Metabolically Healthy Overweight Research Articles

Frequency of prediabetes in individuals with increased adiposity and metabolically healthy or unhealthy phenotypes.

To utilize the estimated glucose disposal rate (eGDR) index of insulin sensitivity, which is based on readily available clinical variables, namely, waist circumference, hypertension and glycated haemoglobin, to discriminate between metabolically healthy and unhealthy phenotypes, and to determine the prevalence of prediabetic conditions. Non-diabetic individuals (n = 2201) were stratified into quartiles of insulin sensitivity based on eGDR index. Individuals in the upper quartiles of eGDR were defined as having metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW) or metabolically healthy obesity (MHO) according to their body mass index, while those in the lower quartiles were classified as having metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW) and metabolically unhealthy obesity (MUO), respectively. The frequency of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and IFG + IGT status was comparable among the MHNW, MHOW and MHO groups, while it increased from those with MUNW status towards those with MUOW and MUO status. As compared with participants with MHNW, the odds ratio of having IFG, IGT, or IFG + IGT was significantly higher in participants with MUOW and MUO but not in those with MUNW, MHOW and MHO, respectively. A metabolically healthy phenotype is associated with lower frequency of IFG, IGT, and IFG + IGT status across all body weight categories.

Metabolic Health, Overweight or Obesity, and Depressive Symptoms among Older Australian Adults.

The relationship between overweight or obesity and depressive symptoms in individuals with or without cardio-metabolic abnormalities is unclear. In a cross-sectional study we examined the odds of experiencing depressive symptoms in overweight or obese older adults with or without metabolic abnormalities. The participants included 3318 older adults from the Hunter Community Study Cohort with a Body Mass Index (BMI) ≥ 18.5 kgm2, stratified by BMI and metabolic health risk. Obesity was defined as BMI ≥ 30 kgm2 and metabolically healthy as the absence of metabolic risk factors, according to International Diabetic Federation criteria for metabolic syndromes. Moderate to severe depressive symptoms were defined as a Centre for Epidemiological Studies Depression Scale (CES-D) score ≥ 16. Compared to the metabolically healthy normal weight (MHNW) group, the odds of experiencing moderate/severe depressive symptoms were higher in those classified as a metabolically unhealthy normal weight (MUNW) (odds ratio (OR) = 1.25, 95% Confidence Interval (CI): 0.76-2.06) or metabolically unhealthy obesity (MUO) (OR = 1.48, 95% CI: 1.00-2.19), but not in those classified as metabolically unhealthy overweight (MUOW) (OR = 0.96, 95% CI: 0.63-1.45), metabolically healthy overweight (MHOW) (OR = 0.80, 95% CI: 0.51-1.26), and metabolically healthy obesity (MHO) (OR = 1.03, 95% CI: 0.65-1.64). Compared with MHNW males, the odds of moderate/severe depressive symptoms were increased in all other BMI category-metabolic health groups for males and females. Our relatively small sample size and cross-sectional design did not allow us to robustly establish causality. The odds of experiencing moderate/severe depressive symptoms were increased in metabolically unhealthy older adults regardless of normal weight or obesity, with the odds of having moderate/severe depressive symptoms being higher in females than in males.

Metabolically Unhealthy Normal Weight: Prevalence and Associated Factors in an Adult Population from Northwest Colombia.

Individuals with a normal weight may have metabolic alterations at risk for chronic non-communicable diseases. The prevalence of this condition and associated factors have not been reported in Latin American populations. We aimed to estimate the presence and associated factors of Metabolically Unhealthy Normal Weight (MUNW) in adults from a public program for the control and prevention of chronic diseases in Medellín, Colombia. Cross-sectional study. Overweight and normal weight were characterized according to the absence or presence of one or more components of the metabolic syndrome, obtaining four phenotypes: Metabolically Healthy Normal Weight (MHNW), MUNW (phenotype of interest), Metabolically Healthy Overweight (MHO), and Metabolically Unhealthy Overweight (MUO). The association of these phenotypes with sociodemographic variables of lifestyles and increased waist circumference was conducted by using logistic regression. In 37,558 individuals (72.7% women), the prevalence of MUNW was 23.3%. Among the additional phenotypes, MUO was found to be more prevalent (71.6%), while MHNW and MHO were very slightly common, 2% and 3.1%, respectively. In a multiple model, the factors associated with MUNW were age over 60 years (trend [OR 1.56 95% CI 0.97-2.52] p-value = 0.066), living in a rural area ([OR 1.58 95% CI 1.09-2.29] p-value = 0.015), and increased waist circumference ([OR 1.68 95% CI 1.45-1.95] p-value < 0.001). Male gender was inversely associated with all phenotypes (P < 0.05). Almost a quarter of the analyzed population presented MUNW. People living in a rural area and over 60 years old were more likely to present MUNW. Men were less likely to present the weight phenotypes studied, although they could have been underrepresented.

Associations between metabolic phenotypes and diabetes risk: A systematic review and meta-analysis

IntroductionIt seems that the risk of developing diabetes cannot be predicted solely based on weight or BMI. Metabolic phenotypes might offer a more precise tool for identifying patients at higher risk of diabetes, thus enabling more effective and targeted preventive interventions. ObjectiveTo determine the association between these metabolic phenotypes and the risk of diabetes. MaterialsSystematic Review (SR) with a meta-analysis of cohort studies. The search was carried out in four databases: Pubmed/Medline, SCOPUS, Web of Science, and EMBASE. Metabolic states were classified into six groups: Metabolically Healthy Normal Weight (MHNW), Metabolically Unhealthy Normal Weight (MUNW), Metabolically Healthy Overweight (MHOW), Metabolically Unhealthy Overweight (MUOW), Metabolically Healthy Obesity (MHO), and Metabolically Unhealthy Obesity (MUO). Association measures were presented as odds ratios (OR) and hazard ratios (HR) along with their 95 % confidence intervals (CI95%). ResultsA total of six studies were evaluated. For the meta-analysis, only studies using OR as the measure of association were included. Compared with individuals with MHNW, a statistically significant association was found for MUNW (OR: 1.82; CI95% 1.62, 2.04), MHOW (OR: 1.19; CI95% 1.07, 1.32), MUOW (OR: 2.44; CI95% 2.19, 2.72), MHO (OR: 2.14; CI95% 1.52, 3.01), and MUO (OR: 3.94; CI95% 3.28, 4.74). ConclusionsMetabolic phenotypes are significantly associated with the risk of diabetes, regardless of BMI. Further research in this field is required, and should be conducted in other regions of the world where obesity and diabetes rates are rapidly increasing.

Assessment of Metabolic Syndrome Risk Based on Body Size Phenotype in Korean Adults: Analysis of Community-based Cohort Data

Purpose: Body size phenotypes can be regarded as an indicator of cardiovascular incidence risk factors. The aim of this study was to investigate the incidence of metabolic syndrome among metabolically healthy adults according to body size phenotype using Korean Genome &amp; Epidemiology Study [KoGES]-community based cohort from 2001-2002 to 2014.Methods: A prospective population-based cohort including 5,068 adults aged 40-69 years free from metabolic syndrome was included and divided into three phenotypes: metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), and metabolically healthy obesity (MHO). The time dependent Cox’s proportional hazards regression model was used to estimate the incidence of the metabolic syndrome after a follow-up 14 years.Results: In the demographic, health related variables-adjusted model (Model 3), the hazard ratio of the metabolic syndrome was 1.92 (CI 1.54-2.40) for MHOW, 2.88 (CI 2.32-3.58) for MHO among males, 1.89 (CI 1.51-2.37) for MHOW, 2.34 (CI 1.88-2.90) for MHO for females compared to MHNW, respectively.Conclusion: In conclusion, MHOW and MHO in both males and females increased the risk of metabolic syndrome compared to MHNW. Obesity management should be considered even in the absence of metabolic syndrome. Programs and resources should be provided for prevention of cardiovascular diseases and better quality of life.

Association between different metabolic phenotypes of obesity and thyroid disorders among Chinese adults: a nationwide cross-sectional study.

Increased body mass index (BMI) and metabolic abnormalities both have potential associations with thyroid disease. The aim of this study was to investigate the correlation between different metabolic phenotypes of obesity and thyroid disorders using nationwide data from China. Data were collected from a cross-sectional survey called the Thyroid Disorders, Iodine Status, and Diabetes Epidemiological Survey conducted between 2015 and 2017 in China. A total of 69007 subjects aged 18 years or older were defined and divided into six groups on the basis of BMI and metabolic health status: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). We estimated the odds ratios (ORs) and confidence intervals [CIs] for different thyroid disorders according to metabolic phenotypes using multivariate logistic regression models. In our study, we found that the prevalence of subclinical hypothyroidism was almost as high in the MUNW group as in the MUO group, the prevalence of overt hyperthyroidism was highest in the MUNW group and Graves' disease was highest in the MHO group. Our results also suggested that the prevalence of goiter and thyroid nodules increased with increased BMI values and that the MUO group had the highest incidence. Multivariate logistic regression analysis indicated that subjects with unhealthy metabolic phenotypes (MUNW, MUOW, and MUO) all had an increased risk of subclinical hypothyroidism, regardless of their BMI. MUNW subjects had an approximately 1.6-fold higher risk of overt hyperthyroidism and a 1.8-fold higher risk of Graves' disease than their metabolically healthy counterparts (MHNW). The present study also demonstrated that the MUO group had the highest risk of goiter and thyroid nodules among the metabolic phenotypes of obesity. Based on our study, we found that metabolic abnormalities and obesity play different roles in various thyroid diseases. Metabolically unhealthy individuals, both with and without obesity, have a higher risk of thyroid disorders than metabolically healthy individuals without obesity.

Abstract P112: Association of Obesity Sub-Phenotypes With Indices of Cardiac Remodeling in the Framingham Heart Study

Introduction: Many studies have reported that obesity-related metabolic abnormalities (such as diabetes and hypertension) lead to myocardial dysfunction and adverse cardiac remodeling. However, it is challenging to disentangle the effects of obesity from those of its associated metabolic comorbidities. Comparing metabolically healthy adults across different body mass index (BMI) categories can help delineate the independent effect of obesity on cardiac remodeling from the impact of metabolic comorbidities. Hypothesis: We hypothesized that metabolically healthy overweight (MHOW) and obese (MHO) participants will manifest echocardiographic measures consistent with adverse cardiac remodeling relative to their metabolically healthy counterparts with a normal BMI. Methods: A total of 6,639 participants of the Framingham Heart Study (mean age of 49 years, 55% women) underwent routine physical examinations and echocardiography. Participants who had prevalent cardiovascular disease were excluded. Individuals were classified into obesity sub-phenotypes based on their metabolic health (metabolically healthy or metabolic unhealthy) and BMI categories (normal weight, overweight, or obese). Obesity sub-phenotypes were related to echocardiographic measures using multivariable regression models adjusting for age, sex, smoking, resting systolic blood pressure (BP), resting diastolic BP, and resting heart rate. Results: Compared to metabolically healthy normal weight, MHOW and MHO were associated with increased left ventricular mass (LVM), left atrium end-systolic dimension (LAD), global longitudinal strain (GLS), and the ratio of early trans-mitral flow velocity to early diastolic mitral annulus velocity (E/e’) ( Table ). Conclusions: Evidence of adverse cardiac remodeling was seen in individuals with increasing BMI regardless of metabolic health classification. These findings indicate that MHOW and MHO states are associated with adverse cardiac remodeling.

Transition patterns of metabolism-weight phenotypes over time: A longitudinal study using the multistate Markov model in China.

A change in weight or metabolic status is a dynamic process, yet most studies have focused on metabolically healthy obesity (MHO) and the transition between MHO and metabolically unhealthy obesity (MUO); therefore, they have not fully revealed the nature of all possible transitions among metabolism-weight phenotypes over the years. This was a longitudinal study based on a retrospective health check-up cohort. A total of 9,742 apparently healthy individuals aged 20-60 years at study entry were included and underwent at least two health check-ups. Six metabolism-weight phenotypes were cross-defined by body mass index (BMI) categories and metabolic status as follows: metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), MHO, metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and MUO. A multistate Markov model was used to analyse all possible transitions among these phenotypes and assess the effects of demographic and blood indicators on the transitions. The transition intensity from MUNW to MHNW was the highest (0.64), followed by the transition from MHO to MUO (0.56). The greatest sojourn time appeared in the MHNW state (3.84 years), followed by the MUO state (2.34 years), and the shortest sojourn time appeared in the MHO state (1.16 years). Transition intensities for metabolic improvement gradually decreased with BMI level as follows: 0.64 for MUNW to MHNW, 0.44 for MUOW to MHNW, and 0.27 for MUO to MHO; however, transition intensities for metabolic deterioration, including MHNW to MUNW, MHOW to MUOW, and MHO to MUO, were 0.15, 0.38, and 0.56, respectively. In the middle-aged male group, elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), and uric acid (UA) increased the risk of deterioration in weight and metabolic status and decreased the possibility of improvement. Maintaining a normal and stable BMI is important for metabolic health. More attention should be given to males and elderly people to prevent their progression to an unhealthy metabolic and/or weight status. MHO is the most unstable phenotype and is prone to convert to the MUO state, and individuals with abnormal ALT, AST and UA are at an increased risk of transitioning to an unhealthy weight and/or metabolic status; therefore, we should be alert to abnormal indicators and MHO. Intervention measures should be taken early to maintain healthy weight and metabolic status.

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.

Association between metabolic phenotype and urinary albumin‐creatinine ratio in Chinese community adults: A cross‐sectional study

BackgroundUrinary albumin‐creatinine ratio (UACR) is a sensitive marker of kidney injury. This study analyzed the prevalence of different metabolic phenotypes and investigated their relationship with UACR in Chinese community adults.MethodsThis study involved 33 303 participants over 40 years old from seven centers across China. They were stratified into six groups according to their body mass index (BMI) and metabolic status: 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). Increased albuminuria was defined as a UACR ≥30 mg/g.ResultsThe percentages of MHNW, MHOW, MHO, MUNW, MUOW, and MUO were 27.6%, 15.9%, 4.1%, 19.8%, 22.5%, and 9.6%, respectively. Multiple logistic regression analysis showed that the MHO group (odds ratio [OR] 1.205; 95% CI, 1.081‐1.343), MUNW group (OR 1.232; 95% CI, 1.021‐1.486), MUOW group (OR 1.447; 95% CI, 1.303‐1.607), and MUO group (OR 1.912; 95% CI, 1.680‐2.176) were at higher risk of increased albuminuria compared to the MHNW group. Subgroup analysis indicated that the risk of increased albuminuria was further elevated among regular smokers in men aged 40 to 55 years old with abdominal obesity.ConclusionsAmong Chinese community adults, increased albuminuria was associated with increased BMI whether metabolism was normal or not, and those with abnormal metabolism were at greater risk of increased albuminuria than those with normal metabolism. These findings suggest that overweight or obesity or metabolic abnormalities are risk factors for chronic kidney disease.

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.

Relationships Between Metabolic Body Composition Status and Rapid Kidney Function Decline in a Community-Based Population: A Prospective Observational Study.

Obesity and metabolic syndrome are strong risk factors for incident chronic kidney disease (CKD). However, the predictive accuracy of metabolic body composition status (MBCS), which combines the status of obesity and metabolic syndrome, for rapid kidney function decline (RKFD) is unclear. The aim of this study was to investigate the relationship between MBCS and RKFD in a healthy population in a prospective community-based cohort study. In the current study, we followed changes in renal function in 731 people residing in northern Taiwan for 5 years. The participants were divided into four groups according to their MBCS, including metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy overweight (MUOW). We evaluated traditional risk factors for CKD and metabolic profiles. The primary outcome was RKFD, which was defined as a 15% decline in estimated glomerular filtration rate (eGFR) within the first 4 years, and a reduction in eGFR which did not improve in the 5th year. During the study period, a total of 731 participants were enrolled. The incidence of RKFD was 17.1% (125/731). Multiple Cox logistic regression hazard analysis revealed that age, cerebrovascular accident, eGFR, urine albumin-to-creatinine ratio, use of painkillers, depressive mood, MUNW and MUOW were independent predictors of RKFD. After adjusting for age, sex, eGFR and total cholesterol, the participants with MUNW and MUOW had higher hazard ratios (HRs) for RKFD [HR: 2.19, 95% confidence interval (CI): 1.22–3.95 for MUNW; HR: 1.86, 95% CI: 1.21–2.87 for MUOW] than those with MHNW. Similar results were also observed in subgroup analysis of those aged above 65 years. On the basis of the results of this study, we conclude that MBCS was independently associated with RKFD, especially in the older adults. On the basis of our results, we suggest that MUNW and MUOW should be considered as risk factors for RKFD.

Metabolically unhealthy and overweight phenotypes are associated with increased levels of inflammatory cytokines: a population-based study

ObjectiveThe aim of this study was to determine the association between cytokine levels in metabolic phenotypes. Our hypothesis was that an unhealthy metabolic profile is associated to higher levels of proinflammatory cytokines. MethodsThe study sample was composed of 743 Brazilian adults classified in four phenotypes: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight (MHOW), and metabolically unhealthy overweight (MUOW). Sociodemographic, anthropometric, clinical, and biochemical parameters were collected. Six different cytokines were analyzed from blood samples using the CBA Human Inflammatory cytokines kit and the values divided in quartiles for analysis. Logistic regression models were constructed to assess the association between metabolic phenotypes and cytokines concentrations, adjusted for potential confounders and P < 0.05 was used. ResultsThe MUOW phenotype showed a higher risk for increased levels of all cytokines analyzed compared with the reference group (MHNW). ConclusionsThese results indicated that excess weight and altered metabolic profile are related to inflammation, especially when both conditions are associated, possibly linked to visceral adiposity. Therefore, the categorization of metabolic phenotypes in populations is an important factor for prevention of chronic diseases, as inflammation is associated with cardiovascular risk and obesity is not the only influencing factor.

Metabolically healthy overweight/obesity and cancer risk: A representative cohort study in Taiwan

ObjectiveMany cancers are caused by overweight; however, cancer risk varies among individuals with obesity. Few studies are addressing the relationship between metabolic obesity phenotypes and cancer. This study investigates the association between metabolically healthy overweight (MHOW) or metabolically healthy obesity (MHO) and cancer incidence. MethodsIn a nationwide, representative community-based prospective cohort study, 5734 Taiwanese adults were classified into eight phenotypes according to body mass index (underweight <18.5; normal weight 18.5–23.9; overweight 24–26.9; and obese ≥27 kg/m2) and metabolic status (healthy/unhealthy). Participants with healthy cardiometabolic blood profiles included in the metabolic syndrome criteria and an absence of hypertension, diabetes, and hyperlipidemia were considered metabolically healthy. We used the Cox proportional hazards models to estimate the adjusted hazard ratio (HR) and 95% confidence intervals (95% CI). ResultsDuring 73,389 person-years of follow-up, 428 incident cancers were identified. Compared to the participants with metabolically healthy normal weight, participants with MHOW (adjusted HR 1.39, 95% CI, 0.90–2.13) or MHO (adjusted HR 1.07, 95% CI, 0.51–2.22) had a tendency toward a higher risk of cancer. These associations were stronger in MHOW (adjusted HR 1.77, 95% CI, 1.09–2.86) or MHO (adjusted HR 1.39, 95% CI, 0.66–2.93) participants younger than 65 years. ConclusionsThis study was the first to investigate the impact of metabolic obesity phenotype on the incidence of cancer in the Taiwanese population. Even in the absence of metabolic abnormalities, overweight, and obesity may cause a modest increase in the risk of developing cancer.

Metabolic Phenotypes and Chronic Kidney Disease: A Cross-Sectional Assessment of Patients from a Large Federally Qualified Health Center.

The purpose of this study is to determine if renal function varies by metabolic phenotype. A total of 9599 patients from a large Federally Qualified Health Center (FQHC) were included in the analysis. Metabolic health was classified as the absence of metabolic abnormalities defined by the National Cholesterol Education Program Adult Treatment Panel III criteria, excluding waist circumference. Obesity was defined as body mass index >30 kg/m2 and renal health as an estimated glomerular filtration rate (eGFR) >60 mL/min/1.73 m2. Linear and logistic regressions were used to analyze the data. The metabolically healthy overweight (MHO) phenotype had the highest eGFR (104.86 ± 28.76 mL/min/1.72 m2) and lowest unadjusted odds of chronic kidney disease (CKD) (OR = 0.46, 95%CI = 0.168, 1.267, p = 0.133), while the metabolically unhealthy normal weight (MUN) phenotype demonstrated the lowest eGFR (91.34 ± 33.28 mL/min/1.72 m2) and the highest unadjusted odds of CKD (OR = 3.63, p < 0.0001). After controlling for age, sex, and smoking status, the metabolically unhealthy obese (MUO) (OR = 1.80, 95%CI = 1.08, 3.00, p = 0.024) was the only phenotype with significantly higher odds of CKD as compared to the reference. We demonstrate that the metabolically unhealthy phenotypes have the highest odds of CKD compared to metabolically healthy individuals.

Metabolically healthy overweight adolescents: definition and components.

PurposeIn adolescents, the definition and clinical implications of metabolically healthy overweight (MHO) status have not been established. This study aimed to investigate the prevalence of MHO according to its most widespread definition, which is based on metabolic syndrome (MS), and to explore further metabolic indicators such as Homeostatic Model Assessment of Insulin Resistance, total cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase, and C-reactive protein levels, together with metabolic health predictors in a sample of adolescents attending a pediatric obesity clinic.MethodsData from 487 adolescents categorized as overweight (52.6% females, 88.1% white), with a mean body mass index (BMI) z-score of 2.74 (±1.07 standard deviation [SD]), and a mean age of 14.4 years (±2.2 SD) were cross-sectionally analyzed. From this original sample, a subsample of 176 adolescents underwent a second assessment at 12 (±6 SD) months for longitudinal analysis.ResultsFrom the 487 adolescents originally analyzed, 200 (41.1%) were categorized as MHO, but only 93 (19.1%) had none of the metabolic indicators considered in this study. According to longitudinal analysis, 30 of the 68 adolescents (44%) categorized as MHO at baseline became non-MHO over time. BMI z-score was the best predictor of metabolic health both in cross-sectional and longitudinal analyses. Increased BMI z-score reduced the odds of being categorized as MHO (odds ratio [OR], 0.6; 95% confidence interval [CI], 0.4–0.9; P=.008) and increased the odds of having hypertension (OR 2.1, 95% CI: 1.4–3.3, P=0.001), insulin resistance (OR, 2.4; 95% CI, 1.4–4.1, P=0.001), or a proinflammatory state (OR, 1.2; 95% CI, 1.1–1.3, P=0.002).ConclusionsDiagnosis of MHO should not be exclusively based on MS parameters, and other metabolic indicators should be considered. Adolescents categorized as overweight should participate in weight-management lifestyle interventions regardless of their metabolic health phenotype.

Association of metabolic phenotypes, grip strength and diabetes risk: The 15-year follow-up of The North West Adelaide Health Study, Australia

BackgroundThe association of diabetes risk in the long-term, metabolic phenotypes (MP) and muscle strength is unclear. We aimed to investigate the association between strictly defined MP, grip strength (GS) and diabetes. Material and methodsMP were defined according to BMI and presence of any individual metabolic abnormality for ≥18 years, in participants of the North West Adelaide Health Study (Australia) free of diabetes at baseline. The association of MP and dominant hand GS with incident diabetes over 15-years follow-up and the moderation effect of GS on the association between diabetes and MP were investigated by logistic regression models. ResultsOf 3039 participants followed over 13.3 years (SD 2.6), 236 (7.8%) developed diabetes. Compared to the metabolically healthy (MH) normal weight phenotype, the metabolically unhealthy (MU) overweight (OR 6.15, 95%CI 2.43–15.59) and obese (OR 12.32, 95%CI 4.97–30.52) phenotypes were associated with a high risk of diabetes, but not the MU normal weight (OR 1.73, 95%CI 0.57–5.25), MH overweight (OR 1.15, 95%CI 0.31–4.31) or MH obese phenotypes (OR 0.77, 0.07–8.89). GS was inversely associated with diabetes (OR 0.97, 95% CI 0.95–0.99) and attenuated the risk associated with MU overweight (beta = −0.296, p = 0.039) and MU normal weight (beta = −0.773; p for interaction = 0.009). ConclusionStrictly defined MP (rather than based on metabolic syndrome criteria) and GS, a proxy of muscle strength, might be useful for stratifying the risk of diabetes in the long-term. Improving muscle strength might be an important strategy to reduce diabetes risk.

All-cause mortality in metabolically healthy individuals was not predicted by overweight and obesity.

BACKGROUNDMetabolically healthy obesity (MHO) and metabolically healthy overweight (MH-OW) have been suggested to be important and emerging phenotypes with an increased risk of cardiovascular disease (CVD). However, whether MHO and MH-OW are associated with all-cause mortality remains inconsistent.METHODSThe association of MHO and MH-OW and all-cause mortality was determined in a Chinese community-based prospective cohort study (the Kailuan study), including 93,272 adults at baseline. Data were analyzed from 2006 to 2017. Participants were categorized into 6 mutually exclusive groups, according to BMI and metabolic syndrome (MetS) status. The primary outcome was all-cause death, and accidental deaths were excluded.RESULTSDuring a median follow-up of 11.04 years (interquartile range, 10.74–11.22 years), 8977 deaths occurred. Compared with healthy participants with normal BMI (MH-NW), MH-OW participants had the lowest risk of all-cause mortality (multivariate-adjusted HR [aHR], 0.926; 95% CI, 0.861–0.997), whereas there was no increased or decreased risk for MHO (aHR, 1.009; 95% CI, 0.886–1.148). Stratified analyses and sensitivity analyses further validated that there was a nonsignificant association between MHO and all-cause mortality.CONCLUSIONSOverweight and obesity do not predict increased risk of all-cause mortality in metabolic healthy Chinese individuals.FUNDINGNational Natural Science Foundation of China (NSFC; 81673247, 81872682 and 81773527), the NSFC Joint Project, and the Australian National Health and Medical Research Council (NHMRC; NSFC 81561128020-NHMRC APP1112767).

The Impact of BMI Categories on Metabolic Abnormality Development in Chinese Adults Who are Metabolically Healthy: A 7-Year Prospective Study.

PurposeTo determine what metabolic abnormalities develop frequently among metabolically healthy adults over time according to different baseline body mass index (BMI) categories.Patients and MethodsA prospective cohort study was performed on 10,805 adults, who were metabolically healthy at the time of the 2008 survey. Participants were divided into four groups: metabolically healthy obese (MHO), metabolically healthy overweight (MHOW), metabolically healthy normal-weight (MHN), and metabolically healthy underweight (MHU). Modified Poisson regression models were used to evaluate the relationship of BMI with the development of metabolic abnormalities. Association rule mining was used to identify the most frequent abnormalities that developed over time.ResultsCompared with the MHN group, the adjusted relative risks of the MHO group were 1.57 (95% CI: 1.09–2.27) and 2.08 (95% CI: 1.59–2.73) for developing elevated fasting glucose and elevated blood pressure, respectively, after adjusting for lifestyle behaviours and dietary factors. At the end of follow-up, 33 (19.1%) MHO subjects and 342 (16.6%) MHOW subjects had elevated blood pressure as the predominant metabolic syndrome component, whereas 236 (9.0%) MHU subjects had elevated plasma glucose. The results were similar after stratification by sex.ConclusionMHO and MHOW subjects developed elevated blood pressure most frequently, and MHU subjects developed elevated blood glucose most commonly, regardless of sex.

Comparative Study of Inflammatory and Oxidative Stress Biomarkers in Different Metabolically Healthy Obesity Phenotypes

Aims: Obesity is the major contributor of the metabolic syndrome (MetS), but a unique phenotype of obesity known as metabolically healthy obese (MHO) shows healthier metabolic profile; however understanding of their biochemical correlates is poorly understood. Obesity is defined by Body mass index (BMI), but controversy exists regarding ethnic-specific BMI cut-offs. The present study used the Asian Indian BMI cut-offs to assess relationships of MHO phenotypes with oxidative stress (OS) and inflammation. Methods: In this case-control study, 299 metabolically-healthy (MH) respondents were divided into four groups as per Asian criteria for obesity: MH non-obese (MHNO), MH overweight (MHOW), MHO and MH severely obese (MHSO). Their oxidative stress and pro-inflammatory markers were measured. Results: Levels of hydroxyl radicals (·OH), fluorescent oxidation products (FLOP), MDA, PCO and inflammatory markers CRP, TNF-α, IL-6 were highest in MHSO phenotype followed by the MHO, MHOW and MHNO groups (p > 0.0001), whereas antioxidant markers, CuZn-SOD, catalase, glutathione peroxidase and total antioxidant activity followed the reverse trend. The MHNO and MHOW groups showed significant difference with regard to (·OH) radicals and FLOP. Moreover, ·OH radicals, FLOP and inflammatory markers were significantly correlated to BMI in MHSO and MHO but not in MHNO and MHOW group. Conclusion: The MHO and MHSO phenotype display differences in terms of OS and inflammatory markers at lower BMI cut-offs, indicating that they may be on the way to becoming “unhealthy” obese. The lower BMI cut-offs proposed by Indian Consensus Group would help in understanding of manifestation of metabolic syndrome.