Cardiovascular Biomarkers of Obesity and Overlap With Cardiometabolic Dysfunction

Author:

Lau Emily S.1,Paniagua Samantha M.12,Zarbafian Shahrooz12,Hoffman Udo3,Long Michelle T.4,Hwang Shih‐Jen56,Courchesne Paul6,Yao Chen67,Ma Jiantao67,Larson Martin G.56,Levy Daniel67,Shah Ravi V.12,Ho Jennifer E.12ORCID

Affiliation:

1. Cardiology Division Department of Medicine Massachusetts General Hospital Boston MA

2. Cardiovascular Research Center Massachusetts General Hospital Boston MA

3. Department of Radiology Massachusetts General Hospital Boston MA

4. Section of Gastroenterology Boston Medical CenterBoston University School of Medicine Boston MA

5. Department of Biostatistics Boston University School of Public Health Boston MA

6. The Framingham Heart Study Framingham MA

7. The Population Sciences Branch Division of Intramural Research National Heart, Lung, and Blood InstituteNational Institutes of Health Bethesda MD

Abstract

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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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