Journal article

Identification of Adipokine Clusters Related to Parameters of Fat Mass, Insulin Sensitivity and Inflammation


Authors listFlehmig, G; Scholz, M; Klöting, N; Fasshauer, M; Tönjes, A; Stumvoll, M; Youn, BS; Blüher, M

Publication year2014

Pagese99785-

JournalPLoS ONE

Volume number9

Issue number6

ISSN1932-6203

DOI Linkhttps://doi.org/10.1371/journal.pone.0099785

PublisherPublic Library of Science


Abstract
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.



Citation Styles

Harvard Citation styleFlehmig, G., Scholz, M., Klöting, N., Fasshauer, M., Tönjes, A., Stumvoll, M., et al. (2014) Identification of Adipokine Clusters Related to Parameters of Fat Mass, Insulin Sensitivity and Inflammation, PLoS ONE, 9(6), p. e99785. https://doi.org/10.1371/journal.pone.0099785

APA Citation styleFlehmig, G., Scholz, M., Klöting, N., Fasshauer, M., Tönjes, A., Stumvoll, M., Youn, B., & Blüher, M. (2014). Identification of Adipokine Clusters Related to Parameters of Fat Mass, Insulin Sensitivity and Inflammation. PLoS ONE. 9(6), e99785. https://doi.org/10.1371/journal.pone.0099785


Last updated on 2025-21-05 at 16:22