Subtyping obesity with microarrays: implications for the diagnosis and treatment of obesity.

Abstract

Obese patients respond differently to weight loss interventions. No efficient diagnostic tool exists to separate obese patients into subtypes as a means to improve prediction of response to interventions. We aimed to separate obese subjects into distinct subgroups using microarray technology to identify gene expression-based subgroups to predict weight loss. A total of 72 obese men and women without family history of diabetes were enrolled in the study; 52 were treated with ephedra and caffeine (E+C) and 20 with placebo for 8 weeks. Adipose and skeletal muscle tissue biopsies were performed at baseline. RNA sample pairs were labeled and hybridized to oligonucleotide microarrays. Quantile normalization and gene shaving were performed, and a clustering algorithm was then applied to cluster subjects based on their gene expression profile. Clusters were visualized using heat maps and related to weight changes. Cluster analysis of gene expression data revealed two distinct subgroups of obesity and predicted weight loss in response to the treatment with E+C. One cluster ('red') decreased to 96.87+/-2.35% body weight, and the second cluster ('green') decreased to 95.59+/-2.75% body weight (P<0.05). 'Red' cluster had less visceral adipose tissue mass (2.77+/-1.08 vs 3.43+/-1.49 kg; P<0.05) and decreased size of the very large fat cells (1.45+/-0.61 vs 2.16+/-1.74 microl; P<0.05) compared to 'green' cluster. Gene expression for both skeletal muscle and adipose tissue was also different between clusters. Our study provides the first evidence that the combined approach of gene expression profiling and cluster analysis can identify discrete subtypes of obesity, these subtypes have different physiological characteristics and respond differently to an adrenergic weight loss therapy. This brings us that into an era of personalized treatment in the obesity clinic.