Utilizing Proteomics to Identify Correlates of Elevated Lipid Levels

<strong><em>Background:</em></strong><em> </em>The Hispanic border communities along the Texas-Mexico border face challenges related to the diagnosis and treatment of obesity related chronic diseases. This population also experiences an increased burden from cardiovascular disease (CVD) and its associated risk factors. Proteomics is a powerful approach for understanding the biological processes underlying lipid abnormalities and cardiometabolic risk in Hispanic border populations, and it can inform the development of cost-effective prevention strategies and treatments for hyperlipidemia in resource-limited settings. We propose to enhance the understanding of the proteomic correlates of elevated lipid levels in Hispanic adults by looking at the association between the expression of 5,511 proteins and the lipids of interest, including LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), total cholesterol, and triglycerides. <strong><em>Methods:</em></strong><em> </em>We used data from the 959 observations from 691 unique observations from the Border Health Research Cohort study. A pooled cross-sectional analysis using mixed-effect linear regression models was used to assess the association between lipid levels and protein expression to determine which specific proteins are associated with these measures. The covariates in our model were age, sex, and genetic PCs (principal components) since these factors can have an effect on a participant&rsquo;s lipid levels. A random intercept was included for each study participant to account for the correlation present in repeated measurements from the same individual, and FDR-adjusted p-values were calculated to determine the significance of an association. <strong><em>Results: </em></strong>The largest intersections of significant proteins were observed among total cholesterol, LDL-C, and triglycerides, whereas fewer shared associations were noted with HDL-C.<strong><em> </em></strong>270 proteins (4.90%) out of 5,511 had significant associations with LDL cholesterol, which included PLA2G7, LDLR, APOM, APCS, and MPRIP. 330 proteins (5.99%) out of 5,511 had significant associations with HDL cholesterol, including APOA1, APOD, BPIFA2, APOM, and PLTP. 389 proteins (7.06%) out of 5,511 had significant associations with total cholesterol, with the significant associations being similar to the ones observed for LDL-C. 474 proteins (8.60%) out of 5,511 had significant associations with triglycerides, including LDLR, CES1, IGFS9, MAMDC4, and MFGE8. <strong><em>Conclusion</em></strong><em>: </em>In our sample from the Border Health Research Cohort Study (BHRC), we found that proteins involved in lipid biology, metabolism, inflammation, and cardiovascular pathways had significant associations with the lipids of interest. These results suggest there could be potential for the development of interventions and treatments for hyperlipidemia.