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.