Protein Markers of Diabetes Discovered in an African American Cohort.
Zsu-Zsu ChenYan GaoMichelle J KeyesShuliang DengMichael MiLaurie A FarrellDongxiao ShenUsman A TahirDaniel E CruzDebby NgoMark D BensonJeremy M RobbinsAdolfo CorreaJames G WilsonRobert E GersztenPublished in: Diabetes (2023)
Proteomics has been used to study type 2 diabetes, but the majority of available data are from White participants. Here, we extend prior work by analyzing a large cohort of self-identified African Americans in the Jackson Heart Study (n = 1,313). We found 325 proteins associated with incident diabetes after adjusting for age, sex, and sample batch (false discovery rate q < 0.05) measured using a single-stranded DNA aptamer affinity-based method on fasting plasma samples. A subset was independent of established markers of diabetes development pathways, such as adiposity, glycemia, and/or insulin resistance, suggesting potential novel biological processes associated with disease development. Thirty-six associations remained significant after additional adjustments for BMI, fasting plasma glucose, cholesterol levels, hypertension, statin use, and renal function. Twelve associations, including the top associations of complement factor H, formimidoyltransferase cyclodeaminase, serine/threonine-protein kinase 17B, and high-mobility group protein B1, were replicated in a meta-analysis of two self-identified White cohorts-the Framingham Heart Study and the Malmö Diet and Cancer Study-supporting the generalizability of these biomarkers. A selection of these diabetes-associated proteins also improved risk prediction. Thus, we uncovered both novel and broadly generalizable associations by studying a diverse population, providing a more complete understanding of the diabetes-associated proteome.
Keyphrases
- type diabetes
- cardiovascular disease
- insulin resistance
- glycemic control
- african american
- heart failure
- protein kinase
- blood glucose
- small molecule
- gold nanoparticles
- adipose tissue
- metabolic syndrome
- body mass index
- coronary artery disease
- weight loss
- high fat diet
- electronic health record
- skeletal muscle
- single molecule
- sensitive detection
- data analysis