People become obese from a variety of physiological, environmental, psychological and genetic causes. In an effort to improve our diagnostic and therapeutic acumen in obesity, we subdivided obese subjects into discrete diagnostic categories based on microarray analysis of gene expression in the adipose tissue and skeletal muscle tissue from 72 healthy obese men and women. Using the micro-array gene expression data, we clustered subjects in a process similar to that employed by oncology research scientists to subtype cancerous tumors. Our analyses revealed that adipose tissue and skeletal muscle mRNA expression separated obese subjects into two distinct subgroups. The analysis of the tissues identified a subgroup of obese patients who lost significantly more weight when treated with the adrenergic herbal pill containing ephedra and caffeine (E+C) as compared to the remaining patients. This study provides the first evidence that gene expression profiling can identify subtypes of obesity, that these subtypes have different physiological characteristics and that these sub-types respond differently to an adrenergic obesity therapy.
Interestingly, all of the men in this study clustered into the `green' cluster. A priori
, we predicted that the presence of mRNAs from the male Y chromosome would place men into a discrete cluster; however, this was not the case. We speculate that women in the `green' cluster have a more `masculine' gene profile or share a pattern of gene expressions that is similar to that of men in the `green' cluster. This interpretation must be investigated further using a candidate gene approach. Clinically, men rarely display a glutealfemoral or `pear shape' pattern of fat, whereas women exhibit a wide variation in fat patterning. Adipose tissue distribution is regulated by both genetic and environmental factors,27
and the differences among these factors between men and women may explain the segregation by gender we observed in our data. Gender differences include a larger subcutaneous adipose tissue in women than men, explainable at least partly by a depot in the gluteal-femoral region in women, which is essentially absent in nonobese men.17
Men, on the other hand, seem to have a larger proportion of their adipose tissue organ localized intra-abdominally.28
In addition, the gluteal-femoral fat cells are specifically enlarged in women and have a higher lipoprotein lipase activity. In premenopausal women, subcutaneous abdominal adipose tissue has a higher lipid turnover than femoral adipose tissue. Results of studies in vitro
indicate that this difference is diminished at the menopause, and restored by estrogen substitution, suggesting that the functional effects of estrogens in women are similar to those of testosterone in men.29
VAT mass has been found to have a strong relationship with insulin resistance and type 2 diabetes.30
Our study showed that subjects with greater VAT mass in the `green' cluster lost more weight than those with less VAT mass in the `red' cluster when treated with ephedra and caffeine (E+C). VAT mass is responsive to weight reduction because the VAT adipocyte is more metabolically active and sensitive to β-adrenergic stimulated lipolysis.9
Two major differences between the `green' and the `red' clusters were VAT mass and VL FCS. Our results indicate that the `green' cluster had more VAT mass and greater VL fat cells compared to the `red' cluster. However, after excluding all men from the `green' cluster, women in that cluster had no difference in VAT mass, but did have larger fat cells, compared to the women in the `red' cluster. Further analysis showed that in addition to a gender dimorphism in FCS, there is also a dimorphism among women in different clusters for the determinants of FCS and suggested that women having greater serum LDL level, TAT and body weight, also have larger fat cells. Historically, it has been shown that the long-term prognosis for weight reduction is worse for those patients with higher fat cell number compared to those patients with larger FCS.31
Our study showed that of the six skeletal muscle genes that contributed to the overall variance in both clusters, three have been connected to the cAMP/PKA pathway (TYR, CDC7 and PDE8B), and others are involved in muscle contraction (KCNMA1) and protein metabolism (MARCH-VI), respectively. Some of the adipose tissue genes are adipocytokines (TNFAIP6, IL1F5), others are involved in extracellular transport (solute carrier family 26, member 3), purine metabolism (NT5C2) and anion transport (SLC26A3), respectively.
This investigation does not imply that there are only two subcategories of obesity. More categories may be uncovered as transcriptome analysis/microarray technology improves (splice-chips, increasing number of genes, and so on), or as additional technologies are brought online that characterize the genome, proteome or lipidome within tissues and cells. Furthermore, the changes that follow the different patterns of gene expression (vis-à-vis changes in protein content and function) will manifest themselves at the structural level of the cells and tissues, which will provide additional opportunities. A combined approach of examining tissues from multiple perspectives simultaneously, for example, may reveal additional subtypes of obesity or strengthen the diagnostic utility of the current subtype definition.
Taken together, the oncology microarray literature demonstrates that knowing the exact genes that discriminate these subtypes is not necessary for the purposes of diagnosis, prognosis of disease risk or treatment planning.13
However, there are several reasons it is important to discover the identity of the genes that discriminate between subtypes. First, the list may provide clues as to why obese patients are different and why some patients have abdominal obesity and others have a gluteal-femoral pattern. Does one subtype have a defect in food intake control and the other in peripheral fat metabolism? Further exploration of these questions should include consideration of the results presented herein. Second, there may be single genes that discriminate between these two predominant `flavors' of obesity. Mini-aspirates of adipose tissue, like routine phlebotomy, are minimally invasive and provide enough material to measure a handful of genes. If the proper genes are chosen, this candidate gene approach could provide a rapid classification of a patient and assist in the choice of an optimal obesity therapy. Clearly more work is needed to answer these questions; however, this investigation provides support for the notion that this approach might yield insights into the diagnosis and/or pharmacogenomics of obesity. Other tissues (for example, liver and hypothalamus) probably contribute in terms of different subtypes of obesity and different skeletal muscle and adipose tissue depots might also respond or contribute differently. The orthogonal question `does the adipose tissue gene expression at baseline influence the response to treatment?' is interesting and merits investigation; however, this investigation was limited to the question of whether the baseline characteristics of the adipose tissue and muscle influence the response to a weight loss treatment. It is logical to consider that the identified `subtypes' might determine the tissue response to a treatment. This concept is being explored in subsequent clinical studies.
In summary, this study provides the first evidence that the combined approach of gene expression profiling plus cluster analysis can identify discrete subtypes of obesity, these subtypes have different physiological characteristics and these subtypes respond differently to an adrenergic therapy in vivo. This opens the door to the development of new clinical diagnostic tools that may lead us to discard the current onesize-fits-all advice given to obese patients in the clinic and enter an era of personalized treatment in the obesity clinic.