Metabolically healthy obese individuals present similar chronic inflammation level but less insulin-resistance than obese individuals with metabolic syndrome

Relationship of Mitochondrial DNA Oxidation and Content with Metabolic Syndrome and Cardiovascular Risk in Obesity Phenotypes

Objective

Obesity, chronic inflammation, and oxidative stress can influence mitochondrial DNA (mtDNA) content. Our objective was to evaluate the oxidation level and content of mtDNA and its relationship with metabolic parameters in metabolically healthy obese (MHO) compared to metabolically unhealthy obese (MUO) and normal weight (NW) controls.

Materials and Methods

We studied 94 NW, 95 MHO, and 97 MUO individuals between 18 and 80 years old. Relative mtDNA content and mtDNA oxidation level (8-oxoguanine, 8-OxoG) were determined in peripheral blood leukocytes by the SYBR Green method of real-time PCR. One-way ANOVA and Tukey test were used to compare biochemical, clinical, and anthropometric characteristics, as well as mtDNA content and 8-OxoG.

Results

A progressive decrease in mtDNA content was observed between NW, MHO, and MUO with significant differences in MUO vs. NW (p: 0.04). An increase in 8-OxoG was observed in MUO patients compared to the other groups (MUO vs. MHO p: 0.01; MUO vs. NW p: 0.04). mtDNA content was directly correlated with HDL-c (p < 0.01) and inversely with waist circumference (p: 0.01) and LDL-c (p: 0.05). mtDNA content decreased, and the oxidation level increased concomitantly with the presence of obesity, the number of MS components, higher coronary risk, and insulin resistance parameters.

Conclusion

MHO presented a similar mtDNA oxidation level to NW and mtDNA content to the MUO, placing the MHO individuals as having an intermediate phenotype. Changes in mtDNA content and oxidation were correlated to the lipid profile related to obesity and/or MS presence, probably associated with oxidative stress and chronic low-grade inflammation.

Metabolically healthy obesity and chronic kidney disease risk: exploring the dynamics

Obesity represents a prevalent global health concern with significant implications for various diseases, including chronic kidney disease (CKD). Within this landscape, the phenomenon of metabolically healthy obesity has emerged, challenging traditional notions about the health risks associated with excess weight. While traditional CKD risk factors involve obesity, metabolic syndrome, diabetes, and hypertension, the metabolically healthy obese (MHO) subgroup disrupts these assumptions. Our main objective in this study is to integrate existing literature on CKD in MHO individuals. In this endeavor, we delve into the pathophysiological foundations, the transition between obesity phenotypes and their impact on renal health, examine the implications of their metabolic resilience on mortality within a renal context, and explore potential management strategies specifically designed for MHO individuals. Offering a comprehensive overview of the pathophysiology, we cover various factors contributing to the risk of CKD in the metabolically healthy obese setting, including inflammation, cytokines, hemodynamics, and the renin-angiotensin-aldosterone system, gastrointestinal microbiota, diet, exercise, adipose distribution, and lipotoxicity. Through this synthesis, we aim to provide a comprehensive understanding of the risk of CKD in those classified as MHO.

Differences in the levels of inflammatory markers between metabolically healthy obese and other obesity phenotypes in adults: A systematic review and meta-analysis

AIMS

The aim of this study was to systematically review and analyze differences in the levels of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) comparing metabolically healthy but obese (MHO) with metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO) subjects.

DATA SYNTHESIS

We searched PubMed, Embase, Web of Science, and Scopus for studies that matched the relevant search terms. Differences in inflammatory marker levels between MHO and the other three phenotypes were pooled as standardized mean differences (SMD) or differences of medians (DM) using a random-effects model. We included 91 studies reporting data on 435,007 individuals. The CRP levels were higher in MHO than in MHNO subjects (SMD = 0.63, 95% CI: 0.49, 0.76; DM = 0.83 mg/L, 95% CI: 0.56, 1.11). The CRP levels were higher in MHO than in MUNO subjects (SMD = 0.16, 95% CI: 0.05, 0.28; DM = 0.39 mg/L, 95% CI: 0.09, 0.69). The CRP levels were lower in MHO than in MUO individuals (SMD = -0.43, 95% CI: -0.54, -0.31; DM = -0.82 mg/L, 95% CI: -1.16, -0.48). The IL-6 levels in MHO were higher than in MHNO while lower than in MUO subjects. The TNF-α levels in MHO were higher than in MHNO individuals.

CONCLUSIONS

This review provides evidence that CRP levels in MHO are higher than in MHNO and MUNO subjects but lower than in MUO individuals. Additionally, IL-6 levels in MHO are higher than in MHNO but lower than in MUO subjects, and TNF-α levels in MHO are higher than in MHNO individuals.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO number: CRD42021234948.

The Association between Cardio-metabolic and hepatic indices and anthropometric measures with metabolically obesity phenotypes: a cross-sectional study from the Hoveyzeh Cohort Study

BACKGROUND

This study aimed to compare the cardio-metabolic, anthropometric, and liver function indices among metabolic obesity phenotypes.

METHODS

In this cross-sectional study, which was carried out in Hoveyzeh, Khuzestan Province, Iran, a total of 7,464 individuals (male: 2859, female: 4605), were recruited and classified into four groups, based on Body Mass Index (obese, BMI ≥ 30 kg/m²; non-obese, BMI = 18.5-29.9 kg/m²) and the National Cholesterol Education Program and Adult Treatment Panel (NCEP ATP) III criteria (Healthy group, ≤ 1 of the criteria; Unhealthy group, ≥ 2 of the criteria), as follows: Metabolically Healthy Non-Obese (MHNO, 28.14%), Metabolically Unhealthy Non-Obese (MUNO, 33.06%), Metabolically Healthy Obese (MHO, 6.54%), and Metabolically Unhealthy Obese (MUO, 32.26%). Anthropometric indices (Waist/Hip Ratio (WHR), Waist/Height Ratio (WHtR), Body Adiposity Index (BAI), Visceral Adiposity Index (VAI), and Weight adjusted Waist Index (WWI)), cardio-metabolic indices (Atherogenic Index of Plasma (AIP), Lipid Accumulation Product (LAP), Cardio-Metabolic Index (CMI), Lipoprotein Combine Index (LCI), Triglyceride-Glucose (TyG), TyG-BMI, TyG-WC, and Thrombolysis In Myocardial Infarction (TIMI) risk index), and hepatic indices (Hepatic Steatosis Index (HSI) and ALD/NAFLD index (ANI)) were calculated and compared between the groups.

RESULTS

WHR,VAI, AIP, LAP, CMI, LCI, TyG, and TIMI risk index values were significantly higher in the MUNO phenotype compared to the MHO phenotype (WHR: 0.97 vs. 0.95; VAI: 3.16 vs. 1.33; AIP: 0.58 vs. 0.25; LAP: 78.87 vs. 55.79; CMI: 2.69 vs. 1.25; LCI: 27.91 vs. 12.11; TyG: 9.21 vs. 8.41; TIMI: 18.66 vs. 15.63; p < 0.001). The highest and lowest HSI and ANI values were detected in the MUO phenotype. After adjustment for age, sex, physical activity, and years of education, VAI showed the highest Odds Ratio for MUNO (OR: 5.65; 95% CI: 5.12, 6.24) and MUO (OR: 5.40; 95% CI: 5.89, 5.95) compared to the MHNO phenotypes (p < 0.001). The ANI indices was associated with a reduced risk of MUO (OR: 0.76; 95% CI: 0.75-0.78), MUNO (OR: 0.88; 95% CI: 0.87-0.90), and MHO (OR: 0.79; 95% CI: 0.77-0.81) phenotypes (p < 0.001).

CONCLUSION

MUNO phenotype was exposed to a higher risk of cardiovascular disease compared to the MHO phenotype. VAI was found to be the optimal index for cardiovascular risk assessment.

Shared and Distinct Gut Microbial Profiles in Saudi Women with Metabolically Healthy and Unhealthy Obesity

BACKGROUND

Mounting evidence suggests a pivotal role for the gut microbiome in energy disequilibrium characteristic of obesity. The clinical utility of microbial profiling for the distinction between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) remains ill-defined. We aim to probe microbial composition and diversity in young adult Saudi females with MHO and MUO. This observational study included anthropometric and biochemical measurements and shotgun sequencing of stool DNA for 92 subjects. α- and β-diversity metrics were calculated to determine the richness and variability in microbial communities, respectively. Results showed that Bacteroides and Bifidobacterium merycicum were less abundant in MUO compared to healthy and MHO groups. BMI was negatively correlated with B. adolescentis, B. longum, and Actinobacteria in MHO, while being positively correlated with Bacteroides thetaiotaomicron in both MHO and MUO. Positive correlations between waist circumference and B. merycicum and B. thetaiotaomicron were observed in MHO and MUO, respectively. Compared to MHO and MUO groups, higher α-diversity was detected in healthy individuals who also had higher β-diversity compared to those with MHO. We conclude that modulation of the gut microbiome cohorts through prebiotics, probiotics, and fecal microbiota transplantation may be a promising preventive and therapeutic approach to obesity-associated disease.

Metabolically healthy obesity: Misleading phrase or healthy phenotype?

Obesity is a heterogenous condition with multiple different phenotypes. Among these a particular subtype exists named as metabolically healthy obesity (MHO). MHO has multiple definitions and its prevalence varies according to study. The potential mechanisms underlying the pathophysiology of MHO include the different types of adipose tissue and their distribution, the role of hormones, inflammation, diet, the intestinal microbiota and genetic factors. In contrast to the negative metabolic profile associated with metabolically unhealthy obesity (MUO), MHO has relatively favorable metabolic characteristics. Nevertheless, MHO is still associated with many important chronic diseases including cardiovascular disease, hypertension, type 2 diabetes, chronic kidney disease as well as certain types of cancer and has the risk of progression into the unhealthy phenotype. Therefore, it should not be considered as a benign condition. The major therapeutic alternatives include dietary modifications, exercise, bariatric surgery and certain medications including glucagon-like peptide-1 (GLP-1) analogs, sodium-glucose cotransporter-2 (SGLT-2) inhibitors and tirzepatide. In this review, we discuss the significance of MHO while comparing this phenotype with MUO.

Pathogenesis, Murine Models, and Clinical Implications of Metabolically Healthy Obesity

Although obesity is commonly associated with numerous cardiometabolic pathologies, some people with obesity are resistant to detrimental effects of excess body fat, which constitutes a condition called “metabolically healthy obesity” (MHO). Metabolic features of MHO that distinguish it from metabolically unhealthy obesity (MUO) include differences in the fat distribution, adipokine types, and levels of chronic inflammation. Murine models are available that mimic the phenotype of human MHO, with increased adiposity but preserved insulin sensitivity. Clinically, there is no established definition of MHO yet. Despite the lack of a uniform definition, most studies describe MHO as a particular case of obesity with no or only one metabolic syndrome components and lower levels of insulin resistance or inflammatory markers. Another clinical viewpoint is the dynamic and changing nature of MHO, which substantially impacts the clinical outcome. In this review, we explore the pathophysiology and some murine models of MHO. The definition, variability, and clinical implications of the MHO phenotype are also discussed. Understanding the characteristics that differentiate people with MHO from those with MUO can lead to new insights into the mechanisms behind obesity-related metabolic derangements and diseases.

The Harm of Metabolically Healthy Obese and the Effect of Exercise on Their Health Promotion

Obesity and obesity-related diseases [type 2 diabetes, cardiovascular disease (CVD), and cancer] are becoming more common, which is a major public health concern. Metabolically healthy obesity (MHO) has become a type of obesity, accounting for a large proportion of obese people. MHO is still harmful to health. It was discovered that MHO screening criteria could not well reflect health hazards, whereas visceral fat, adiponectin pathway, oxidative stress, chronic inflammation, and histological indicators at the microlevel could clearly distinguish MHO from health control, and the biological pathways involved in these micro indicators were related to MHO pathogenesis. This review reveals that MHO’s micro metabolic abnormality is the initial cause of the increase of disease risk in the future. Exploring the biological pathway of MHO is important in order to develop an effective mechanism-based preventive and treatment intervention strategy. Exercise can correct the abnormal micro metabolic pathway of MHO, regulate metabolic homeostasis, and enhance metabolic flexibility. It is a supplementary or possible alternative to the traditional healthcare prevention/treatment strategy as well as an important strategy for reducing MHO-related health hazards.

Relationship Between Serum Magnesium Level and Insulin Resistance in Turkey Non-obese Adult Population

The homeostasis model assessment of insulin resistance (HOMA-IR) is widely used in clinical practice to estimate insulin resistance. In particular, magnesium (Mg) is an extensively studied mineral that has been shown to function in the management of hyperglycemia and insulin resistance (IR) action. The pathophysiology of IR in non-obese patients has not been clearly demonstrated. From this point of view, we aimed to investigate the relationship between serum Mg level and IR in non-obese patients. We analyzed 957 patients who are not obese and estimated glomerular filtration rate (e-GFR) ≥ 60 mL/min/1.73 m². Patients were divided into two groups, with and without IR. The results of the IR detected group (HOMA-IR ≥ 2.5, n = 544) and the IR undetected group (HOMA-IR < 2.5, n = 413) were compared. The median Mg value of the patients was 1.76 [0.21] mg/dL. A statistically significant difference was observed between the two groups regarding serum Mg levels (p = 0.043). A negative correlation was found between the HOMA-IR index and serum Mg levels among patients (r =  - 0.064, p = 0.049). Multivariable logistic regression analysis revealed that serum Mg level (p = 0.039, odds ratio [OR] = 0.770[95%CI: [0.917-0.989]) was independent risk factors for IR. HOMA-IR increases as the Mg level decreases in advanced ages without obesity, especially in men with low e-GFR.

Effect of obstructive sleep apnea on glucose metabolism

BACKGROUND

Obstructive sleep apnea (OSA) is prevalent in people with obesity and is a major risk factor for type 2 diabetes (T2D). The effect of OSA on metabolic function and the precise mechanisms (insulin resistance, β-cell dysfunction, or both) responsible for the increased T2D risk in people with OSA are unknown.

DESIGN AND METHODS

We used a two-stage hyperinsulinemic-euglycemic clamp procedure in conjunction with stable isotopically labeled glucose and palmitate tracer infusions and 18F-fluorodeoxyglucose injection and positron emission tomography to quantify multi-organ insulin action and oral and intravenous tolerance tests to evaluate glucose-stimulated insulin secretion in fifteen people with obesity and OSA and thirteen people with obesity without OSA.

RESULTS

OSA was associated with marked insulin resistance of adipose tissue triglyceride lipolysis and glucose uptake into both skeletal muscles and adipose tissue, whereas there was no significant difference between the OSA and control groups in insulin action on endogenous glucose production, basal insulin secretion, and glucose-stimulated insulin secretion during both intravenous and oral glucose tolerance tests.

CONCLUSIONS

These data demonstrate that OSA is a key determinant of insulin sensitivity in people with obesity and underscore the importance of taking OSA status into account when evaluating metabolic function in people with obesity. These findings may also have important clinical implications because disease progression and the risk of diabetes-related complications vary by T2D subtype (i.e. severe insulin resistance vs insulin deficiency). People with OSA may benefit most from the targeted treatment of peripheral insulin resistance and early screening for complications associated with peripheral insulin resistance.

Association of Metabolically Healthy and Unhealthy Obesity Phenotype with Markers Related to Obesity, Diabetes among Young, Healthy Adult Men. Analysis of MAGNETIC Study

Adipose tissue secretes many regulatory factors called adipokines. Adipokines affect the metabolism of lipids and carbohydrates. They also influence the regulation of the immune system and inflammation. The current study aimed to evaluate the association between markers related to obesity, diabesity and adipokines and metabolically healthy and unhealthy obesity in young men. The study included 98 healthy participants. We divided participants into three subgroups based on body mass index and metabolic health definition: 49 metabolically healthy normal-weight patients, 27 metabolically healthy obese patients and 22 metabolically unhealthy obese patients. The 14 metabolic markers selected were measured in serum or plasma. The analysis showed associations between markers related to obesity, diabesity and adipokines in metabolically healthy and unhealthy obese participants. The decreased level of adipsin (p < 0.05) was only associated with metabolically healthy obesity, not with metabolically unhealthy obesity. The decreased level of ghrelin (p < 0.001) and increased level of plasminogen activator inhibitor-1 (p < 0.01) were only associated with metabolically unhealthy obesity, not with metabolically healthy obesity. The decreased level of adiponectin and increased levels of leptin, c-peptide, insulin and angiopoietin-like 3 protein were associated with metabolically healthy and unhealthy obesity. In conclusion, our data show that metabolically healthy obesity was more similar to metabolically unhealthy obesity in terms of the analyzed markers related to obesity and diabesity.

Contribution of insulin resistance to the relationship between sugar-sweetened beverage intake and a constellation of cardiometabolic abnormalities in adolescents

BACKGROUND

Insulin resistance (IR) is a pathophysiological construct that derives a series of metabolic disturbances that promote cardiometabolic dysfunction. This study evaluated mediating and modifying effects of homeostatic model assessment-based IR (HOMA-IR) on the association between sugar-sweetened beverage (SSB) consumption and a constellation of adolescent cardiometabolic abnormalities.

METHODS

Comprehensive data on sociodemographics, diet, physical activity, and anthropometric and biochemical parameters for 1454 adolescents were obtained from a large-scale representative study for adolescent metabolic syndrome (MetS) conducted in Taiwan. The original (HOMA1-IR) and updated nonlinear (HOMA2-IR) HOMA-IR indicators were used as IR biomarkers. Principal component (PC) analysis was employed to create reduced groups of variables and risk scores for retained PCs.

RESULTS

Higher SSB intake was associated with higher levels of HOMA1-IR and HOMA2-IR, and the two IR biomarkers were positively correlated with metabolic dysfunction clustering. Compared with SSB nondrinkers, adolescents who consumed >500 mL/day of hand-shaken high-fructose corn syrup beverages (HHB) had a 0.22 increase in the number of abnormal MetS components, and HOMA-IR mediation explained 33.9-37.9% of the effect. IR biomarkers accounted for 26.5-31.0% of the relationship between >500 mL/day of SSB consumption and bodyweight-enhanced PC scores. The effects of HOMA-IR indicators on all bodyweight-related factors were consistently intensified among >350 mL/day HHB drinkers (all P_interaction < 0.05).

CONCLUSIONS

Fructose-rich SSB intake correlates with a constellation of cardiometabolic abnormalities in adolescents, and this association may be partly mediated by HOMA-IR levels. The adverse effects of HOMA-IR on bodyweight-associated cardiometabolic risk factors depend on the type of SSB consumption, with enhanced risks observed in the intake of high amounts of HFCS-containing SSBs.

Associations of Metabolic/Obesity Phenotypes with Insulin Resistance and C-Reactive Protein: Results from the CNTR Study

BACKGROUND

Obesity is a heterogeneous condition in terms of metabolic status. Different obesity phenotypes have various health risks. The aim of this work was to define different subtypes of obesity and investigate their relationship with inflammatory-cardiometabolic abnormalities among Chinese adult twins.

METHODS

The analyses used data from 1113 adult twins in 4 provinces (Shandong, Zhejiang, Jiangsu and Sichuan) from Chinese National Twin Registry (CNTR) which collected detailed information. We defined those with 0 or 1 metabolic syndrome (MetS) components excluding waist circumference as metabolically healthy, and those with waist circumference ≥90 cm (for men) and ≥85 cm (for women) as obese. The two-category obesity status and metabolic states are combined to generate four metabolic/obesity phenotypes. High sensitivity C reactive protein (hsCRP) was measured to assess underlying inflammation and homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as surrogate measure of insulin resistance. Mixed-effect linear regression models and fixed-effect linear regression models were used to analyse the correlation between HOMA-IR, hsCRP and different metabolic/obesity phenotypes.

RESULTS

In cross-sectional analyses of 1113 individuals (mean [SD] age, 46.6 [12.9] years; 463 obese [41.6%]), 20.3% obese twins were metabolic healthy and 64.2% non-obese twins were metabolic unhealthy. Serum HOMA-IR level was higher in metabolically unhealthy non-obesity (MUNO) (β=0.42, 95% CI: 0.21-0.64), metabolically healthy obesity (MHO) (β=0.68, 95% CI: 0.36-1.00) and metabolically unhealthy obesity (MUO) (β=0.69, 95% CI: 0.46-0.91) twins, compared with their metabolically healthy non-obesity (MHNO) counterparts. HsCRP was similar between MHO and MUO, which differed significantly to metabolic healthy non-obesity (MHNO).

CONCLUSION

MHO and MUNO phenotypes were common in Chinese twin population. Both phenotypes were associated with elevated IR and hsCRP which may not be benign and need to be concerned.

BMI, high-sensitivity C-reactive protein and the conversion from metabolically healthy to unhealthy phenotype in Chinese adults: a cohort study

OBJECTIVE

We performed the cohort study to evaluate the association between BMI, high-sensitivity C-reactive protein (hs-CRP) and the conversion from metabolically healthy to unhealthy phenotype in Chinese adults.

DESIGN

Metabolically healthy was defined as participants without history of metabolic diseases and with normal fasting blood glucose level, glycated Hb A1c level, blood pressure, lipid profile, serum uric acid level and liver ultrasonographic findings at baseline. Participants were either classified into normal weight (18·5 ≤ BMI < 24·0 kg/m2) and overweight (BMI ≥ 24·0 kg/m2) based on baseline BMI, or low (<1 mg/l) and high (≥1 mg/l) groups based on baseline hs-CRP. The conversion from metabolically healthy to unhealthy phenotype was deemed if any of the metabolic abnormalities had been confirmed twice or more during 5 years of follow-up.

RESULTS

Included were 4855 (1942 men and 2913 women, aged 36·0 ± 8·9 years) metabolically healthy Chinese adults. We identified 1692 participants who converted to metabolically unhealthy phenotype during the follow-up. Compared with their counterparts, the adjusted hazards ratio of the conversion was 1·19 (95 % CI 1·07, 1·33) for participants with overweight, while it was 1·15 (95 % CI 1·03, 1·29) for those with high hs-CRP level (≥1 mg/l). Further adjustment of hs-CRP did not materially change the association between BMI and the conversion. However, the association between hs-CRP and the conversion was not significant after further adjustment of BMI. The sensitivity analysis generated similar results to main analysis.

CONCLUSION

BMI was associated with the risk of the conversion from metabolically healthy to unhealthy status in Chinese adults.

Association Metabolic Obesity Phenotypes with Cardiometabolic Index, Atherogenic Index of Plasma and Novel Anthropometric Indices: A Link of FTO-rs9939609 Polymorphism

Background

The role of metabolic states in cardiovascular risks among individuals with varying degrees of obesity is unknown. The study aimed to compare cardiometabolic index (CMI), atherogenic index of plasma (AIP), lipid accumulation product (LAP) and novel anthropometric indices in metabolic and non-metabolically obese individual with regard to the role of FTO gene in Iranian adults.

Methods

In total, 165 individuals were recruited into this cross-sectional study. Individuals grouped into four groups: metabolic healthy normal-weight (MHNW) individuals, metabolically unhealthy normal-weight (MUNW) individuals, metabolically healthy obese (MHO) individuals and metabolic unhealthy obese (MUO) individuals. The dietary intake was evaluated by food frequency questionnaire (FFQ). The cardiovascular indices (CMI, AIP and LAP) were calculated. A variety of anthropometric indices were calculated, including body adiposity Index (BAI), weight-adjusted-waist index (WWI), A body shape index (ABSI) and waist-height ratio (WHR). The genotypes of FTO-rs9939609 subjects were detected by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP).

Results

The individuals with metabolically unhealthy phenotypes (MUO, MUNW) have higher levels of triglyceride and cardiovascular indices (AIP, LAP and CMI) than the individuals with metabolic healthy phenotypes (MHO, MHNW). With a similar degree of obesity, the anthropometric indices (BAI, WWI and WHR) levels were higher in metabolic unhealthy groups than metabolically healthy groups. The highest frequency of obesity-risk allele AA of FTO gene was observed in MUO, MHO, MUNW and MHNW, respectively.

Conclusion

Normal-weight individuals with metabolic unhealthy status are at higher risk for cardiovascular diseases than obese individuals with metabolically healthy status. The genotype frequencies of obesity-risk allele AA of FTO gene were higher in obesity phenotypes than metabolic phenotypes.

Emerging concepts in metabolically healthy obesity

Obesity is a major risk factor for noncommunicable diseases that is responsible for more than 70% of early deaths in the world. In the 1980's decade, some studies started to describe a "benign" obesity phenotype, named "metabolically healthy obesity" (MHO), which represents obesity without comorbidities such as hypertension, cardiovascular diseases, insulin resistance, diabetes, dyslipidemia or metabolic syndrome. However, it is still unclear if this "benign" obesity phenotype is really favorable or just a transition status to unhealthy obesity and if these subjects presented subclinical levels of cardiovascular risk that are not commonly detected. To further elucidate these issues, the investigation of pathophysiological mechanisms that can increase cardiovascular risk in MHO individuals, such as hormones and cytokines, may offer some responses. In parallel, the evaluation of subclinical cardiovascular derangement, using the systemic microcirculation as a proxy, may be an alternative to anticipate overt cardiovascular disease. Overall, further studies are needed to better understand the pathophysiology of MHO as well as to identify high-risk individuals who deserve more intensive management.

Metabolically healthy obesity: facts and fantasies

Although obesity is typically associated with metabolic dysfunction and cardiometabolic diseases, some people with obesity are protected from many of the adverse metabolic effects of excess body fat and are considered "metabolically healthy." However, there is no universally accepted definition of metabolically healthy obesity (MHO). Most studies define MHO as having either 0, 1, or 2 metabolic syndrome components, whereas many others define MHO using the homeostasis model assessment of insulin resistance (HOMA-IR). Therefore, numerous people reported as having MHO are not metabolically healthy, but simply have fewer metabolic abnormalities than those with metabolically unhealthy obesity (MUO). Nonetheless, a small subset of people with obesity have a normal HOMA-IR and no metabolic syndrome components. The mechanism(s) responsible for the divergent effects of obesity on metabolic health is not clear, but studies conducted in rodent models suggest that differences in adipose tissue biology in response to weight gain can cause or prevent systemic metabolic dysfunction. In this article, we review the definition, stability over time, and clinical outcomes of MHO, and discuss the potential factors that could explain differences in metabolic health in people with MHO and MUO - specifically, modifiable lifestyle factors and adipose tissue biology. Better understanding of the factors that distinguish people with MHO and MUO can produce new insights into mechanism(s) responsible for obesity-related metabolic dysfunction and disease.

Metabolically Healthy Obesity and Bariatric Surgery

A peculiar category of persons with obesity lacking common metabolic disturbances has been depicted and termed as metabolically healthy obesity (MHO). Yet, although MHO patients are free of obesity-associated complications, they might not be entirely precluded from developing cardio-metabolic disorders. Among patients with morbid obesity (MO) who are referred to bariatric surgery, a subset of metabolically healthy MO (MHMO) has been identified and the question arises if these patients would benefit from surgery in terms of mitigating the peril of cardio-metabolic complications. We revisited the pathophysiological mechanisms that define MHO, the currently available data on the cardio-metabolic risk of these patients and finally we reviewed the benefits of bariatric surgery and the urge to better characterize MHMO before submission to surgery.

Beta-Cell Function, Self-rated Health, and Lifestyle Habits in 64-Year-Old Swedish Women with Metabolically Healthy Obesity Phenotype

Background

A subset of obese individuals do not present metabolic abnormalities that commonly define the metabolic syndrome (MetS). This is referred to as a metabolically healthy obese (MHO) phenotype. The aim of the present study was to evaluate the prevalence of the MHO phenotype and its relationship with beta cell dysfunction by measuring C-peptide and proinsulin, anthropometric-, metabolic- and lipid appearance, as well as lifestyle behaviors and self-rated health in a cohort of 64-year-old Swedish women.

Methods

The National Cholesterol Education Program definition was used to assess MetS. We defined normal weight as body mass index (BMI) 18.5–24.9 kg/m² and obesity as BMI ≥30 kg/m² to categorize participants as metabolically healthy normal weight, MHO, and metabolically unhealthy obese.

Results

The MHO phenotype represented 36.3% of obese participants and 16.3% of total participants. The MHO group were at greater risk of having proinsulin levels >11 pmol/L, indicating impaired beta cell function. Further, homeostatic model assessment for insulin resistance, fasting plasma levels of insulin, and C-peptide showed significant trends, with the MHO phenotype group having intermediate levels among three groups. Health behaviors such as leisure time physical activity and alcohol intake were also intermediate in individuals with the MHO phenotype.

Conclusion

In this study, we demonstrate that over a third of the obese women in our sample were MHO. Further, women with the MHO phenotype showed intermediate profiles considering beta cell function and insulin resistance, as well as metabolic variables, and tended to rate their general health as worse than otherwise similar individuals of normal weight.

Metabolic abnormalities, but not obesity per se, associated with chronic kidney disease in a Taiwanese population

BACKGROUND AND AIMS

It is inconclusive whether obesity itself or metabolic abnormalities are linked to chronic kidney disease (CKD). The aim of this study was to examine the association between different subtypes of obesity and metabolic abnormalities with CKD in adults.

METHODS AND RESULTS

This study enrolled 14,983 eligible subjects stratified into metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and metabolically unhealthy obesity (MUO) according to body mass index and metabolic syndrome status (ATP-III criteria). The metabolic healthy phenotype was defined as the absence of both metabolic syndrome and any known diabetes, coronary artery disease, stroke, hypertension or dyslipidemia. Early and advanced CKD were defined as eGFR<60, proteinuria, or structural abnormalities as detected by renal sonography. The prevalence of CKD was 2.5, 3.0, 4.0, 10.6, 9.5, and 10.5% in subjects with MHNW, MHOW, MHO, MUNW, MUOW, and MUO, respectively. In the multivariate analysis, the MUNW (OR:2.22, P < 0.001), MUOW (OR:2.22, P < 0.001), and MUO (OR:2.45, P < 0.001) groups were associated with early CKD. For advanced CKD, the OR was 2.56 (P < 0.001), 2.31 (P < 0.001), and 3.49 (P < 0.001) in the MUNW, MUOW, and MUO groups, respectively. The associated risks of early and advanced CKD were not significant in the MHOW and MHO group. MUOW and MUO were associated with higher risk of CKD compared with MHOW and MHO after adjusting other variables.

CONCLUSIONS

Metabolic abnormalities, but neither overweight nor obesity, were associated with a higher risk of CKD in adults.

Metabolically Healthy, but Obese Individuals and Associations with Echocardiographic Parameters and Inflammatory Biomarkers: Results from the CARLA Study

INTRODUCTION

The research on heterogeneity among obese individuals has identified the metabolically healthy, but obese (MHO) phenotype as a distinct group that does not experience the typical cardiovascular-related diseases (CVD). It is unclear if this group differs with regard to preconditions for CVDs. Our aim was to assess differences in echocardiographic parameters and inflammatory biomarkers between MHO and metabolically healthy, normal weight individuals (MHNW).

METHODS

The analyses used data from 1412 elderly participants from a German population-based cohort study (CARLA), which collected detailed information on demographic, biochemical, and echocardiographic variables. Participants were subdivided into four groups (MHNW, MHO, MUNW (metabolically unhealthy, normal weight) and MUO (metabolically unhealthy, obese)) based on BMI≥30 kg/m² (obese or normal weight) and presence of components of the metabolic syndrome. The clinical characteristics of the 4 groups were compared with ANOVA or Chi-Square test, in addition to two linear regression models for 16 echocardiographic parameters. The difference in inflammatory biomarkers (hsCRP, IL-6 and sTNF-RI) between the groups was examined with a multinomial logistic regression model.

RESULTS

The MHO individuals were on average 64.2±8.4 years old, with a higher proportion of women (71.6%), low percentage of smokers, larger waist circumference (109.3±10.5 cm vs 89.1±10.8 cm, p<0.0001) and higher odds ratios for hsCRP, IL-6 and sTNF-RI compared to MHNW individuals. Linear regression models revealed greater left atrial (LA) diameter (2.73 (95% CI: 1.35-4.11) mm), LA volume (7.86 (95% CI: 2.88-12.83) mL), and left ventricular mass index (LVMI) (11.82 (95% CI: 4.43-19.22) g/m^1.7) in the MHO group compared to the MHNW group.

CONCLUSION

The MHO phenotype is associated with echocardiographic markers of cardiac remodeling (LA diameter, volume and LVMI) and higher odds ratios for inflammatory biomarkers.

The influence of the TNFα rs1800629 polymorphism on some inflammatory biomarkers in 45-60-year-old women with metabolic syndrome

Introduction: There are reports that the TNFα gene (rs1800629) can be involved in the pathogenesis of metabolic syndrome through an increased production of pro-inflammatory cytokines. Therefore, we have decided to search for the relationship between the TNFα gene polymorphisms and serum levels of proinflammatory cytokines (IL-1α, IL-1β, IL-6, TNFα, IFNγ) and CRP in women with metabolic syndrome.

Material and methods: The study sample consisted of 416 women aged 45-60 years, including 118 with metabolic syndrome. The participants were surveyed and subjected to anthropometric, biochemical and genetic analysis.

Results: We noticed that in the group meeting the criteria for metabolic syndrome, the G/G genotype of the TNFα gene was related to higher IL-6 levels than in the remainder group. The carriers of the A/G genotype in the metabolic syndrome group had significantly higher levels of IFNγ than those in the group without this syndrome. CRP was significantly higher in the group with metabolic syndrome, irrespective of the women’s genotypes.

Conclusions: The upregulation of IFNγ and IL-6 and CRP suggests that autoinflammatory process may play a significant role in the pathogenesis of metabolic syndrome. However, a direct relationship between the TNFα gene polymorphisms and inflammatory biomarkers analyzed in our study was not confirmed.

Metabolically healthy versus metabolically unhealthy obesity

Obesity-related disease complications reduce life quality and expectancy and increase health-care costs. Some studies have suggested that obesity not always entails metabolic abnormalities and increased risk of cardiometabolic complications. Because of the lack of universally accepted criteria to identify metabolically healthy obesity (MHO), its prevalence varies widely among studies. Moreover, the prognostic value of MHO is hotly debated, mainly because it likely shifts gradually towards metabolically unhealthy obesity (MUO). In this review, we outline the differential factors contributing to the metabolic heterogeneity of obesity by discussing the behavioral, genetic, phenotypical, and biological aspects associated with each of the two metabolic phenotypes (MHO and MUO) of obesity and their clinical implications. Particular emphasis will be laid on the role of adipose tissue biology and function, including genetic determinants of body fat distribution, depot-specific fat metabolism, adipose tissue plasticity and, particularly, adipogenesis. Finally, the emerging role of gut microbiota in obesity and adipose tissue dysfunction as well as the search for novel biomarkers for the obesity-related metabolic traits and associated diseases will be briefly presented. A better understanding of the main determinants of a healthy metabolic status in obesity would allow promotion of this favorable condition by targeting the relevant pathways.

Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes-these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

Single-nucleotide polymorphisms in a cohort of significantly obese women without cardiometabolic diseases

BACKGROUND/OBJECTIVES

Obesity is an important risk factor for the development of diseases such as diabetes mellitus, hypertension, and dyslipidemia; however, a small number of individuals with long-standing obesity do not present with these cardiometabolic diseases. Such individuals are referred to as metabolically healthy obese (MHO) and potentially represent a subgroup of the general population with a protective genetic predisposition to obesity-related diseases. We hypothesized that individuals who were metabolically healthy, but significantly obese (BMI ≥ 35 kg/m2) would represent a highly homogenous subgroup, with which to investigate potential genetic associations to obesity. We further hypothesized that such a cohort may lend itself well to investigate potential genotypes that are protective with respect to the development of cardiometabolic disease.

SUBJECTS/METHODS

In the present study, we implemented this novel selection strategy by screening 892 individuals diagnosed as Class 2 or Class 3 obese and identified 38 who presented no manifestations of cardiometabolic disease. We then assessed these subjects for single-nucleotide polymorphisms (SNPs) that associated with this phenotype.

RESULTS

Our analysis identified 89 SNPs that reach statistical significance (p < 1 × 10-5), some of which are associated with genes of biological pathways that influences dietary behavior; others are associated with genes previously linked to obesity and cardiometabolic disease as well as neuroimmune disease. This study, to the best of our knowledge, represents the first genetic screening of a cardiometabolically healthy, but significantly obese population.

Metabolically Healthy versus Unhealthy Morbidly Obese: Chronic Inflammation, Nitro-Oxidative Stress, and Insulin Resistance

Metabolically heathy obesity is characterised by the presence of obesity in the absence of metabolic disturbances. The aim of our study was to analyse pro-inflammatory, nitro-oxidative stress, and insulin-resistance (IR) markers in metabolically healthy morbidly obese (MHMO) with respect to metabolically unhealthy morbidly obese (MUHMO) with metabolic syndrome (MS) and to identify the potential predictors of MS in the MHMO group. Two groups of MHMO and MUHMO with MS were analysed. We evaluated serum high sensitivity C reactive protein (hsCRP), tumor necrosis factor alpha (TNF-α), chemerin, nitrite and nitrate (NOx), total oxidant status (TOS), total antioxidant response (TAR), fasting blood glucose, insulin, and homeostasis model assessment of insulin resistance (HOMA-IR.) MHMO have similar hsCRP and TNF-α values as the MUHMO with MS, while chemerin was significantly lower in MHMO. NOx was higher in MUHMO with MS patients, while no difference regarding TOS and TAR was found between the two groups. HOMA-IR and insulin values were lower in MHMO as compared to the MUHMO with MS group. Insulin, HOMA-IR, and chemerin were identified predictors of MS in MHMO. In conclusion, MHMO and MUHMO display similarities and differences in terms of chronic inflammation, nitro-oxidative stress, and IR. Markers of IR and chemerin are possible predictors of MS in MHMO.

Metabolically Healthy or Metabolically Unhealthy Obese HIV-Infected Patients: Mostly a Matter of Age?

Background: Life expectancy of HIV-infected patients has increased with antiretroviral treatment (ART). Chronic diseases associated with aging, including metabolic and cardiovascular diseases are becoming more prevalent in this population. We aimed to evaluate the association of obesity and aging with cardiometabolic comorbidities and metabolic health status among patients with HIV infection. Methods: We evaluated 580 HIV-1 infected patients (71.7% male, mean age of 47.7 ± 11.5 years). We analyzed the association of age and obesity (defined by and by central obesity) with gender, duration of HIV infection, and ART, anthropometric parameters, cardiometabolic comorbidities, Framingham risk score (FRS), blood pressure, lipid profile, uric acid, liver biochemical tests, and glycemic profile. Furthermore, we analyzed the above-mentioned associations according to the category and central obesity into the metabolically healthy (MH) and unhealthy (MUH) categories. To evaluate the association of anthropometric parameters with cardiometabolic comorbidities, we performed unadjusted and adjusted logistic regression models. Results: The prevalence of excessive weight and cardiometabolic comorbidities increased with age. Patients with normal weight were younger and there was a higher proportion of female patients in the obesity group. The prevalence of hypertension and metabolic syndrome were higher among patients who were overweight or with obesity. The FRS was higher among patients with obesity. The proportion of MUH patients was higher among patients with excessive weight and central obesity. MUH patients had more cardiometabolic comorbidities and a higher FRS. In the normal weight group, MUH patients were older, and in the obesity group they were more likely to be male. The anthropometric parameter most associated with metabolic syndrome was waist circumference and that most associated with hypertension was waist-to-height ratio. The anthropometric parameter most associated with diabetes and FRS was waist-to-hip ratio. Conclusion: Patients with HIV present a high prevalence of obesity and related comorbidities. Ageing significantly contributes to metabolic dysfunction in this population. The proportion of MUH patients is higher among groups with excessive weight and central obesity, with those patients presenting a higher cardiovascular risk. Our results highlight the importance of evaluating and addressing obesity in patients with HIV, as well as metabolic comorbidities and cardiovascular risk.