Metabolically Unhealthy Normal Weight 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.

Abstract 4772: Metabolic phenotype, age acceleration, and obesity-related cancer risk in Women's Health Initiative and Atherosclerosis Risk in Communities cohorts

Abstract Introduction: While normal physiologic aging causes metabolic decline, metabolic dysregulation owing to excess nutrient intake, physical inactivity, and obesity may drive accelerated aging. However, the relationship of accelerated aging with metabolic dysfunction across different levels of BMI (i.e., metabolic obesity phenotype) and whether it may mediate the relationship with obesity-related cancer (ORC) risk is not well established. Methods: To test this hypothesis, we are leveraging existing blood-based DNA methylation (DNAm) data within the Women’s Health Initiative (WHI) and the Atherosclerosis Risk in Communities (ARIC) cohorts. Weight, height and blood pressure were measured by trained technicians in the respective cohorts and fasting blood glucose, triglycerides and HDL cholesterol were quatified by standard clinican assays. Metabolic obesity phenotypes are classified as metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obese (MHO), and metabolically unhealthy overweight/obese (MUO) when defined by ATP III criteria. Age acceleration was measured as a residual from regressing pan-tissue Horvath’s DNAmAge on chronological age. Results: The mean baseline age was 64 ± 6.6 years and 54 ± 5.7 year among WHI and ARIC participants, respectively and the age acceleration was measured at an mean follow-up time of 16 years from baseline for WHI and 3.5 years from baseline for ARIC cohort. The outcome of interest, ORC incidence was observed among 474 participants of which 387 were women. The median time for cancer diagnosis from baseline was 25 year and from DNAm measurement was 18.6 years. Of the 4341 (70% women) participants included in the analysis, 23.9% were MHNW, 3.2% MUNW, 39% MHO, and 33.5% MUO. A 31.5% of MHNW, 40.4% of MUNW, 28.7% of MHO, and 35.2% of MUO showed age acceleration. MUNW (OR 1.47, 95% CI: 1.03-2.11) and MUO (OR 1.18, 95% CI: 1.00-1.40) individuals at baseline had increased odds of experiencing age acceleration at a follow-up visit. The risk of all ORC combined was significantly elevated among MUO (HR 1.63, 95% CI: 1.27-2.09) compared to MHNW. Age acceleration at the follow-up visit was not significantly associated with ORC risk (HR 0.91, 95% CI: 0.75-1.11). These analyses were not adjusted for confounders. Conclusions: MUNW and MUO individuals had increased odds of age acceleration at a follow-up visit. Furthermore, MUO individuals were at an elevated risk of ORC, compared to individuals with MHNW. Accelerated aging does not appear to mediate this association, as accelerated aging was not associated with ORC. Citation Format: Prasoona Karra, Garnet L. Anderson, Mary C. Playdon, Sheetal Hardikar, Aladdin Shadyab, Kari E. North, Anne E. Justice, Elizabeth A. Platz, Jan Bressler, Anna Prizment, Weihua Guan, Dominique S. Michaud, Eric A. Whitsel, Brock C. Christensen, Lucas A. Salas. Metabolic phenotype, age acceleration, and obesity-related cancer risk in Women's Health Initiative and Atherosclerosis Risk in Communities cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4772.

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.

Metabolic health status and renal disorders: a cross-sectional study

Previous surveys suggests that body mass index (BMI) may be positively related to development of chronic kidney disease (CKD). However, this association might be altered by metabolic syndrome. Therefore, we aimed to evaluate the association of metabolic health status with CKD. The present cross-sectional study was carried out on 3322 representative sample of Iranian adults. Metabolic syndrome was identified based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) and BMI was assessed by anthropometric measurements. Estimated glomerular filtration rate (eGFR) was calculated by modification of diet in renal disease-Chronic Kidney Disease Epidemiology Collaboration (MDRD-EPI) formula. Subjects were categorized into four phenotypes: metabolically healthy normal weight (MHNW), metabolically healthy overweight and obesity (MHO), metabolically unhealthy normal weight (MUHNW), and metabolically unhealthy overweight and obesity (MUHO). Based on multivariate-adjusted models, the risk of CKD was significantly higher in MUHO compared with MHNW (OR: 1.48; p < 0.05). Although MUHNW and MUHO were associated with lower eGFR and albuminuria, the significant association was not observed in case of hematuria. Furthermore, subjects with kidney stones tended to be in MHO (OR: 1.42; p < 0.05) and MUHO phenotypes (OR: 1.64; p < 0.05), in comparison to the MHNW phenotype. The odds of kidney disorders were higher in adults with metabolic syndrome, regardless of BMI. However, this relationship might be strengthened by the concomitance of metabolic syndrome and obesity. To verify our findings, clarify the causality, and elucidate the underlying mechanisms, further research are warranted.

Risk of incident chronic kidney disease in metabolically healthy obesity and metabolically unhealthy normal weight: A systematic review and meta-analysis.

Studies have reported inconsistent results about the risk of incident chronic kidney disease (CKD) in people with metabolically healthy obesity (MHO). We designed this systematic review and meta-analysis to evaluate the risk of developing CKD in people with MHO and metabolically unhealthy normal weight (MUNW). We used a predefined search strategy to retrieve eligible studies from multiple databases up to June 20, 2022. Random-effects model meta-analyses were implied to estimate the overall hazard ratio (HR) of incident CKD in obesity phenotypes. Eight prospective cohort studies, including approximately 5 million participants with a median follow-up ranging between 3 and 14 years, were included in this meta-analysis. Compared to the metabolically healthy normal weight (MHNW), the mean differences in cardiometabolic and renal risk factors in MHO, MUNW, and metabolically unhealthy obesity (MUO) were evaluated with overall HR of 1.42, 1.49, and 1.84, respectively. Compared to MHNW, the mean estimated glomerular filtration rate (eGFR) and high-density lipoprotein (HDL) were significantly lower, and low-density lipoprotein (LDL), blood pressure, blood glucose, and triglycerides were higher in MHO and MUNW. In conclusion, MHO and MUNW are not benign conditions and pose a higher risk for incident CKD. Obesity, whether in the presence or absence of metabolic health, is a risk factor for CKD.

Phenotyping obesity: A focus on metabolically healthy obesity and metabolically unhealthy normal weight.

Over the past 4decades, research has shown that having a normal body weight does not automatically imply preserved metabolic health and a considerable number of lean individuals harbour metabolic abnormalities typically associated with obesity. Conversely, excess adiposity does not always equate with an abnormal metabolic profile. In fact, evidence exists for the presence of a metabolically unhealthy normal weight (MUHNW) and a metabolically healthy obese (MHO) phenotype. It has become increasingly recognised that different fat depots exert different effects on the metabolic profile of each individual by virtue of their location, structure and function, giving rise to these different body composition phenotypes. Furthermore, other factors have been implicated in the aetiopathogenesis of the body composition phenotypes, including genetics, ethnicity, age and lifestyle/behavioural factors. Even though to date both MHO and MUHNW have been widely investigated and documented in the literature, studies report different outcomes on long-term cardiometabolic morbidity and mortality. Future large-scale, observational and population-based studies are required for better profiling of these phenotypes as well as to further elucidate the pathophysiological role of the adipocyte in the onset of metabolic disorders to allow for better risk stratification and a personalised treatment paradigm.

Asociación entre parámetros hematológicos y fenotipos metabólicamente poco saludables en niños y adolescentes

Introduction. Metabolic syndrome has been associated with changes in several hematological parameters, such as red blood cells, platelets, and leucocytes. Therefore, hematologic parameters can be used to identify the subjects at risk of metabolically unhealthy phenotypes (MUP). The current study investigated if hematological parameters can serve as biomarkers to distinguish metabolically healthy phenotype (MHP) from MUP in children and adolescents. Methods. Two hundred ninety-two children and adolescents were enrolled in this cross-sectional study. The MUP was diagnosed using consensus-based criteria. Group comparisons were performed using one-way ANOVA. Multiple logistic regression analysis was used to determine if sex, age group, nutritional status, puberty, hematological parameters, and insulin resistance were associated with MUP. Results. The subject's age mean was 11 years (SD: 2.61). RDW values were significantly lower in children in the metabolically unhealthy normal weight (MUNW) group compared to children with metabolically unhealthy obesity (MUO) group (12.33 ± 0.90 vs. 13.67 ± 0.52; p = 0.01) and in metabolically healthy obesity (MHO) compared to MUO group (13.15 ± 0.53 vs. 13.67 ± 0.52; p = 0.04). In adolescents, the platelet-to-lymphocyte ratio was higher in the MHNW group, with a mean value of 152.60 (SD 62.97) compared to 111.16 (SD 44.12) for the MHO group. However, after adjusting for age, nutritional status, and puberty, hematological indices were not associated with MUP. Conclusions. The study demonstrates that hematologic parameters are not independently associated with the MUP, and it is unlikely that they represent reliable biomarkers for screening for the MUP in the pediatric population.

Macronutrient quality and the incidence of metabolically unhealthy phenotypes in adults with normal weight and overweight/obesity.

We aimed to investigate the associations of macronutrient quality indices with the incident metabolically unhealthy normal weight (MUNW) and metabolically unhealthy overweight/obesity (MUO) phenotypes. This prospective study included 512 metabolically healthy normal weight and 787 metabolically healthy overweight/obese adults from the third study examination of the Tehran Lipid and Glucose Study. The participants were followed through the sixth study examination. Diet was measured with a food frequency questionnaire. The macronutrient quality index (MQI), carbohydrate quality index (CQI), fat quality index (FQI), and healthy plate quality index (HPPQI) were calculated. Hazard ratio (HR) and 95 % confidence interval (95 % CI) were estimated for incident unhealthy phenotypes using Cox regression. After controlling all possible confounding factors, a one-point higher HPPQI was linked to a 28 % lower risk of MUNW (HR = 0.72; 95 % CI = 0.59, 0.87). Compared to the lowest quartile, the incident MUNW was also lower in the two last quartiles of the HPPQI. A one-unit increase in MQI was associated with a 5 % lower incident MUO (HR = 0.95; 95 % CI = 0.92, 0.99). The incident MUO was also higher for the highest compared to the lowest MQI quartile. In quartiles 2-4 of the HPPQI, incident MUO was lower with respective HRs (95 % CI) of 0.71 (0.54, 0.93), 0.60 (0.45, 0.80), and 0.66 (0.50, 0.86) in the fully-adjusted model. A higher overall macronutrient quality was independently associated with a lower incident MUO. A higher dietary protein quality was related to a lower risk for MUNW and MUO.

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.

Investigating Adiposity-Related Metabolic Health Phenotypes in Patients with Hidradenitis Suppurativa: A Cross-Sectional Study.

Obesity and hidradenitis suppurativa (HS) are related through meta-inflammation and are both associated with increased cardiometabolic risk. Notwithstanding, cardiometabolic pathology is not uniform in obesity and a subset of individuals with excess adiposity exhibit a healthy metabolic profile. Whilst the incidence of cardiometabolic endpoints and transitions across different adiposity-related body composition phenotypes within several populations and across different ethnicities have been investigated, data regarding metabolic health (MetH) and body composition phenotypes in individuals with HS are lacking. The objective of this study was to evaluate the relationship between different body composition phenotypes in individuals with HS. This was a cross-sectional study of 632 individuals with and without HS from a population with a high prevalence of both obesity and HS. A total of four body composition phenotypes were generated based on BMI and metabolic status (defined using either the metabolic syndrome definition or the homeostasis model of insulin resistance (HOMA-IR)): metabolically healthy overweight/obese (MHOWOB), metabolically unhealthy overweight/obese (MUOWOB), metabolically healthy normal weight (MHNW), and metabolically unhealthy normal weight (MUNW). Generally, subjects with HS exhibited a worse metabolic profile with higher levels of indices of central adiposity measures (including Visceral Adiposity Index and waist circumference), systolic blood pressure and markers of insulin resistance, as well as a higher prevalence of the metabolic syndrome. Moreover, when sub-stratified into the different body composition phenotypes, individuals with HS typically also demonstrated adverse metabolic characteristics relative to controls matched for both adiposity and metabolic health, particularly in the normal weight category and despite being classified as metabolically healthy. Being metabolically unhealthy in addition to being overweight/obese increases an individual's risk of HS. Metabolic risk-assessment should be prioritized in the clinical management of individuals with HS even in those who are lean. Patients attending HS clinics provide a valuable opportunity for targeted cardiovascular risk reduction with respect to the management of both obesity and metabolic health.

Association of body mass index, metabolic health status and clinical outcomes in acute myocardial infarction patients: a national registry-based study.

Obesity is an important risk factor for acute myocardial infarction (AMI), but the interplay between metabolic health and obesity on AMI mortality has been controversial. In this study, we aimed to elucidate the risk of short- and long-term all-cause mortality by obesity and metabolic health in AMI patients using data from a multi-ethnic national AMI registry. A total of 73,382 AMI patients from the national Singapore Myocardial Infarction Registry (SMIR) were included. These patients were classified into four groups based on the presence or absence of metabolic diseases, diabetes mellitus, hyperlipidaemia, and hypertension, and obesity: (1) metabolically-healthy-normal-weight (MHN); (2) metabolically-healthy-obese (MHO); (3) metabolically-unhealthy-normal-weight (MUN); and (4) metabolically-unhealthy-obese (MUO). MHO patients had reduced unadjusted risk of all-cause in-hospital, 30-day, 1-year, 2-year, and 5-year mortality following the initial MI event. However, after adjusting for potential confounders, the protective effect from MHO on post-AMI mortality was lost. Furthermore, there was no reduced risk of recurrent MI or stroke within 1-year from onset of AMI by the MHO status. However, the risk of 1-year mortality was higher in female and Malay AMI patients with MHO compared to MHN even after adjusting for confounders. In AMI patients with or without metabolic diseases, the presence of obesity did not affect mortality. The exception to this finding were female and Malay MHO who had worse long-term AMI mortality outcomes when compared to MHN suggesting that the presence of obesity in female and Malay patients may confer worsened outcomes.

Metabolic obesity phenotypes and obesity-related cancer risk in the National Health and Nutrition Examination Survey.

Body mass index (BMI) fails to identify up to one-third of normal weight individuals with metabolic dysfunction who may be at increased risk of obesity-related cancer (ORC). Metabolic obesity phenotypes, an alternate metric to assess metabolic dysfunction with or without obesity, were evaluated for association with ORC risk. National Health and Nutrition Examination Survey participants from 1999 to 2018 (N = 19,500) were categorized into phenotypes according to the metabolic syndrome (MetS) criteria and BMI: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obese (MHO) and metabolically unhealthy overweight/obese (MUO). Adjusted multivariable logistic regression models were used to evaluate associations with ORC. With metabolic dysfunction defined as ≥1 MetS criteria, ORC cases (n = 528) had higher proportions of MUNW (28.2% vs. 17.4%) and MUO (62.6% vs. 60.9%) phenotypes than cancer-free individuals (n = 18,972). Compared with MHNW participants, MUNW participants had a 2.2-times higher ORC risk [OR (95%CI) = 2.21 (1.27-3.85)]. MHO and MUO participants demonstrated a 43% and 56% increased ORC risk, respectively, compared to MHNW, but these did not reach statistical significance [OR (95% CI) = 1.43 (0.46-4.42), 1.56 (0.91-2.67), respectively]. Hyperglycaemia, hypertension and central obesity were all independently associated with higher ORC risk compared to MHNW. MUNW participants have a higher risk of ORC than other abnormal phenotypes, compared with MHNW participants. Incorporating metabolic health measures in addition to assessing BMI may improve ORC risk stratification. Further research on the relationship between metabolic dysfunction and ORC is warranted.

RELATIONSHIP BETWEEN SKELETAL MASS INDEX AND DIFFERENT OBESITY METABOLIC PHENOTYPES AMONG ADOLESCENTS IN CHINA

Objective: To explore the relationship between skeletal mass index (SMI) and different obesity metabolic phenotypes among adolescents,and to provide a theoretical basis for the prevention and control of obesity and related metabolic diseases in adolescents. Design and method: A total of 1352 adolescents aged 12-18 were randomly selected for questionnaire survey,physical measurements,body composition determination and laboratory testing. Multiple logistic regression analysis was used to analyze the correlation intensity between SMI and different metabolic phenotypes of obesity. Results: The metabolically unhealthy normal weight (MUNW) phenotype, metabolically healthy obesity(MHO) phenotype and metabolically unhealthy obesity(MUO) phenotype had lower levels of SMI compared to the metabolically healthy normal weight(MHNW) phenotype among adolescents,with all differences being statistically significant(P-values were all less than 0.001). Similar results were found after stratification by sex. Multiple logistic regression analysis showed that after adjusting for sex, SMI were protective factors for MUNW phenotype, MHO phenotype and MUO phenotype in adolescents(P-values were all less than 0.001),with the 0.86(95%CI:0.85,0.92),0.78(0.75,0.82) and 0.73(0.70,0.76) increased risks of MUNW phenotype, MHO phenotype and MUO phenotype associated with each unit rise in SMI, similar results were found after stratification by sex. Conclusions: SMI were protective factors for MUNW phenotype, MHO phenotype and MUO phenotype among adolescents. Attention should be paid to the assessment and supervision of adolescent skeletal muscle quality when prevented obesity and related metabolic risks,reducing the risk of abnormal cardiovascular metabolism in adolescents.

Association between thyroid function and obesity phenotypes in healthy euthyroid individuals: an investigation based on Tehran Thyroid Study

AimsWe investigated whether thyroid function could be associated with obesity phenotypes amongst euthyroid individuals.Materials and methodsA cross-sectional analysis was conducted among healthy, euthyroid subjects. The study participants were chosen from the Tehran Thyroid Study (TTS). We analyzed 2988 euthyroid adults and classified them into four obesity phenotype groups: metabolically healthy normal weight (MHNW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy obese (MUO). The statistical differences between thyroid hormones between various obesity phenotypes according to age and sex was compared using analysis of covariance (ANCOVA).ResultsIt was found that MHNW participants had higher levels of FT4 when compared with metabolically healthy or unhealthy obese subjects (P < 0.001), even after adjustment for the confounding variables. No difference was observed in the levels of TSH (P = 0.260) among obesity phenotypes. In the subgroup analysis according to the age, a significant difference was observed in the level of FT4 only in subjects with age < 55 years (P = 0.001). However, analyzing men and women separately did not show a significant difference in the FT4 level among obesity phenotypes (P > 0.05).Conclusion“Metabolically abnormality” was independently related to low normal FT4 levels in overweight/obese euthyroid individuals. There is a need for further research to understand how low FT4 levels are linked to metabolically unhealthy states in euthyroid individuals.

Ultra-processed foods consumption and the risk of metabolically unhealthy phenotype in normal-weight and overweight/obese adults: a prospective investigation

This study investigated the association between ultra-processed foods (UPF) and the risk of metabolically unhealthy normal weight (MUNW) and metabolically unhealthy overweight/obese (MUO). We included 512 normal-weight and 787 overweight/obese adults with a metabolically healthy phenotype from the Tehran and Lipid Glucose Study, who were followed from the third (baseline) to the sixth study examinations. Each 10% increase in energy intake from UPF was related to a 54% (95% CI = 21–96%) and 2% (95% CI = 1–3%) higher risk of MUNW and MUO, respectively. The risk of MUNW was significantly higher in quartile 4 compared to quartile 1. The restricted cubic splines suggested that the risk of MUNW increases monotonically when UPF accounts for at least 20% of energy intake. No non-linear association was observed between UPF and the risk of MUO. Energy intake from UPF was positively related to the risk of MUNW and MUO.

Relationship between skeletal muscle mass index and metabolic phenotypes of obesity in adolescents

To study the relationship between skeletal muscle mass index (SMI) and metabolic phenotypes of obesity in adolescents, and to provide a basis for the prevention and control of adolescent obesity and related metabolic diseases. A total of 1 352 adolescents aged 12 to 18 years were randomly selected by stratified cluster sampling in Yinchuan City from October 2017 to September 2020, and they were surveyed using questionnaires, physical measurements, body composition measurements, and laboratory tests. According to the diagnostic criteria for metabolic abnormalities and the definition of obesity based on the body mass index, the subjects were divided into four metabolic phenotypes: metabolically healthy normal weight, metabolically healthy obesity, metabolically unhealthy normal weight, and metabolically unhealthy obesity. The association between SMI and the metabolic phenotypes was analyzed using multivariate logistic regression. The SMI level in the metabolically unhealthy normal weight, metabolically healthy obesity, and metabolically unhealthy obesity groups was lower than that in the metabolically healthy normal weight group (P<0.001). Multivariate logistic regression analysis showed that after adjusting for gender and age, a higher SMI level was a protective factors for adolescents to develop metabolic unhealthy normal weight, metabolically healthy obesity, and metabolically unhealthy obesity phenotypes (OR=0.74, 0.60, and 0.54, respectively; P<0.001). Increasing SMI can reduce the risk of the development of metabolic unhealthy/obesity.

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.

Short-chain fatty acids are associated with adiposity, energy and glucose homeostasis among different metabolic phenotypes in the Nutritionists' Health Study.

The gut microbiome is associated with obesity, mainly mediated by bacteria-produced short-chain fatty acids (SCFAs). It is unknown how SCFA concentrations are associated with the phenotypes metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy obese/overweight (MHO), and metabolically unhealthy obese/overweight (MUO). We compared plasma and fecal SCFA concentrations among adult women categorized according to the metabolic phenotypes mentioned above and examined associations between SCFA and adiposity and components of energy and glucose homeostasis. This was a cross-sectional study involving 111 participants. Body composition was assessed by DEXA. Energy and glycemic homeostasis were assessed by the standard mixed-meal tolerance test coupled with indirect calorimetry. SCFAs were quantified by gas chromatography and mass spectrometry. Only plasma propionate was increased in the MHNW phenotype compared to the MHO and MUO phenotypes [p < 0.05]. Fecal propionate and butyrate concentrations and plasma propionate concentrations were inversely associated with total and visceral adiposity [p < 0.05]. Fecal and plasma SCFA concentrations were associated with reduced glucose, insulin and HbA1c levels, increased fasting and postprandial GLP-1 levels; and more preserved beta-cell function [p < 0.05]. Fecal and plasma SCFA concentrations were positively correlated with resting energy expenditure and lipid oxidation rate and inversely correlated with the oxidation rate of carbohydrates [p < 0.05]. These findings reinforce the concept that fecal and plasma SCFA concentrations are linked to specific components of energy and glucose homeostasis; and body adiposity. However, it was not possible to discriminate the different metabolic phenotypes of adiposity based on the determination of fecal SCFA concentrations.