Effects of alcohol, coffee, and milk intake on cardiometabolic disease via observational analysis and Mendelian randomization

通过观察分析和孟德尔随机化研究酒精、咖啡和牛奶摄入对心脏代谢疾病的影响

• 批准号: 10021413
• 负责人: Joanna Lankester
• 金额: $ 7.86万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10021413
• 关键词: Accounting; Address; Alcohol consumption; Alcohol dehydrogenase; Alcohols; Atrial Fibrillation; Caffeine; Cardiovascular Diseases; Centers for Disease Control and Prevention (U.S.); Cessation of life; Coffee; Conceptions; Consumption; Coronary heart disease; Dairy Products; Data; Diabetes Mellitus; Diet; Dietary Factors; Dietary Intervention; Disease; Disease Outcome; Ethics; Etiology; Genes; Genetic; Genetic Risk; Genomics; Health; Hospital Records; Hypertension; Individual; Intake; Intervention; Intervention Trial; Ischemic Stroke; Lactase; Life Style; Meta-Analysis; Milk; Morbidity - disease rate; Non-Insulin-Dependent Diabetes Mellitus; Nutritionist; Obesity; Outcome; Participant; Physicians; Population; Prevalence; Randomized; Randomized Controlled Trials; Risk; Risk Factors; Sample Size; Socioeconomic Status; Time; Variant; alcohol effect; alcohol risk; base; biobank; burden of illness; cardiometabolic risk; cardiometabolism; cardiovascular disorder risk; disorder risk; genetic makeup; genetic variant; genome wide association study; large datasets; large scale data; milk intake; mortality; phenotypic data; sex

Project Summary Cardiovascular diseases are the global leading cause of morbidity and mortality, accounting for 32% of deaths (> 17 million) annually as of 2017. Diabetes is also a major health problem, with a prevalence of 463 million globally and 29 million in the US. Obesity is thought to be a risk factor for both cardiovascular diseases (e.g. coronary heart disease, atrial fibrillation, ischemic stroke) and type 2 diabetes. Furthermore, obesity has continued to rise in the US population. Diet is an important risk factor for obesity and cardiometabolic outcomes, as well as a modifiable factor for intervention that could reduce disease risk. The contribution of specific dietary factors to cardiometabolic disease is poorly understood due to ethical, economical, and practical issues with randomizing a sufficient number of individuals to dietary interventions. Thus, we rely on observational data, which is prone to confounding by factors such as socioeconomic status and lifestyle choices. Because genes are fixed at conception and randomly assorted within a population, Mendelian Randomization (MR) mimics a randomized controlled trial on the basis of genetic makeup, allowing inference of causality. MR requires large-scale data, and with the availability of data such as that in the UK Biobank (> 500k participants), we can now use MR to address questions regarding dietary exposures that have so far been beyond our reach. I will combine observational, genetic, and MR analyses in the UK Biobank with external genome-wide association study (GWAS) meta-analyses of risk factors and cardiometabolic disease outcomes to answer important dietary questions that have so far evaded us. I aim to elucidate the relationship of several dietary factors with cardiometabolic disease using observational and MR studies. In Aim 1, I will characterize alcohol using observational association, standard MR, and non-linear MR analyses based on variation in the alcohol dehydrogenase gene. In Aim 2, I will characterize coffee using observational analysis, perform GWAS to create a genetic risk score for coffee intake and subtypes of coffee, and use the resulting genetic risk scores for MR and non-linear MR analyses. In Aim 3, I will characterize various dairy intake types using observational analysis and perform an MR analysis based on variation in the lactase persistence gene. In summary, the combination of observational, genetic, and MR analyses will allow us to characterize the risk associated with ubiquitous alcohol, caffeine, and dairy consumption in a much more meaningful way than correlations drawn from observational analyses alone. This has tremendous potential to influence the advice given by nutritionists and physicians to the hundreds of millions of people who suffer from or are at risk of cardiovascular disease, diabetes, and obesity.

项目概要 心血管疾病是全球发病率和死亡率的主要原因，占全球发病率和死亡率的32% 截至 2017 年，每年死亡人数（> 1700 万）。糖尿病也是一个主要的健康问题，患病率为 463 全球有 100 万，美国有 2900 万。肥胖被认为是这两种心血管疾病的危险因素 （例如冠心病、心房颤动、缺血性中风）和 2 型糖尿病。此外，肥胖还 美国人口持续增长。 饮食是肥胖和心脏代谢结果的重要危险因素，也是可改变的因素 可以降低疾病风险的干预措施。特定饮食因素对心脏代谢的贡献 由于随机化足够多的伦理、经济和实际问题，人们对这种疾病知之甚少。 接受饮食干预的人数。 因此，我们依赖观察数据，而这些数据很容易受到社会经济等因素的混淆。 地位和生活方式的选择。因为基因在受孕时就被固定并在群体中随机分类， 孟德尔随机化 (MR) 模拟基于基因构成的随机对照试验，允许 因果关系的推断。 MR需要大规模数据，并且数据可用，例如英国的数据 生物银行（> 500k 参与者），我们现在可以使用 MR 来解决有关饮食暴露的问题 到目前为止是我们力所能及的。我将结合英国生物银行的观察、遗传和 MR 分析 风险因素和心脏代谢疾病的外部全基因组关联研究 (GWAS) 荟萃分析 结果来回答迄今为止我们一直回避的重要饮食问题。 我的目的是利用以下方法阐明几种饮食因素与心脏代谢疾病的关系 观察和 MR 研究。在目标 1 中，我将使用观察关联、标准 MR 来表征酒精， 以及基于乙醇脱氢酶基因变异的非线性 MR 分析。在目标 2 中，我将描述 使用观察分析的咖啡，执行 GWAS 来创建咖啡摄入量和亚型的遗传风险评分 咖啡，并使用所得的遗传风险评分进行 MR 和非线性 MR 分析。在目标 3 中，我将 使用观察分析来描述各种乳制品摄入类型的特征，并基于以下内容进行 MR 分析： 乳糖酶持久性基因的变异。 总之，观察、遗传和 MR 分析的结合将使我们能够表征 与无处不在的酒精、咖啡因和乳制品消费相关的风险比饮酒更有意义 仅通过观察分析得出的相关性。这对影响建议有巨大的潜力 由营养学家和医生为数亿患有或有风险的人提供 心血管疾病、糖尿病和肥胖症。