BACKGROUND
diabetes affects hundreds of millions world wide, contributing to cardiovascular disease, blindness, amputation, kidney failure and many other diseases.
obesity and impaired insulin sensitivity are among the major factors responsible for development of type  <dig> diabetes .
skeletal muscle and white adipose tissue are believed to play a major role in insulin resistance  <cit> .
however, long-term studies indicate that even major factors such as insulin resistance are not sufficient to fully predict the onset of disease  <cit> .
recently a series of papers connected insulin sensitivity and type  <dig> diabetes to expression of a group of oxidation phosphorylation genes that are co-regulated by the peroxisome proliferator activator protein   <cit> .
these experiments, as well as others  <cit> , suggest that mitochondrial dysfunction plays a role in the genesis of dm <dig> and have fuelled discussions about energy metabolism as a primary factor in insulin resistance.
microarray expression profiling allows researchers to monitor expression levels of thousands of genes in a single analysis.
classification of samples by such molecular signatures allows for improved stratification of patients, rational application of treatment, and better risk assessment.
importantly, these techniques often uncover previously unanticipated pathways and identify of new targets for therapy.
these experimental and computational approaches were first developed and applied in numerous cancer-related research projects  <cit> .
applying these same techniques to classify skeletal muscle samples of dm <dig> and non-diabetic patients has encountered very serious problems.
when comparing the differences in gene expression of dm <dig> and non-diabetic patients, the differences are modest, with analytic noise masking the underlying informative changes in gene expression.
two different approaches have been suggested to counter this challenge.
mootha et al.
have developed the gene set enrichment approach .
in the absence of significantly over- or under-expressed genes, they identified groups of genes to discriminate between dm <dig> and normal samples based on function, gene ontology  annotation, chromosomal location and other factors.
joining genes from common functional groups is effectively the same as using multiple replicates as it dramatically increases the power of the experiment.
similarly, patti et al.
found no single gene differentially expressed between diabetic and non-diabetic muscle samples after correction for multiple comparisons.
they also used extensive functional annotation to identify genes differentially expressed between dm <dig> and non-diabetic patients.
even though statistical significance of differential expression of these genes was lacking, classification by occurrence of go terms  <cit>  revealed disparate expression of genes involved in energy metabolism between dm <dig> and normal.
taken together, both papers implicate genes involved in energy metabolism as the major contributors to dm <dig> status of the patients.
these findings are logical from a biological standpoint and build upon prior data  <cit> .
the analytical strategies employed by mootha et al.
and patti et al.
were based on presumption of two distinct categories  and that these clinical categories should manifest themselves through the gene expression patterns in skeletal muscle.
instinctively, we perceive diabetic and non-diabetic patients in two different categories.
however, the onset of diabetes depends on other factors such as lipotoxicity  <cit> , a failure of leptin signaling  <cit> , abnormalities in hypothalamic function  <cit> , to name a few.
these and many other factors can mitigate the effect of gene expression in skeletal muscle with regards to the onset of diabetes.
taking into account the complexity of the disease, the very existence of distinct categories such as diabetics and non-diabetics when analyzing gene expression data cannot be taken for granted.
the approach described herein does not assign patients' transcriptomes  to a diagnostic category .
instead, we rely on the "natural classification" to identify the groups of samples within the data that are similar to each other by their transcriptome.
the concept of natural classification is well established in computer analysis of biological data  <cit> .
in the first step of our analysis, our goal was to identify natural categories  in the expression data.
to accomplish this goal we applied a high-dimension unsupervised cluster analysis algorithm developed based on forel .
this algorithm performs a "class discovery" type of clustering  <cit>  without pre-selection of a small set of genes to reduce dimensionality.
the output consists of a set of finished clusters, which can be further analyzed and hyper-clustered in order to establish the relationship between natural classes.
the second step of the analysis is to relate the observations in the data set to the clinical characteristics, and to identify the underlying discriminant genes implicated in formation of specific clusters.
our strategy is based entirely on the observation of similarities within the data and avoids speculative assumptions about gene function.
when using a "natural classification" strategy the only assumption being made is that the most common gene expression patterns associated with the development of diabetes are expected to be found many times, providing that a sufficiently large number of samples are included and the microarray technique accurately reflects the underlying molecular mechanisms.
RESULTS
forel analysis of the mootha et al.
data set revealed  <dig> clusters and  <dig> singletons.
no clear-cut categories emerged that were related to the presence or absence of diabetes in these patients.
in other words, diabetic and non-diabetic subjects did not form dichotomous groups in the expression space.
however, one of the clusters  was found to contain mostly non-diabetics and one glucose-tolerance impaired  sample.
the centroids of all  <dig> clusters were stretched along a single line .
to visualize this trend we used the centroids of clusters as control points to reduce the dimensionality down to the number of clusters.
the clusters were projected into the space of the first  <dig> principal components and plotted as 3d spheres with radius equal to the distance between the centroid and the most distant member  of the corresponding cluster.
unsupervised class discovery analysis suggests that the population of samples is highly variable along a single smoothly arched line in the multidimensional expression space.
to simplify the presentation of the data, all clusters were graphed along a line connecting the centroids of the clusters in the expression space .
there is a modest gradient of the average insulin sensitivity and the position of the cluster along the "main line" of variation.
on one end of this line there is a cluster composed of  <dig> normal samples and one sample with slightly impaired glucose tolerance.
the subjects in this cluster are also the most metabolically sound among the data set.
for the purpose of this paper, metabolically sound is defined as having they have high insulin sensitivity and an ideal body mass index .
the juxtaposition and separability of resulting clusters has been analyzed using manova and hierarchical classification of clusters  based on inter-centroid and inter-class distances.
the results of this analysis are presented in the supplementary materials .
considering visualization and statistic analysis we suggest the following interpretation: the data set represents one core cluster of the most metabolically sound samples  and a continuum of samples extended in one direction, like a head and a tail of a comet.
the imaginary line stretched between the core and the most distant forel cluster represents the major trend in gene expression space.
although there is no clear separation between diabetic and non-diabetic samples as two categories, the distance along this main line of variation coincides with the occurrence of diabetes and severity of the common risk factors.
differences in gene expression between the forel clusters
next, we identified genes that were most differentially expressed between cluster  <dig>  and cluster  <dig> .
to select such genes we applied cageda tools  <cit>  on unabbreviated list of genes .
we found that clusters occupying the extremes ends of the line connecting the centroids varied with respect to the expression of genes encoding ribosomal proteins.
other significant function categories included actin, myosin, hemo- and myoglobins, enzymes involved in glucose and fructose metabolism, lipid metabolism and genes encoding proteins involved in oxidative phosphorylation .
we also performed functional annotation of these same genes at the extremes of clusters  <dig> and  <dig> using ease  <cit> .
we found several categories of genes that were significantly overrepresented as compared to their expected occurrence .
the most prominent functional categories included ribosomal genes, genes involved in skeletal muscle contraction, actin, cytoskeleton and protein biosynthesis.
the dataset produced at the east carolina university  <cit>  has a different purpose, it is focused on skeletal muscle fatty acid metabolism in connection with obesity, but utilized the affymetrix genechip .
this chip is different from those used in experiments of mootha et al.
and patti et al.
.
given that obesity and insulin resistance are clearly linked  <cit>  it is interesting to compare this study with our re-analyses of the mootha et al.
dataset.
the  <dig> samples were collected from  <dig> groups of patients with normal weight , obese  and morbidly obese .
surprisingly, the forel clustering results from the hulver dataset presented on figure  <dig> are highly similar to the clustering results from the mootha et al.
dataset.
four large clusters, produced by forel analysis form a line in the gene expression space, one end of the line is occupied by a cluster composed almost entirely by the most metabolically sound specimens, with the opposite cluster again containing the least sound samples.
analysis of the genes most differentially expressed between the most distant clusters along this line reveals a remarkably similar list of genes to the first analysis .
the other data set used in this study  is much smaller; it contains  <dig> samples of which  <dig> represent normal and  <dig> represent dm <dig>  again using the forel clustering process, we found that all samples fell into  <dig> clusters.
the largest cluster  contains  <dig> samples, of which  <dig> are dm <dig>  cluster # <dig> is entirely composed of samples from normoglycemic subjects, clusters # <dig> and # <dig> include mostly normoglycemic subjects but each includes one dm <dig> subject.
it is reasonable to suppose that clusters # <dig> and # <dig> represent the most contrast groups of samples in the data and the line that stretches between centroids of these clusters would be analogous to the trend we observe in the mootha et al.
data set.
the results of our re-analysis of the patti et al.
data can be found in supporting materials  <cit>  .
when comparing these three studies, the samples are absolutely independent and differ in many ways , yet the genes that are most differentially expressed between the most and least metabolically sound are again remarkably similar to those found in our re-analyses of the mootha and hulver datasets.
these discriminant genes include gapdh, myoglobins, titin, fhl <dig> and a score of ribosomal proteins.
although genes involved in oxidative phosphorylation are among those differentially expressed in all three datasets, they are not at the top of the list when ranked by the fold change between clusters.
thus these analyses identified common genes and functional categories that were unanticipated based on the earlier analyses.
after these efforts to cluster the subjects, we next clustered the genes independent of the subjects.
cluster analysis of the genes throughout the data set revealed a number of highly correlated clusters.
again using the forel technique with the mootha dataset, we found several interesting clusters of genes.
based on a functional annotation analysis  <cit>  of these genes, we found that one of the clusters contains a group of genes related to lipid transport and metabolism.
however, the largest cluster includes  <dig> genes and among these genes are the same ribosomal, glucose and fructose metabolism genes that discriminated between the most and least metabolically sound subjects.
natural classification of subjects based on their transcriptome separates subjects into categories recognizable by their clinical characteristics
we next asked whether the natural classification of the molecular phenotypes using the forel analysis corresponded to the clinical phenotype as measured by bmi, insulin sensitivity, etc.
the clinical data, provided by mootha et al.
was used to compare the 'metabolic soundness' of the patients across cluster membership from the forel analysis .
patients from clusters  <dig>   <dig>   <dig> and  <dig> have a higher body mass index  and lower insulin sensitivity  when compared to the 'normal' cluster.
this visual analysis was confirmed by anova .
from this and the other results  the subjects represented in cluster  <dig> are in exceptional physical form for their age.
they form a tight group with a normal bmi and normal insulin sensitivity.
interestingly, maximal aerobic capacity  does not clearly discriminate this group from the least metabolically sound cluster with a high proportion of dm <dig>  the two singletons, both ngt have either normal  or almost normal  bmi and high insulin sensitivity .
a few ngt samples are also found in clusters  <dig>   <dig>   <dig>  even less are found in clusters  <dig>  and  <dig> that are dominated by dm <dig> and igt samples.
thus in spite of the high bmi and low insulin sensitivity these individuals manage to remain normo-glycemic.
this is consistent with the natural history of dm <dig> where beta cell failure is a late event with a variable onset but invariably preceded by insulin resistance.
we speculate that the metabolically unsound individuals are at risk for the development of dm <dig> as beta cell apoptosis ensues and islet neogenesis fails to compensate  <cit> .
these 'natural' clusters coincided with the classic markers of diabetes risk, namely obesity and insulin resistance, but did not follow the accepted clinical diagnosis of dm <dig> as defined by the presence or absence of hyperglycemia.
unfortunately, there are other important parameters missing in all three data sets, such as time of onset of hyperglycemia and estimation of physical activity.
since all samples were collected from the patients of general hospitals we can speculate that the level of physical activity and its distribution among sample donors is typical for the urban population of us east coast and on average not very high.
it is possible that molecular mechanisms of insulin resistance discussed below are significantly influenced by the level of patients' physical activity.
discussion
type  <dig> diabetes  is a complex multifactorial disease where dozens or perhaps even hundreds of different genetic and environmental factors play a role.
it is likely that there is more than one reason for a patient to develop hyperglycemia , although insulin resistance is a dominant factor.
similarly, it is likely that genetic and environmental factors play a role in the development of insulin resistance, a metabolic feature that invariably precedes beta cell failure.
moreover, dm <dig> is not a disorder caused by malfunction of a single tissue.
skeletal muscle plays very important, but not exclusive role along with pancreas, adipose tissue, etc.
dm <dig> is diagnosed on the level of the whole organism as increased blood sugar  <cit> .
thus, there is no reason to believe that muscle samples from a single tissue would contain just one single gene expression signature associated with the onset of the dm <dig>  classification models based on only two classes separated by dm <dig> diagnosis may be at a disadvantage as they have limited separation performance.
our initial motivation for application of unsupervised clustering algorithms was based on the hypothesis that dm <dig> samples as well as normal ngt controls could possibly be subdivided into many classes with more distinctive gene expression signatures than those revealed by the original analysis.
indeed this was the case.
we found that individuals could be separated based on their gene expression profiles along a single continuum.
relationship of the forel classifications with clinical characteristics
in the next step of analysis we superimposed the clusters in the space of first principal component.
for all three datasets, the centroids of the clusters were situated along a single line or plane.
clusters with the most metabolically sound subjects with ngt and the least sound subjects  occupy the extreme ends of this line, while most igt samples are found in the mixed clusters between the extremes.
in the mootha dataset, the healthiest group is represented by a single cluster, which has  <dig> non-diabetic and  <dig> igt subjects.
on the opposite end, two dominant clusters contain only one non-diabetic sample each, while all other members are either diabetic or insulin resistant.
this picture suggests a simple interpretation: instead of categorical classes of normal and diabetic samples we observe a continuous trend.
on one end of the trend are the samples from the most metabolically sound individuals with a low prevalence of dm <dig>  prevalence of dm <dig> increases along the line, but can be mitigated by a number of factors.
many of these factors are beyond the scope of this research and probably relate to β-cell apoptosis and/or failure of beta cell neogenesis/replication  <cit> .
as expected, unsupervised clustering revealed a few distinct classes.
however, we did not anticipate that these clusters would be aligned along a single principal component.
rather than multiple 'subtypes' these analyses suggest a single underlying disorder.
importantly, these natural classes differ in average clinical characteristics and in the number of diabetic and non-diabetic members.
discriminant genes
once the direction of the trend is established it is relatively easy to select and annotate the genes that are most differentially expressed between the extremities of the trend.
the list of such genes does include those involved in the energy metabolism and oxidative phosphorylation, as reported by mootha et al.
and patti et al.
the expression level of such genes changes dramatically between clusters on the extreme ends of the trend.
oxidative phosphorylation genes possibly play an important role in the etiology of dm <dig> although this role is not decisive and is insufficient for discrimination between classes.
this also explains the difficulties mootha et al.
had selecting the genes that discriminate between dm <dig> and ngt samples.
these discriminant genes fall into several interesting gene ontogeny categories.
first, we observed an increase in expression of genes encoding proteins required for glycolytic rather than oxidative metabolism such as gapdh.
this is consistent with prior data demonstrating a reduction in oxidative capacity of skeletal muscle from insulin resistant diabetics  <cit> .
second, we discovered that myoglobin, lactate dehydrogenase a, gapdh, aldolase a, and pyruvate kinase, genes that are classically upregulated when oxygen tension falls  <cit>  were upregulated in the least metabolically sound samples.
human skeletal muscle adaptation to altitude hypoxia has been well characterized and includes a seemingly paradoxical reduction in oxidative capacity, an increase in myoglobin, and a shift to glycolytic metabolism  <cit> .
all of these are dominant features of the gene expression data reported herein.
altitude hypoxia reduces the overall mitochondrial volume by 20%, but with a preferential reduction in sub-sarcolemmal mitochondria   <cit>  compared to a modest change in the intra-myofibrilar mitochondria , a finding strikingly similar to the mitochondrial defects observed in type  <dig> diabetes  <cit> .
reduced oxidative enzymes are also observed in s. cerevisiae as hypoxia ensues  <cit> .
hypoxia/anoxia also shifts substrate metabolism from aerobic to glycolytic in diverse species from s. cerevisiae to man  <cit> .
multiple transcription factors are involved in the transcriptional response to hypoxia in s. cerevisiae including rox <dig>  <cit> , hap1– <dig>  sut <dig>  upc <dig>  and mox 1– <dig>   and in man hif-1a  <cit> .
the role of these transcription factors in dm <dig> is unknown and warrants exploration.
if hypoxia is involved in the observed molecular and cellular phenotype it does not cause a compensatory effect on the capillary density.
the capillary density and maximum oxygen capacity  as reported in the supplementary materials  by mootha et al.
shows no significant difference across forel clusters.
this again, is consistent with the literature on structural adaptations to hypoxia where no change in capillary density are seen unless intense exercise was employed as part of the experimental paradigm  <cit> .
this finding does not necessarily present a contradiction: a shift from oxidative to glycolytic pathway metabolism in skeletal muscle tissue may reduce demand for oxygen as an alternative to capillary growth.
alternately, insulin is capable of activating the bundle of genes that are similar to those activated by hif-1a.
in vivo, insulin upregulates aldolase a, and gapdh, both of which are classic targets of hif-1a  <cit> .
there is some controversy whether these effects are dependent on changes hif- <dig> mrna  <cit> , occur through the stabilization of hif-1a protein or through an unknown hif response element binding protein  <cit> .
it is established, however, that insulin upregulation of these genes requires phosphoinositol 3-kinase  <cit> .
in summary, the unsupervised forel analysis identified upregulation of a cassette of genes strikingly similar to hypoxia inducible genes in the obese, insulin resistant state.
lastly, we discovered a set of genes encoding ribosomal proteins separated metabolically sound from unsound subjects with dm <dig> and igt.
on the surface, this is an anomalous finding.
however, microarray studies of petite, mitochondria deficient  yeast data demonstrate an upregulation of genes encoding ribosomal proteins as compared to mitochondria replete  yeast  <cit> .
similarly, as yeast become anoxic and shift from an oxidative to ethanol producing metabolism  many of these same genes encoding ribosomal proteins are up/downregulated.
this is congruous with prior data showing a downregulation of oxidative phosphorylation genes and disrupted mitochondria in dm <dig>  <cit> .
an alternate explanation for the increased expression of ribosomal and other genes associated with cell growth reflects alterations upstream of the mammalian target of rapamycin  pathway.
mtor is a major regulator of metabolic processes in cell.
it has been reported to stimulate ribosomal biogenesis and repress nutrient turnover  <cit> .
prior studies have implicated mtor in nutrient sensing and diabetes  <cit> .
acute insulin infusion upregulates the expression of many ribosomal genes  <cit> , providing an alternate pathway for the observed changes in ribosomal gene expression.
in contrast to the subtle changes in gene expression of the oxidative phosphorylation genes identified by mootha et al., we found several genes that discriminanted lean healthy people and individuals with dm <dig> or igt.
for example, aldolase a was  <dig>  times overexpressed in obesity/insulin resistance and gapdh  <dig> times higher.
similarly, hemoglobin was upregulated in obesity/insulin resistance; by  <dig>   <dig>  and  <dig>  fold for subunits alpha  <dig>  beta and alpha  <dig> respectively.
the novel transcription factor fhl <dig> was ~ <dig>  fold upregulated.
the second and third data sets  although limited by sample size and without complete clinical phenotypes, produced almost identical cluster layout with a single group of the most metabolically sound subjects and several mixed groups with differing prevalence of dm <dig>  similarly, the genes most differentially expressed between the two most and least metabolically sound clusters were remarkably similar across the three datasets.
this suggests all three datasets identified similar underlying pathophysiological mechanisms leading to dm <dig>
further study will be required to determine which factor plays the primary role, whether impaired oxidative phosphorylation causes a shift to glycolytic metabolism or oxidative phosphorylation is repressed as a result of unknown upstream transcriptional events  <cit> .
regardless, the observed upregulation of ribosomal gene expression might be a marker of the risk to develop type  <dig> diabetes.
CONCLUSIONS
in summary, using a novel unsupervised clustering technique, forel, we found a single set of genes in skeletal muscle that separate subjects that are metabolically sound from those with insulin resistance, igt and diabetes.
this finding was confirmed in two additional datasets from different populations and suggests a single underlying pathophysiological process lies underneath the clinical diagnosis of insulin resistance.
the genes that discriminate between metabolically sound and metabolically unsound patients include the anticipated gene categories of fat oxidation, oxidative phosphorylation, and glycolytic metabolism.
we also found several categories of genes not previously associated with insulin resistance or diabetes, namely, genes involved in ribosomal function and oxygen sensing .
a separate analysis, where we identified clusters of genes that were co-regulated within the datasets, showed a large degree of overlap.
based on prior literature in energy metabolism and mitochondrial dysfunction in saccharomyces cerevisiae, these results suggest that hypoxia, changes in ribosomal function, and glycolytic metabolism as key players in insulin resistance and diabetes.

 diabetes affects hundreds of millions world wide, contributing to cardiovascular disease, blindness, amputation, kidney failure and many other diseases. obesity and impaired insulin sensitivity are among the major factors responsible for development of type diabetes. skeletal muscle and white adipose tissue are believed to play a major role in insulin resistance. however, long-term studies indicate that even major factors such as insulin resistance are not sufficient to fully predict the onset of disease. recently a series of papers connected insulin sensitivity and type diabetes to expression of a group of oxidation phosphorylation genes that are co-regulated by the peroxisome proliferator activator protein. these experiments, as well as others, suggest that mitochondrial dysfunction plays a role in the genesis of dm and have fuelled discussions about energy metabolism as a primary factor in insulin resistance. microarray expression profiling allows researchers to monitor expression levels of thousands of genes in a single analysis. classification of samples by such molecular signatures allows for improved stratification of patients, rational application of treatment, and better risk assessment. importantly, these techniques often uncover previously unanticipated pathways and identify of new targets for therapy. these experimental and computational approaches were first developed and applied in numerous cancer-related research projects. applying these same techniques to classify skeletal muscle samples of dm and non-diabetic patients has encountered very serious problems. when comparing the differences in gene expression of dm and non-diabetic patients, the differences are modest, with analytic noise masking the underlying informative changes in gene expression. two different approaches have been suggested to counter this challenge. mootha et al. have developed the gene set enrichment approach. in the absence of significantly over- or under-expressed genes, they identified groups of genes to discriminate between dm and normal samples based on function, gene ontology annotation, chromosomal location and other factors. joining genes from common functional groups is effectively the same as using multiple replicates as it dramatically increases the power of the experiment. similarly, patti et al. found no single gene differentially expressed between diabetic and non-diabetic muscle samples after correction for multiple comparisons. they also used extensive functional annotation to identify genes differentially expressed between dm and non-diabetic patients. even though statistical significance of differential expression of these genes was lacking, classification by occurrence of go terms revealed disparate expression of genes involved in energy metabolism between dm and normal. taken together, both papers implicate genes involved in energy metabolism as the major contributors to dm status of the patients. these findings are logical from a biological standpoint and build upon prior data. the analytical strategies employed by mootha et al. and patti et al. were based on presumption of two distinct categories and that these clinical categories should manifest themselves through the gene expression patterns in skeletal muscle. instinctively, we perceive diabetic and non-diabetic patients in two different categories. however, the onset of diabetes depends on other factors such as lipotoxicity, a failure of leptin signaling, abnormalities in hypothalamic function, to name a few. these and many other factors can mitigate the effect of gene expression in skeletal muscle with regards to the onset of diabetes. taking into account the complexity of the disease, the very existence of distinct categories such as diabetics and non-diabetics when analyzing gene expression data cannot be taken for granted. the approach described herein does not assign patients' transcriptomes to a diagnostic category. instead, we rely on the "natural classification" to identify the groups of samples within the data that are similar to each other by their transcriptome. the concept of natural classification is well established in computer analysis of biological data. in the first step of our analysis, our goal was to identify natural categories in the expression data. to accomplish this goal we applied a high-dimension unsupervised cluster analysis algorithm developed based on forel. this algorithm performs a "class discovery" type of clustering without pre-selection of a small set of genes to reduce dimensionality. the output consists of a set of finished clusters, which can be further analyzed and hyper-clustered in order to establish the relationship between natural classes. the second step of the analysis is to relate the observations in the data set to the clinical characteristics, and to identify the underlying discriminant genes implicated in formation of specific clusters. our strategy is based entirely on the observation of similarities within the data and avoids speculative assumptions about gene function. when using a "natural classification" strategy the only assumption being made is that the most common gene expression patterns associated with the development of diabetes are expected to be found many times, providing that a sufficiently large number of samples are included and the microarray technique accurately reflects the underlying molecular mechanisms.  forel analysis of the mootha et al. data set revealed clusters and singletons. no clear-cut categories emerged that were related to the presence or absence of diabetes in these patients. in other words, diabetic and non-diabetic subjects did not form dichotomous groups in the expression space. however, one of the clusters was found to contain mostly non-diabetics and one glucose-tolerance impaired sample. the centroids of all clusters were stretched along a single line. to visualize this trend we used the centroids of clusters as control points to reduce the dimensionality down to the number of clusters. the clusters were projected into the space of the first principal components and plotted as 3d spheres with radius equal to the distance between the centroid and the most distant member of the corresponding cluster. unsupervised class discovery analysis suggests that the population of samples is highly variable along a single smoothly arched line in the multidimensional expression space. to simplify the presentation of the data, all clusters were graphed along a line connecting the centroids of the clusters in the expression space. there is a modest gradient of the average insulin sensitivity and the position of the cluster along the "main line" of variation. on one end of this line there is a cluster composed of normal samples and one sample with slightly impaired glucose tolerance. the subjects in this cluster are also the most metabolically sound among the data set. for the purpose of this paper, metabolically sound is defined as having they have high insulin sensitivity and an ideal body mass index. the juxtaposition and separability of resulting clusters has been analyzed using manova and hierarchical classification of clusters based on inter-centroid and inter-class distances. the results of this analysis are presented in the supplementary materials. considering visualization and statistic analysis we suggest the following interpretation: the data set represents one core cluster of the most metabolically sound samples and a continuum of samples extended in one direction, like a head and a tail of a comet. the imaginary line stretched between the core and the most distant forel cluster represents the major trend in gene expression space. although there is no clear separation between diabetic and non-diabetic samples as two categories, the distance along this main line of variation coincides with the occurrence of diabetes and severity of the common risk factors. differences in gene expression between the forel clusters next, we identified genes that were most differentially expressed between cluster and cluster. to select such genes we applied cageda tools on unabbreviated list of genes. we found that clusters occupying the extremes ends of the line connecting the centroids varied with respect to the expression of genes encoding ribosomal proteins. other significant function categories included actin, myosin, hemo- and myoglobins, enzymes involved in glucose and fructose metabolism, lipid metabolism and genes encoding proteins involved in oxidative phosphorylation. we also performed functional annotation of these same genes at the extremes of clusters and using ease. we found several categories of genes that were significantly overrepresented as compared to their expected occurrence. the most prominent functional categories included ribosomal genes, genes involved in skeletal muscle contraction, actin, cytoskeleton and protein biosynthesis. the dataset produced at the east carolina university has a different purpose, it is focused on skeletal muscle fatty acid metabolism in connection with obesity, but utilized the affymetrix genechip. this chip is different from those used in experiments of mootha et al. and patti et al.. given that obesity and insulin resistance are clearly linked it is interesting to compare this study with our re-analyses of the mootha et al. dataset. the samples were collected from groups of patients with normal weight, obese and morbidly obese. surprisingly, the forel clustering results from the hulver dataset presented on figure are highly similar to the clustering results from the mootha et al. dataset. four large clusters, produced by forel analysis form a line in the gene expression space, one end of the line is occupied by a cluster composed almost entirely by the most metabolically sound specimens, with the opposite cluster again containing the least sound samples. analysis of the genes most differentially expressed between the most distant clusters along this line reveals a remarkably similar list of genes to the first analysis. the other data set used in this study is much smaller; it contains samples of which represent normal and represent dm again using the forel clustering process, we found that all samples fell into clusters. the largest cluster contains samples, of which are dm cluster # is entirely composed of samples from normoglycemic subjects, clusters # and # include mostly normoglycemic subjects but each includes one dm subject. it is reasonable to suppose that clusters # and # represent the most contrast groups of samples in the data and the line that stretches between centroids of these clusters would be analogous to the trend we observe in the mootha et al. data set. the results of our re-analysis of the patti et al. data can be found in supporting materials. when comparing these three studies, the samples are absolutely independent and differ in many ways, yet the genes that are most differentially expressed between the most and least metabolically sound are again remarkably similar to those found in our re-analyses of the mootha and hulver datasets. these discriminant genes include gapdh, myoglobins, titin, fhl and a score of ribosomal proteins. although genes involved in oxidative phosphorylation are among those differentially expressed in all three datasets, they are not at the top of the list when ranked by the fold change between clusters. thus these analyses identified common genes and functional categories that were unanticipated based on the earlier analyses. after these efforts to cluster the subjects, we next clustered the genes independent of the subjects. cluster analysis of the genes throughout the data set revealed a number of highly correlated clusters. again using the forel technique with the mootha dataset, we found several interesting clusters of genes. based on a functional annotation analysis of these genes, we found that one of the clusters contains a group of genes related to lipid transport and metabolism. however, the largest cluster includes genes and among these genes are the same ribosomal, glucose and fructose metabolism genes that discriminated between the most and least metabolically sound subjects. natural classification of subjects based on their transcriptome separates subjects into categories recognizable by their clinical characteristics we next asked whether the natural classification of the molecular phenotypes using the forel analysis corresponded to the clinical phenotype as measured by bmi, insulin sensitivity, etc. the clinical data, provided by mootha et al. was used to compare the'metabolic soundness' of the patients across cluster membership from the forel analysis. patients from clusters and have a higher body mass index and lower insulin sensitivity when compared to the'normal' cluster. this visual analysis was confirmed by anova. from this and the other results the subjects represented in cluster are in exceptional physical form for their age. they form a tight group with a normal bmi and normal insulin sensitivity. interestingly, maximal aerobic capacity does not clearly discriminate this group from the least metabolically sound cluster with a high proportion of dm the two singletons, both ngt have either normal or almost normal bmi and high insulin sensitivity. a few ngt samples are also found in clusters even less are found in clusters and that are dominated by dm and igt samples. thus in spite of the high bmi and low insulin sensitivity these individuals manage to remain normo-glycemic. this is consistent with the natural history of dm where beta cell failure is a late event with a variable onset but invariably preceded by insulin resistance. we speculate that the metabolically unsound individuals are at risk for the development of dm as beta cell apoptosis ensues and islet neogenesis fails to compensate. these'natural' clusters coincided with the classic markers of diabetes risk, namely obesity and insulin resistance, but did not follow the accepted clinical diagnosis of dm as defined by the presence or absence of hyperglycemia. unfortunately, there are other important parameters missing in all three data sets, such as time of onset of hyperglycemia and estimation of physical activity. since all samples were collected from the patients of general hospitals we can speculate that the level of physical activity and its distribution among sample donors is typical for the urban population of us east coast and on average not very high. it is possible that molecular mechanisms of insulin resistance discussed below are significantly influenced by the level of patients' physical activity. discussion type diabetes is a complex multifactorial disease where dozens or perhaps even hundreds of different genetic and environmental factors play a role. it is likely that there is more than one reason for a patient to develop hyperglycemia, although insulin resistance is a dominant factor. similarly, it is likely that genetic and environmental factors play a role in the development of insulin resistance, a metabolic feature that invariably precedes beta cell failure. moreover, dm is not a disorder caused by malfunction of a single tissue. skeletal muscle plays very important, but not exclusive role along with pancreas, adipose tissue, etc. dm is diagnosed on the level of the whole organism as increased blood sugar. thus, there is no reason to believe that muscle samples from a single tissue would contain just one single gene expression signature associated with the onset of the dm classification models based on only two classes separated by dm diagnosis may be at a disadvantage as they have limited separation performance. our initial motivation for application of unsupervised clustering algorithms was based on the hypothesis that dm samples as well as normal ngt controls could possibly be subdivided into many classes with more distinctive gene expression signatures than those revealed by the original analysis. indeed this was the case. we found that individuals could be separated based on their gene expression profiles along a single continuum. relationship of the forel classifications with clinical characteristics in the next step of analysis we superimposed the clusters in the space of first principal component. for all three datasets, the centroids of the clusters were situated along a single line or plane. clusters with the most metabolically sound subjects with ngt and the least sound subjects occupy the extreme ends of this line, while most igt samples are found in the mixed clusters between the extremes. in the mootha dataset, the healthiest group is represented by a single cluster, which has non-diabetic and igt subjects. on the opposite end, two dominant clusters contain only one non-diabetic sample each, while all other members are either diabetic or insulin resistant. this picture suggests a simple interpretation: instead of categorical classes of normal and diabetic samples we observe a continuous trend. on one end of the trend are the samples from the most metabolically sound individuals with a low prevalence of dm prevalence of dm increases along the line, but can be mitigated by a number of factors. many of these factors are beyond the scope of this research and probably relate to β-cell apoptosis and/or failure of beta cell neogenesis/replication. as expected, unsupervised clustering revealed a few distinct classes. however, we did not anticipate that these clusters would be aligned along a single principal component. rather than multiple'subtypes' these analyses suggest a single underlying disorder. importantly, these natural classes differ in average clinical characteristics and in the number of diabetic and non-diabetic members. discriminant genes once the direction of the trend is established it is relatively easy to select and annotate the genes that are most differentially expressed between the extremities of the trend. the list of such genes does include those involved in the energy metabolism and oxidative phosphorylation, as reported by mootha et al. and patti et al. the expression level of such genes changes dramatically between clusters on the extreme ends of the trend. oxidative phosphorylation genes possibly play an important role in the etiology of dm although this role is not decisive and is insufficient for discrimination between classes. this also explains the difficulties mootha et al. had selecting the genes that discriminate between dm and ngt samples. these discriminant genes fall into several interesting gene ontogeny categories. first, we observed an increase in expression of genes encoding proteins required for glycolytic rather than oxidative metabolism such as gapdh. this is consistent with prior data demonstrating a reduction in oxidative capacity of skeletal muscle from insulin resistant diabetics. second, we discovered that myoglobin, lactate dehydrogenase a, gapdh, aldolase a, and pyruvate kinase, genes that are classically upregulated when oxygen tension falls were upregulated in the least metabolically sound samples. human skeletal muscle adaptation to altitude hypoxia has been well characterized and includes a seemingly paradoxical reduction in oxidative capacity, an increase in myoglobin, and a shift to glycolytic metabolism. all of these are dominant features of the gene expression data reported herein. altitude hypoxia reduces the overall mitochondrial volume by 20%, but with a preferential reduction in sub-sarcolemmal mitochondria compared to a modest change in the intra-myofibrilar mitochondria, a finding strikingly similar to the mitochondrial defects observed in type diabetes. reduced oxidative enzymes are also observed in s. cerevisiae as hypoxia ensues. hypoxia/anoxia also shifts substrate metabolism from aerobic to glycolytic in diverse species from s. cerevisiae to man. multiple transcription factors are involved in the transcriptional response to hypoxia in s. cerevisiae including rox, hap1– sut upc and mox 1– and in man hif-1a. the role of these transcription factors in dm is unknown and warrants exploration. if hypoxia is involved in the observed molecular and cellular phenotype it does not cause a compensatory effect on the capillary density. the capillary density and maximum oxygen capacity as reported in the supplementary materials by mootha et al. shows no significant difference across forel clusters. this again, is consistent with the literature on structural adaptations to hypoxia where no change in capillary density are seen unless intense exercise was employed as part of the experimental paradigm. this finding does not necessarily present a contradiction: a shift from oxidative to glycolytic pathway metabolism in skeletal muscle tissue may reduce demand for oxygen as an alternative to capillary growth. alternately, insulin is capable of activating the bundle of genes that are similar to those activated by hif-1a. in vivo, insulin upregulates aldolase a, and gapdh, both of which are classic targets of hif-1a. there is some controversy whether these effects are dependent on changes hif- mrna, occur through the stabilization of hif-1a protein or through an unknown hif response element binding protein. it is established, however, that insulin upregulation of these genes requires phosphoinositol 3-kinase. in summary, the unsupervised forel analysis identified upregulation of a cassette of genes strikingly similar to hypoxia inducible genes in the obese, insulin resistant state. lastly, we discovered a set of genes encoding ribosomal proteins separated metabolically sound from unsound subjects with dm and igt. on the surface, this is an anomalous finding. however, microarray studies of petite, mitochondria deficient yeast data demonstrate an upregulation of genes encoding ribosomal proteins as compared to mitochondria replete yeast. similarly, as yeast become anoxic and shift from an oxidative to ethanol producing metabolism many of these same genes encoding ribosomal proteins are up/downregulated. this is congruous with prior data showing a downregulation of oxidative phosphorylation genes and disrupted mitochondria in dm. an alternate explanation for the increased expression of ribosomal and other genes associated with cell growth reflects alterations upstream of the mammalian target of rapamycin pathway. mtor is a major regulator of metabolic processes in cell. it has been reported to stimulate ribosomal biogenesis and repress nutrient turnover. prior studies have implicated mtor in nutrient sensing and diabetes. acute insulin infusion upregulates the expression of many ribosomal genes, providing an alternate pathway for the observed changes in ribosomal gene expression. in contrast to the subtle changes in gene expression of the oxidative phosphorylation genes identified by mootha et al., we found several genes that discriminanted lean healthy people and individuals with dm or igt. for example, aldolase a was times overexpressed in obesity/insulin resistance and gapdh times higher. similarly, hemoglobin was upregulated in obesity/insulin resistance; by and fold for subunits alpha beta and alpha respectively. the novel transcription factor fhl was ~ fold upregulated. the second and third data sets although limited by sample size and without complete clinical phenotypes, produced almost identical cluster layout with a single group of the most metabolically sound subjects and several mixed groups with differing prevalence of dm similarly, the genes most differentially expressed between the two most and least metabolically sound clusters were remarkably similar across the three datasets. this suggests all three datasets identified similar underlying pathophysiological mechanisms leading to dm further study will be required to determine which factor plays the primary role, whether impaired oxidative phosphorylation causes a shift to glycolytic metabolism or oxidative phosphorylation is repressed as a result of unknown upstream transcriptional events. regardless, the observed upregulation of ribosomal gene expression might be a marker of the risk to develop type diabetes.  in summary, using a novel unsupervised clustering technique, forel, we found a single set of genes in skeletal muscle that separate subjects that are metabolically sound from those with insulin resistance, igt and diabetes. this finding was confirmed in two additional datasets from different populations and suggests a single underlying pathophysiological process lies underneath the clinical diagnosis of insulin resistance. the genes that discriminate between metabolically sound and metabolically unsound patients include the anticipated gene categories of fat oxidation, oxidative phosphorylation, and glycolytic metabolism. we also found several categories of genes not previously associated with insulin resistance or diabetes, namely, genes involved in ribosomal function and oxygen sensing. a separate analysis, where we identified clusters of genes that were co-regulated within the datasets, showed a large degree of overlap. based on prior literature in energy metabolism and mitochondrial dysfunction in saccharomyces cerevisiae, these results suggest that hypoxia, changes in ribosomal function, and glycolytic metabolism as key players in insulin resistance and diabetes. 
our findings are concordant with the changes seen in skeletal muscle with altitude hypoxia. the distance along this line coincided with the classic markers of diabetes risk, namely obesity and insulin resistance, but did not follow the accepted clinical diagnosis of dm as defined by the presence or absence of hyperglycemia. genes implicated in this expression pattern are those controlling skeletal muscle fiber type and glycolytic metabolism. this suggests that hypoxia and shift to glycolytic metabolism may also drive insulin resistance. using multi-dimensional unsupervised forel algorithm we have re-analyzed three public datasets of skeletal muscle gene expression in connection with insulin resistance and type diabetes. our analysis revealed the major line of variation between expression profiles of normal, insulin resistant, and diabetic skeletal muscle. additionally myoglobin and hemoglobin were upregulated and ribosomal genes deregulated in insulin resistant patients.
 classification of large volumes of data produced in a microarray experiment allows for the extraction of important clues as to the nature of a disease.  using multi-dimensional unsupervised forel algorithm we have re-analyzed three public datasets of skeletal muscle gene expression in connection with insulin resistance and type diabetes. our analysis revealed the major line of variation between expression profiles of normal, insulin resistant, and diabetic skeletal muscle. a cluster of most "metabolically sound" samples occupied one end of this line. the distance along this line coincided with the classic markers of diabetes risk, namely obesity and insulin resistance, but did not follow the accepted clinical diagnosis of dm as defined by the presence or absence of hyperglycemia. genes implicated in this expression pattern are those controlling skeletal muscle fiber type and glycolytic metabolism. additionally myoglobin and hemoglobin were upregulated and ribosomal genes deregulated in insulin resistant patients.  our findings are concordant with the changes seen in skeletal muscle with altitude hypoxia. this suggests that hypoxia and shift to glycolytic metabolism may also drive insulin resistance. 