Alcohol consumption and the risk of all-cause and cause-specific mortality-a linear and nonlinear Mendelian randomization study

INTERNATIONAL JOURNAL OF EPIDEMIOLOGY(2024)

引用 0|浏览14
暂无评分
摘要
Background Many observational studies support light-to-moderate alcohol intake as potentially protective against premature death. We used a genetic approach to evaluate the linear and nonlinear relationships between alcohol consumption and mortality from different underlying causes.Methods We used data from 278 093 white-British UK Biobank participants, aged 37-73 years at recruitment and with data on alcohol intake, genetic variants, and mortality. Habitual alcohol consumption was instrumented by 94 variants. Linear Mendelian randomization (MR) analyses were conducted using five complementary approaches, and nonlinear MR analyses by the doubly-ranked method.Results There were 20 834 deaths during the follow-up (median 12.6 years). In conventional analysis, the association between alcohol consumption and mortality outcomes was 'J-shaped'. In contrast, MR analyses supported a positive linear association with premature mortality, with no evidence for curvature (Pnonlinearity >= 0.21 for all outcomes). The odds ratio [OR] for each standard unit increase in alcohol intake was 1.27 (95% confidence interval [CI] 1.16-1.39) for all-cause mortality, 1.30 (95% CI 1.10-1.53) for cardiovascular disease, 1.20 (95% CI 1.08-1.33) for cancer, and 2.06 (95% CI 1.36-3.12) for digestive disease mortality. These results were consistent across pleiotropy-robust methods. There was no clear evidence for an association between alcohol consumption and mortality from respiratory diseases or COVID-19 (1.32, 95% CI 0.96-1.83 and 1.46, 95% CI 0.99-2.16, respectively; Pnonlinearity >= 0.21).Conclusion Higher levels of genetically predicted alcohol consumption had a strong linear association with an increased risk of premature mortality with no evidence for any protective benefit at modest intake levels.
更多
查看译文
关键词
Alcohol consumption,Mendelian randomization,nonlinear analysis,doubly-ranked method,mortality
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要