Perfluoroalkyl substances and incident type 2 diabetes in a multi-ethnic population: A metabolome-genome investigation

项目摘要 2型糖尿病（T2 D）患病率的增加伴随着种族/民族差异，但病因因素 促进T2 D流行病和T2 D差异尚未得到充分理解。越来越多的实验证据表明 暴露于内分泌干扰化学品（EDCs），如全氟和多氟烷基物质（PFAS）， 促进T2 D发展，可能与已知的风险因素如遗传变异协同作用。PFAS是 扰乱新陈代谢的普遍存在且持久的化学物质。然而，很少有前瞻性研究检查 PFAS和T2 D风险之间的相关性，并且这些几乎仅在白色人群中存在。先前 研究还缺乏临床确定的T2 D诊断，仅调查了许多潜在的T2 D诊断中的少数几个。 有害的PFAS，并没有检查PFAS混合物或基因PFAS相互作用的潜在影响。国家 现有技术的集成组学方法可以克服这些障碍以推进该领域。我们建议第一个 综合代谢组-基因组方法（1）表征PFAS浓度之间的关联 诊断前血浆样本中的PFAS（单个PFAS和混合物）以及T2 D事件风险和潜在影响 使用多基因风险评分作为研究T2 D遗传易感性的创新解决方案 相互作用，（2）识别潜在的失调代谢途径，（3）识别代谢特征， 根据EDC暴露量和T2 D相关内源性代谢产物定义的诊断前血浆样本 风险我们将利用BioMe进行一项嵌套病例对照研究，BioMe是一种正在进行的电子健康记录链接， 自2007年以来，超过55，000名参与者在西奈山医院（纽约州）寻求初级保健时登记。 T2 D发病病例与BioMe无T2 D对照组（N = 1，700）匹配（1：1），均为非裔美国人， 西班牙裔和白色血统，采血和T2 D诊断之间的平均时间约为6年。我们将使用 使用最先进的高分辨率测量PFAS和代谢途径 代谢组学（HRM）方法。我们将在新发T2 D病例和匹配的对照组中重复研究结果 来自洛杉矶和夏威夷基于人群的多种族队列（MEC）研究， 以及在同一实验室测量的PFAS和HRM的诊断前血浆浓度作为BioMe样本。在 与以前的研究相比，我们纳入了一系列广泛的传统和新兴的PFAS，混合物效应， 和基因环境相互作用，利用最先进的代谢组-基因组方法和 在两个独特的、表型良好的多种族队列中进行严格的发现-复制设计， 血浆样本以鉴定与T2 D相关的早期生物标志物。这项研究依赖于多学科 经验丰富的研究人员团队，具有环境/遗传流行病学，PFAS和T2 D研究方面的专业知识， 以及最先进的人力资源管理、基因组学和生物统计学混合方法。调查结果将告知精度 T2 D预防和治疗的医学方法，特别是针对高风险的多民族人群。

PROJECT SUMMARY Increasing prevalence of type 2 diabetes (T2D) is accompanied by racial/ethnic disparities, but etiological factors promoting the T2D epidemic and T2D disparities are not fully understood. Growing experimental evidence shows that exposures to endocrine-disrupting chemicals (EDCs), such as per- and polyfluoroalkyl substances (PFAS), promote T2D development, likely in synergy with known risk factors such as genetic variations. PFAS are ubiquitous and persistent chemicals that perturb metabolism. However, few prospective studies examined the association between PFAS and T2D risk, and those were almost exclusively in White populations. Previous studies also lacked clinically ascertained T2D diagnosis, investigated only a few of the many potentially hazardous PFAS, and did not examine potential effects of PFAS mixtures or gene–PFAS interactions. State-of- the-art integrated omics approaches can overcome these barriers to advance the field. We propose the first integrated metabolome–genome approach to (1) characterize the associations between PFAS concentrations (individual PFAS and mixtures) in prediagnostic plasma samples and incident T2D risk and potential effect modification by genetic predisposition to T2D using polygenic risk scores as an innovative solution for studying interactions, (2) identify underlying dysregulated metabolic pathways, and (3) identify metabolic signatures in prediagnostic plasma samples defined by EDC exposures and endogenous metabolites associated with T2D risk. We will perform a nested case–control study leveraging BioMe, an ongoing electronic health record-linked biobank with >55,000 participants enrolled while seeking primary care at Mount Sinai Hospital (NY) since 2007. Incident T2D cases are matched (1:1) to BioMe T2D-free controls (N = 1,700) and are of African American, Hispanic and White ancestry, with ~6 years average time between blood draw and T2D diagnosis. We will use prediagnostic plasma to measure PFAS and metabolic pathways using state-of-the-art high-resolution metabolomics (HRM) approaches. We will replicate findings among incident T2D cases and matched controls from the population-based Multiethnic Cohort (MEC) study in Los Angeles and Hawaii with extant genome data and prediagnostic plasma concentrations of PFAS and HRM measured at the same lab as BioMe samples. In contrast to prior studies, we incorporate a wide suite of legacy and emerging PFAS, exposure-mixture effects, and gene–environment interactions by leveraging state-of-the-art metabolome–genome approaches and a rigorous discovery–replication design in two unique, well-phenotyped multiethnic cohorts with prediagnostic plasma samples to identify early biomarkers associated with T2D. This research relies on a multidisciplinary team of seasoned investigators with expertise in environmental/genetic epidemiology, PFAS and T2D research, and state-of-the-art HRM, genomics, and biostatistical exposure–mixture methods. Findings will inform precision medicine approaches for T2D prevention and treatment, particularly for high-risk multiethnic populations.