Integrated exposome profiling to identify environmental risk factors of metabolic disease in mid- and late-life

综合暴露组分析可识别中晚年代谢疾病的环境危险因素

• 批准号: 10638457
• 负责人: Sung Kyun Park
• 金额: $ 66.94万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-17 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10638457
• 关键词: Address; Age Years; Air Pollutants; Air Pollution; Area; Biological; Biological Markers; Blood; Blood Glucose; Blood specimen; Body mass index; Chemicals; Clinical; Communities; Diagnosis; Diagnostic; Diet; Discrimination; Disease; Disease Progression; Early Diagnosis; Early Intervention; Elderly; Environmental Exposure; Environmental Health; Environmental Risk Factor; Ethnic Origin; Evaluation; Evolution; Exposure to; Family; Fasting; Future; Genotype; Goals; Human; Individual; Life Style; Link; Longitudinal cohort; Mass Spectrum Analysis; Measures; Metabolic; Metabolic Diseases; Metabolic syndrome; Metabolism; Metals; Natural History; Newly Diagnosed; Nitrogen Dioxide; Non-Insulin-Dependent Diabetes Mellitus; Organophosphates; Outcome; Ozone; Parabens; Particulate Matter; Pathogenesis; Perimenopause; Pesticides; Phenols; Play; Poly-fluoroalkyl substances; Population; Precision Health; Predisposition; Prevalence; Prevention; Process; Prospective Studies; Public Health; Recording of previous events; Research; Resolution; Risk; Risk Assessment; Risk Factors; Risk Marker; Role; Sampling; Serum; Study of Women' s Health Across the Nation; Testing; Toxic effect; United States; Vulnerable Populations; Woman; cohort; cost; demographics; diagnostic signature; disorder risk; effective intervention; environmental chemical; epidemiology study; fine particles; health data; improved; inorganic phosphate; insight; machine learning method; metabolic phenotype; metabolome; metabolomics; middle age; multi-ethnic; multi-racial; novel; phthalates; response; risk prediction; risk prediction model; risk stratification

PROJECT SUMMARY/ABSTRACT Exposure to environmental chemicals may play an important role in the pathogenesis of type-2 diabetes (T2D) and metabolic syndrome (MetS) as these exposures are widespread in the population, and the prevalence of these metabolic disorders has dramatically increased over the past several decades. In the US, >42,000 types of chemicals are actively produced, but only a handful of chemicals are tested for their toxicity. Epidemiologic studies have also been restricted to a few targeted metabolism-disrupting chemicals, while relatively little is known about the potential metabolic toxicity of the majority of these chemicals. Analyses spanning wide classes of chemicals are critically needed to identify novel areas of risk, as well as specific associations with metabolic phenotypes. In addition, the underlying biological mechanisms linking environmental chemicals and T2D and MetS are still not well understood. Although risk assessment is critical for early detection and prevention of T2D and MetS, current risk prediction models generally use only a few known risk factors such as demographics, clinical measures including fasting blood glucose and body mass index, and family history and susceptible genotypes. Integrated exposomic signatures may improve risk stratification/discrimination to identify vulnerable populations and provide a more effective intervention approach at the individual- and population-level (i.e., precision health). To address these scientific gaps, we propose to conduct an exposome- metabolome wide association study in relation to incident T2D and Mets in the Study of Women’s Health Across the Nation (SWAN), a multi-racial/ethnic longitudinal cohort of women from midlife to late-life. Using untargeted, high-resolution mass spectrometry that provides the high-quality measures needed for sequencing the human blood exposome, we aim to establish a new paradigm for environmental health research addressing metabolic disease risk. This can be achieved by taking advantage of the rich longitudinal features of the SWAN. Specifically, we aim to 1) conduct an integrated exposome- and metabolome-wide association study of incident T2D and MetS using pre-diagnosis blood samples collected at baseline to characterize known and novel environmental exposures detected using untargeted high-resolution mass spectrometry linked to future T2D and MetS risk and identify endogenous metabolite and disease-associated alterations underlying T2D and MetS; 2) evaluate temporal evolution of exposome and metabolome profiles in T2D and MetS cases and metabolically healthy controls; 3) identify pre-diagnostic metabolomic signatures that link targeted exposures of metabolism disrupting chemicals and novel exposome biomarkers to incident T2D and MetS; and 4) develop and validate exposome-based risk prediction models for T2D and MetS. Successful completion of these aims will identify critical pre-diagnostic exposome biomarkers that may improve risk prediction for T2D and MetS.

项目总结/文摘