Characterization of metabolically healthy obese Brazilians and cardiovascular risk prediction

Trends and characteristics of the metabolically healthy obese phenotype in an Arab population

The metabolically healthy obesity (MHO) phenotype represents a complex and distinctive trait, the trends and characteristics of which remain unknown in the Saudi Arabian adult population. The present study aims to fill that gap. A combined total of 10,220 Saudi adults from 2 independent cohorts [2008-2019, N = 7,896 (2,903 males and 4,993 females), and 2021-2023, N = 2,324 (830 males and 1,494 females)] aged 19-70 years old was screened, of whom 9,631 (3,428 males and 6,203 females) were included. Anthropometric data were measured, and fasting blood samples were collected to assess glucose, lipids, adipocytokines and inflammatory markers using routine methods and commercially available assays. Obesity was defined as a body mass index (BMI) ≥30 kg/m2. Screening for MHO was done using the empiric definition proposed by Zembic and colleagues and the by the National Cholesterol Education Program's Adult Treatment Panel III (NCEP ATPIII). Of the 3,949 (41.0%) participants with obesity, 33.4% (95% confidence interval, CI, 32-35) were considered MHO using the empiric definition, and 32.8% (95% CI, 31-34) using NCEP-ATPIII. The overall age and gender adjusted prevalence of MHO in the Saudi adult population was 31.6% (95% CI, 30-33) and 30.1% (29-31) by the two definitions, respectively. Females had a higher age-adjusted prevalence of MHO than males (OR = 1.22, 95% CI 1.1-1.4, p = 0.009) as per the ATPIII criteria. MHO prevalence substantially increased over time from 2008 to 2023 (p < 0.001) for both definitions. Circulating leptin levels and insulin resistance were significantly higher in the MUO group than the MHO group independent of the definition used, suggesting the presence of a more severe form of leptin resistance in the MUO group which may explain the worse cardiometabolic profile as compared to the MHO group. In summary, the study highlights the first time the characteristics and trends of the MHO phenotype among Saudi Arabian adults. The pluripotent effects of leptin and its resistance may be central to MHO's progression, or lack thereof, to the MUO phenotype, and this needs further investigation.

Exploring New Tools for Risk Classification among Adults with Several Degrees of Obesity

The epidemic of obesity worldwide has been recognized as a very important challenge. Within its complexity, the identification of higher-risk patients is essential, as it is unsustainable to offer access to treatment to all people with obesity. Several new approaches have recently been presented as important tools for risk stratification. In this research, we applied several of these tools in a cross-sectional study involving adults with obesity classes I, II, III, and super-obesity. The participants had their cardiometabolic risk profiles assessed. The study included adults with obesity aged 18 to 50 years (n = 404), who were evaluated using anthropometric, body composition, hemodynamic, physical fitness, and biochemical assessments. These variables were used to identify the prevalence of risk factors for cardiometabolic diseases according to the classes of obesity by gender and age group. The results showed high prevalence of risk factors, especially among the upper classes of obesity (BMI > 35 kg/m2) using single parameters as the waist circumference, with almost 90% above the cut-off point. For smaller numbers such as Glycated Hemoglobin, however, the prevalence was around 30%. Indexes such as the atherogenic index of plasma (AIP) had the highest prevalence, with 100% of the male participants identified as being at increased risk for cardiovascular disease.

Natural course of metabolically healthy phenotype and risk of developing Cardiometabolic diseases: a three years follow-up study

BACKGROUND

Whether the metabolically healthy obese (MHO) phenotype is a single, stable or a transitional, fluctuating state is currently unknown. The Mexican-Mestizo population has a genetic predisposition for the development of type 2 diabetes (T2D) and other cardiometabolic complications. Little is known about the natural history of metabolic health in this population. The aim of this study was to analyze the transitions over time among individuals with different degrees of metabolic health and body mass index, and evaluate the incidence of cardiometabolic outcomes according to phenotype.

METHODS

The study population consisted of a metabolic syndrome cohort with at least 3 years of follow up. Participants were apparently-healthy urban Mexican adults ≥20 years with a body mass index (BMI) ≥20 kg/m2. Metabolically healthy phenotype was defined using the criteria of the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) metabolic syndrome criteria and the subjects were stratified into 4 groups according to their BMI and metabolic health. For cardiometabolic outcomes we estimated the incidence of cardiometabolic outcomes and standardized them per 1, 000 person-years of follow-up. Finally, to evaluate the risk for transition and development of cardiometabolic outcomes, we fitted Cox Proportional Hazard regression models.

RESULTS

Amongst the 5541 subjects, 54.2% were classified as metabolically healthy and 45.8% as unhealthy. The MHO prevalence was 39.3%. Up to a third of the population changed from their initial category to another and the higher transition rate was observed in MHO (42.9%). We also found several novel factors associated to transition to metabolically unhealthy phenotype; socioeconomic status, number of pregnancies, a high carbohydrate intake, history of obesity and consumption of sweetened beverages. Similarly, visceral adipose tissue (VAT) was a main predictor of transition; loss of VAT ≥5% was associated with reversion from metabolically unhealthy to metabolically healthy phenotype (hazard ratio (HR) 1.545, 95%CI 1.266-1.886). Finally, we observed higher incidence rates and risk of incident T2D and hypertension in the metabolically unhealthy obesity (MUHO) and metabolically unhealthy lean (MUHL) phenotypes compared to MHO.

CONCLUSIONS

Metabolic health is a dynamic and continuous process, at high risk of transition to metabolically unhealthy phenotypes over time. It is imperative to establish effective processes in primary care to prevent such transitions.

Gut microbiota of adults with different metabolic phenotypes

OBJECTIVE

The aim of this study was to investigate the gut microbiota of individuals with different metabolic phenotypes and to compare their characteristics.

METHODS

This was a cross-sectional study with 109 adults, classified according to metabolic status, by body mass index, and homeostasis model assessment. Anthropometric and biochemical characteristics and blood pressure were evaluated. Level of physical activity was evaluated by means of a questionnaire, and dietary consumption was evaluated using a 3-d food record. Feces samples were collected from each participant, and gut microbiota profile was analyzed by sequencing of the 16S rRNA gene. The Kruskal-Wallis and χ² tests were used, according to the scale of measurement of the variables, on the statistical program SPSS version 25, with significance level of 5%.

RESULTS

Significantly lower values of saturated fat intake and fasting glycemia were observed in metabolically healthy individuals who were overweight and obese compared with their unhealthy counterparts. Diversity and richness of bacterial communities were lower in individuals who were obese and in the unhealthy phenotype. The genus Eubacterium rectale and genera of the family Prevotellaceae were more abundant in the metabolically healthy obese and metabolically healthy overweight groups than in the metabolically healthy normal weight one, whereas Bifidobacterium was more abundant in the metabolically healthy normal weight group. The genera Coprococcus and Ruminococcus were more abundant in the metabolically healthy overweight group than in the metabolically unhealthy overweight one.

CONCLUSIONS

We observed distinct characteristics in the gut microbiota of different metabolic phenotypes. The intestines of individuals with unhealthy phenotypes hosted inflammation-associated microbiota, with lower butyrate production potential and reduced bacterial diversity.

An Overview of the Role of Adipokines in Cardiometabolic Diseases

Obesity as an independent risk factor for cardiovascular diseases (CVDs) leads to an increase in morbidity, mortality, and a shortening of life span. The changes in heart structure and function as well as metabolic profile are caused by obese people, including those free of metabolic disorders. Obesity alters heart function structure and affects lipid and glucose metabolism, blood pressure, and increase inflammatory cytokines. Adipokines, specific cytokines of adipocytes, are involved in the progression of obesity and the associated co-morbidities. In the current study, we review the scientific evidence on the effects of obesity on CVDs, focusing on the changes in adipokines. Several adipokines have anti-inflammatory and cardioprotective effects comprising omentin, apelin, adiponectin, and secreted frizzled-related protein (Sfrp-5). Other adipokines have pro-inflammatory impacts on the cardiovascular system and obesity including leptin, tumor necrosis factor (TNF), retinol-binding protein4 (RBP-4), visfatin, resistin, and osteopontin. We found that obesity is associated with multiple CVDs, but can only occur in unhealthy metabolic patients. However, more studies should be designed to clarify the association between obesity, adipokine changes, and the occurrence of CVDs.

The Prevalence of Metabolically Healthy and Unhealthy Obesity according to Different Criteria

OBJECTIVE

Obesity-related disease risks may vary depending on whether the subject has metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO). At least 5 definitions/criteria of obesity and metabolic disorders have been documented in the literature, yielding uncertainties in a reliable international comparison of obesity phenotype prevalence. This report aims to compare differences in MHO and MUO prevalence according to the 5 most frequently used definitions.

METHODS

A random sample of 4,757 adults aged 35 years and older (male 51.1%) was enrolled. Obesity was defined either according to body mass index or waist circumference, and the definitions of metabolic abnormalities were derived from 5 different criteria.

RESULTS

In MHO, the highest prevalence was obtained when using the homeostasis model assessment (HOMA) criteria (13.6%), followed by the Chinese Diabetes Society (11.4%), Adult Treatment Panel III (10.3%), Wildman (5.2%), and Karelis (4.2%) criteria; however, the MUO prevalence had an opposite trend to MHO prevalence. The magnitude of differences in the age-specific prevalence of MHO and MUO varied greatly and ranked in different orders. The proportion of insulin resistance for MHO and MUO individuals differed significantly regardless of which metabolic criterion was used.

CONCLUSION

The prevalence of MHO and MUO in the Chinese population varies according to different definitions of obesity and metabolic disorders.

Visceral adiposity index is a better predictor of unhealthy metabolic phenotype than traditional adiposity measures: results from a population-based study

OBJECTIVE

The present study aimed to investigate whether the visceral adiposity index (VAI) is an effective predictor to identify unhealthy metabolic phenotype by comparing normal-weight and overweight individuals.

DESIGN

A population-based cross-sectional study. Data were collected by interviews, anthropometric evaluation, dietetic, clinical and laboratory tests. The area under the receiver-operating characteristic curve (AUC) and prevalence ratio (PR), obtained from Poisson regression, were used to compare the predictive capacity of the obesity indicators evaluated (VAI, BMI, waist and neck circumference, waist-to-height and waist-to-hip ratios) and their association with the unhealthy metabolic phenotype. All analyses were stratified by sex and by nutritional status.

SETTING

Viçosa, Minas Gerais, Brazil.ParticipantsA total of 854 Brazilian adults (20-59 years old) of both sexes.

RESULTS

VAI was the best predictor for unhealthy metabolic phenotype among men (AUC = 0·865) and women (AUC = 0·843) at normal weight. VAI also had the best predictive capacity among overweight women (AUC = 0·903). Among overweight men, its accuracy (AUC = 0·830) was higher than that of waist-to-hip ratio. In the adjusted regression models, VAI was the indicator most strongly associated with the unhealthy metabolic phenotype, especially among those with normal weight (PR = 6·74; 95 % CI 3·15, 14·42 for men; PR = 7·14; 95 % CI 3·79, 13·44 for women).

CONCLUSIONS

VAI has better predictive capacity in detecting unhealthy metabolic phenotype than conventional anthropometric indicators, regardless of nutritional status and sex.

Is it Possible to Easily Identify Metabolically Healthy Obese Women?

Background: Obesity is recognized as a major risk factor for the development of several metabolic complications. However, some obese individuals have a favorable metabolic profile.

Objective: The aim of this study was to identify an easy parameter for recognizing metabolically healthy obese (MHO) women.

Methods: A total of 292 non-diabetic women with a body mass index (BMI) ≥ 30 kg/m² were selected, and 239 composed the final cohort. We classified the participants according to their metabolic state determined by homeostasis model assessment (HOMA) into MHO or metabolically unhealthy obese (MUO). Both groups were compared regarding biochemical, anthropometric, and body composition characteristics.

Results: The average age of the cohort was 43.9 ± 10.9 years and the average BMI was 37.2 ± 5.3 kg/m². In total, 75.7% of the participants were classified as MHO by HOMA. A cutoff of 108.2 cm for waist circumference (WC) identified MHO participants with a sensitivity of 72.4% (95% confidence interval [CI]: 59.8-82.3%), specificity of 66.9% (95% CI: 59.71-73.3%), and negative likelihood ratio of 0.41 (95% CI: 0.36-0.47). Additionally, a visceral adiposity index cutoff value of 99.2 identified MHO women with a sensitivity of 89.7% (95% CI: 79.2-95.2%), specificity of 48.6% (95% CI: 41.4-55.9%), and negative likelihood ratio of 0.21 (95% CI: 0.15-0.30).

Conclusion: Women classified as MHO exhibited smaller WC measurements and lower body fat percentages, as well as lower blood glucose and insulin levels. WC emerged as an easy parameter for identifying MHO women.

The prevalence, metabolic risk and effects of lifestyle intervention for metabolically healthy obesity: a systematic review and meta-analysis: A PRISMA-compliant article

BACKGROUND

We conducted a systematic review and meta-analysis to firstly obtain a reliable estimation of the prevalence of metabolically healthy obese (MHO) individuals in obesity, then assessed the risk of developing metabolic abnormalities (MA) among MHO individuals. At last, we evaluated the effects of traditional lifestyle interventions on metabolic level for MHO subjects.

METHODS

A systematic review and meta-analysis (PRISMA) guideline were conducted, and original studies were searched up to December 31, 2016. The prevalence of MHO in obesity from each study was pooled using random effects models. The relative risks (RRs) were pooled to determine the risk of developing MA for MHO compared with metabolically healthy normal-weight (MHNW) subjects. For the meta-analysis of intervention studies, the mean difference and standardized mean differences were both estimated for each metabolic parameter within each study, and then pooled using a random-effects model.

RESULTS

Overall, 40 population-based studies reported the prevalence of MHO in obesity, 12 cohort studies and 7 intervention studies were included in the meta-analysis. About 35.0% obese individuals were metabolically healthy in the obese subjects. There were dramatic differences in the prevalence among different areas. However, 0.49 (95% confidence intervals [CI]: 0.38 to 0.60) of the MHO individuals would develop one or more MA within 10 years. Compared with MHNW subjects, the MHO subjects presented higher risk of incident MA (pooled RR = 1.80, 95%CI: 1.53-2.11). Following intervention, there was certain and significant improvement of metabolic state for metabolically abnormal obesity (MAO) subjects. Only diastolic blood pressure had reduced for MHO individuals after intervention.

CONCLUSIONS

Almost one-third of the obese individuals are in metabolic health. However, they are still at higher risk of advancing to unhealthy state. Therefore, it is still needed to advise MHO individuals to maintain or adopt a healthy lifestyle, so as to counterbalance the adverse effects of obesity.

Leptin as a cardiovascular risk marker in metabolically healthy obese: Hyperleptinemia in metabolically healthy obese

Adipokines contribute to the inflammatory process which can lead to obesity-associated cardiometabolic complications. Metabolically healthy obese individuals seem to be protected or more resistant to develop these complications and it is intriguing why some individuals develop comorbidities and others do not. Thus, we questioned whether the differences between metabolically healthy and unhealthy obese relied on the alterations in metabolic profile, characterized by serum leptin and adiponectin. A total of 142 obese adults were divided into 2 groups - metabolically healthy obese (MHO) or unhealthy obese (MUO) - and they were evaluated for anthropometric measures, body composition, blood pressure, dietary intakes and plasma levels of leptin and adiponectin. Leptin/adiponectin ratio (L/A) was calculated. Age, BMI and blood pressure were higher in the MUO. No differences in anthropometric measurements, body composition, dietary intake and dietary quality were observed between groups. Leptin were significantly higher in the MUO (53.07 ± 34.56 versus 36.27 ± 24.02 ng/ml in the MHO, r < 0.04). The logistic regression analysis demonstrated that leptin was an important factor associated with not being healthy, independent of age, body weight and BMI. There were no differences between groups for adiponectin and L/A. Leptin correlated positively with body weight (r = 0.25, r < 0.05), BMI (r = 0.38, r < 0.05) and BF (r = 0.74, r < 0.05), and negatively with FFM (r = -0.74, r < 0.05). Our findings suggest that leptin is an important cardiovascular disease marker to obese population and can contribute to evaluate metabolic risks in these individuals.

Identifying metabolic syndrome in a clinical cohort: Implications for prevention of chronic disease

In the clinical setting, calculating cardiovascular disease (CVD) risk is commonplace but the utility of the harmonised equation for metabolic syndrome (MetS) (Alberti et al., 2009) is less well established. The aims of this study were to apply this equation to an overweight clinical cohort to identify risk factors for being metabolically unhealthy and explore associations with chronic disease.

Baseline data were analysed from a lifestyle intervention trial of Illawarra residents recruited in 2014/2015. Participants were aged 25–54 years with a BMI 25–40 kg/m². Data included MetS, CVD risk, insulin sensitivity, weight, body fat, diet, peripheral artery disease (PAD), physical activity, socio-economic position and psychological profile. Backward stepwise regression tested the association of covariates with MetS status and linear or logistic regression tested associations between MetS and risk of CVD, coronary heart disease, PAD and insulin resistance. 374 participants were included in the analysis with 127 (34.0%) categorised with MetS. Covariates significantly and positively associated with MetS were higher BMI (odds 1.26, p < 0.01) and older age (odds 1.08, p < 0.01). MetS participants (n = 351) had a 4.50% increase in CVD risk and were 8.1 and 12.7 times (respectively) more likely to be at risk of CHD and insulin resistance, compared to participants without MetS.

The utility of the harmonised equation in the clinical setting was confirmed in this overweight clinical cohort. Those classified as having MetS were more likely to be older, overweight/obese individuals and they had a substantially higher risk of developing CVD and insulin resistance than those without MetS.

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Older, obese individuals at greatest risk of having Metabolic Syndrome (MetS)

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Stress, socioeconomic, fitness and diet factors were not associated with MetS.

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MetS individuals had increased cardiovascular risk of 4.50%.

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MetS were 12.7 times more likely to be insulin resistant.

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Study identifies those at risk in order to implement early lifestyle changes.

Branched-chain amino acids are associated with cardiometabolic risk profiles found already in lean, overweight and obese young

Cardiovascular risk is increased in obese subjects. Nevertheless, some overweight and obese remain cardiometabolically healthy (CMH), and normal-weight persons develop cardiovascular disease (CVD). Herein, we investigate the potential of branched-chain amino acids (BCAAs) to identify an increased CVD risk in a cross-sectional study of 666 adults and juveniles (age 25.3±12.8years), classified as lean, overweight or obese. Cardiometabolic groups were defined by cutoffs of systolic blood pressure<130mmHg, diastolic blood pressure<85mmHg, glucose<125mg/dl, triglycerides<150mg/dl, HDL-cholesterol>40mg/dl (males), HDL-cholesterol>50mg/dl (females) and HOMA-IR<5. CMH had ≤1 cutoff, and cardiometabolically abnormal (CMA) had ≥2 cutoffs. Amino acids were measured by high-pressure lipid chromatography after precipitation of serum with perchloric acid and derivatization with o-phthalaldehyde. Valine correlated with 5, leucine correlated with 3 and isoleucine correlated with 5 of the cardiac risk classification factors. Valine and leucine were significantly higher in the obese (P<.001, P=.015, respectively), overweight (P<.001, P=.015, respectively) and lean (P=.024, P=.012, respectively) CMA compared to CMH subjects. Isoleucine showed except of the lean group the same results. Taken together, BCAAs, especially valine and leucine, are proposed as a cardiometabolic risk marker independent of body mass index (BMI) category.