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Large-Scale Study Details on Type 2 Diabetes and Metabolomics
A thorough metabolome-wide association study (MWAS) for incident Type 2 Diabetes (T2D) was conducted utilizing data from ten prospective cohorts, including the Nurses’ Health Study (NHS), NHS2, health professionals Follow-Up Study (HPFS), Hispanic Community Health Study/Study of Latinos (SOL), Women’s Health Initiative (WHI), Atherosclerosis Risk in Communities (ARIC) study, Framingham Heart Study Offspring cohort (FHS), Multi-Ethnic Study of Atherosclerosis (MESA), Boston Puerto Rican Health Study (BPRHS), and the Prevención con Dieta Mediterránea Study (PREDIMED) [[3]].
study Participants & Data Collection: The study included a total of 6,890 participants from NHS, 3,692 from NHS2, 2,529 from HPFS, 2,821 from SOL, 1,392 from WHI, 1,288 white and 1,433 black participants from ARIC, 1,424 from FHS, 902 from MESA, 378 from BPRHS and 885 from PREDIMED [[3]].Data collected included demographics, medical and family history, diet, lifestyle factors, and blood samples collected at baseline and during follow-up periods. Participants were required to have qualified metabolomics data and be free of diabetes, cardiovascular disease, and cancer at the study’s start [[3]].
T2D Ascertainment: Incident T2D was defined as a new diagnosis during longitudinal follow-up in participants without diabetes at baseline.Diagnostic criteria varied slightly by cohort, utilizing fasting glucose levels, HbA1c measurements, medication use, and self-reported diagnoses, adhering to guidelines from the National Diabetes Data Group and the american Diabetes Association [[3]].
Metabolomic Profiling & Harmonization: Metabolomic profiling was performed using LC–MS methods at the Broad Institute and Metabolon Inc. Data underwent rigorous quality control, including filtering for detection rates and coefficient of variation. A total of 407 metabolites were harmonized across cohorts for analysis, ensuring representation from multiple platforms and cohorts [[3]].
Statistical Analysis: Cox and logistic regression models were used to assess the association between metabolites and T2D risk, adjusted for various covariates including age, sex, lifestyle factors, BMI, and waist-to-hip ratio. Meta-analysis was performed to combine results across cohorts, and associations were considered statistically meaningful with a meta-analyzed false revelation rate (FDR) < 0.05 [[3]]. The novelty of identified associations was assessed by comparing findings to previously published literature.
Research suggests that plasma metabolomic signatures are linked to obesity and the risk of T2D [[2]],and can even demonstrate decent prediction performance for incident T2D risk [[3]]. Further analysis of the data is ongoing, with code examples available on GitHub for figure generation [[1]].