Abstract
Objective
Visceral adipose tissue (VAT) produces several adipokines; however, VAT adipocytes’ gene expression has not been related with cardio-metabolic components. The present study aimed to explore associations between gene expression in human primary VAT adipocytes’ and cardio-metabolic components in subjects who live with Metabolically Unhealthy Obesity (MUO) compared with subjects who live with Metabolically Healthy Obesity (MHO).
Methods
Cross-sectional study of patients who live with obesity, who underwent bariatric/metabolic surgery.
Results
Forty-five patients (18 MHO and 27 MUO) aged 44 years, main co-morbidities Diabetes and Hypertension, were analyzed. MUO showed younger age and higher cardio-metabolic risk, as well as larger adipocyte´s sizes. Primary cultured adipocytes were isolated from trans-surgical VAT sample, then cDNA was analyzed for the expression of 92 genes, further distributed as MUO vs. MHO and clustered according to their linkage with cardio-metabolic components. Higher expression of genes like EDN1, IL-6, ADIPOQ, GSTA2, TGFβ, WNT3A, KDR, MAPK3, vWF (p<0.04); as well as reduced NOS2, PDGFA, HAMP and RARRES2 (p<0.01) were observed across clusters of hyperglycemia, hypertension, dyslipidaemia, and central obesity, broadly implicating pathways related to inflammation, oxidative stress, endothelial dysfunction, insulin resistance, and vascular regulation.
Conclusion
Findings from the present exploratory analysis show that cardio-metabolic risk is associated to VAT adipocytes’ gene expression; particularly those genes related to oxidative stress, insulin resistance, inflammation, endothelial dysfunction, athero-thrombosis, and lower vasodilation. Further longitudinal and mechanistic studies are required to clarify the directionality and clinical significance of these associations.
Keywords
Introduction
Obesity is a chronic and multifactorial disease, in which environmental and genetic factors develop an energy imbalance. Resulting in increased adipose tissue mass and increased secretion of adipokines, generating a chronic inflammatory state. 1 This inflammatory condition is characterized by activation of innate and adaptive immune responses within adipose tissue, including macrophage infiltration and altered cytokine production, which contribute to systemic insulin resistance and metabolic dysfunction. 2
To better characterize metabolic heterogeneity among individuals with obesity; clinical and biochemical criteria, such as those proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III), are commonly used in order to identify patients with metabolic syndrome (MS) and to distinguish metabolically unhealthy obesity (MUO) from metabolically healthy obesity (MHO).3,4 Individuals with MUO typically present with greater metabolic dysregulation, including insulin resistance, dyslipidemia, hypertension, and systemic inflammation, which confer a higher risk of morbidity and mortality compared with MHO individuals. 4 In this context, chronic low-grade inflammation represents a central mechanism linking obesity to metabolic syndrome, where dysregulated cytokine signaling pathways exacerbate metabolic impairment 2,5. In contrast, MHO subjects display a more favorable metabolic profile despite increased adipose tissue accumulation.
Phenotypic differences between MUO and MHO individuals have been consistently associated with the development of chronic degenerative diseases, particularly cardiometabolic disorders. 4 These differences are strongly influenced by body fat distribution, especially visceral adipose tissue (VAT), which has been identified as a key determinant of metabolic dysfunction. VAT exhibits greater endocrine and inflammatory activity than subcutaneous adipose tissue, contributing to endothelial dysfunction, atherogenesis, and systemic metabolic impairment. 5
Despite increasing recognition of these metabolic phenotypes, relatively few studies have explored the molecular mechanisms underlying their differences. In recent years, adipose tissue biology has gained significant attention, particularly the role of adipocyte gene expression as a determinant of metabolic health. Altered expression of genes involved in inflammation, oxidative stress, insulin signaling, endothelial dysfunction, and lipid metabolism has been associated with an increased risk of type 2 Diabetes Mellitus (T2DM) and Cardiovascular Disease (CVD). 6 Notably, dysregulation of inflammatory and immune-response genes has been associated with impaired insulin signaling and endothelial dysfunction, further contributing to cardiometabolic risk. 7 Comparative analyses between MHO and MUO individuals have identified specific patterns of gene overexpression and downregulation related to cellular function and MS components. 8
Our group hypothesized that primary VAT adipocytes from individuals who live with MUO exhibit a particular gene expression signature, which significantly associate with cardiometabolic components of MS. Such expression profile in MUO adipocytes is characterized by higher expression of pathways related to inflammation, oxidative stress, impaired insulin signaling, endothelial dysfunction, and altered lipid metabolism, as compared with MHO.
Therefore, the aim of this study was to determine the association between metabolic components and the gene expression profile of primary visceral adipocytes in patients who live with obesity, classified according to metabolic risk. In this regards, specific objectives included to compare MHO vs. MUO differences regarding clinical-demographic characteristics and gene expression from their primary visceral adipocytes related to inflammation, insulin signaling, adipokine secretion, and lipid metabolism. Likewise, to identify molecular pathways associated with metabolic dysfunction and cardiometabolic risk and to explore potential biomarkers that distinguish metabolically healthy from unhealthy obesity.
We expected to identify distinct gene expression signatures associated with metabolic phenotypes in obesity, particularly pathways related to inflammation, insulin resistance, adipokine dysregulation, and lipid metabolism. These findings may contribute to a better understanding of the biological mechanisms underlying metabolic heterogeneity in obesity and support the identification of novel therapeutic targets and precision medicine strategies for preventing T2DM and cardiovascular complications.
Material and method
Design and study type
Observational, cross-sectional, analytical, retrospective and comparative, carried out according to recommendations guidelines. The study was designed, conducted, and reported in accordance with the STROBE guidelines for observational studies. 9
Study population and selection criteria
We consecutively selected patients older than 18 years who lived with morbid obesity (BMI> 40 kg/m2) undergoing bariatric surgery at the bariatric service of the Centro Médico Nacional “20 de Noviembre” ISSSTE, Mexico City, during a time period from May 2018 to February 2023. They were classified in two groups: MHO and MUO, based on the NCEP ATP III metabolic syndrome components. Specifically, MUO was defined as the presence of ≥3 of the following criteria: (1) waist circumference ≥102 cm in men or ≥88 cm in women; (2) triglycerides ≥150 mg/dL; (3) HDL cholesterol <40 mg/dL in men or <50 mg/dL in women; (4) blood pressure ≥130/85 mmHg or antihypertensive treatment; and (5) fasting glucose ≥100 mg/dL or previously diagnosed type 2 diabetes. Subjects not meeting these criteria were classified as MHO. A multivariable regression model was constructed to evaluate the association between metabolic phenotype (MUO vs. MHO) and cardiometabolic risk markers, adjusting for age and sex. All patients signed the informed consent. We excluded patients under medication with potential effect on adipose tissue or cardiovascular risk, serious infections in the last month and clinically unstable conditions. The study was approved by the Research, Biosafety and Ethics Committee of the Centro Médico Nacional “20 de Noviembre” (Protocol ID No. 386.2013, approval date November 2013); and it was conducted in accordance with the Helsinki Declaration of 1975 as revised in 2024.
All patients provided written informed consent for participation, and their data were fully de-identified prior to analysis in accordance with applicable ethical guidelines to protect participant privacy and confidentiality. The main study (Biomarkers Derived from Adipose Tissue Useful for Diagnosis and Prognosis of Cardiovascular Risk in Obese Patient (CROP)) is registered at Clinical-Trials.gov, NCT03561987.
Adipose tissue sample and primary culture of human adipocytes
Two cm3 of VAT was obtained during bariatric surgery, and it was divided into two pieces, one half was fixed in paraformaldehyde and further processed; while the other half was placed in a culture medium M199 at room temperature and taken to laboratory for immediate processing and culture. The biopsy was washed with HBSS supplemented with penicillin (100 IU/ml), cut and digested with 1 mg/ml collagenase I in a water bath and shaking (100 rpm) at 37 ° C for 30 minutes. The suspension was filtered by means of a cell strainer (size 40 μm) and subsequently centrifuged at 404 g for 2 min. The supernatant was discarded, and the cell pellet was resuspended in M199 medium supplemented with penicillin–streptomycin (100 IU/mL), HEPES (15 mM), gentamicin (0.1 mg/mL), and 10% fetal bovine serum (FBS, v/v). Cells were seeded into 6-well plates and incubated at 37 °C in a humidified atmosphere containing 5% CO2. After 24 hours, the culture medium was replaced, and sterile coverslips were placed in each well to promote cell adherence. Cultures were maintained for 15 days. 10
Viability and size of adipocytes
The viability of the adipocytes was assessed at 15 days in one of the wells cultured, as follows: the coverslip was turned upside, then the cells were fixed with 10% formalin for 5 min, washed with distilled water, and 60% isopropanol was placed for 5 min. Then, isopropanol was removed and Oil Red stain was added for 5 min at room temperature. After a wash with distilled water, counterstaining with hematoxylin was performed.
The scanning electron microscopy of the adipocytes was performed in other well seeded, following recommendations from the Electronic Microscopy Unit, Institute of Cellular Physiology, Universidad Autónoma de México (UNAM). Since previous study from our group have demonstrated the potential relevance of adipocyte size, 11 such evaluation was performed. Briefly, staining toluidine blue was performed by means of histological sections (thickness of 3μM), mounted on slides treated with poly-L-lysine. Deparaffination was performed in an oven at 60 °C for 30 minutes, then submerged in a progressive rehydration train (xylol and alcohols) and incubated with toluidine blue for 10 minutes at 37 °C. The stained was added and washed with running water. Afterwards, samples were dehydrated by the alcohol train, and further mounted with Entellan resin and coverslips. Deparaffinization was performed by incubating the slides in an oven at 60 °C for 30 min. Sections were subsequently immersed in xylene, followed by a graded alcohol series for progressive rehydration. Samples were then incubated with toluidine blue at 37 °C for 10 min. Excess stain was removed by rinsing under running water. Thereafter, the sections were dehydrated through a graded alcohol series, cleared in xylene, and mounted with resin and coverslips. Stained tissues were observed, digitalized and measured in a light microscope using the software Image-Pro Plus 7.0.
Extraction of RNA and gene expression analysis
For the gene expression assay, 5 MHO and 10 MUO subjects (1:2 ratio) were selected to maintain representative characteristics of their respective groups, and were included in the analysis of VAT adipocyte gene expression. We selected 92 genes associated with the metabolic profile (reflecting pathways of inflammation, angiogenesis, fibrosis, endothelial dysfunction, oxidant stress, lipid metabolism and glucose) from those reported in the literature.
After adipocyte isolation and culture, cells were recovered from the culture plates and trizol was used to extract total RNA from adipocytes lysates using zymo-spin RNA extraction columns (Zymo Research). RNA quality was assessed by UV spectrophotometry using the A260/A280 ratio, and RNA concentration was measured with a NanoDrop 2000 spectrophotometer (Thermo Scientific). For gene expression analysis, a panel of 92 genes was selected based on key mechanistic pathways involved in adipose tissue dysfunction and cardiometabolic risk. Total RNA from each sample was normalized to a concentration of 70 ng/μL and reverse-transcribed into complementary DNA (cDNA) using a commercially available reverse transcription kit. The resulting cDNA was used to prepare the qPCR mastermix (RT 2 SYBR® Green, Cat. No. 330529, Qiagen) and analyzed using a custom RT 2 Profiler PCR Array (Qiagen), based on quantitative real-time PCR technology. Quality control procedures were performed according to the manufacturer’s instructions. All samples were analyzed in technical triplicates, and gene expression levels were normalized using GAPDH as the endogenous control. cDNA array analysis was carried out through the online software of the QIAGEN website at GeneGlobe.
Statistical analysis
The clinical and biochemical variables, as well as adipocyte size, were summarized using descriptive statistics and expressed as mean ± SD for continuous variables. Data distribution was evaluated using the Shapiro–Wilk test to assess normality. Categorical variables were presented as n (%). Comparisons between groups were performed using the independent samples Student’s t-test for normally distributed continuous variables. In cases where normality assumptions were not met, the Mann–WhitneyU test was applied. Effect sizes were calculated to estimate the magnitude of group differences. Statistical analyses were conducted using SPSS (v.27, IBM Corp., Armonk, NY, USA) and R software (v. 4.2), ensuring reproducibility and robustness of the results. Statistical significance was considered if p <0.05. The gene expression data were analyzed using an integrative bioinformatic framework. After quality control and normalization to minimize technical variability, multivariate modeling was performed using Partial Least Squares Regression (PLS-R). This method is particularly suitable for high-dimensional datasets and allows the identification of latent components that maximize the covariance between predictor variables (clinical and metabolic phenotypes) and gene expression profiles. To evaluate the stability and predictive performance of the model, Jackknife cross-validation with a leave-one-out (LOO) approach was applied, and 95%CI were calculated to determine the robustness and statistical significance of gene–phenotype associations. In addition, unsupervised bioinformatic analyses, including principal component analysis and hierarchical clustering, were conducted to explore the global structure of the dataset and identify molecular signatures associated with metabolically healthy and metabolically unhealthy obesity. Functional enrichment and pathway analyses based on Gene Ontology and KEGG databases were performed to characterize biological processes related to inflammation, adipokine signaling, insulin resistance, mitochondrial dysfunction, and cardiometabolic risk. This comprehensive bioinformatic strategy allowed the evaluation of the combined effects of multiple interrelated metabolic predictors on gene regulation patterns, strengthening the mechanistic interpretation of metabolic phenotypes in obesity.
Results
Study population
Clinical and biochemical characteristics (n=45).
Continuous variables presented as mean ± SD and categorical variables as n (%). p-value of mean difference was adjusted by age and sex. Abbreviatures: BMI, body mass index; WC, Waist Circumference; WtHR, Waist-to-Height Ratio; T2DM, type 2 Diabetes Mellitus; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; HbA1c, glycated hemoglobin; HDL, High density lipoproteins; LDL, Low density lipoproteins; AIP, Atherogenic Index of Plasma
The MUO phenotype was independently associated with higher cardiometabolic risk, including increased diastolic blood pressure (β 10.4; p 0.04) and Atherogenic Index of Plasma (AIP) (β = 41.9, p 0.004), after adjustment for age and sex.
Characteristics of adipocytes
Typical morphometric characteristics of cultured primary adipocytes, which were further used for gene expression assay are shown in Figure 1; where it became evident that adipocyte size was differently distributed between metabolic phenotypes, being the largest sized those obtained from MUO phenotype (Figure 1(c) vs. Figure1(d) and (e)). Adipocyte´s characteristics. The figure shows morphometric features of cultured primary adipocytes used for gene expression assay, either stained with Oil Red (1A) or under scanning electron microscopy (1B). Distribution of adipocyte size was compared between MUO and MHO phenotypes (1C and 1D, respectively) and quantification is shown (1E). Abbreviatures: MUO, adults living with Metabolically Unhealthy Obesity; MHO, adults living with Metabolically Healthy Obesity.
Gene expression profile
Transcription rate of genes from isolated cultured human primary adipocytes, shown either as general distribution (Figure 2(a)), or according to metabolic phenotype (Figure 2(b)). Adipocyte’s gene expression. Left. Transcription rate of genes in human primary culture of adipocytes. Right. Gene expression pattern according to metabolic phenotype (1MHO, 2MUO). Abbreviations: MUO, adults living with Metabolically Unhealthy Obesity; MHO, adults living with Metabolically Healthy Obesity.
During analysis of adipocyte’s gene expression in MUO phenotype (when MHO phenotype was considered as reference), there were 33 genes overexpressed (Figure 3(a)) and 19 under-expressed (Figure 3(b)). Clusters of gene expression in human primary adipocytes. A. Overexpressed and B. Underexpressed genes in human primary culture of adipocytes, distributed as expression clusters. Axes t1 and t2 represent latent variable scores derived from PLS analysis and are dimensionless. Multivariate modeling was performed using Partial Least Squares Regression (PLS-R) to identify latent components that maximize the covariance between predictor variables (metabolic components) and gene expression profiles. Statistical significance was considered if p <0.05.
Adipocytes’ gene expression and metabolic components
Gene expression in human primary adipocytes were further distributed as clusters, and analyzed either as general distribution (Figure 3) or correlated with specific metabolic components (Figure 4). Such analyses resulted in the following gene characterization: for hyperglycemia, most linked overexpressed genes were EDN1, IL-6, ADIPOQ, GSTA2, MMP2, RPLP0 and NQO1 (Figure 3(a), PLS-DA cluster distribution: 0.63, 0.63, 0.63, 0.63, 0.63, 0.63 and 0.55, respectively); while most linked underexpressed genes were NOS2 and PDGFA (Figure 3(a): 0.76 and 0.75, respectively). Expression profile for hypertension was different, depending on systolic or diastolic evaluation; overexpressed genes were TGF-β1, MMP2, EDN1, GSTA2, NAMPT, IL-6, RPLP0 and HIF1A (Figure 3(b): TGF-β1: SBP 0.61, DBP 0.71; MMP2: SBP 0.61, DBP 0.71; EDN1: SBP 0.61, DBP 0.70; GSTA2: SBP 0.61, DBP 0.70; NAMPT: SBP 0.61, DBP 0.70; IL-6: SBP 0.61, DBP 0.70; RPLP0: SBP 0.61, DBP 0.70 and HIF1A: SBP 0.58, DBP 0.69); whereas underexpressed genes were HAMP and RARRES2 (Figure 3(b): HAMP: SBP 0.65, DBP 0.58; RARRES2: SBP 0.37, DBP 0.55). Regarding lipoproteins HDLc, LDLc and triglycerides, the former particularly linked following overexpressed genes: WNT3A, KDR, and GPX3 (Figure 3(c): WNT3A (0.79), KDR (0.79) and GPX3 (0.79)), while none gene underexpression profile was significantly linked to HDLc, LDLc nor triglycerides (Figure 3(c) and 3(e)). Finally, waist circumference linked to overexpression of vWF (0.65) (Figure 3(d)) without link to underexpressed genes. Clusters of gene expression in human primary adipocytes and metabolic components. A. Overexpressed and (b). Underexpressed genes in human primary culture of adipocytes, distributed as expression clusters linked to each metabolic component. Axes t1 and t2 represent latent variable scores derived from PLS analysis and are dimensionless. Multivariate modeling was performed using Partial Least Squares Regression (PLS-R) to identify latent components that maximize the covariance between predictor variables (metabolic components) and gene expression profiles. Statistical significance was considered if p <0.05. (a). hyperglycemia; (b). systolic and diastolic hypertension; (c). lower HDLc or higher LDLc; (d). waist circumference; (e). triglycerides. BMI remained constant along all metabolic components. Abbreviatures: HDLc, High Density Lipoprotein cholesterol; LDLc, Low Density Lipoprotein cholesterol.
Since the study population showed morbid obesity, BMI was used to weight linking analyses, in order to reduce potential bias. BMI was linked to overexpression of MAPK3 (0.86) for all phenotypes except for waist circumference.
Metabolic components and adipocyte’s gene expression profile.
Bold highlight indicates genes that were more over- or under-expressed.
*Indicates particular expression in Diabetes Mellitus.
†Indicates particular expression in Systemic Arterial Hypertension.
Discussion
The main finding of the present study was that gene profile of VAT adipocytes associates with specific cardiometabolic risk components shown by the MUO phenotype. Despite being younger, MUO subjects exhibited a markedly adverse cardiometabolic profile, characterized by a higher prevalence of T2DM, elevated systolic blood pressure, glucose, HbA1c, and AIP levels, along with lower HDL-C, and remained independently associated with increased diastolic blood pressure and AIP after adjustment for age and sex; which allowed an appropriate interpretation regarding differential gene expression in adipocytes. However, the younger age in the MUO group may be attributable to selection bias driven by referral patterns inherent to a bariatric surgery setting.
As first finding, VAT adipocytes from MUO were larger than those from MHO phenotype. In this regard, our results are consistent with previous observations that adipocyte´s size is related to metabolic flexibility, insulin resistance, pro-inflammatory cytokines and lipid metabolism in patients with obesity undergoing bariatric surgery; and being particularly true for VAT´s derived adipocytes of higher size.12–14
While evaluating adipocyte´s expression profiles of cardiometabolic components, we noticed that hyperglycemia was related with increased IL-6 gene, which may be involved in the deregulation of glucose metabolism and homeostasis directly and indirectly by adipocytes, 15 insulin resistance and increased body mass index. 16
Furthermore, we noticed that hyperglycemia and hypertension shared a similar gene expression, suggesting common pathophysiological pathways involving endothelial dysfunction, vascular remodeling, inflammation, oxidative stress and atherogenesis. Particularly, lower expression of NOS2 as well as higher HIF1A gene expression suggest the role of hypoxia in the VAT tissue may be related to hyperglycemia and hypertension,16–18 with potential synergistic effects on GSTA2 during regulation of oxidative stress and endothelial dysfunction. Interestingly, prior studies have shown that leptin can inhibit angiotensin II-induced intracellular calcium increase and vasoconstriction in the rat aorta, highlighting a potential modulatory role of adipokines on vascular tone and endothelial function. 19 Consistently, increased expression of HIF1A in VAT, has been observed in experimental diabetes, further correlating with LPL expression 20 as well as the variation of HIF1A expression in VAT induced by an ERβ agonist, which owns anti-inflammatory effects, in obese mice. 21
Regarding dyslipidemia, we observed an increased expression of WNT3A and GPX3. This gene profile may participate through different mechanisms contributing to dyslipidemia. For example, WNT3A has modulatory effects on adipogenesis and lipolysis, mediated by PPARγ, c/EBPα and FABP4; as it has been suggested in cultured 3T3 adipocytes. 22 Likewise, human carriers of GPX3 polymorphic variant rs3828599, as well as transfected animal model, show hypertriglyceridemia and low cHDL. 23 Interestingly, inflammation and obesity have been associated with a decrease in the expression of key lipogenic factors in visceral adipose tissue, potentially reducing lipid synthesis while favoring alterations in lipolytic pathways.24,25 These combined data suggest that WNT3A and GPX3 associate to dyslipidemia, accelerated atherogenesis and increased cardiovascular risk.
Finally, higher waist circumference was related to higher levels of vWf in our study population. Such association is particularly interesting, since vWf may reflect a higher risk of angiogenesis and pro-thrombotic effects due to its biological activity, especially when coexisting with increased waist circumference. Consistently, similar associations were observed in other studies in patients with hyperlipidemia and vascular disease, where increased vWF correlated with waist-to-hip ratio, specifically in individuals with vascular disease. 26 Likewise, other studies have addressed vWF and PAI-1 as markers of endothelial cell dysfunction, showing correlations with waist/hip circumference in children with metabolic syndrome. 27
Notably, these associations may also be influenced by additional adipostatic hormones and other endocrine factors that regulate energy homeostasis, lipid metabolism, and vascular function, which could contribute to the observed effects of VAT on endothelial markers. 28 In this context, leptin has been proposed as a key mediator linking adiposity with cardiovascular risk, as higher body fat percentage is associated with increased leptin concentrations, which in turn correlate with multiple cardiovascular risk factors, including dyslipidemia, insulin resistance, and endothelial dysfunction. 29 These findings support the notion that adipose tissue–derived hormones may act synergistically with central adiposity to promote a pro-inflammatory and pro-thrombotic milieu, potentially amplifying endothelial activation and vWF expression.
Dysfunctional adiposity triggers the release of a broader spectrum of adipokines and bioactive molecules with relevant roles in cardiometabolic regulation and vascular homeostasis. Beyond classical adipostatic hormones, VAT has been shown to secrete membrane-associated proteins, acute-phase reactants, and components of the innate immune system that may contribute to endothelial dysfunction and low-grade inflammation. For instance, increased circulating levels of serum amyloid A have been described in morbid obesity, with significant reductions following weight loss after bariatric surgery, supporting its role as a dynamic marker of adipose tissue–driven inflammation. 30 Likewise, aquaporins, particularly those involved in glycerol and water transport, have emerged as key regulators of adipocyte metabolism and lipid handling, linking adipose tissue expansion with altered metabolic fluxes and systemic insulin resistance. 31 In addition, complement-related proteins such as complement factor H have been associated with adiposity and metabolic dysfunction, suggesting a crosstalk between adipose tissue and innate immunity pathways that may further amplify cardiometabolic risk. 32 Collectively, these findings reinforce the concept that adipose tissue dysfunction extends beyond fat accumulation, encompassing a complex endocrine and immunometabolic network capable of modulating endothelial activation, pro-thrombotic pathways, and systemic inflammation. Such mechanisms may partially explain the associations observed between visceral adiposity and endothelial markers such as vWF, as well as the broader cardiometabolic alterations identified in the MUO phenotype.
The metabolic and inflammatory pathways identified in VAT adipocytes from the MUO phenotype may also represent potential therapeutic targets through drug repurposing. GLP-1 receptor agonists have shown pleiotropic cardiometabolic effects, including anti-inflammatory, anti-oxidative, and endothelial protective actions that could modulate adipocyte dysfunction and improve metabolic flexibility. Likewise, targeting the ubiquitin–proteasome system, particularly the 20S proteasome, has been proposed as an immunomodulatory strategy capable of regulating oxidative stress, cytokine signaling, and hypoxia-related pathways. These mechanisms are especially relevant given the increased IL-6 and HIF1A expression observed in VAT from MUO subjects.33,34 Therefore, repurposing drugs that enhance GLP-1 signaling or selectively regulate proteasome activity may represent a promising prophylactic approach to attenuate adipose tissue inflammation, endothelial dysfunction, and cardiometabolic risk progression.
This study has several limitations that should be acknowledged: 1) the relatively small sample size, together with heterogeneity in sex and age between groups, may have influenced the observed results. These factors are particularly relevant in the context of gene expression analyses, where biological variability can significantly affect transcriptional profiles. However, the potential impact of these variables was not formally assessed, and therefore their contribution to the findings cannot be fully excluded; 2) likewise, gene-clinical correlations were performed in cross-sectional analysis, whereas a long term, prospective study involving follow up of metabolic components after surgery would have revealed a more dynamic effect of a particular gene risk profile; 3) potential bias derived from missing genes, not considered in the present study. Nevertheless, the strength of our study is the specific design targeting the identification of gene-metabolic clusters, within a comparative analysis of MHO vs. MUO patients, whereas other study designs have addressed to compare gene expression between groups with presence/absence of disease.
Conclusion
Comparative analysis of VAT adipocytes’ gene expression, as clustered by cardio-metabolic components, MUO vs. MHO, suggests that cardio-metabolic risk is associated to adipocytes’ genes driving oxidative stress, insulin resistance, inflammation, endothelial dysfunction, atherosclerosis and pro-thrombotic effects; as well as lower vasodilator factors; highlighting the cardio-metabolic role of VAT adipocytes.
Footnotes
Acknowledgements
Special thanks to Dr. María Angélica Díaz-Aranda for her excellent comments and English translation.
Author contributions
J.A.S-C, A.H-P, E.V-G and P.M-T conceived the design and experiments. J.A.G-B, A.S.R-H, K.D.V-M, G.D.P-S, J.M-R, A.M-L, M.A.D-A, Y.B-F, G.I.M-T, and J.G-S collected data and analyzed the results. S.L.A-E, S.G, J.A.P-J, C. G.T-L, D.C.R-V, C.A.D.-W and M.A.T-G. conducted experiments and critically revised the manuscript. All authors reviewed the final manuscript.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors gratefully acknowledge support from Institutional Program E015, ISSSTE.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
The datasets generated and analyzed during the current study are not publicly available due to privacy policies of the hospital and patients information; but are available from the corresponding author on reasonable request.