Metabolically healthy obese subjects display preserved insulin sensitivity and a beneficial white adipose tissue gene expression pattern. However, this observation stems from fasting studies when insulin levels are low. We investigated adipose gene expression by 5′Cap-mRNA sequencing in 17 healthy non-obese (NO), 21 insulin-sensitive severely obese (ISO), and 30 insulin-resistant severely obese (IRO) subjects, before and 2 hr into a hyperinsulinemic euglycemic clamp. ISO and IRO subjects displayed a clear but globally similar transcriptional response to insulin, which differed from the small effects observed in NO subjects. In the obese, 231 genes were altered; 71 were enriched in ISO subjects (e.g., phosphorylation processes), and 52 were enriched in IRO subjects (e.g., cellular stimuli). Common cardio-metabolic risk factors and gender do not influence these findings. This study demonstrates that differences in the acute transcriptional response to insulin are primarily driven by obesity per se, challenging the notion of healthy obese adipose tissue, at least in severe obesity.

Although ISO and IRO individuals display different clinical phenotypes, it has been a matter of debate whether they also confer different risks for cardiovascular morbidity and/or mortality (). In fact, several recent meta-analyses have refuted the notion that “healthy obesity“or preserved insulin sensitivity protects against cardiometabolic complications (). These controversies prompted us to investigate how gene expression in the sWAT of ISO individuals responds to insulin stimulation (hyperinsulinemia). Assuming that the ISO group displays a more “beneficial” gene expression profile in the fasting state compared to the IRO group, we hypothesized that the differences in transcriptional response profiles between the ISO and IRO groups would be more pronounced upon hyperinsulinemia and that the ISO group would be more similar to the NO group than the IRO group. Therefore, we assessed global transcriptional profiles in sWAT from healthy NO subjects and from obese subjects subdivided into ISO and IRO groups according to hyperinsulinemic euglycemic clamp measures. Subcutaneous WAT biopsies were taken before and at the end of the 2-hr clamp. We chose this short period of hyperinsulinemia in order to evaluate direct transcriptional effects of insulin, assuming that longer duration of stimulation may cause secondary effects on gene transcription. Samples were analyzed using global transcriptional profiling with the 5′cap analysis of gene expression (CAGE) (). CAGE is based on sequencing the 5′ end of mRNA, thereby assessing the transcriptional start sites (TSS) and their usage (expression) with high resolution and reproducibility (). Adjacent TSSs for presumably the same transcripts are collapsed into “tag clusters” corresponding to gene promoters (). Individual genes typically have several tag clusters depending on tissue type (). CAGE provides less biased results than conventional expression arrays, allows for more in-depth analysis, and also detects uncharacterized novel gene transcripts (). However, in order to obtain biologically interpretable insights, the present work was focused on analyses of tag clusters corresponding to annotated genes.

Up to 30% of obese subjects display normal fasting plasma glucose/lipid levels and normotension, a phenotype referred to as “metabolically healthy obesity,” which implies that a significant proportion of obese individuals may need less vigorous interventions to avoid metabolic/cardiovascular complications (). A hallmark characteristic among these individuals is high insulin sensitivity. Several studies have shown that insulin-sensitive obese (ISO) subjects have lower visceral fat accumulation, less ectopic fat and arterial atherosclerosis, higher plasma adiponectin levels, and a more favorable inflammation profile than insulin-resistant obese (IRO) individuals (). It is also well established that the two obesity phenotypes differ in the subcutaneous white adipose tissue (sWAT) itself (). ISO individuals have smaller fat cells and less pronounced inflammation than IRO individuals, which is also reflected at the gene expression level (). However, the transcriptional profiles of sWAT have been investigated in the fasting state, when insulin levels are low (). As insulin is expected to induce profound alterations in gene expression, it is not clear how such changes relate to insulin sensitivity and clinical profiles. This has prompted some investigators to determine the transcriptional response to insulin in sWAT collected before and during hyperinsulinemic euglycemic clamp for 6 hr. Comparisons in limited numbers of lean ISO and IRO subjects have reported some between-group differences (). Although relevant, these studies were not designed to address the transcriptional response to insulin in subjects matched for BMI. Thus, in order to fully evaluate the idea of a healthy obese state, insulin responses need to be determined in obese subjects discordant in insulin sensitivity and ideally compared with those in healthy non-obese (NO) subjects.

Most of the included subjects were female, but there were men in the NO and IRO groups. Exclusion of male subjects did not impact our findings in a major way, as exemplified by PCA plots ( Figures S2 D and S2E).

As indicated for insulin responses in Figure 2 and further supported by the global comparisons in Figure S2 , the predominant difference between the controls and the IRO and ISO groups appeared to be obesity per se. This made it difficult to assess the possible differences between the ISO and IRO groups when including all three groups. To further evaluate what drives the differences in insulin-induced transcriptional response in obesity, we performed a multiple regression analysis of the data from the obese groups from Figure 2 A in relation to individual insulin sensitivity (i.e., M value) and other, possibly, contributing factors. This demonstrated that the M value and BMI contributed to the variations. However, the results were not influenced by gender or common risk factors such as waist-to-hip ratio, fasting lipid/insulin levels, pulse rate, or blood pressure ( Table 2 ), indicating that obesity and insulin sensitivity, rather than associated cardio-metabolic risk factors, explains the differences in gene expression upon hyperinsulinemia. A comparison of the two obese groups showed that 380 tag clusters (FDR < 0.05), corresponding to 231 genes, were altered in hyperinsulinemia ( Figure 3 A; Table S1 ). Almost half were responding in both the ISO and IRO groups (161 tag clusters, 42%; 108 genes, 47%), and among these, the degree of upregulation was more pronounced in the ISO group (116 out of the 133 upregulated tag clusters, 87%; Figure 3 B). The 231 genes mapped to KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways involved in, e.g., metabolic function, insulin signaling/resistance, MAPK signaling, circadian rhythm, and cancer ( Figure 3 C). Genes in these pathways were found among common as well as ISO- and IRO-group-associated genes. In order to gain some further insight into the biological meaning of the findings, a gene ontology (GO) analysis focusing on biological processes was also performed. This revealed that the top five most significantly enriched GO terms (p < 0.05) were response to lipid, cellular response to lipid, response to organic cyclic compound, response to steroid hormone, and fat cell differentiation ( Table S2 ). Because, in the present article, we focused on the effects of obesity and insulin resistance, we used these two MeSH (Medical Subject Headings) terms in a PubMatrix ( http://pubmatrix.irp.nia.nih.gov/ ) search of the genes listed in Table S1 . This revealed 34 ISO- and 27 IRO-enriched genes, as well as 48 common genes ( Figure 3 D). The genes (down- or upregulated) displaying the most pronounced differences between the ISO and IRO groups are displayed in Figures 3 E and 3F. Among those most prominent in ISO individuals were RORC, RPGR, KLF9, IRS2, and DDIT4. Those in the IRO group included PPARGC1 and TAGAP. Expression changes of these seven genes were confirmed by qPCR ( Figure S3 ). The five most significant and biologically relevant GO processes for the genes identified in Figure 3 D are summarized in Table 3 . The common ones belonged to biological pathways involved in lipid metabolism and cell differentiation (e.g., NPC1, PPARGC1B, and CEBPD). The ISO-enriched genes (e.g., IRS2, TRIB1, and MIDN) were primarily involved in phosphorylation processes, while the genes significantly altered in the IRO group (e.g., PIK3R1, FOS, and PPARGC1A) were associated with fat cell differentiation and cellular stimuli such as responses to endogenous/exogenous factors.

The top five most significant and biologically relevant Gene Ontology (GO):Biological processes are listed for the genes identified in Figure 3 D. The p values are Bonferroni corrected in the GO analysis. ISO, insulin-sensitive obese; IRO, insulin-resistant obese.

In the present dataset, it is possible to compare the groups in several different ways, as outlined in Figures S1 A–S1C. Principal-component analysis (PCA) for all differentially expressed tag clusters between groups and conditions, corresponding to the comparisons in Figure S1 B, showed a clear distinction between the NO group and both obese groups ( Figures S2 A and S2B). As expected, the ISO and IRO groups in the fasting state displayed a higher expression of genes in pro-inflammatory pathways compared with the NO group (data not shown). When focusing on the actual insulin response, i.e., the changes from fasting to hyperinsulinemia (f/hi; see comparison in Figure S1 C), the majority of the differentially expressed tag clusters between the ISO group (246 out of 295; 83%) and the IRO group (190 out of 246; 77%) were upregulated (f/hi; Figure 2 A). Surprisingly, after correction for multiple testing, insulin altered only the expression of four tag clusters in the NO group, two of which were not annotated, while two represented the genes FRMD6-AS2 and NPC1 ( Figure 2 A). The effect on NPC1 expression was confirmed by qPCR ( Figure S2 C). PCA of data from the fasting and hyperinsulinemic states showed that the two obese groups were indistinguishable but that both were clearly separated from the NO group ( Figure 2 B). Principal component (PC) 1 primarily reflected the insulin effect ( Figure 2 C). In contrast, in the fasting state, PC2 separated the NO group from the obese groups but not the IRO group from the ISO group ( Figure 2 D). Upon hyperinsulinemia, there was no further change in the NO group, while the ISO and IRO groups were altered but remained different from the NO group ( Figure 2 D).

(B) Principal-component (PC) analysis plot summarizing the high-dimensional transcriptional data from (A) for NO, ISO, and IRO groups along PC1 and PC2, explaining the most variance in the data. Circles represent 95% confidence intervals.

(A) The number of differentially expressed CAGE tag clusters altered by fasting/hyperinsulinemia (f/hi) according to the way of comparison in Figure S1 C.

Taking into account the expression data from all the subjects put together, there was a clear overall expression response to hyperinsulinemia, including several genes involved in insulin signaling. Using a false discovery rate (FDR) of <0.05, we found 786 CAGE tag clusters to be altered during the clamp (expressed as fasting divided by hyperinsulinemia; f/hi) ( Figure 1 B). Out of these, 493 corresponded to annotated genes, and there was a more pronounced transcriptional upregulation (641 out of 786 tag clusters; 82%) upon hyperinsulinemia (i.e., the f/hi quotient was decreased). Eight insulin responding tag clusters ( Figure 1 B) corresponded to eight genes in the insulin signaling pathway and included PPP1R3B, PPP1R3C, PIK3R, and IRS2 ( Figure 1 C). A subset of these genes (SREBF1, PIK3R3, and IRS2) was validated by qPCR in the NO group and confirmed the findings obtained by CAGE ( Figure 1 D).

Clinical data are summarized in Table 1 . As expected, there were major differences in the clinical profile between the NO group and the obese groups, in particular for the IRO group. Compared with the ISO group, the IRO group displayed significantly higher values for waist-to-hip ratio, systolic blood pressure, fasting plasma glucose, insulin, and triglycerides, as well as lower HDL (high-density lipoprotein)-cholesterol. Mean plasma insulin levels during clamp were also slightly higher in the IRO group. M values in the NO group were, on average, ∼40% higher than those in the ISO group, but there was a considerable overlap ( Figure 1 A).

(B) Volcano plot of the 30,331 detected CAGE tag clusters, corresponding to 15,518 genes, expressed as log fold change fasting/hyperinsulinemia (f/hi) along the x axis and false discovery rate (FDR) on the y axis. In blue: 29,964 tag clusters (15,398 genes) responding to the clamp. In red: 367 tag clusters (122 genes) involved in the insulin signaling pathway. The eight tag clusters corresponding to eight genes involved in the insulin signaling pathway are highlighted. The horizontal line shows an FDR < 0.05.

Discussion

Kawaji et al., 2014 Kawaji H.

Lizio M.

Itoh M.

Kanamori-Katayama M.

Kaiho A.

Nishiyori-Sueki H.

Shin J.W.

Kojima-Ishiyama M.

Kawano M.

Murata M.

et al. FANTOM Consortium

Comparison of CAGE and RNA-seq transcriptome profiling using clonally amplified and single-molecule next-generation sequencing. Herein, we report findings on global transcriptional profiles in the sWAT of NO and obese subjects, using a sequencing method that allows for broader analyses than conventional expression arrays (). We found a clear transcriptional response to insulin in our global analysis. However, after correction for multiple testing, this was only significant among the obese individuals except for four tag clusters in the NO group. Thus, NO individuals may regulate their short-term insulin response in WAT in a different way, possibly via post-transcriptional mechanisms. The small effects in NO also exclude the possibility that we used an insulin infusion rate during clamp that was too high, potentially masking differences between the ISO and IRO groups. Because of the strong influence of obesity per se, it was necessary to omit the NO group in the analyses in order to allow identification of the differences between the ISO and IRO groups. Around half of the genes were common for the ISO and IRO groups, although the magnitude of the insulin response was more pronounced for the upregulated genes in the ISO group, most probably reflecting these subjects’ higher level of insulin sensitivity.

Our analyses identified 108 insulin-responding genes that were common and 123 that were enriched in either the ISO or the IRO grooup. This shows that there are small but quantifiable differences between the ISO and IRO groups in the adipose response to insulin at the gene expression level. Notably, the obesity/insulin resistance-linked genes (i.e., with at least one publication in the literature) constituted less than half (109 out of 231) of all the insulin-responsive genes in obesity (individual genes exemplified in Results ). The remaining 122 genes may be of interest in future studies of the pathophysiological mechanisms of insulin action in human WAT. Nevertheless, it should be stressed that, although statistically significant, the differences between the ISO and IRO groups were small and primarily of quantitative nature, supporting the conclusion that the ISO and IRO groups display a strong similarity, at least in the short-term response to insulin.

Fan et al., 2013 Fan J.

Song Y.

Chen Y.

Hui R.

Zhang W. Combined effect of obesity and cardio-metabolic abnormality on the risk of cardiovascular disease: a meta-analysis of prospective cohort studies. Kramer et al., 2013 Kramer C.K.

Zinman B.

Retnakaran R. Are metabolically healthy overweight and obesity benign conditions?: A systematic review and meta-analysis. Roberson et al., 2014 Roberson L.L.

Aneni E.C.

Maziak W.

Agatston A.

Feldman T.

Rouseff M.

Tran T.

Blaha M.J.

Santos R.D.

Sposito A.

et al. Beyond BMI: The “metabolically healthy obese” phenotype & its association with clinical/subclinical cardiovascular disease and all-cause mortality – a systematic review. Viguerie et al., 2012 Viguerie N.

Montastier E.

Maoret J.J.

Roussel B.

Combes M.

Valle C.

Villa-Vialaneix N.

Iacovoni J.S.

Martinez J.A.

Holst C.

et al. Determinants of human adipose tissue gene expression: impact of diet, sex, metabolic status, and cis genetic regulation. The observation that the two obese phenotypes displayed very similar insulin responses could provide a clue for why ISO subjects display an increased morbidity and cardiovascular risk, compared with NO individuals (). The sWAT in ISO individuals may simply not be as metabolically normal as previously believed. Our present findings are in line with results following weight reduction induced by low-calorie diet (). This study showed similar improvements in sWAT gene expression in obese subjects with or without the metabolic syndrome. Thus, the transcriptional control upon either hyperinsulinemia or weight loss appears to be very similar in ISO and IRO subjects.

Hoffstedt et al., 2010 Hoffstedt J.

Arner E.

Wahrenberg H.

Andersson D.P.

Qvisth V.

Löfgren P.

Rydén M.

Thörne A.

Wirén M.

Palmér M.

et al. Regional impact of adipose tissue morphology on the metabolic profile in morbid obesity. The obese subjects were subdivided based on the 25th upper percentile of insulin sensitivity (). However, this has no important bearing on the results, as we obtained similar findings using individual insulin sensitivity values as a continuous variable (in multiple regression analyses). There is no consensus on how to express clamp data. We used the most common way, i.e., M value/body weight. However, correcting M values for lean body mass yielded virtually the same results. Insulin levels during clamp were slightly higher in the IRO group than in the ISO group ( Table 1 ). These small differences can hardly have influenced our findings on gene expression. Furthermore, as mentioned earlier, fasting insulin levels had no bearing on the results.

Persson et al., 2015 Persson H.

Kwon A.T.

Ramilowski J.A.

Silberberg G.

Söderhäll C.

Orsmark-Pietras C.

Nordlund B.

Konradsen J.R.

de Hoon M.J.

Melén E.

et al. Transcriptome analysis of controlled and therapy-resistant childhood asthma reveals distinct gene expression profiles. In the present work, we obtained sequencing-based data on 136 samples from 68 individuals. This can be regarded as very large numbers, using a genome-wide sequencing technique by today’s standards. Moreover, based on results from a smaller study on 40 subjects using CAGE (), our present cohort was sufficiently large to detect gene expression differences between the three groups. Despite this, we did not have sufficient statistical power to allow a subgroup analysis of, e.g., the influence of different age groups.

Klimcáková et al., 2011 Klimcáková E.

Roussel B.

Márquez-Quiñones A.

Kovácová Z.

Kováciková M.

Combes M.

Siklová-Vítková M.

Hejnová J.

Srámková P.

Bouloumié A.

et al. Worsening of obesity and metabolic status yields similar molecular adaptations in human subcutaneous and visceral adipose tissue: decreased metabolism and increased immune response. Felländer et al., 1994 Felländer G.

Nordenström J.

Tjäder I.

Bolinder J.

Arner P. Lipolysis during abdominal surgery. Elbein et al., 2011 Elbein S.C.

Kern P.A.

Rasouli N.

Yao-Borengasser A.

Sharma N.K.

Das S.K. Global gene expression profiles of subcutaneous adipose and muscle from glucose-tolerant, insulin-sensitive, and insulin-resistant individuals matched for BMI. There are some caveats with the present study. Because the obese subjects were scheduled to undergo bariatric surgery, our results may only pertain to individuals with severe obesity. We investigated solely sWAT as it is, for obvious ethical reasons, virtually impossible to obtain visceral WAT biopsies during clamp. Still, there is no obvious reason to believe that regional differences in gene expression are important for the present findings. In fact, previous studies of subcutaneous and visceral WAT have found that the impact of obesity and metabolic status is very similar in the two regions in the fasting state (). Moreover, studies of visceral WAT would require intraoperative fat biopsies during general anesthesia. It has been convincingly demonstrated that this procedure induces an acute insulin-resistant state immediately following the incision of the intra-abdominal wall (). This makes valid assessments of acute insulin responses in any target tissue during general surgery uncertain. Another aspect is the possibility that the gene expression response during fasting and hyperinsulinemia may differ between sWAT and tissues such as skeletal muscle and liver. However, previous comparisons have shown that differences in gene expression between BMI-matched individuals discordant in insulin sensitivity are more pronounced in sWAT than in skeletal muscle (). Furthermore, with regard to influence of gender, the number of men was too small to allow a detailed comparison in this respect. Again, for lack of statistical power, we had to keep the male subjects in the analyses. Nevertheless, gender distribution did not influence our correlation analysis, and PCA of insulin gene responses in women was similar to that in all subjects ( Figures S2 D and S2E). Therefore, we do not believe that sex impacts on our results to any significant degree. Finally, because both pre- and postmenopausal women were included, we cannot exclude that menopausal state may influence our results. Nevertheless, as evident from Table 1 , the three groups were well matched for this factor.

In summary, obese subjects with preserved insulin sensitivity have globally almost the same WAT gene expression response to insulin as BMI- and body-fat-matched insulin-resistant individuals. This is independent of cardio-metabolic risk factors, thereby questioning the notion of a “healthy obese state,” at least in the sWAT of women with severe obesity. A comparison with NO subjects confirms that the major factor explaining the differences in short-term insulin response is obesity per se. Nevertheless, small but clear quantitative differences in the transcriptional response to insulin in the ISO and IRO groups are observed that are linked to specific biological pathways involving, e.g., phosphorylation processes, cellular stimuli, and fat cell differentiation. The clinical and pathophysiological relevance of these differences will be addressed in additional studies, which we encourage by making the full tag cluster dataset publically available. The dataset can also be used for future studies addressing other gene regulatory mechanisms, e.g., those facilitated by specific gene promoters, enhancer usage, and long non-coding RNAs.