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Luo L, Lv J. An evolutionary theory on virus mutation in COVID-19. Virus Res 2024; 344:199358. [PMID: 38508401 PMCID: PMC10979259 DOI: 10.1016/j.virusres.2024.199358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 03/22/2024]
Abstract
With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n-distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2.
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Affiliation(s)
- Liaofu Luo
- Faculty of Physical Science and Technology, Inner Mongolia University, 235 West College Road, Hohhot 010021, China.
| | - Jun Lv
- College of Science, Inner Mongolia University of Technology, 49 Aymin Street, Hohhot 010051, China.
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2
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Weng C, Shao Z, Xiao M, Song M, Zhao Y, Li A, Pang Y, Huang T, Yu C, Lv J, Li L, Sun D. Association of sex hormones with non-alcoholic fatty liver disease: An observational and Mendelian randomization study. Liver Int 2024; 44:1154-1166. [PMID: 38345150 DOI: 10.1111/liv.15866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/20/2024] [Accepted: 01/28/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND AND AIMS Sex-specific associations of sex hormone-binding globulin (SHBG) and bioavailable testosterone (BAT) with NAFLD remain indeterminate. We aimed to explore observational and genetically determined relationships between each hormone and NAFLD. METHODS We included 187 395 men and 170 193 women from the UK Biobank. Linear and nonlinear Cox regression models and Mendelian randomization (MR) analysis were used to test the associations. RESULTS During 12.49 years of follow-up, 2209 male and 1886 female NAFLD cases were documented. Elevated SHBG levels were linearly associated with a lower risk of NAFLD in women (HR (95% CI), .71 (.63, .79)), but not in men (a "U" shape, pnon-linear < .001). Higher BAT levels were associated with a lower NAFLD risk in men (HR (95% CI), .81 (.71, .93)) but a higher risk in women (HR (95% CI): 1.25 (1.15, 1.36)). Genetically determined SHBG and BAT levels were linearly associated with NAFLD risk in women (OR (95% CI): .57 (.38, .87) and 2.21 (1.41, 3.26) respectively); in men, an "L-shaped" MR association between SHBG levels and NAFLD risk was found (pnon-linear = .016). The bidirectional MR analysis further revealed the effect of NAFLD on SHBG and BAT levels in both sexes. CONCLUSIONS Consistently, linear associations of lower SHBG and higher BAT levels with increased NAFLD risk were both conventionally and genetically found in women, while in men, SHBG acts in a nonlinear manner. In addition, NAFLD may affect SHBG and BAT levels.
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Affiliation(s)
- Chenghao Weng
- School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Zilun Shao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Meng Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Mingyu Song
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Yuxuan Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Aolin Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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Miao K, Hong X, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Hu R, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Association between epigenetic age and type 2 diabetes mellitus or glycemic traits: A longitudinal twin study. Aging Cell 2024:e14175. [PMID: 38660768 DOI: 10.1111/acel.14175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/18/2024] [Accepted: 04/04/2024] [Indexed: 04/26/2024] Open
Abstract
Epigenetic clocks based on DNA methylation have been known as biomarkers of aging, including principal component (PC) clocks representing the degree of aging and DunedinPACE representing the pace of aging. Prior studies have shown the associations between epigenetic aging and T2DM, but the results vary by epigenetic age metrics and people. This study explored the associations between epigenetic age metrics and T2DM or glycemic traits, based on 1070 twins (535 twin pairs) from the Chinese National Twin Registry. It also explored the temporal relationships of epigenetic age metrics and glycemic traits in 314 twins (157 twin pairs) who participated in baseline and follow-up visits after a mean of 4.6 years. DNA methylation data were used to calculate epigenetic age metrics, including PCGrimAge acceleration (PCGrimAA), PCPhenoAge acceleration (PCPhenoAA), DunedinPACE, and the longitudinal change rate of PCGrimAge/PCPhenoAge. Mixed-effects and cross-lagged modelling assessed the cross-sectional and temporal relationships between epigenetic age metrics and T2DM or glycemic traits, respectively. In the cross-sectional analysis, positive associations were identified between DunedinPACE and glycemic traits, as well as between PCPhenoAA and fasting plasma glucose, which may be not confounded by shared genetic factors. Cross-lagged models revealed that glycemic traits (fasting plasma glucose, HbA1c, and TyG index) preceded DunedinPACE increases, and TyG index preceded PCGrimAA increases. Glycemic traits are positively associated with epigenetic age metrics, especially DunedinPACE. Glycemic traits preceded the increases in DunedinPACE and PCGrimAA. Lowering the levels of glycemic traits may reduce DunedinPACE and PCGrimAA, thereby mitigating age-related comorbidities.
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Affiliation(s)
- Ke Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Xuanming Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Runhua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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4
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Song S, Wu Z, Lv J, Yu C, Sun D, Pei P, Pan L, Yang L, Chen Y, Du H, Chen L, Schmidt D, Avery D, Duan L, Chen J, Chen Z, Li L, Pang Y. Dietary factors and patterns in relation to risk of later-onset ulcerative colitis in Chinese: A prospective study of 0.5 million people. Aliment Pharmacol Ther 2024. [PMID: 38654428 DOI: 10.1111/apt.17963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/27/2024] [Accepted: 03/10/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND There is limited evidence on the associations of dietary factors and patterns with risk of later-onset ulcerative colitis (UC) in Chinese adults. AIMS To investigate the associations of dietary factors and patterns with risk of later-onset UC in Chinese. METHODS The prospective China Kadoorie Biobank cohort study recruited 512,726 participants aged 30-79. Dietary habits were assessed using food frequency questionnaires. Dietary patterns were derived by factor analysis with a principal component method. Cox regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS During a median follow-up of 12.1 years, 312 cases of newly diagnosed UC were documented (median age of diagnosis 60.1 years). Egg consumption was associated with higher risk of UC (HR for daily vs. never or rarely: 2.29 [95% CI: 1.26-4.16]), while spicy food consumption was inversely associated with risk of UC (HR: 0.63 [0.45-0.88]). The traditional northern dietary pattern, characterised by high intake of wheat and low intake of rice, was associated with higher risk of UC (HR for highest vs. lowest quartile of score: 2.79 [1.93-4.05]). The modern dietary pattern, characterised by high intake of animal-origin foods and fruits, was associated with higher risk of UC (HR: 2.48 [1.63-3.78]). Population attributable fraction was 13.04% (7.71%-19.11%) for daily/almost daily consumption of eggs and 9.87% (1.94%-18.22%) for never/rarely consumption of spicy food. CONCLUSIONS The findings highlight the importance of evaluating dietary factors and patterns in the primary prevention of later-onset UC in Chinese adults.
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Affiliation(s)
- Shuyao Song
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zhiyu Wu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Lang Pan
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lingli Chen
- Tongxiang Center for Disease Control and Prevention, Tongxiang, China
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liping Duan
- Department of Gastroenterology, Peking University Third Hospital, Beijing, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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5
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Yao P, Iona A, Pozarickij A, Said S, Wright N, Lin K, Millwood I, Fry H, Kartsonaki C, Mazidi M, Chen Y, Bragg F, Liu B, Yang L, Liu J, Avery D, Schmidt D, Sun D, Pei P, Lv J, Yu C, Hill M, Bennett D, Walters R, Li L, Clarke R, Du H, Chen Z. Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes. Diabetes Care 2024:dc232145. [PMID: 38623619 DOI: 10.2337/dc23-2145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 03/09/2024] [Indexed: 04/17/2024]
Abstract
OBJECTIVE Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D. RESULTS Overall, 33 proteins were significantly associated (false discovery rate < 0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D. CONCLUSIONS Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.
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Affiliation(s)
- Pang Yao
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Andri Iona
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Alfred Pozarickij
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Saredo Said
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Neil Wright
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Kuang Lin
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Iona Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Hannah Fry
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Mohsen Mazidi
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Fiona Bragg
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Bowen Liu
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Junxi Liu
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Michael Hill
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Derrick Bennett
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Robin Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
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6
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Kartsonaki C, Yao P, Butt J, Jeske R, de Martel C, Plummer M, Sun D, Clark S, Walters RG, Chen Y, Lv J, Yu C, Hill M, Peto R, Li L, Waterboer T, Chen Z, Millwood IY, Yang L. Infectious pathogens and risk of esophageal, gastric and duodenal cancers and ulcers in China: A case-cohort study. Int J Cancer 2024; 154:1423-1432. [PMID: 38108203 PMCID: PMC7615747 DOI: 10.1002/ijc.34814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/16/2023] [Accepted: 10/24/2023] [Indexed: 12/19/2023]
Abstract
Infection by certain pathogens is associated with cancer development. We conducted a case-cohort study of ~2500 incident cases of esophageal, gastric and duodenal cancer, and gastric and duodenal ulcer and a randomly selected subcohort of ~2000 individuals within the China Kadoorie Biobank study of >0.5 million adults. We used a bead-based multiplex serology assay to measure antibodies against 19 pathogens (total 43 antigens) in baseline plasma samples. Associations between pathogens and antigen-specific antibodies with risks of site-specific cancers and ulcers were assessed using Cox regression fitted using the Prentice pseudo-partial likelihood. Seroprevalence varied for different pathogens, from 0.7% for Hepatitis C virus (HCV) to 99.8% for Epstein-Barr virus (EBV) in the subcohort. Compared to participants seronegative for the corresponding pathogen, Helicobacter pylori seropositivity was associated with a higher risk of non-cardia (adjusted hazard ratio [HR] 2.73 [95% CI: 2.09-3.58]) and cardia (1.67 [1.18-2.38]) gastric cancer and duodenal ulcer (2.71 [1.79-4.08]). HCV was associated with a higher risk of duodenal cancer (6.23 [1.52-25.62]) and Hepatitis B virus was associated with higher risk of duodenal ulcer (1.46 [1.04-2.05]). There were some associations of antibodies again some herpesviruses and human papillomaviruses with risks of gastrointestinal cancers and ulcers but these should be interpreted with caution. This first study of multiple pathogens with risk of gastrointestinal cancers and ulcers demonstrated that several pathogens are associated with risks of gastrointestinal cancers and ulcers. This will inform future investigations into the role of infection in the etiology of these diseases.
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Affiliation(s)
- Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Pang Yao
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Julia Butt
- Infections and Cancer Epidemiology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rima Jeske
- Infections and Cancer Epidemiology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Catherine de Martel
- Early Detection, Prevention and Infections Branch, International Agency for Research on Cancer, Lyon, France
| | - Martyn Plummer
- Department of Statistics, University of Warwick, Coventry, UK
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Sarah Clark
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G. Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Michael Hill
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Richard Peto
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Tim Waterboer
- Infections and Cancer Epidemiology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y. Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Chen S, Wei F, Cheng X, Luo Y, Meng F, Zhang Y, Huang W, Lv J, Pan H, Wu Q, Zhao G. Regioselective Deacetylation of Peracetylated Deoxy- C-glycopyranosides by Boron Trichloride (BCl 3). J Org Chem 2024; 89:4802-4817. [PMID: 38477972 DOI: 10.1021/acs.joc.4c00026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
A general approach for regioselective deacetylation at sugar 3-OH of peracetylated 6-deoxy-C-glucopyranosides mediated by BCl3 was developed. The approach could be extended to other sugar-derived 6-deoxy-C-glycopyranosides, such as those derived from mannose, galactose, and rhamnose, with deacetylation occurring at varied sugar hydroxyl groups, and further extended to 4-deoxy-C-glucopyranosides with deacetylation occurring at sugar 3-OH. The approach would enable access to synthetically challenging carbohydrate derivatives. A possible mechanism of the regioselectivity was proposed.
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Affiliation(s)
- Shuangyuan Chen
- College of Pharmacy, Guizhou Medical University, Guiyang 561113, China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
| | - Feifei Wei
- College of Pharmacy, Guizhou Medical University, Guiyang 561113, China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
| | - Xinqiang Cheng
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
| | - Ying Luo
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
| | - Fancui Meng
- National Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Institute of Pharmaceutical Research, Tianjin 300301, China
| | - Yuanwen Zhang
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
- School of Chinese Medicinal Resource, Guangdong Pharmaceutical University, Guangzhou 510006, China
| | - Wenqian Huang
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jun Lv
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hong Pan
- School of Intelligent Medical Technology, Dazhou Vocational and Technical College, Dazhou 635001, China
| | - Qingqing Wu
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
| | - Guilong Zhao
- College of Pharmacy, Guizhou Medical University, Guiyang 561113, China
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
- Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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8
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Levy M, Buckell J, Clarke R, Wu N, Pei P, Sun D, Avery D, Zhang H, Lv J, Yu C, Li L, Chen Z, Yip W, Chen Y, Mihaylova B. Association between health insurance cost-sharing and choice of hospital tier for cardiovascular diseases in China: a prospective cohort study. Lancet Reg Health West Pac 2024; 45:101020. [PMID: 38380231 PMCID: PMC10876671 DOI: 10.1016/j.lanwpc.2024.101020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/13/2023] [Accepted: 01/16/2024] [Indexed: 02/22/2024]
Abstract
Background Hospitals in China are classified into tiers (1, 2 or 3), with the largest (tier 3) having more equipment and specialist staff. Differential health insurance cost-sharing by hospital tier (lower deductibles and higher reimbursement rates in lower tiers) was introduced to reduce overcrowding in higher tier hospitals, promote use of lower tier hospitals, and limit escalating healthcare costs. However, little is known about the effects of differential cost-sharing in health insurance schemes on choice of hospital tiers. Methods In a 9-year follow-up of a prospective study of 0.5 M adults from 10 areas in China, we examined the associations between differential health insurance cost-sharing and choice of hospital tiers for patients with a first hospitalisation for stroke or ischaemic heart disease (IHD) in 2009-2017. Analyses were performed separately in urban areas (stroke: n = 20,302; IHD: n = 19,283) and rural areas (stroke: n = 21,130; IHD: n = 17,890), using conditional logit models and adjusting for individual socioeconomic and health characteristics. Findings About 64-68% of stroke and IHD cases in urban areas and 27-29% in rural areas chose tier 3 hospitals. In urban areas, higher reimbursement rates in each tier and lower tier 3 deductibles were associated with a greater likelihood of choosing their respective hospital tiers. In rural areas, the effects of cost-sharing were modest, suggesting a greater contribution of other factors. Higher socioeconomic status and greater disease severity were associated with a greater likelihood of seeking care in higher tier hospitals in urban and rural areas. Interpretation Patient choice of hospital tiers for treatment of stroke and IHD in China was influenced by differential cost-sharing in urban areas, but not in rural areas. Further strategies are required to incentivise appropriate health seeking behaviour and promote more efficient hospital use. Funding Wellcome Trust, Medical Research Council, British Heart Foundation, Cancer Research UK, Kadoorie Charitable Foundation, China Ministry of Science and Technology, and National Natural Science Foundation of China.
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Affiliation(s)
- Muriel Levy
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, UK
| | - John Buckell
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, UK
| | - Nina Wu
- School of Public Health, Capital Medical University, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Dianjianyi Sun
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Hua Zhang
- NCDs Prevention and Control Department, Qingdao Centre for Disease Control and Prevention, Qingdao, China
| | - Jun Lv
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Canqing Yu
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Liming Li
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Winnie Yip
- Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, UK
| | - Borislava Mihaylova
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, UK
- Health Economics and Policy Research Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
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9
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Elbasheer MMA, Bohrmann B, Chen Y, Lv J, Sun D, Wu X, Yang X, Avery D, Li L, Chen Z, Kartsonaki C, Chan KH, Yang L. Reproductive factors and risk of lung cancer among 300,000 Chinese female never-smokers: evidence from the China Kadoorie Biobank study. BMC Cancer 2024; 24:384. [PMID: 38532314 PMCID: PMC10964706 DOI: 10.1186/s12885-024-12133-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/17/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Lung cancer is the leading cause of cancer mortality among Chinese females despite the low smoking prevalence among this population. This study assessed the roles of reproductive factors in lung cancer development among Chinese female never-smokers. METHODS The prospective China Kadoorie Biobank (CKB) recruited over 0.5 million Chinese adults (0.3 million females) from 10 geographical areas in China in 2004-2008 when information on socio-demographic/lifestyle/environmental factors, physical measurements, medical history, and reproductive history collected through interviewer-administered questionnaires. Cox proportional hazard regression was used to estimate adjusted hazard ratios (HRs) of lung cancer by reproductive factors. Subgroup analyses by menopausal status, birth year, and geographical region were performed. RESULTS During a median follow-up of 11 years, 2,284 incident lung cancers occurred among 282,558 female never-smokers. Ever oral contraceptive use was associated with a higher risk of lung cancer (HR = 1.16, 95% CI: 1.02-1.33) with a significant increasing trend associated with longer duration of use (p-trend = 0.03). Longer average breastfeeding duration per child was associated with a decreased risk (0.86, 0.78-0.95) for > 12 months compared with those who breastfed for 7-12 months. No statistically significant association was detected between other reproductive factors and lung cancer risk. CONCLUSION Oral contraceptive use was associated with an increased risk of lung cancer in Chinese female never-smokers. Further studies are needed to assess lung cancer risk related to different types of oral contraceptives in similar populations.
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Affiliation(s)
- Marwa M A Elbasheer
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Bastian Bohrmann
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council (MRC) Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Xia Wu
- Pengzhou Centre for Disease Control and Prevention (CDC), Pengzhou, China
| | - Xiaoming Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council (MRC) Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council (MRC) Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK
| | - Ka Hung Chan
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Oxford BHF Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Medical Research Council (MRC) Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK.
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10
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Zhang Y, Sun Q, Yu C, Sun D, Pang Y, Pei P, Du H, Yang L, Chen Y, Yang X, Chen X, Chen J, Chen Z, Li L, Lv J. Associations of traditional cardiovascular risk factors with 15-year blood pressure change and trajectories in Chinese adults: a prospective cohort study. J Hypertens 2024:00004872-990000000-00438. [PMID: 38525868 DOI: 10.1097/hjh.0000000000003717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
OBJECTIVE How traditional cardiovascular disease (CVD) risk factors are related to long-term blood pressure change (BPC) or trajectories remain unclear. We aimed to examine the independent associations of these factors with 15-year BPC and trajectories in Chinese adults. METHODS We included 15 985 participants who had attended three surveys, including 2004-2008 baseline survey, and 2013-2014 and 2020-2021 resurveys, over 15 years in the China Kadoorie Biobank (CKB). We measured systolic and diastolic blood pressure (SBP and DBP), height, weight, and waist circumference (WC). We asked about the sociodemographic characteristics and lifestyle factors, including smoking, alcohol drinking, intake of fresh vegetables, fruits, and red meat, and physical activity, using a structured questionnaire. We calculated standard deviation (SD), cumulative blood pressure (cumBP), coefficient of variation (CV), and average real variability (ARV) as long-term BPC proxies. We identified blood pressure trajectories using the latent class growth model. RESULTS Most baseline sociodemographic and lifestyle characteristics were associated with cumBP. After adjusting for other characteristics, the cumSBP (mmHg × year) increased by 116.9 [95% confidence interval (CI): 111.0, 122.7] for every 10 years of age. The differences of cumSBP in heavy drinkers of ≥60 g pure alcohol per day and former drinkers were 86.7 (60.7, 112.6) and 48.9 (23.1, 74.8) compared with less than weekly drinkers. The cumSBP in participants who ate red meat less than weekly was 29.4 (12.0, 46.8) higher than those who ate red meat daily. The corresponding differences of cumSBP were 127.8 (120.7, 134.9) and 70.2 (65.0, 75.3) for BMI per 5 kg/m2 and WC per 10 cm. Most of the findings of other BPC measures by baseline characteristics were similar to the cumBP, but the differences between groups were somewhat weaker. Alcohol drinking was associated with several high-risk trajectories of SBP and DBP. Both BMI and WC were independently associated with all high-risk blood pressure trajectories. CONCLUSIONS Several traditional CVD risk factors were associated with unfavorable long-term BPC or blood pressure trajectories in Chinese adults.
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Affiliation(s)
- Yiqian Zhang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
| | - Qiufen Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University
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11
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Wang H, Xun M, Tang H, Zhao J, Hu S, Zhang L, Lv J, Wang D, Chen Y, Liu J, Li GL, Wang W, Shu Y, Li H. Hair cell-specific Myo15 promoter-mediated gene therapy rescues hearing in DFNB9 mouse model. Mol Ther Nucleic Acids 2024; 35:102135. [PMID: 38404504 PMCID: PMC10883836 DOI: 10.1016/j.omtn.2024.102135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/30/2024] [Indexed: 02/27/2024]
Abstract
Adeno-associated viral (AAV) vectors are increasingly used as vehicles for gene delivery to treat hearing loss. However, lack of specificity of the transgene expression may lead to overexpression of the transgene in nontarget tissues. In this study, we evaluated the expression efficiency and specificity of transgene delivered by AAV-PHP.eB under the inner ear sensory cell-specific Myo15 promoter. Compared with the ubiquitous CAG promoter, the Myo15 promoter initiates efficient expression of the GFP fluorescence reporter in hair cells, while minimizing non-specific expression in other cell types of the inner ear and CNS. Furthermore, using the Myo15 promoter, we constructed an AAV-mediated therapeutic system with the coding sequence of OTOF gene. After inner ear injection, we observed apparent hearing recovery in Otof-/- mice, highly efficient expression of exogenous otoferlin, and significant improvement in the exocytosis function of inner hair cells. Overall, our results indicate that gene therapy mediated by the hair cell-specific Myo15 promoter has potential clinical application for the treatment of autosomal recessive deafness and yet for other hereditary hearing loss related to dysfunction of hair cells.
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Affiliation(s)
- Hui Wang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - MengZhao Xun
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Honghai Tang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Jingjing Zhao
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Shaowei Hu
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Longlong Zhang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Jun Lv
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Daqi Wang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Yuxin Chen
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Jianping Liu
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Geng-lin Li
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Wuqing Wang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Yilai Shu
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Huawei Li
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
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12
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Zhu Y, Zhuang Z, Lv J, Sun D, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Liu F, Stevens R, Chen J, Chen Z, Li L, Yu C. A genome-wide association study based on the China Kadoorie Biobank identifies genetic associations between snoring and cardiometabolic traits. Commun Biol 2024; 7:305. [PMID: 38461358 PMCID: PMC10924953 DOI: 10.1038/s42003-024-05978-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 02/27/2024] [Indexed: 03/11/2024] Open
Abstract
Despite the high prevalence of snoring in Asia, little is known about the genetic etiology of snoring and its causal relationships with cardiometabolic traits. Based on 100,626 Chinese individuals, a genome-wide association study on snoring was conducted. Four novel loci were identified for snoring traits mapped on SLC25A21, the intergenic region of WDR11 and FGFR, NAA25, ALDH2, and VTI1A, respectively. The novel loci highlighted the roles of structural abnormality of the upper airway and craniofacial region and dysfunction of metabolic and transport systems in the development of snoring. In the two-sample bi-directional Mendelian randomization analysis, higher body mass index, weight, and elevated blood pressure were causal for snoring, and a reverse causal effect was observed between snoring and diastolic blood pressure. Altogether, our results revealed the possible etiology of snoring in China and indicated that managing cardiometabolic health was essential to snoring prevention, and hypertension should be considered among snorers.
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Affiliation(s)
- Yunqing Zhu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Zhenhuang Zhuang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Fang Liu
- Suzhou Centers for Disease Control, NO.72 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China.
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13
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Man S, Deng Y, Ma Y, Yang X, Wang X, Fu J, Yu C, Lv J, Du J, Wang B, Li L. Association between weight change, waist circumference change, and the risk of nonalcoholic fatty liver disease in individuals with metabolically healthy overweight or obesity and metabolically unhealthy overweight or obesity. Obes Res Clin Pract 2024:S1871-403X(24)00030-9. [PMID: 38443283 DOI: 10.1016/j.orcp.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND This study aimed to explore and compare the effect of weight change, and waist circumference (WC) change, on the risk of nonalcoholic fatty liver disease (NAFLD) in individuals with metabolically healthy overweight or obesity (MHOW/O) and metabolically unhealthy overweight or obesity (MUOW/O) in a health check-up cohort in China. METHODS 5625 adults with overweight or obesity, and free from NAFLD at baseline were included. Metabolically healthy was defined as not having any components of metabolic syndrome. Weight/WC changes were calculated as the relative difference between the first and second visits of check-up. NAFLD was assessed based on abdominal ultrasound. RESULTS During a median follow-up of 2.1 (IQR: 1.1-4.3) years, 1849 participants developed NAFLD. In MHOW/O participants, the multivariable adjusted HRs (95 % CIs) for NAFLD in weight change ≤ -5.0 %, and - 4.9-- 1.0 % were 0.36 (0.23-0.59), 0.59 (0.43-0.80), respectively, compared to the weight stable group (-0.9% to 0.9 %). The corresponding HRs (95 % CIs) for the association between WC change (≤ 6.0 %, - 5.9 to -3.0 %) and NAFLD in MHOW/O participants were 0.41 (0.27-0.62), and 0.74 (0.54-1.01), respectively, compared to the WC stable group (-2.9-2.9 %). Similar patterns were observed in MUOW/O participants. A more marked gradient of cumulative incidence of NAFLD across weight/WC change categories was observed in MHOW/O than in MUOW/O individuals. CONCLUSIONS A more evident association between weight/WC loss and risk of NAFLD was observed in MHOW/O than in MUOW/O individuals. Our findings indicate the practical significance of encouraging all individuals with overweight and obesity to achieve a clinically relevant level of weight/WC loss to prevent NAFLD, even among metabolic healthy groups.
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Affiliation(s)
- Sailimai Man
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Meinian Institute of Health, Beijing 100083, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Yuhan Deng
- Meinian Institute of Health, Beijing 100083, China; Chongqing Research Institute of Big Data, Peking University, Chongqing 400000, China
| | - Yuan Ma
- Meinian Institute of Health, Beijing 100083, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Xiaochen Yang
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing 100191, China
| | - Xiaona Wang
- Beijing MJ Health Check-up Center, Beijing 100000, China
| | - Jingzhu Fu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Jing Du
- Beijing Centre for Disease Prevention and Control, Beijing 100013, China.
| | - Bo Wang
- Meinian Institute of Health, Beijing 100083, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China.
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China.
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14
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Zhu Y, Zhuang Z, Lv J, Sun D, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Wu X, Schmidt D, Avery D, Chen J, Chen Z, Li L, Yu C. Causal association between snoring and stroke: a Mendelian randomization study in a Chinese population. Lancet Reg Health West Pac 2024; 44:101001. [PMID: 38304719 PMCID: PMC10832459 DOI: 10.1016/j.lanwpc.2023.101001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/24/2023] [Accepted: 12/19/2023] [Indexed: 02/03/2024]
Abstract
Background Previous observational studies established a positive relationship between snoring and stroke. We aimed to investigate the causal effect of snoring on stroke. Methods Based on 82,339 unrelated individuals with qualified genotyping data of Asian descent from the China Kadoorie Biobank (CKB), we conducted a Mendelian randomization (MR) analysis of snoring and stroke. Genetic variants identified in the genome-wide association analysis (GWAS) of snoring in CKB and UK Biobank (UKB) were selected for constructing genetic risk scores (GRS). A two-stage method was applied to estimate the associations of the genetically predicted snoring with stroke and its subtypes. Besides, MR analysis among the non-obese group (body mass index, BMI <24.0 kg/m2), as well as multivariable MR (MVMR), were performed to control for potential pleiotropy from BMI. In addition, the inverse-variance weighted (IVW) method was applied to estimate the causal association with genetic variants identified in CKB GWAS. Findings Positive associations were found between snoring and total stroke, hemorrhagic stroke (HS), and ischemic stroke (IS). With GRS of CKB, the corresponding HRs (95% CIs) were 1.56 (1.15, 2.12), 1.50 (0.84, 2.69), 2.02 (1.36, 3.01), and the corresponding HRs (95% CIs) using GRS of UKB were 1.78 (1.30, 2.43), 1.94 (1.07, 3.52), and 1.74 (1.16, 2.61). The associations remained stable in the MR among the non-obese group, MVMR analysis, and MR analysis using the IVW method. Interpretation This study suggests that, among Chinese adults, genetically predicted snoring could increase the risk of total stroke, IS, and HS, and the causal effect was independent of BMI. Funding National Natural Science Foundation of China, Kadoorie Charitable Foundation Hong Kong, UK Wellcome Trust, National Key R&D Program of China, Chinese Ministry of Science and Technology.
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Affiliation(s)
- Yunqing Zhu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Zhenhuang Zhuang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Iona Y. Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Robin G. Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Xianping Wu
- Suzhou Centers for Disease Control, NO.72 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
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15
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Shi K, Zhu Y, Lv J, Sun D, Pei P, Du H, Chen Y, Yang L, Han B, Stevens R, Chen J, Chen Z, Li L, Yu C. Association of physical activity with risk of chronic kidney disease in China: A population-based cohort study. J Sport Health Sci 2024; 13:204-211. [PMID: 37532222 PMCID: PMC10980896 DOI: 10.1016/j.jshs.2023.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/29/2023] [Accepted: 07/06/2023] [Indexed: 08/04/2023]
Abstract
BACKGROUND Information on the association between physical activity (PA) and the risk of chronic kidney disease (CKD) is limited. We aimed to explore the associations of total, domain-specific, and intensity-specific PA with CKD and its subtypes in China. METHODS The study included 475,376 adults from the China Kadoorie Biobank aged 30-79 years during 2004-2008 at baseline. An interviewer-administered questionnaire was used to collect the information about PA, which was quantified as metabolic equivalent of task hours per day (MET-h/day) and categorized into 4 groups based on quartiles. Cox regression was used to analyze the association between PA and CKD risk. RESULTS During a median follow-up of 12.1 years, 5415 incident CKD cases were documented, including 1159 incident diabetic kidney disease (DKD) cases and 362 incident hypertensive nephropathy (HTN) cases. Total PA was inversely associated with CKD risk, with an adjusted hazard ratio (HR, 95% confidence interval (95%CI)) of 0.83 (0.75-0.92) for incident CKD in the highest quartile of total PA as compared with participants in the lowest quartile. Similar results were observed for risk of DKD and HTN, and the corresponding HRs (95%CIs) were 0.75 (0.58-0.97) for DKD risk and 0.56 (0.37-0.85) for HTN risk. Increased nonoccupational PA, low-intensity PA, and moderate-to-vigorous-intensity PA were significantly associated with a decreased risk of CKD, with HRs (95%CIs) of 0.80 (0.73-0.88), 0.85 (0.77-0.94), and 0.85 (0.76-0.95) in the highest quartile, respectively. CONCLUSION PA, including nonoccupational PA, low-intensity PA, and moderate-to-vigorous-intensity PA, was inversely associated with the risk of CKD, including DKD, HTN, and other CKD, and such associations were dose dependent.
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Affiliation(s)
- Kexiang Shi
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Yunqing Zhu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
| | - Bing Han
- NCDs Prevention and Control Department, Henan Center for Disease Control and Prevention, Zhengzhou 450016, China
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China.
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16
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Wei X, Sun D, Gao J, Zhang J, Zhu M, Yu C, Ma Z, Fu Y, Ji C, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Jin G, Chen Z, Hu Z, Li L, Shen H, Lv J, Ma H. Development and evaluation of a polygenic risk score for lung cancer in never-smoking women: A large-scale prospective Chinese cohort study. Int J Cancer 2024; 154:807-815. [PMID: 37846649 DOI: 10.1002/ijc.34765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 10/18/2023]
Abstract
The proportion of lung cancer in never smokers is rising, especially among Asian women, but there is no effective early detection tool. Here, we developed a polygenic risk score (PRS), which may help to identify the population with higher risk of lung cancer in never-smoking women. We first performed a large GWAS meta-analysis (8595 cases and 8275 controls) to systematically identify the susceptibility loci for lung cancer in never-smoking Asian women and then generated a PRS using GWAS datasets. Furthermore, we evaluated the utility and effectiveness of PRS in an independent Chinese prospective cohort comprising 55 266 individuals. The GWAS meta-analysis identified eight known loci and a novel locus (5q11.2) at the genome-wide statistical significance level of P < 5 × 10-8 . Based on the summary statistics of GWAS, we derived a polygenic risk score including 21 variants (PRS-21) for lung cancer in never-smoking women. Furthermore, PRS-21 had a hazard ratio (HR) per SD of 1.29 (95% CI = 1.18-1.41) in the prospective cohort. Compared with participants who had a low genetic risk, those with an intermediate (HR = 1.32, 95% CI: 1.00-1.72) and high (HR = 2.09, 95% CI: 1.56-2.80) genetic risk had a significantly higher risk of incident lung cancer. The addition of PRS-21 to the conventional risk model yielded a modest significant improvement in AUC (0.697 to 0.711) and net reclassification improvement (24.2%). The GWAS-derived PRS-21 significantly improves the risk stratification and prediction accuracy for incident lung cancer in never-smoking Asian women, demonstrating the potential for identification of high-risk individuals and early screening.
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Affiliation(s)
- Xiaoxia Wei
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Jiaxin Gao
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jing Zhang
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Zhimin Ma
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yating Fu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Ji
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Melloni GEM, Kanoni S, Rayner NW, Bocher O, Arruda AL, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Thangam M, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Hakaste L, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kamanu FK, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Melander O, Metspalu A, Mo H, Morris AD, Moura FA, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YDI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Ahlqvist E, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Marston NA, Ruff CT, van Heel DA, Finer S, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, Zeggini E. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 2024; 627:347-357. [PMID: 38374256 PMCID: PMC10937372 DOI: 10.1038/s41586-024-07019-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 01/03/2024] [Indexed: 02/21/2024]
Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
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Affiliation(s)
- Ken Suzuki
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Henry J Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Xianyong Yin
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Kim M Lorenz
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Giorgio E M Melloni
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Nigel W Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Graduate School of Experimental Medicine, Technical University of Munich, Munich, Germany
- Munich School for Data Science, Helmholtz Munich, Neuherberg, Germany
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Simon S K Lee
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren E Petty
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip Schroeder
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Brett Vanderwerff
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London NorthWest Healthcare NHS Trust, London, UK
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sanghoon Moon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Jung-Jin Lee
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian Pan
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Daniel Taliun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Esteban J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yang Hai
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tamar Sofer
- Department of Biostatistics, Harvard University, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard University, Boston, MA, USA
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Chloé Sarnowski
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Darryl Nousome
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Soo-Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Meng Sun
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Lin Tong
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suraj S Nongmaithem
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Victor J Y Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Claudia H T Tam
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Yoonjung Yoonie Joo
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bram Peter Prins
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Edmond Kabagambe
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Academics, Ochsner Health, New Orleans, LA, USA
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Anny H Xiang
- Department of Research and Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Hyeok Sun Choi
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jingyi Tan
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Jinrui Cui
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Manonanthini Thangam
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A Aguilar-Salinas
- Unidad de Investigación en Enfermedades Metabólicas and Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sonia S Anand
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Thomas A Buchanan
- Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus, University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, China
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ji Chen
- Exeter Centre of Excellence in Diabetes (ExCEeD), Exeter Medical School, University of Exeter, Exeter, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Shyh-Huei Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Mary Cushman
- Department of Medicine, University of Vermont, Colchester, VT, USA
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus, University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Swapan K Das
- Section of Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Latchezar Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Oscar H Franco
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Barry I Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Pauline Genter
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hertzel C Gerstein
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Maria Elena González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Lindsay A Guare
- Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sophie Hackinger
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Liisa Hakaste
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | | | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Annie-Green Howard
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Willa Hsueh
- Department of Internal Medicine, Diabetes and Metabolism Research Center, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mengna Huang
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
| | - Wei Huang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Yi-Jen Hung
- Division of Endocrine and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | | | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Farzana Jasmine
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Guozhi Jiang
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Jost B Jonas
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Torben Jørgensen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Frederick K Kamanu
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fouad R Kandeel
- Department of Clinical Diabetes, Endocrinology and Metabolism, Department of Translational Research and Cellular Therapeutics, City of Hope, Duarte, CA, USA
| | | | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Varinderpal Kaur
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Jacob M Keaton
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Abel N Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Muhammad G Kibriya
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Duk-Hwan Kim
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Kyung Min Lee
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Myung-Shik Lee
- Soochunhyang Institute of Medi-bio Science and Division of Endocrinology, Department of Internal Medicine, Soochunhyang University College of Medicine, Cheonan, South Korea
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Nanette R Lee
- USC-Office of Population Studies Foundation, University of San Carlos, Cebu City, Philippines
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Symen Ligthart
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St Louis, MO, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Andrea O Luk
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Xi Luo
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Julie A Lynch
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Shiro Maeda
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan
| | - Vasiliki Mamakou
- Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Sohail Rafik Mansuri
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Koichi Matsuda
- Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Olle Melander
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Huan Mo
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew D Morris
- Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Filipe A Moura
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jerry L Nadler
- Department of Medicine and Pharmacology, New York Medical College, Valhalla, NY, USA
| | - Michael A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Uma Nayak
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Sanjay R Patel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Snehal Patil
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Mark A Pereira
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Fraser J Pirie
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Hannah G Polikowsky
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Campus, Ghaziabad, India
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michael Roden
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katheryn Roll
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Kevin Sandow
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alagu Sankareswaran
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Mohammad Shahriar
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Botong Shen
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jinxiu Shi
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Dong Mun Shin
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Nobuhiro Shojima
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wing Yee So
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, China
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | | | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Biostatistics, Epidemiology, and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Chair of Genetic Epidemiology, Institute of Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Tam C Tran
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fuu-Jen Tsai
- Department of Medical Genetics and Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Jaakko Tuomilehto
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
- National School of Public Health, Madrid, Spain
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxiología Ambiental, Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Miriam S Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Adan Valladares-Salgado
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jan B van Klinken
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Chemistry, Laboratory of Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Hospital, Los Angeles, CA, USA
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniel R Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Chittaranjan S Yajnik
- Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, India
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Matthew Zawistowski
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Liang Zhang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Leslie J Raffel
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UCI Irvine School of Medicine, Irvine, CA, USA
| | - Michiya Igase
- Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Touon, Japan
| | - Eli Ipp
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael A Province
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Craig L Hanis
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Erik Ingelsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ya-Xing Wang
- Beijing Institute of Ophthalmology, Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Diane M Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | | | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James C Engert
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Amélie Bonnefond
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Wayne H H Sheu
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, China
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tiinamaija Tuomi
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Department of Endocrinology, Helsinki University Hospital, Helsinki, Finland
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- Science and Engineering Research Board (SERB), Department of Science and Technology, Ministry of Science and Technology, Government of India, New Delhi, India
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dwaipayan Bharadwaj
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Michèle M Sale
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Habibul Ahsan
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyong-Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Miguel Cruz
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Roberta McKean-Cowdin
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Josee Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Woon-Puay Koh
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Donald W Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Colin N A Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, University of Dundee, Dundee, UK
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London NorthWest Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
- Department of Medicine, Brown University Alpert School of Medicine, Providence, RI, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Danish Saleheen
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Juyoung Lee
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kari Stefansson
- deCODE Genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Emma Ahlqvist
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Jose C Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daniel J Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Translational Medicine and Therapeutics, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Center for Precision Medicine, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Nicholas A Marston
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian T Ruff
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sarah Finer
- Institute for Population Health Sciences, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Joshua C Denny
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Toranomon Hospital, Tokyo, Japan
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London NorthWest Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jennifer E Below
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip S Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - James B Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Cassandra N Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Josep M Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Benjamin F Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK.
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany.
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18
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Wang Y, Hong X, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Age effect on the shared etiology of glycemic traits and serum lipids: evidence from a Chinese twin study. J Endocrinol Invest 2024; 47:535-546. [PMID: 37524979 DOI: 10.1007/s40618-023-02164-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 07/24/2023] [Indexed: 08/02/2023]
Abstract
PURPOSE Diabetes and dyslipidemia are among the most common chronic diseases with increasing global disease burdens, and they frequently occur together. The study aimed to investigate differences in the heritability of glycemic traits and serum lipid indicators and differences in overlapping genetic and environmental influences between them across age groups. METHODS This study included 1189 twin pairs from the Chinese National Twin Registry and divided them into three groups: aged ≤ 40, 41-50, and > 50 years old. Univariate and bivariate structural equation models (SEMs) were conducted on glycemic indicators and serum lipid indicators, including blood glucose (GLU), glycated hemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), in the total sample and three age groups. RESULTS All phenotypes showed moderate to high heritability (0.37-0.64). The heritability of HbA1c demonstrated a downward trend with age (HbA1c: 0.50-0.79), while others remained relatively stable (GLU: 0.55-0.62, TC: 0.58-0.66, TG: 0.50-0.63, LDL-C: 0.24-0.58, HDL-C: 0.31-0.57). The bivariate SEMs demonstrated that GLU and HbA1c were correlated with each serum lipid indicator (0.10-0.17), except HDL-C. Except for HbA1c and LDL-C, as well as HbA1c and HDL-C, differences in genetic correlations underlying glycemic traits and serum lipids between age groups were observed, with the youngest group showing a significantly higher genetic correlation than the oldest group. CONCLUSION Across the whole adulthood, genetic influences were consistently important for GLU, TC, TG, LDL-C and HDL-C, and age may affect the shared genetic influences between glycemic traits and serum lipids. Further studies are needed to elucidate the role of age in the interactions of genes related to glycemic traits and serum lipids.
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Affiliation(s)
- Y Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - X Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - W Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - J Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - C Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - T Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - D Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - C Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Y Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Z Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - M Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - H Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - X Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Y Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - W Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
| | - L Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
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19
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Wang Y, Huang Q, Zhong G, Lv J, Guo Q, Ma Y, Wang X, Zeng J. Corrigendum: Sequential PET/CT and pathological biomarker crosstalk predict response to PD-1 blockers alone or combined with sunitinib in propensity score-matched cohorts of cancer of unknown primary treatment. Front Oncol 2024; 14:1376408. [PMID: 38476368 PMCID: PMC10931807 DOI: 10.3389/fonc.2024.1376408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 03/14/2024] Open
Abstract
[This corrects the article DOI: 10.3389/fonc.2023.1191611.].
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Affiliation(s)
- Youlong Wang
- Hainan Hospital of PLA General Hospital, Department of General Surgery, Haitang District, Sanya, China
| | - Qi Huang
- Department of Clinical Laboratory, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guanqing Zhong
- Department of Clinical Laboratory, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jun Lv
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qinzhi Guo
- Pancreas Center of Guangdong Provincial People’s Hospital, Guangzhou, China
| | - Yifei Ma
- Department of Spine Surgery, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Xinjia Wang
- Department of Spine Surgery, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Jiling Zeng
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Center, Guangzhou, China
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20
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Liu G, Zhang X, Wang J, Li L, Cao J, Yin C, Liu Y, Chen G, Lv J, Xu X, Wang J, Huang X, Xu D. Facile preparation of biomimetic mineralized COFs based on magnetic silk fibroin and its effective extraction of sulforaphane from cruciferous vegetables. Food Chem 2024; 434:137482. [PMID: 37722339 DOI: 10.1016/j.foodchem.2023.137482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/11/2023] [Accepted: 09/11/2023] [Indexed: 09/20/2023]
Abstract
A novel biomimetic mineralized covalent organic framework (BM-COF) was prepared based on magnetic silk fibroin and a new sulforaphane pretreatment technology was constructed. First, metal coordination was performed on the surface of silk fibroin, and nanoparticles were deposited by in-situ mineralization after co-precipitation. Then, biomineralized COFs were prepared by in-situ self-assembly of a COF layer on Fe3O4@silk fibroin surface guided by interfacial directional growth technology. The BM-COFs had a multilayer structure, large specific surface area and pore volume, and superparamagnetic properties, which make them an ideal adsorbent. The adsorption of sulforaphane by BM-COFs is mainly multi-molecular layer adsorption and chemisorption, there might be electrostatic action, π-stacking and hydrogen bonding in the adsorption process. The composite material was successfully used for the pretreatment of sulforaphane in cruciferous vegetables. An extraction time of 30 min gave extraction efficiencies as high as 92%, and the recovery could reach more than 73%.
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Affiliation(s)
- Guangyang Liu
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China; Hebei Key Laboratory of Quality and Safety Analysis-Testing for Agro-Products and Food, Hebei North University, Zhangjiakou 075000, China.
| | - Xuan Zhang
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China; Hebei Key Laboratory of Quality and Safety Analysis-Testing for Agro-Products and Food, Hebei North University, Zhangjiakou 075000, China; Southwest University, Chongqing 400715, China.
| | - Jian Wang
- Hebei Key Laboratory of Quality and Safety Analysis-Testing for Agro-Products and Food, Hebei North University, Zhangjiakou 075000, China.
| | - Lingyun Li
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China.
| | - Jiayong Cao
- Hebei Key Laboratory of Quality and Safety Analysis-Testing for Agro-Products and Food, Hebei North University, Zhangjiakou 075000, China.
| | - Chen Yin
- Hebei Key Laboratory of Quality and Safety Analysis-Testing for Agro-Products and Food, Hebei North University, Zhangjiakou 075000, China.
| | - Yuan Liu
- Hebei Key Laboratory of Quality and Safety Analysis-Testing for Agro-Products and Food, Hebei North University, Zhangjiakou 075000, China.
| | - Ge Chen
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China.
| | - Jun Lv
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China.
| | - Xiaomin Xu
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Jing Wang
- Institute of Quality Standard and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Product Quality and Safety, Ministry of Agriculture Beijing, 100081 Beijing, China.
| | - Xiaodong Huang
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China.
| | - Donghui Xu
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China; Southwest University, Chongqing 400715, China.
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Chen M, Xia H, Zuo X, Tang D, Zhou H, Huang Z, Guo A, Lv J. Screening and characterization of lactic acid bacteria and fermentation of gamma-aminobutyric acid-enriched bamboo shoots. Front Microbiol 2024; 15:1333538. [PMID: 38374919 PMCID: PMC10876094 DOI: 10.3389/fmicb.2024.1333538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/23/2024] [Indexed: 02/21/2024] Open
Abstract
In order to produce fermented bamboo shoots with functional properties, two strains of lactic acid bacteria were selected for inoculation and fermentation. One strain, Lactiplantibacillus plantarum R1, exhibited prominent potential probiotic properties (including gastrointestinal condition tolerance, adhesion ability, antimicrobial ability, and antibiotic resistance), while the other, Levilactobacillus brevis R2, demonstrated the capability of high γ-aminobutyric acid (GABA) production (913.99 ± 14.2 mg/L). The synergistic inoculation of both strains during bamboo shoot fermentation led to a remarkable increase in GABA content (382.31 ± 12.17 mg/kg), surpassing that of naturally fermented bamboo shoots by more than 4.5 times and outperforming mono-inoculated fermentation. Simultaneously, the nitrite content was maintained at a safe level (5.96 ± 1.81 mg/kg). Besides, inoculated fermented bamboo shoots exhibited an increased crude fiber content (16.58 ± 0.04 g/100 g) and reduced fat content (0.39 ± 0.02 g/100 g). Sensory evaluation results indicated a high overall acceptability for the synergistically inoculated fermented bamboo shoots. This study may provide a strategy for the safe and rapid fermentation of bamboo shoots and lay the groundwork for the development of functional vegetable products enriched with GABA.
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Affiliation(s)
- Meilin Chen
- College of Food Science and Technology, Huazhong Agriculture University, Wuhan, Hubei, China
| | - Hongqiu Xia
- Liunan District Modern Agricultural Industry Service Center of Liuzhou City, Liuzhou, Guangxi, China
| | - Xifeng Zuo
- College of Food Science and Technology, Huazhong Agriculture University, Wuhan, Hubei, China
| | - Danping Tang
- Liunan District Modern Agricultural Industry Service Center of Liuzhou City, Liuzhou, Guangxi, China
| | - Haoyu Zhou
- College of Food Science and Technology, Huazhong Agriculture University, Wuhan, Hubei, China
| | - Zijun Huang
- College of Food Science and Technology, Huazhong Agriculture University, Wuhan, Hubei, China
| | - Ailing Guo
- College of Food Science and Technology, Huazhong Agriculture University, Wuhan, Hubei, China
| | - Jun Lv
- Institute of Infection and Immunity, Taihe Hospital, Hubei University of Medicine, Shiyan, China
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22
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Hu W, Cheng B, Su L, Lv J, Zhu J. Uric acid is negatively associated with cognition in the first- episode of schizophrenia. Encephale 2024; 50:54-58. [PMID: 36907671 DOI: 10.1016/j.encep.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 12/14/2022] [Accepted: 01/10/2023] [Indexed: 03/12/2023]
Abstract
BACKGROUND We explored the relationship between levels of serum uric acid (UA) and cognitive impairment in people with schizophrenia to order to better protect and improve cognitive function in such patients. METHODS A uricase method evaluated serum UA levels in 82 individuals with first-episode schizophrenia and in 39 healthy controls. The Brief Psychiatric Rating Scale (BPRS) and the event-related potential P300 were used to assess the patient's psychiatric symptoms and cognitive functioning. The link between serum UA levels, BPRS scores, and P300 was investigated. RESULTS Prior to treatment, serum UA levels and latency N3 in the study group were significantly higher than in the control group, whereas the amplitude P3 was considerably lower. After therapy, the study group's BPRS scores, serum UA levels, latency N3, and amplitude P3 were lower than before treatment. According to correlation analysis, serum UA levels in the pre-treatment study group significantly positively correlated with BPRS score and latency N3 but not amplitude P3. After therapy, serum UA levels were no longer substantially related to the BPRS score or amplitude P3 but strongly and positively correlated with latency N3. CONCLUSIONS First-episode schizophrenia patients have higher serum UA levels than the general population which partly reflects poor cognitive performance. Improving patients' cognitive function may be facilitated by lowering serum UA levels.
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Affiliation(s)
- W Hu
- Department of Psychiatry, The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China; Key Laboratory of Brain Diseases Bioinformation (Xuzhou Medical University), Xuzhou, Jiangsu, China; The Key Lab of Psychiatry, Xuzhou Medical University, Xuzhou, Jiangsu, China.
| | - B Cheng
- Department of Psychiatry, The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China; Key Laboratory of Brain Diseases Bioinformation (Xuzhou Medical University), Xuzhou, Jiangsu, China; The Key Lab of Psychiatry, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - L Su
- Yangzhou Sida Health Consulting Co., LTD, Yangzhou, Jiangsu, China
| | - J Lv
- Department of Psychiatry, The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - J Zhu
- Department of Psychiatry, The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China; Key Laboratory of Brain Diseases Bioinformation (Xuzhou Medical University), Xuzhou, Jiangsu, China; The Key Lab of Psychiatry, Xuzhou Medical University, Xuzhou, Jiangsu, China.
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Pang Y, Lv J, Wu T, Yu C, Guo Y, Chen Y, Yang L, Millwood IY, Walters RG, Yang X, Stevens R, Clarke R, Chen J, Li L, Chen Z, Kartsonaki C. Associations of diabetes, circulating protein biomarkers, and risk of pancreatic cancer. Br J Cancer 2024; 130:504-510. [PMID: 38129526 PMCID: PMC10844301 DOI: 10.1038/s41416-023-02533-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Type 2 diabetes (T2D) is associated with higher risk of pancreatic cancer (PC), but the underlying mechanisms are not fully understood. METHODS We conducted a case-subcohort study involving 610 PC cases and 623 subcohort participants with 92 protein biomarkers measured in baseline plasma samples. Genetically-instrumented T2D was derived using 86 single-nucleotide polymorphisms (SNPs), including insulin resistance (IR) SNPs. RESULTS In observational analyses of 623 subcohort participants (mean age, 52 years; 61% women), T2D was positively associated with 13 proteins (SD difference: IL6: 0.52 [0.23-0.81]; IL10: 0.41 [0.12-0.70]), of which 8 were nominally associated with incident PC. The 8 proteins potentially mediated 36.9% (18.7-75.0%) of the association between T2D and PC. In MR, no associations were observed for genetically-determined T2D with proteins, but there were positive associations of genetically-determined IR with IL6 and IL10 (SD difference: 1.23 [0.05-2.41] and 1.28 [0.31-2.24]). In two-sample MR, fasting insulin was associated with both IL6 and PC, but no association was observed between IL6 and PC. CONCLUSIONS Proteomics were likely to explain the association between T2D and PC, but were not causal mediators. Elevated fasting insulin driven by insulin resistance might explain the associations of T2D, proteomics, and PC.
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Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ting Wu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Yu Guo
- National Clinical Research Center of Cardiovascular Diseases, National Center for Cardiovascular Diseases, Beijing, China
- Fuwai Hospital, Chinese Academy of Medical Sciences, 167 Beilishi Road, Xicheng District, 100037, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, 37 Guangqu Road, 100021, Beijing, China
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Xue J, Lv J, Liu L, Duan F, Shi A, Ji X, Ding L. Maltodextrin-binding protein as a key factor in Cronobacter sakazakii survival under desiccation stress. Food Res Int 2024; 177:113871. [PMID: 38225116 DOI: 10.1016/j.foodres.2023.113871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/07/2023] [Accepted: 12/14/2023] [Indexed: 01/17/2024]
Abstract
Cronobacter sakazakii (C. sakazakii) is a notorious pathogen responsible for infections in infants and newborns, often transmitted through contaminated infant formula. Despite the use of traditional pasteurization methods, which can reduce microbial contamination, there remains a significant risk of pathogenic C. sakazakii surviving due to its exceptional stress tolerance. In our study, we employed a comparative proteomic approach by comparing wild-type strains with gene knockout strains to identify the essential genes crucial for the successful survival of C. sakazakii during desiccation. Our investigation revealed the significance of envZ-ompR, recA, and flhD gene cassettes in contributing to desiccation tolerance in C. sakazakii. Furthermore, through our comparative proteomic profiling, we identified the maltodextrin-binding protein encoded by ESA_03421 as a potential factor influencing dry tolerance. This protein is regulated by EnvZ-OmpR, RecA, and FlhD. Notably, the knockout of ESA_03421 resulted in a 150% greater reduction in Log CFU compared to the wild-type C. sakazakii. Overall, our findings offer valuable insights into the mechanisms underlying C. sakazakii desiccation tolerance and provide potential targets for the development of new antimicrobial strategies aimed at reducing the risk of infections in infants and newborns.
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Affiliation(s)
- Juan Xue
- Institute of Infection and Immunity, Department of Neurology, Department of Critical Care Medicine,Hubei Provincial Clinical Research Center for Umbilical Cord Blood Hematopoietic Stem Cells, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Jun Lv
- Institute of Infection and Immunity, Department of Neurology, Department of Critical Care Medicine,Hubei Provincial Clinical Research Center for Umbilical Cord Blood Hematopoietic Stem Cells, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Lanfang Liu
- Shiyan Center for Disease Control and Prevention, Shiyan, Hubei, China
| | - Fangfang Duan
- Institute of Infection and Immunity, Department of Neurology, Department of Critical Care Medicine,Hubei Provincial Clinical Research Center for Umbilical Cord Blood Hematopoietic Stem Cells, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Aiying Shi
- School of Medicine, Nankai University, Tianjin, China
| | - Xuemeng Ji
- School of Medicine, Nankai University, Tianjin, China.
| | - Li Ding
- Institute of Infection and Immunity, Department of Neurology, Department of Critical Care Medicine,Hubei Provincial Clinical Research Center for Umbilical Cord Blood Hematopoietic Stem Cells, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
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25
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Wang X, Chen L, Shi K, Lv J, Sun D, Pei P, Yang L, Chen Y, Du H, Liu J, Yang X, Barnard M, Chen J, Chen Z, Li L, Yu C. Diabetes and chronic kidney disease in Chinese adults: a population-based cohort study. BMJ Open Diabetes Res Care 2024; 12:e003721. [PMID: 38267203 PMCID: PMC10823934 DOI: 10.1136/bmjdrc-2023-003721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/05/2023] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION Cohort evidence of the association of diabetes mellitus (DM) with chronic kidney disease (CKD) is limited. Previous studies often describe patients with kidney disease and diabetes as diabetic kidney disease (DKD) or CKD, ignoring other subtypes. The present study aimed to assess the prospective association of diabetes status (no diabetes, pre-diabetes, screened diabetes, previously diagnosed controlled/uncontrolled diabetes with/without antidiabetic treatment) and random plasma glucose (RPG) with CKD risk (including CKD subtypes) among Chinese adults. RESEARCH DESIGN AND METHODS The present study included 472 545 participants from the China Kadoorie Biobank, using baseline information on diabetes and RPG. The incident CKD and its subtypes were collected through linkage with the national health insurance system during follow-up. Cox regression models were used to calculate the HR and 95% CI. RESULTS During 11.8 years of mean follow-up, 5417 adults developed CKD. Screened plus previously diagnosed diabetes was positively associated with CKD (HR=4.52, 95% CI 4.23 to 4.83), DKD (HR=33.85, 95% CI 29.56 to 38.76), and glomerulonephritis (HR=1.66, 95% CI 1.40 to 1.97). In those with previously diagnosed diabetes, participants with uncontrolled diabetes represented higher risks of CKD, DKD, and glomerulonephritis compared with those with controlled RPG. The risk of DKD was found to rise in participants with pre-diabetes and increased with the elevated RPG level, even in those without diabetes. CONCLUSIONS Among Chinese adults, diabetes was positively associated with CKD, DKD, and glomerulonephritis. Screen-detected and uncontrolled DM had a high risk of CKD, and pre-diabetes was associated with a greater risk of DKD, highlighting the significance of lifelong glycemic management.
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Affiliation(s)
- Xue Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Lu Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Kexiang Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jiaqiu Liu
- NCDs Prevention and Control Department, Pengzhou CDC, Pengzhou, China
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Maxim Barnard
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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26
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Lv J, Wang H, Cheng X, Chen Y, Wang D, Zhang L, Cao Q, Tang H, Hu S, Gao K, Xun M, Wang J, Wang Z, Zhu B, Cui C, Gao Z, Guo L, Yu S, Jiang L, Yin Y, Zhang J, Chen B, Wang W, Chai R, Chen ZY, Li H, Shu Y. AAV1-hOTOF gene therapy for autosomal recessive deafness 9: a single-arm trial. Lancet 2024:S0140-6736(23)02874-X. [PMID: 38280389 DOI: 10.1016/s0140-6736(23)02874-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/21/2023] [Accepted: 12/21/2023] [Indexed: 01/29/2024]
Abstract
BACKGROUND Autosomal recessive deafness 9, caused by mutations of the OTOF gene, is characterised by congenital or prelingual, severe-to-complete, bilateral hearing loss. However, no pharmacological treatment is currently available for congenital deafness. In this Article, we report the safety and efficacy of gene therapy with an adeno-associated virus (AAV) serotype 1 carrying a human OTOF transgene (AAV1-hOTOF) as a treatment for children with autosomal recessive deafness 9. METHODS This single-arm, single-centre trial enrolled children (aged 1-18 years) with severe-to-complete hearing loss and confirmed mutations in both alleles of OTOF, and without bilateral cochlear implants. A single injection of AAV1-hOTOF was administered into the cochlea through the round window. The primary endpoint was dose-limiting toxicity at 6 weeks after injection. Auditory function and speech were assessed by appropriate auditory perception evaluation tools. All analyses were done according to the intention-to-treat principle. This trial is registered with Chinese Clinical Trial Registry, ChiCTR2200063181, and is ongoing. FINDINGS Between Oct 19, 2022, and June 9, 2023, we screened 425 participants for eligibility and enrolled six children for AAV1-hOTOF gene therapy (one received a dose of 9 × 1011 vector genomes [vg] and five received 1·5 × 1012 vg). All participants completed follow-up visits up to week 26. No dose-limiting toxicity or serious adverse events occurred. In total, 48 adverse events were observed; 46 (96%) were grade 1-2 and two (4%) were grade 3 (decreased neutrophil count in one participant). Five children had hearing recovery, shown by a 40-57 dB reduction in the average auditory brainstem response (ABR) thresholds at 0·5-4·0 kHz. In the participant who received the 9 × 1011 vg dose, the average ABR threshold was improved from greater than 95 dB at baseline to 68 dB at 4 weeks, 53 dB at 13 weeks, and 45 dB at 26 weeks. In those who received 1·5 × 1012 AAV1-hOTOF, the average ABR thresholds changed from greater than 95 dB at baseline to 48 dB, 38 dB, 40 dB, and 55 dB in four children with hearing recovery at 26 weeks. Speech perception was improved in participants who had hearing recovery. INTERPRETATION AAV1-hOTOF gene therapy is safe and efficacious as a novel treatment for children with autosomal recessive deafness 9. FUNDING National Natural Science Foundation of China, National Key R&D Program of China, Science and Technology Commission of Shanghai Municipality, and Shanghai Refreshgene Therapeutics.
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Affiliation(s)
- Jun Lv
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Institutes of Biomedical Science, Fudan University, Shanghai, China
| | - Hui Wang
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Xiaoting Cheng
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Yuxin Chen
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Daqi Wang
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Longlong Zhang
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Qi Cao
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Honghai Tang
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Shaowei Hu
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Kaiyu Gao
- Research and Development Department, Shanghai Refreshgene Therapeutics, Shanghai, China
| | - Mengzhao Xun
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Jinghan Wang
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Zijing Wang
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Biyun Zhu
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Chong Cui
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Institutes of Biomedical Science, Fudan University, Shanghai, China
| | - Ziwen Gao
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Luo Guo
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Sha Yu
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Luoying Jiang
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Institutes of Biomedical Science, Fudan University, Shanghai, China
| | - Yanbo Yin
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Jiajia Zhang
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Institutes of Biomedical Science, Fudan University, Shanghai, China
| | - Bing Chen
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Wuqing Wang
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Renjie Chai
- State Key Laboratory of Digital Medical Engineering, Southeast University, Nanjing, China; Department of Otolaryngology Head and Neck Surgery of Zhongda Hospital, Southeast University, Nanjing, China; Advanced Institute for Life and Health, Southeast University, Nanjing, China; Jiangsu Province High-Tech Key Laboratory for Bio-Medical Research, Southeast University, Nanjing, China; Co-Innovation Center of Neuroregeneration, Nantong University, Nantong, China; Department of Neurology of Aerospace Center Hospital, Beijing Institute of Technology, Beijing, China; School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Zheng-Yi Chen
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA; Graduate Program in Speech and Hearing Bioscience and Technology and Program in Neuroscience, Harvard Medical School, Boston, MA, USA; Eaton-Peabody Laboratory, Massachusetts Eye and Ear, Boston, MA, USA
| | - Huawei Li
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Institutes of Biomedical Science, Fudan University, Shanghai, China
| | - Yilai Shu
- ENT Institute and Otorhinolaryngology Department, Eye & ENT Hospital, Fudan University, Shanghai, China; NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Institutes of Biomedical Science, Fudan University, Shanghai, China.
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27
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Hua Y, Liu G, Lin Z, Jie Z, Zhao C, Han J, Chen G, Li L, Huang X, Liu Z, Lv J, Xu D. Engineering of zeolitic imidazolate frameworks based on magnetic three-dimensional graphene as effective and reusable adsorbent to enhance the adsorption and removal of caffeine from tea samples. Food Chem 2024; 431:137143. [PMID: 37604003 DOI: 10.1016/j.foodchem.2023.137143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/23/2023]
Abstract
As more and more adverse health effects of caffeine are being discovered, decaffeinated drinks are receiving increasing attention. In this work, a magnetic imidazole zeolite backbone compounded with three-dimensional graphene was successfully synthesized as a solid adsorbent using a layer-by-layer self-assembly technique, which can rapidly and effectively adsorb caffeine from tea. Meanwhile, the structure and properties of caffeine in tea were investigated by various physicochemical characterization tools. The analytical data showed that Fe3O4@3DGA@ZIF-8 had a specific surface area of 162.9754 m2/g and an adsorption capacity of up to 19.57 mg/g with a maximum adsorption rate of 96.55%, which could be maintained with good adsorption repeated utilization three times. The adsorption isotherm and the adsorption kinetic better fit with the Langmuir model and the preudo-second order kinetic model, respectively. Therefore, Fe3O4@3DGA@ZIF-8 is a good magnetic adsorbent for the separation and the effective removal of caffeine from tea sample.
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Affiliation(s)
- Yuwei Hua
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Guangyang Liu
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China.
| | - Zhihao Lin
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Zhou Jie
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Chenxi Zhao
- College of Horticulture, Northeast Agricultural University, Harbin 150030, PR China
| | - Jiatong Han
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Ge Chen
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Linyun Li
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Xiaodong Huang
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Zhongxiao Liu
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Jun Lv
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China
| | - Donghui Xu
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Key Laboratory of Vegetables Quality and Safety Control, Ministry of Agriculture and Rural Affairs of China, Beijing 100081, China.
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28
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Holmes MV, Kartsonaki C, Boxall R, Lin K, Reeve N, Yu C, Lv J, Bennett DA, Hill MR, Yang L, Chen Y, Du H, Turnbull I, Collins R, Clarke RJ, Tobin MD, Li L, Millwood IY, Chen Z, Walters RG. PCSK9 genetic variants and risk of vascular and non-vascular diseases in Chinese and UK populations. Eur J Prev Cardiol 2024:zwae009. [PMID: 38198221 DOI: 10.1093/eurjpc/zwae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/15/2023] [Accepted: 01/08/2024] [Indexed: 01/12/2024]
Abstract
AIM Lowering low-density lipoprotein cholesterol (LDL-C) through PCSK9 inhibition represents a new therapeutic approach to preventing and treating cardiovascular disease (CVD). Phenome-wide analyses of PCSK9 genetic variants in large biobanks can help to identify unexpected effects of PCSK9 inhibition. METHODS In the prospective China Kadoorie Biobank, we constructed a genetic score using three variants at the PCSK9 locus associated with directly-measured LDL-C (PCSK9-GS). Logistic regression gave estimated odds ratios (ORs) for PCSK9-GS associations with CVD and non-CVD outcomes, scaled to 1SD lower LDL-C. RESULTS PCSK9-GS was associated with lower risks of carotid plaque (n=8340 cases; OR=0.61 [95%CI: 0.45-0.83]; P=0.0015), major occlusive vascular events (n=15,752; 0.80 [0.67-0.95]; P=0.011), and ischaemic stroke (n=11,467; 0.80 [0.66-0.98]; P=0.029). However, PCSK9-GS was also associated with higher risk of hospitalisation with chronic obstructive pulmonary disease (COPD: n=6836; 1.38 [1.08-1.76]; P=0.0089), and with even higher risk of fatal exacerbations among individuals with pre-existing COPD (n=730; 3.61 [1.71-7.60]; P=7.3x10-4). We also replicated associations for a PCSK9 variant, reported in UK Biobank, with increased risks of acute upper respiratory tract infection (URTI) (pooled OR after meta-analysis of 1.87 ([1.38-2.54]; P=5.4x10-5) and self-reported asthma (pooled OR 1.17 ([1.04-1.30]; P=0.0071). There was no association of a polygenic LDL-C score with COPD hospitalisation, COPD exacerbation, or URTI. CONCLUSIONS LDL-C-lowering PCSK9 genetic variants are associated with lower risk of subclinical and clinical atherosclerotic vascular disease, but higher risks of respiratory diseases. Pharmacovigilance studies may be required to monitor patients treated with therapeutic PCSK9 inhibitors for exacerbations of respiratory diseases or respiratory tract infections.
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Affiliation(s)
- Michael V Holmes
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ruth Boxall
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kuang Lin
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicola Reeve
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Derrick A Bennett
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Michael R Hill
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iain Turnbull
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robert J Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Martin D Tobin
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- National Institute for Health and Care Research, Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Fu J, Deng Y, Ma Y, Man S, Yang X, Yu C, Lv J, Wang B, Li L. National and Provincial-Level Prevalence and Risk Factors of Carotid Atherosclerosis in Chinese Adults. JAMA Netw Open 2024; 7:e2351225. [PMID: 38206625 PMCID: PMC10784858 DOI: 10.1001/jamanetworkopen.2023.51225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/21/2023] [Indexed: 01/12/2024] Open
Abstract
Importance Epidemiologic studies on carotid atherosclerosis (CAS) based on nationwide ultrasonography measurements can contribute to understanding the future risk of cardiovascular diseases and identifying high-risk populations, thereby proposing more targeted prevention and treatment measures. Objectives To estimate the prevalence of CAS within the general population of China and to investigate its distribution among populations with potential risk factors and variation across diverse geographic regions. Design, Setting, and Participants This multicenter, population-based cross-sectional study used China's largest health check-up chain database to study 10 733 975 individuals aged 20 years or older from all 31 provinces in China who underwent check-ups from January 1, 2017, to June 30, 2022. Main Outcomes and Measures Carotid atherosclerosis was assessed and graded using ultrasonography as increased carotid intima-media thickness (cIMT), carotid plaque (CP), and carotid stenosis (CS). The overall and stratified prevalences were estimated among the general population and various subpopulations based on demographic characteristics, geographic regions, and cardiovascular disease risk factors. Mixed-effects regression models were used to analyze the risk factors for CAS. Results Among 10 733 975 Chinese participants (mean [SD] age, 47.7 [13.4] years; 5 861 566 [54.6%] male), the estimated prevalences were 26.2% (95% CI, 25.0%-27.4%) for increased cIMT, 21.0% (95% CI, 19.8%-22.2%) for CP, and 0.56% (95% CI, 0.36%-0.76%) for CS. The prevalence of all CAS grades was higher among older adults (eg, increased cIMT: aged ≥80 years, 92.7%; 95% CI, 92.2%-93.3%), male participants (29.6%; 95% CI, 28.4%-30.7%), those residing in northern China (31.0%; 95% CI, 29.1%-32.9%), and those who had comorbid conditions, such as hypertension (50.8%; 95% CI, 49.7%-51.9%), diabetes (59.0%; 95% CI, 57.8%-60.1%), dyslipidemia (32.1%; 95% CI, 30.8%-33.3%), and metabolic syndrome (31.0%; 95% CI, 29.1%-32.9%). Most cardiovascular disease risk factors were independent risk factors for all CAS stages (eg, hypertension: 1.60 [95% CI, 1.60-1.61] for increased cIMT, 1.62 [95% CI, 1.62-1.63] for CP, and 1.48 [95% CI, 1.45-1.51] for CS). Moreover, the magnitude of the association between several cardiovascular disease risk factors and increased cIMT and CP differed between the sexes and geographic regions. Conclusions and Relevance These findings suggest that nearly one-quarter of Chinese adults have increased cIMT or CP. The burden of this disease is unevenly distributed across geographic regions and subpopulations and may require different levels of local planning, support, and management. Addressing these disparities is crucial for effectively preventing and managing cardiovascular and cerebrovascular diseases in China.
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Affiliation(s)
- Jingzhu Fu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuhan Deng
- Meinian Institute of Health, Beijing, China
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
- Chongqing Research Institute of Big Data, Peking University, Chongqing, China
| | - Yuan Ma
- Meinian Institute of Health, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Sailimai Man
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Meinian Institute of Health, Beijing, China
| | - Xiaochen Yang
- Meinian Institute of Health, Beijing, China
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Bo Wang
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Meinian Institute of Health, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Meinian Public Health Institute, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
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Wang Z, Cao Q, Hu S, Fan X, Lv J, Wang H, Wang W, Li H, Shu Y. [Study on gene therapy for DPOAE and ABR threshold changes in adult Otof-/- mice]. Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2024; 38:49-56. [PMID: 38297849 DOI: 10.13201/j.issn.2096-7993.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Indexed: 02/02/2024]
Abstract
Objective:This study aims to analyze the threshold changes in distortion product otoacoustic emissions(DPOAE) and auditory brainstem response(ABR) in adult Otof-/- mice before and after gene therapy, evaluating its effectiveness and exploring methods for assessing hearing recovery post-treatment. Methods:At the age of 4 weeks, adult Otof-/- mice received an inner ear injection of a therapeutic agent containing intein-mediated recombination of the OTOF gene, delivered via dual AAV vectors through the round window membrane(RWM). Immunofluorescence staining assessed the proportion of inner ear hair cells with restored otoferlin expression and the number of synapses.Statistical analysis was performed to compare the DPOAE and ABR thresholds before and after the treatment. Results:AAV-PHP. eB demonstrates high transduction efficiency in inner ear hair cells. The therapeutic regimen corrected hearing loss in adult Otof-/- mice without impacting auditory function in wild-type mice. The changes in DPOAE and ABR thresholds after gene therapy are significantly correlated at 16 kHz. Post-treatment,a slight increase in DPOAE was observeds,followed by a recovery trend at 2 months post-treatment. Conclusion:Gene therapy significantly restored hearing in adult Otof-/- mice, though the surgical delivery may cause transient hearing damage. Precise and gentle surgical techniques are essential to maximize gene therapy's efficacy.
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Affiliation(s)
- Zijing Wang
- Department of Otolaryngology,Second Affiliated Hospital of South China University Hengyang,421001,China
- Otolaryngology Department of Fudan University Affiliated Eye,Ear,Nose and Throat Hospital
| | - Qi Cao
- Department of Otolaryngology,Second Affiliated Hospital of South China University Hengyang,421001,China
- Otolaryngology Department of Fudan University Affiliated Eye,Ear,Nose and Throat Hospital
| | - Shaowei Hu
- Otolaryngology Department of Fudan University Affiliated Eye,Ear,Nose and Throat Hospital
| | - Xintai Fan
- Otolaryngology Department of Fudan University Affiliated Eye,Ear,Nose and Throat Hospital
| | - Jun Lv
- Otolaryngology Department of Fudan University Affiliated Eye,Ear,Nose and Throat Hospital
| | - Hui Wang
- Otolaryngology Department of Fudan University Affiliated Eye,Ear,Nose and Throat Hospital
| | - Wuqing Wang
- Otolaryngology Department of Fudan University Affiliated Eye,Ear,Nose and Throat Hospital
| | - Huawei Li
- Otolaryngology Department of Fudan University Affiliated Eye,Ear,Nose and Throat Hospital
| | - Yilai Shu
- Department of Otolaryngology,Second Affiliated Hospital of South China University Hengyang,421001,China
- Otolaryngology Department of Fudan University Affiliated Eye,Ear,Nose and Throat Hospital
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31
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Hong X, Miao K, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Hu R, Pang Z, Yu M, Wang H, Wu X, Liu Y, Gao W, Li L. Association of psychological distress and DNA methylation: A 5-year longitudinal population-based twin study. Psychiatry Clin Neurosci 2024; 78:51-59. [PMID: 37793011 DOI: 10.1111/pcn.13606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 10/06/2023]
Abstract
AIM To identify the psychological distress (PD)-associated 5'-cytosine-phosphate-guanine-3' sites (CpGs), and investigate the temporal relationship between dynamic changes in DNA methylation (DNAm) and PD. METHODS This study included 1084 twins from the Chinese National Twin Register (CNTR). The CNTR conducted epidemiological investigations and blood withdrawal twice in 2013 and 2018. These included twins were used to perform epigenome-wide association studies (EWASs) and to validate the previously reported PD-associated CpGs selected from previous EWASs in PubMed, Embase, and the EWAS catalog. Next, a cross-lagged study was performed to examine the temporality between changes in DNAm and PD in 308 twins who completed both 2013 and 2018 surveys. RESULTS The EWAS analysis of our study identified 25 CpGs. In the validation analysis, 741 CpGs from 29 previous EWASs on PD were selected for validation, and 101 CpGs were validated to be significant at a false discovery rate <0.05. The cross-lagged analysis found a unidirectional path from PD to DNAm at 14 CpGs, while no sites showed significance from DNAm to PD. CONCLUSIONS This study identified and validated PD-related CpGs in a Chinese twin population, and suggested that PD may be the cause of changes in DNAm over time. The findings provide new insights into the molecular mechanisms underlying PD pathophysiology.
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Affiliation(s)
- Xuanming Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Ke Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Runhua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education, Peking University, Beijing, China
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Xiao M, Li A, Wang Y, Yu C, Pang Y, Pei P, Yang L, Chen Y, Du H, Schmidt D, Avery D, Sun Q, Chen J, Chen Z, Li L, Lv J, Sun D. A wide landscape of morbidity and mortality risk associated with marital status in 0.5 million Chinese men and women: a prospective cohort study. Lancet Reg Health West Pac 2024; 42:100948. [PMID: 38357394 PMCID: PMC10865043 DOI: 10.1016/j.lanwpc.2023.100948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/26/2023] [Accepted: 10/16/2023] [Indexed: 02/16/2024]
Abstract
Background A comprehensive depiction of long-term health impacts of marital status is lacking. Methods Sex-stratified phenome-wide association analyses (PheWAS) of marital status (living with vs. without a spouse) were performed using baseline (2004-2008) and follow-up information (ICD10-coded events till Dec 31, 2017) from the China Kadoorie Biobank (CKB). We estimated adjusted hazard ratios (aHRs) to evaluate the associations of marital status with morbidity risks of phenome-wide significant diseases or sex-specific top-10 death causes in China documented in 2017. Additionally, the association between marital status and mortality risks among participants with major chronic diseases at baseline was assessed. Findings During up to 11.1 years of the median follow-up period, 1,946,380 incident health events were recorded among 210,202 men and 302,521 women aged 30-79. Marital status was found to have phenome-wide significant associations with thirteen diseases among men (p < 9.92 × 10-5) and nine diseases among women (p < 9.33 × 10-5), respectively. After adjusting for all disease-specific covariates in the final model, participants living without a spouse showed increased risks of schizophrenia, schizotypal and delusional disorders (aHR [95% CI]: 2.55, [1.83-3.56] for men; 1.49, [1.13-1.97] for women) compared with their counterparts. Additional higher risks in overall mental and behavioural disorder (1.31, 1.13-1.53), cardiovascular disease (1.07, 1.04-1.10) and cancer (1.06, 1.00-1.12) were only observed among men without a spouse, whereas women living without a spouse were at lower risks of developing genitourinary diseases (0.89, 0.85-0.93) and injury & poisoning (0.93, 0.88-0.97). Among 282,810 participants with major chronic diseases at baseline, 39,166 deaths were recorded. Increased mortality risks for those without a spouse were observed in 12 of 21 diseases among male patients and one of 23 among female patients. For patients with any self-reported disease at baseline, compared with those living with a spouse, the aHRs (95% CIs) of mortality risk were 1.29 (1.24-1.34) and 1.04 (1.00-1.07) among men and women without a spouse (pinteraction<0.0001), respectively. Interpretation Long-term associations of marital status with morbidity and mortality risks are diverse among middle-aged Chinese adults, and the adverse impacts due to living without a spouse are more profound among men. Marital status may be an influential factor for health needs. Funding The National Natural Science Foundation of China, the Kadoorie Charitable Foundation, the National Key R&D Program of China, the Chinese Ministry of Science and Technology, and the UK Wellcome Trust.
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Affiliation(s)
- Meng Xiao
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Aolin Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Yueqing Wang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Dan Schmidt
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Qiang Sun
- NCDs Prevention and Control Department, Pengzhou CDC, Pengzhou, Sichuan, 611930, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
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Bennett DA, Parish S, Millwood IY, Guo Y, Chen Y, Turnbull I, Yang L, Lv J, Yu C, Davey Smith G, Wang Y, Wang Y, Peto R, Collins R, Walters RG, Li L, Chen Z, Clarke R. MTHFR and risk of stroke and heart disease in a low-folate population: a prospective study of 156 000 Chinese adults. Int J Epidemiol 2023; 52:1862-1869. [PMID: 37898918 PMCID: PMC10749746 DOI: 10.1093/ije/dyad147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 10/12/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND The relevance of folic acid for stroke prevention in low-folate populations such as in China is uncertain. Genetic studies of the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism, which increases plasma homocysteine (tHcy) levels, could clarify the causal relevance of elevated tHcy levels for stroke, ischaemic heart disease (IHD) and other diseases in populations without folic acid fortification. METHODS In the prospective China Kadoorie Biobank, 156 253 participants were genotyped for MTHFR and 12 240 developed a stroke during the 12-year follow-up. Logistic regression was used to estimate region-specific odds ratios (ORs) for total stroke and stroke types, IHD and other diseases comparing TT genotype for MTHFR C677T (two thymine alleles at position 677 of MTHFR C677T polymorphism) vs CC (two cytosine alleles) after adjustment for age and sex, and these were combined using inverse-variance weighting. RESULTS Overall, 21% of participants had TT genotypes, but this varied from 5% to 41% across the 10 study regions. Individuals with TT genotypes had 13% (adjusted OR 1.13, 95% CI 1.09-1.17) higher risks of any stroke [with a 2-fold stronger association with intracerebral haemorrhage (1.24, 1.17-1.32) than for ischaemic stroke (1.11, 1.07-1.15)] than the reference CC genotype. In contrast, MTHFR C677T was unrelated to risk of IHD or any other non-vascular diseases, including cancer, diabetes and chronic obstructive lung disease. CONCLUSIONS In Chinese adults, the MTHFR C677T polymorphism was associated with higher risks of stroke. The findings warrant corroboration by further trials of folic acid and implementation of mandatory folic acid fortification programmes for stroke prevention in low-folate populations.
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Affiliation(s)
- Derrick A Bennett
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sarah Parish
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, National Center for Cardiovascular Diseases, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iain Turnbull
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | | | - Yongjun Wang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yilong Wang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Richard Peto
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Zhu M, Lv J, Huang Y, Ma H, Li N, Wei X, Ji M, Ma Z, Song C, Wang C, Dai J, Tan F, Guo Y, Walters R, Millwood IY, Hung RJ, Christiani DC, Yu C, Jin G, Chen Z, Wei Q, Amos CI, Hu Z, Li L, Shen H. Ethnic differences of genetic risk and smoking in lung cancer: two prospective cohort studies. Int J Epidemiol 2023; 52:1815-1825. [PMID: 37676847 DOI: 10.1093/ije/dyad118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 08/23/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND The role of genetic background underlying the disparity of relative risk of smoking and lung cancer between European populations and East Asians remains unclear. METHODS To assess the role of ethnic differences in genetic factors associated with smoking-related risk of lung cancer, we first constructed ethnic-specific polygenic risk scores (PRSs) to quantify individual genetic risk of lung cancer in Chinese and European populations. Then, we compared genetic risk and smoking as well as their interactions on lung cancer between two cohorts, including the China Kadoorie Biobank (CKB) and the UK Biobank (UKB). We also evaluated the absolute risk reduction over a 5-year period. RESULTS Differences in compositions and association effects were observed between the Chinese-specific PRSs and European-specific PRSs, especially for smoking-related loci. The PRSs were consistently associated with lung cancer risk, but stronger associations were observed in smokers of the UKB [hazard ratio (HR) 1.26 vs 1.15, P = 0.028]. A significant interaction between genetic risk and smoking on lung cancer was observed in the UKB (RERI, 11.39 (95% CI, 7.01-17.94)], but not in the CKB. Obvious higher absolute risk was observed in nonsmokers of the CKB, and a greater absolute risk reduction was found in the UKB (10.95 vs 7.12 per 1000 person-years, P <0.001) by comparing heavy smokers with nonsmokers, especially for those at high genetic risk. CONCLUSIONS Ethnic differences in genetic factors and the high incidence of lung cancer in nonsmokers of East Asian ethnicity were involved in the disparity of smoking-related risk of lung cancer.
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Affiliation(s)
- Meng Zhu
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yanqian Huang
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoxia Wei
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Mengmeng Ji
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhimin Ma
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Ci Song
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Cheng Wang
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Fengwei Tan
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Guo
- Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Robin Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
| | - David C Christiani
- Department of Environmental Health, Harvard School of Public Health, Department of Medicine, Harvard Medical School/Massachusetts General Hospital, Boston, USA
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Guangfu Jin
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Qingyi Wei
- Duke Cancer Institute, Duke University Medical Center, Durham, USA
- Department of Population Health Sciences, Duke University School of Medicine, Durham, USA
| | - Christopher I Amos
- Baylor College of Medicine, Institute for Clinical and Translational Research, Houston, USA
| | - Zhibin Hu
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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Wang Y, Huang Q, Zhong G, Lv J, Guo Q, Ma Y, Wang X, Zeng J. Sequential PET/CT and pathological biomarker crosstalk predict response to PD-1 blockers alone or combined with sunitinib in propensity score-matched cohorts of cancer of unknown primary treatment. Front Oncol 2023; 13:1191611. [PMID: 38205137 PMCID: PMC10777842 DOI: 10.3389/fonc.2023.1191611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/26/2023] [Indexed: 01/12/2024] Open
Abstract
Introduction The efficacy of immune checkpoint inhibitors (ICIs), including toripalimab and pembrolizumab, has not been confirmed in the treatment of cancer of unknown primary (CUP), which has a very poor prognosis. Combined with anti-angiogenic therapies, ICIs are hypothesized to be effective in prolonging overall survival. The study aims to give evidence on the treatment effects of sunitinib combined with ICIs, find pathological biomarkers associated with changes in volumetric 18F FDG PET/CT parameters, and investigate inner associations among these markers associated with response on PET/CT. Methods The study recruited patients receiving combined treatment (ICIs + sunitinib), compared the effects of combined treatment with those of separate treatment and age-matched negative controls, and analyzed propensity score-matched (PSM) pairs. Markers associated with survival were identified, and their inner associations were tested using structural equation modeling. Results A total of 292 patients were enrolled in the final analysis, with 53 patients receiving combined treatment. Survival analysis demonstrated significantly prolonged survival in either combined or separate treatment, with the combined arm showing better response when PSM-paired using pre-treatment whole-body PET/CT parameters. The angiogenic markers KDR and VEGF mediate the PD-1 blockade impact on volumetric value changes in positive and negative manners. Conclusion The anti-angiogenic agent sunitinib may potentiate PD-1 blockade by diminishing angiogenesis or its downstream effects. The combined separate treatment increased the survival of CUP patients, and the responses could be evaluated using volumetric PET/CT parameters.
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Affiliation(s)
- Youlong Wang
- Hainan Hospital of PLA General Hospital, Department of General Surgery, Haitang District, Sanya, China
| | - Qi Huang
- Department of Clinical Laboratory, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guanqing Zhong
- Department of Clinical Laboratory, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jun Lv
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qinzhi Guo
- Pancreas Center of Guangdong Provincial People’s Hospital, Guangzhou, China
| | - Yifei Ma
- Department of Spine Surgery, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Xinjia Wang
- Department of Spine Surgery, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Jiling Zeng
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Center, Guangzhou, China
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Chan KH, Xia X, Liu C, Kan H, Doherty A, Yim SHL, Wright N, Kartsonaki C, Yang X, Stevens R, Chang X, Sun D, Yu C, Lv J, Li L, Ho KF, Lam KBH, Chen Z. Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China. Sci Total Environ 2023; 904:166647. [PMID: 37647956 PMCID: PMC10804935 DOI: 10.1016/j.scitotenv.2023.166647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/20/2023] [Accepted: 08/26/2023] [Indexed: 09/01/2023]
Abstract
BACKGROUND Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours. METHODS We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May-September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326-80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed. FINDINGS Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2-4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6-85.3] μg/m3 vs 41.6 [37.3-46.5] μg/m3), kitchen (102.4 [90.4-116.0] μg/m3 vs 52.3 [44.8-61.2] μg/m3) and living room (62.1 [57.3-67.3] μg/m3 vs 41.0 [37.1-45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700-1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64-0.66) and kitchen (0.52-0.59) levels, but only weakly correlated with community levels, especially in summer (0.15-0.34) and among solid fuel users (0.11-0.31). CONCLUSION Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5.
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Affiliation(s)
- Ka Hung Chan
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK; Oxford British Heart Foundation Centre of Research Excellence, University of Oxford, UK.
| | - Xi Xia
- School of Public Health, Xi'an Jiaotong University, China; The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong
| | - Cong Liu
- School of Public Health, Key Lab of Public Health Safety of the ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, China
| | - Aiden Doherty
- Oxford British Heart Foundation Centre of Research Excellence, University of Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK; National Institute of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, UK
| | - Steve Hung Lam Yim
- Asian School of the Environment, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Earth Observatory of Singapore, Nanyang Technological University, Singapore
| | - Neil Wright
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK; MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, UK
| | - Xiaoming Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK
| | - Rebecca Stevens
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK
| | - Xiaoyu Chang
- NCDs Prevention and Control Department, Sichuan CDC, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, China; Peking University Center for Public Health and Epidemic Preparedness and Response, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, China; Peking University Center for Public Health and Epidemic Preparedness and Response, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, China; Peking University Center for Public Health and Epidemic Preparedness and Response, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, China; Peking University Center for Public Health and Epidemic Preparedness and Response, China
| | - Kin-Fai Ho
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong.
| | - Kin Bong Hubert Lam
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, UK; MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, UK
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Zhang L, Wang H, Xun M, Tang H, Wang J, Lv J, Zhu B, Chen Y, Wang D, Hu S, Gao Z, Liu J, Chen ZY, Chen B, Li H, Shu Y. Preclinical evaluation of the efficacy and safety of AAV1-hOTOF in mice and nonhuman primates. Mol Ther Methods Clin Dev 2023; 31:101154. [PMID: 38027066 PMCID: PMC10679773 DOI: 10.1016/j.omtm.2023.101154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/07/2023] [Indexed: 12/01/2023]
Abstract
Pathogenic mutations in the OTOF gene cause autosomal recessive hearing loss (DFNB9), one of the most common forms of auditory neuropathy. There is no biological treatment for DFNB9. Here, we designed an OTOF gene therapy agent by dual-adeno-associated virus 1 (AAV1) carrying human OTOF coding sequences with the expression driven by the hair cell-specific promoter Myo15, AAV1-hOTOF. To develop a clinical application of AAV1-hOTOF gene therapy, we evaluated its efficacy and safety in animal models using pharmacodynamics, behavior, and histopathology. AAV1-hOTOF inner ear delivery significantly improved hearing in Otof-/- mice without affecting normal hearing in wild-type mice. AAV1 was predominately distributed to the cochlea, although it was detected in other organs such as the CNS and the liver, and no obvious toxic effects of AAV1-hOTOF were observed in mice. To further evaluate the safety of Myo15 promoter-driven AAV1-transgene, AAV1-GFP was delivered into the inner ear of Macaca fascicularis via the round window membrane. AAV1-GFP transduced 60%-94% of the inner hair cells along the cochlear turns. AAV1-GFP was detected in isolated organs and no significant adverse effects were detected. These results suggest that AAV1-hOTOF is well tolerated and effective in animals, providing critical support for its clinical translation.
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Affiliation(s)
- Longlong Zhang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Hui Wang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Mengzhao Xun
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Honghai Tang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Jinghan Wang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Jun Lv
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Biyun Zhu
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Yuxin Chen
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Daqi Wang
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Shaowei Hu
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Ziwen Gao
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Jianping Liu
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Zheng-Yi Chen
- Department of Otolaryngology-Head and Neck Surgery, Graduate Program in Speech and Hearing Bioscience and Technology and Program in Neuroscience, Harvard Medical School, Boston, MA 02115, USA
- Eaton-Peabody Laboratory, Massachusetts Eye and Ear, 243 Charles Street, Boston, MA 02114, USA
| | - Bing Chen
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Huawei Li
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
| | - Yilai Shu
- ENT Institute and Otorhinolaryngology Department of Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200031, China
- Institutes of Biomedical Science, Fudan University, Shanghai 200032, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai 200031, China
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Cai A, Xia P, Zhou X, He Y, Lv J. MiR-1275 Targeting SPARC Promotes Gambogic Acid-Induced Inhibition of Gastric Cancer. Biochem Genet 2023; 61:2481-2495. [PMID: 37118619 DOI: 10.1007/s10528-023-10381-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/11/2023] [Indexed: 04/30/2023]
Abstract
Gambogic acid (GA) has been observed to effectively impede the progression of numerous types of cancers. In this study, we investigated the effects of miR-1275 and Secreted Protein Acidic and Cysteine Rich (SPARC) on GA in gastric cancer (GC). miR-1275 and SPARC expression were determined in GC cell lines and tissues using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The correlation between miR-1275 and SPARC expression was ascertained using Pearson's correlation coefficient. Cell proliferation was assessed using the cell counting kit-8 (CCK-8) assay. The Transwell assay was conducted to examine cell migration. A dual-luciferase reporter assay was used to verify the regulatory relationship between miR-1275 and SPARC. The levels of SPARC, Bcl-2, and Bax proteins were estimated using western blotting. To verify the effects of GA on the growth of GC cells in vivo, a tumorigenesis experiment was performed in nude mice. GA suppressed GC cell viability and migration, facilitated apoptosis, and inhibited tumor growth in vivo and in vitro. Low levels of miR-1275 been observed in GC cell lines and tissues. GA-treated GC cells manifested high miR-1275 levels. In functional experiments, miR-1275 enhanced the influence of GA on cell apoptosis, migration, and proliferation. Furthermore, GA treatment suppressed SPARC upregulation in GC cell lines and tissues. Pearson's correlation coefficient revealed that miR-1275 expression negatively correlated with SPARC expression. Mechanistically, miR-1275 promoted growth inhibition in GA-treated GC cells by targeting SPARC. Our study indicates that miR-1275 enhances the suppressive effect of GA on GC progression by inhibiting SPARC expression. Through this study, we contribute to the knowledge of a new mechanism by which GA suppresses GC progression.
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Affiliation(s)
- Ang Cai
- Department of Oncology, Wuhan Hospital of Traditional Chinese Medicine, No. 49, Lihuangpi Road, Jiang'an District, Wuhan, 430014, Hubei, People's Republic of China
| | - Pengfei Xia
- Department of Gastric Diseases and Liver-Gallbladder (Department of Gastroenterology), Wuhan Hospital of Traditional Chinese Medicine, Wuhan, 430014, Hubei, People's Republic of China
| | - Xiaokang Zhou
- Department of Oncology, Wuhan Hospital of Traditional Chinese Medicine, No. 49, Lihuangpi Road, Jiang'an District, Wuhan, 430014, Hubei, People's Republic of China
| | - Yao He
- Department of Oncology, Wuhan Hospital of Traditional Chinese Medicine, No. 49, Lihuangpi Road, Jiang'an District, Wuhan, 430014, Hubei, People's Republic of China
| | - Jun Lv
- Department of Oncology, Wuhan Hospital of Traditional Chinese Medicine, No. 49, Lihuangpi Road, Jiang'an District, Wuhan, 430014, Hubei, People's Republic of China.
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Millwood IY, Im PK, Bennett D, Hariri P, Yang L, Du H, Kartsonaki C, Lin K, Yu C, Chen Y, Sun D, Zhang N, Avery D, Schmidt D, Pei P, Chen J, Clarke R, Lv J, Peto R, Walters RG, Li L, Chen Z. Alcohol intake and cause-specific mortality: conventional and genetic evidence in a prospective cohort study of 512 000 adults in China. Lancet Public Health 2023; 8:e956-e967. [PMID: 38000378 PMCID: PMC7615754 DOI: 10.1016/s2468-2667(23)00217-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Genetic variants that affect alcohol use in East Asian populations could help assess the causal effects of alcohol consumption on cause-specific mortality. We aimed to investigate the associations between alcohol intake and cause-specific mortality using conventional and genetic epidemiological methods among more than 512 000 adults in China. METHODS The prospective China Kadoorie Biobank cohort study enrolled 512 724 adults (210 205 men and 302 519 women) aged 30-79 years, during 2004-08. Residents with no major disabilities from ten diverse urban and rural areas of China were invited to participate, and alcohol use was self-reported. During 12 years of follow-up, 56 550 deaths were recorded through linkage to death registries, including 23 457 deaths among 168 050 participants genotyped for ALDH2-rs671 and ADH1B-rs1229984. Adjusted hazard ratios (HRs) for cause-specific mortality by self-reported and genotype-predicted alcohol intake were estimated using Cox regression. FINDINGS 33% of men drank alcohol most weeks. In conventional observational analyses, ex-drinkers, non-drinkers, and heavy drinkers had higher risks of death from most major causes than moderate drinkers. Among current drinkers, each 100 g/week higher alcohol intake was associated with higher mortality risks from cancers (HR 1·18 [95% CI 1·14-1·22]), cardiovascular disease (CVD; HR 1·19 [1·15-1·24]), liver diseases (HR 1·51 [1·27-1·78]), non-medical causes (HR 1·15 [1·08-1·23]), and all causes (HR 1·18 [1·15-1·20]). In men, ALDH2-rs671 and ADH1B-rs1229984 genotypes predicted 60-fold differences in mean alcohol intake (4 g/week in the lowest group vs 255 g/week in the highest). Genotype-predicted alcohol intake was uniformly and positively associated with risks of death from all causes (n=12 939; HR 1·07 [95% CI 1·05-1·10]) and from pre-defined alcohol-related cancers (n=1274; 1·12 [1·04-1·21]), liver diseases (n=110; 1·31 [1·02-1·69]), and CVD (n=6109; 1·15 [1·10-1·19]), chiefly due to stroke (n=3285; 1·18 [1·12-1·24]) rather than ischaemic heart disease (n=2363; 1·06 [0·99-1·14]). Results were largely consistent using a polygenic score to predict alcohol intake, with higher intakes associated with higher risks of death from alcohol-related cancers, CVD, and all causes. Approximately 2% of women were current drinkers, and although power was low to assess observational associations of alcohol with mortality, the genetic evidence suggested that the excess risks in men were due to alcohol, not pleiotropy. INTERPRETATION Higher alcohol intake increased the risks of death overall and from major diseases for men in China. There was no genetic evidence of protection from moderate drinking for all-cause and cause-specific mortality, including CVD. FUNDING Kadoorie Charitable Foundation, National Natural Science Foundation of China, British Heart Foundation, Cancer Research UK, GlaxoSmithKline, Wellcome Trust, Medical Research Council, and Chinese Ministry of Science and Technology.
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Affiliation(s)
- Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Pek Kei Im
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Derrick Bennett
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Parisa Hariri
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kuang Lin
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Ningmei Zhang
- NCD Prevention and Control Department, Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan, China
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dan Schmidt
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Richard Peto
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Lv J, Liang M, Wang L, Zhang M, Liu R, Liang H, Wang C, Jia L, Zeng Q, Zhu P. Clinical characteristics and outcomes of patients with COVID-19 and tuberculosis coinfection. Infect Dis (Lond) 2023; 55:839-846. [PMID: 37624684 DOI: 10.1080/23744235.2023.2245885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Data on the coincidence of Tuberculosis (TB) and Coronavirus disease 2019 (COVID-19) are limited. We sought to investigate the clinical characteristics and outcomes of coinfected patients in Henan and identify whether TB disease is associated with an increased risk of intensive care unit (ICU) admission and mortality. METHOD We conducted a retrospective matched cohort study of COVID-19 inpatients involving 41 TB-positive patients with 82 patients without TB. Leveraging data was collected from electronic medical records. RESULTS There were no significant differences in clinical manifestations, the need for mechanical ventilation and vasopressors, ICU admission, or in-hospital mortality between 2 groups. TB-positive patients had a lower lymphocyte counts (1.24 ± 0.54 vs. 1.59 ± 0.58, p = 0.01), B cells (99/µl vs. 201/µl, p < 0.01), CD4+ T cells (382/µl vs. 667/µl, p < 0.01), CD8+ T cells (243/µl vs. 423/µl, p < 0.01), NK cells (145/µl vs. 216/µl, p = 0.01), IL-2 (14.18 ± 11.23 vs. 31.86 ± 34.55, p < 0.01) and TNF-α (3.42 ± 2.93 vs. 5.62 ± 3.69, p < 0.01). Notably, the TB-positive group had a longer duration of SARS-CoV-2 shedding (67 days vs. 22 days, p < 0.01). CONCLUSIONS Concomitant TB does not significantly impact clinical outcomes of hospitalised patients with acute COVID-19. However, TB-positive patients had longer duration of SARS-COV-2-RNA positivity.
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Affiliation(s)
- Jun Lv
- Department of Infectious Diseases and Hepatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Mengyu Liang
- Department of Infectious Diseases and Hepatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Lin Wang
- Department of Infectious Diseases and Hepatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Minmin Zhang
- Department of Infectious Diseases, The Sixth People's Hospital of Zhengzhou City, Zhengzhou, P.R. China
| | - Renjie Liu
- Department of Respiratory and Critical Care Medicine, The Zhengzhou First People's Hospital, Zhengzhou, P.R. China
| | - Han Liang
- Department of Ultrasound, Shangqiu First People's Hospital, Shangqiu, P.R. China
| | - Chen Wang
- Department of Infectious Diseases and Hepatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Liuqun Jia
- Department of Respiration, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Qinglei Zeng
- Department of Infectious Diseases and Hepatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Pengfei Zhu
- Department of Clinical Laboratory & Key Clinical Laboratory of Henan province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
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Hamilton EM, Yang L, Wright N, Turnbull I, Mentzer AJ, Matthews PC, Chen Y, Du H, Kartsonaki C, Pang Y, Pei P, Tian H, Yang X, Avery D, Yu C, Lv J, Clarke R, Li L, Millwood IY, Chen Z. Chronic Hepatitis B Virus Infection and Risk of Stroke Types: A Prospective Cohort Study of 500 000 Chinese Adults. Stroke 2023; 54:3046-3053. [PMID: 37942646 PMCID: PMC10664797 DOI: 10.1161/strokeaha.123.043327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/12/2023] [Accepted: 10/17/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Stroke is a leading cause of mortality and permanent disability in China, with large and unexplained geographic variations in rates of different stroke types. Chronic hepatitis B virus infection is prevalent among Chinese adults and may play a role in stroke cause. METHODS The prospective China Kadoorie Biobank included >500 000 adults aged 30 to 79 years who were recruited from 10 (5 urban and 5 rural) geographically diverse areas of China from 2004 to 2008, with determination of hepatitis B surface antigen (HBsAg) positivity at baseline. During 11 years of follow-up, a total of 59 117 incident stroke cases occurred, including 11 318 intracerebral hemorrhage (ICH), 49 971 ischemic stroke, 995 subarachnoid hemorrhage, and 3036 other/unspecified stroke. Cox regression models were used to estimate adjusted hazard ratios (HRs) for risk of stroke types associated with HBsAg positivity. In a subset of 17 833 participants, liver enzymes and lipids levels were measured and compared by HBsAg status. RESULTS Overall, 3.0% of participants were positive for HBsAg. HBsAg positivity was associated with an increased risk of ICH (adjusted HR, 1.29 [95% CI, 1.16-1.44]), similarly for fatal (n=5982; adjusted HR, 1.36 [95% CI, 1.16-1.59]) and nonfatal (n=5336; adjusted HR, 1.23 [95% CI, 1.06-1.44]) ICH. There were no significant associations of HBsAg positivity with risks of ischemic stroke (adjusted HR, 0.97 [95% CI, 0.92-1.03]), subarachnoid hemorrhage (adjusted HR, 0.87 [95% CI, 0.57-1.33]), or other/unspecified stroke (adjusted HR, 1.12 [95% CI, 0.89-1.42]). Compared with HBsAg-negative counterparts, HBsAg-positive individuals had lower lipid and albumin levels and higher liver enzyme levels. After adjustment for liver enzymes and albumin, the association with ICH from HBsAg positivity attenuated to 1.15 (0.90-1.48), suggesting possible mediation by abnormal liver function. CONCLUSIONS Among Chinese adults, chronic hepatitis B virus infection is associated with an increased risk of ICH but not other stroke types, which may be mediated through liver dysfunction and altered lipid metabolism.
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Affiliation(s)
- Elizabeth M. Hamilton
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (L.Y., C.K., I.Y.M., Z.C), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Neil Wright
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Iain Turnbull
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Alexander J. Mentzer
- Wellcome Centre for Human Genetics (A.J.M.), University of Oxford, United Kingdom
| | - Philippa C. Matthews
- Division of Infection and Immunity, University College London, United Kingdom (P.C.M.)
- Matthews lab HBV Elimination Laboratory, The Francis Crick Institute, London, United Kingdom (P.C.M.)
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (L.Y., C.K., I.Y.M., Z.C), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China (Y.P., C.Y., J.L., L.L.)
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Huizi Tian
- Non-Communicable Diseases Prevention and Control Department, Henan, China (H.T.)
| | - Xiaoming Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China (Y.P., C.Y., J.L., L.L.)
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China (Y.P., C.Y., J.L., L.L.)
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China (Y.P., C.Y., J.L., L.L.)
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China (Y.P., P.P., C.Y., J.L., L.L.)
| | - Iona Y. Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (L.Y., C.K., I.Y.M., Z.C), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (E.M.H., L.Y., N.W., I.T., Y.C., H.D., C.K., X.Y., D.A., R.C., I.Y.M., Z.C.), Nuffield Department of Population Health, University of Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (L.Y., C.K., I.Y.M., Z.C), Nuffield Department of Population Health, University of Oxford, United Kingdom
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Wang X, Wu Z, Lv J, Yu C, Sun D, Pei P, Yang L, Millwood IY, Walters R, Chen Y, Du H, Yuan M, Schmidt D, Barnard M, Chen J, Chen Z, Li L, Pang Y. Life-course adiposity and severe liver disease: a Mendelian randomization analysis. Obesity (Silver Spring) 2023; 31:3077-3085. [PMID: 37869961 DOI: 10.1002/oby.23913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/21/2023] [Accepted: 08/04/2023] [Indexed: 10/24/2023]
Abstract
OBJECTIVE There is little evidence on the genetic associations between life-course adiposity (including birth weight, childhood BMI, and adulthood BMI) and severe liver disease (SLD; including cirrhosis and liver cancer). The current study aimed to examine and contrast these associations. METHODS Genetic variants were obtained from genome-wide association studies. Two-sample Mendelian randomization (MR) analyses were performed to assess the genetic associations of life-course adiposity with SLD and liver biomarkers. Cox regression was used to estimate adjusted hazard ratios for SLD associated with genetic risk scores of life-course adiposity and adulthood weight change in the China Kadoorie Biobank. RESULTS In observational analyses, genetic predispositions to childhood adiposity and adulthood adiposity were each associated with SLD. There was a U-shaped association between adulthood weight change and risk of SLD. In meta-analyses of MR results, genetically predicted 1-standard deviation increase in birth weight was inversely associated with SLD at a marginal significance (odds ratio: 0.81 [95% CI: 0.65-1.00]), whereas genetically predicted 1-standard deviation higher childhood BMI and adulthood BMI were positively associated with SLD (odds ratio: 1.27 [95% CI: 1.05-1.55] and 1.79 [95% CI: 1.59-2.01], respectively). The results of liver biomarkers mirrored those of SLD. CONCLUSIONS The current study provided genetic evidence on the associations between life-course adiposity and SLD.
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Affiliation(s)
- Xinyu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zhiyu Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mingqiang Yuan
- Pengzhou Center for Disease Control and Prevention, Pengzhou, China
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Maxim Barnard
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
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Luo L, Zhang H, Zhang S, Luo C, Kan X, Lv J, Zhao P, Tian Z, Li C. Extracellular vesicle-derived silk fibroin nanoparticles loaded with MFGE8 accelerate skin ulcer healing by targeting the vascular endothelial cells. J Nanobiotechnology 2023; 21:455. [PMID: 38017428 PMCID: PMC10685683 DOI: 10.1186/s12951-023-02185-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/02/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Reduced supplies of oxygen and nutrients caused by vascular injury lead to difficult-to-heal pressure ulcers (PU) in clinical practice. Rapid vascular repair in the skin wound is the key to the resolution of this challenge, but clinical measures are still limited. We described the beneficial effects of extracellular vesicle-derived silk fibroin nanoparticles (NPs) loaded with milk fat globule EGF factor 8 (MFGE8) on accelerating skin blood vessel and PU healing by targeting CD13 in the vascular endothelial cells (VECs). METHODS CD13, the specific targeting protein of NGR, and MFGE8, an inhibitor of ferroptosis, were detected in VECs and PU tissues. Then, NPs were synthesized via silk fibroin, and MFGE8-coated NPs (NPs@MFGE8) were assembled via loading purified protein MFGE8 produced by Chinese hamster ovary cells. Lentivirus was used to over-express MFGE8 in VECs and obtained MFGE8-engineered extracellular vesicles (EVs-MFGE8) secreted by these VECs. The inhibitory effect of EVs-MFGE8 or NPs@MFGE8 on ferroptosis was detected in vitro. The NGR peptide cross-linked with NPs@MFGE8 was assembled into NGR-NPs@MFGE8. Collagen and silk fibroin were used to synthesize the silk fibroin/collagen hydrogel. After being loaded with NGR-NPs@MFGE8, silk fibroin/collagen hydrogel sustained-release carrier was synthesized to investigate the repair effect on PU in vivo. RESULTS MFGE8 was decreased, and CD13 was increased in PU tissues. Similar to the effect of EVs-MFGE8 on inhibiting ferroptosis, NPs@MFGE8 could inhibit the mitochondrial autophagy-induced ferroptosis of VECs. Compared with the hydrogels loaded with NPs or NPs@MFGE8, the hydrogels loaded with NGR-NPs@MFGE8 consistently released NGR-NPs@MFGE8 targeting CD13 in VECs, thereby inhibiting mitochondrial autophagy and ferroptosis caused by hypoxia and accelerating wound healing effectively in rats. CONCLUSIONS The silk fibroin/collagen hydrogel sustained-release carrier loaded with NGR-NPs@MFGE8 was of great significance to use as a wound dressing to inhibit the ferroptosis of VECs by targeting CD13 in PU tissues, preventing PU formation and promoting wound healing.
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Affiliation(s)
- Liwen Luo
- Department of Orthopaedics, Xinqiao Hospital, Army Medical University (Third Military Medical University), 83, Xinqiao St, Shapingba District, Chongqing, 400037, China
| | - Hongyu Zhang
- Department of Emergency, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shiyu Zhang
- Department of Orthopaedics, Xinqiao Hospital, Army Medical University (Third Military Medical University), 83, Xinqiao St, Shapingba District, Chongqing, 400037, China
| | - Chengqin Luo
- Department of Emergency, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuewei Kan
- Department of Dermatology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Jun Lv
- Department of Pharmacy, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Ping Zhao
- State Key Laboratory of Silkworm Genome Biology, Biological Science Research Center, Southwest University, 2, Tiansheng Road, Beibei District, Chongqing, 400715, China.
| | - Zhiqiang Tian
- Institute of Immunology, PLA, Army Medical University (Third Military Medical University), 30 Gaotanyan St, Shapingba District, Chongqing, 400038, China.
- State Key Laboratory of Silkworm Genome Biology, Biological Science Research Center, Southwest University, 2, Tiansheng Road, Beibei District, Chongqing, 400715, China.
| | - Changqing Li
- Department of Orthopaedics, Xinqiao Hospital, Army Medical University (Third Military Medical University), 83, Xinqiao St, Shapingba District, Chongqing, 400037, China.
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Yu C, Lan X, Tao Y, Guo Y, Sun D, Qian P, Zhou Y, Walters R, Li L, Zhu Y, Zeng J, Millwood I, Guo R, Pei P, Yang T, Du H, Yang F, Yang L, Ren F, Chen Y, Chen F, Jiang X, Ye Z, Dai L, Wei X, Xu X, Yang H, Wang J, Chen Z, Zhu H, Lv J, Jin X, Li L. A high-resolution haplotype-resolved Reference panel constructed from the China Kadoorie Biobank Study. Nucleic Acids Res 2023; 51:11770-11782. [PMID: 37870428 PMCID: PMC10681741 DOI: 10.1093/nar/gkad779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 08/02/2023] [Accepted: 09/12/2023] [Indexed: 10/24/2023] Open
Abstract
Precision medicine depends on high-accuracy individual-level genotype data. However, the whole-genome sequencing (WGS) is still not suitable for gigantic studies due to budget constraints. It is particularly important to construct highly accurate haplotype reference panel for genotype imputation. In this study, we used 10 000 samples with medium-depth WGS to construct a reference panel that we named the CKB reference panel. By imputing microarray datasets, it showed that the CKB panel outperformed compared panels in terms of both the number of well-imputed variants and imputation accuracy. In addition, we have completed the imputation of 100 706 microarrays with the CKB panel, and the after-imputed data is the hitherto largest whole genome data of the Chinese population. Furthermore, in the GWAS analysis of real phenotype height, the number of tested SNPs tripled and the number of significant SNPs doubled after imputation. Finally, we developed an online server for offering free genotype imputation service based on the CKB reference panel (https://db.cngb.org/imputation/). We believe that the CKB panel is of great value for imputing microarray or low-coverage genotype data of Chinese population, and potentially mixed populations. The imputation-completed 100 706 microarray data are enormous and precious resources of population genetic studies for complex traits and diseases.
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Affiliation(s)
- Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Xianmei Lan
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Shenzhen 518083, China
| | - Ye Tao
- BGI Research, Shenzhen 518083, China
| | - Yu Guo
- National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Puyi Qian
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Yuwen Zhou
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Shenzhen 518083, China
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Linxuan Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Shenzhen 518083, China
| | - Yunqing Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Jingyu Zeng
- BGI Research, Shenzhen 518083, China
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | | | - Pei Pei
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
| | - Tao Yang
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Fan Yang
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Fangyi Ren
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Fengzhen Chen
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Xiaosen Jiang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Shenzhen 518083, China
| | - Zhiqiang Ye
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Lanlan Dai
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Xiaofeng Wei
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Xun Xu
- BGI Research, Shenzhen 518083, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI Research, Shenzhen 518083, China
| | - Huanming Yang
- BGI Research, Shenzhen 518083, China
- Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, BGI, Shenzhen 518083, China
- James D. Watson Institute of Genome Sciences, Hangzhou 310013, China
| | | | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | | | - Jun Lv
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100191, China
| | - Xin Jin
- BGI Research, Shenzhen 518083, China
- School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
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Wang M, Gong W, Sun D, Pei P, Lv J, Yu C, Yu M. Associations between experience of stressful life events and cancer prevalence in China: results from the China Kadoorie Biobank study. BMC Cancer 2023; 23:1142. [PMID: 38001425 PMCID: PMC10675951 DOI: 10.1186/s12885-023-11659-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 11/20/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Studies examining the relationships of stressful life events and cancer yielded inconsistent findings, while relevant evidence in mainland China is scarce. The current study sought to determine whether experience of stressful life events was associated with cancer prevalence in Chinese population. METHODS We used cross-sectional data from the China Kadoorie Biobank study which that recruited 0.5 million Chinese adults aged 30 to 79 from 2004 to 2008. Logistic regression models were used to estimate adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for cancer associated with stressful life events reported at baseline. RESULTS Among the 461,696 participants included in this analysis, 2,122 (0.46%) had self-reported cancer with the mean (SD) age was 57.12 (9.71) years. Compared to those without any stressful life event, participants who experienced 1 and 2 or more events had significantly higher odds of cancer, with the ORs of 1.80 (95% CI: 1.58-2.05) and 3.05 (2.18-4.28). For categories of work-, family-, and personal-related events, the OR of cancer was 1.48 (1.07-2.05), 2.06 (1.80-2.35), and 1.65 (1.17-2.33), respectively. Regarding the specific stressful life events, loss of income/living on debt, major conflict within family, death/major illness of other close family member, and major injury/traffic accident were significantly associated with increased odds of cancer, with the ORs of 2.64 (1.81-3.86), 1.73 (1.20-2.50), 2.36 (2.05-2.72), and 2.11 (1.43-3.13). CONCLUSION Our findings suggested that experiences of cumulative and specific stressful life events were significantly associated with increased cancer prevalence in Chinese population.
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Grants
- 82192900, 82192901, 82192904, 81390540, 91846303 National Natural Science Foundation of China
- 82192900, 82192901, 82192904, 81390540, 91846303 National Natural Science Foundation of China
- 82192900, 82192901, 82192904, 81390540, 91846303 National Natural Science Foundation of China
- 2016YFC0900500 National Key Research and Development Program of China
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Affiliation(s)
- Meng Wang
- Department of NCDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Hangzhou, 310051, China
| | - Weiwei Gong
- Department of NCDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Hangzhou, 310051, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Xueyuan Road, Haidian District, Beijing, 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Xueyuan Road, Haidian District, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Xueyuan Road, Haidian District, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Xueyuan Road, Haidian District, Beijing, 100191, China
| | - Min Yu
- Department of NCDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, 3399 Binsheng Road, Hangzhou, 310051, China.
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Chen Y, Chan S, Bennett D, Chen X, Wu X, Ke Y, Lv J, Sun D, Pan L, Pei P, Yang L, Chen Y, Chen J, Chen Z, Li L, Du H, Yu C, Doherty A. Device-measured movement behaviours in over 20,000 China Kadoorie Biobank participants. Int J Behav Nutr Phys Act 2023; 20:138. [PMID: 38001522 PMCID: PMC10668372 DOI: 10.1186/s12966-023-01537-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Movement behaviours, including physical activity, sedentary behaviour, and sleep have been shown to be associated with several chronic diseases. However, they have not been objectively measured in large-scale prospective cohort studies in low-and middle-income countries. We aim to describe the patterns of device-measured movement behaviours collected in the China Kadoorie Biobank (CKB) study. METHODS During 2020 and 2021, a random subset of 25,087 surviving CKB individuals participated in the 3rd resurvey of the CKB. Among them, 22,511 (89.7%) agreed to wear an Axivity AX3 wrist-worn triaxial accelerometer for seven consecutive days to assess their habitual movement behaviours. We developed a machine-learning model to infer time spent in four movement behaviours [i.e. sleep, sedentary behaviour, light intensity physical activity (LIPA), and moderate-to-vigorous physical activity (MVPA)]. Descriptive analyses were performed for wear-time compliance and patterns of movement behaviours by different participant characteristics. RESULTS Data from 21,897 participants (aged 65.4 ± 9.1 years; 35.4% men) were received for demographic and wear-time analysis, with a median wear-time of 6.9 days (IQR: 6.1-7.0). Among them, 20,370 eligible participants were included in movement behavior analyses. On average, they had 31.1 mg/day (total acceleration) overall activity level, accumulated 7.7 h/day (32.3%) of sleep time, 8.8 h/day (36.6%) sedentary, 5.7 h/day (23.9%) in light physical activity, and 104.4 min/day (7.2%) in moderate-to-vigorous physical activity. There was an inverse relationship between age and overall acceleration with an observed decline of 5.4 mg/day (17.4%) per additional decade. Women showed a higher activity level than men (32.3 vs 28.8 mg/day) and there was a marked geographical disparity in the overall activity level and time allocation. CONCLUSIONS This is the first large-scale accelerometer data collected among Chinese adults, which provides rich and comprehensive information about device-measured movement behaviour patterns. This resource will enhance our knowledge about the potential relevance of different movement behaviours for chronic disease in Chinese adults.
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Affiliation(s)
- Yuanyuan Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Shing Chan
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Derrick Bennett
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- National Institute of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Xiaofang Chen
- Department of Epidemiology and Statistics, Chengdu Medical College, Chengdu, Sichuan, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan, China
| | - Yalei Ke
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | - Lang Pan
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | - Pei Pei
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK.
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
| | - Aiden Doherty
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
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Liu X, Lv J, Tang W, Hu Y, Wen Y, Shen H. METTL3-mediated maturation of miR-192-5p targets ATG7 to prevent Schwann cell autophagy in peripheral nerve injury. J Neuropathol Exp Neurol 2023; 82:1010-1019. [PMID: 37964653 DOI: 10.1093/jnen/nlad091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023] Open
Abstract
The inhibition of miR-192-5p can promote nerve repair in rats with peripheral nerve injury (PNI) but the precise mechanisms underlying this effect remain unclear. Schwann cell (SC) autophagy mediated by autophagy-related gene (ATG) proteins has a key role in PNI but it is uncertain whether miR-192-5p affects the involvement of SC autophagy in PNI. In this study, we investigated the impact of methyltransferase-like protein 3 (METTL3)/miR-192-5p/ATG7 on SC autophagy in a rat PNI model and in an SC oxygen and glucose deprivation model. The results revealed that METTL3 stimulated miR-192-5p maturation via m6A methylation to depress ATG7 and SC autophagy and aggravate PNI. These findings provide a new target and potential basis for the treatment of patients with PNI.
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Affiliation(s)
- Xing Liu
- Department of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, P.R. China
| | - Jun Lv
- Department of Orthopaedics, Heilongjiang Beidahuang Group General Hospital, Harbin, Heilongjiang, P.R. China
| | - Weilong Tang
- Department of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, P.R. China
| | - Yuanbai Hu
- Department of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, P.R. China
| | - Yiwei Wen
- Department of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, P.R. China
| | - Hongtao Shen
- Department of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, P.R. China
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Yang K, Huang Y, Li Z, Zeng Q, Dai X, Lv J, Zong X, Deng K, Zhang J. Overexpression of Nta-miR6155 confers resistance to Phytophthora nicotianae and regulates growth in tobacco ( Nicotiana tabacum L.). Front Plant Sci 2023; 14:1281373. [PMID: 38053762 PMCID: PMC10694243 DOI: 10.3389/fpls.2023.1281373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/26/2023] [Indexed: 12/07/2023]
Abstract
Tobacco black shank induced by Phytophthora nicotianae causes significant yield losses in tobacco plants. MicroRNAs (miRNAs) play a pivotal role in plant biotic stress responses and have great potential in tobacco breeding for disease resistance. However, the roles of miRNAs in tobacco plants in response to P. nicotianae infection has not been well characterized. In this study, we found that Nta-miR6155, a miRNA specific to Solanaceae crops, was significantly induced in P. nicotianae infected tobacco. Some of predicted target genes of Nta-miR6155 were also observed to be involved in disease resistance. To further investigate the function of miR6155 in tobacco during P. nicotianae infection, Nta-miR6155 overexpression plants (miR6155-OE) were generated in the Honghua Dajinyuan tobacco variety (HD, the main cultivated tobacco variety in China). We found that the Nta-miR6155 overexpression enhanced the resistance in tobacco towards P. nicotianae infections. The level of reactive oxygen species (ROS) was significantly lower and antioxidant enzyme activities were significantly higher in miR6155-OE plants than those in control HD plants during P. nicotianae infection. In addition, we found that the accumulation of salicylic acid and the expression of salicylic acid biosynthesis and signal transduction-related genes is significantly higher in miR6155-OE plants in comparison to the control HD plants. Furthermore, we found that Nta-miR6155 cleaved target genes NtCIPK18 to modulate resistance towards P. nicotianae in tobacco plants. Additionally, phenotypic analysis of miR6155-OE plants showed that Nta-miR6155 could inhibit the growth of tobacco by suppressing nitrogen uptake and photosynthesis. In conclusion, our findings indicated that miR6155 plays a crucial role in the regulation of growth and resistance against P. nicotianae infections in tobacco plants.
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Affiliation(s)
- Kaiyue Yang
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Yuanyuan Huang
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Zexuan Li
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Qian Zeng
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Xiumei Dai
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
- Chongqing Tobacco Science Research Institute, Chongqing, China
| | - Jun Lv
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
| | - Xuefeng Zong
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
| | - Kexuan Deng
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
- Chongqing Tobacco Science Research Institute, Chongqing, China
| | - Jiankui Zhang
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
- Chongqing Tobacco Science Research Institute, Chongqing, China
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Mazidi M, Wright N, Yao P, Kartsonaki C, Millwood IY, Fry H, Said S, Pozarickij A, Pei P, Chen Y, Avery D, Du H, Schmidt DV, Yang L, Lv J, Yu C, Chen J, Hill M, Holmes MV, Howson JMM, Peto R, Collins R, Bennett DA, Walters RG, Li L, Clarke R, Chen Z. Plasma Proteomics to Identify Drug Targets for Ischemic Heart Disease. J Am Coll Cardiol 2023; 82:1906-1920. [PMID: 37940228 PMCID: PMC10641761 DOI: 10.1016/j.jacc.2023.09.804] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/11/2023] [Accepted: 09/05/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Integrated analyses of plasma proteomic and genetic markers in prospective studies can clarify the causal relevance of proteins and discover novel targets for ischemic heart disease (IHD) and other diseases. OBJECTIVES The purpose of this study was to examine associations of proteomics and genetics data with IHD in population studies to discover novel preventive treatments. METHODS We conducted a nested case-cohort study in the China Kadoorie Biobank (CKB) involving 1,971 incident IHD cases and 2,001 subcohort participants who were genotyped and free of prior cardiovascular disease. We measured 1,463 proteins in the stored baseline samples using the OLINK EXPLORE panel. Cox regression yielded adjusted HRs for IHD associated with individual proteins after accounting for multiple testing. Moreover, cis-protein quantitative loci (pQTLs) identified for proteins in genome-wide association studies of CKB and of UK Biobank were used as instrumental variables in separate 2-sample Mendelian randomization (MR) studies involving global CARDIOGRAM+C4D consortium (210,842 IHD cases and 1,378,170 controls). RESULTS Overall 361 proteins were significantly associated at false discovery rate <0.05 with risk of IHD (349 positively, 12 inversely) in CKB, including N-terminal prohormone of brain natriuretic peptide and proprotein convertase subtilisin/kexin type 9. Of these 361 proteins, 212 had cis-pQTLs in CKB, and MR analyses of 198 variants in CARDIOGRAM+C4D identified 13 proteins that showed potentially causal associations with IHD. Independent MR analyses of 307 cis-pQTLs identified in Europeans replicated associations for 4 proteins (FURIN, proteinase-activated receptor-1, Asialoglycoprotein receptor-1, and matrix metalloproteinase-3). Further downstream analyses showed that FURIN, which is highly expressed in endothelial cells, is a potential novel target and matrix metalloproteinase-3 a potential repurposing target for IHD. CONCLUSIONS Integrated analyses of proteomic and genetic data in Chinese and European adults provided causal support for FURIN and multiple other proteins as potential novel drug targets for treatment of IHD.
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Affiliation(s)
- Mohsen Mazidi
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Neil Wright
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Pang Yao
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Christiana Kartsonaki
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Iona Y Millwood
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Hannah Fry
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Saredo Said
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Alfred Pozarickij
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Huaidong Du
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Dan Valle Schmidt
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Jun Lv
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major (Peking University), Ministry of Education, Beijing, China
| | - Junshi Chen
- China National Center for Food Risk Assessment, Beijing, China
| | - Michael Hill
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Michael V Holmes
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | - Richard Peto
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Rory Collins
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Derrick A Bennett
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robin G Walters
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Liming Li
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major (Peking University), Ministry of Education, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
| | - Zhengming Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
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50
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Xia C, Wei T, Tang Q, Zheng H, Sun M, Chen G, Lv J. Anxiety, Depression, Quality of Life, and Family Support Among Family Caregivers of Children with Disabilities. Int J Gen Med 2023; 16:5063-5075. [PMID: 37942475 PMCID: PMC10629400 DOI: 10.2147/ijgm.s434900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/20/2023] [Indexed: 11/10/2023] Open
Abstract
Purpose To investigate the family support, anxiety, depression, health-related quality of life (HRQoL), and their associations among parents and grandparents of children with disabilities. Methods This is a cross-sectional study and a total of 327 family caregivers were included. Chi-square test, Mann-Whitney U-test, and two independent t-test were used to identify the intergenerational differences in sociodemographic characteristics, received family support, anxiety, depression, and HRQoL. Eight generalized linear models were developed to examine the associations in both generations. Results Parents and grandparents were most in need of rehabilitation and financial support, but both populations received the least amount of financial support. 33.6% and 36.1% of family caregivers had the risk of anxiety and depression and found no significant intergenerational differences. As for HRQoL, intergenerational differences were found in the physical component score, but not found in the mental component score. Among parents, childcare support of respite care and household tasks assistance was negatively associated with their depression (p<0.05), professional support of appropriate surgery for children (p<0.05) and psychological support from professional psychologists (p<0.01) were negatively associated with their anxiety and depression, psychological support from relatives and friends was negatively associated with their depression (p<0.05) whereas was positively associated with their mental HRQoL (p<0.01). As for grandparents, financial support for daily living expenses was negatively associated with depression (p<0.05), and psychological support from families was negatively associated with depression (p<0.05) whereas was positively associated with mental HRQoL (p<0.05). However, no relationship was found between family support and anxiety among grandparents. Notably, no association was found between family support and physical HRQoL among both populations. Conclusion Both parents and grandparents had high risks of anxiety, depression and low levels of mental HRQoL. To efficiently improve psychological health, care providers and policymakers may consider intergenerational differences and provide targeted family support.
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Affiliation(s)
- Cong Xia
- School of Health Management Anhui Medical University, Hefei, People’s Republic of China
| | - Ting Wei
- School of Public Health, Fudan University, Shanghai, People’s Republic of China
- China Research Center on Disability, Fudan University, Shanghai, People’s Republic of China
| | - Qi Tang
- School of Public Health, Fudan University, Shanghai, People’s Republic of China
- China Research Center on Disability, Fudan University, Shanghai, People’s Republic of China
| | - Hongying Zheng
- School of Public Health, Fudan University, Shanghai, People’s Republic of China
- China Research Center on Disability, Fudan University, Shanghai, People’s Republic of China
| | - Mei Sun
- School of Public Health, Fudan University, Shanghai, People’s Republic of China
- China Research Center on Disability, Fudan University, Shanghai, People’s Republic of China
| | - Gang Chen
- School of Public Health, Fudan University, Shanghai, People’s Republic of China
- China Research Center on Disability, Fudan University, Shanghai, People’s Republic of China
| | - Jun Lv
- School of Public Health, Fudan University, Shanghai, People’s Republic of China
- China Research Center on Disability, Fudan University, Shanghai, People’s Republic of China
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