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Background: Chronic kidney disease (CKD) is a major global health concern with increasing prevalence and associated complications. Obesity and obesity-related metabolic disorders have been linked to chronic kidney disease (CKD), although the evidence is still inconsistent. To investigate the relationship between different obesity phenotypes and the risk of CKD, we conducted a systematic review and meta-analysis., Method: We conducted a comprehensive search of databases for all relevant studies up to February 2024, without imposing a specific start date, for observational studies assessing the relationship between obesity phenotypes and incident kidney dysfunction. We used fixed and random effects models for the meta-analysis, subgroup analyses were carried out to explore heterogeneity, and Egger's and Begg's tests were used to assess publication bias., Result: This meta-analysis included 13 eligible observational studies with 492,829 participants. Pooling the studies regardless of the effect measure showed that individuals with metabolically unhealthy normal weight (MUNW) (ES = 1.58, 95% CI = 1.43-1.76), metabolically healthy obese (MHO) (ES = 1.20, 95% CI = 1.06-1.34), and metabolically unhealthy obese (MUO) (ES = 1.90, 95% CI = 1.63-2.20) phenotypes had a significantly increased risk for kidney dysfunction (KD) events. On the other hand, MUOW individuals did not significantly correlate with risk of CKD (ES = 1.63, 95% CI = 0.97-2.23) compared to the MHNW phenotype., Conclusion: Healthy overweight and obese individuals had higher risk for the incident KD events; refuting the notion that metabolically healthy overweight and obese phenotypes are benign conditions., Level of Evidence: III Evidence obtained from well-designed cohort or cross-sectional studies., (© 2024. The Author(s).)

Background: Obesity is major cause of heart failure (HF), but it is related with a better prognosis among the elderly. Therefore, we aimed to examine whether metabolically healthy obesity (MHO) in late life increases HF risk and is reflected in impaired left ventricular (LV) function., Methods: The participants were grouped into four metabolic phenotypes based on obesity and metabolic status: metabolically healthy non-obesity (MHN), MHO, metabolically unhealthy non-obesity (MUN), metabolically unhealthy obesity (MUO). Association of metabolic phenotypes with LV function was evaluated using multiple linear regression models. And association between metabolic phenotypes and risk of HF was assessed using multivariable logistic regression models. In addition, we validated the association of metabolic phenotypes and HF risk in a separate longitudinal cohort., Results: In the primary cohort of 6335 participant, there were 434 participants diagnosed with HF. Compared to MHN participants, the risk of HF was higher among older individuals with MUN (OR = 1.51 [95% CI: 1.14-1.99]) and MUO (OR = 2.01 [95% CI: 1.39-2.91]), but not older individuals with MHO (OR = 0.86 [95% CI: 0.30-2.43). Regarding to LV function, worse LV diastolic function was noted among MUN and MUO individuals rather than MHO individuals. Older adults with MHO were also not associated with risk of HF in the validation cohort., Conclusion: Among older individuals, the metabolic health status might modify the association of obesity with risk of HF and LV diastolic dysfunction. Worse LV diastolic function and higher risk of HF were just noted in individuals with MUO, but not in those with MHO., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Obesity is as an important risk factor for chronic diseases. Metabolically healthy obesity (MHO) is considered a benign state. The association between metabolic health and obesity categories and cancer risk remains unclear. This study aimed to investigate the relationship between metabolic health status combined with obesity phenotypes and the risk of cancer., Methods: Data from 91,834 participants in the Kailuan cohort were analyzed, excluding individuals with a body mass index (BMI) < 18.5 kg/m² and those with a history of cancer. Obesity phenotypes were classified based on BMI and waist circumference (WC) combined with metabolic health status, resulting in six phenotypes. Cox proportional hazard regression models were used to assess the association between metabolic health and obesity phenotypes with cancer risk and all-cause mortality., Results: The prevalence of metabolically healthy obesity and metabolically unhealthy obesity defined by BMI was 6.86% and 12.18%, while that defined by WC was 20.79% and 25.76%, respectively. Compared to metabolically healthy participants, individuals with an unhealthy metabolic status had a significantly higher risk of cancer (HR, 1.09; 95% CI, 1.03-1.15; p=0.004). The hazard ratios for cancer were 1.19, 1.23, 1.20, and 1.55 for individuals with one, two, three, and four metabolic disorders, respectively. Among those classified as metabolically unhealthy, both overweight and obesity were associated with a protective effect on cancer risk (HR, 0.88; 95% CI, 0.80-0.96; p=0.006 for overweight; HR, 0.87; 95% CI, 0.78-0.97; p=0.010 for obesity). However, abdominal obesity significantly increased cancer risk in both metabolically healthy and unhealthy participants. In subgroup analysis, simple obesity showed a protective trend against cancer in those with respiratory cancers, while abdominal obesity consistently posed a risk for various cancer types., Conclusion: Metabolically unhealthy status and abdominal obesity are risk factors for cancer and all-cause mortality, whereas simple obesity offers protective effects against cancer and all-cause mortality in metabolically unhealthy individuals. These findings suggest that maintaining metabolic health and reducing the metabolic risks associated with abdominal obesity should be key targets for cancer prevention., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zheng, Wang, Chen, Liu, Liu, Lin, Xie, Ma, Wang, Shi, Zhang, Yang, Liu, Deng, Zhang and Shi.)

Aim: To examine physical activity levels in association with metabolic health and estimate the stability of metabolically healthy obese (MHO) phenotypes over a 2-year period., Methods: In total, 2848 men and women from families at risk of the development of diabetes were recruited. Participants were classified as obese or non-obese and metabolic health was defined using five existing definitions. Physical activity was estimated with the International Physical Activity Questionnaire and pedometers., Results: Prevalence of the MHO phenotype varied among definitions (0% to 20.2%). Overall, the MHO were more active than the metabolically unhealthy obese (MUO). Daily sitting hours (odds ratio [OR] = 1.055, 95% confidence interval [CI]: 1.009-1.104) and daily steps (per 500; OR = 0.934, 95% CI: 0.896-0.973) were remarkable predictors of metabolic health in individuals with obesity; and likewise, in individuals without obesity. After 2 years, 44.1% of baseline MHO adults transitioned to MUO, while 84.0% of the MUO at baseline remained at the same phenotype. Although physical activity was not a major determinant in phenotype transitioning, daily steps were associated with the maintenance of metabolic health over time in the non-obese group., Conclusion: A universally accepted definition for MHO is needed. Being physically active can contribute to a metabolically healthy profile even in the presence of obesity; still, MHO is a transient condition and physical activity alone may not be an adequate factor for its maintenance., (© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Obesity represents a prevalent global health concern with significant implications for various diseases, including chronic kidney disease (CKD). Within this landscape, the phenomenon of metabolically healthy obesity has emerged, challenging traditional notions about the health risks associated with excess weight. While traditional CKD risk factors involve obesity, metabolic syndrome, diabetes, and hypertension, the metabolically healthy obese (MHO) subgroup disrupts these assumptions. Our main objective in this study is to integrate existing literature on CKD in MHO individuals. In this endeavor, we delve into the pathophysiological foundations, the transition between obesity phenotypes and their impact on renal health, examine the implications of their metabolic resilience on mortality within a renal context, and explore potential management strategies specifically designed for MHO individuals. Offering a comprehensive overview of the pathophysiology, we cover various factors contributing to the risk of CKD in the metabolically healthy obese setting, including inflammation, cytokines, hemodynamics, and the renin-angiotensin-aldosterone system, gastrointestinal microbiota, diet, exercise, adipose distribution, and lipotoxicity. Through this synthesis, we aim to provide a comprehensive understanding of the risk of CKD in those classified as MHO.

Aims: 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., Methods: 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., Results: 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., Conclusions: A metabolically healthy phenotype is associated with lower frequency of IFG, IGT, and IFG + IGT status across all body weight categories., (© 2024 John Wiley & Sons Ltd.)

Background: Metabolically healthy obesity is not always a benign condition. It is associated with an increased incidence of cardiovascular disease and all-cause mortality. We investigated the prognostic significance of metabolically healthy obesity by comparing clinical profile-matched metabolically healthy obesity and non-obesity groups., Methods: We analyzed a health insurance dataset with annual health checkup data from Japan. The analyzed data included 168,699 individuals aged <65 years. Obesity was defined as ≥25 kg/m 2 body mass index. Metabolically healthy was defined as ≤1 metabolic risk factor (high blood pressure, low high-density lipoprotein cholesterol, high low-density lipoprotein cholesterol, or high hemoglobin A1c). Incidence rates of stroke, myocardial infarction, and all-cause mortality identified from the insurance data were compared between metabolically healthy obesity and non-obesity groups (n = 8644 each) using a log-rank test., Results: The stroke (obesity: 9.2 per 10,000 person-years; non-obesity: 10.5; log-rank test p = 0.595), myocardial infarction (obesity: 3.7; non-obesity: 3.1; p = 0.613), and all-cause mortality (obesity: 26.6; non-obesity: 23.2; p = 0.304) incidence rates did not differ significantly between the metabolically healthy obesity and non-obesity groups, even when the abdominal obesity was considered in the analysis. The lack of association was also observed in the comparison between the metabolically unhealthy obesity and non-obesity groups (n = 10,965 each). The population with metabolically healthy obesity reported negligibly worse metabolic profiles than the population with non-obesity at the 5.6-year follow-up., Conclusion: Obesity, when accompanied by a healthy metabolic profile, did not increase the risk of cardiovascular outcomes and all-cause mortality., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Objective: The study objective was to investigate whether changes in metabolic phenotype affect the risk of cardiovascular events., Methods: All 117,589 participants were included in this retrospective cohort study. The metabolic phenotypes of the participants were assessed at two points (the second evaluation was set 2 years after the first evaluation), and the incidence rate of cardiovascular events was observed for 11 years. The main outcome was 3-point major adverse cardiac events (MACE), which comprises cardiovascular death, nonfatal coronary artery disease, and nonfatal stroke incidence., Results: Of the participants, 2748 (2.3%) cases of 3-point MACE were identified during follow-up. The stable metabolically healthy obesity group had a higher risk of 3-point MACE than those with stable metabolically healthy nonobesity (MHNO). Additionally, the change from metabolically healthy obesity to MHNO for 2 years decreased the risk of 3-point MACE (hazard ratio [HR], 1.12: 95% CI: 0.84-1.47) to the same level as stable MHNO. However, the change from metabolically abnormal nonobesity and metabolically abnormal obesity to MHNO for 2 years maintained a higher risk of 3-point MACE (HR, 1.66 [95% CI: 1.36-2.01]; HR, 1.91 [95% CI: 1.22-2.81]) than those with stable MHNO., Conclusions: Change in metabolic phenotype is associated with incident 3-point MACE., (© 2024 The Obesity Society.)

Purpose: The triglycerides and glucose (TyG) index is a reliable biomarker for estimating insulin resistance; however, evidence regarding the use of the TyG index in individuals with metabolically healthy obesity (MHO) is scarce. Thus, we examined the association between the TyG index and the MHO phenotype., Methods: Apparently healthy men and women aged 18 years or more with obesity (body mass index [BMI] ≥ 30 kg/m 2 ) were allocated into the following groups: MHO and metabolically unhealthy obesity (MUO). The MHO phenotype was defined by obesity and the absence of the following metabolic disorders: elevated triglyceride concentrations, elevated glucose levels, elevated blood pressure, and low HDL-C. The MUO was defined by individuals with obesity and at least one of the aforementioned cardiovascular risk factors., Results: A total 827 individuals, 605 (73.1%) women and 222 (26.9%) men were enrolled and allocated into the MHO (n = 104) and MUO (n = 723) groups. The adjusted regression analysis by age, sex, BMI, and waist circumference showed that fasting glucose (OR = 0.90; 95% CI: 0.88-0.93), and triglycerides (OR = 0.97; 95% CI: 0.96-0.98), as well as the triglycerides/HDL-C (OR = 0.18; 95% CI: 0.13-0.26), lipid accumulation product (OR = 0.95; 95% CI: 0.93-0.96), visceral adipose index (OR = 0.38; 95% CI: 0.31-0.46), and TyG index (OR = 0.001; 95% CI: 0.000-0.004) are inversely associated with the MHO, while the HDL-C (OR = 1.10; 95% CI: 1.07-1.12) had a direct association., Conclusions: Our results show that the TyG index is more strongly associated with the MHO phenotype than the lipid and obesity indices., (© 2023. The Author(s), under exclusive licence to Italian Society of Endocrinology (SIE).)

Background: The association between obesity and depression may partly depend on the contextual metabolic health. The effect of change in metabolic health status over time on subsequent depression risk remains unclear. We aimed to assess the prospective association between metabolic health and its change over time and the risk of depression across body mass index (BMI) categories., Methods: Based on a nationally representative cohort, we included participants enrolled at the wave 2 (2004-2005) of the English Longitudinal Study of Ageing and with follow-up for depression at wave 8 (2016-2017). Participants were cross-classified by BMI categories and metabolic health (defined by the absence of hypertension, diabetes, and hypercholesterolemia) at baseline or its change over time (during waves 3-6). Logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the risk of depression at follow-up stratified by BMI category and metabolic health status with adjustment for potential confounders., Results: The risk of depression was increased for participants with metabolically healthy obesity compared with healthy nonobese participants, and the risk was highest for those with metabolically unhealthy obesity (OR 1.62, 95% CI 1.18-2.20). Particularly hypertension and diabetes contribute most to the increased risk. The majority of metabolically healthy participants converted to unhealthy metabolic phenotype (50.1% of those with obesity over 8 years), which was associated with an increased risk of depression. Participants who maintained metabolically healthy obesity were still at higher risk (1.99, 1.33-2.72), with the highest risk observed for those with stable unhealthy metabolic phenotypes., Conclusions: Obesity remains a risk factor for depression, independent of whether other metabolic risk factors are present or whether participants convert to unhealthy metabolic phenotypes over time. Long-term maintenance of metabolic health and healthy body weight may be beneficial for the population mental well-being.

Aim: To assess the effect of obesity phenotype on the incidence of diabetes, considering phenotype as a time-varying exposure., Methods: We used community-based cohort data from the Korean Genome and Epidemiology Study, with a 16-year follow-up period. Obesity phenotype was determined using body mass index and metabolic syndrome criteria. The influence of obesity phenotype on the occurrence of diabetes was evaluated using a Cox proportional hazard model and a marginal structural model (MSM)., Results: Obesity phenotypes were defined in 6265 individuals, with diabetes identified in 903 (14.4%) during the follow-up period. Individuals with metabolically healthy obesity (MHO) exhibited a higher risk of diabetes compared to those with metabolically healthy normal weight (MHNW), with a hazard ratio (HR) of 1.48 (95% confidence interval [CI] 1.15-1.90). This association remained significant after applying the MSM (HR 1.49, 95% CI 1.01-2.20). Moreover, various sensitivity analyses consistently demonstrated a higher risk of diabetes in individuals with MHO compared to those with MHNW., Conclusions: Even when obesity phenotype was treated as a time-varying exposure, individuals with MHO were still at higher risk for developing diabetes than those with MHNW. Consequently, such individuals should aim to avoid transitioning to a metabolically unfavourable state and strive to reduce their body weight to a normal range., (© 2023 John Wiley & Sons Ltd.)

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., (© 2023 World Obesity Federation.)

Background: There has been lack of evidence on the association between healthy dietary patterns and metabolic health status of adolescents. The present study aimed to evaluate the association between alternative healthy eating index (AHEI) and metabolic health status among a relatively representative sample of Iranian adolescents with overweight/obesity., Methods: Adolescents with extra body weight (n = 203, aged 12-18 y), were selected for this cross-sectional study by a multistage cluster random-sampling method. Habitual dietary intakes and diet quality of individuals were assessed using validated food frequency questionnaire and AHEI-2010, respectively. Data on other covariates were also gathered by pre-tested questionnaires. To determine fasting glucose, insulin and lipid profiles, fasting blood samples were collected. Participants were categorized as having metabolically healthy overweight/obesity (MHO) or metabolically unhealthy overweight/obesity (MUO) phenotypes, based on two approaches (International Diabetes Federation (IDF) and combination of IDF with Homeostasis Model Assessment Insulin Resistance (HOMA-IR))., Results: The overall prevalence of MUO was 38.9% (based on IDF criteria) and 33.0% (based on IDF/HOMA-IR criteria). After considering all potential confounders, participants in highest tertiles of AHEI-2010 had lower odds of MUO profile according to both IDF (OR = 0.05; 95% CI: 0.01-0.15) and IDF/HOMA-IR (OR = 0.05; 95% CI: 0.02-0.19) definitions. This association was stronger in adolescents with overweight compared with obese ones and also among girls than boys. Moreover, each unit increase in AHEI-2010 score was associated with lower risk of MUO based on both criteria., Conclusions: Higher adherence to AHEI-2010 was inversely associated with odds of MUO in Iranian adolescents with overweight/obesity., (© 2024. The Author(s).)

There is a lack of consensus regarding universally accepted criteria for metabolic health (MH). A simple definition of MH was systematically derived in a recent prospective cohort study. The present cross-sectional study aimed to explore the applicability of these criteria in Korean population, using coronary calcification as an indicator of cardiovascular risk. In total, 1049 healthy participants, who underwent coronary artery calcification testing at university hospital health promotion centers between January and December 2022, were included. Applying the main components of the newly derived definition, MH was defined as follows: (1) systolic blood pressure < 130 mmHg and no use of blood pressure-lowering medication; (2) waist circumference < 90 cm for males and < 85 cm for females; and (3) absence of diabetes. Multivariate logistic regression was conducted to examine the odds ratio (OR) and 95 % confidence interval (CI) for coronary artery calcium score across different phenotypes. The prevalence of coronary artery calcification in this study was 41.1 %. Compared with metabolically healthy, normal weight subjects, those with the metabolically healthy obesity phenotype did not exhibit increased odds for coronary atherosclerosis. (OR 0.93 [95 % CI 0.48-1.79]) Conversely, metabolically unhealthy subjects had increased risk, regardless of their body mass index category (OR 3.10 [95 % CI 1.84-5.24] in metabolically unhealthy normal weight; OR 3.21 [95 % CI 1.92-5.37] in metabolically unhealthy overweight; OR 2.73 [95 % CI 1.72-4.33] in metabolically unhealthy obese phenotype). These findings suggest that the new definition for MH has the potential to effectively distinguish individuals at risk for cardiovascular disease from those who are not., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.)

Purpose: Although obesity is associated with numerous diseases, the risks of disease may depend on metabolically healthy status. Nevertheless, it is unclear to whether metabolically healthy status affects risk of gastrointestinal (GI) cancer in general Chinese population., Materials and Methods: A total of 114,995 participants who met the criteria were included from the Kailuan Study. The study participants were divided into four groups according to body mass index (BMI)/waist circumference (WC) and metabolic status. Incident of GI cancer (esophageal cancer, gastric cancer, liver cancer, biliary cancer, pancreatic cancer, and colorectal cancer) during 2006-2020 were confirmed by review of medical records. The Cox proportional hazard regression models were used to assess the association metabolically healthy status with the risk of GI cancer by calculating the hazard ratios (HR) and 95% confidence interval (CI)., Results: During a mean 13.76 years of follow-up, we documented 2,311 GI cancers. Multivariate Cox regression analysis showed that compared with the metabolically healthy normal-weight group, metabolically healthy obese (MHO) participants demonstrated an increased risk of developing GI cancer (HR, 1.54; 95% CI, 1.11 to 2.13) by BMI categories. However, such associations were not found for WC category. These associations were moderated by age, sex, and anatomical site of the tumor. Individuals with metabolic unhealthy normal-weight or metabolic unhealthy obesity phenotype also have an increased risk of GI cancer., Conclusion: MHO phenotype was associated with increased risk of GI cancer. Moreover, individuals who complicated by metabolic unhealthy status have an increased risk of developing GI cancer. Hence, clinicians should consider the risk of incident GI cancer in people with abnormal metabolically healthy status and counsel them about metabolic fitness and weight control.

Introduction: The cardiometabolic risk associated with metabolically healthy obesity remains the subject of debate. It is unclear whether changes in metabolically healthy obesity status affect premature cardiovascular disease (CVD) risk. Authors aimed to investigate the association of metabolically healthy obesity and its transition over time with incident CVD by age at onset., Methods: In a community-based, prospective cohort study, 54,441 adults without CVD in or before 2010 were followed for incident CVD until 2020. This sample was analyzed in 2022. Four age groups were examined (<55, 55-65, 65-75, and ≥75 years) for CVD onset. In each age group, participants were cross-classified by BMI categories and metabolic health. The Cox proportional hazards model with age as the underlying time scale was used to examine the associations of metabolic health status and its transition with CVD across BMI categories., Results: During a median follow-up of 9.59 years, 3,038 participants developed CVD. Individuals with metabolically unhealthy obesity at baseline had the highest hazard ratio for CVD onset at any age, ranging from 2.68 (95% CI=2.02, 3.55) for CVD onset in those aged <55 years to 1.55 (95% CI=1.09, 2.10) for CVD onset in those aged ≥75 years. Individuals who had metabolically healthy obesity at baseline or even remained metabolically healthy during 2006-2010 were still at increased risk of premature CVD, and the association attenuated with increasing age of CVD onset., Conclusions: The metabolically healthy obesity phenotype is dynamic and its transition to a metabolically unhealthy phenotype or even stable metabolically healthy obesity is associated with an increased risk of CVD. The associations were more evident for CVD onset at younger ages., (Copyright © 2023. Published by Elsevier Inc.)

Background: Metabolically healthy obesity is hypothesized to be a benign condition but whether this is the case for dementia remains debated. We examined the role of age at assessment of metabolic-obesity phenotypes in associations with incident dementia., Methods: Obesity (body mass index ≥ 30 kg/m 2 ) and poor metabolic health (≥ 2 of elevated serum triglycerides, low HDL-C, elevated blood pressure, and elevated serum fasting glucose) were used to define four metabolic-obesity phenotypes (metabolically healthy (MHNO) and unhealthy non-obesity (MUNO), metabolically healthy (MHO) and unhealthy obesity (MUO)) at < 60, 60 to < 70, and ≥ 70 years using 6 waves of data from the Whitehall II study and their associations with incident dementia was examined using Cox regression., Results: Analyses with exposures measured < 60, 60 to < 70, and ≥ 70 years involved 410 (5.8%), 379 (5.6%), and 262 (7.4%) incident dementia cases over a median follow-up of 20.8, 10.3, and 4.2 years respectively. In analyses of individual components, obesity before 60 years (HR 1.41, 95% CI: [1.08, 1.85]) but not at older ages was associated with dementia; unhealthy metabolic status when present < 60 years (HR 1.33, 95% CI: [1.08, 1.62]) and 60 to < 70 years (HR 1.32, 95% CI: [1.07, 1.62]) was associated with dementia. Compared to the metabolically healthy non-obesity group, the risk of dementia was higher in those with metabolically healthy obesity before 60 years (1.69; 95% CI: [1.16, 2.45]); this was not the case when metabolic-obesity phenotype was present at 60 to < 70 years or ≥ 70 years. Analyses at older ages were on smaller numbers due to death and drop-out but inverse probability weighting to account for missing data yielded similar results., Conclusions: Individuals with metabolically healthy obesity before age 60 had a higher risk of incident dementia over a 27-year follow-up; the excess risk dissipates when metabolic health and obesity are measured after 70 years., (© 2023. The Author(s).)

Background and Aims: Although the associations of vitamin D with obesity and metabolic abnormalities have been reported, the role of vitamin D in the transition of obesity phenotype remains unclear but is highly desired since it is crucial to identify potential methods for obesity management. Therefore, we aimed to investigate the relationship between vitamin D and the risk for metabolically unhealthy obesity (MUO) or metabolically healthy obesity (MHO) in metabolically healthy children with 2 years of follow-up., Methods: Data were collected from a population-based cohort consisting of 6424 metabolically healthy children aged 6-16 years at baseline. Metabolic abnormalities including hypertension, high triglycerides (TG), low high-density lipoprotein cholesterol (HDL-C), hyperglycemia, and hyperuricemia were assessed both at baseline and follow-up. Baseline serum 25-hydroxyvitamin D (25[OH]D) concentrations were measured as exposure. The obesity phenotype transition was evaluated by weight status with the combination of metabolic health status from baseline to follow-up., Results: During a 2-year follow-up, 889 (13.8 %) incident MUO cases occurred. For participants with obesity, each 10 nmol/L increment in 25(OH)D concentrations was associated with a 21 % (95%CI: 13 %∼43 %) and a 7 % (95%CI: 1 %∼14 %) decreased risk in high TG and hyperuricemia, respectively. A 51 % (95%CI: 22 %∼69 %) lower risk of MUO was observed in participants with sufficient vitamin D levels (≥50 nmol/L) compared to those with vitamin D deficiency (<30 nmol/L). Besides, among children who were MHO at baseline, those with sufficient vitamin D levels (≥50 nmol/L) were more likely to transition to metabolically healthy normal weight (MHNW) than vitamin D deficient individuals (<30 nmol/L)., Conclusions: Vitamin D may prevent the development of MUO and help increase the transition from MHO to MHNW. The findings highlight that vitamin D might be an effective nutrient for obesity management., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Pei Xiao reports financial support was provided by National Natural Science Foundation of China. Jie Mi reports financial support was provided by National Natural Science Foundation of China., (Copyright © 2023. Published by Elsevier Ltd.)

Given the increasing number of people living with obesity and related chronic metabolic disease, precision nutrition approaches are required to increase the effectiveness of prevention strategies. This review addresses these approaches in different metabolic phenotypes (metabotypes) in obesity. Although obesity is typically associated with an increased cardiometabolic disease risk, some people with obesity are relatively protected against the detrimental effects of excess adiposity on cardiometabolic health, also referred to as 'metabolically healthy obesity' (MHO). Underlying mechanisms, the extent to which MHO is a transient state as well as lifestyle strategies to counteract the transition from MHO to metabolically unhealthy obesity (MUO) are discussed. Based on the limited resources that are available for dietary lifestyle interventions, it may be reasonable to prioritize interventions for people with MUO, since targeting high-risk patients for specific nutritional, lifestyle or weight-loss strategies may enhance the cost-effectiveness of these interventions. Additionally, the concept of tissue insulin resistant (IR) metabotypes is discussed, representing distinct etiologies towards type 2 diabetes (T2D) as well as cardiovascular disease (CVD). Recent evidence indicates that these tissue IR metabotypes, already present in individuals with obesity with a normal glucose homeostasis, respond differentially to diet. Modulation of dietary macronutrient composition according to these metabotypes may considerably improve cardiometabolic health benefits. Thus, nutritional or lifestyle intervention may improve cardiometabolic health, even with only minor or no weight loss, which stresses the importance of focusing on a healthy lifestyle and not on weight loss only. Targeting different metabotypes towards T2D and cardiometabolic diseases may lead to more effective lifestyle prevention and treatment strategies. Age and sex-related differences in tissue metabotypes and related microbial composition and functionality (fermentation), as important drivers and/or mediators of dietary intervention response, have to be taken into account. For the implementation of these approaches, more prospective trials are required to provide the knowledge base for precision nutrition in the prevention of chronic metabolic diseases., (© 2023. The Author(s).)

Background: The terms metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) categorize subjects with obesity based on the presence or absence of cardio-metabolic risk factors. Detecting MUO phenotype is crucial due to the high risk of cardio-metabolic complications, requiring tailored and intensive follow-up. However, diagnosing MUO is time-consuming and costly. Thus, we aimed to investigate the role of Mediterranean diet (MD) in determining MHO/MUO phenotypes and whether adherence to MD could serve as an additional screening tool for MUO phenotype., Methods: The study population of this cross-sectional observational study consisted of 275 subjects with obesity. We assessed their lifestyle habits (physical activity and smoking habits), anthropometric measurements (weight, height, waist circumference, body mass index), blood pressure, metabolic parameters, inflammatory marker (high sensitivity C reactive protein levels), adherence to MD (by PREvención con DIetaMEDiterránea (PREDIMED) questionnaire), and MHO/MUO phenotypes., Results: The study included 275 individuals with obesity (256F/19M; 34.0 ± 10.5 years; BMI 38.3 ± 5.95 kg/m 2 ). Among them, 114 (41.5%) exhibited MHO phenotype, while 161 (58.5%) had MUO phenotype. MHO phenotype exhibited favorable anthropometric and cardio-metabolic profiles, characterized by lower waist circumference (p < 0.001), BMI (p < 0.001), insulin resistance (p < 0.001), blood pressure (p < 0.001), inflammation (p < 0.001), and lipid levels (p < 0.001) compared to MUO phenotype. Notably, we found that MHO phenotype had higher adherence to MD (p < 0.001) and consumed more extra virgin olive oil (EVOO) (p < 0.001), vegetables (p < 0.001), fruits (p < 0.001), legumes (p = 0.001), fish (p < 0.001), wine (p = 0.008), and nuts (p = 0.001), while reporting lower intake of red/processed meats (p < 0.001), butter, cream, margarine (p = 0.008), soda drinks (p = 0.006), and commercial sweets (p = 0.002) compared to MUO phenotype. Adherence to MD (p < 0.001) and EVOO (p = 0.015) intake were identified as influential factors in determining the presence of MUO/MHO phenotypes. Furthermore, a PREDIMED score < 5 proved to be the most sensitive and specific cut-point value for predicting the presence of MUO phenotype (p < 0.001)., Conclusion: High adherence to MD was associated with MHO phenotype. Moreover, we suggest that a specific cut-off of the PREDIMED score could be an indicator to discriminate patients with MUO/MHO phenotypes and therefore help in identifying patients at higher cardiovascular risk who will require specific dietary intervention., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Obesity has been confirmed to be associated with infertility. However, the association between metabolically healthy obesity (MHO), a subset of obesity with no metabolic abnormalities, and female infertility has not yet been investigated. This study aimed to examine the association between MHO and the risk of female infertility among United States., Methods: This study utilized a cross-sectional design and included 3542 women aged 20-45 years who were selected from the National Health and Nutrition Examination Survey (NHANES) 2013-2020 database. The association between MHO and the risk of infertility was evaluated using risk factor-adjusted logistic regression models., Results: Higher BMI and WC were associated with increased infertility risk after adjusting for potential confounding factors (OR (95% CI): 1.04(1.02, 1.06), P = 0.001; OR (95% CI): 1.02 (1.01, 1.03), P < 0.001; respectively). After cross-classifying by metabolic health and obesity according to BMI and WC categories, individuals with MHO had a higher risk of infertility than those with MHN (OR (95% CI): 1.75(0.88, 3.50) for BMI criteria; OR (95% CI): 2.01(1.03, 3.95) for WC criteria). A positive linear relationship was observed between BMI/WC and infertility risk among metabolically healthy women (P non-linearity =0.306, 0.170; respectively)., Conclusions: MHO was associated with an increased risk of infertility among reproductive-aged women in the US. Obesity itself, regardless of metabolic health status, was associated with a higher infertility risk. Our results support implementing lifestyle changes aimed at achieving and maintaining a healthy body weight in all individuals, even those who are metabolically healthy., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Objectives: Various diseases are associated with obesity and metabolism. We sought to investigate the risk of cardiovascular disease (CVD) in diverse metabolic obesity phenotypes. Methods and Results: A prospective observational study of 1517 participants ≥25 years of age without CVD at baseline was conducted. Participants were categorized into four groups based on the condition of central obesity and metabolic health status: metabolically healthy normal weight, metabolically healthy obesity (MHO), metabolically unhealthy normal weight, and metabolically unhealthy obese (MUO). A multivariate Cox regression analysis was used to analyze the relationship between different obesity phenotypes and CVD. During 14830.49 person-years of follow-up, there were 244 incident cases of CVD. Of the 1517 participants, 72 (4.75%) and 812 (53.53%) were classified as having MHO and MUO, respectively. MHO and MUO had a tendency toward a higher risk of CVD [adjusted hazard ratios (HRs) = 1.49, 95% confidence interval (CI): 1.11-2.02 and HR = 1.25, 95% CI: 1.00-1.55, respectively] based on the waist circumference criterion. Conclusion: MHO and MUO can increase the risk of CVD.

Metabolically healthy obesity or metabolically healthy overweight (MHO) is best described as being absent of any major metabolic disorder or cardiovascular diseases such as type 2 diabetes mellitus, dyslipidemia, hypertension, and atherosclerotic cardiovascular disease despite being obese or overweight. Nevertheless, MHO is being recognized as an important risk factor for the development of cardiovascular, cerebrovascular, and peripheral artery disease. In addition, these patients are at a high risk of conversion to the metabolically unhealthy phenotype. Tirzepatide is a newly developed glucose-lowering agent which acts on the glucose-dependent insulinotropic peptide (GIP) and glucagon-like peptide-1 (GLP-1) receptors. It has been shown to result in several highly beneficial outcomes including weight loss and a significant improvement in important metabolic parameters such as HbA1c, fasting serum glucose, and triglyceride/lipoprotein levels. These findings suggest that tirzepatide could potentially be beneficial to metabolically healthy obese or metabolically healthy overweight patients in reducing their risk of adverse cardiovascular outcomes and conversion to the metabolically unhealthy phenotype. In this review, we aim to discuss the potential benefits of using the novel anti-diabetic tirzepatide in the management of MHO to prevent the development of cardiovascular events and to decrease the likelihood of conversion to the unhealthy phenotype. We initially describe the clinical outcomes of MHO as well as the association of MHO with developing future cardiovascular events. We then delineate the currently available evidence behind the clinical effects of tirzepatide. We finally discuss the potential advantages of using tirzepatide in the management of MHO., Competing Interests: Declaration of Competing Interest KRT reports consultancy fees from AstraZeneca, Boehringer Ingelheim, Lilly, Goldfinch Bio, Novo Nordisk, and Travere; grant support from the National Institutes of Health (NIDDK, NHLBI, NIMHD), the Centers for Disease Control and Prevention,  Bayer, Goldfinch Bio, and Travere; and speaker fees from AstraZeneca, Lilly, Janssen, and Novo Nordisk. Other authors have no conflict of interest., (Copyright © 2023 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.)

Objective: Birth weight (BW) and sibling's status are two important indicators of early intrauterine environment and subsequent living environment, but no evidence has emerged on their joint associations on metabolic obesity phenotype. To determine the joint associations between BW and single-child status with childhood metabolic obesity phenotype was our purpose., Methods: A cross-sectional assessment of children and adolescents aged 7-18 years was performed in Chinese seven provinces in 2013. We obtained anthropometric, blood pressure and biochemical measurements, and distributed questionnaires covering demographic, neonatal and lifestyle characteristics. The metabolic obesity phenotype was defined by 2018 consensus-based criteria. Logistic regression and restricted cubic spline models were applied to evaluate the associations of BW and metabolic obesity phenotype, and estimate the multiplicative interactions and the combined associations of BW and single-child status with metabolic obesity phenotype., Results: Of enrolled 12 346 children and adolescents, the prevalence of metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) was 1.96% and 3.03%. There were 8.95% and 4.03% children with high BW or low BW, and 67.55% did not have siblings. High BW was positively associated with MHO (OR = 1.94, 95%CI = 1.28-2.94). Single-child also had increased odds of MHO and MUO (p < 0.05), and it had joint associations with high BW showing 0.85- to 2.58-fold higher odds of MUO and MHO., Conclusions: High BW and single-child status have joint positive associations with the subsequent odds of MHO and MUO, which should be jointly prevented through earlier screening and subsequent preventive strategies., (© 2023 World Obesity Federation.)

Background and Objective: Obesity has become a serious public health problem and brings a heavy burden of cardiovascular disease. Metabolically healthy obesity (MHO) is defined as individuals with obesity with no or only minor metabolic complications. Whether individuals with MHO have a lower cardiovascular risk remains controversial. In this study, a new criterion was used to define MHO and assess its predictive value for cardiovascular events and death. At the same time, the new criterion and the traditional criterion are compared to analyze the differences between different diagnostic criteria., Methods: A prospective cohort was established in northeast rural China from 2012 to 2013. Follow-up was conducted in 2015 and 2018 to investigate the incidence of cardiovascular events and survival. Subjects were grouped according to the metabolic health and obesity status. Kaplan-Meier curves were drawn to describe the cumulative risk of endpoint events in the four groups. Cox regression analysis model was constructed to evaluate the risk of endpoint events. Analysis of variance and post hoc analyses were used to calculate and compare differences in metabolic markers between MHO subjects diagnosed by novel and traditional criteria., Results: A total of 9345 participants 35 years of age or older without a history of cardiovascular disease were included in this study. After a median follow-up of 4.66 years, the data showed that participants in the MHO group had no significant increase in the risk of composite cardiovascular events and stroke, but had a 162% increase in the risk of coronary heart disease (HR: 2.62; 95%CI: 1.21-5.67). However, when using conventional criteria for metabolic health, mMHO group had a 52% increase in combined CVD risk (HR: 1.52; 95%CI: 1.14-2.03). By comparing the differences of metabolic indicators between MHO subjects diagnosed by the two criteria, MHO subjects diagnosed by the new criterion had higher WC, WHR, TG, FPG, and lower HDL-C levels except for lower blood pressure, showing more exposure to cardiovascular risk factors., Conclusions: The risk of combined CVD and stroke was not increased in MHO subjects. The new metabolic health criterion is superior to the traditional criterion and can effectively identify individuals with obesity with a lower risk of combined CVD. Blood pressure levels may be responsible for the inconsistent risk of combined CVD in MHO subjects diagnosed with both criteria., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Wang, Ma, Guo, Sun and Zhang.)

Background: Studies of obesity with or without metabolic aberrations, commonly termed metabolically unhealthy or healthy obesity, in relation to cancer risk are scarce., Methods: We investigated body mass index (normal weight, overweight, obesity) jointly and in interaction with metabolic health status in relation to obesity-related cancer risk (n = 23 630) among 797 193 European individuals. A metabolic score comprising mid-blood pressure, plasma glucose, and triglycerides was used to define metabolically healthy and unhealthy status. Hazard ratios (HRs) and multiplicative interactions were assessed using Cox regression, and additive interactions were assessed using the relative excess risk for interaction. All statistical tests were 2-sided., Results: Metabolically unhealthy obesity, with a baseline prevalence of 7%, was, compared with metabolically healthy normal weight, associated with an increased relative risk of any obesity-related cancer and of colon, rectal, pancreas, endometrial, liver, gallbladder, and renal cell cancer (P < .05), with the highest risk estimates for endometrial, liver, and renal cell cancer (HR = 2.55-3.00). Metabolically healthy obesity showed a higher relative risk for any obesity-related cancer and colon (in men), endometrial, renal cell, liver, and gallbladder cancer, though the risk relationships were weaker. There were no multiplicative interactions, but there were additive, positive interactions between body mass index and metabolic health status on obesity-related and rectal cancer among men and on endometrial cancer (P < .05)., Conclusions: This study highlights that the type of metabolic obesity phenotype is important when assessing obesity-related cancer risk. In general, metabolic aberrations further increased the obesity-induced cancer risk, suggesting that obesity and metabolic aberrations are useful targets for prevention., (© The Author(s) 2023. Published by Oxford University Press.)

Background: The impact of metabolically healthy obesity (MHO) on kidney dysfunction remains debatable. Moreover, few studies have focused on the early stages of kidney dysfunction indicated by hyperfiltration and mildly reduced eGFR. Thus, we aimed to investigate the association between the MHO and early kidney dysfunction, which is represented by hyperfiltration and mildly reduced estimated glomerular filtration rate (eGFR), and to further explore whether serum uric acid affects this association., Methods: This cross-sectional study enrolled 1188 residents aged ≥ 40 years old from Yonghong Communities. Metabolically healthy phenotypes were categorized based on Adult Treatment Panel III criteria. Obesity was defined as body mass index (BMI) ≥ 25 kg/m 2 . Mildly reduced eGFR was defined as being in the range 60 < eGFR ≤ 90 ml/min/1.73m 2 . Hyperfiltration was defined as eGFR > 95th percentile after adjusting for sex, age, weight, and height., Results: Overall, MHO accounted for 12.8% of total participants and 24.6% of obese participants. Compared to metabolically healthy non-obesity (MHNO), MHO was significantly associated with an increased risk of mildly reduced eGFR (odds ratio [OR] = 1.85, 95% confidence interval [CI]  1.13-3.01) and hyperfiltration (OR = 2.28, 95% CI 1.03-5.09). However, upon further adjusting for uric acid, the association between the MHO phenotype and mildly reduced eGFR was reduced to null. Compared with MHNO/non-hyperuricemia, MHO/non-hyperuricemia was associated with an increased risk of mildly reduced eGFR (OR = 2.04, 95% CI  1.17-3.58), whereas MHO/hyperuricemia was associated with an observably increased risk (OR = 3.07, 95% CI  1.34-7.01)., Conclusions: MHO was associated with an increased risk of early kidney dysfunction, and the serum uric acid partially mediated this association. Further prospective studies are warranted to clarify the causality., (© 2023. The Author(s).)

Background and Aims: Obesity without metabolic alterations (Metabolically Healthy Obesity, MHO) is a condition with a risk of death and cardiovascular disease lower than that of obesity associated with metabolic alterations (Metabolically Unhealthy Obesity, MUO) and similar to that of healthy non obese individuals. Inflammation is considered as a key risk factor mediating the adverse health outcomes in obesity., Methods and Results: We compared circulating levels of thirteen major cytokines and adipokines and the expression profiles of fifteen pro-inflammatory and two anti-inflammatory genes in visceral and subcutaneous adipose tissue in a series of 16 MHO patients and in 32 MUO patients that underwent bariatric surgery. MHO was defined according to the most applied definition in current literature. Serum levels of a large set of major cytokines and adipokines did not differ between MHO and MUO patients (p ≥ 0.15). Analyses of the expression profile of pro-inflammatory and anti-inflammatory genes in subcutaneous and visceral adipose tissue failed to show differences between MHO and MUO patients (p ≥ 0.07). Sensitivity analyses applying two additional definitions of MHO confirmed the results of the primary analysis., Conclusion: In a series of metabolically healthy obese patients neither circulating levels of major cytokines and adipokines nor the gene expression profile of a large set of pro-inflammatory and anti-inflammatory genes in subcutaneous and visceral fat differed from those in metabolically unhealthy obese patients., Competing Interests: Declaration of competing interest All the authors declare no conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

Purpose: Dietary behavior is an important part of lifestyle interventions for obesity and its cardiovascular comorbidities. However, little is known about associations between dietary patterns and obesity phenotypes in Southwest China, a region with unique dietary patterns and significant heterogeneity in obesity., Methods: Data from the baseline survey of the China Multi-Ethnic Cohort in Southwest China were analyzed (n = 64,448). Dietary intakes during the past year were measured with the semi-quantitative Food Frequency Questionnaire (s-FFQ). Principal component factor analysis (PCFA) was used to identify dietary patterns. Multinomial logistic regressions were used to examine the associations between dietary patterns and obesity phenotypes and stratified analyses were performed to assess whether the associations differed across demographic variables., Results: Three dietary patterns were identified and then named according to their apparent regional gathering characteristics: the Sichuan Basin dietary pattern (characterized by high intakes of various foods), the Yunnan-Guizhou Plateau dietary pattern (characterized by agricultural lifestyles), and the Qinghai-Tibet Plateau dietary pattern (characterized by animal husbandry lifestyles), respectively. Higher adherence to the Sichuan Basin dietary pattern was positively associated with metabolically healthy overweight/obesity (MHO, OR 1.13, 95% CI 1.05-1.21) but negatively associated with metabolically unhealthy normal weight (MUNW, OR 0.78, 95% CI 0.65-0.95). Higher adherence to the other two dietary patterns was positively associated with MHO and metabolically unhealthy overweight/obesity (MUO). Besides, differences in socioeconomic status also affected the relationship between dietary patterns and obesity phenotypes., Conclusions: Adherence to the more diverse Sichuan basin dietary pattern performed a mixed picture, while the other two may increase the risk of obesity phenotypes, which indicates nutritional interventions are urgently needed., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Background: Previous studies indicated that obesity would accelerate frailty progression. However, obesity is heterogeneous by different metabolic status. The associations of metabolic heterogeneity of obesity with frailty progression remain unclear., Methods: A total of 6730 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 4713 from the English Longitudinal Study of Ageing (ELSA) were included at baseline. Metabolic heterogeneity of obesity was evaluated based on four obesity and metabolic phenotypes as metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy overweight/obesity (MUOO). Frailty status was assessed by the frailty index (FI) ranging from 0 to 100 and frailty was defined as FI ≥ 25. Linear mixed-effect models were used to analyse the associations of metabolic heterogeneity of obesity with frailty progression., Results: In the CHARLS, MUOO and MUNW presented the accelerated FI progression with additional annual increases of 0.284 (95% CI: 0.155 to 0.413, P < 0.001) and 0.169 (95% CI: 0.035 to 0.303, P = 0.013) as compared with MHNW. MHOO presented no accelerated FI progression (β: -0.011, 95% CI: -0.196 to 0.173, P = 0.904) as compared with MHNW. In the ELSA, the accelerated FI progression was marginally significant for MUOO (β: 0.103, 95% CI: -0.005 to 0.210, P = 0.061) and MUNW (β: 0.157, 95% CI: -0.011 to 0.324, P = 0.066), but not for MHOO (β: -0.047, 95% CI: -0.157 to 0.062, P = 0.396) in comparison with MHNW. The associations of MUOO and MUNW with the accelerated FI progression were stronger after excluding the baseline frail participants in both cohorts. The metabolic status changed over time. When compared with stable MHNW, participants who changed from MHNW to MUNW presented the accelerated FI progression with additional annual increases of 0.356 (95% CI: 0.113 to 0.599, P = 0.004) and 0.255 (95% CI: 0.033 to 0.477, P = 0.024) in the CHARLS and ELSA, respectively. The accelerated FI progression was also found in MHOO participants who transitioned to MUOO (CHARLS, β: 0.358, 95% CI: 0.053 to 0.663, P = 0.022; ELSA, β: 0.210, 95% CI: 0.049 to 0.370, P = 0.011)., Conclusions: Metabolically unhealthy overweight/obesity and normal weight, but not metabolically healthy overweight/obesity, accelerated frailty progression as compared with metabolically healthy normal weight. Regardless of obesity status, transitions from healthy metabolic status to unhealthy metabolic status accelerated frailty progression as compared with stable metabolically healthy normal weight. Our findings highlight the important role of metabolic status in frailty progression and recommend the stratified management of obesity based on metabolic status., (© 2022 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.)

Introduction: Obesity contributes to the pathogenesis of diverse metabolic diseases, yet the mechanism underlying metabolically healthy obesity (MHO) remains elusive. Thyroid hormones and sensitivity to them have a major impact on metabolism. Our study aimed to investigate the association between MHO and thyroid hormone sensitivity., Methods: Thyroid hormone indices, including the thyroid-stimulating hormone (TSH) index (TSHI), the Thyrotroph Thyroxine Sensitivity Index (TTSI), the Thyroid Feedback Quantile-Based Index (TFQI), and the Parametric Thyroid Feedback Quantile-Based Index (PTFQI), were calculated based on a non-institutionalized US sample in the National Health and Nutrition Examination Survey (NHANES, 2007-2012). Participants were divided into four groups (metabolically healthy non-obesity [MHNO], metabolically unhealthy non-obesity [MUNO], MHO, and metabolically unhealthy obesity [MUO]) according to their body mass index and metabolic profiles. Linear regression, logistic regression, and restricted cubic splines were employed to analyze the association between thyroid hormone indices and metabolic phenotypes., Results: A total of 4,857 participants (49.6% men; mean age, 42.6 years) were included, with 1,539 having obesity and 235 identified as MHO. Participants in the MHO group exhibited lower levels of TSH, TSHI, TTSI, TFQI, and PTFQI compared with the MHNO group (all p &lt; 0.05), while the differences among MHNO, MUNO, and MUO groups were not statistically significant (all p &gt; 0.05). Among participants with obesity, TSH, TSHI, TTSI, TFQI, and PTFQI were positively associated with metabolic abnormality (all p &lt; 0.05)., Conclusion: Participants with MHO exhibited higher thyroid hormone sensitivity among various obesity phenotypes, even when compared with those with MHNO. A positive association was observed between metabolic abnormality and thyroid hormone sensitivity, while the trend of TSH was observed to be consistent with sensitivity to thyroid hormone indices in discriminating metabolic abnormality. Hence, TSH has the potential to serve as a convenient index for detecting sensitivity to thyroid hormones and further metabolic conditions., (© 2023 The Author(s). Published by S. Karger AG, Basel.)

Background: The risks of NAFLD and NAFLD with fibrosis progression among metabolically healthy obesity (MHO) individuals are largely unexplored. This cohort study investigated the association between MHO as well as other metabolic syndrome-obesity combined phenotypes and NAFLD and its fibrosis progression., Methods: Participants included 31,010 adults from a health check-up cohort free from NAFLD and intermediate or high probability of advanced fibrosis at baseline. Metabolically healthy was defined as not having any component of metabolic syndrome. Obesity was identified by body mass index (BMI) and waist circumference (WC). Participants were cross-classified by metabolic health and obesity at baseline. The outcomes were NAFLD, and NAFLD with fibrosis progression, as assessed by abdominal B-type ultrasound and noninvasive fibrosis score., Results: During a median follow-up of 2.2 (interquartile range, 1.2-4.9) years, 7,393 participants developed NAFLD. MHO individuals (HR 5.51, 95% CI 4.98, 6.09 for BMI criteria; HR 6.76, 95% CI 6.04, 7.57 for WC criteria) had a significantly higher risk of NAFLD than those with metabolically healthy normal weight or low WC. The corresponding HRs (95% CIs) for metabolically healthy overweight (defined by BMI) and medium WC were 2.74 (2.49-3.02) and 2.93 (2.65-3.24), respectively. Furthermore, 557 participants developed NAFLD with fibrosis progression. The association between different obesity phenotypes and NAFLD with fibrosis progression also showed a similar pattern., Conclusion: MHO was associated with significantly higher risks of NAFLD and its fibrosis progression, suggesting that regarding NAFLD prevention, MHO individuals might still benefit from lifestyle interventions aimed at body weight and WC maintenance., (© 2022. Asian Pacific Association for the Study of the Liver.)

Background: Obesity is commonly associated with a high risk of metabolic disorders, and obesity-related metabolic abnormalities are affected by some specific obesity phenotypes, regional fat distribution, and body mass index. However, few studies have investigated the relationship between obesity phenotypes and regional fat distribution in Korean subjects. This study aimed to assess regional fat distribution by gender using magnetic resonance imaging (MRI), and to identify a link between fat distribution and metabolic disorders in Korean subjects. Methods: This study included 35 Korean subjects (20 women, 15 men) who were classified into two groups by gender, and further divided into two groups based on their obesity phenotype: a metabolically abnormal obesity (MAO) and metabolically healthy obesity (MHO) group. Fat distribution was measured using MRI. The blood parameters were measured using a commercially available kit. Results: Women in the MAO group had more risk factors for metabolic abnormalities than those in the MHO group. Serum glucose, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels were also significantly higher in women with MAO than in those with MHO. The intermuscular adipose tissue (IMAT) of women with MAO was significantly higher than that of women with MHO. Serum HDL-C level was negatively correlated with IMAT, whereas leptin showed a positive correlation with IMAT in all subjects. Conclusions: Metabolic abnormalities according to obesity phenotype posed a higher risk in women than that in men. These findings suggest that an understanding of gender differences in relation to the association between obesity and metabolic risk would be helpful to reduce the prevalence of obesity.

Background and Aims: The association between obesity severity and duration with the transition from metabolically healthy obese/overweight (MHO) phenotype to metabolically unhealthy obese (MUO) phenotype is not well understood., Methods and Results: This study includes the Tehran Lipid and Glucose Study participants who were initially classed as MHO. Cumulative excess weight (CEW) and cumulative excess waist circumference (CEWC) scores, which represent the accumulation of body mass index and waist circumference deviations from expected values over time (kg/m 2 ∗ y and cm ∗ y, respectively), were calculated until the transition from MHO to MUO or the end of follow-up. The sex-stratified association of CEW and CWEC with the transition from MHO to MUO was investigated by time-dependent Cox models, adjusting for confounders. Out of 2525 participants, 1732 (68.5%) were women. During 15 years of follow-up, 1886 (74.6%) participants transitioned from MHO to MUO. A significant association was found between CEW and CEWC quartiles with the development of MUO among women participants (fully adjusted hazard ratios in the fourth quartile of CEW and CEWC [95% (CI)]:1.65 [1.37-1.98] and [95% CI]: 1.83 [1.53-2.19]). There was no significant association between CEW and CEWC with the MHO transition to MUO among men participants., Conclusion: Over 15 years of follow-up in TLGS, general and central obesity accumulation was associated with the increased transition from MHO to MUO among women participants. More research with a larger sample size is needed to confirm and explain why the results are different for men and women., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to disclose., (Copyright © 2022 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.)

Background: Although the gut microbiota may be involved in obesity onset and progression, the exact association of the gut microbiota in metabolically healthy obesity (MHO) remains largely unknown., Methods: An integrated paired-sample metagenomic analysis was conducted to investigate the gut microbial network and biomarkers of microbial species from the MHO and healthy non-obese subjects in the GMrepo database. Further explorations were performed in the MHO mice model using a multiomics analysis to detect changes in the composition and function of the intestinal microbiome and associated metabolites., Results: In the human study, 314 matched metagenomic data were qualified for the final analysis. We identified seven significantly changed species possibly involved in MHO pathogenesis (MHO-enriched: Bacteroides vulgatus , Megamonas sp ; MHO-depleted: Butyrivibrio crossotus , Faecalibacterium prausnitzii , Bacteroides cellulosilyticus ; Eubacterium siraeum ; Bacteroides massiliensis ). In the murine study, we found 79 significantly-changed species which may have possible associations with the MHO phenotype. The depletion of Bacteroides cellulosilyticus was commonly recognized in the human and murine MHO phenotype. Consistent with the metagenomic data, liquid chromatography-mass spectrometry (LC/MS) revealed significantly changed gut metabolites, which may promote MHO pathogenesis by altering the amino acids and lipid metabolic pathways. In the microbe-metabolites interaction analysis, we identified certain fatty acids (Dodecanedioic acid, Arachidic Acid, Mevalonic acid, etc.) that were significantly correlated with the MHO-enriched or depleted species., Conclusion: This study provides insights into identifying specific microbes and metabolites that may involve in the development of obesity without metabolic disorders. Future modalities for MHO intervention may be further validated by targeting these bacteria and metabolites., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Tang, Ye, Yu, Yang, Cheng, Zhang and Zhou.)

Purpose: This study evaluated the association between metabolic health status and incident kidney cancer among obese participants., Materials and Methods: A total of 514,866 individuals were included from the Korean National Health Insurance Service-National Health Screening Cohort. Changes in metabolic health status and obesity from the baseline examination in 2009-2010 to the next biannual examination in 2011-2012 were determined. Based on the status change, obese participants were divided into four groups: stable metabolically healthy obesity, metabolically healthy obesity to metabolically unhealthy obesity, metabolically unhealthy obesity to metabolically healthy obesity, and stable metabolically unhealthy obesity., Results: The stable metabolically healthy obesity phenotype did not confer an increased risk of incident kidney cancer, compared to the stable metabolically healthy non-obese group. In contrast, the metabolically healthy obesity to metabolically unhealthy obesity group had a significantly higher risk of incident kidney cancer than the stable metabolically healthy non-obese group. Among patients with metabolically unhealthy obesity at baseline, those who transitioned to the metabolically healthy obese group had no increased risk of kidney cancer, whereas those who remained in metabolically unhealthy obesity status had a higher risk of incident kidney cancer than the stable metabolically healthy non-obese group. The transition or maintenance of metabolic health was a decisive factor for kidney cancer in obese patients., Conclusions: Maintaining or restoring metabolic health should be stressed upon in obese patients to reduce the risk of kidney cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cho, Kim, Park, Lee, Kim and Jung.)

The evidence linking sleep duration and metabolic body size phenotypes is limited, especially in young adulthood. In this study, we aimed to examine the association between sleep duration and metabolic body size phenotypes among Chinese young workers and investigate whether discrepancies exist among shift and non-shift workers. A cross-sectional study was performed between 2018 and 2019 in Wuhan, China and 7,376 young adults aged 20-35 years were included. Self-reported sleep duration was coded into four groups: <7, 7-8, 8-9, and ≥9 h per day. Participants were classified into four metabolic body size phenotypes according to their body mass index and metabolic health status: metabolically healthy normal weight, metabolically unhealthy normal weight, metabolically healthy overweight/obesity (MHO), and metabolically unhealthy overweight/obesity (MUO). Multinomial logistic regression models were used to explore the associations between sleep duration and metabolic body phenotypes. Compared with those who slept 7-8 h each night, those with sleep duration <7 h per day had higher odds of MHO (OR 1.27, 95% CI: 1.02-1.56) and MUO (OR 1.22, 95% CI: 1.03-1.43), irrespective of multiple confounders. Stratification analyses by shift work showed that the association between short nighttime sleep and increased odds of MUO was only observed in shift workers (OR 1.26, 95% CI 1.03-1.54). Sleep duration is independently associated with metabolic body size phenotypes among Chinese young adults, while shift work could possibly modulate the association. These results may provide evidence for advocating adequate sleep toward favorable metabolic body size phenotypes in young workers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Xue, Shi, Xia, Chen, Liu, Liu, Wang and Ye.)

Background: China has the world's highest rail transportation network density, and the prevalence of obesity among railway workers in China is more than twice that of adults in the world. Carotid artery plaque (CAP) is a simple and noninvasive predictor of early atherosclerosis, while the association between different obese phenotypes and CAP risk among Chinese male railway drivers is unclear., Methods: This cross-sectional study was performed among 8,645 Chinese male railway drivers. Obese phenotypes were assessed based on the obesity status (the body mass index ≥ 28 kg/m 2 as obesity vs. < 28 kg/m 2 as non-obesity) and metabolic status (metabolically healthy vs. metabolically unhealthy). Metabolically unhealthy was defined as the presence of at least one dysfunction, including elevated blood pressure, elevated fasting blood glucose, elevated triglyceride, and reduced high-density-lipoprotein cholesterol. Four obese phenotypes were defined based on the body mass index and metabolic status, i.e., metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), and metabolically unhealthy non-obesity (MUNO). Multivariable logistic regression was employed to estimate the association between different obese phenotypes and the risk of CAP. Subgroup analysis was performed to examine the variation of the association by age, circadian rhythm disorders, and history of smoking and drinking., Results: The prevalence of CAP among male railway drivers in MHO, MUO, MUNO, and MHNO was 8.75%, 18.67%, 17.82%, and 5.36%, respectively. Compared to those with MHNO, an increased risk for CAP was observed among those with MHO (OR = 2.18, 95% CI: 0.82, 5.10), MUO (OR = 1.78, 95% CI:1.44, 2.21), and MUNO (OR = 2.20, 95% CI: 1.67, 2.89). The subgroup analysis showed that both of the metabolically unhealthy groups (MUNO and MUO) aged < 45 years were prone to a higher risk of CAP (for the MUNO group, OR = 4.27, 95% CI:2.71, 7.10; for the MUO group, OR = 4.00, 95%CI: 2.26, 7.17)., Conclusion: The obese phenotypes are associated with CAP risk in male railway drivers, especially those with metabolically unhealthy conditions aged < 45 years., (© 2022. The Author(s).)

Background and Aims: It is still debatable whether metabolic status in normal weight population increases the risk of mortality (all-cause mortality (ACM), cardiovascular mortality (CVM)) and major adverse cardiac events (MACE) as compared to the obese population. Therefore, this meta-analysis aims to evaluate the association of the metabolically unhealthy normal weight (MUH-NW) phenotype with all-cause mortality, cardiovascular mortality, and MACE in comparison to metabolically healthy obesity (MH-O), along with the association of metabolically unhealthy obesity (MUH-O) phenotype regarding the same outcomes compared to MUH-NW., Methods: A systematic literature search was conducted using online databases from inception to June 20, 2022, to comprehensively search all prospective cohort studies comprising three variables including adults aged ≥18 years, obesity and four metabolic phenotypes, and interest outcomes (ACM, CVM, and MACE)., Results: Forty-one prospective cohort studies with a total of 4,028,750 participants was included in this study. Compared to MH-O, MUH-NW had a substantially higher risk of ACM (RR = 1.47 (95%CI = 1.32-1.64); P < 0.001; I 2  = 89.8%,P-heterogeneity<0.001), CVM (RR = 2.37 (95%CI = 1.97-2.86); P < 0.001; I 2  = 83.7%,P-heterogeneity<0.001), and MACE (RR = 1.73 (95%CI = 1.49-2.00); P < 0.001; I 2  = 74.3%,P-heterogeneity<0.001). Moreover, MUH-O did not have a significantly elevated risk of ACM (RR = 0.97 (95%CI = 0.82-1.15); P = 0.736; I 2  = 98.3%,P-heterogeneity<0.001), CVM (RR = 0.96 (95%CI = 0.88-1.05); P = 0.394; I 2  = 77.0%,P-heterogeneity<0.001), and MACE (RR = 0.95 (95%CI = 0.80-1.13); P = 0.570; I 2  = 92.2%,P-heterogeneity<0.001) compared to MUH-NW., Conclusion: In conclusion, MUH-NW was superior but not inferior to MH-O and MUH-O in terms of increased risk of interest outcomes, refuting the notion that normal weight population is a benign condition. Hence, in normal weight population, metabolic screening is highly suggested to measure the baseline of obesity and metabolic phenotypes, thus preventing the risk of CVD and mortality in the future., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2022 Diabetes India. Published by Elsevier Ltd. All rights reserved.)

Introduction: The incidence of metabolically unhealthy obesity is rising nationally. In this study, we compare wound and overall complications between metabolically unhealthy obese and healthy patients undergoing elective plastic surgery and model how operative time influences a complication risk., Methods: Patients undergoing elective breast and body plastic surgery procedures in the 2009-2019 National Surgical Quality Improvement Program (NSQIP) dataset were identified. Complications were compared between metabolically unhealthy obese (body mass index [BMI] > 30 with diabetes and/or hypertension) versus metabolically healthy obese patients (BMI > 30 without diabetes or hypertension). Logistic regression was used to model the probability of wound complications across operative times stratified by metabolic status., Results: Of 139,352 patients, 13.4% (n = 18,663) had metabolically unhealthy obesity and 23.8% (n = 33,135) had metabolically healthy obesity. Compared to metabolically healthy patients, metabolically unhealthy patients had higher incidence of wound complications (6.9% versus 5.6%; P < 0.001) and adverse events (12.4% versus 9.6%; P < 0.001), in addition to higher 30-d readmission, returns to the operating room, and length of stay (all P < 0.001). After adjustment, BMI (Odds ratio [OR] 7.86), hypertension (OR 1.15), and diabetes (OR 1.25) were independent risk factors for wound complications (all P < 0.001). Among metabolically unhealthy patients, the operative time was log-linear with a wound complication risk (OR 1.21; P < 0.001)., Conclusions: Diabetes and hypertension are additive risk factors with obesity for wound complications in elective plastic surgery. Among patients with metabolically unhealthy obesity, a risk of wound complications increases logarithmically with operative time. This distinction with regard to metabolic state might explain the unclear impact of obesity on surgical outcomes within existing surgical literature., (Copyright © 2022 Elsevier Inc. All rights reserved.)

It has been argued that metabolically healthy obesity (MHO) does not increase the risk of cardiovascular disease. The aim of this study is to evaluate whether, in a population of obese children/adolescents, the metabolically unhealthy obesity (MUO) phenotype is associated with higher left ventricular mass index and/or higher prevalence of left ventricular hypertrophy than the MHO phenotype. We also tested whether the addition of an insulin resistance index (HOMA-index >90th percentile by sex and age) and the presence of hyperuricemia (serum uric acid >90th percentile by sex and age) to the definition of MUO better identified obese children with early cardiac damage. Left ventricular hypertrophy was defined as the presence of left ventricular mass index greater than or equal to the age- and sex-specific 95th percentile. The study population included 459 obese children (males 53.2%, mean age 10.6 [standard deviation, 2.6] years), of whom 268 (58.4%) were MUO. The left ventricular mass index was higher in MUO children than in MHO children (37.8 vs 36.3 g/m 2.7 , p=0.015), whereas the percentage of MUO children presenting left ventricular hypertrophy was only slightly higher in MUO children (31.1 vs 40%, p=0.06). Multiple linear regression analyses showed that the variables significantly associated with higher left ventricular mass index were male gender (p<0.01), Body Mass Index z-score (p<0.001) and Waist-to-Height-ratio (p<0.001). Multiple logistic regression analyses showed that the presence of left ventricular hypertrophy was only significantly associated with higher Body Mass Index z-score (p<0.05) and Waist-to-Height-ratio (p<0.05). In spite of the higher left ventricular mass index of MUO as compared to MHO children, the MUO phenotype was not a significant predictor of either higher left ventricular mass index or higher left ventricular hypertrophy prevalence. The MUO phenotype had a low predictive ability on the presence of left ventricular hypertrophy. The area under the receiver operating characteristic curve was 0.57 (sensitivity 0.64, 1-specificity 0.55). The addition of insulin resistance and hyperuricemia to the definition of MUO did not change the results observed with the standard definition of MUO at multivariable analysis. The MUO phenotype appears to be of little usefulness in identifying the early presence of cardiac damage in a large population of obese children and adolescents. Excess weight and abdominal obesity are confirmed as an important determinant of early organ damage in obese children., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Genovesi, Tassistro, Giussani, Lieti, Patti, Orlando, Montemerlo, Antolini and Parati.)

Introduction: Children with obesity in the absence of traditional cardiometabolic risk factors (CRF) have been described as metabolically healthy obese (MHO). Children with MHO phenotype has a favorable metabolic profile with normal glucose metabolism, lipids, and blood pressure compared to children with metabolically unhealthy obese (MUO) phenotype. This study aimed to compare several parameters related to obesity between these two groups and to examine the predictors associated with the MHO phenotype., Methods: This study included a cross-sectional baseline data of 193 children with obesity (BMI z-score > +2 SD) aged 8-16 years enrolled in MyBFF@school program, a school-based intervention study conducted between January and December 2014. Metabolic status was defined based on the 2018 consensus-based criteria with MHO children had no CRF (HDL-cholesterol > 1.03 mmol/L, triglycerides ≤ 1.7 mmol/L, systolic and diastolic blood pressure ≤ 90 th percentile, and fasting plasma glucose ≤ 5.6 mmol/L). Those that did not meet one or more of the above criteria were classified as children with MUO phenotype., Results: The prevalence of MHO was 30.1% (95% CI 23.7 - 37.1) among schoolchildren with obesity and more common in younger and prepubertal children. Compared to MUO, children with MHO phenotype had significantly lower BMI, lower waist circumference, lower uric acid, higher adiponectin, and higher apolipoprotein A-1 levels (p < 0.01). Multivariate logistic regression showed that adiponectin (OR: 1.33, 95% CI 1.05 - 1.68) and apolipoprotein A-1 (OR: 1.02, 95% CI 1.01 - 1.03) were independent predictors for MHO phenotype in this population., Conclusions: MHO phenotype was more common in younger and prepubertal children with obesity. Higher serum levels of adiponectin and apolipoprotein A-1 increased the possibility of schoolchildren with obesity to be classified into MHO phenotype., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wan Mohd Zin, Jalaludin, Yahya, Nur Zati Iwani, Md Zain, Hong, Mokhtar and Wan Mohamud.)

(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.

Background: Urinary 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., Methods: This 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., Results: The 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., Conclusions: Among 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., (© 2022 The Authors. Journal of Diabetes published by Ruijin Hospital, Shanghai JiaoTong University School of Medicine and John Wiley & Sons Australia, Ltd.)

Objective: Obesity is associated with an increased risk of digestive polyps, whereas all obesity are not created equally. The role of metabolic states in occurrence risks of polyps among individuals with varying degrees of obesity remains unknown. Our study aimed to evaluate the association between metabolic obesity phenotypes and the occurrence of digestive polyps., Research Design and Methods: Data from 9,278,949 patients between 2016 and 2018 from the National Inpatient Sample (NIS) database, a nationally representative database of all discharges from US health-care hospitals, were analyzed. According to obesity phenotype, the study population was classified into four groups: metabolically healthy nonobese (MHNO), metabolically unhealthy nonobese (MUNO), metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). We calculated the incidence rates of various digestive polyps (stomach/duodenum, colon and rectum polyps) among these participants by searching the hospital records for ICD-10 diagnosis codes indicating each gastric, duodenum, colon or rectal polyps. The multiple stepwise regression analysis and further in-depth subgroup analysis were used to determine the associations between metabolic obesity phenotypes and the occurrence of digestive polyps., Results: In the total or female population, those with the MUNO and MUO phenotypes had significantly higher prevalence of digestive polyps compared with individuals with the MHNO or MHO phenotypes (all p < 0.05) and a significant difference was not found between MUNO and MUO phenotypes (p > 0.05). Obese subjects seem to be more likely to develop stomach and duodenum polyps or colon polyps than non-obese subjects in metabolically healthy people of males (MHO vs. MHNO, p < 0.05), whereas obesity status seems to have little effect on the occurrence of digestive polyps in metabolically healthy people of females (MHO vs. MHNO, p>0.05). After adjusting for the potential confounders, the MHO, MUNO and MUO phenotypes were all risk factors for stomach and duodenum polyps (OR = 1.46, 95% CI: 1.36-1.58, p< 0.01; OR = 1.19, 95% CI: 1.14-1.25, p< 0.01; OR = 1.44, 95% CI: 1.35-1.55, p< 0.01, respectively) or colon polyps (OR = 1.28, 95% CI: 1.21-1.35, p< 0.01; OR = 1.18, 95% CI: 1.14-1.22, p< 0.01; OR = 1.46, 95% CI: 1.38-1.54, p< 0.01, respectively) compared with the MHNO phenotype，especially in menopausal female. Interestingly, we also observed in further in-depth subgroup analysis that metabolic abnormalities may have a greater impact on the occurrence of digestive polyps than obesity (all p < 0.05)., Conclusions: Both metabolic abnormities and obesity were associated with a higher risk of digestive polyps. The effect of metabolism on digestive polyp occurrence may be stronger than that of obesity, highlighting the importance of abnormal metabolic status modification regardless of obesity status. Clinical intervention should not only focus on obesity, but also on metabolic abnormalities to decrease digestive polyp risk., (Copyright © 2022. Published by Elsevier Inc.)

Background: The obesity and hepatocellular carcinoma (HCC) risk association may differ by individuals' metabolic health status., Aim: To investigate the association between obesity categories and HCC risk among individuals with different metabolic health phenotypes., Methods: 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)., Results: 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., Conclusion: The association between BMI and HCC appears to be restricted to individuals with underlying metabolic abnormalities., Competing Interests: Conflict of interest None declared., (Copyright © 2021. Published by Elsevier Ltd.)

Nonalcoholic fatty liver disease (NAFLD) is the leading risk factor for common chronic liver disease and is often regarded as a prevalent metabolic disorder tightly associated with obesity. However, the existence of metabolically healthy obesity (MHO) indicates that some important factors may participate in protecting individuals with MHO free of NAFLD, even with excessive adiposity. To explore factors independent of obesity that may be involved in the occurrence of NAFLD, we performed an iTRAQ-based proteomic study to identify proteins differentially expressed in serum between NAFLD and MHO subjects. Compared with the MHO group, ten proteins were upregulated and five were downregulated significantly in the NAFLD group. Gene Ontology analysis indicated significant changes in the immune response and triglyceride metabolism-related pathways between MHO and NAFLD. We further validated three candidates markedly dysregulated in NAFLD by Western blotting and ELISA, including two upregulated proteins (afamin and apolipoprotein H) and one downregulated protein (apolipoprotein C-1). Detection of serum apolipoprotein H levels in a large-scale cohort with MHO and different stages of NAFLD indicated that apolipoprotein H may be a potential blood biomarker for distinguishing NAFLD from MHO and an independent risk factor for predicting NAFLD., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Objective: Some studies have demonstrated a bidirectional association between obesity and depression, whereas others have not. This discordance might be due to the metabolic health status. We aimed to determine whether the relationship between obesity and depression is dependent on metabolic health status., Methods: In total, 9,022,089 participants were enrolled and classified as one of four obesity phenotypes: metabolically healthy nonobesity (MHNO), metabolically unhealthy nonobesity (MUNO), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). We then divided the population into eight phenotypes based on obesity and the number of metabolic risk factors. Furthermore, the associations of eight phenotypes, based on obesity and specific metabolic risk factors, with depression were assessed., Result: Among all participants, a higher risk of depression was observed for MUNO, MHO and MUO than for MHNO. The risk was highest for MUO (OR = 1.442; 95% CI = 1.432, 1.451). However, the association between MHO and depression was different for men and women (OR = 0.941, men; OR = 1.132, women). The risk of depression increased as the number of metabolic risk factors increased. Dyslipidemia was the strongest metabolic risk factor. These relationships were consistent among patients ≥ 45 years of age., Conclusions: The increased risk of obesity-related depression appears to partly depend on metabolic health status. The results highlight the importance of a favorable metabolic status, and even nonobese populations should be screened for metabolic disorders., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Cheng, Li, Han, Yuan, Li, Zhong, Wu, Fan, Bo and Gao.)