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Ban HJ, Lee S, Jin HJ. Exploring Stroke Risk through Mendelian Randomization: A Comprehensive Study Integrating Genetics and Metabolic Traits in the Korean Population. Biomedicines 2024; 12:1311. [PMID: 38927518 PMCID: PMC11201557 DOI: 10.3390/biomedicines12061311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Numerous risk factors play a role in the causation of stroke, and the cardiometabolic condition is a one of the most important. In Korea, various treatment methods are employed based on the constitutional type, which is known to differ significantly in cardiometabolic disease. In this study, we compared the estimates obtained for different groups by applying the Mendelian randomization method to investigate the causal effects of genetic characteristics on stroke, according to constitutional type. In clinical analysis, the subtypes differ significantly in diabetes or dyslipidemia. The genetic association estimates for the stroke subtype risk were obtained from MEGASTROKE, the International Stroke Genetics Consortium (ISGC), UKbiobank, and BioBank Japan (BBJ), using group-related SNPs as instrumental variables. The TE subtypes with higher risk of metabolic disease were associated with increased risk (beta = 4.190; s.e. = 1.807; p = 0.035) of cardioembolic stroke (CES), and the SE subtypes were associated with decreased risk (beta = -9.336, s.e. = 1.753; p = 3.87 × 10-5) of CES. The findings highlight the importance of personalized medicine in assessing disease risk based on an individual's constitutional type.
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Affiliation(s)
| | | | - Hee-Jeong Jin
- Korean Medicine (KM) Data Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (H.-J.B.); (S.L.)
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Chen X, Wang S, Shen W. The causal relationship between severe mental illness and risk of lung carcinoma. Medicine (Baltimore) 2024; 103:e37355. [PMID: 38489734 PMCID: PMC10939700 DOI: 10.1097/md.0000000000037355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 03/17/2024] Open
Abstract
Observational studies have suggested a link between severe mental illness (SMI) and risk of lung carcinoma (LC); however, causality has not been established. In this study, we conducted a two-sample, two-step Mendelian randomization (MR) investigation to uncover the etiological influence of SMI on LC risk and quantify the mediating effects of known modifiable risk factors. We obtained summary-level datasets for schizophrenia, major depressive disorder (MDD), and bipolar disorder (BD) from the Psychiatric Genomics Consortium (PGC). Data on single nucleotide polymorphisms (SNPs) associated with lung carcinoma (LC) were sourced from a recent large meta-analysis by McKay et al. We employed two-sample MR and two-step MR utilizing the inverse variance weighted method for causal estimation. Sensitivity tests were conducted to validate causal relationships. In two-sample MR, we identified schizophrenia as a risk factor for LC (OR = 1.06, 95% CI 1.02-1.11, P = 3.48E-03), while MDD (OR = 1.18, 95% CI 0.98-1.42, P = .07) and BD (OR = 1.07, 95% CI 0.99-1.15, P = .09) showed no significant association with LC. In the two-step MR, smoking accounted for 24.66% of the schizophrenia-LC risk association, and alcohol consumption explained 7.59% of the effect. Schizophrenia is a risk factor for lung carcinoma, and smoking and alcohol consumption are the mediating factors in this causal relationship. LC screening should be emphasized in individuals with schizophrenia, particularly in those who smoke and consume alcohol regularly.
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Affiliation(s)
- Xiaohan Chen
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
| | - Shudan Wang
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
| | - Weiyu Shen
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
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Zhang W, Zhang L, Xiao C, Wu X, Cui H, Yang C, Yan P, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Zhang L, Yang C, Yao Y, Li J, Liu Z, Jiang X, Zhang B. Bidirectional relationship between type 2 diabetes mellitus and coronary artery disease: Prospective cohort study and genetic analyses. Chin Med J (Engl) 2024; 137:577-587. [PMID: 38062574 DOI: 10.1097/cm9.0000000000002894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND While type 2 diabetes mellitus (T2DM) is considered a putative causal risk factor for coronary artery disease (CAD), the intrinsic link underlying T2DM and CAD is not fully understood. We aimed to highlight the importance of integrated care targeting both diseases by investigating the phenotypic and genetic relationships between T2DM and CAD. METHODS We evaluated phenotypic associations using data from the United Kingdom Biobank ( N = 472,050). We investigated genetic relationships by leveraging genomic data conducted in European ancestry for T2DM, with and without adjustment for body mass index (BMI) (T2DM: Ncase / Ncontrol = 74,124/824,006; T2DM adjusted for BMI [T2DM adj BMI]: Ncase / Ncontrol = 50,409/523,897) and for CAD ( Ncase / Ncontrol = 181,522/984,168). We performed additional analyses using genomic data conducted in multiancestry individuals for T2DM ( Ncase / Ncontrol = 180,834/1,159,055). RESULTS Observational analysis suggested a bidirectional relationship between T2DM and CAD (T2DM→CAD: hazard ratio [HR] = 2.12, 95% confidence interval [CI]: 2.01-2.24; CAD→T2DM: HR = 1.72, 95% CI: 1.63-1.81). A positive overall genetic correlation between T2DM and CAD was observed ( rg = 0.39, P = 1.43 × 10 -75 ), which was largely independent of BMI (T2DM adj BMI-CAD: rg = 0.31, P = 1.20 × 10 -36 ). This was corroborated by six local signals, among which 9p21.3 showed the strongest genetic correlation. Cross-trait meta-analysis replicated 101 previously reported loci and discovered six novel pleiotropic loci. Mendelian randomization analysis supported a bidirectional causal relationship (T2DM→CAD: odds ratio [OR] = 1.13, 95% CI: 1.11-1.16; CAD→T2DM: OR = 1.12, 95% CI: 1.07-1.18), which was confirmed in multiancestry individuals (T2DM→CAD: OR = 1.13, 95% CI: 1.10-1.16; CAD→T2DM: OR = 1.08, 95% CI: 1.04-1.13). This bidirectional relationship was significantly mediated by systolic blood pressure and intake of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, with mediation proportions of 54.1% (95% CI: 24.9-83.4%) and 90.4% (95% CI: 29.3-151.5%), respectively. CONCLUSION Our observational and genetic analyses demonstrated an intrinsic bidirectional relationship between T2DM and CAD and clarified the biological mechanisms underlying this relationship.
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Affiliation(s)
- Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chenghan Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Ling Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Iatrical Polymer Material and Artificial Apparatus, School of Polymer Science and Engineering, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yuqin Yao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Zhenmi Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17177, Sweden
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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Zhang W, Lei X, Tu Y, Ma T, Wen T, Yang T, Xue L, Ji J, Xue H. Coffee and the risk of osteoarthritis: a two-sample, two-step multivariable Mendelian randomization study. Front Genet 2024; 15:1340044. [PMID: 38362204 PMCID: PMC10867243 DOI: 10.3389/fgene.2024.1340044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/17/2024] [Indexed: 02/17/2024] Open
Abstract
Purpose: To investigate the potential causal relationship between coffee consumption and osteoarthritis (OA), and to disentangle whether body mass index (BMI) and Bone mineral density (BMD) mediate this relationship. Methods: We performed two-sample and two-step Mendelian randomization (MR) analyses utilizing publicly available genome-wide association studies (GWAS) summary statistics to estimate the association between coffee intake and OA risk (including knee OA, hip OA, knee or hip OA, and total OA), as well as the possible mediating effects of BMI and BMD. In addition, data of different coffee types (decaffeinated coffee, instant coffee, ground coffee-including espresso, filter, etc., and other coffee types) were used to explore the effect of coffee type on the risk of OA. Results: In two-sample MR, coffee intake increased the risk of OA in various sites, with the most significant impact observed in knee osteoarthritis (KOA) (odds ratio [OR] 2.03, 95% confidence interval [CI] 1.57-2.61, p < 0.001). The effect on self-reported OA was minimal (OR 1.03, 95% CI 1.01-1.05, p = 0.006). Further analysis of different types of coffee revealed that only decaffeinated coffee was causally associated with both KOA (OR 4.40, 95% CI 1.71-11.33, p = 0.002) and self-reported OA (OR 1.13, 95% CI 1.02-1.26, p = 0.022). In two-step MR, BMI explained over half of the coffee intake-all OA risk association, while BMD accounted for less than 5% of the mediation effect. Conclusion: Our study suggests that coffee intake increase the risk of OA, with BMI playing a significant mediating role. Decaffeinated coffee appears to have the greatest impact on OA risk compared to other types of coffee. Therefore, managing BMI and selecting appropriate types of coffee should be included in the health management of individuals who frequently consume coffee.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Huaming Xue
- Department of Orthopaedics, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, China
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Ji L, Ahmann AJ, Ahrén B, Capehorn MS, Hu P, Lingvay I, Liu W, Rodbard HW, Shen Z, Sorli C. Proportion of participants with type 2 diabetes achieving a metabolic composite endpoint with once-weekly semaglutide treatment versus comparators: Post hoc pooled analysis from SUSTAIN 1-5, 7-10 and SUSTAIN China. Diabetes Obes Metab 2024; 26:233-241. [PMID: 37822270 DOI: 10.1111/dom.15309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/05/2023] [Accepted: 09/15/2023] [Indexed: 10/13/2023]
Abstract
AIM To compare the proportion of participants with type 2 diabetes (T2D) treated with once-weekly (OW) subcutaneous (SC) semaglutide versus comparators who achieved a composite metabolic endpoint. MATERIALS AND METHODS SUSTAIN 1-5, 7-10 and SUSTAIN China trial data were pooled. Participants with T2D (aged ≥18 years) and glycated haemoglobin ≥7.0% (≥53 mmol/mol) who had been randomized to OW SC semaglutide (0.5 or 1.0 mg) or comparator in addition to background medication. Using patient-level data pooled by treatment, proportions of participants achieving the metabolic composite endpoint, defined as glycated haemoglobin <7% (<53 mmol/mol), blood pressure <140/90 mmHg and non-high-density lipoprotein cholesterol <130 mg/dl (<3.37 mmol/L), were evaluated following baseline adjustments. Endpoints were analysed per trial using a binomial logistic regression model with treatment, region/country and stratification factor as fixed effects and baseline value as covariate. Pooled analysis used logistic regression with treatment and trial as fixed effects and baseline value as covariate. RESULTS This post hoc analysis included data from 7633 participants across 10 trials. The proportion of participants who achieved the metabolic composite endpoint was significantly higher with OW SC semaglutide 0.5 and 1.0 mg versus comparators (23.7% and 32.0% vs. 11.5%, respectively; p < .0001). Likewise, when the OW SC semaglutide doses were pooled, significantly higher proportions of patients receiving semaglutide achieved the composite metabolic endpoint versus comparators (29.1% vs. 11.4%, respectively; p < .0001). CONCLUSIONS Treatment with OW SC semaglutide versus comparators was associated with increased proportions of participants with T2D meeting the composite metabolic endpoint.
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Affiliation(s)
- Linong Ji
- Peking University People's Hospital, Beijing, China
| | - A J Ahmann
- Oregon Health and Science University, Portland, Oregon, USA
| | - B Ahrén
- Lund University, Lund, Sweden
| | | | - P Hu
- Novo Nordisk (Shanghai) Pharma Trading Co., Ltd, Beijing, China
| | - I Lingvay
- University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - W Liu
- Novo Nordisk (Shanghai) Pharma Trading Co., Ltd, Beijing, China
| | - H W Rodbard
- Endocrine and Metabolic Consultants, Rockville, Maryland, USA
| | - Z Shen
- Novo Nordisk (Shanghai) Pharma Trading Co., Ltd, Beijing, China
| | - C Sorli
- Acerus Pharma, Toronto, Ontario, Canada
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Zhang W, Zhang L, Zhu J, Xiao C, Cui H, Yang C, Yan P, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Wu X, Zhang L, Yang C, Yao Y, Li J, Liu Z, Jiang X, Zhang B. Additional Evidence for the Relationship Between Type 2 Diabetes and Stroke Through Observational and Genetic Analyses. Diabetes 2023; 72:1671-1681. [PMID: 37552871 DOI: 10.2337/db22-0954] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 08/01/2023] [Indexed: 08/10/2023]
Abstract
While type 2 diabetes mellitus (T2DM) is commonly considered a putative causal risk factor for stroke, the effect of stroke on T2DM remains unclear. The intrinsic link underlying T2DM and stroke has not been thoroughly examined. We aimed to evaluate the phenotypic and genetic relationships underlying T2DM and stroke. We evaluated phenotypic associations using data from the UK Biobank (N = 472,050). We then investigated genetic relationships by leveraging genomic data in European ancestry for T2DM, with and without adjusting (adj) for BMI (T2DM: n = 74,124 case subjects/824,006 control subjects; T2DMadjBMI: n = 50,409 case subjects/523,897 control subjects), and for stroke (n = 73,652 case subjects/1,234,808 control subjects). We performed additional analyses using genomic data in East Asian ancestry for T2DM (n = 77,418 case subjects/356,122 control subjects) and for stroke (n = 27,413 case subjects/237,242 control subjects). Observational analyses suggested a significantly increased hazard of stroke among individuals with T2DM (hazard ratio 2.28 [95% CI 1.97-2.64]), but a slightly increased hazard of T2DM among individuals with stroke (1.22 [1.03-1.45]) which attenuated to 1.14 (0.96-1.36) in sensitivity analysis. A positive global T2DM-stroke genetic correlation was observed (rg = 0.35; P = 1.46 × 10-27), largely independent of BMI (T2DMadjBMI-stroke: rg = 0.27; P = 3.59 × 10-13). This was further corroborated by 38 shared independent loci and 161 shared expression-trait associations. Mendelian randomization analyses suggested a putative causal effect of T2DM on stroke in Europeans (odds ratio 1.07 [95% CI 1.06-1.09]), which remained significant in East Asians (1.03 [1.01-1.06]). Conversely, despite a putative causal effect of stroke on T2DM also observed in Europeans (1.21 [1.07-1.37]), it attenuated to 1.04 (0.91-1.19) in East Asians. Our study provides additional evidence to underscore the significant relationship between T2DM and stroke. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Jingwei Zhu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Chenghan Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Ling Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
- Department of Iatrical Polymer Material and Artificial Apparatus, School of Polymer Science and Engineering, Sichuan University, Chengdu, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Yuqin Yao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
| | - Zhenmi Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C.C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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7
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Georgiou AN, Zagkos L, Markozannes G, Chalitsios CV, Asimakopoulos AG, Xu W, Wang L, Mesa‐Eguiagaray I, Zhou X, Loizidou EM, Kretsavos N, Theodoratou E, Gill D, Burgess S, Evangelou E, Tsilidis KK, Tzoulaki I. Appraising the Causal Role of Risk Factors in Coronary Artery Disease and Stroke: A Systematic Review of Mendelian Randomization Studies. J Am Heart Assoc 2023; 12:e029040. [PMID: 37804188 PMCID: PMC7615320 DOI: 10.1161/jaha.122.029040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/27/2023] [Indexed: 10/09/2023]
Abstract
BACKGROUND Mendelian randomization (MR) offers a powerful approach to study potential causal associations between exposures and health outcomes by using genetic variants associated with an exposure as instrumental variables. In this systematic review, we aimed to summarize previous MR studies and to evaluate the evidence for causality for a broad range of exposures in relation to coronary artery disease and stroke. METHODS AND RESULTS MR studies investigating the association of any genetically predicted exposure with coronary artery disease or stroke were identified. Studies were classified into 4 categories built on the significance of the main MR analysis results and its concordance with sensitivity analyses, namely, robust, probable, suggestive, and insufficient. Studies reporting associations that did not perform any sensitivity analysis were classified as nonevaluable. We identified 2725 associations eligible for evaluation, examining 535 distinct exposures. Of them, 141 were classified as robust, 353 as probable, 110 as suggestive, and 926 had insufficient evidence. The most robust associations were observed for anthropometric traits, lipids, and lipoproteins and type 2 diabetes with coronary artery; disease and clinical measurements with coronary artery disease and stroke; and thrombotic factors with stroke. CONCLUSIONS Despite the large number of studies that have been conducted, only a limited number of associations were supported by robust evidence. Approximately half of the studies reporting associations presented an MR sensitivity analysis along with the main analysis that further supported the causality of associations. Future research should focus on more thorough assessments of sensitivity MR analyses and further assessments of mediation effects or nonlinearity of associations.
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Affiliation(s)
- Andrea N. Georgiou
- Department of Hygiene and EpidemiologyUniversity of Ioannina School of MedicineIoanninaGreece
| | - Loukas Zagkos
- Department of Epidemiology and BiostatisticsSchool of Public Health, Imperial College LondonLondonUK
| | - Georgios Markozannes
- Department of Hygiene and EpidemiologyUniversity of Ioannina School of MedicineIoanninaGreece
- Department of Epidemiology and BiostatisticsSchool of Public Health, Imperial College LondonLondonUK
| | - Christos V. Chalitsios
- Department of Hygiene and EpidemiologyUniversity of Ioannina School of MedicineIoanninaGreece
| | | | - Wei Xu
- Centre for Global Health, Usher InstituteThe University of EdinburghEdinburghUK
| | - Lijuan Wang
- Centre for Global Health, Usher InstituteThe University of EdinburghEdinburghUK
| | | | - Xuan Zhou
- Centre for Global Health, Usher InstituteThe University of EdinburghEdinburghUK
| | - Eleni M. Loizidou
- Department of Hygiene and EpidemiologyUniversity of Ioannina School of MedicineIoanninaGreece
- Biobank Cyprus Center of Excellence in Biobanking and Biomedical ResearchUniversity of CyprusNicosiaCyprus
| | - Nikolaos Kretsavos
- Department of Hygiene and EpidemiologyUniversity of Ioannina School of MedicineIoanninaGreece
| | - Evropi Theodoratou
- Centre for Global Health, Usher InstituteThe University of EdinburghEdinburghUK
- Cancer Research UK Edinburgh Centre, Institute of Genetics and CancerThe University of EdinburghEdinburghUK
| | - Dipender Gill
- Department of Epidemiology and BiostatisticsSchool of Public Health, Imperial College LondonLondonUK
- Medical Research Council Biostatistics UnitUniversity of CambridgeCambridgeUK
| | - Stephen Burgess
- Medical Research Council Biostatistics UnitUniversity of CambridgeCambridgeUK
- Cardiovascular Epidemiology UnitUniversity of CambridgeCambridgeUK
| | - Evangelos Evangelou
- Department of Hygiene and EpidemiologyUniversity of Ioannina School of MedicineIoanninaGreece
- Department of Epidemiology and BiostatisticsSchool of Public Health, Imperial College LondonLondonUK
- Department of Biomedical Research, Institute of Molecular Biology and BiotechnologyFoundation for Research and Technology‐HellasIoanninaGreece
| | - Konstantinos K. Tsilidis
- Department of Hygiene and EpidemiologyUniversity of Ioannina School of MedicineIoanninaGreece
- Department of Epidemiology and BiostatisticsSchool of Public Health, Imperial College LondonLondonUK
| | - Ioanna Tzoulaki
- Department of Epidemiology and BiostatisticsSchool of Public Health, Imperial College LondonLondonUK
- Centre for Systems Biology, Biomedical Research FoundationAcademy of AthensAthensGreece
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8
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Wang Z, Zhou H, Zhang S, Wang F, Huang H. The causal relationship between COVID-19 and seventeen common digestive diseases: a two-sample, multivariable Mendelian randomization study. Hum Genomics 2023; 17:87. [PMID: 37752570 PMCID: PMC10523605 DOI: 10.1186/s40246-023-00536-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 09/20/2023] [Indexed: 09/28/2023] Open
Abstract
OBJECTIVES In clinical practice, digestive symptoms such as nausea, vomiting are frequently observed in COVID-19 patients. However, the causal relationship between COVID-19 and digestive diseases remains unclear. METHODS We extracted single nucleotide polymorphisms associated with the severity of COVID-19 from summary data of genome-wide association studies. Summary statistics of common digestive diseases were primarily obtained from the UK Biobank study and the FinnGen study. Two-sample Mendelian randomization analyses were then conducted using the inverse variance-weighted (IVW), Mendelian randomization-Egger regression (MR Egger), weighted median estimation, weighted mode, and simple mode methods. IVW served as the primary analysis method, and Multivariable Mendelian randomization analysis was employed to explore the mediating effect of body mass index (BMI) and type 2 diabetes. RESULTS MR analysis showed that a causal association between SARS-CoV-2 infection (OR = 1.09, 95% CI 1.01-1.18, P = 0.03), severe COVID-19 (OR = 1.02, 95% CI 1.00-1.04, P = 0.02), and COVID-19 hospitalization (OR = 1.04, 95% CI 1.01-1.06, P = 0.01) with gastroesophageal reflux disease (GERD). Mediation analysis indicated that body mass index (BMI) served as the primary mediating variable in the causal relationship between SARS-CoV-2 infection and GERD, with BMI mediating 36% (95% CI 20-53%) of the effect. CONCLUSIONS We found a causal relationship between SARS-CoV-2 infection and gastroesophageal reflux disease. Furthermore, we found that the causal relationship between SARS-CoV-2 infection and GERD is mainly mediated by BMI.
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Affiliation(s)
- Zhiqi Wang
- Jiangnan University Affiliated Wuxi Fifth People's Hospital, Wuxi, 214000, Jiangsu, China
| | - Huanyu Zhou
- Jiangnan University Affiliated Wuxi Second People's Hospital, Wuxi, 214000, Jiangsu, China
| | - Shurui Zhang
- The Shangyou People's Hospital, Ganzhou, 341000, Jiangxi, China
| | - Fei Wang
- Jiangnan University Affiliated Wuxi Fifth People's Hospital, Wuxi, 214000, Jiangsu, China
| | - Haishan Huang
- The Affiliated Suqian Hospital of Xuzhou Medical University, Suqian, 223800, Jiangsu, China.
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9
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Cui Y, Lu W, Shao T, Zhuo Z, Wang Y, Zhang W. Severe mental illness and the risk of breast cancer: A two-sample, two-step multivariable Mendelian randomization study. PLoS One 2023; 18:e0291006. [PMID: 37656762 PMCID: PMC10473543 DOI: 10.1371/journal.pone.0291006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/21/2023] [Indexed: 09/03/2023] Open
Abstract
BACKGROUND Based on epidemiological reports, severe mental illness (SMI) and breast cancer (BC) risk are linked positively. However, it is susceptible to clinical confounding factors, such as smoking, alcohol consumption, etc. Here, we performed a two-sample, two-step multivariable Mendelian randomization (MR) research to explore how the SMI etiologically influences BC risk and to quantify mediating effects of known modifiable risk factors. METHODS Data concerning the single nucleotide polymorphism (SNP)-associated with schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), and BC were obtained from two large consortia: the Breast Cancer Association Consortium (BCAC) and the Psychiatric Genomics Consortium (PGC). Then, the correlations of the previous SMI with the BC prevalence and the potential impact of mediators were explored through the two-sample and two-step MR analyses. RESULTS In two-sample MR, schizophrenia increased BC incidence (odds ratio (OR) 1.06, 95% confidence interval (CI) 1.02-1.10, P = 0.001). In subgroup analysis, schizophrenia increased ER+ BC (OR 1.06, 95% CI 1.03-1.10, P = 0.0009) and ER-BC (OR 1.06, 95% CI 1.01-1.11, P = 0.0123) incidences. Neither MDD nor BD elevated the BC risk. In two-step MR, smoking explained 11.29% of the schizophrenia-all BC risk association. CONCLUSIONS Our study indicates that schizophrenia increases susceptibility to breast cancer, with smoking playing a certain mediating role. Therefore, BC screening and smoking should be incorporated into the health management of individuals with schizophrenia.
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Affiliation(s)
- Yongjia Cui
- Oncology Department, China Academy of Chinese Medical Sciences Guang’anmen Hospital, Beijing, China
| | - Wenping Lu
- Oncology Department, China Academy of Chinese Medical Sciences Guang’anmen Hospital, Beijing, China
| | - Tianrui Shao
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Zhili Zhuo
- Oncology Department, China Academy of Chinese Medical Sciences Guang’anmen Hospital, Beijing, China
| | - Ya’nan Wang
- Oncology Department, China Academy of Chinese Medical Sciences Guang’anmen Hospital, Beijing, China
| | - Weixuan Zhang
- Oncology Department, China Academy of Chinese Medical Sciences Guang’anmen Hospital, Beijing, China
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10
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Li K, Cao B, Wang X, Chai T, Ke J, Zhao D. Sex differences in the non-linear association between BMI and LDL cholesterol in type 2 diabetes. Front Endocrinol (Lausanne) 2023; 14:1180012. [PMID: 37484947 PMCID: PMC10360932 DOI: 10.3389/fendo.2023.1180012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 06/26/2023] [Indexed: 07/25/2023] Open
Abstract
Background A data-based study reported the linear relationship between body mass index (BMI) and low-density lipoprotein cholesterol (LDL-C) in a normal population. However, there were no studies giving the suggestion for diabetes patients limited by sample size. This study aimed to investigate the non-linear dose-response relationship between BMI and LDL-C in type 2 diabetes mellitus (T2DM). Method The study participants registered at the National Metabolic Management Center (MMC) of Beijing Luhe hospital from June 2017 to June 2021. T2DM was diagnosed according to the 1999 World Organization criteria. The generalized additive models (GAMs) were used to investigate the non-linear association between BMI and LDL-C. The relationship between BMI and LDL-C was visualized via the smooth splines function plot by sex. Segmented regressions were fitted to calculate the slopes with different estimated breakpoints. Results After data cleaning, a total of 2500 participants with T2DM aged 30 to 70 years were included in this study. Compared with females, the spline between BMI and LDL-C showed an Inverted U shape in males. In males, the slopes below and above the breakpoint (26.08. 95% CI: 24.13 to 28.03) were 2.38 (95%CI: 1.06, 3.70) and -0.36 (95%CI: -1.20, 0.48), respectively. Conclusion There was an Inverted U shape association between BMI and LDL-C in male participants with T2DM, for which the LDL-C was increased with BMI in the lean population, while LDL-C gradually tended to be flat or even decreased in the obese population. However, the Inverted U-shape between BMI and LDL-C was not found in female patients with T2DM.
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Affiliation(s)
- Kun Li
- Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Diabetes Research and Care, Capital Medical University, Beijing, China
| | - Bin Cao
- Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Diabetes Research and Care, Capital Medical University, Beijing, China
| | - Xiaojing Wang
- Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Diabetes Research and Care, Capital Medical University, Beijing, China
| | - Tao Chai
- Physical Examination Center, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Jing Ke
- Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Diabetes Research and Care, Capital Medical University, Beijing, China
| | - Dong Zhao
- Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Diabetes Research and Care, Capital Medical University, Beijing, China
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11
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Huang J. Assessment of the causal association between celiac disease and cardiovascular diseases. Front Cardiovasc Med 2022; 9:1017209. [PMID: 36386312 PMCID: PMC9644835 DOI: 10.3389/fcvm.2022.1017209] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/20/2022] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Epidemiological studies have reported inconsistent results of the association between celiac disease (CD) and cardiovascular diseases. Moreover, the causality remains largely unknown. Therefore, we aimed to investigate whether CD is causally associated cardiovascular diseases, including ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke, coronary heart disease, myocardial infarction, angina, heart failure, atrial fibrillation, and venous thromboembolism using an mendelian randomization (MR) approach. METHODS Summary-level data for CD were derived from a large-sample genome-wide association study (GWAS) including 12,041 CD cases and 12,228 controls of European ancestry. The corresponding data for ischemic stroke (34,217 cases and 406,111 controls), large artery stroke (4,373 cases and 406,111 controls), cardioembolic stroke (7,193 cases and 406,111 controls), small vessel stroke (5,386 cases and 192,662 controls), coronary heart disease (22,233 cases and 64,762 controls), myocardial infarction (11,622 cases and 187,840 controls), angina (18,168 cases and 187,840 controls), heart failure (47,309 cases and 930,014 controls), atrial fibrillation (60,620 cases and 970,216 controls), and venous thromboembolism (9,176 cases and 209,616 controls) were obtained from the IEU GWAS database. We calculated the causal effect using the inverse variance weighted method. Sensitivity analyses and leave-one-out analyses were performed to ensure the consistency and robustness of causal estimates. RESULTS The MR inverse variance weighted estimates indicated no causal effect of genetically predicted CD on ischemic stroke (OR = 1.001, 95% CI: 0.984-1.018), large artery stroke (OR = 1.003, 95% CI: 0.961-1.048), cardioembolic stroke (OR = 1.009, 95% CI: 0.977-1.042), small vessel stroke (OR = 1.023, 95% CI: 0.981-1.066), coronary heart disease (OR = 0.995, 95% CI: 0.977-1.013), myocardial infarction (OR = 0.994, 95% CI: 0.959-1.030), angina (OR = 1.006, 95% CI: 0.981-1.032), heart failure (OR = 0.999, 95% CI: 0.982-1.016), atrial fibrillation (OR = 1.000, 95% CI: 0.990-1.011), and venous thromboembolism (OR = 1.001, 95% CI: 0.971-1.032). Sensitivity analyses using the MR-Egger, weighted median, and simple mode methods yielded similar results. No evidence of horizontal pleiotropy was identified (MR Pleiotropy Residual Sum and Outlier global test and MR-Egger intercept with P > 0.05). CONCLUSION Our findings do not support a causal contribution of CD itself to ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke, coronary heart disease, myocardial infarction, angina, heart failure, atrial fibrillation, and venous thromboembolism risk.
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Affiliation(s)
- Jian Huang
- Clinical Laboratory Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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