ABSTRACT Cardiometabolic diseases (CMD), including obesity, dyslipidemia, type 2 diabetes, and hypertension, are the leading cause of disease burden in the world with a disproportionate impact on historically marginalized populations. In the first funding period of our project, The Hispanic/Latino Lipid Consortium (HLLC), our efforts centered on discovering genetic factors impacting serum lipid levels, obesity, and T2D in self-identified Hispanic/Latinos (HL), a population with significant CMD health disparities. This highly impactful research, which has resulted in 39 published papers to-date, leveraged extant genetic data as well as new genetic data generated for the project in >63k participants to identify multiple new CMD loci. We also characterized the regulatory mechanisms influencing lipid levels using a new resource of whole blood (WB) gene expression profiles in 880 HL participants. Yet, the mechanism of action of most GWAS signals and the molecular pathways disrupted in metabolic tissues are still not well understood. As such, in the second funding period of the HLLC, we propose to build on our remarkable success and experience generating and analyzing transcriptomic data in HL. Here, we aim to investigate the role of multi-tissue gene expression (WB and subcutaneous adipose tissue [SAT]) and changes in WB expression over time with the goal of identifying key modifiable molecular signatures associated with CMDs in an even larger, more diverse sample of HL. Specifically, we propose to: first, identify multi-tissue transcriptomic patterns associated with CMD and related traits (obesity, type 2 diabetes, dyslipidemia, hypertension measures) in recently acquired WB RNA sequencing data from 14k HL participants as well as in 300 SAT tissue specimens from HL participants recruited for the present application; second, identify longitudinal changes in WB transcriptomic data associated with changes in CMD-related risk factors in participants from the HLCC (1500 RNA measures from 750 participants with an average of 5 years between the two RNA sequencing measures for each person); and third, conduct integrative analyses of genetic and transcriptomic data to establish causality via Mendelian Randomization and characterize existing genomic findings with functional evidence. Our aims are entirely independent, exceptionally well powered, and designed to answer critical questions about the causal pathways underlying observed transcriptomic differences in CMD. This work will result in creation of a publicly available resource of eQTL information for metabolic tissues in HL and identify novel targets for early prevention and pharmaceutical intervention. Significantly, by addressing CMD risk in an under-represented population, our work contributes directly to the NIH’s mission to promote disease prevention and treatment in historically marginalized populations.