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

项目摘要 心血管疾病是全球发病率和死亡率的主要原因， 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2900万，美国2900万。肥胖被认为是两种心血管疾病的危险因素 (e.g.冠心病、心房纤维性颤动、缺血性中风）和2型糖尿病。此外，肥胖症 在美国人口中继续上升。 饮食是肥胖和心脏代谢结果的重要危险因素，也是一个可改变的因素 进行干预以降低疾病风险。特定膳食因素对心脏代谢的影响 由于伦理、经济和实际问题，对疾病的了解很少， 接受饮食干预的人数。 因此，我们依赖于观察数据，这些数据容易受到社会经济等因素的干扰。 地位和生活方式的选择。因为基因在受孕时是固定的，在种群中是随机排列的， 孟德尔随机化（MR）模拟了基于遗传组成的随机对照试验， 因果关系的推论。MR需要大规模的数据，而随着数据的可用性，如在英国 生物库（> 50万参与者），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，我们还没有达到。我将联合收割机结合英国生物库中的观察、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、 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 咖啡使用观察分析，执行GWAS以创建咖啡摄入量和亚型的遗传风险评分 的咖啡，并使用由此产生的遗传风险分数的MR和非线性MR分析。在目标3中，我将 使用观察分析表征各种乳制品摄入类型，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， 仅从观察分析得出的相关性。这有巨大的潜力影响建议 营养学家和医生给数亿患有或有风险的人 心血管疾病、糖尿病和肥胖症。

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.