Cardiology Division Department of Medicine Massachusetts General Hospital Boston MA.
Cardiovascular Research Center Massachusetts General Hospital Boston MA.
J Am Heart Assoc. 2021 Jul 20;10(14):e020215. doi: 10.1161/JAHA.120.020215. Epub 2021 Jul 3.
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 <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 <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.
肥胖可能与一系列心血管代谢表现有关。我们假设蛋白质组学分析可能为阐明导致各种肥胖相关心血管代谢特征的生物学途径提供新的见解。我们试图确定肥胖的蛋白质组学特征，并研究其与相关心血管代谢特征的重叠，包括腹部肥胖、胰岛素抵抗和脂肪组织。
我们对 6981 名参与者（54%为女性，平均年龄为 49 岁）的 71 种循环心血管疾病蛋白生物标志物进行了测量。我们使用多变量回归模型，检查肥胖、计算机断层扫描（CT）测量的肥胖指标、心血管代谢特征和代谢综合征的发生与生物标志物之间的关联。在检查的 71 种生物标志物中，有 45 种与肥胖显著相关，其中 32 种与肥胖呈正相关，13 种与肥胖呈负相关（所有生物标志物的错误发现率均<0.05）。肥胖和心血管代谢特征的生物标志物谱存在显著重叠，但有 23 种生物标志物仅与代谢特征相关，包括黑色素瘤细胞黏附分子（MCAM）、生长分化因子 15（GDF15）和脂蛋白（a）（LPA）。通过层次聚类，我们发现蛋白质生物标志物沿着 3 个主要特征轴聚类：脂肪、代谢和脂质特征。在纵向分析中，有 6 种生物标志物与代谢综合征的发生显著相关：载脂蛋白 B（apoB）、胰岛素样生长因子结合蛋白 2（IGFBP2）、血浆激肽释放酶（KLKB1）、补体 C2（C2）、纤维蛋白原（FBN）和 N 端前 B 型利钠肽（NT-proBNP）；所有生物标志物的错误发现率均<0.05。
我们发现肥胖的蛋白质组学结构与相关心血管代谢特征有很大的重叠，表明存在共同的途径。尽管存在重叠，但蛋白质组学特征的层次聚类确定了 3 个不同的心血管代谢特征聚类：脂肪、代谢和脂质。进一步探索这些新的蛋白质靶点和相关途径可能为阐明从肥胖到心血管代谢疾病的进展机制提供新的思路。
