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Project description:Some people with obesity are resistant to the adverse metabolic effects of excess adiposity and are considered to have “metabolically healthy obesity” (MHO). However, the mechanisms responsible for MHO are not clear. We performed comprehensive cardiometabolic profiling in 20 adults with MHO (defined by features of normal insulin sensitivity), 20 age-, sex-, and BMI-matched adults with metabolically unhealthy obesity (MUO), and 15 adults who were metabolically healthy and lean (MHL). More than 120 variables were evaluated, including body composition, beta-cell function, 24-hour plasma metabolic profile, rates of de novo lipogenesis and cholesterol synthesis, plasma adipokine concentrations, oxidative stress, plasma lipoprotein profiles, cardiovascular structure/function, skeletal muscle bioactive lipids, and both adipose tissue and skeletal muscle transcriptomics. Using dimensionality reduction approaches, measures of insulinemia and skeletal muscle ceramides were the strongest discriminators of MHO and MUO status. These data clarify the characteristics associated with preserved metabolic health in response to obesity.
2024-03-06 | GSE244118 | GEO
Project description:Some people with obesity are resistant to the adverse metabolic effects of excess adiposity and are considered to have “metabolically healthy obesity” (MHO). However, the mechanisms responsible for MHO are not clear. We performed comprehensive cardiometabolic profiling in 20 adults with MHO (defined by features of normal insulin sensitivity), 20 age-, sex-, and BMI-matched adults with metabolically unhealthy obesity (MUO), and 15 adults who were metabolically healthy and lean (MHL). More than 120 variables were evaluated, including body composition, beta-cell function, 24-hour plasma metabolic profile, rates of de novo lipogenesis and cholesterol synthesis, plasma adipokine concentrations, oxidative stress, plasma lipoprotein profiles, cardiovascular structure/function, skeletal muscle bioactive lipids, and both adipose tissue and skeletal muscle transcriptomics. Using dimensionality reduction approaches, measures of insulinemia and skeletal muscle ceramides were the strongest discriminators of MHO and MUO status. These data clarify the characteristics associated with preserved metabolic health in response to obesity.
2024-03-06 | GSE244120 | GEO
Project description:Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity
| PRJNA1021253 | ENA
Project description:Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity [AbdomFat]
| PRJNA1021256 | ENA
Project description:Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity [ThighMuscle]
| PRJNA1021259 | ENA
Project description:Identifying those parameters that could potentially predict the deterioration of metabolically healthy phenotype is a matter of debate. In this field, epigenetics, in particular DNA methylation deserves special attention. The aim of the present study was to analyze the long-term evolution of methylation patterns in a subset of metabolically healthy subjects in order to search for epigenetic markers that could predict the progression to an unhealthy state. Twenty-six CpG sites were significantly differentially methylated, both at baseline and 11-year follow-up. These sites were related to 19 genes or pseudogenes; a more in-depth analysis of the methylation sites of these genes showed that CYP2E1 had 50% of the collected CpG sites differently methylated between stable metabolically healthy obesity (MHO) and unstable MHO, followed by HLA-DRB1 (33%), ZBTB45 (16%), HOOK3 (14%), PLCZ1 (14%), SLC1A1 (12%), MUC2 (12%), ZFPM2 (12.5%) and HLA-DQB2 (8%). Pathway analysis of the selected 26 CpG sites showed enrichment in pathways linked to th1 and th2 activation, antigen presentation, allograft rejection signals and metabolic processes. Higher methylation levels in the cg20707527 (ZFPM2) could have a protective effect against the progression to unstable MHO (OR: 0.21, 95%CI (0.067-0.667), p < 0.0001), whilst higher methylation levels in cg11445109 (CYP2E1) would increase the progression to MUO; OR: 2.72, 95%CI (1.094-6.796), p < 0.0014; respectively). DNA methylation status is associated with the stability/worsening of MHO phenotype. Two potential biomarkers of the transition to an unhealthy state were identified and deserve further investigation (cg20707527 and cg11445109). Moreover, the described differences in methylation could alter immune system-related pathways, highlighting these pathways as therapeutic targets to prevent metabolic deterioration in MHO patients.
| S-EPMC8508854 | biostudies-literature
Project description:ObjectivesTo analyze sex differences in the prevalence of obesity phenotypes and their risk factors among children and adolescents aged 7-18 years in China.MethodsWe enrolled 15,114 children and adolescents aged 7-18 years into the final analysis. Obesity phenotypes were classified by body mass index (BMI) and metabolic status as metabolically healthy or unhealthy obesity. In addition, we collected four possible influencing factors on obesity phenotypes through questionnaires, including demographic, parental, early life, and lifestyle indicators. Multinomial logistic regression analysis in a generalized linear mixed model (GLMM) was selected to estimate the odds ratio (OR) and 95% confidence interval (95% CI) for identifying risk factors and control the cluster effects of schools. More importantly, the interaction terms of sex and each indicator were established to demonstrate the sex differences.ResultsThe prevalence of metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), metabolically healthy overweight and obesity (MHOO), and metabolically unhealthy overweight and obesity (MUOO) were 3.5%, 5.6%, 11.1%, and 13.0% respectively, with higher prevalence in boys (5.3% vs. 1.6%, 7.9% vs. 3.1%, 14.3% vs. 7.7%, 15.6% vs. 10.1%). In addition, younger ages, single children, parental smoking, parental history of diseases (overweight, hypertension, diabetes), caesarean, premature, and delayed delivery time, high birth weight, insufficient sleep time, and excessive screen time were considered as important risk factors of MHO and MUO among children and adolescents (p < 0.05). More notably, boys were at higher risks of MUO when they were single children (boys: OR = 1.56, 95% CI: 1.24-1.96; girls: OR = 1.12, 95% CI: 0.82-1.54), while girls were more sensitive to MUO with parental smoking (girls: OR = 1.34, 95% CI: 1.02-1.76; boys: OR = 1.16, 95% CI: 0.97-1.39), premature delivery (girls: OR = 3.11, 95% CI: 1.59-6.07; boys: OR = 1.22, 95% CI: 0.67-2.22), high birth weight (girls: OR = 2.45, 95% CI: 1.63-3.69; boys: OR = 1.28, 95% CI: 0.96-1.70), and excessive screen time (girls: OR = 1.47, 95% CI: 1.06-2.04; boys: OR = 0.97, 95% CI: 0.79-1.20), with significant interaction term for sex difference (pinteraction < 0.05).ConclusionsMHO and MUO are becoming prevalent among Chinese children and adolescents. Significant sex differences in the prevalence of obesity phenotypes as well as their environmental and genetic risk factors suggest it might be necessary to manage obesity phenotypes problems from a sex perspective.
| S-EPMC9613922 | biostudies-literature
Project description:IntroductionChildren 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.MethodsThis 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 ≤ 90th 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.ResultsThe 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.ConclusionsMHO 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.
| S-EPMC9441792 | biostudies-literature
Project description:BackgroundControversial evidence about the association between cancer risk and metabolic status among individuals with obesity has been reported, but pooled data remain absent. This study aims to present pooled data comparing cancer risk between patients with metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO).MethodsThe current study systematically searched pieces of literature on January 4, 2021, of prospective cohorts that compare the incidence of cancer between MHO and MUO. The quality of included studies was assessed using Newcastle-Ottawa scale, and publication bias was evaluated using funnel plots.ResultsEleven high-quality studies were eventually selected. Quantitative analysis indicates that a lower cancer incidence exists for MHO phenotype than that for MUO (odds ratio [OR], 0.71; 95% confidential interval [CI], 0.61-0.84). Consistent outcomes are presented by subgroup analyses, which are grouped by cohort region (western population: [OR, 0.84; 95% CI, 0.75-0.93]; Asian population: [OR, 0.64; 95% CI, 0.54-0.77]); definition of metabolic unhealthiness (≥3 metabolic abnormalities: [OR, 0.62; 95% CI, 0.54-0.71]; ≥1 metabolic abnormality: [OR, 0.76; 95% CI, 0.62-0.94]); and definition of obesity (body mass index (BMI), ≥30 kg/m2: [OR, 0.84; 95% CI, 0.73-0.98]; BMI, ≥25 kg/m2: [OR, 0.53; 95% CI, 0.52-0.55]).ConclusionIn conclusion, this study suggests a reduced cancer risk for MHO compared to MUO regardless of population heterogeneity, or the definitions of obesity and metabolic status.
| S-EPMC8914254 | biostudies-literature
Project description:There is actually no consensus about the possibility that in some instances, obesity may be a benign metabolically healthy (MH) condition as opposed to a normal-weight but metabolically unhealthy (MUH) state. The aim of this study was to characterize MH condition and to investigate possible associations with metabolic and cardiovascular complications. One thousand nineteen people (range of age 18-90 years) of the cohort of the ABCD_2 study were investigated. Participants were classified as normal weight (BMI < 24.9 kg/m2) or overweight-obese (BMI ≥25 kg/m2); they were also classified as MH in the presence of 0-1 among the following conditions: (a) prediabetes/type 2 diabetes, (b) hypertension, (c) hypertriglyceridemia or low HDL cholesterolemia, and (d) hypercholesterolemia. MUH condition was diagnosed if ≥2 of the conditions listed were found. The prevalence of overweight/obese people was 71.1%, of whom 27.4% were found to be MH. In addition, 36.7% of the normal-weight participants were MUH. HOMA-IR, high sensitivity C-reactive protein, and the carotid intima-media thickness were significantly different in the 4 subgroups (P < 0.001), with higher values observed in the MUH normal-weight and obese groups. In conclusion, this study highlights the importance of identifying a MH condition in normal-weight and in obese people in order to offer better treatment.
| S-EPMC5559951 | biostudies-other