Metabolically Healthy Obese Phenotype Research Articles

Prevalence and clinical characteristics of metabolically healthy obese versus metabolically unhealthy obese school children.

IntroductionChildren with obesity in the absence of traditional cardiometabolic risk factors (CRF) have been described as metabolically healthy obese (MHO). Children with MHO phenotype has a favorable metabolic profile with normal glucose metabolism, lipids, and blood pressure compared to children with metabolically unhealthy obese (MUO) phenotype. This study aimed to compare several parameters related to obesity between these two groups and to examine the predictors associated with the MHO phenotype.MethodsThis study included a cross-sectional baseline data of 193 children with obesity (BMI z-score > +2 SD) aged 8-16 years enrolled in MyBFF@school program, a school-based intervention study conducted between January and December 2014. Metabolic status was defined based on the 2018 consensus-based criteria with MHO children had no CRF (HDL-cholesterol > 1.03 mmol/L, triglycerides ≤ 1.7 mmol/L, systolic and diastolic blood pressure ≤ 90th percentile, and fasting plasma glucose ≤ 5.6 mmol/L). Those that did not meet one or more of the above criteria were classified as children with MUO phenotype.ResultsThe prevalence of MHO was 30.1% (95% CI 23.7 – 37.1) among schoolchildren with obesity and more common in younger and prepubertal children. Compared to MUO, children with MHO phenotype had significantly lower BMI, lower waist circumference, lower uric acid, higher adiponectin, and higher apolipoprotein A-1 levels (p < 0.01). Multivariate logistic regression showed that adiponectin (OR: 1.33, 95% CI 1.05 – 1.68) and apolipoprotein A-1 (OR: 1.02, 95% CI 1.01 – 1.03) were independent predictors for MHO phenotype in this population.ConclusionsMHO phenotype was more common in younger and prepubertal children with obesity. Higher serum levels of adiponectin and apolipoprotein A-1 increased the possibility of schoolchildren with obesity to be classified into MHO phenotype.

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.

Obesity phenotypes related to musculoskeletal disorders; a cross-sectional study from RaNCD cohort

BackgroundThis study was evaluated the association between obesity phenotypes and risk of lower torso musculoskeletal disorders including low back pain (LBP), low back stiffness (LBS), arthralgia, and joint stiffness in Ravansar non-communicable diseases (RaNCD) cohort study.MethodsIn this cross-sectional study, 6940 adults were examined for the presence of lower torso musculoskeletal disorders by a physician. Obesity phenotypes including metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) were defined based on the International Diabetes Federation, as well as, body mass index > 30 kg/m2. Metabolically unhealthy non-obesity (MUNO) phenotype was considered as unhealthy metabolic without obesity.ResultsThe prevalence of LBP, LBS, arthralgia, and joint stiffness in MHO, MUO, and MUNO were significantly higher than in healthy participants compared to obesity phenotypes. Logistic regression showed that MHO phenotype was significantly increased with risk of LBP (OR: 1.19, CI 95%: 1.01–1.41), LBS (OR: 1.44, CI 95%: 1.12–1.86), arthralgia (OR: 1.54, CI 95%: 1.33–1.78), and joint stiffness (OR: 1.84, CI 95%: 1.35–2.52). Moreover, MUO phenotype was positively associated with risk of LBS (OR: 1.46, CI 95%: 1.09–1.94) and arthralgia (OR: 1.66, CI 95%: 1.41–1.96). In addition, MUNO phenotype was associated with a higher risk of arthralgia (OR: 1.21, CI 95%: 1.06–1.37).ConclusionAll three phenotypes, MHO, MUO and MUNO were significantly increased the risk of arthralgia. However, MHO phenotype was significantly associated with a higher risk of all examined lower torso musculoskeletal disorders in the current study.

Hyperuricemia is Associated with the Presence of Metabolically Obese Normal-Weight and Metabolically Healthy Obese Phenotypes

ABSTRACT Background It is well-recognized that hyperuricemia is a common abnormality among individuals with metabolic syndrome. Aims The objective of this study was to determine whether hyperuricemia is associated with the metabolically obese normal-weight (MONW) and metabolically healthy obese (MHO) phenotypes. Methods Men and women equal or greater than 18 years of age were enrolled in a cross-sectional study. Normal-weight subjects were allocated into the MONW or healthy normal-weight (HNW) groups; while obese individuals were divided into the MHO and metabolically unhealthy obese (MUO) subgroups. MONW phenotype was defined by body mass index (BMI) <25.0 kg/m2 accompanied by at least one cardiovascular risk factor (hyperglycemia, elevated blood pressure, hypertriglyceridemia, and low high-density lipoprotein cholesterol), and MHO phenotype was considered in obese subjects (BMI ≥30 kg/m2) without metabolic abnormalities. Results A total of 567 individuals were enrolled; of them, normal-weight subjects were allocated into the MONW (n = 101) and control (n = 72) groups, whereas obese individuals into the MHO (n = 61) and MUO (n = 333) groups. The multiple logistic regression analysis adjusted by age, gender, and body mass index revealed that hyperuricemia is significantly associated with MONW (OR = 5.14; 95% CI: 1.37–19.29) and MHO (OR = 0.34; 95% CI: 0.14–0.82) phenotypes. Conclusion Results of our study showed that hyperuricemia is associated with both MONW and MHO phenotypes.

Association of metabolic health phenotypes, obesity, and hepatocellular carcinoma risk.

The obesity and hepatocellular carcinoma (HCC) risk association may differ by individuals' metabolic health status. To investigate the association between obesity categories and HCC risk among individuals with different metabolic health phenotypes. A case-control study among 518 HCC cases and 1,036 frequency-matched controls was conducted. Body mass index (BMI) was assessed before diagnosis. Pre-diagnosis data on dyslipidemia, hypertension, and diabetes were used to categorize participants as metabolically healthy or metabolically unhealthy. Participants were further categorized into metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and metabolically unhealthy obese (MHO). We used logistic regression to calculate multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Being overweight (OR=1.68, 95%CI=1.21-2.34) or obese (OR=1.49, 95%CI=1.11-1.89) was associated with higher HCC risk. Among metabolically healthy participants, no association was found between being overweight or obese and HCC risk. However, among the metabolically unhealthy participants, being overweight (OR=1.89, 95%CI=1.31-2.72) or obese (OR=1.50, 95%CI=1.07-2.09) was associated with higher HCC risk. Compared to the MHNW phenotype, no association was found between the MHOW and MHO phenotypes and HCC risk, but the MUNW (OR=1.94, 95%CI=1.09-3.43), MUOW (OR=3.78, 95%CI=2.15-6.65), and MUO (OR=2.93, 95%CI=1.70-5.05) phenotypes were associated with higher HCC risk. The association between BMI and HCC appears to be restricted to individuals with underlying metabolic abnormalities.

RELATIONSHIP BETWEEN METABOLICALLY HEALTHY OBESITY AND THE DEVELOPMENT OF HYPERTENSION: A NATIONWIDE POPULATION-BASED STUDY

Objective: Metabolically healthy obesity (MHO), which does not have a general definition, has been recognized as a transient phenotype with few cardiometabolic diseases. This study aimed to assess the relationship between the development of hypertension and metabolically healthy abdominal obesity (MHAO) and metabolically healthy general obesity (MHGO) phenotype. Design and method: This was a retrospective cohort study of 4764 adults who were enrolled from the China Health and Nutrition Survey (CHNS) study from 2009 to 2015. Obesity was classified as abdominal obesity based on waist circumference and as general obesity based on body mass index. Logistic regression was used to analyze the relationship between metabolic health combined with obesity and prehypertension and hypertension. Results: There were 412 (37.9%) participants with prehypertension and 446 (41.0%) participants with hypertension with the MHAO phenotype. Subjects with the MHAO phenotype had significantly higher risks of prehypertension [risk ratio (RR) = 1.89 (1.51–2.36), P &lt; 0.001] and hypertension [RR = 2.58 (2.02–3.30), P &lt; 0.001] than subjects with the metabolically healthy nonabdominal obesity (MHNAO) phenotype. A similar trend existed with regard to the subjects with the MHGO phenotype, particularly in young individuals aged under 64 years. Men with the MHAO phenotype seemed to have higher risks of prehypertension [men: 2.42 (1.52–3.86) vs. women: 1.76 (1.36–2.29)] and hypertension [men: 3.80 (2.38–6.06) vs. women: 2.22 (1.64–3.00)] than women when compared with those with the MHNAO phenotype. Conclusions: The MHO phenotype, regardless of the presence of general or abdominal obesity, showed a worse effect on the development of prehypertension and hypertension, particularly in young adults. Abdominal adiposity with healthy metabolic status is more strongly associated with incident hypertension in men than in women, guiding decision-making for sex-specific and risk-stratified obesity treatment.

Prevalence of liver cirrhosis based on the metabolic health and weight criteria: Report from the Korea National Health and Nutrition Examination Survey (KNHANES) data analysis

Introduction and ObjectivesRecent studies have proposed two distinctive types of obesity, metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO), based on various physiological factors. This study sought to explore the relationship between the metabolic obesity types and the incidence of liver cirrhosis (LC) in a large nationally-representative population. Materials and MethodsData on 27,629 adults with MHO or MUHO, were analyzed from the Korea National Health and Nutrition Examination Survey (KNHANES) obtained from 2015 through 2019. Four categories of metabolic health and weight (MHW) were generated for analysis: (1) MHO, (2) MUHO, (3) Metabolically unhealthy normal weight (MUHNW), and (4) Metabolically healthy normal weight (MHNW). Statistical analyzes were performed with univariate and multivariate logistic regression. ResultsThe prevalence of LC did not show statistically significant differences among the MHW categories: 0.5% in MHO, 0.4% in MUHO, 0.2% in MHNW, and 0.3% in MUHNW. The unadjusted analysis showed a significant association between self-reported LC and MUHO, but this association was not evident in the adjusted analysis. In the adjusted analysis of the prevalence of laboratory LC, a significant association emerged in the MUHO group, followed in descending order of magnitude by the MHO and MUHNW groups. A favorable fasting blood glucose level was the only factor associated with increased prevalence of reported LC in MUHO. ConclusionsThe study demonstrated a difference in the prevalence of LC between MHO and MUHO. Our study concludes that the MHO phenotype is a transient status with regard to metabolic abnormalities, and caution is necessary when evaluating MHO.

Circulating Inflammatory Markers May Mediate the Relationship between Healthy Plant-Based Diet and Metabolic Phenotype Obesity in Women: A Cross-Sectional Study

Background It has been posited that both metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO) could be emergent from diet and inflammatory markers. Thus, we sought to investigate the influence of plant-based diet on MHO and MUHO phenotypes mediated by inflammatory markers in overweight and obese women. Methods This cross-sectional study was conducted on 289 women aged ≥18 years, with a body mass index (BMI) ≥25 kg/m2. Dietary intake was measured using 147 item food frequency questionnaire, as well as anthropometrics and biochemistry panel, in all participants. Metabolic health phenotypes were considered using Karelis score, while plant-based diet indices (PDI) were evaluated based on 18 food groups, where healthy and unhealthy PDI were identified. Results Accordingly, 26.9% of women had MHO and 73.1% had MUHO phenotypes. After adjusting for potential confounders, TGF-β1 had a significant inverse association with hPDI (β: −0.28; 95% CI: 452.99, −85.25; P: 0.004). Moreover, we found that women with higher hPDI had lower odds of MUHO (OR: 0.95; 95% CI: 0.39, 2.30; P: 0.03). Regarding the mediatory effect of the inflammatory markers, TGF-β1 (P: 0.73), IL-β1 (P: 0.14), and MCP1 (P: 0.51) played a role in decreasing the odds of MUHO among hPDI tertiles. Conclusion There was a significant inverse relationship between adherence to hPDI and MUHO phenotype in overweight and obese Iranian women. This association appeared to be mediated by TGF-β1, IL-β1, and MCP1.

The value of laboratory indicators estimation in patients with metabolically healthy obesity: analysis of a clinical case with literature review

Introduction. Obesity currently is a relevant issue of modern medicine due to its global prevalence, heterogeneity of clinical and laboratory manifestations, as well as the association with various comorbid conditions. Depending on the metabolic status, metabolically unhealthy obesity (MUO) and metabolically healthy obesity (MHO) are distinguished. MUO is defined with the presence of criteria for metabolic syndrome (MS) and is associated with an increased risk of cardiovascular and metabolic complications. MHO is characterized by a «metabolically healthy» profile, but the probability of a favorable course of the disease is controversial; many studies indicate the instability of the MHO phenotype and the possibility of further development of MUO. The aim of study: to analyze the features of laboratory indicators associated with MHO and determine the risk factors for the MUO development on the example of a clinical case. Materials and methods. A clinical case of 24 year old female patient diagnosed with alimentary-constitutional obesity class III. Objectively: height – 174 cm, weight – 124.7 kg, body mass index (BMI) – 41.21 kg/m2, waist circumference – 107 cm, hips circumference – 144 cm; white striae on the abdomen; excessive subcutaneous fat stores, mostly distributed in the abdomen, thighs; blood pressure (BP) – 125/80 mm Hg. Investigation data: hyperleptinemia – 86.82 ng/ml, increased level of HOMA index – 4.6, hyperuricemia – 6.8 mg/dl, vitamin D deficiency – 9.19 ng/ml; lipid profile, fasting plasma glucose, glycated haemoglobin (HbA1c), thyroid stimulating hormone (TSH), triiodothyronine (T3), thyroxine (T4), anti-thyroid peroxidase (anti-TPO) antibodies, cortisol, blood electrolytes, liver function tests – within normal limits. Electrocardiography (ECG), ultrasound of the heart and abdominal organs – without pathology. Results. Normal indicators of lipid metabolism, blood glucose and BP measurement in our patient are characteristic for MHO. However, the combination of hyperleptinemia with insulin resistance, hyperuricemia and vitamin D3 deficiency indicate metabolic and hormonal imbalance and are considered as a risk factors for the development of MS and the further transition of MHO to MUO. Conclusion. MHO should be considered as a transient state, the management of such patients requires careful laboratory monitoring with early detection of metabolic disorders and its adequate and timely correction.

Metabolic obesity phenotypes: a friend or foe of digestive polyps?—An observational study based on National Inpatient Database

ObjectiveObesity is associated with an increased risk of digestive polyps, whereas all obesity are not created equally. The role of metabolic states in occurrence risks of polyps among individuals with varying degrees of obesity remains unknown. Our study aimed to evaluate the association between metabolic obesity phenotypes and the occurrence of digestive polyps. Research design and methodsData from 9,278,949 patients between 2016 and 2018 from the National Inpatient Sample (NIS) database, a nationally representative database of all discharges from US health-care hospitals, were analyzed. According to obesity phenotype, the study population was classified into four groups: metabolically healthy nonobese (MHNO), metabolically unhealthy nonobese (MUNO), metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). We calculated the incidence rates of various digestive polyps (stomach/duodenum, colon and rectum polyps) among these participants by searching the hospital records for ICD-10 diagnosis codes indicating each gastric, duodenum, colon or rectal polyps. The multiple stepwise regression analysis and further in-depth subgroup analysis were used to determine the associations between metabolic obesity phenotypes and the occurrence of digestive polyps. ResultsIn the total or female population, those with the MUNO and MUO phenotypes had significantly higher prevalence of digestive polyps compared with individuals with the MHNO or MHO phenotypes (all p < 0.05) and a significant difference was not found between MUNO and MUO phenotypes (p > 0.05). Obese subjects seem to be more likely to develop stomach and duodenum polyps or colon polyps than non-obese subjects in metabolically healthy people of males (MHO vs. MHNO, p < 0.05), whereas obesity status seems to have little effect on the occurrence of digestive polyps in metabolically healthy people of females (MHO vs. MHNO, p>0.05). After adjusting for the potential confounders, the MHO, MUNO and MUO phenotypes were all risk factors for stomach and duodenum polyps (OR = 1.46, 95% CI: 1.36–1.58, p< 0.01; OR = 1.19, 95% CI: 1.14–1.25, p< 0.01; OR = 1.44, 95% CI: 1.35–1.55, p< 0.01, respectively) or colon polyps (OR = 1.28, 95% CI: 1.21–1.35, p< 0.01; OR = 1.18, 95% CI: 1.14–1.22, p< 0.01; OR = 1.46, 95% CI: 1.38–1.54, p< 0.01, respectively) compared with the MHNO phenotype，especially in menopausal female. Interestingly, we also observed in further in-depth subgroup analysis that metabolic abnormalities may have a greater impact on the occurrence of digestive polyps than obesity (all p < 0.05). ConclusionsBoth metabolic abnormities and obesity were associated with a higher risk of digestive polyps. The effect of metabolism on digestive polyp occurrence may be stronger than that of obesity, highlighting the importance of abnormal metabolic status modification regardless of obesity status. Clinical intervention should not only focus on obesity, but also on metabolic abnormalities to decrease digestive polyp risk.

Metabolically healthy versus unhealthy obese phenotypes in relation to hypertension incidence; a prospective cohort study

BackgroundAlthough obesity increases the risk of hypertension, the effect of obesity based on metabolic status on the incidence of hypertension is not known. This study aimed to determine the association between obesity phenotypes including metabolically unhealthy obesity (MUO) and metabolically healthy obesity (MHO) and the risk of hypertension incidence.MethodsWe conducted a prospective cohort study on 6747 adults aged 35–65 from Ravansar non-communicable diseases (RaNCD) study. Obesity was defined as body mass index above 30 kg/m2 and metabolically unhealthy was considered at least two metabolic disorders based on the International Diabetes Federation criteria. Obesity phenotypes were categorized into four groups including MUO, MHO, metabolically unhealthy non obesity (MUNO), and metabolically healthy non obesity (MHNO). Cox proportional hazards regression models were applied to analyze associations with hypertension incidence.ResultsThe MHO (HR: 1.37; 95% CI: 1.03–1.86) and MUO phenotypes (HR: 2.44; 95% CI: 1.81–3.29) were associated with higher hypertension risk compared to MHNO. In addition, MUNO phenotype was significantly associated with risk of hypertension incidence (HR: 1.65; 95% CI: 1.29–2.14).ConclusionsBoth metabolically healthy and unhealthy obesity increased the risk of hypertension incidence. However, the increase in metabolically unhealthy phenotype was higher.

Metabolically Healthy and Unhealthy Obese Phenotypes among Arabs and South Asians: Prevalence and Relationship with Cardiometabolic Indicators.

Obesity is a public health crisis in Kuwait. However, not all obese individuals are metabolically unhealthy (MuHO) given the link between obesity and future cardiovascular events. We assessed the prevalence of the metabolically healthy obese (MHO) phenotype and its relationship with high sensitivity C-reactive protein (hs-CRP), serum alanine aminotransferase (ALT), and insulin resistance (HOMA-IR) in Arab and South Asian ethnic groups in Kuwait. The national cross-sectional survey of diabetes and obesity in Kuwait adults aged 18–60 years were analysed. The harmonised definition of metabolic syndrome was used to classify metabolic health. Multinomial logistic regression analysis was used to model the relationship between the MHO and MuHO phenotypes and hs-CRP, ALT and HOMA-IR levels. Overall, the prevalence of MHO for body mass index (BMI)- and waist circumference (WC)-defined obesity was 30.8% and 56.0%, respectively; it was greater in women (60.4% and 61.8%, respectively) than men (39.6% and 38.2%, respectively). Prevalence rates were also lower for South Asians than for Arabs. The MHO phenotype had hs-CRP values above 3 µg/mL for each age group category. Men compared to women, and South Asians compared to Arabs had a lower relative risk for the MHO group relative to the MuHO group. This study shows there is high prevalence of MHO in Kuwait.

Effects of Body Weight Change on Development of Chronic Kidney Disease in Obese Metabolic Phenotypes

Introduction: Obesity is a risk factor for chronic kidney disease (CKD), but whether the reduction of body mass index (BMI) helps prevent CKD is controversial. Recently, obese metabolic phenotypes have raised considerable interest. We thus investigated the effect of BMI change on CKD development. Methods: We analyzed the data of 6,959 subjects who underwent annual health checkups in both 2013 and 2018. The subjects were categorized into five groups according to their BMI percentage change (ΔBMI) and classified into four obese metabolic phenotypes. By a multivariate logistic regression analysis, we investigated the association between BMI change and CKD development within the 5 years. Results: In total subjects, compared with the maintained BMI group (ΔBMI ≥0% but <2.5%), the odd ratios (ORs) and 95% confidence intervals (CIs) of CKD development were 0.70 (95% CI 0.54–0.91) for the severe BMI decrease group (ΔBMI <−2.5%), and 1.40 (95% CI 1.08–1.81) for the severe BMI increase group (ΔBMI ≥5%). In the metabolically healthy obese (MHO) phenotype, the risks of CKD development were significantly higher in the moderate BMI increase group (ΔBMI ≥2.5% but <5%) (OR 3.04, 95% CI 1.19–7.78) and a severe BMI increase group (OR 2.88, 95% CI 1.13–7.35). Regarding the metabolically unhealthy nonobese (MUNO) phenotype, the risks of CKD development were significantly lower in the severe BMI decrease group (OR 0.43, 95% CI 0.25–0.74) and the moderate BMI decrease group (ΔBMI ≥−2.5% but <0%) (OR 0.58, 95% CI 0.35–0.98). Conclusions: In the MHO phenotype, an increased BMI deteriorated CKD development, and a decreased BMI ameliorated CKD development in the MUNO phenotype.

Prevalence and predictors of metabolically healthy obesity in severely obese Asian children.

Obese individuals who have little or no metabolic syndrome components are proposed to be "metabolically healthy obese (MHO)". This study aim to evaluate the prevalence of MHO and examine the predictors associated with MHO in a multi-ethnic Asian cohort of severely obese children. This study included a cross-sectional cohort of 406 Chinese, Malay and Indian children aged 5-20 years old with BMI for age ≥ 97th percentile. Metabolic syndrome (MS) and metabolic health (MH) definitions based on the presence or absence of metabolic abnormalities (High triglycerides, low HDL cholesterol, elevated blood pressure and high glucose) were used to define MHO in the cohort. The prevalence of MHO is 63.5% by MS definition and 22.4% by MH definition. Maternal healthy metabolic status (OR: 2.47), age (OR: 0.83, 0.80), paternal obesity (OR: 0.48, 0.53), Malay (OR: 1.97) and Indian ethnicity (OR: 6.38, 3.21) (compared to Chinese ethnicity) are independent predictors for MHO phenotype based on different MHO definitions. Adiposity measures are not associated with MHO phenotype, but instead younger age, maternal healthy metabolic status, absence of paternal obesity, Malay and Indian ethnicity are independent predictors for MHO phenotype in a multi-ethnic Asian cohort of severely obese children. The prevalence of metabolically healthy obese (MHO) in our multi-ethnic Asian cohort of severely obese children is 63.5% and 22.4%, respectively, based on different MHO definitions. Adiposity measures are not associated with the MHO phenotype. There are other factors that contribute to the metabolic phenotype in obese individuals. Younger age, maternal healthy metabolic status, absence of paternal obesity, Malay and Indian ethnicity are independent predictors for MHO phenotype. Parental influence is important in predicting metabolic health in obese individuals.

Evolution of Metabolic Phenotypes of Obesity in Coronary Patients after 5 Years of Dietary Intervention: From the CORDIOPREV Study.

Background: Obesity phenotypes with different metabolic status have been described previously. We analyzed metabolic phenotypes in obese coronary patients during a 5-year follow-up, and examined the factors influencing this evolution. Methods: The CORDIOPREV study is a randomized, long-term secondary prevention study with two healthy diets: Mediterranean and low-fat. All obese patients were classified as either metabolically healthy obese (MHO) or metabolically unhealthy obese (MUO). We evaluated the changes in the metabolic phenotypes and related variables after 5 years of dietary intervention. Results: Initially, 562 out of the 1002 CORDIOPREV patients were obese. After 5 years, 476 obese patients maintained their clinical and dietary visits; 71.8% of MHO patients changed to unhealthy phenotypes (MHO-Progressors), whereas the MHO patients who maintained healthy phenotypes (MHO-Non-Progressors) lost more in terms of their body mass index (BMI) and had a lower fatty liver index (FLI-score) (p < 0.05). Most of the MUO (92%) patients maintained unhealthy phenotypes (MUO-Non-Responders), but 8% became metabolically healthy (MUO-Responders) after a significant decrease in their BMI and FLI-score, with improvement in all metabolic criteria. No differences were found among dietary groups. Conclusions: A greater loss of weight and liver fat is associated with a lower progression of the MHO phenotype to unhealthy phenotypes. Likewise, a marked improvement in these parameters is associated with regression from MUO to healthy phenotypes.

Metabolically Healthy Obesity (MHO) vs. Metabolically Unhealthy Obesity (MUO) Phenotypes in PCOS: Association with Endocrine-Metabolic Profile, Adherence to the Mediterranean Diet, and Body Composition.

Obesity and obesity-related low-grade inflammation are common findings in polycystic ovary syndrome (PCOS), the most common endocrine-metabolic disorder-affecting women in reproductive age. The terms metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) have been introduced to define individuals with obesity in whom cardio-metabolic risk factors are absent or present, respectively. To date, evidence investigating differences in body composition and adherence to the Mediterranean diet (MD) between MHO and MUO-PCOS women are lacking. Aim of this study was to better characterize the determinants of the metabolic health status in PCOS patients with obesity according to MHO and MUO phenotypes by evaluating endocrine-metabolic profile, inflammatory status, adherence to the MD, and body composition. The study population consisted of 94 treatment-naïve women with PCOS and obesity (BMI = 38.23 ± 6.62 kg/m2 and age = 24.12 ± 3.68 years). Compared PCOS MHO with PCOS MUO patients, the latter had higher levels of high-sensitivity C-reactive protein (hs-CRP) (p < 0.001), testosterone (p < 0.001), and insulin (p < 0.001), worse metabolic parameters, and higher Homeostatic Model Assessment of Insulin Resistance (HoMA-IR), Visceral Adiposity Index (VAI), and Fatty liver Index (FLI) (p < 0.001). Furthermore, PCOS MUO patients had lower adherence to the MD (p < 0.001) in spite of the same total energy intake (p = 0.102) as compared to PCOS MHO. The presence of MUO was associated with highest hs-CRP levels (OR = 1.49, p < 0.001), more severe hyperandrogenism and cardio-metabolic indices (p < 0.001). On the contrary, being PCOS MUO was associated with lower adherence to the MD (OR = 0.28, p < 0.001), and smaller PhAs (OR = 0.04, p < 0.001). Using a regression linear analysis model PREDIMED score entered at the first step (p < 0.001), followed by VAI (p < 0.001), and FLI (p = 0.032) in this analysis. At ROC analysis, a PREDIMED score of ≤4 (p < 0.001, AUC 0.926) could serve as a threshold for a significantly increased risk of presence the MUO-PCOS phenotype. To the best of our knowledge, this is the first study that characterized MHO and MUO-PCOS women on the basis of their adherence to the MD, body composition, and cardio-metabolic indices, providing evidence of the usefulness of adjunctive diagnostic parameters to better differentiate the MHO/MHO phenotypes in this cohort of PCOS patients with obesity.

Magnesium intake is associated with the metabolically healthy obese phenotype

Although magnesium intake is inversely associated with the risk of metabolic abnormalities, whether magnesium intake plays a role on metabolically healthy obese (MHO) phenotype has not been explored. Therefore, the...

Metabolically healthy obesity: predictors of transformation to unhealthy phenotype in St Petersburg population (according to the ESSE-RF study)

Objective. The purpose of the study was to determine the dynamics of the metabolically healthy obesity (MHO) status according to the Meigs criteria, and to establish the predictors of the transformation of healthy obesity phenotype into an unhealthy (MUHO) one in the population of residents of St Petersburg (Russia) at 6,5-year follow-up. Design and methods. Within the epidemiology study ESSE-RF a random sample of 1600 St Petersburg inhabitants stratified according to gender and age was formed. Examination of participants included anthropometry with measurement of waist circumference and calculation of body mass index (BMI), measurement of blood pressure (BP), fasting blood glucose, insulin (index of insulin resistance was calculated), creatinine, cortisol, lipid spectrum, C-reactive protein, adiponectin, leptin, and uric acid. Meigs MHO criteria (2006) were used in obese subjects (BMI &gt; 30 kg/m²). Obese patients, who were identified as metabolically healthy in 2012–2013, were invited for follow-up in 2018–2019. Results. At the first stage obesity was diagnosed in 430 (26,9 %) participants, according to the BMI, 116 (27,0 %) of them were metabolically healthy according to the Meigs criteria. At follow-up, 44,4% individuals with the MHO phenotype transformed to the MUHO category on average after 6,5 years. Individuals who retained the MHO phenotype over time had significantly lower baseline systolic BP and diastolic BP levels, more favorable lipid levels and lower levels of uric acid, insulin, and index of insulin resistance. Glucose increase by every 0,5 mmol/l and higher was associated with elevated probability of transformation MHO to MUHO phenotype by 10,9 times (adjusted for sex and age). Conclusions. Significantly higher levels of BP, insulin resistance, low density lipoprotein and uric acid at baseline, as well as an increase in glucose levels over time, were associated with the transformation of the metabolically healthy to the unhealthy phenotype in obese individuals at 6,5-year follow-up. In all individuals with the MHO phenotype, there was a significant increase in waist circumference over time, accompanied by an increase in BMI only in those who transformed into the MUHO status.

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.

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.