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.

抽象的 不健康的饮食是全球糖尿病和死亡率的主要危险因素。作为糖尿病和肥胖症 流行病继续增加，饮食危险因素对全球疾病负担的贡献也在增加。有一个 迫切需要确定饮食因果关系的哪些方面影响代谢疾病，以指导更有效的饮食 建议。与因果关系分开相关性仍然是一个挑战，而许多 流行病学研究已与糖尿病联系在一起，在 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影响 摄入量，最近的工作建立了重要的遗传力和数百种遗传关联 不同的食物和饮食方式。但是，将饮食特征结合在研究中以进行遗传分析 由于研究，文化和偏好的研究差异，仍然是一个挑战。我们假设共享 遗传对饮食摄入的影响可以作为识别整个研究中可比饮食的常见参考。 在几个同类群体中，均具有遗传和饮食数据，我们将启动新的合作，得出定量 食品特征和饮食模式，并进行全基因组关联研究（GWAS）以创建同源 具有研究特异性饮食表型和一组遗传标记的GWAS数据集。一系列遗传 将进行相关分析，以确定各种研究的可比食品和饮食模式。 一旦确定，可比饮食表型的GWAS荟萃分析将提高发现新颖和 多种族遗传关联。阐明饮食摄入的直接和间接遗传机制 我们将进行精细映射和基因优先序列，富集和途径的基因座和基因组水平 分析以及遗传相关性和全现象的关联研究（PHEWAS）。解决限制 观察性研究，Mendelian随机分组（MR）因果推断将使用遗传学进行。 预测饮食摄入量和与糖尿病相关结果的公共GWA，以优先考虑因果关系 干预试验协会。通过表型相关和因果效应将遗传基因座聚类将 查明因果影响代谢疾病的饮食的遗传机制。我们将把MR扩展到英国 生物库结果与饮食绘制全面的因果双向关系。 总体而言，我们将确定新颖的和多种族的遗传关联与遍布可比的饮食表型 多种研究以阐明饮食摄入量的机制和饮食之间的因果关系， 糖尿病和整体人类健康。为了实现成为成为独立调查员的目标 营养学和代谢性疾病研究，我设计了一个详细的K99计划，其中包括教学课程和 博士的联合授权。 Florez，Hirschhorn和Willett在代谢，统计遗传学和营养中 流行病学。在R00阶段，进行独立研究并继续进行研究 技能，我将维护和培养营养和遗传学方面的合作，并发展我的研究计划。