Identification of adipokine clusters related to parameters of fat mass, insulin sensitivity and inflammation

• Published in: PloS one Vol. 9; no. 6; p. e99785
• Main Authors: Flehmig, Gesine, Scholz, Markus, Klöting, Nora, Fasshauer, Mathias, Tönjes, Anke, Stumvoll, Michael, Youn, Byung-Soo, Blüher, Matthias
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.06.2014; Public Library of Science (PLoS)
• Subjects: Adipokines - blood; Adiponectin; Adult; Analysis; Biology and Life Sciences; Biomarkers - blood; Blood glucose; Body fat; Body mass index; Body weight; Bone morphogenetic protein 7; Bone morphogenetic proteins; Clusterin; Clusters; Diabetes; Diabetes mellitus; Diabetes Mellitus, Type 2 - blood; Female; Gene expression; Glucose; Glucose metabolism; Heparan sulfate proteoglycans; Humans; Hyperglycemia; Hyperglycemia - blood; Inflammation; Inflammation - blood; Insulin; Insulin Resistance; Leptin; Lipid Metabolism; Male; Medicine; Medicine and Health Sciences; Metabolic disorders; Metabolism; Middle Aged; Obesity; Obesity - blood; Parameter identification; Parameter sensitivity; Physiological aspects; Preadipocyte factor 1; Proteins; Regression analysis; Research and Analysis Methods; Secretion; Sensitivity; Sensitivity analysis; Subgroups; Type 2 diabetes
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0099785

In obesity, elevated fat mass and ectopic fat accumulation are associated with changes in adipokine secretion, which may link obesity to inflammation and the development of insulin resistance. However, relationships among individual adipokines and between adipokines and parameters of obesity, glucose metabolism or inflammation are largely unknown. Serum concentrations of 20 adipokines were measured in 141 Caucasian obese men (n = 67) and women (n = 74) with a wide range of body weight, glycemia and insulin sensitivity. Unbiased, distance-based hierarchical cluster analyses were performed to recognize patterns among adipokines and their relationship with parameters of obesity, glucose metabolism, insulin sensitivity and inflammation. We identified two major adipokine clusters related to either (1) body fat mass and inflammation (leptin, ANGPTL3, DLL1, chemerin, Nampt, resistin) or insulin sensitivity/hyperglycemia, and lipid metabolism (vaspin, clusterin, glypican 4, progranulin, ANGPTL6, GPX3, RBP4, DLK1, SFRP5, BMP7, adiponectin, CTRP3 and 5, omentin). In addition, we found distinct adipokine clusters in subgroups of patients with or without type 2 diabetes (T2D). Logistic regression analyses revealed ANGPTL6, DLK1, Nampt and progranulin as strongest adipokine correlates of T2D in obese individuals. The panel of 20 adipokines predicted T2D compared to a combination of HbA1c, HOMA-IR and fasting plasma glucose with lower sensitivity (78% versus 91%) and specificity (76% versus 94%). Therefore, adipokine patterns may currently not be clinically useful for the diagnosis of metabolic diseases. Whether adipokine patterns are relevant for the predictive assessment of intervention outcomes needs to be further investigated.