Dissecting Genetic and Environmental Determinants of Plasma Molecular Signatures and Their Link to Type 2 Diabetes Risk
Sevilla-Gonzalez, M.; Wang, N.; Hanson, P.; Bebo, A.; Hitchcock, D.; Hsu, S.; Westerman, K.; Cromer, S. J.; Barry, V. G.; Borns-Weil, Y.; Zhang, Y.; Ben-Yossef, O.; Patel, C. J.; Franceschini, N. J.; Taylor, K.; Pacheco, J. A.; Clish, C. B.; Gerszten, R. E.; Raffield, L. M.; Kooperberg, C.; Rich, S.; Dupuis, J.; Rotter, J.; Liu, C.-T.; Meigs, J.; Manning, A. K.
Show abstract
BackgroundType 2 diabetes (T2D) is a heterogeneous disease shaped by both genetic, environmental, cultural, and socioeconomic factors, with well-documented disparities in incidence across populations. The molecular pathways underlying these disparities, however, remain poorly understood. Plasma metabolites and proteins integrate both genetic and environmental influences on type 2 diabetes (T2D) risk, providing insight into disease mechanisms. We aimed to quantify the variance in these molecular profiles explained by environmental and genetic ancestry domains and to apply causal inference approaches to identify environmentally and genetic ancestry influenced pathways contributing to T2D risk. MethodsWe analyzed plasma proteomic and metabolomic profiles from 3,360 MESA participants (51.6% female), and in 1,333 participants from the Womens Health Initiative. To characterize the sources of variance in plasma proteomic and metabolomic profiles, we performed variance decomposition partitioning into four domains: biological (age, sex, BMI), genetic ancestry (principal components), lifestyle (smoking, alcohol intake, diet), and social determinants (self-reported race and ethnicity, income, education). To assess causal pathways towards T2D risk, we applied two-sample Mendelian Randomization to disentangle environmental and genetic contributors to T2D risk. ResultsThe largest share of variance in proteomic and metabolomic profiles was explained by biological and lifestyle factors, while race and ethnicity and genetic ancestry accounted for smaller but non-redundant contributions. Genetic ancestry was primarily associated with lipid and apolipoprotein variation, whereas race and ethnicity and socioeconomic factors were associated with immune and inflammatory signatures. Environmentally influenced metabolites (e.g., diacylglycerols, phosphatidylethanolamines, lysophosphatidylcholines) and vascular-inflammatory proteins were consistently linked to higher T2D risk, while genetic ancestry influenced triglycerides and IGFBP3 reflected inherited risk pathways. Mediation analyses showed that selected lipids and proteins (e.g., IGFBP2, HGF, SSC4D) explained 10-25% of racial/ethnic disparities in T2D. Mendelian randomization identified causal roles for seven lipid species and IGFBP3 in T2D risk. ConclusionsOur results reveal both genetic and non-genetic sources of variation in proteomic and metabolomic profiles, uncovering environmental and genetic pathways contributing to T2D risk. These findings advance precision medicine by identifying modifiable molecular mediators of disparities and potential causal targets for prevention.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Metabolome-wide Mendelian randomization characterizes heterogeneous and shared causal effects of metabolites on human health 97%
- Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank 96%
- Comprehensive genetic analysis of the human lipidome identifies novel loci controlling lipid homeostasis with links to coronary artery disease 96%
Similar papers in this journal
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship 95%
- Blood-based epigenome-wide analyses of chronic low-grade inflammation across diverse population cohorts 94%
- Multi-trait genome-wide association study in 34,394 Chinese women reveals the genetic architecture of plasma metabolites during pregnancy 94%
Similar papers in this journal
- Characterizing common and rare variations in non-traditional glycemic biomarkers using multivariate approaches on multi-ancestry ARIC study 96%
- Integrative proteogenomic analyses provide novel interpretations of type 1 diabetes risk loci through circulating proteins 95%
- Plasma proteomic signatures of adiposity are associated with cardiovascular risk factors and type 2 diabetes risk in a multi-ethnic Asian population 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.