1
|
Kim MS, Lee I, Natarajan P, Do R, Kwon Y, Shin JI, Solmi M, Kim JY, Won HH, Park S. Integration of observational and causal evidence for the association between adiposity and 17 gastrointestinal outcomes: An umbrella review and meta-analysis. Obes Rev 2024; 25:e13823. [PMID: 39233338 DOI: 10.1111/obr.13823] [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: 11/08/2022] [Revised: 07/10/2024] [Accepted: 08/08/2024] [Indexed: 09/06/2024]
Abstract
We systematically reviewed observational and Mendelian randomization (MR) articles that evaluated the association between obesity and 17 gastrointestinal (GI) diseases to integrate causal and observational evidence. A total of 594 observational studies from 26 systematic reviews and meta-analyses and nine MR articles were included. For every 5 kg/m2 increase in body mass index (BMI), there was an increased risk of GI diseases ranging from 2% for rectal cancer (relative risk [RR]: 1.02, 95% confidence interval [CI]: 1.01 to 1.03) to 63% for gallbladder disease (RR: 1.63, 95% CI: 1.50 to 1.77). MR articles indicated that risks of developing GI diseases elevated with each 1 standard deviation increase in genetically predicted BMI, ranging from 11% for Crohn's disease to 189% for nonalcoholic fatty liver disease. Moreover, upper GI conditions were less susceptible, whereas hepatobiliary organs were more vulnerable to increased adiposity. Among the associations between obesity and the 17 GI conditions, causal relationships were inferred from only approximately half (10/17, 59%). This study reveals a substantial gap between observational and causal evidence, indicating that a combined approach is necessary to effectively inform public health policies and guide epidemiological research on obesity and GI diseases.
Collapse
Affiliation(s)
- Min Seo Kim
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Inhyeok Lee
- Division of Foregut Surgery, Korea University College of Medicine, Seoul, Republic of Korea
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yeongkeun Kwon
- Division of Foregut Surgery, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jae Il Shin
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Marco Solmi
- Department of Psychiatry, University of Ottawa, Ontario, Canada
- Department of Mental Health, The Ottawa Hospital, Ontario, Canada
- Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program, University of Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Jong Yeob Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Sungsoo Park
- Division of Foregut Surgery, Korea University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
2
|
Tatsuno I, Gerlier L, Olivieri AV, Baker-Knight J, Lamotte M. Assessing the health and economic burden of obesity-related complications in East-Asian populations: implementation of risk equations in the Core Obesity Model for Japan and model validation. BMJ PUBLIC HEALTH 2024; 2:e000302. [PMID: 40018224 PMCID: PMC11812756 DOI: 10.1136/bmjph-2023-000302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 05/22/2024] [Indexed: 03/01/2025]
Abstract
Objective Obesity is associated with a significant clinical and economic burden and its prevalence has reached epidemic proportions worldwide. An ethnicity-specific impact of excess weight has been demonstrated, with Asian individuals exhibiting weight-related health problems at lower body mass indexes (BMIs) than Caucasians. We aimed to adapt the core obesity model (COM) to predict incidences of weight-associated diseases, including type 2 diabetes, acute coronary syndrome (ACS), stroke, cancers, sleep apnoea, hyperuricaemia/gout, total knee replacement (TKR) and non-alcoholic fatty liver disease (NAFLD) in a Japanese population. Methods and analysis Literature was searched to identify studies reporting the association between risk factors and comorbidities in Japanese populations. Data were extracted to update the COM risk prediction equations. Internal and external validation were performed. Results Overall, good internal validity was achieved, with mild underestimation for diabetes, cardiovascular and all-cause death taken together (ordinary least squares linear regression [OLS-LRL] 0.8844), moderate overestimation of TKR and cancers (OLS-LRL 1.267) and a slight underestimation for NAFLD and hyperuricaemia (OLS-LRL 0.934). External validation results were aligned with known geographical patterns: complications occurred at lower BMI in Japanese individuals, with a threefold higher incidence of diabetes and twofold higher obstructive sleep apnoea, gout prevalence and colorectal cancer at equal BMI. Conversely, the 10-year cumulative ACS incidences predicted in a Japanese population were less than half of those in a Western population. Conclusion The Japanese COM adaptation addresses ethnicity-specific patterns of overweight/obesity, with better sensitivity to lower BMIs for several associated complications. It may support regional public health policy and research.
Collapse
Affiliation(s)
- Ichiro Tatsuno
- Chiba Prefecture University of Health Sciences, Chiba City, Japan
| | | | | | | | | |
Collapse
|
3
|
Pang Y, Åberg F, Chen Z, Li L, Kartsonaki C. Predicting risk of chronic liver disease in Chinese adults: External validation of the CLivD score. J Hepatol 2024; 80:e264-e266. [PMID: 38181826 PMCID: PMC7617155 DOI: 10.1016/j.jhep.2023.12.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/07/2024]
Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, China.
| | - Fredrik Åberg
- Transplantation and Liver Surgery, HUCH Meilahti Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Zhengming Chen
- Clinical Trial Service Unit & 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; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom; Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom.
| |
Collapse
|
4
|
Luo T, Lin S, Zhang W, Li X, Wang Y, Zhou J, Liu T, Wu G. Relationship between socioeconomic status and hypertension incidence among adults in southwest China: a population-based cohort study. BMC Public Health 2024; 24:1211. [PMID: 38693482 PMCID: PMC11064324 DOI: 10.1186/s12889-024-18686-5] [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: 12/17/2023] [Accepted: 04/22/2024] [Indexed: 05/03/2024] Open
Abstract
PURPOSE To investigate the correlation between socioeconomic status (SES) and the incidence of hypertension among adults aged 18 or above in southwest China. METHODS A multistage proportional stratified cluster sampling method was employed to recruited 9280 adult residents from 12 counties in southwest China, with all participants in the cohort tracked from 2016 to 2020. The questionnaire survey gathered information on demographics, lifestyle habits, and household income. The physical exam recorded height, weight, and blood pressure. Biochemical tests measured cholesterol levels. The chi-square test was employed to assess the statistical differences among categorical variables, while the Cox proportional hazards regression model was applied to evaluate the association between socioeconomic status (SES) and the incidence of hypertension. RESULTS The finally effective sample size for the cohort study was 3546 participants, after excluding 5734 people who met the exclusion criteria. Adults in the highest household income group had a significantly lower risk of hypertension compared to those in the lowest income group (HR = 0.636, 95% CI: 0.478-0.845). Besides, when compared to individuals in the illiterate population, the risk of hypertension among adults with elementary school, junior high school, senior high school and associate degree educational level decreased respectively by 34.4% (HR = 0.656, 95%CI: 0.533-0.807), 44.9% (HR = 0.551, 95%CI: 0.436-0.697), 44.9% (HR = 0.551, 95%CI: 0.405-0.750), 46.1% (HR = 0.539, 95%CI: 0. 340-0.854). After conducting a thorough analysis of socioeconomic status, compared with individuals with a score of 6 or less, the risk of hypertension in participants with scores of 8, 10, 11, 12, and greater than 12 decreased respectively by 23.9% (HR = 0.761, 95%CI: 0.598-0.969), 29.7% (HR = 0.703, 95%CI: 0.538-0.919), 34.0% (HR = 0.660, 95%CI: 0.492-0.885), 34.3% (HR = 0.657, 95%CI: 0.447-0.967), 43.9% (HR = 0.561, 95%CI: 0.409-0.769). CONCLUSION The findings indicate a negative correlation between socioeconomic status and hypertension incidence among adults in southwest China, suggesting that individuals with higher socioeconomic status are less likely to develop hypertension.
Collapse
Affiliation(s)
- Tao Luo
- Department of Emergency, The Affiliated Hospital of Guizhou Medical University, Guizhou Medical University, Guiyang, 550004, China
- School of Basic Medical Sciences, Guizhou Medical University, Guiyang, 550025, China
| | - Shenrong Lin
- Medical College, Guizhou University, Guiyang, 550025, China
| | - Wenying Zhang
- Clinical College of Guizhou Medical University, Guiyang, 550004, China
| | - Xuejiao Li
- School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China
| | - Yiying Wang
- Guizhou Province Centre for Disease Control and Prevention, 101 Bageyan Road, Yunyan District, Guiyang City, Guizhou Province, China
| | - Jie Zhou
- Guizhou Province Centre for Disease Control and Prevention, 101 Bageyan Road, Yunyan District, Guiyang City, Guizhou Province, China
| | - Tao Liu
- Medical College, Guizhou University, Guiyang, 550025, China.
- Guizhou Province Centre for Disease Control and Prevention, 101 Bageyan Road, Yunyan District, Guiyang City, Guizhou Province, China.
| | - Guofeng Wu
- Department of Emergency, The Affiliated Hospital of Guizhou Medical University, Guizhou Medical University, Guiyang, 550004, China.
| |
Collapse
|
5
|
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: 0.5] [Reference Citation Analysis] [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.
Collapse
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
| |
Collapse
|
6
|
Fang S, Hemani G, Richardson TG, Gaunt TR, Davey Smith G. Evaluating and implementing block jackknife resampling Mendelian randomization to mitigate bias induced by overlapping samples. Hum Mol Genet 2023; 32:192-203. [PMID: 35932451 PMCID: PMC9840213 DOI: 10.1093/hmg/ddac186] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/02/2022] [Accepted: 08/03/2022] [Indexed: 02/02/2023] Open
Abstract
Participant overlap can induce overfitting bias into Mendelian randomization (MR) and polygenic risk score (PRS) studies. Here, we evaluated a block jackknife resampling framework for genome-wide association studies (GWAS) and PRS construction to mitigate overfitting bias in MR analyses and implemented this study design in a causal inference setting using data from the UK Biobank. We simulated PRS and MR under three scenarios: (1) using weighted SNP estimates from an external GWAS, (2) using weighted SNP estimates from an overlapping GWAS sample and (3) using a block jackknife resampling framework. Based on a P-value threshold to derive genetic instruments for MR studies (P < 5 × 10-8) and a 10% variance in the exposure explained by all SNPs, block-jackknifing PRS did not suffer from overfitting bias (mean R2 = 0.034) compared with the externally weighted PRS (mean R2 = 0.040). In contrast, genetic instruments derived from overlapping samples explained a higher variance (mean R2 = 0.048) compared with the externally derived score. Overfitting became considerably more severe when using a more liberal P-value threshold to construct PRS (e.g. P < 0.05, overlapping sample PRS mean R2 = 0.103, externally weighted PRS mean R2 = 0.086), whereas estimates using jackknife score remained robust to overfitting (mean R2 = 0.084). Using block jackknife resampling MR in an applied analysis, we examined the effects of body mass index on circulating biomarkers which provided comparable estimates to an externally weighted instrument, whereas the overfitted scores typically provided narrower confidence intervals. Furthermore, we extended this framework into sex-stratified, multivariate and bidirectional settings to investigate the effect of childhood body size on adult testosterone levels.
Collapse
Affiliation(s)
- Si Fang
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8 2BN, UK
| | - Gibran Hemani
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8 2BN, UK
| | - Tom G Richardson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8 2BN, UK
- Novo Nordisk Research Centre, Headington, Oxford OX3 7FZ, UK
| | - Tom R Gaunt
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8 2BN, UK
| | - George Davey Smith
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8 2BN, UK
| |
Collapse
|
7
|
Pang Y, Han Y, Yu C, Kartsonaki C, Guo Y, Chen Y, Yang L, Du H, Hou W, Schmidt D, Stevens R, Chen J, Chen Z, Lv J, Li L. The role of lifestyle factors on comorbidity of chronic liver disease and cardiometabolic disease in Chinese population: A prospective cohort study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2022; 28:100564. [PMID: 35991535 PMCID: PMC9386629 DOI: 10.1016/j.lanwpc.2022.100564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Background Lifestyle factors are associated with chronic liver disease (CLD) and death after CLD diagnosis. However, their associations with pathways of CLD progression have been unclear, particularly transition to cardiometabolic disease (CMD), a major comorbid condition with CLD. We assessed the associations of lifestyle factors with CLD progression. Methods The study population involved 486,828 participants of the prospective China Kadoorie Biobank (CKB) aged 30-79 years without a history of cardiovascular disease, diabetes, CLD, or cancer at baseline. Liver-cardiometabolic comorbidity (LCC) was defined as developing CMD subsequently after first CLD (FCLD) in an individual. A multi-state model was used to estimate the associations of high-risk lifestyle factors (smoking, alcohol, physical inactivity, and central adiposity) with CLD progression from healthy to FCLD, subsequently to LCC, and further to death. Findings During a median follow-up of 11 years, 5046 participants developed FCLD, 519 developed LCC, and 157 died afterwards. There were positive associations between the number of high-risk lifestyle factors and risks of all transitions. The hazard ratios (95% CIs) per 1-factor increase were 1.30 (1.25-1.35) for transitions from baseline to FCLD, 1.21 (1.09-1.34) for FCLD to LCC, 1.20 (1.17-1.23) for baseline to death, 1.15 (1.09-1.22) for FCLD to death, and 1.17 (1.06-1.31) for LCC to death. For CLD subtypes, lifestyle factors showed different associations with disease-specific transitions even within the same transition stage. Interpretation High-risk lifestyle factors played a key role in all disease transition stages from healthy to FCLD, subsequently to LCC, and then to death, with different magnitude of associations. Funding Kadoorie Charitable Foundation, Chinese MoST and NSFC.
Collapse
Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yuting Han
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, 38 Xueyuan Road, Beijing 100191, China
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, UK
| | - Yu Guo
- Chinese Academy of Medical Sciences, 9 Dongdan San Tiao, Beijing 100730, 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, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of 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, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, UK
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, UK
| | - Wei Hou
- Licang Center for Disease Prevention and Control, 20 Yongnian Road, Licang District, Qingdao 266041, China
| | - Danile Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of 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, UK
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, 37 Guangqu Road, Beijing 100021, 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, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, UK
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, 38 Xueyuan Road, Beijing 100191, China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, 38 Xueyuan Road, Beijing 100191, China
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, 38 Xueyuan Road, Beijing 100191, China
| |
Collapse
|
8
|
The timing of adiposity and changes in the life course on the risk of cancer. Cancer Metastasis Rev 2022; 41:471-489. [PMID: 35908000 DOI: 10.1007/s10555-022-10054-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 07/23/2022] [Indexed: 11/02/2022]
Abstract
Excess body weight has been established as a risk factor for at least twelve cancer sites, though questions remain as to the timing of associations for adiposity and cancer risk throughout the life course. We conducted a narrative review summarizing existing evidence to provide insights into the complex timing relationship between adiposity and risk of seven common obesity-related cancers. We considered five types of studies, including traditional epidemiologic studies examining adiposity at different time points, studies examining weight gain in specific life phases, studies examining weight loss over a period including from bariatric surgery, life course trajectory analysis, and Mendelian randomization studies. The results showed that lifetime excess body weight is associated with increased risk of cancers of endometrium, colorectum, liver, kidney, and pancreas. Early life obesity is one of the strongest risk factors for pancreatic cancer but less directly important than adult obesity for endometrial and kidney cancer. Interestingly, heavy weight during childhood, adolescence, and early adulthood is protective against pre- and postmenopausal breast cancer and possibly advanced prostate cancer. It is apparent that preventing weight gain later in adulthood would likely reduce risk of many cancers, including postmenopausal breast cancer, endometrial cancer, colorectal cancer (especially in men), liver cancer, kidney cancer, and probably advanced prostate cancer. Furthermore, weight loss even late in life may confer benefits for cancers of breast, endometrium, colorectum, and liver among patients with obesity, as mostly demonstrated by studies of bariatric surgery. Overall, maintaining a healthy weight throughout the life course will help prevent a large number of cancers.
Collapse
|
9
|
Pang Y, Kartsonaki C, Lv J, Millwood IY, Fairhurst-Hunter Z, Turnbull I, Bragg F, Hill MR, Yu C, Guo Y, Chen Y, Yang L, Clarke R, Walters RG, Wu M, Chen J, Li L, Chen Z, Holmes MV. Adiposity, metabolomic biomarkers, and risk of nonalcoholic fatty liver disease: a case-cohort study. Am J Clin Nutr 2022; 115:799-810. [PMID: 34902008 PMCID: PMC8895224 DOI: 10.1093/ajcn/nqab392] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 11/06/2021] [Accepted: 11/18/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Globally, the burden of obesity and associated nonalcoholic fatty liver disease (NAFLD) are rising, but little is known about the role that circulating metabolomic biomarkers play in mediating their association. OBJECTIVES We aimed to examine the observational and genetic associations of adiposity with metabolomic biomarkers and the observational associations of metabolomic biomarkers with incident NAFLD. METHODS A case-subcohort study within the prospective China Kadoorie Biobank included 176 NAFLD cases and 180 subcohort individuals and measured 1208 metabolites in stored baseline plasma using a Metabolon assay. In the subcohort the observational and genetic associations of BMI with biomarkers were assessed using linear regression, with adjustment for multiple testing. Cox regression was used to estimate adjusted HRs for NAFLD associated with biomarkers. RESULTS In observational analyses, BMI (kg/m2; mean: 23.9 in the subcohort) was associated with 199 metabolites at a 5% false discovery rate. The effects of genetically elevated BMI with specific metabolites were directionally consistent with the observational associations. Overall, 35 metabolites were associated with NAFLD risk, of which 15 were also associated with BMI, including glutamate (HR per 1-SD higher metabolite: 1.95; 95% CI: 1.48, 2.56), cysteine-glutathione disulfide (0.44; 0.31, 0.62), diaclyglycerol (C32:1) (1.71; 1.24, 2.35), behenoyl dihydrosphingomyelin (C40:0) (1.92; 1.42, 2.59), butyrylcarnitine (C4) (1.91; 1.38, 2.35), 2-hydroxybehenate (1.81; 1.34, 2.45), and 4-cholesten-3-one (1.79; 1.27, 2.54). The discriminatory performance of known risk factors was increased when 28 metabolites were also considered simultaneously in the model (weighted C-statistic: 0.84 to 0.90; P < 0.001). CONCLUSIONS Among relatively lean Chinese adults, a range of metabolomic biomarkers are associated with NAFLD risk and these biomarkers may lie on the pathway between adiposity and NAFLD.
Collapse
Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - 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 (PKU-PHEPR), Peking University, Beijing, China
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Zammy Fairhurst-Hunter
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Iain Turnbull
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Fiona Bragg
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Michael R Hill
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - 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 (PKU-PHEPR), Peking University, Beijing, China
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ming Wu
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, Beijing, China
| | - 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 (PKU-PHEPR), Peking University, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Michael V Holmes
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, United Kingdom
| |
Collapse
|
10
|
Wang X, Cheng S, Lv J, Yu C, Guo Y, Pei P, Yang L, Millwood IY, Walters R, Chen Y, Du H, Duan H, Gilbert S, Avery D, Chen J, Pang Y, Chen Z, Li L. Liver biomarkers, genetic and lifestyle risk factors in relation to risk of cardiovascular disease in Chinese. Front Cardiovasc Med 2022; 9:938902. [PMID: 36035906 PMCID: PMC9403237 DOI: 10.3389/fcvm.2022.938902] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background and aims Liver biomarkers and metabolic associated fatty liver disease (MAFLD) have been shown to be associated with cardiovascular disease (CVD). However, there is limited evidence on CVD subtypes [myocardial infarction (MI), ischemic stroke (IS), and intracerebral hemorrhage (ICH)], especially in the Chinese population. We examined these associations overall, by genetic predisposition to non-alcoholic fatty liver disease (NAFLD), and by lifestyle risk factors. Approach and results This is a nested case-control study of CVD (10,298 cases and 5,388 controls) within the China Kadoorie Biobank. Cox regression was used to estimate adjusted hazard ratios (HRs) for CVD associated with liver biomarkers and MAFLD and by stratum of genetic risk and a combined high-risk lifestyle score. For liver enzymes, there were positive associations with MI and IS, but no associations with ICH or carotid plaque. There were positive associations of NAFLD with risks of MI, IS, and ICH (HR 1.43 [95% CI 1.30-1.57], 1.25 [1.16-1.35], and 1.12 [1.02-1.23]) as well as carotid plaque (odds ratio 2.36 [1.12-4.96]). The associations of NAFLD with CVD and carotid plaque were stronger among individuals with a high genetic risk (ICH: p-interaction < 0.05), while the associations with stroke were stronger among those with a favorable lifestyle (p-interaction < 0.05). The results for MAFLD mirrored those for NAFLD. Conclusion In Chinese adults, liver biomarkers and MAFLD were associated with risk of CVD, with different magnitudes of associations by CVD subtypes. Genetic predisposition to NAFLD and lifestyle factors modified the associations of fatty liver with stroke.
Collapse
Affiliation(s)
- Xinyu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Si Cheng
- 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 Molecular Cardiovascular Sciences, Ministry of Education, Peking University, 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
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Pei Pei
- Chinese Academy of Medical Sciences, Beijing, China
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Iona Y. Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robin Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Haiping Duan
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Simon Gilbert
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit and 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
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- *Correspondence: Yuanjie Pang,
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - 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
- Liming Li,
| |
Collapse
|
11
|
Larsson SC, Burgess S. Causal role of high body mass index in multiple chronic diseases: a systematic review and meta-analysis of Mendelian randomization studies. BMC Med 2021; 19:320. [PMID: 34906131 PMCID: PMC8672504 DOI: 10.1186/s12916-021-02188-x] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Obesity is a worldwide epidemic that has been associated with a plurality of diseases in observational studies. The aim of this study was to summarize the evidence from Mendelian randomization (MR) studies of the association between body mass index (BMI) and chronic diseases. METHODS PubMed and Embase were searched for MR studies on adult BMI in relation to major chronic diseases, including diabetes mellitus; diseases of the circulatory, respiratory, digestive, musculoskeletal, and nervous systems; and neoplasms. A meta-analysis was performed for each disease by using results from published MR studies and corresponding de novo analyses based on summary-level genetic data from the FinnGen consortium (n = 218,792 individuals). RESULTS In a meta-analysis of results from published MR studies and de novo analyses of the FinnGen consortium, genetically predicted higher BMI was associated with increased risk of type 2 diabetes mellitus, 14 circulatory disease outcomes, asthma, chronic obstructive pulmonary disease, five digestive system diseases, three musculoskeletal system diseases, and multiple sclerosis as well as cancers of the digestive system (six cancer sites), uterus, kidney, and bladder. In contrast, genetically predicted higher adult BMI was associated with a decreased risk of Dupuytren's disease, osteoporosis, and breast, prostate, and non-melanoma cancer, and not associated with Alzheimer's disease, amyotrophic lateral sclerosis, or Parkinson's disease. CONCLUSIONS The totality of the evidence from MR studies supports a causal role of excess adiposity in a plurality of chronic diseases. Hence, continued efforts to reduce the prevalence of overweight and obesity are a major public health goal.
Collapse
Affiliation(s)
- Susanna C Larsson
- Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| |
Collapse
|
12
|
Parra-Landazury NM, Cordova-Gallardo J, Méndez-Sánchez N. Obesity and Gallstones. Visc Med 2021; 37:394-402. [PMID: 34722722 PMCID: PMC8543292 DOI: 10.1159/000515545] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 02/23/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The prevalence of obesity has been increasing globally and represents the main risk factor for the development of gallstone disease (GD). SUMMARY Excess body weight represents the main cause for the development of GD; nevertheless, there have been described multiple risk factors for its development, among them modifiable risk factors as diet, lifestyle, physical inactivity, and non-modifiable risk factors as ethnicity, female sex, advanced age, parity, and genetic mutations. Body mass index, abdominal perimeter, and waist-hip index have been used to determine the degree of adiposity of a person. Hence, central abdominal fat has been mostly associated with insulin resistance with the consequent increase in the hepatic cholesterol secretion; contributing as one of the multiple mechanisms associated with the development of gallstones. This disease has a low mortality; however, it has been associated with multiple diseases such as cardiovascular diseases, carotid atherosclerosis, metabolic associated fatty liver disease, and gallbladder cancer, probably because they share many of the risk factors. KEY MESSAGES GD continues to be considered a disease with a high medical burden, in which it is sought to intervene in modifiable risk factors to reduce its development.
Collapse
Affiliation(s)
| | - Jacqueline Cordova-Gallardo
- Department of Hepatology, Service of Surgery and Obesity Clinic, General Hospital “Dr. Manuel Gea González”, Mexico City, Mexico
- Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
| | - Nahum Méndez-Sánchez
- Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
- Liver Research Unit, Medica Sur Clinic and Foundation, Mexico City, Mexico
| |
Collapse
|
13
|
Pang Y, Lv J, Kartsonaki C, Yu C, Guo Y, Chen Y, Yang L, Millwood IY, Walters RG, Wang S, Chen J, Chen Z, Li L. Metabolic risk factors, genetic predisposition, and risk of severe liver disease in Chinese: a prospective study of 0.5 million people. Am J Clin Nutr 2021; 114:496-504. [PMID: 33964851 DOI: 10.1093/ajcn/nqab099] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 03/09/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Metabolic risk factors have been shown to be associated with severe liver disease (SLD) in Chinese populations. However, there is limited evidence on the combined impact of these factors, or the genetic variants associated with SLD. OBJECTIVES We examined the associations of combined metabolic risk factors with risks of SLD, both overall and by genetic predisposition to SLD. METHODS The study population involved 486,828 participants of the prospective China Kadoorie Biobank aged 30-79 years from 10 diverse areas in China without a history of cancer or liver disease at baseline. Cox regression was used to estimate adjusted HRs for SLD associated with combined metabolic risk factors (central adiposity, physical inactivity, and diabetes) by stratum of genetic risk [assessed separately by a PNPLA3 variant (rs738409) and a BMI genetic risk score]. RESULTS During ∼10 years of follow-up, 3279 incident cases of SLD were recorded. The overall mean BMI was 23.8 kg/m2 (SD, 3.4 kg/m2), and 5.9% participants had diabetes. Compared with those with 3 metabolic factors, participants with 2, 1, and 0 metabolic factors had 31% (HR, 0.69; 95% CI: 0.65-0.73), 43% (HR, 0.57; 95% CI: 0.53-0.60), and 52% (HR, 0.48; 95% CI: 0.42-0.56) lower risks of SLD, respectively. For both BMI and nonalcoholic fatty liver disease variants, participants with fewer metabolic factors had a lower risk of SLD, lower levels of gamma-glutamyl transferase, and lower fatty liver index scores, in participants with low and high genetic risks (P value for interaction > 0.05). CONCLUSIONS In relatively lean Chinese adults, individuals with fewer metabolic risk factors had a lower relative risk of SLD and a more favorable profile of liver biomarkers across all strata of genetic risk.
Collapse
Affiliation(s)
- Yuanjie Pang
- 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 (PKU-PHEPR), Peking University, Beijing, China
| | - 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, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response (PKU-PHEPR), Peking University, Beijing, China
| | - Yu Guo
- Chinese Academy of Medical Sciences, 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, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - 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, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - 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, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - 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, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Sisi Wang
- Liuzhou Center for Disease Prevention and Control, Liuzhou, China
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, 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, United Kingdom
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response (PKU-PHEPR), Peking University, Beijing, China
| |
Collapse
|