Metabolically Unhealthy 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.

Myocardial mechano-energetic efficiency is not impaired in patients with metabolically healthy overweight and obesity.

Reduced myocardial mechano-energetic efficiency (MEE) was associated with BMI. Subgroups of individuals with increased BMI but favorable cardiovascular risk profile were identified as individuals with "metabolically healthy overweight" (MHOW) and "metabolically healthy obesity" (MHO), respectively. We aim to investigate whether those with MHOW/MHO, defined as those having none of the components of metabolic syndrome, exhibit impaired MEE compared with their unhealthy counterparts. Myocardial MEE per gram of left ventricular mass (MEEi) was assessed by echocardiography in 2190 nondiabetic individuals participating in the CATAnzaro MEtabolic RIsk factors (CATAMERI) study who were divided, according to BMI and metabolic status, into groups of individuals with metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), MHOW, metabolically unhealthy overweight (MUOW), MHO, and metabolically unhealthy obesity (MUO). After adjusting for age and sex, no differences in myocardial MEEi were observed among individuals with MHNW, MHOW, and MHO (p = 0.56). Myocardial MEEi was comparable among individuals with MUNW, MUOW, and MUO (p = 0.21). Individuals with MHNW, MHOW, and MHO displayed significantly higher myocardial MEEi compared with their unhealthy counterparts. Increased BMI is not an obligate determinant for reduced myocardial MEEi. Other known components of metabolic syndrome rather than increased BMI contributed to reduced myocardial MEEi.

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.

Association between sugar-free beverage intake and childhood obesity among Chinese children and adolescents.

The relationship between sugar-free beverage (SFB) intake and childhood obesity among Chinese children is unknown. To describe the status of SFB consumption among children and adolescents in China and assess the association between SFB intake and different types of obesity. The study was based on the baseline data of an ongoing cohort project named Evaluation and Monitoring on School-based Nutrition and Growth in Shenzhen (EMSNGS). Food frequency questionnaires were used to collect information on SFB consumption in 3227 students aged 9-17. Physical and clinical examinations were conducted by trained investigators and clinicians. Multivariable binary logistic regression models were performed to assess the association between SFB intake and general obesity, overweight/obesity, abdominal obesity, metabolically unhealthy overweight (MUOW)/metabolically unhealthy obesity (MUO). The median age of the participants was 13.28 years. Among the participants, 55.2% were boys, and 66.1% were adolescents. The median SFB consumption was 16.67 mL/d. After adjusting for potential confounding factors, each 100 mL increase in daily SFB intake was associated with an increased risk of overweight/obesity (OR = 1.14; 95%CI: 1.06-1.23), abdominal obesity (OR = 1.12; 95%CI: 1.03-1.23), and MUOW/MUO (OR = 1.12; 95%CI: 1.02-1.21), respectively. Stratified analyses showed that family income may have an impact on the association between SFB intake and overweight/obesity (P for interaction = 0.021) and abdominal obesity (P for interaction = 0.031). SFB intake was positively associated with childhood obesity in Chinese children, particularly among individuals with high-income families.

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.

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.

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.

Prevalence of metabolically unhealthy obesity, overweight, and normal weight and the associated risk factors in a southern coastal region, Iran (the PERSIAN cohort study): a cross-sectional study

BackgroundObesity is a substantial risk factor for cardiovascular and metabolic diseases. Epidemiologic studies have shown that some obese and overweight individuals are metabolically healthy. We aimed to determine the prevalence of metabolically unhealthy obesity (MUO), metabolically unhealthy overweight (MUOW), and metabolically unhealthy normal weight (MUNW) in a southern coastal area of Iran, Bandare-Kong Non-Communicable Diseases (BKNCD) Cohort Study.MethodsThis population-based study included the participants of BKNCD, as part of the Prospective Epidemiological Research Studies in IrAN (PERSIAN). Metabolic health was defined as not fulfilling the metabolic syndrome (MetS) criteria.ResultsAmong the 3917 participants in this study with the mean age of 48.29 ± 9.39 years, including 1691 (43.2%) males, the age- and sex-standardized prevalence of MUO, MUOW, and MUNW was 13.9, 16.8, and 6.4%, respectively. Binary logistic regression analysis revealed that the adjusted odds of all metabolically unhealthy states were higher in older age groups, except for MUO whose adjusted odds were lower in the 65–70 age group compared to the 55–64 age group. Illiteracy was significantly correlated with MUOW (adjusted OR: 1.43, 95% CI 1.09–1.87, P = 0.010); however, it was not associated with MUO or MUNW. Higher body mass index (BMI) was significantly correlated with MUNW but it was not associated with MUOW or MUO. Higher waist circumference (WC) was also significantly associated with all metabolically unhealthy states.ConclusionThe age- and sex-standardized prevalence of MUO, MUOW, and MUNW was 13.9, 16.8, and 6.4%, respectively in the current study. Advanced age and higher WC were significantly correlated with all metabolically unhealthy states, while illiteracy and higher BMI were only associated with MUOW and MUNW, respectively. Metabolic health rather than weight loss should be the focus and objective of public prevention programs.

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

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

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.

Sex influence in ischemic stroke severity and outcome among metabolically unhealthy overweight patients

IntroductionThe role of metabolically unhealthy (MU) overweight in influencing stroke prognosis has not been fully assessed and conclusive data about the impact of sex are lacking. Aim of this study was to evaluate the possible correlations among MU overweight, sex and ischemic stroke severity and outcome. MethodsAll consecutive patients with acute non-lacunar ischemic stroke observed over a one-year period were considered. MU subjects were selected and classified according to the body mass index (BMI) values. The relationships among sex, BMI and National Institutes of Health Stroke Scale (NIHSS) score at admission and modified Rankin scale (mRS) score at discharge were analysed with multivariate models. Results180 patients were included. A significantly increasing trend in the NIHSS (p = .030) and in the mRS (p = .001) scores along with the increase of the BMI category was detected in women. Men showed a significantly decreasing trend in the NIHSS (p = .040) and a non-significant decreasing trend in the mRS (p = .290) scores with increasing BMI category. ConclusionsOur findings show that sex can influence stroke severity and outcome among MU patients. The worse outcome observed in women suggest the need of considering differential sex-based clinical approaches in stroke overweight patients.

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

Background and aimsIt is inconclusive whether obesity itself or metabolic abnormalities are linked to chronic kidney disease (CKD). The aim of this study was to examine the association between different subtypes of obesity and metabolic abnormalities with CKD in adults. Methods and resultsThis study enrolled 14,983 eligible subjects stratified into metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and metabolically unhealthy obesity (MUO) according to body mass index and metabolic syndrome status (ATP-III criteria). The metabolic healthy phenotype was defined as the absence of both metabolic syndrome and any known diabetes, coronary artery disease, stroke, hypertension or dyslipidemia. Early and advanced CKD were defined as eGFR<60, proteinuria, or structural abnormalities as detected by renal sonography. The prevalence of CKD was 2.5, 3.0, 4.0, 10.6, 9.5, and 10.5% in subjects with MHNW, MHOW, MHO, MUNW, MUOW, and MUO, respectively. In the multivariate analysis, the MUNW (OR:2.22, P < 0.001), MUOW (OR:2.22, P < 0.001), and MUO (OR:2.45, P < 0.001) groups were associated with early CKD. For advanced CKD, the OR was 2.56 (P < 0.001), 2.31 (P < 0.001), and 3.49 (P < 0.001) in the MUNW, MUOW, and MUO groups, respectively. The associated risks of early and advanced CKD were not significant in the MHOW and MHO group. MUOW and MUO were associated with higher risk of CKD compared with MHOW and MHO after adjusting other variables. ConclusionsMetabolic abnormalities, but neither overweight nor obesity, were associated with a higher risk of CKD in adults.

320-LB: Metabolically Unhealthy Overweight Individuals Have High Lysophosphatide Levels, Phospholipase Activity, and Oxidative Stress

Background and Aims: Metabolically unhealthy overweight (MUO) individuals and metabolically healthy overweight (MHO) individuals differ in biomarkers of atherogenesis. Metabolomic approaches enable studies of the metabolic variables underlying these differences. Methods: We determined the metabolomes in plasma samples from 34 MUO and 34 MHO individuals matched for sex, age, and body mass index (BMI) to identify potential metabolic markers or pathways associated with atherogenic traits. Results: This analysis revealed that the MUO group had significantly higher levels of glycolic acid, 6 lysophosphatidylethanolamines (lysoPEs), and 12 lysophosphatidylcholines (lysoPCs). Although the two groups had similar total body fat percentages and lean body masses, MUO individuals had larger visceral fat areas (VFAs). They also had greater circulating lipoprotein-associated phospholipase A2 (Lp-PLA2) activity and higher levels of oxidized low-density lipoprotein (ox-LDL) and urinary 8-epi-prostaglandin F2α (8-epi-PGF2α), reflecting higher risks for oxidative and lipid-related tissue damage. The following measures were positively correlated: VFA and ox-LDL; ox-LDL and Lp-PLA2 activity; and lysoPC, lysoPE, and 8-epi-PGF2α levels. Chronic plasma lysoPC increases were associated with atherogenic traits, higher levels of mean ox-LDL, 8-epi-PGF2α, Lp-PLA2, and visceral fat accumulation in MUO compared to MHO individuals. Conclusions: This panel of plasma metabolites distinguishes MUO from MHO individuals and will enable future research on fat dysregulation and obesity. Disclosure Y. Han: None. M. Kim: None. H. Yoo: None. J. Lee: None. Funding National Research Foundation of Korea (NRF-2017R1C1B2007195, NRF-2012M3A9C4048762)

Metabolically unhealthy overweight individuals have high lysophosphatide levels, phospholipase activity, and oxidative stress

Metabolically unhealthy overweight (MUO) individuals and metabolically healthy overweight (MHO) individuals differ in biomarkers of atherogenesis. Metabolomic approaches enable studies of the metabolic variables underlying these differences. We determined the metabolomes in plasma samples from 34 MUO and 34 MHO individuals matched for sex, age, and body mass index (BMI) to identify potential metabolic markers or pathways associated with atherogenic traits. This analysis revealed that the MUO group had significantly higher levels of glycolic acid, 6 lysophosphatidylethanolamines (lysoPEs), and 12 lysophosphatidylcholines (lysoPCs). Although the two groups had similar total body fat percentages and lean body masses, MUO individuals had larger visceral fat areas (VFAs). They also had greater circulating lipoprotein-associated phospholipase A2 (Lp-PLA2) activity and higher levels of oxidized low-density lipoprotein (ox-LDL) and urinary 8-epi-prostaglandin F2α (8-epi-PGF2α), reflecting higher risks for oxidative and lipid-related tissue damage. The following measures were positively correlated: VFA and ox-LDL; ox-LDL and Lp-PLA2 activity; and lysoPC, lysoPE, and 8-epi-PGF2α levels. Chronic plasma lysoPC increases were associated with atherogenic traits, higher levels of mean ox-LDL, 8-epi-PGF2α, Lp-PLA2, and visceral fat accumulation in MUO compared to MHO individuals. This panel of plasma metabolites distinguishes MUO from MHO individuals and will enable future research on fat dysregulation and obesity.