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Aims/hypothesisPeople with obesity and a normal metabolic profile are sometimes referred to as having ‘metabolically healthy obesity’ (MHO). However, whether this group of individuals are actually ‘healthy’ is uncertain. This study aims to examine the associations of MHO with a wide range of obesity-related outcomes.MethodsThis is a population-based prospective cohort study of 381,363 UK Biobank participants with a median follow-up of 11.2 years. MHO was defined as having a BMI ≥ 30 kg/m2 and at least four of the six metabolically healthy criteria. Outcomes included incident diabetes and incident and fatal atherosclerotic CVD (ASCVD), heart failure (HF) and respiratory diseases.ResultsCompared with people who were not obese at baseline, those with MHO had higher incident HF (HR 1.60; 95% CI 1.45, 1.75) and respiratory disease (HR 1.20; 95% CI 1.16, 1.25) rates, but not higher ASCVD. The associations of MHO were generally weaker for fatal outcomes and only significant for all-cause (HR 1.12; 95% CI 1.04, 1.21) and HF mortality rates (HR 1.44; 95% CI 1.09, 1.89). However, when compared with people who were metabolically healthy without obesity, participants with MHO had higher rates of incident diabetes (HR 4.32; 95% CI 3.83, 4.89), ASCVD (HR 1.18; 95% CI 1.10, 1.27), HF (HR 1.76; 95% CI 1.61, 1.92), respiratory diseases (HR 1.28; 95% CI 1.24, 1.33) and all-cause mortality (HR 1.22; 95% CI 1.14, 1.31). The results with a 5 year landmark analysis were similar.Conclusions/interpretationWeight management should be recommended to all people with obesity, irrespective of their metabolic status, to lower risk of diabetes, ASCVD, HF and respiratory diseases. The term ‘MHO’ should be avoided as it is misleading and different strategies for risk stratification should be explored.

The concept of metabolic health, particularly in obesity, has attracted a lot of attention in the scientific community, and is being increasingly used to determine the risk of cardiovascular diseases and diabetes mellitus-related complications. This Review assesses the current understanding of metabolically healthy obesity (MHO). First, we present the historical evolution of the concept. Second, we discuss the evidence for and against its existence, the usage of different definitions of MHO over the years and the efforts made to provide novel definitions of MHO. Third, we highlight epidemiological data with regard to cardiovascular risk in MHO, which is estimated to be moderately elevated using widely used definitions of MHO when compared with individuals with metabolically healthy normal weight, but potentially not elevated using a novel definition of MHO. Fourth, we discuss novel findings about the physiological mechanisms involved in MHO and how such knowledge helps to identify and characterize both people with MHO and those with metabolically unhealthy normal weight. Finally, we address how the concept of MHO can be used for risk stratification and treatment in clinical practice. This Review discusses the current understanding of the concept of metabolically healthy obesity (MHO), the challenges in defining MHO and how the MHO concept can be used to improve the prevention and treatment of cardiometabolic disease. Key points The concept of metabolically healthy obesity (MHO) is attracting a huge amount of attention in the scientific community. Total cardiovascular risk in individuals with MHO, when compared with individuals with metabolically healthy normal weight, is moderately elevated using widely used definitions of MHO. Cardiovascular mortality risk in individuals with MHO, when compared with individuals with metabolically healthy normal weight, might not be elevated when using a novel definition of MHO. Novel genetic data strongly support the hypothesis that body adipose tissue distribution, including the ability to expand adipose tissue mass in the gluteofemoral adipose tissue compartment, is an important determinant of MHO. As of today, the concept of MHO can be used for risk stratification and treatment in clinical practice.

The risk of nonalcoholic fatty liver disease (NAFLD) among obese individuals without obesity-related metabolic abnormalities, a condition referred to as metabolically healthy obese (MHO), is largely unexplored. Therefore, we examined the association between body mass index (BMI) categories and the development of NAFLD in a large cohort of metabolically healthy men and women. A cohort study was conducted in 77,425 men and women free of NAFLD and metabolic abnormalities at baseline, who were followed-up annually or biennially for an average of 4.5 years. Being metabolically healthy was defined as not having any metabolic syndrome component and having a homeostasis model assessment of insulin resistance <2.5. The presence of fatty liver was determined using ultrasound. During 348,193.5 person-years of follow-up, 10,340 participants developed NAFLD (incidence rate, 29.7 per 1,000 person-years). The multivariable adjusted hazard ratios (95% confidence intervals) for incident NAFLD comparing overweight and obese with normal-weight participants were 2.15 (2.06-2.26) and 3.55 (3.37-3.74), respectively. In detailed dose-response analyses, increasing baseline BMI showed a strong and approximately linear relationship with the incidence of NAFLD, with no threshold at no risk. This association was present in both men and women, although it was stronger in women (P for interaction <0.001), and it was evident in all clinically relevant subgroups evaluated, including participants with low inflammation status. In a large cohort of strictly defined metabolically healthy men and women, overweight and obesity were strongly and progressively associated with an increased incidence of NAFLD, suggesting that the obese phenotype per se, regardless of metabolic abnormalities, can increase the risk of NAFLD.

Obesity now stands as a paramount public health challenge globally. The classification of individuals with obesity extends into two categories: those with metabolically healthy obesity (MHO) and those with metabolically unhealthy obesity (MUO), differentiated by the presence or absence of metabolic irregularities. This study aimed to explore the independent correlates of MHO or MUO in children and adolescents with depressive disorders. In a study conducted at the Third People’s Hospital of Fuyang throughout 2021, 515 pediatric adolescent in-patient patients diagnosed with depressive disorders according to the ICD-10 criteria were examined. Comprehensive demographic and clinical data were gathered for these individuals. Using regression analysis, the research delved into the distinct impacts of MHO and MUO on these patients. This approach aimed to discern the varying contributions of metabolic health statuses to depressive symptoms in this demographic group. The detection rates of MHO and MUO were 3.7% (19/515) and 8.0% (41/515), respectively. Compared with the MHO group, patients in the MUO group showed older age, older ages of onset and first hospitalization of depressive disorders, higher systolic and diastolic blood pressure, higher levels of TG, TC/HDL, TG/HDL, TyG index and AST, and lower levels of HDL. Binary regression analysis showed that a high level of LDL ( OR  = 2.76, P  = 0.007) was an independent risk factor for MHO, whereas older age at the onset of the disorders ( OR  = 0.69, P  = 0.002) was a protective factor for MHO. In addition, high levels of TC/HDL ( OR  = 2.66, P  = 0.003), TG/HDL ( OR  = 1.81, P  = 0.034), AST ( OR  = 1.03, P  < 0.001), and uric acid ( OR  = 1.004, P  = 0.018) were independent risk factors for MUO. Children and adolescents suffering from depressive disorders exhibit increased rates of both MHO and MUO. It is imperative in clinical settings to monitor these conditions closely. Proactive measures are essential to address the underlying risk factors, thereby mitigating the progression from MHO to MUO and enhancing patient outcomes.

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.

Introduction Metabolically healthy obesity may be a transient phenotype, but studies with long follow-up, especially covering late-life, are lacking. We describe conversions between cross-categories of body mass index (BMI) and metabolic health in 786 Swedish twins with up to 27 years of follow-up, from midlife to late-life. Methods Metabolic health was defined as the absence of metabolic syndrome (MetS). We first visualized conversions between BMI-metabolic health phenotypes in 100 individuals with measurements available at ages 50–64, 65–79, and ≥80. Next, we modeled conversion in metabolic health status by BMI category in the full sample using Cox proportional hazards regression. Results The proportion of individuals with MetS and with overweight or obesity increased with age. However, one-fifth maintained a metabolically healthy overweight or obesity across all three age categories. Among those metabolically healthy at baseline, 59% converted to MetS during follow-up. Conversions occurred 56% more often among individuals with metabolically healthy obesity, but not overweight, compared to normal weight. Among those with MetS at baseline, 60% regained metabolic health during follow-up, with no difference between BMI categories. Conclusions Conversions between metabolically healthy and unhealthy status occurred in both directions in all BMI categories. While conversions to MetS were more common among individuals with obesity, many individuals maintained or regained metabolic health during follow-up.

Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.

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.

Background Insulin resistance (IR)-related indices are validated prognostic markers in metabolic disorders, but have not been applied to preclinical or clinical obesity. This study aimed to investigate the relationship between IR-related indices and cardiovascular disease (CVD) incidence, considering genetic factors and biomarkers. Methods This prospective study analyzed 112,866 UK Biobank participants with preclinical or clinical obesity. IR-related indices were evaluated: triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), and TyG-waist-to-height ratio (TyG-WHtR). Genetic risk was estimated using the polygenic risk score. Outcomes, including total CVD, coronary artery disease (CAD), and stroke, were ascertained through medical records linkage. Cox proportional hazard models were used to evaluate the associations and modification effects of genetic risk. Incremental predictive value was assessed by net reclassification index (NRI) and integrated discrimination improvement index (IDI). Mediation analyses explored the role of inflammatory, hepatic, and renal biomarkers. Results Over a median follow-up period of 13.45 years, 21,601 total CVD, 11,942 CAD, and 3347 stroke cases were documented. Compared with the lowest quartile of IR-related indices, participants in the highest quartile presented increased CVD risk. For total CVD, hazard ratios (HRs) (95% confidence intervals, CIs) for the fourth versus the first quartiles were 1.33 (1.28–1.39) for TyG-BMI, 1.41 (1.34–1.48) for TyG-WC, and 1.25 (1.20–1.31) for TyG-WHtR. All IR-related indices demonstrated significant associations with CAD. Borderline significant associations were observed for stroke. Distinct dose-response association patterns with total CVD were observed: TyG-BMI and TyG-WHtR exhibited nonlinear relationships, while TyG-WC demonstrated a linear association. The CVD risk was highest in individuals with high genetic risk and high IR indices, with an additive interaction between TyG-WC and genetic risk being observed. Significantly higher NRI and IDI were observed for TyG-WC, TyG-BMI, and TyG-WHtR in predicting CVD, with TyG-WC achieving the highest performance. Mediation analyses indicated that inflammation, liver, and renal biomarkers might partially mediate the relationship. Conclusion Elevated IR-related indices, particularly TyG-WC, were associated with increased total CVD and CAD risks in preclinical or clinical obesity. Additive effects of TyG-WC and genetic risk on CVD were revealed, with mediating biomarkers suggesting potential targeted interventions for CVD risk reduction. Graphical abstract