A panel of 20 adipokines predicted type 2 diabetes with lower sensitivity (78% vs 91%) and specificity (76% vs 94%) compared to a combination of HbA1c, HOMA-IR, and fasting plasma glucose.
Cross-Sectional (n=141)
No
Adipokine patterns are currently not clinically useful for the diagnosis of metabolic diseases like type 2 diabetes compared to standard clinical parameters.
Absolute Event Rate: 78.3% vs 91.3%
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.
Flehmig et al. (2014) conducted a cross-sectional in Obesity (n=141). Adipokine patterns (20 adipokines) vs. Classical clinical parameters (HbA1c, HOMA-IR, FPG) was evaluated on Prediction of Type 2 Diabetes (Sensitivity). A panel of 20 adipokines predicted type 2 diabetes with lower sensitivity (78% vs 91%) and specificity (76% vs 94%) compared to a combination of HbA1c, HOMA-IR, and fasting plasma glucose.