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.

项目摘要 心血管疾病是发病率和死亡率的全球主要原因，占32％ 截至2017年，每年死亡（> 1700万）。糖尿病也是一个主要的健康问题，患病率为463 全球百万，美国2900万。肥胖被认为是两种心血管疾病的危险因素 （例如冠状动脉疾病，心房颤动，缺血性中风）和2型糖尿病。此外，肥胖具有 美国人口继续增加。 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 用于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 由于道德，经济和实际问题，疾病的理解很少 饮食干预措施的个体数量。 因此，我们依靠观察数据，这很容易被社会经济等因素混淆 地位和生活方式的选择。因为基因是固定在构思上的，并且在人群中随机分类，所以 Mendelian随机化（MR）基于遗传构成模仿随机对照试验，允许 因果关系的推断。 MR需要大规模数据，并且具有诸如英国之类的数据的可用性 生物库（> 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，我们无法触及。我将在英国生物库中结合观察性，遗传和MR分析与 全基因组协会研究（GWAS）危险因素和心脏代谢疾病的荟萃分析 迄今为止逃避我们的重要饮食问题的结果。 我旨在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AIM 1中，我将使用观察性关联，标准MR来表征酒精， 基于酒精脱氢酶基因变异的非线性MR分析。在AIM 2中，我会描述 使用观察性分析的咖啡，执行GWAS为咖啡摄入和亚型创建遗传风险评分 咖啡，并使用由此产生的遗传风险评分进行MR和非线性MR分析。在AIM 3中，我会 使用观察性分析表征各种乳制品摄入类型，并基于 乳糖酶持续基因的变异。 总而言之，观测，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 仅根据观察分析得出的相关性。这具有影响建议的巨大潜力 由营养学家和医生给予数亿人遭受或有遭受危险的人 心血管疾病，糖尿病和肥胖症。