Proteomics networks linking diet to cardiometabolic risk factors: the Framingham Heart Study

Proteomics has facilitated the identification of key pathways linking diet to diseases. However, a key challenge in high-throughput proteomics is identifying functional units of proteins that act together in biological processes.

We aimed to identify protein networks associated with diet quality and cardiovascular disease (CVD) risk factors.

We analyzed 740 Framingham Heart Study participants (mean age 52 y; 46% female). Weighted gene coexpression network analysis was applied to construct protein networks (i.e. modules) using 2651 plasma proteins. We assessed cross-sectional associations of modules with the Dietary Approach to Stop Hypertension (DASH) diet score, and body mass index (BMI). We examined the prospective association of the diet- and BMI-associated modules with incident fatty liver and type 2 diabetes (T2D). Furthermore, we conducted Mendelian randomization (MR) analysis to investigate protein-protein relationships.

There were 39 protein modules identified, and each was assigned an arbitrary color name. Four protein modules were associated with both diet and BMI. For example, a 10-unit higher DASH score was associated with 0.27 SD lower darkgrey module eigenvalues (95% confidence interval [CI]; 0.12, 0.42; P = 0.0008), and per 0.27 SD lower darkgrey module was associated with 1.17 kg/m² lower BMI (95% CI: 1.07, 1.27; P = 2.4e-88). Furthermore, we found that the darkgrey module was associated with both incident fatty liver and T2D, and the association with incident fatty liver remained after BMI adjustment (odds ratio 3.22 per SD increase), (95% CI: 1.68, 6.19; P = 0.0005). The darkgrey module comprises 39 proteins, including 8 proteins such as fatty acid-binding protein, adipocyte 4, leptin, and interleukin-1 receptor antagonist protein that may drive with the association with diet and BMI. MR analysis revealed 3 putative causal protein pairs from the darkgrey module.

Our findings highlight proteomic networks potentially linking diet and CVD risk and demonstrate the usefulness of proteomics for identifying high-risk individuals for future interventions.