Metabolically Healthy Obese Individuals Research Articles

Distinguishing health-related parameters between metabolically healthy and metabolically unhealthy obesity in women.

Obesity represents a global health crisis, yet a dichotomy is emerging with classification according to the metabolic state into metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). This study aimed to identify distinctive systemic clinical/endocrinological parameters between MHO individuals, employing a comprehensive comparative analysis of 50 biomarkers. Our emphasis was on routine analytes, ensuring cost-effectiveness for widespread use in diagnosing metabolic health. The study included 182 women diagnosed with obesity referred for bariatric surgery at the Endocrinology, Diabetes, and Metabolism Service of São João Hospital and University Centre in Portugal. MUO was defined by the presence of at least one of the following metabolic disorders: diabetes, hypertension, or dyslipidemia. Patients were stratified based on the diagnosis of these pathologies. Significantly divergent health-related parameters were observed between MHO and MUO patients. Notable differences included: albumin (40.1 ± 2.2 vs 40,98 ± 2.6 g/L, p value = 0.017), triglycerides (110.7 ± 51.1 vs 137.57 ± 82.6 mg/dL, p value = 0.008), glucose (99.49 ± 13.0 vs 119.17 ± 38.9 mg/dL, p value < 0.001), glycated hemoglobin (5.58 ± 0.4 vs 6.15 ± 1.0%, p value < 0.001), urea (31.40 ± 10.0 vs 34.61 ± 10.2 mg/dL, p value = 0.014), total calcium (4.64 ± 0.15 vs 4.74 ± 0.17 mEq/L, 1 mEq/L = 1 mg/L, p value < 0.001), ferritin (100.04 ± 129.1 vs 128.55 ± 102.1 ng/mL, p value = 0.005), chloride (104.68 ± 1.5 vs 103.04 ± 2.6 mEq/L, p value < 0.001), prolactin (13.57 ± 6.3 vs 12.47 ± 7.1 ng/mL, p value = 0.041), insulin (20.36 ± 24.4 vs 23.87 ± 19.6 μU/mL, p value = 0.021), c peptide (3.78 ± 1.8 vs 4.28 ± 1.7 ng/mL, p value = 0.003), albumin/creatinine ratio (15.41 ± 31.0 vs 48.12 ± 158.7 mg/g creatinine, p value = 0.015), and whole-body mineral density (1.27 ± 0.1 vs 1.23 ± 0.1 g/cm2, p value = 0.016). Our findings highlight potential additional parameters that should be taken into consideration alongside the commonly used biomarkers for classifying metabolic health in women. These include albumin, urea, total calcium, ferritin, chloride, prolactin, c-peptide, albumin-creatinine ratio, and whole-body mineral density. Moreover, our results also suggest that MHO may represent a transitional phase preceding the development of the MUO phenotype.

Computed tomography evaluation of skeletal muscle quality and quantity in people with morbid obesity with and without metabolic abnormality.

We investigated the differences in quantity and quality of skeletal muscle between metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) individuals using abdominal CT. One hundred and seventy-two people with morbid obesity who underwent bariatric surgery and 64 healthy control individuals participated in this retrospective study. We divided the people with morbid obesity into an MHO and MUO group. In addition, nonobese metabolic healthy people were included analysis to provide reference levels. CT evaluation of muscle quantity (at the level of the third lumbar vertebra [L3]) was performed by calculating muscle anatomical cross-sectional area (CSA), which was normalized to patient height to produce skeletal muscle index (SMI). Muscle quality was assessed as skeletal muscle density (SMD), which was calculated from CT muscle attenuation. To characterize intramuscular composition, muscle attenuation was classified into three categories using Hounsfield unit (HU) thresholds: -190 HU to -30 HU for intermuscular adipose tissue (IMAT), -29 to +29 HU for low attenuation muscle (LAM), and +30 to +150 HU for normal attenuation muscle (NAM). People with morbid obesity comprised 24 (14%) MHO individuals and 148 (86%) MUO individuals. The mean age of the participants was 39.7 ± 12.5 years, and 154 (65%) participants were women. MUO individuals had a significantly greater total skeletal muscle CSA than MHO individuals in the model that adjusted for all variables. Total skeletal muscle SMI, SMD, NAM index, LAM index, and IMAT index did not differ between MHO and MUO individuals for all adjusted models. Total skeletal muscle at the L3 level was not different in muscle quantity, quality, or intramuscular composition between the MHO and MUO individuals, based on CT evaluation. MHO individuals who are considered "healthy" should be carefully monitored and can have a similar risk of metabolic complications as MUO individuals, at least based on an assessment of skeletal muscle.

Association between metabolically healthy obesity and kidney stones: results from the 2011-2018 National Health and Nutrition Examination Survey.

The risk of kidney stones in metabolically healthy obesity (MHO) individuals is largely unexplored. This study using percent body fat (%BF) to categorize obesity, to investigate the association between MHO as well as other metabolic syndrome-obesity combined phenotypes and kidney stones in a national representative population. This cross-sectional study included 4,287 participants in the National Health and Nutrition Examination Survey from 2011 to 2018. Metabolically healthy status was defined as not having any component of metabolic syndrome or insulin resistance. Obesity was identified by %BF, which was measured and assessed by dual-energy x-ray absorptiometry (DXA) scan. Participants were cross-classified by metabolic health and obesity status. The outcome was self-report kidney stones. Multivariable logistic regression model was used to examine the association between MHO and kidney stones. A total of 358 participants had kidney stones [weighted prevalence (SE): 8.61% (0.56%)]. The weighted prevalence (SE) of kidney stones in MHN, MHOW, and MHO groups was 3.13% (1.10%), 4.97% (1.36%), and 8.55% (2.09%), respectively. After adjusting for age, sex, race and ethnicity, education level, smoking status, alcohol consumption, physical activity, daily water intake, CKD stage 3-5, and hyperuricemia, MHO individuals (OR: 2.90, 95% CI: 1.18, 7.0) had a significantly higher risk of kidney stones than those with metabolically healthy normal weight. In metabolically healthy participants, a 5% increment in %BF was associated with a significantly higher risk of kidney stones (OR: 1.60, 95% CI: 1.20, 2.14). Furthermore, a nonlinear dose-response relationship between %BF and the kidney stones was observed in metabolically healthy participants (P for non-linearity = 0.046). Using %BF to define obesity, MHO phenotype was significantly associated with higher risks of kidney stones, suggesting that obesity can independently contribute to kidney stones in the absence of metabolic abnormalities and insulin resistance. Regarding kidney stones prevention, MHO individuals might still benefit from lifestyle interventions aimed at healthy body composition maintenance.

Abstract MP31: Serum Metabolite Signatures for Metabolic Healthy and Unhealthy Obesity: The Bogalusa Heart Study

Introduction: Mechanisms for metabolically healthy obesity (MHO) are not well studied. Methods: We performed cross-sectional analyses among 1,261 participants of the Bogalusa Heart Study 2013-2016 survey. A total of 887 known serum metabolites were profiled using an untargeted approach. Participants who met all the following five criteria were considered metabolically healthy, otherwise, unhealthy: 1) SBP &lt;130 mm Hg and DBP &lt;85 mm Hg; 2) fasting glucose &lt;100 mg/dl; 3) triglycerides &lt;150 mg/dl; 4) HDL ≥40 mg/dl in men and ≥50 mg/dl in women; and 5) not taking medications for hypertension, diabetes, or dyslipidemia. We tested associations of metabolites with metabolic health status, controlling for age, sex, race, education, smoking, drinking, and kidney function. Results: Among 648 obese participants (BMI≥30 kg/m 2 ), 66 (10.2%) had MHO. After Bonferroni correction, 26 metabolites differed between MHO and metabolically unhealthy obesity (MUO) groups. Amino acids serine , glycine, and histidine , fatty acid N-linoleoylserine , 4 sphingolipids, and 3 plasmalogens were significantly higher among MHO individuals. These metabolites are functionally relevant to metabolic health ( Table ). Carbohydrates mannose , lactate, and pyruvate , urate , amino acids glutamate , 1-carboxyethylvaline , 1-carboxyethylleucine , 3-methyl-2-oxovalerate , and 3-methyl-2-oxobutyrate , PE(18:0/18:2(9Z,12Z)) , and 5 diacylglycerols were significantly higher in the MUO group. These metabolites are mostly food additives or reduce gut microbiota diversity ( Table ). Furthermore, histidine was associated with metabolic health only among overweight and obese participants. Odds ratios of being metabolically healthy per SD increase of histidine among normal weight, overweight, and obese participants were 1.05 (95% CI: 0.83-1.32), 1.54 (95% CI: 1.10-2.14), and 2.12 (95% CI: 1.56-2.89), respectively (P for group difference=1.41E-4). Conclusion: We identified potential intervention targets to improve metabolic health in obesity.

Association between metabolically healthy obesity and non-alcoholic fatty liver disease.

The risks of NAFLD and NAFLD with fibrosis progression among metabolically healthy obesity (MHO) individuals are largely unexplored. This cohort study investigated the association between MHO as well as other metabolic syndrome-obesity combined phenotypes and NAFLD and its fibrosis progression. Participants included 31,010 adults from a health check-up cohort free from NAFLD and intermediate or high probability of advanced fibrosis at baseline. Metabolically healthy was defined as not having any component of metabolic syndrome. Obesity was identified by body mass index (BMI) and waist circumference (WC). Participants were cross-classified by metabolic health and obesity at baseline. The outcomes were NAFLD, and NAFLD with fibrosis progression, as assessed by abdominal B-type ultrasound and noninvasive fibrosis score. During a median follow-up of 2.2 (interquartile range, 1.2-4.9) years, 7,393 participants developed NAFLD. MHO individuals (HR 5.51, 95% CI 4.98, 6.09 for BMI criteria; HR 6.76, 95% CI 6.04, 7.57 for WC criteria) had a significantly higher risk of NAFLD than those with metabolically healthy normal weight or low WC. The corresponding HRs (95% CIs) for metabolically healthy overweight (defined by BMI) and medium WC were 2.74 (2.49-3.02) and 2.93 (2.65-3.24), respectively. Furthermore, 557 participants developed NAFLD with fibrosis progression. The association between different obesity phenotypes and NAFLD with fibrosis progression also showed a similar pattern. MHO was associated with significantly higher risks of NAFLD and its fibrosis progression, suggesting that regarding NAFLD prevention, MHO individuals might still benefit from lifestyle interventions aimed at body weight and WC maintenance.

Metabolic Obesity Phenotypes and Risk of Lung Cancer: A Prospective Cohort Study of 450,482 UK Biobank Participants.

(1) Background: The association between metabolic obesity phenotypes and incident lung cancer (LC) remains unclear. (2) Methods: Based on the combination of baseline BMI categories and metabolic health status, participants were categorized into eight groups: metabolically healthy underweight (MHUW), metabolically unhealthy underweight (MUUW), metabolically healthy normal (MHN), metabolically unhealthy normal (MUN), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). The Cox proportional hazards model and Mendelian randomization (MR) were applied to assess the association between metabolic obesity phenotypes with LC risk. (3) Results: During a median follow-up of 9.1 years, 3654 incident LC patients were confirmed among 450,482 individuals. Compared with participants with MHN, those with MUUW had higher rates of incident LC (hazard ratio (HR) = 3.24, 95% confidence interval (CI) = 1.33–7.87, p = 0.009). MHO and MHOW individuals had a 24% and 18% lower risk of developing LC, respectively (MHO: HR = 0.76, 95% CI = 0.61–0.95, p = 0.02; MHO: HR = 0.82, 95% CI = 0.70–0.96, p = 0.02). No genetic association of metabolic obesity phenotypes and LC risk was observed in MR analysis. (4) Conclusions: In this prospective cohort study, individuals with MHOW and MHO phenotypes were at a lower risk and MUUW were at a higher risk of LC. However, MR failed to reveal any evidence that metabolic obesity phenotypes would be associated with a higher risk of LC.

Can Gamma-glutamyl Transferase Predict Unhealthy Metabolic Phenotypes Among Healthcare Workers in Azar Cohort Study?

Background: Although various studies have assessed the correlation between gamma-glutamyl transferase (GGT) and cardiometabolic risk factors in obesity, no research has differentiated among metabolically-healthy obese (MHO) and metabolically unhealthy obese (MUHO), metabolically-healthy lean (MHL), and metabolically-unhealthy lean (MUHL). Objectives: Accordingly, this study evaluated the correlation between GGT and cardiometabolic phenotypes among healthcare workers. Methods: In this study, there were anthropometric measurements as well as the measurements of fasting blood sugar (FBS), GGT, cholesterol, triglyceride (TG), high lipoprotein density (HDL), and blood pressure in 1458 healthcare workers enrolled in the Azar Cohort Study. Metabolic syndrome (MetS) was defined according to the National Cholesterol Education Program Adult Treatment Panel III (ATP III). Accordingly, the participants were divided into four cardiometabolic phenotypes. Results: In this cross-sectional study, there was a significant difference in the prevalence of cardiometabolic phenotypes regarding the GGT tertiles (P ≤ 0.001). The highest prevalence of MHO was observed in the third GGT tertile. The mean waist circumference, TG, FBS, HDL, and systolic and diastolic blood pressure levels increased in the MHO, MUHO, and MHL groups in a dose dependent manner with an increase in the GGT tertiles (P &lt; 0.05). In comparing the highest and lowest GGT tertile, the risk of MHO and MUHO increased by 2.84 (95%CI 2.01 - 4.01) and 9.12 (95%CI 5.54 - 15), respectively. However, the correlation between the GGT tertile and MUHL did not reveal a similar trend. The ROC curve shows the cutoff value of 18.5 U/L for GGT, which allowed us to distinguish between the MUHO and MHO individuals. Conclusions: The findings revealed that GGT can indicate the risk of MetS as such, it can be used to detect at-risk MHO individuals and administer proper interventions.

Metabolically healthy obesity: Is it really healthy for type 2 diabetes mellitus?

Metabolically healthy obese (MHO) individuals are reported to have a lower risk of developing cardiovascular diseases in comparison with individuals with metabolic syndrome. However, the association between MHO and type 2 diabetes (T2DM) is still controversial. Some studies indicated that MHO is a favorable phenotype for T2DM, but more studies showed that MHO individuals have an increased risk of developing T2DM compared with metabolically healthy normal-weight individuals, especially among those who would acquire metabolically unhealthy obesity. This has been supported by finding insulin resistance and low-grade inflammatory responses in MHO individuals with a tendency for impaired beta-cell dysfunction. Studies also showed that liver fat accumulation increased the risk of incidence of T2DM in MHO. Here, we reviewed current literature on the relationship between MHO and T2DM, discussed the determinants for the development of diabetes in MHO, and summarized the measures for the prevention of T2DM in MHO.

Metabolic phenotypes and risk of colorectal cancer: a systematic review and meta-analysis of cohort studies

BackgroundThe association of obesity with colorectal cancer (CRC) may vary depending on metabolic status.ObjectiveThis meta-analysis aimed to investigate the combined impacts of obesity and metabolic status on CRC risk.MethodsThe Scopus, PubMed, and web of sciences databases were systematically searched up to Jun 2021 to find all eligible publications examining CRC risk in individuals with metabolically unhealthy normal-weight (MUHNW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUHO) phenotypes.ResultsA total of 7 cohort studies with a total of 759,066 participants were included in this meta-analysis. Compared with healthy normal-weight people, MUHNW, MHO, and MUHO individuals indicated an increased risk for CRC with a pooled odds ratio of 1.19 (95% CI = 1.09–1.31) in MUHNW, 1.14 (95% CI = 1.06–1.22) in MHO, and 1.24 (95% CI = 1.19–1.29) in MUHO subjects. When analyses were stratified based on gender, associations remained significant for males. However, the elevated risk of CRC associated with MHO and MUHO was not significant in female participants.ConclusionsThe individuals with metabolic abnormality, although at a normal weight, have an increased risk for CRC. Moreover, obesity is associated with CRC irrespective of metabolic status.

Evaluation of Liver Metabolism Biomarkers in Metabolic Associated Fatty Liver Disease According to Obesity Phenotype

Objective To analyze the relationship between the biochemical markers of liver metabolism in different stages of Metabolic Associated Fatty Liver Disease (MAFLD) according to the obesity phenotype. Methodology This is a cross-sectional study with individuals with class III obesity classified according to the obesity phenotypes proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. Biochemical and anthropometric variables were analyzed according to the staging of MAFLD and obesity phenotype. Results A total of 50 subjects with MAFLD, 62% (n = 31) with steatosis and 38% (n = 19) with steatohepatitis without fibrosis; 36% were classified as metabolically healthy obesity (MHO) and 64% as metabolically unhealthy obesity (MUHO), respectively. Mean values of alkaline phosphatase were 85.44 ± 27.27 vs. 61.92 ± 17.57 (p = 0.006); gamma-glutamyl transpeptidase, 25.77 ± 15.36 vs. 30.63 ± 19.49 (p = 0.025); and albumin, 3.99 ± 0.34 vs. 4.24 ± 0.23 (p = 0.037), were lower and statistically significant in the MHO group with steatosis. The results show when considering individuals with IR, only AP is a predictor of unhealthy phenotype (B-0.934, 0.848- 1.029, p = 0.031). Conclusion MHO individuals with steatosis present lower severe changes related to markers of liver damage and function and AP is considered the predictor of MUHO phenotype.

Proteomic and Metabolomic Characterization of Metabolically Healthy Obesity: A Descriptive Study from a Swedish Cohort

Method Associations between different biomarkers (proteomics, lipidomics, and metabolomics) coupled to either MHO or metabolically unhealthy obese (MUO) individuals were analyzed through principal component analysis (PCA). Subjects were identified from a subsample of 416 obese individuals, selected from the Malmö Diet and Cancer study—Cardiovascular arm (MDCS-CV, n = 3,443). They were further divided into MHO (n = 143) and MUO (n = 273) defined by a history of hospitalization, or not, at baseline inclusion, and nonobese subjects (NOC, n = 3,027). Two distinctive principle components (PL2, PP5) were discovered with a significant difference and thus further investigated through their main loadings. Results MHO individuals had a more metabolically favorable lipid and glucose profile than MUO subjects, that is, lower levels of traditional blood glucose and triglycerides, as well as a trend of lower metabolically unfavorable lipid biomarkers. PL2 (lipidomics, p=0.02) showed stronger associations of triacylglycerides with MUO, whereas phospholipids correlated with MHO. PP5 (proteomics, p=0.01) included interleukin-1 receptor antagonist (IL-1ra) and leptin with positive relations to MUO and galanin that correlated positively to MHO. The group differences in metabolite profiles were to a large extent explained by factors included in the metabolic syndrome. Conclusion Compared to MUO individuals, corresponding MHO individuals present with a more favorable lipid metabolic profile, accompanied by a downregulation of potentially harmful proteomic biomarkers. This unique and extensive biomarker profiling presents novel data on potentially differentiating traits between these two obese phenotypes.

Current prospects of metabolically healthy obesity

Obesity is a heterogeneous disorder posing a great risk of developing cardiovascular disease and type 2 diabetes. Metabolically healthy obesity (MHO) is a phenotype claimed to be protected against these risks, but its mechanisms remain unclear. Overview of current evidence indicating MHO and its metabolic characteristics, highlighting possible factors underlying MHO. PubMed, Medline, Clinical trials.gov, Science Direct, ADI, and WHO databases were searched through to June 2021 . Recent evidence shows MHO phenotype with a prevalence of 6–75% and claims protection from cardiometabolic complications, though its precise definition and diagnosis are not yet established. Although MHO individuals are not healthy and may represent a subclinical state of obesity, they are closer to normal than those with other obesity phenotypes concerning adipocyte size and distribution, immune cell infiltration, adipokine secretion, cardiorespiratory fitness, proinflammatory biomarkers, insulin sensitivity, and dietary response. Notably, 36–52% of MHO individuals may become unhealthy later in life, though the evidence is inconclusive. MHO phenotype remains a controversial scientific issue, especially for its definition. MHO individuals are classified to have fewer or milder metabolic risks than those with unhealthy phenotypes; some maintain a healthy lifestyle and fat and fit. Thus, in-depth research is warranted. • Metabolic healthy obesity (MHO) shows reduced cardiometabolic risks. • There is no precise definition and diagnosis of MHO yet. • Although not healthy, MHO individuals are closer to normal than other phenotypes. • Evidence that MHO individuals may become unhealthy later in life is inconclusive. • The MHO phenotype remains a controversial scientific issue.

Effect of Metabolic Health and Obesity Phenotype on Risk of Diabetes Mellitus: A Population-Based Longitudinal Study.

BackgroundEpidemiologic evidence on body mass index (BMI)-metabolic status phenotypes and diabetes risk remains controversial, especially for metabolically healthy obesity (MHO). We aimed to examine the effect of metabolic health and obesity phenotype on diabetes risk in the Chinese population.MethodsA population-based cohort study was carried out. The baseline survey was conducted in 2017, with two follow-up visits in 2018 and 2020. Diabetes was defined based on the criteria of the World Health Organization. Robust generalized estimating equation models with a binary distribution using a log link and exchange structure were applied for the pooled analysis sample.ResultsA total sample of 9623 observations was pooled for the longitudinal data analysis. The average follow-up time was 1.64 years per person and the overall incidence density of diabetes was 6.94% person-years. Decreased diabetes risk was found in metabolically healthy overweight phenotype (RR = 0.65; 95% CI = 0.47–0.90) and no significant associations were detected for the MHO individuals (RR = 0.99; 95% CI = 0.63–1.53) compared with those of metabolically healthy normal weight, in contrast to metabolically unhealthy normal weight (MU-NW) (RR = 1.81; 95% CI = 1.28–2.55), metabolically unhealthy overweight (MU-OW) (RR = 2.02; 95% CI = 1.57–2.61) and metabolically unhealthy obesity (MUO) (RR = 2.48; 95% CI = 1.89–3.26) phenotypes. Significant associations between BMI-metabolic status phenotypes and diabetes were found in both males and females.ConclusionThe MUO phenotype needs to be accorded much more importance. MU-NW and MU-OW are also important component for targeted prevention. Our findings can be targeted for optimizing preventive strategies to mitigate the obviously increased prevalence of diabetes.

Transitions in metabolic health status over time and risk of heart failure: A prospective study

Aims- Evidence for the effects of metabolically healthy obese (MHO) status on heart failure (HF) is limited and ignores the dynamic change of metabolic health and obesity phenotypes. We aimed to investigate the associations of metabolic health and its transition with HF across body mass index (BMI) and waist circumference (WC) categories. Methods- This prospective cohort study was conducted with 93,288 Chinese adults who were free of cardiovascular disease, cancer or HF at baseline (2006–2007). Metabolic health was defined as having no or only one abnormality in blood pressure, glucose, high-density lipoprotein cholesterol, or triglyceride levels. Participants were cross-classified at baseline by metabolic health and obesity (defined by BMI and WC criteria). Transitions in metabolic health status from 2006 to 2007 to 2010 to 2011 were considered. The hazard ratios (HRs) and 95% confidence intervals (CIs) for HF were assessed by Cox proportional hazards regression. Results- During a mean ± standard deviation follow-up of 9.7 ± 1.5 years, 1,628 participants developed HF. Individuals with MHO (HR: 1.78, 95% CI: 1.45, 2.19 for BMI criteria; HR: 1.51, 95% CI: 1.30, 1.76 for WC criteria) had higher risk of HF than those with metabolically healthy normal weight (MHNW). Individuals with initial MHO who shifted to metabolically unhealthy phenotype during follow-up had higher risk of HF compared with stable MHNW individuals (HR 3.12; 95% CI: 2.01, 4.85 for BMI categories; HR 1.98; 95% CI: 1.42, 2.77 for WC categories). Even stable MHO individuals were at an increased risk of HF compared with stable MHNW individuals (HR: 2.17; 95% CI: 1.39, 3.39 for BMI categories; HR: 1.33; 95% CI: 0.96, 1.85 for WC categories). Conclusions- MHO phenotype is dynamic and its transition to metabolically unhealthy phenotype or even stable MHO is associated with increased risk of HF. Maintaining metabolic health may provide a clue for preventing HF.

Obesity, metabolic health and omics: Current status and future directions.

The growing obesity epidemic is becoming a major public health concern, and the associated costs represent a considerable burden on societies. Among the most common complications of severe obesity are the development of hypertension, dyslipidemia, type 2 diabetes, cardiovascular disease, and various types of cancer. Interestingly, some obese individuals have a favorable metabolic profile and appear to be somehow protected from the detrimental effects of excessive adipose tissue accumulation. These individuals remain normoglycemic, insulin sensitive, and hypotensive with proper blood lipid levels, despite their high body mass index and/or waist circumference. Multiple independent observations have led to the concept of the metabolically healthy obese (MHO) phenotype, yet no consensus has been reached to date regarding a universal definition or the main mechanism behind this phenomenon. Recent technological advances and the use of high-throughput analysis techniques have revolutionized different areas of biomedical research. A multi-omics approach, which is used to investigate changes at different molecular levels in an organism or tissue, may provide valuable insights into the interplay between the molecules or pathways and the roles of different factors involved in the mechanisms underlying metabolic health deterioration. The aim of this review is to present the current status regarding the use of omics technologies to investigate the MHO phenotype, as well as the results of targeted analyses conducted in MHO individuals.

Metabolically healthy obese vs. Metabolic syndrome – The crosslink between nutritional exposure to bisphenols and physical exercise

Obesity has become a worldwide pandemic as well as a major contributing factor to the increasing rate of type 2 diabetes (T2D). However, there is an intriguing variance demonstrated by a subset of obesity defined as metabolically healthy obesity (MHO). MHO individuals are less prone to develop obesity-related metabolic complications, such as metabolic syndrome (MetS) and further T2D. The exact reason why an MHO person does not present the cluster of risk factors associated with insulin resistance is unknown due to the challenge to mimic MHO in experimental settings. However, MHO individuals present lower sedentary behaviors in comparison to individuals with MetS, which might indicate that an adaptation to skeletal muscle, such as increased insulin sensitivity and glucose transporter (GLUT4), could play a major role in their healthy characteristics. The hypothesis invoked in this paper is that lower exposure to bisphenol together with increased levels of physical exercise underlie the physiological aspects behind MHO characteristics. Evidence suggests that exposure to “obseogens,” such as bisphenol A (BPA), appears to impair insulin secretion and insulin response in cells containing GLUT4. Epidemiological studies have associated higher levels of BPA, as well as bisphenol S and F, in children with a risk for MetS development. Therefore, the combination between low bisphenol exposure and increased physical exercise may not necessarily affect body weight, but it could modify several metabolic pathways inhibiting insulin resistance, which characterize the heathy status of the MHO. If confirmed, this hypothesis could lead to therapeutic approaches to reverse MetS and inhibit T2D onset.

Differences in the vascular and metabolic profiles between metabolically healthy and unhealthy obesity

Individuals suffering from severe obesity but not presenting the typical metabolic alterations, are included in a subclass of obesity defined Metabolically Healthy Obesity (MHO). The physiological factors underlying what seems a protective and favourable metabolic profile remain unclear. MHO individuals are more insulin-sensitive, have relatively lower visceral/ectopic fat accumulation and reduced levels of chronic low-grade inflammation, compared to obese subjects with co-morbidities. The study of MHO subjects represents a great opportunity for the recognition of the mechanisms that lead to the vascular and metabolic complications in obesity. Finding the differences among the metabolic profile of visceral adipose tissue between metabolically healthy and unhealthy obesity may lead to future personalized and stratified therapies.This review article summarizes the pathomechanisms and metabolic changes in MHO and metabolically unhealthy obesity (MUO), reviews clinical studies on the subject, and discusses preventive and therapeutic options.

Metabolically healthy obesity, transition to unhealthy metabolic status, and vascular disease in Chinese adults: A cohort study.

Metabolically healthy obesity (MHO) and its transition to unhealthy metabolic status have been associated with risk of cardiovascular disease (CVD) in Western populations. However, it is unclear to what extent metabolic health changes over time and whether such transition affects risks of subtypes of CVD in Chinese adults. We aimed to examine the association of metabolic health status and its transition with risks of subtypes of vascular disease across body mass index (BMI) categories. The China Kadoorie Biobank was conducted during 25 June 2004 to 15 July 2008 in 5 urban (Harbin, Qingdao, Suzhou, Liuzhou, and Haikou) and 5 rural (Henan, Gansu, Sichuan, Zhejiang, and Hunan) regions across China. BMI and metabolic health information were collected. We classified participants into BMI categories: normal weight (BMI 18.5-23.9 kg/m²), overweight (BMI 24.0-27.9 kg/m²), and obese (BMI ≥ 28 kg/m²). Metabolic health was defined as meeting less than 2 of the following 4 criteria (elevated waist circumference, hypertension, elevated plasma glucose level, and dyslipidemia). The changes in obesity and metabolic health status were defined from baseline to the second resurvey with combination of overweight and obesity. Among the 458,246 participants with complete information and no history of CVD and cancer, the mean age at baseline was 50.9 (SD 10.4) years, and 40.8% were men, and 29.0% were current smokers. During a median 10.0 years of follow-up, 52,251 major vascular events (MVEs), including 7,326 major coronary events (MCEs), 37,992 ischemic heart disease (IHD), and 42,951 strokes were recorded. Compared with metabolically healthy normal weight (MHN), baseline MHO was associated with higher hazard ratios (HRs) for all types of CVD; however, almost 40% of those participants transitioned to metabolically unhealthy status. Stable metabolically unhealthy overweight or obesity (MUOO) (HR 2.22, 95% confidence interval [CI] 2.00-2.47, p < 0.001) and transition from metabolically healthy to unhealthy status (HR 1.53, 1.34-1.75, p < 0.001) were associated with higher risk for MVE, compared with stable healthy normal weight. Similar patterns were observed for MCE, IHD, and stroke. Limitations of the analysis included lack of measurement of lipid components, fasting plasma glucose, and visceral fat, and there might be possible misclassification. Among Chinese adults, MHO individuals have increased risks of MVE. Obesity remains a risk factor for CVD independent of major metabolic factors. Our data further suggest that metabolic health is a transient state for a large proportion of Chinese adults, with the highest vascular risk among those remained MUOO.

Effect of body shape on the development of cardiovascular disease in individuals with metabolically healthy obesity.

Studies about the effects of metabolically healthy obesity on cardiovascular disease (CVD) have yielded conflicting results. These heterogeneous results could be due to the limited usefulness of BMI in measuring general adiposity, as body mass index (BMI) does not accurately reflect body composition. This study aimed to evaluate the effect of body shape on CVD outcomes across different obesity phenotypes, and to provide an explanation for the heterogeneous effects of metabolically healthy obese (MHO) phenotype on CVD.We analyzed data from the Korean Genome and Epidemiology Study, a population-based cohort study conducted between 2001 and 2012. We divided the participants into 4 groups: metabolically healthy non-obese (MHNO), MHO, metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO). To assess body shape, we calculated the z-score of the log-transformed a body shape index (LBSIZ). We computed Pearson correlation coefficients to examine the association of LBSIZ with muscle mass index, percentage of total fat mass (%Total FM), and percentage of abdominal fat mass (%Abdominal FM). We also used Cox proportional hazards regression to evaluate the effect of LBSIZ on CVD events according to the obesity phenotypes.A total of 9460 participants were assessed in this study. The incidence of CVD was 8.53 cases per 1000 person-year. LBSIZ showed strong positive correlation with %Total FM and %Abdominal FM, but negative correlation with muscle mass index. In Cox regression, MHO individuals did not show increased risk of CVD compared with MHNO individuals (hazard ratio [HR], 1.29; 95% confidence interval [CI], 0.96-1.73). However, MHO individuals in the 3rd (HR, 2.40; 95% CI, 1.28-4.51) and 4th (HR, 3.67; 95% CI, 1.99-6.74) quarters of LBSIZ showed significantly higher risk of CVD compared with MHNO individuals in the 1st quarter of LBSIZ. Moreover, LBSIZ showed a linear relationship with CVD among MHO individuals.While the MHO individuals showed similar CVD risk to the MHNO individuals, CVD risk increases with LBSIZ among the MHO individuals. LBSIZ appears to be a useful measure for CVD risk assessment in clinical practice and epidemiologic studies, especially for MHO patients.

Exploring Therapeutic Targets to Reverse or Prevent the Transition from Metabolically Healthy to Unhealthy Obesity.

The prevalence of obesity and obesity-related metabolic comorbidities are rapidly increasing worldwide, placing a huge economic burden on health systems. Excessive nutrient supply combined with reduced physical exercise results in positive energy balance that promotes adipose tissue expansion. However, the metabolic response and pattern of fat accumulation is variable, depending on the individual’s genetic and acquired susceptibility factors. Some develop metabolically healthy obesity (MHO) and are resistant to obesity-associated metabolic diseases for some time, whereas others readily develop metabolically unhealthy obesity (MUO). An unhealthy response to excess fat accumulation could be due to susceptibility intrinsic factors (e.g., increased likelihood of dedifferentiation and/or inflammation), or by pathogenic drivers extrinsic to the adipose tissue (e.g., hyperinsulinemia), or a combination of both. This review outlines the major transcriptional factors and genes associated with adipogenesis and regulation of adipose tissue homeostasis and describes which of these are disrupted in MUO compared to MHO individuals. It also examines the potential role of pathogenic insulin hypersecretion as an extrinsic factor capable of driving the changes in adipose tissue which cause transition from MHO to MUO. On this basis, therapeutic approaches currently available and emerging to prevent and reverse the transition from MHO to MUO transition are reviewed.