Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity.

There is considerable heterogeneity in the cardiometabolic abnormalities associated with obesity. We evaluated multi-organ system metabolic function in 20 adults with metabolically healthy obesity (MHO; normal fasting glucose and triglycerides, oral glucose tolerance, intrahepatic triglyceride content, and whole-body insulin sensitivity), 20 adults with metabolically unhealthy obesity (MUO; prediabetes, hepatic steatosis, and whole-body insulin resistance), and 15 adults who were metabolically healthy lean. Compared with MUO, people with MHO had (1) altered skeletal muscle biology (decreased ceramide content and increased expression of genes involved in BCAA catabolism and mitochondrial structure/function); (2) altered adipose tissue biology (decreased expression of genes involved in inflammation and extracellular matrix remodeling and increased expression of genes involved in lipogenesis); (3) lower 24-h plasma glucose, insulin, non-esterified fatty acids, and triglycerides; (4) higher plasma adiponectin and lower plasma PAI-1 concentrations; and (5) decreased oxidative stress. These findings provide a framework of potential mechanisms responsible for MHO and the metabolic heterogeneity of obesity. This study was registered at ClinicalTrials.gov (NCT02706262).

Cardiometabolic profiles and proteomics associated with obesity phenotypes in a longitudinal cohort of young adults.

To assess cardiometabolic profiles and proteomics to identify biomarkers associated with the metabolically healthy and unhealthy obesity. Young adults (N = 156) enrolled were classified as not having obesity, metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO) based on NCEP ATP-III criteria. Plasma proteomics at study entry were measured using Olink Cardiometabolic Explore panel. Linear regression was used to assess associations between proteomics and obesity groups as well as cardiometabolic traits of glucose, insulin, and lipid profiles at baseline and follow-up visits. Enriched biological pathways were further identified based on the significant proteomic features. Among the baseline 95 (61%) and 61 (39%) participants classified as not having obesity and having obesity (8 MHO and 53 MUHO), respectively. Eighty of the participants were followed-up with an average 4.6 years. Forty-one proteins were associated with obesity (FDR < 0.05), 29 of which had strong associations with insulin-related traits and lipid profiles (FDR < 0.05). Inflammation, immunomodulation, extracellular matrix remodeling and endoplasmic reticulum lumen functions were enriched by 40 proteins. In this study population, obesity and MHO were associated with insulin resistance and dysregulated lipid profiles. The underlying mechanism included elevated inflammation and deteriorated extracellular matrix remodeling function.

Sex differences in the relationships between macronutrients and all-cause mortality in individuals with metabolically unhealthy overweight/obesity.

This study investigates sex differences in the effects of macronutrient quantity, quality, and timing on mortality in metabolically unhealthy overweight/obesity (MUO) populations. The study included 18,345 participants, including 9204 men and 9141 women. The Cox proportional risk model and isocaloric substitution effects were used to examine the association of macronutrient intake and subtype with all-cause mortality in the MUO populations. After adjusting for the potential covariates, The risk of all-cause mortality was elevated in men in the highest 25% percentile of poor-quality carbohydrates compared with men in the lowest quartile (odds ratio [OR]: 2.04; 95% confidence interval [CI], 1.40-2.98). Compared with women in the lowest quartile, the risk of all-cause mortality for women in the highest 25% percentile for high-quality carbohydrates (OR: 0.74; 95% CI, 0.55-0.99) and unsaturated fatty acids (OR: 0.54; 95% CI, 0.32-0.93) were decreased. In women, replacing low-quality carbohydrates with high-quality carbohydrates on an isocaloric basis reduces the risk of all-cause mortality by approximately 9%. We find that different macronutrient consumption subtypes are associated with all-cause mortality in MUO populations, with differential effects between men and women, and that the risk of all-cause mortality is influenced by macronutrient quality and meal timing.

The transition of metabolic phenotypes and cardiovascular events: Panasonic cohort study 16.

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.

Metabolic health's central role in chronic kidney disease progression: a 20-year study of obesity-metabolic phenotype transitions.

This study investigates the risk of chronic kidney disease (CKD) across four metabolic phenotypes: Metabolically Healthy-No Obesity (MH-NO), Metabolically Unhealthy-No obesity (MU-NO), Metabolically Healthy-Obesity (MH-O), and Metabolically Unhealthy-Obesity (MU-O). Data from the Tehran Lipid and Glucose Study, collected from 1999 to 2020, were used to categorize participants based on a BMI ≥ 30 kg/m2 and metabolic health status, defined by the presence of three or four of the following components: high blood pressure, elevated triglycerides, low high-density lipoprotein, and high fasting blood sugar. CKD, characterized by a glomerular filtration rate < 60 ml/min/1.72 m2. The hazard ratio (HR) of CKD risk was evaluated using Cox proportional hazard models. The study included 8731 participants, with an average age of 39.93 years, and identified 734 incidents of CKD. After adjusting for covariates, the MU-O group demonstrated the highest risk of CKD progression (HR 1.42-1.87), followed by the MU-NO group (HR 1.33-1.67), and the MH-O group (HR 1.18-1.54). Persistent MU-NO and MU-O posed the highest CKD risk compared to transitional states, highlighting the significance of exposure during early adulthood. These findings emphasize the independent contributions of excess weight and metabolic health, along with its components, to CKD risk. Therefore, preventive strategies should prioritize interventions during early-adulthood.

Relationship between metabolically healthy obesity and coronary artery calcification.

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.

Transition in metabolic health phenotypes across general adiposity categories and association with the risk of depression: a prospective analysis.

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.

Metabolically healthy obesity, transition from metabolic healthy to unhealthy status, and carotid atherosclerosis.

BACKGROUND: Evidence of the effects of metabolically healthy obesity (MHO) on atherosclerosis is limited; the transition effects of metabolic health and obesity phenotypes have been ignored. We examined the association between metabolic health and the transition to atherosclerosis risk across body mass index (BMI) categories in a community population. METHODS: This cross-sectional study was based on a national representative survey that included 50,885 community participants aged ≥40 years. It was conducted from 01 December 2017 to 31 December 2020, in 13 urban and 13 rural regions across Hunan China. Metabolic health was defined as meeting less than three abnormalities in blood pressure, glucose, high-density lipoprotein cholesterol, triglycerides, or waist circumference. The participants were cross-classified at baseline based on their metabolic health and obesity. In addition, the relationship between atherosclerosis and transitions in metabolic health status based on 4733 participants from baseline to the second survey after 2 years was considered. The relationship between metabolic health status and the risk of transition to Carotid atherosclerosis (CA) was assessed using logistic regression and Cox proportional hazards regression analyses. RESULTS: In this study, the mean age of the participants was 60.7 years (standard deviation [SD], 10.91), 53.0% were female, and 51.2% had CA. As compared with metabolically healthy normal weight (MHN), those with MHO phenotype (odd ratio [OR] 1.10, 95% confidence interval [CI] 1.02-1.21), metabolically unhealthy normal weight (OR 1.27, 95% CI 1.19-1.35), metabolically unhealthy overweight (OR 1.41, 95% CI 1.33-1.48), and metabolically unhealthy obese (OR 1.54, 95% CI 1.44-1.64) had higher risk for CA. However, during the follow-up of 2 years, almost 33% of the participants transitioned to a metabolically unhealthy status. As compared with stable healthy normal weight, transition from metabolically healthy to unhealthy status (hazard ratios [HR] 1.21, 95% [CI] 1.02-1.43) and stable metabolically unhealthy overweight or obesity (MUOO) (HR 1.32, 95% CI 1.17-1.48) were associated with higher risk of CA. CONCLUSIONS: In the community population, obesity remains a risk factor for CA despite metabolic health. However, the risks were highest for metabolically unhealthy status across all BMI categories. A large proportion of metabolically healthy overweight or participants with obesity converts to an unhealthy phenotype over time, which is associated with an increased risk of CA.

Healthy overweight and obesity in the young: Prevalence and risk of major adverse cardiovascular events.

AIMS: To investigate the prevalence of metabolically healthy overweight/obesity and to study its longitudinal association with major adverse cardiovascular and renal events (MARCE). METHODS AND RESULTS: The study was conducted in 1210 young-to-middle-age subjects grouped according to their BMI and metabolic status. The risk of MARCE was evaluated during 17.4 years of follow-up. Forty-eight-percent of the participants had normal weight, 41.9% had overweight, and 9.3% had obesity. Metabolically healthy status was found in 31.1% of subjects with normal weight and in 20.0% of those with overweight/obesity. During the follow-up, there were 108 MARCE. In multivariate Cox analysis adjusted for confounders and risk factors, no association was found between MARCE and overweight/obesity (p = 0.49). In contrast, metabolic status considered as a two-class variable (0 versus at least one metabolic abnormality) was a significant predictor of MARCE (HR, 2.11; 95%CI, 1.21-3.70, p = 0.009). Exclusion of atrial fibrillation from MARCE (N = 87) provided similar results (HR, 2.11; 95%CI, 1.07-4.16, p = 0.030). Inclusion of average 24 h BP in the regression model attenuated the strength of the associations. Compared to the group with healthy metabolic status, the metabolically unhealthy overweight/obesity participants had an increased risk of MARCE with an adjusted HR of 2.33 (95%CI, 1.05-5.19, p = 0.038). Among the metabolically healthy individuals, the CV risk did not differ according to BMI group (p = 0.53). CONCLUSION: The present data show that the risk of MARCE is not increased in young metabolically healthy overweight/obesity suggesting that the clinical approach to people with high BMI should focus on parameters of metabolic health rather than on BMI.

Association of metabolic health and obesity with coronary heart disease in adult cancer survivors.

BACKGROUND: The metabolically healthy obese (MHO) phenotype is associated with an increased risk of coronary heart disease (CHD) in the general population. However, association of metabolic health and obesity phenotypes with CHD risk in adult cancer survivors remains unclear. We aimed to investigate the associations between different metabolic health and obesity phenotypes with incident CHD in adult cancer survivors. METHODS: We used National Health Insurance Service (NHIS) to identify a cohort of 173,951 adult cancer survivors aged more than 20 years free of cardiovascular complications. Metabolically healthy nonobese (MHN), MHO, metabolically unhealthy nonobese (MUN), metabolically unhealthy obese (MUO) phenotypes were created using as at least three out of five metabolic health criteria along with obesity (body mass index ≥ 25.0 kg/m2). We used Cox proportional hazards model to assess CHD risk in each metabolic health and obesity phenotypes. RESULTS: During 1,376,050 person-years of follow-up, adult cancer survivors with MHO phenotype had a significantly higher risk of CHD (hazard ratio [HR] = 1.52; 95% confidence intervals [CI]: 1.41 to 1.65) as compared to those without obesity and metabolic abnormalities. MUN (HR = 1.81; 95% CI: 1.59 to 2.06) and MUO (HR = 1.92; 95% CI: 1.72 to 2.15) phenotypes were also associated with an increased risk of CHD among adult cancer survivors. CONCLUSIONS: Adult cancer survivors with MHO phenotype had a higher risk of CHD than those who are MHN. Metabolic health status and obesity were jointly associated with CHD risk in adult cancer survivors.

MHO or MUO? White adipose tissue remodeling.

In this review, we delve into the intricate relationship between white adipose tissue (WAT) remodeling and metabolic aspects in obesity, with a specific focus on individuals with metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). WAT is a highly heterogeneous, plastic, and dynamically secreting endocrine and immune organ. WAT remodeling plays a crucial role in metabolic health, involving expansion mode, microenvironment, phenotype, and distribution. In individuals with MHO, WAT remodeling is beneficial, reducing ectopic fat deposition and insulin resistance (IR) through mechanisms like increased adipocyte hyperplasia, anti-inflammatory microenvironment, appropriate extracellular matrix (ECM) remodeling, appropriate vascularization, enhanced WAT browning, and subcutaneous adipose tissue (SWAT) deposition. Conversely, for those with MUO, WAT remodeling leads to ectopic fat deposition and IR, causing metabolic dysregulation. This process involves adipocyte hypertrophy, disrupted vascularization, heightened pro-inflammatory microenvironment, enhanced brown adipose tissue (BAT) whitening, and accumulation of visceral adipose tissue (VWAT) deposition. The review underscores the pivotal importance of intervening in WAT remodeling to hinder the transition from MHO to MUO. This insight is valuable for tailoring personalized and effective management strategies for patients with obesity in clinical practice.

Association between alternative healthy eating index (AHEI) with metabolic health status in adolescents with overweight and obesity.

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.

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

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

Predictive markers of metabolically healthy obesity in children and adolescents: can AST/ALT ratio serve as a simple and reliable diagnostic indicator?

This study aimed to estimate the prevalence of metabolically healthy obesity (MHO) according to two different consensus-based criteria and to investigate simple, measurable predictive markers for the diagnosis of MHO. Five hundred and ninety-three obese children and adolescents aged 6-18 years were included in the study. The frequency of MHO was calculated. ROC analysis was used to estimate the predictive value of AST/ALT ratio, waist/hip ratio, MPV, TSH, and Ft4 cut-off value for the diagnosis of MHO. The prevalence of MHO was 21.9% and 10.2% according to 2018 and 2023 consensus-based MHO criteria, respectively. AST/ALT ratio cut-off value for the diagnosis of MHO was calculated as ≥ 1 with 77% sensitivity and 52% specificity using Damanhoury et al.'s criteria (AUC = 0.61, p = 0.02), and 90% sensitivity and 51% specificity using Abiri et al.'s criteria (AUC = 0.70, p = 0.01). Additionally, using binomial regression analysis, only the AST/ALT ratio is independently and significantly associated with the diagnosis of MHO (p = 0.03 for 2018 criteria and p = 0.04 for 2023 criteria). CONCLUSION: The ALT/AST ratio may be a useful indicator of MHO in children and adolescents. WHAT IS KNOWN: • Metabolically healthy obesity refers to people who are obese but do not have any of the standard cardio-metabolic risk factors. • Metabolically healthy obesity is not entirely harmless; the metabolic characteristics of individuals with this phenotype are less favorable than those of healthy lean groups. Moreover, it is not a constant state, and there may be a transition to metabolically unhealthy phenotypes over time. WHAT IS NEW: • The prevalence of MHO is 21.9% and 10.2% according to 2018 and 2023 consensus-based metabolically healthy obesity criteria, respectively. • The ALT/AST ratio may be a useful indicator of metabolically healthy obesity in children and adolescents.

Differences in the levels of inflammatory markers between metabolically healthy obese and other obesity phenotypes in adults: A systematic review and meta-analysis.

AIMS: The aim of this study was to systematically review and analyze differences in the levels of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) comparing metabolically healthy but obese (MHO) with metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO) subjects. DATA SYNTHESIS: We searched PubMed, Embase, Web of Science, and Scopus for studies that matched the relevant search terms. Differences in inflammatory marker levels between MHO and the other three phenotypes were pooled as standardized mean differences (SMD) or differences of medians (DM) using a random-effects model. We included 91 studies reporting data on 435,007 individuals. The CRP levels were higher in MHO than in MHNO subjects (SMD = 0.63, 95% CI: 0.49, 0.76; DM = 0.83 mg/L, 95% CI: 0.56, 1.11). The CRP levels were higher in MHO than in MUNO subjects (SMD = 0.16, 95% CI: 0.05, 0.28; DM = 0.39 mg/L, 95% CI: 0.09, 0.69). The CRP levels were lower in MHO than in MUO individuals (SMD = -0.43, 95% CI: -0.54, -0.31; DM = -0.82 mg/L, 95% CI: -1.16, -0.48). The IL-6 levels in MHO were higher than in MHNO while lower than in MUO subjects. The TNF-α levels in MHO were higher than in MHNO individuals. CONCLUSIONS: This review provides evidence that CRP levels in MHO are higher than in MHNO and MUNO subjects but lower than in MUO individuals. Additionally, IL-6 levels in MHO are higher than in MHNO but lower than in MUO subjects, and TNF-α levels in MHO are higher than in MHNO individuals. SYSTEMATIC REVIEW REGISTRATION: PROSPERO number: CRD42021234948.

Integrated genetic and epigenetic analyses uncovered GLP1R association with metabolically healthy obesity.

BACKGROUND: Both genetic and epigenetic variations of GLP1R influence the development and progression of obesity. However, the underlying mechanism remains elusive. This study aims to explore the mediation roles of obesity-related methylation sites in GLP1R gene variants-obesity association. METHODS: A total of 300 Chinese adult participants were included in this study and classified into two groups: 180 metabolically healthy obesity (MHO) cases and 120 metabolically healthy normal-weight (MHNW) controls. Questionnaire investigation, physical measurement and laboratory examination were assessed in all participants. 18 single nucleotide polymorphisms (SNPs) and 31 CpG sites were selected for genotype and methylation assays. Causal inference test (CIT) was performed to evaluate the associations between GLP1R genetic variation, DNA methylation and MHO. RESULTS: The study found that rs4714211 polymorphism of GLP1R gene was significantly associated with MHO. Additionally, methylation sites in the intronic region of GLP1R (GLP1R-68-CpG 7.8.9; GLP1R-68-CpG 12.13; GLP1R-68-CpG 17; GLP1R-68-CpG 21) were associated with MHO, and two of these methylation sites (GLP1R-68-CpG 7.8.9; GLP1R-68-CpG 17) partially mediated the association between genotypes and MHO. CONCLUSIONS: Not only the gene polymorphism, but also the DNA methylation of GLP1R was associated with MHO. Epigenetic changes in the methylome may in part explain the relationship between genetic variants and MHO.

Association between Metabolically Healthy Status and Risk of Gastrointestinal Cancer.

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.

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

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

Metabolically healthy obese individuals are still at high risk for diabetes: Application of the marginal structural model.

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.