Key result
Obesity was significantly associated with 45 of 71 cardiovascular protein biomarkers (false discovery rate q <0.05), with proteomic profiles clustering into distinct adipose, metabolic, and lipid axes.
Why the study?
Obesity is linked to diverse cardiometabolic manifestations, but the biological pathways driving these traits remain incompletely understood. Proteomic profiling was evaluated to identify signatures of obesity and their overlap with related cardiometabolic traits.
Cohort (n=6,981)
p-value: p=q <0.05
Proteomic profiling identifies significant overlap between obesity and cardiometabolic traits, highlighting shared pathways and identifying 6 biomarkers associated with incident metabolic syndrome.
May inform cardiometabolic risk assessment in obesity; extends pathway insights but remains hypothesis-generating.
Background Obesity may be associated with a range of cardiometabolic manifestations. We hypothesized that proteomic profiling may provide insights into the biological pathways that contribute to various obesity‐associated cardiometabolic traits. We sought to identify proteomic signatures of obesity and examine overlap with related cardiometabolic traits, including abdominal adiposity, insulin resistance, and adipose depots. Methods and Results We measured 71 circulating cardiovascular disease protein biomarkers in 6981 participants (54% women; mean age, 49 years). We examined the associations of obesity, computed tomography measures of adiposity, cardiometabolic traits, and incident metabolic syndrome with biomarkers using multivariable regression models. Of the 71 biomarkers examined, 45 were significantly associated with obesity, of which 32 were positively associated and 13 were negatively associated with obesity (false discovery rate q <0.05 for all). There was significant overlap of biomarker profiles of obesity and cardiometabolic traits, but 23 biomarkers, including melanoma cell adhesion molecule (MCAM), growth differentiation factor‐15 (GDF15), and lipoprotein(a) (LPA) were unique to metabolic traits only. Using hierarchical clustering, we found that the protein biomarkers clustered along 3 main trait axes: adipose, metabolic, and lipid traits. In longitudinal analyses, 6 biomarkers were significantly associated with incident metabolic syndrome: apolipoprotein B (apoB), insulin‐like growth factor‐binding protein 2 (IGFBP2), plasma kallikrein (KLKB1), complement C2 (C2), fibrinogen (FBN), and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP); false discovery rate q <0.05 for all. Conclusions We found that the proteomic architecture of obesity overlaps considerably with associated cardiometabolic traits, implying shared pathways. Despite overlap, hierarchical clustering of proteomic profiles identified 3 distinct clusters of cardiometabolic traits: adipose, metabolic, and lipid. Further exploration of these novel protein targets and associated pathways may provide insight into the mechanisms responsible for the progression from obesity to cardiometabolic disease.
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Lau et al. (2021) conducted a cohort in Obesity and cardiometabolic dysfunction (n=6,981). Obesity and cardiometabolic traits was evaluated on Association of 71 circulating cardiovascular disease protein biomarkers with obesity (p=q <0.05). Obesity was significantly associated with 45 of 71 cardiovascular protein biomarkers (false discovery rate q <0.05), with proteomic profiles clustering into distinct adipose, metabolic, and lipid axes.
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