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

Abstract

Objective: 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.

Design: 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.

Results: 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.

Conclusions: 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.

Figures

Figure 1

Cluster analysis of gene expression data reveals two distinct subgroups of obesity. Microarrays were used to measure gene expression in both adipose tissue and skeletal muscle in a population of 72 healthy obese patients. On the basis of the ratio of adipose to skeletal muscle tissue gene expression, the subjects were clustered into two distinct subgroups (see text for details), which are represented as a heat map (Figure 1a). Heat maps depict the variations in the expression of genes across patients and are shown in red (downregulated) or green (upregulated) for each gene and each subject. The decision to limit the clusters to two categories was based on the power curve (Figure 1b), which shows a trivial increase in power as the number of clusters increases. Power was determined as the goodness of fit, R2, for each iteration of the clustering; that is, the amount of variance explained with the addition of subsequent principal components.

Figure 2

Subgroups of subjects.

Figure 3

Weight losses by clusters.

Figure 4

Cluster dimorphism in visceral adipose tissue and very large fat cell size. Gene expression was measured in both adipose and skeletal muscle tissue in a population of 72 healthy obese patients using microarrays. On the basis of their gene expression profile, subjects were clustered into two distinct subgroups of either `green' or `red'. All eight men were grouped into the `green' cluster based on their gene expression profile. Visceral adipose tissue (VAT) mass (a) and very large fat cell size (b) were significantly different between the `green' and `red' clusters. These relationships were reanalyzed without the men and VAT mass (c) was no longer different, but very large fat cell size (d) remained significantly different in the `green' and `red' clusters.