Abstract
Metabolic syndrome is linked with obesity and is often first identified clinically by elevated BMI and elevated levels of fasting blood glucose that are generally secondary to insulin resistance. Using the highly translatable rhesus monkey (Macaca mulatta) model, we asked if metabolic syndrome risk could be identified earlier. The study involved 16 overweight but healthy, euglycemic monkeys, one-half of which spontaneously developed metabolic syndrome over the course of 2 years while the other half remained healthy. We conducted a series of biometric and plasma measures focusing on adiposity, lipid metabolism, and adipose tissue-derived hormones, which led to a diagnosis of metabolic syndrome in the insulin-resistant animals. Plasma fatty acid composition was determined by gas chromatography for cholesteryl ester, FFA, diacylglycerol (DAG), phospholipid, and triacylglycerol lipid classes; plasma lipoprotein profiles were generated by NMR; and circulating levels of adipose-derived signaling peptides were determined by ELISA. We identified biomarker models including a DAG model, two lipoprotein models, and a multiterm model that includes the adipose-derived peptide adiponectin. Correlations among circulating lipids and lipoproteins revealed shifts in lipid metabolism during disease development. We propose that lipid profiling may be valuable for early metabolic syndrome detection in a clinical setting.