Genetically harmonized dietary intake and causal relationships with diabetes-related outcomes

遗传协调的饮食摄入量及其与糖尿病相关结果的因果关系

• 批准号: 10730298
• 负责人: Joanne Burnette Cole
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-10 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10730298
• 关键词: Address; Adherence; Alcohols; Biological Markers; Body mass index; Cardiometabolic Disease; Career Mobility; Clinical; Clinical Markers; Collaborations; Complex; Consumption; Data; Data Set; Diabetes Mellitus; Diet; Dietary Intervention; Dietary Practices; Dietary intake; Disease; Education; Enzymes; Ethnic Origin; Etiology; Faculty; Food; Food Patterns; Food Preferences; Foundations; Fruit; Genes; Genetic; Genetic Determinism; Genetic Markers; Genetic Variation; Genetic study; Genomics; Goals; Guidelines; Health; Heritability; Heterogeneity; Human; Human Genetics; Individual Differences; Intake; Intervention Studies; Intervention Trial; Life Style; Link; Maps; Mendelian randomization; Mentors; Meta-Analysis; Metabolic; Metabolic Diseases; Metabolism; Methodology; Methods; Milk; Nomenclature; Obesity; Obesity Epidemic; Observational Study; Outcome; Pathway Analysis; Phase; Phenotype; Population; Positioning Attribute; Recommendation; Research; Research Personnel; Risk Factors; Role; Series; Tea; Tissues; Training; Translations; Work; biobank; burden of illness; career; cohort; design; dietary; epidemiology study; experience; follow-up; food consumption; food environment; genetic analysis; genetic association; genetic epidemiology; genetic variant; genome wide association study; genome-wide; genomic locus; improved; mortality; multi-ethnic; novel; nutrition; nutritional epidemiology; nutritional genomics; olfactory receptor; phenome; preference; prevent; programs; skills; success; trait

ABSTRACT Unhealthful diet is a leading risk factor for diabetes and mortality worldwide. As the diabetes and obesity epidemics continue to rise, so does the contribution of dietary risk factors to global disease burden. There is an urgent need to identify which aspects of diet causally influence metabolic disease to guide more effective dietary recommendations. Teasing apart correlation from causation remains a challenge, and while numerous epidemiological studies have observationally linked diet to diabetes, there has been limited success with translation to intervention studies. Normal human genetic variation has both direct and indirect effects on dietary intake, with recent work establishing significant heritability and hundreds of genetic associations with numerous different foods and dietary patterns. However, combining dietary traits across studies for genetic analysis remains a challenge due to study differences in design, cultures, and preferences. We hypothesize shared genetic influences on dietary intake can act as the common reference to identify comparable diets across studies. In each of several cohorts, with both genetic and diet data, we will initiate new collaborations, derive quantitative food traits and dietary patterns, and conduct genome-wide association studies (GWAS) to create homologous GWAS datasets with study-specific dietary phenotypes and a common set of genetic markers. A series of genetic correlation analyses will be conducted to identify comparable foods and dietary patterns across diverse studies. Once identified, GWAS meta-analysis of comparable dietary phenotypes will improve power to detect novel and multi-ethnic genetic associations. To elucidate the direct and indirect genetic mechanisms of dietary intake at the locus and genome-wide levels we will conduct fine-mapping and gene prioritization, enrichment and pathway analysis, and genetic correlation and phenome-wide association studies (PheWAS). To address limitations with observational studies, Mendelian randomization (MR) causal inference will be performed using genetically predicted dietary intake and publicly available GWAS on diabetes-related outcomes to prioritize causal associations for intervention trials. Clustering of genetic loci by phenotypic correlations and causal effects will pinpoint genetic mechanisms of diet that causally influence metabolic disease. We will extend MR to all UK Biobank outcomes to map comprehensive causal bidirectional relationships with diet. Overall we will identify novel and multi-ethnic genetic associations with comparable dietary phenotypes across diverse studies to elucidate the mechanisms of dietary intake and uncover causal relationships between diet, diabetes, and overall human health. To achieve my goal of becoming an independent investigator in nutrigenomic and metabolic disease research, I have designed a detailed K99 plan with didactic coursework and co-mentoring by Drs. Florez, Hirschhorn, and Willett in metabolism, statistical genetics, and nutritional epidemiology. During the R00 phase, while conducting independent research and continuing to develop research skills, I will maintain and cultivate collaborations in nutrition and genetics and grow my research program.

摘要 不健康的饮食是全球糖尿病和死亡率的主要危险因素。糖尿病和肥胖症 流行病继续增加，饮食风险因素对全球疾病负担的影响也在增加。有一个 迫切需要确定饮食的哪些方面会影响代谢疾病，以指导更有效的饮食 建议.将相关性与因果关系区分开来仍然是一个挑战， 流行病学研究已经观察到饮食与糖尿病有关，但成功有限， 转化为干预研究。正常的人类遗传变异对饮食有直接和间接的影响。 摄入量，最近的工作建立了显着的遗传性和数百个遗传协会与许多 不同的食物和饮食模式。然而，将研究中的饮食特征结合起来进行遗传分析， 仍然是一个挑战，由于研究设计，文化和偏好的差异。我们假设 遗传对饮食摄入量的影响可以作为共同的参考，以确定跨研究的可比饮食。 在几个队列中的每一个中，我们将利用遗传和饮食数据，启动新的合作， 食物性状和饮食模式，并进行全基因组关联研究（GWAS）以创建同源 GWAS数据集具有研究特定的饮食表型和一组常见的遗传标记。一系列的基因 将进行相关性分析，以确定不同研究中可比较的食物和饮食模式。 一旦确定，GWAS对可比饮食表型的荟萃分析将提高检测新的和 多种族基因关联。阐明饮食摄入的直接和间接遗传机制， 基因座和全基因组水平，我们将进行精细定位和基因优先级，富集和途径 分析，以及遗传相关性和全表型关联研究（PheWAS）。为了解决限制， 观察性研究，孟德尔随机化（MR）因果推断将使用遗传学方法进行。 预测饮食摄入量和公开可用的GWAS对糖尿病相关结局的影响， 协会进行干预试验。通过表型相关性和因果效应对遗传位点进行聚类， 确定饮食对代谢疾病的遗传机制。我们将把MR扩展到全英国 生物库结果，以映射与饮食的全面因果双向关系。 总体而言，我们将确定新的和多种族的遗传关联与不同地区的可比饮食表型 不同的研究阐明了饮食摄入的机制，并揭示了饮食， 糖尿病和整体人类健康。为了实现我成为一名独立调查员的目标， 营养基因组学和代谢疾病的研究，我已经设计了一个详细的K99计划与教学课程， Florez、赫什霍恩和Willett博士在代谢、统计遗传学和营养学方面的共同指导 流行病学在R00阶段，在进行独立研究和继续开发研究的同时， 技能，我将保持和培养营养和遗传学的合作，并发展我的研究计划。