Metabolically Unhealthy Obese Group Research Articles

Metabolic Obesity Phenotypes and Incident Cardiovascular Outcomes in Middle-Aged and Older Korean Adults: A Longitudinal 10-Year Analysis of the Korean Genome and Epidemiology Study.

Background: This study investigated the association of four metabolic obesity phenotypes with incident coronary artery disease and stroke in a large-scale, community population-based, prospective Korean cohort observed for over 10 years. Methods: The study participants included 7374 adults aged 40-69 years, drawn from the Korean Genome and Epidemiology Study. Participants with different metabolic obesity phenotypes were categorized according to body weight and metabolic health status into four groups: metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy nonobese (MUHNO), and metabolically unhealthy obese (MUHO). Combined cardiovascular events were defined as coronary artery disease and stroke. We used multivariate Cox proportional hazards regression models to prospectively assess hazard ratios (HRs) with 95% confidence intervals (CIs) for incident coronary artery disease or stroke over 10 years after the baseline survey. Results: During the follow-up period, newly developed coronary artery disease, stroke, and combined cardiovascular events were diagnosed in 151 (2.0%), 137 (1.9%), and 283 (3.8%) participants, respectively. After adjusting for confounding variables, the HRs (95% CIs) for incident combined cardiovascular events were 1.81 (1.34-2.46) in the MUHO group, 1.29 (0.92-1.81) in the MUHNO group, and 1.21 (0.81-1.79) in the MHO group compared with those in the MHNO group. Conclusions: This study revealed distinct risks associated with four metabolic obesity phenotypes concerning incident coronary artery disease and stroke. After adjusting for potential confounding variables, the results indicated that MUHO, but not MUHNO or MHO, showed a higher risk of developing coronary artery disease and stroke than MHNO.

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.

Impact of metabolic unhealthiness and its changes on the risk of erosive esophagitis: a cohort study.

We aimed to compare the risk of erosive esophagitis (EE) among individuals with different phenotypes based on metabolic health status and obesity and investigate the role of changes in metabolic health in EE risk. A cohort of 258 892 asymptomatic adults without EE at baseline who underwent ollow-up esophagogastroduodenoscopy (EGD) were categorized into the following four groups according to metabolic health and obesity status: (i) metabolically healthy (MH) non-obese; (ii) metabolically unhealthy (MU) non-obese; (iii) MH obese; and (iv) MU obese. EE was defined as the presence of grade A or higher mucosal breaks on EGD. During a median follow-up of 4.5years, the incidence rates of EE were 0.6/103 person-years (PY), 1.7/103 PY, 1.7/103 PY, and 3.1/103 PY in the MH non-obese, MU non-obese, MH obese, and MU obese groups, respectively. The multivariable-adjusted hazard ratio (HR) (95% confidence intervals [CI]) for developing EE comparing the MH obese, MU non-obese, and MU obese groups with the MH non-obese group were 1.49 (1.29-1.71), 1.56 (1.25-1.94), and 2.18 (1.90-2.49), respectively. The multivariable-adjusted HR (95% CI) comparing the progression of MH to MU, regression of MU to MH, and persistent MU with the persistent MH group were 1.39 (1.10-1.76), 1.39 (1.09-1.77), and 1.86 (1.56-2.21), respectively. The increased risk of EE among the persistent MU group was consistently observed in individuals without obesity or abdominal obesity. Metabolic unhealthiness and obesity were independent risk factors for the development of EE, suggesting that maintaining both normal weight and metabolic health may help reduce the risk of EE.

Computed tomography evaluation of skeletal muscle quality and quantity in people with morbid obesity with and without metabolic abnormality.

We investigated the differences in quantity and quality of skeletal muscle between metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) individuals using abdominal CT. One hundred and seventy-two people with morbid obesity who underwent bariatric surgery and 64 healthy control individuals participated in this retrospective study. We divided the people with morbid obesity into an MHO and MUO group. In addition, nonobese metabolic healthy people were included analysis to provide reference levels. CT evaluation of muscle quantity (at the level of the third lumbar vertebra [L3]) was performed by calculating muscle anatomical cross-sectional area (CSA), which was normalized to patient height to produce skeletal muscle index (SMI). Muscle quality was assessed as skeletal muscle density (SMD), which was calculated from CT muscle attenuation. To characterize intramuscular composition, muscle attenuation was classified into three categories using Hounsfield unit (HU) thresholds: -190 HU to -30 HU for intermuscular adipose tissue (IMAT), -29 to +29 HU for low attenuation muscle (LAM), and +30 to +150 HU for normal attenuation muscle (NAM). People with morbid obesity comprised 24 (14%) MHO individuals and 148 (86%) MUO individuals. The mean age of the participants was 39.7 ± 12.5 years, and 154 (65%) participants were women. MUO individuals had a significantly greater total skeletal muscle CSA than MHO individuals in the model that adjusted for all variables. Total skeletal muscle SMI, SMD, NAM index, LAM index, and IMAT index did not differ between MHO and MUO individuals for all adjusted models. Total skeletal muscle at the L3 level was not different in muscle quantity, quality, or intramuscular composition between the MHO and MUO individuals, based on CT evaluation. MHO individuals who are considered "healthy" should be carefully monitored and can have a similar risk of metabolic complications as MUO individuals, at least based on an assessment of skeletal muscle.

From Metabolically Healthy Obesity to Metabolically Unhealthy Obesity Populations: Decreased Bone Turnover Bioactivity.

A bone turnover marker reflects bone bioactivity. The effects of metabolically healthy compared with metabolically unhealthy obesity phenotypes on bone metabolism are not well understood. The aim of the study was to evaluate differences of bone transformation indexes in these two obesity phenotypes. A total of 419 obese subjects were recruited, 64 with metabolically healthy obesity (MHO) and 351 with metabolically unhealthy obesity (MuHO). BTMs and clinical parameters were measured. Bone metabolism indexes, including tartrate resistant acid phosphatase (TRACP, p < 0.05), β carboxyl terminal peptide of collagen (β-CTX, p < 0.01), and bone alkaline phosphatase (BAP, p < 0.01), were higher in subjects with MHO than MuHO, but parathyroid hormone (PTH) was lower (p < 0.05). The between-group difference in serum calcium was not significant. Low bone turnover activity was associated with significant hyperglycemia, insulin resistance, and body fat index (p < 0.05). Multivariate logistic regression found that TRACP, β-CTX, and BAP were independently associated with the presence of MHO. Receiver operating characteristic curve analysis found that the maximum area under the curve value for the definition of MHO was (0.8221) and was obtained when sex, age, body mass index (BMI), TRACP, β-CTX and BAP were included simultaneously, resulting in a sensitivity of 81.25% and specificity: 72.3%. The MHO group had significantly increased circulating TRACP and β-CTX compared with the MuHO group and BAP levels were within the physiological range. Obesity with the metabolically healthy phenotype had slightly increased bone turnover activity that may be an early compensatory response of skeletal metabolism to the increased BMI.

Correlation analysis of obesity phenotypes with leptin and adiponectin

Obesity can be categorized as metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). However, individuals with MHO are characterized by the absence of metabolic syndrome (MS) and appear to have lower inflammation levels compared to MUO. This study aimed to investigate the association of obesity phenotypes with leptin (LEP) and adiponectin (ADP). According to the inclusion and exclusion criteria, we selected 178 subjects from the previous cross-sectional survey. Based on the body mass index (BMI) and diagnostic criteria of MS, we divided the individuals into three groups, including healthy control group (HC group), metabolically healthy obesity group (MHO group) and metabolically unhealthy obesity group (MUO group). The concentrations of LEP and ADP in serum were measured, and the association of these two cytokines with different obesity phenotypes were subsequently analyzed. Compared to both the HC and MHO groups, the MUO group showed significantly higher BMI, waist circumference (WC), waist-hip ratio (WHR), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance (Homa-IR) and blood pressure (P < 0.05). In contrast, serum high-density lipoprotein cholesterol (HDL-C) was notably lower in the MUO group (P < 0.05). ADP was found to have a positive correlation with systolic blood pressure (SBP) and a negative correlation with FPG in the MHO group. In the MUO group, LEP demonstrated a positive correlation with fasting insulin (FINS) and Homa-IR, while ADP showed a positive correlation with TC and SBP. Linear regression analysis further indicated that SBP (β = 0.234, P = 0.043), TG (β = − 0.292, P = 0.001) and LDL-C (β = 0.626, P = 0.000) were independently correlated with ADP, and BMI (β = 0.398, P = 0.002) was independently correlated with LEP in obese individuals. In conclusion, ADP and LEP were closely related with glucose and lipid metabolism in obese individuals, these two cytokines might play critical roles in obesity-associated metabolic disorders.

Ceramide d18:1/24:1 as a potential biomarker to differentiate obesity subtypes with unfavorable health outcomes

BackgroundThe criteria for metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) remain controversial. This research aimed to identify a potential biomarker to differentiate the subtypes of obesity.MethodsThe study conducted a lipidomic evaluation of ceramide in the serum of 77 Chinese adults who had undergone hyperinsulinemic-euglycemic clamps. These adults were divided into three groups according to the clinical data: normal weight control group (N = 21), MHO (N = 20), and MUO (N = 36).ResultsThe serum Cer d18:1/24:1 level in the MHO group was lower than that in the MUO group. As the Cer d18:1/24:1 level increased, insulin sensitivity decreased, and the unfavorable parameters increased in parallel. Multivariate logistic regression analysis revealed that serum Cer d18:1/24:1 levels were independently correlated with MUO in obesity. Individuals with higher levels of Cer d18:1/24:1 also had an elevated risk of cardiovascular disease. Most ceramide subtype levels increased in obesity compared to normal-weight individuals, but the levels of serum Cer d18:0/18:0 and Cer d18:1/16:0 decreased in obesity.ConclusionsThe relationships between ceramide subtypes and metabolic profiles might be heterogeneous in populations with different body weights. Cer d18:1/24:1 could be a biomarker that can be used to differentiate MUO from MHO, and to better predict who will develop unfavorable health outcomes among obese individuals.Trial registrationThe First Affiliated Hospital of Nanjing Medical University’s Institutional Review Board authorized this study protocol, and all participants provided written informed consent (2014-SR-003) prior to study entry.

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

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

The triglycerides and glucose index is more strongly associated with metabolically healthy obesity phenotype than the lipid and obesity indices.

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. Apparently healthy men and women aged 18years or more with obesity (body mass index [BMI] ≥ 30kg/m2) 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. 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. Our results show that the TyG index is more strongly associated with the MHO phenotype than the lipid and obesity indices.

Shared and Distinct Gut Microbial Profiles in Saudi Women with Metabolically Healthy and Unhealthy Obesity.

Mounting evidence suggests a pivotal role for the gut microbiome in energy disequilibrium characteristic of obesity. The clinical utility of microbial profiling for the distinction between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) remains ill-defined. We aim to probe microbial composition and diversity in young adult Saudi females with MHO and MUO. This observational study included anthropometric and biochemical measurements and shotgun sequencing of stool DNA for 92 subjects. α- and β-diversity metrics were calculated to determine the richness and variability in microbial communities, respectively. Results showed that Bacteroides and Bifidobacterium merycicum were less abundant in MUO compared to healthy and MHO groups. BMI was negatively correlated with B. adolescentis, B. longum, and Actinobacteria in MHO, while being positively correlated with Bacteroides thetaiotaomicron in both MHO and MUO. Positive correlations between waist circumference and B. merycicum and B. thetaiotaomicron were observed in MHO and MUO, respectively. Compared to MHO and MUO groups, higher α-diversity was detected in healthy individuals who also had higher β-diversity compared to those with MHO. We conclude that modulation of the gut microbiome cohorts through prebiotics, probiotics, and fecal microbiota transplantation may be a promising preventive and therapeutic approach to obesity-associated disease.

Metabolic phenotypes and risk of end-stage kidney disease in patients with type 2 diabetes.

Obesity often initiates or coexists with metabolic abnormalities. This study aimed to investigate the pathological characteristics and the independent or mutual relations of obesity and metabolic abnormalities with end-stage kidney disease (ESKD) in patients with type 2 diabetes (T2D) and associated diabetic kidney disease (DKD). A total of 495 Chinese patients with T2D and biopsy-confirmed DKD between 2003 and 2020 were enrolled in this retrospective study. The metabolic phenotypes were based on the body weight index (BMI)-based categories (obesity, BMI ≥ 25.0 kg/m2) and metabolic status (metabolically unhealthy status, ≥ 1 criterion National Cholesterol Education Program Adult Treatment Panel III (NCEP/ATP III) excluding waist circumference and hyperglycemia) and were categorized into four types: metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO), and metabolically unhealthy obesity (MUO). The pathological findings were defined by the Renal Pathology Society classification. Cox proportional hazards models were used to estimate hazard ratios (HRs) for ESKD. There are 56 (11.3%) MHNO patients, 28 (5.7%) MHO patients, 176 (35.6%) MUNO patients, and 235 (47.5%) MUO patients. The high prevalence of the Kimmelstiel-Wilson nodule and severe mesangial expansion were associated with obesity, whereas severe IFTA was related to metabolically unhealthy status. In the multivariate analysis, the adjusted HR (aHR) was 2.09 [95% confidence interval (CI) 0.99-4.88] in the MHO group, 2.16 (95% CI 1.20-3.88) in the MUNO group, and 2.31 (95% CI 1.27-4.20) in the MUO group compared with the MHNO group. Furthermore, the presence of obesity was insignificantly associated with ESKD compared with non-obese patients (aHR 1.22, 95% CI 0.88-1.68), while the metabolically unhealthy status was significantly associated with ESKD compared to the metabolically healthy status in the multivariate analysis (aHR 1.69, 95% CI 1.10-2.60). Obesity itself was insignificantly associated with ESKD; however, adding a metabolically unhealthy status to obesity increased the risk for progression to ESKD in T2D and biopsy-proven DKD.

Metabolome Profiling and Pathway Analysis in Metabolically Healthy and Unhealthy Obesity among Chinese Adolescents Aged 11-18 Years.

The underlying mechanisms of the development of unhealthy metabolic phenotypes in obese children and adolescents remain unclear. We aimed to screen the metabolomes of individuals with the unhealthy obesity phenotype and identify the potential metabolic pathways that could regulate various metabolic profiles of obesity in Chinese adolescents. A total of 127 adolescents aged 11-18 years old from China were investigated using a cross-sectional study. The participants were classified as having metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO) based on the presence/absence of metabolic abnormalities defined by metabolic syndrome (MetS) and body mass index (BMI). Serum-based metabolomic profiling using gas chromatography-mass spectrometry (GC-MS) was undertaken on 67 MHO and 60 MUO individuals. ROC analyses showed that palmitic acid, stearic acid, and phosphate could predict MUO, and that glycolic acid, alanine, 3-hydroxypropionic acid, and 2-hydroxypentanoic acid could predict MHO (all p < 0.05) from selected samples. Five metabolites predicted MUO, 12 metabolites predicted MHO in boys, and only two metabolites predicted MUO in girls. Moreover, several metabolic pathways may be relevant in distinguishing the MHO and MUO groups, including the fatty acid biosynthesis, fatty acid elongation in mitochondria, propanoate metabolism, glyoxylate and dicarboxylate metabolism, and fatty acid metabolism pathways. Similar results were observed for boys except for phenylalanine, tyrosine and tryptophan biosynthesis, which had a high impact [0.098]. The identified metabolites and pathways could be efficacious for investigating the underlying mechanisms of the development of different metabolic phenotypes in obese Chinese adolescents.

Association between different metabolic phenotypes of obesity and thyroid disorders among Chinese adults: a nationwide cross-sectional study.

Increased body mass index (BMI) and metabolic abnormalities both have potential associations with thyroid disease. The aim of this study was to investigate the correlation between different metabolic phenotypes of obesity and thyroid disorders using nationwide data from China. Data were collected from a cross-sectional survey called the Thyroid Disorders, Iodine Status, and Diabetes Epidemiological Survey conducted between 2015 and 2017 in China. A total of 69007 subjects aged 18 years or older were defined and divided into six groups on the basis of BMI and metabolic health status: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). We estimated the odds ratios (ORs) and confidence intervals [CIs] for different thyroid disorders according to metabolic phenotypes using multivariate logistic regression models. In our study, we found that the prevalence of subclinical hypothyroidism was almost as high in the MUNW group as in the MUO group, the prevalence of overt hyperthyroidism was highest in the MUNW group and Graves' disease was highest in the MHO group. Our results also suggested that the prevalence of goiter and thyroid nodules increased with increased BMI values and that the MUO group had the highest incidence. Multivariate logistic regression analysis indicated that subjects with unhealthy metabolic phenotypes (MUNW, MUOW, and MUO) all had an increased risk of subclinical hypothyroidism, regardless of their BMI. MUNW subjects had an approximately 1.6-fold higher risk of overt hyperthyroidism and a 1.8-fold higher risk of Graves' disease than their metabolically healthy counterparts (MHNW). The present study also demonstrated that the MUO group had the highest risk of goiter and thyroid nodules among the metabolic phenotypes of obesity. Based on our study, we found that metabolic abnormalities and obesity play different roles in various thyroid diseases. Metabolically unhealthy individuals, both with and without obesity, have a higher risk of thyroid disorders than metabolically healthy individuals without obesity.

Myocardial Tissue-Level Characteristics of Adults With Metabolically Healthy Obesity

BackgroundIt remains unclear whether adults with metabolically healthy obesity (MHO) have altered myocardial tissue-level characteristics. ObjectivesThis study aims to assess the subclinical myocardial tissue-level characteristics of adults with MHO. MethodsThe EARLY-MYO-OBESITY (EARLY Assessment of MYOcardial Tissue Characteristics in OBESITY; NCT05277779) registry was a prospective, 3-center, cardiac imaging study of obese nondiabetic individuals without cardiac symptoms who underwent cardiac magnetic resonance. Myocardial tissue-level characteristics, including extracellular volume fraction (ECV) and native T2 values, were measured as indicators of myocardial fibrosis and edema. Global longitudinal peak systolic strain and early diastolic longitudinal strain rate were assessed by tissue tracking analysis to detect subclinical systolic and diastolic dysfunction. ResultsA total of 120 participants were included: MHO (n = 32; mean age, 38 years; 41% men), metabolically healthy controls without obesity (n = 32; mean age: 37 years; 41% men), and metabolically unhealthy obesity (MUHO) (n = 56; mean age: 37 years; 55% men). The MHO group had higher ECV and native T2 values than healthy controls (both P < 0.001); furthermore, the ECV was higher in the MUHO group than in the MHO group (P = 0.002). The prevalence of myocardial fibrosis was 44% (14 of 32) in the MHO group and 71% (40 of 56) in the MUHO group. Although there was no intergroup difference in left ventricular ejection fraction, the MHO group had reduced global longitudinal peak systolic and early diastolic longitudinal strain rates, indicating subclinical systolic and diastolic dysfunction. Multivariate regression analysis identified increased body mass index to be an independent risk factor for myocardial fibrosis (OR: 6.28 [95% CI: 3.17-12.47]; P < 0.001). ConclusionsThis study provides the first evidence of subclinical myocardial tissue-level remodeling in adults with obesity, regardless of metabolic health. Early identification of cardiac impairment may facilitate preventive strategies against heart failure in the MHO population. (EARLY Assessment of MYOcardial Tissue Characteristics in OBESITY [EARLY-MYO-OBESITY]; NCT05277779)

Abstract MP31: Serum Metabolite Signatures for Metabolic Healthy and Unhealthy Obesity: The Bogalusa Heart Study

Introduction: Mechanisms for metabolically healthy obesity (MHO) are not well studied. Methods: We performed cross-sectional analyses among 1,261 participants of the Bogalusa Heart Study 2013-2016 survey. A total of 887 known serum metabolites were profiled using an untargeted approach. Participants who met all the following five criteria were considered metabolically healthy, otherwise, unhealthy: 1) SBP &lt;130 mm Hg and DBP &lt;85 mm Hg; 2) fasting glucose &lt;100 mg/dl; 3) triglycerides &lt;150 mg/dl; 4) HDL ≥40 mg/dl in men and ≥50 mg/dl in women; and 5) not taking medications for hypertension, diabetes, or dyslipidemia. We tested associations of metabolites with metabolic health status, controlling for age, sex, race, education, smoking, drinking, and kidney function. Results: Among 648 obese participants (BMI≥30 kg/m 2 ), 66 (10.2%) had MHO. After Bonferroni correction, 26 metabolites differed between MHO and metabolically unhealthy obesity (MUO) groups. Amino acids serine , glycine, and histidine , fatty acid N-linoleoylserine , 4 sphingolipids, and 3 plasmalogens were significantly higher among MHO individuals. These metabolites are functionally relevant to metabolic health ( Table ). Carbohydrates mannose , lactate, and pyruvate , urate , amino acids glutamate , 1-carboxyethylvaline , 1-carboxyethylleucine , 3-methyl-2-oxovalerate , and 3-methyl-2-oxobutyrate , PE(18:0/18:2(9Z,12Z)) , and 5 diacylglycerols were significantly higher in the MUO group. These metabolites are mostly food additives or reduce gut microbiota diversity ( Table ). Furthermore, histidine was associated with metabolic health only among overweight and obese participants. Odds ratios of being metabolically healthy per SD increase of histidine among normal weight, overweight, and obese participants were 1.05 (95% CI: 0.83-1.32), 1.54 (95% CI: 1.10-2.14), and 2.12 (95% CI: 1.56-2.89), respectively (P for group difference=1.41E-4). Conclusion: We identified potential intervention targets to improve metabolic health in obesity.

Identification of Biomarkers Related to Metabolically Unhealthy Obesity in Korean Obese Adolescents: A Cross-Sectional Study

The current study aimed to screen for relationships and different potential metabolic biomarkers involved between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) in adolescents. The study included 148 obese adolescents aged between 14 and 16. The study participants were divided into MUO and MHO groups based on the age-specific adolescent metabolic syndrome (MetS) criteria of the International Diabetes Federation. The current study was conducted to investigate the clinical and metabolic differences between the MHO and MUO groups. Multivariate analyses were conducted to investigate the metabolites as independent predictors for the odds ratio and the presence of the MetS. There were significant differences in the three acylcarnitines, five amino acids, glutamine/glutamate ratio, three biogenic amines, two glycerophospholipids, and the triglyceride-glucose index between the MUO group and those in the MHO group. Moreover, several metabolites were associated with the prevalence of MUO. Additionally, several metabolites were inversely correlated with MHO in the MUO group. In this study, the biomarkers found in this study have the potential to reflect the clinical outcomes of the MUO group. These biomarkers will lead to a better understanding of MetS in obese adolescents.

Differences in the Impact of Obesity and Metabolic Unhealthiness on the Risk of Gallbladder Polyp.

Differences in the impact of obesity and metabolic health status on the risk of gallbladder polyp (GBP) remain uncertain. Herein, we aimed to compare the risk of GBP ≥5 mm among individuals with different phenotypes based on obesity and metabolic health status. A cohort of 253485 asymptomatic adults who underwent abdominal ultrasonography screening were categorized into the following four groups according to obesity and metabolic health status: 1) metabolically healthy non-obese (MHNO), 2) metabolically unhealthy and non-obese (MUNO), 3) metabolically healthy but obese (MHO), and 4) metabolically unhealthy obese (MUO). The prevalences of GBP ≥5 mm were 2.4%, 3.1%, 3.7%, and 4.0% in the MHNO, MUNO, MHO, and MUO groups, respectively. The multivariable-adjusted odds ratio (OR) values for prevalence of GBP ≥5 mm by comparing the MUNO, MHO, and MUO with the MHNO group were 1.11 [95% confidence interval (CI), 1.04-1.19], 1.30 (95% CI, 1.15-1.47), and 1.37 (95% CI, 1.28-1.45), respectively. The risk of GBP ≥5 mm in the MHO group was significantly higher than that in the MUNO group, but not significantly different from that in the MUO group. Obesity and metabolic unhealthiness appear to be independent risk factors for the prevalence of GBP, and the impact of obesity is greater than that of metabolic unhealthiness, suggesting that maintaining both normal weight and metabolic health may help reduce the risk of GBP.

Evaluating the Impact of Obesity and Different Metabolic Statuses on the Prognosis of Hospitalized Patients with Inflammatory Bowel Disease: A Cohort Study

Introduction: Obesity is associated with an increased risk of inflammatory bowel disease (IBD), whereas not all obese individuals have the same effect. In individuals with obesity, the role of metabolic status in the readmission of IBD remains unclear. Our study aimed to evaluate the association between different obesity metabolic phenotypes and the prognosis of IBD patients. Methods: We conducted a longitudinal cohort study using Nationwide Readmissions Database (NRD) (2018 sample). Out of 12,928,231 discharge records, 63,748 records with a discharge diagnosis of IBD were identified for analysis. Cox proportional hazard ratio (HR) with 95% confidence interval (CI) was calculated, adjusting for potential confounders. Results: During a 180-day follow-up in IBD patients with different obesity metabolic phenotypes, all-cause readmission rate, inpatient mortality rate, unplanned readmission rate, total charge, hospitalized length of stay were statistically different (all p < 0.001). After multivariate Cox regression analysis, IBD patients with metabolically unhealthy nonobese (MUNO) had higher risk of readmission (all-cause and unplanned) (HR 1.04, 95% CI: 1.00–1.08 and HR 1.06, 95% CI: 1.02–1.10), and those with metabolically unhealthy obesity (MUO) had higher risk of unplanned readmission (HR 1.08, 95% CI: 1.02–1.15). In subgroup analysis, both the MUNO group and MUO group had higher risk of readmission (all-cause and unplanned) in the ulcerative colitis (UC) subgroup, but only the MUNO group had higher risk of readmission (all-cause and unplanned) (HR 1.05, 95% CI: 1.00–1.10 and HR 1.06, 95% CI: 1.01–1.12) in the Crohn’s disease (CD) subgroup. Conclusion: Metabolic abnormalities were associated with an increased risk of readmission in patients with IBD, regardless of obesity.

Relationship between obesity severity, metabolic status and cardiovascular disease in obese adults.

Evidence about the associations between obesity severity, metabolic status and risk of incident cardiovascular disease (CVD) in adults with obesity remains limited. The study included 109,301 adults with obesity free of prior CVD based on the UK Biobank cohort. Metabolic status was categorized into metabolically healthy obesity (MHO; free of hypertension, hypercholesterolemia and diabetes) and metabolically unhealthy obesity (MUO). Obesity severity was classified into three levels: class I (body mass index of 30.0-34.9kg/m2 ), II (35.0-39.9) and III (≥40.0). Cox proportional hazards models were used for analyses. There were 8059 incident CVD events during a median follow-up of 8.1 years. MUO was significantly associated with a 74% increased CVD risk compared with MHO (HR=1.74, 95% CI: 1.62-1.83). There was a significant interaction between obesity severity and metabolic status on an additive scale regarding CVD risk. When taking class I obesity as reference, class II was nonsignificantly associated with an increased risk of CVD in the MHO group (HR=1.07, 95% CI: 0.90-1.27), while class III was significantly related to increased risks of CVD (HR=1.48, 95% CI: 1.12-1.96). In the MUO group, both classes II and III were significantly related to increased risks of CVD. Significant subgroup effects of age (p= .009) and sex (p= .047) were observed among participants with MUO but not in the MHO group. Both elevated obesity severity and MUO were significantly associated with increased risks of CVD in adults with obesity, while metabolic status could modify the relationship between obesity severity and CVD risk. More research is needed to further clarify the relationship.

Night eating syndrome, pola tidur, dan kebiasaan konsumsi sugar-sweetened beverage berdasarkan tipe metabolik pada mahasiswa obese

Obesity prevalence in students is increasing and can lead to metabolic syndrome at a young age. The habit of harmful sleeping patterns, night eating syndrome, and excessive sugar-sweetened-beverage consumption can increase the risk of metabolic syndrome in obese students. This research aimed to analyze the differences between night eating syndrome, sleeping patterns, and consumption habits of sugar-sweetened beverages based on metabolic types in obese students. This research used a case-control design in Semarang in 2020, with 52 subjects aged 19-24 selected by consecutive sampling. The data included body weight using digital scales, height using microtoise, waist size using Medline, blood pressure using tensimeters, and laboratory tests to check triglyceride levels, HDL cholesterol, fasting blood glucose, and insulin. The instruments used were The Night Eating Questioner to assess the night eating syndrome, Pittsburg Sleep Quality to assess sleeping patterns, and the Semi-Quantitative Food Frequency Questionnaire to see the subject’s sugar-sweetened beverage consumption. The Chi-Square test analyzed the data. There was a difference between night eating syndrome (p=0,006) and sleeping pattern (p=0,012) in Metabolically Healthy Obesity (MHO) and Metabolically Unhealthy Obesity (MUO). There was no significant difference between sugar-sweetened beverage consumption in the two subject groups (p=0,714). In the MUO group, more people experienced poor sleep patterns and night-eating syndrome. Meanwhile, sugar-sweetened beverage consumption in the MHO and MUO groups was still considered normal.