Elevated remnant cholesterol was associated with the increased metabolically unhealthy obesity risk in Chinese youth.

BACKGROUND AND OBJECTIVES: Metabolically unhealthy obesity is characterized by the presence of cardiovascular metabolic risks such as hypertension, dyslipidemia, and hyperglycemia. Research has shown a correlation between remnant cholesterol (RC) concentrations and abdominal obesity in children. However, the effect of RC concentration on metabolically unhealthy obesity remains unclear. METHODS AND STUDY DESIGN: This study included 3114 Chinese adolescents who received health check-ups. We used logistic regression models and receiver operating characteristic analysis to evaluate the correlation between RC concentration and metabolically unhealthy obesity in a cross-sectional design. RESULTS: After controlling for possible confounding variables, we found that individuals in the top and fourth quintiles of RC concentrations had a significantly higher likelihood of developing metabolically unhealthy obesity compared to those in the bottom quintile (ORs, 4.810 and 1.836; 95% CIs, 3.209-7.212 and 1.167-2.890, respectively). The risk of metabolically unhealthy obesity tended to increase with RC concentration (ptrend<0.001). In addition, boys showed positive associations between RC concentration and both BMI (r = 0.305, p<0.001) and waist circumference (r = 0.306, p<0.001). According to the analysis, the predictive accuracy of metabolically unhealthy obesity was 0.736 (95% CI, 0.690-0.781) for boys and 0.630 (95% CI, 0.573-0.687) for girls. The ideal prediction threshold was 0.66 for boys and 0.59 for girls. CONCLUSIONS: Our findings indicate that elevated RC concen-tration is linked to a higher likelihood of developing metabolically unhealthy obesity in young individuals, regardless of other known risk factors.

Why do some individuals with obesity remain metabolically healthy?

A recent study by Peterson et al. that characterized individuals with metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO) in depth provides insights into the potential pathogenesis of MUO that accounts for much of the cardiometabolic disease and excess mortality caused by the obesity epidemic.

In-hospital outcomes in metabolically healthy and unhealthy individuals over 65 years of age with obesity undergoing percutaneous intervention for acute coronary syndrome: A nationwide propensity-matched analysis (2016-2020).

Background: The obesity paradox in patients with coronary artery disease is well established, but the role of the metabolic syndrome associated with obesity is not well studied. Our study aims to evaluate the in-hospital outcomes of percutaneous coronary intervention (PCI) in metabolically healthy individuals with obesity (MHO) and metabolically unhealthy (MUHO) individuals with obesity over 65 years of age with acute coronary syndrome (ACS) between 2016 and 2020. Methods: This was a retrospective and observational study. Patients were identified through utilizing the National Inpatient Sample (NIS) Database (2016-2020) and ICD-10 codes. By employing a t-test and Pearson's Chi-square test, we assessed and contrasted the initial attributes, concurrent conditions, and results pertaining to all-cause mortality (ACM), cardiogenic shock (CS), length of stay (LOS), and hospitalization expense. Moreover, propensity score matching was conducted in a 1:1 ratio with respect to age, gender, and race. We also utilized multivariable logistic regression to compare MHO and MUHO in terms of the impact on all-cause mortality. Results: Out of a total of 135,395 patients identified, 2995 patients with MUHO were matched with 2995 MHO patients. Patients in the MUHO group had a higher prevalence of chronic pulmonary disease (24.9 % vs. 19.5 %), peripheral vascular disease (9.3 % vs. 6.7 %), hypothyroidism (16 % vs. 11.5 %), prior myocardial infarction (15.9 % vs. 6.2 %), and prior stroke (7.5 % vs. 2.8 %). Patients in the MHO group had a higher ACM (12.4 % vs. 2.8 %, p < 0.001), CS (18.55 % vs. 7 %, p < 0.001), stroke (2.2 % vs. 1 %, p < 0.001), ventricular assist device insertions (5.2 % vs. 2.7 %, p < 0.001), and IABP insertions (8.8 % vs. 3.8 %) compared to the MUHO cohort. Conclusion: Our study revealed an obesity paradox in individuals over 65 years of age undergoing PCI demonstrating worse outcomes, including higher in-hospital mortality, CS, stroke, Ventricular assist device and IABP insertion in MHO patients compared to the MUHO cohort.

Gut microbiota and fecal volatilome profile inspection in metabolically healthy and unhealthy obesity phenotypes.

BACKGROUND: People with metabolically healthy (MHO) and metabolically unhealthy obesity (MUO) differ for the presence or absence of cardio-metabolic complications, respectively. OBJECTIVE: Based on these differences, we are interested in deepening whether these obesity phenotypes could be linked to changes in microbiota and metabolome profiles. In this respect, the overt role of microbiota taxa composition and relative metabolic profiles is not completely understood. At this aim, biochemical and nutritional parameters, fecal microbiota, metabolome and SCFA compositions were inspected in patients with MHO and MUO under a restrictive diet regimen with a daily intake ranging from 800 to 1200 kcal. METHODS: Blood, fecal samples and food questionnaires were collected from healthy controls (HC), and an obese cohort composed of both MHO and MUO patients. Most impacting biochemical/anthropometric variables from an a priori sample stratification were detected by applying a robust statistics approach useful in lowering the background noise. Bacterial taxa and volatile metabolites were assessed by qPCR and gas chromatography coupled with mass spectrometry, respectively. A targeted GC-MS analyses on SCFAs was also performed. RESULTS: Instructed to follow a controlled and restricted daily calorie intake, MHO and MUO patients showed differences in metabolic, gut microbial and volatilome signatures. Our data revealed higher quantities of specific pro-inflammatory taxa (i.e., Desulfovibrio and Prevotella genera) and lower quantities of Clostridium coccoides group in MUO subset. Higher abundances in alkane, ketone, aldehyde, and indole VOC classes together with a lower amount of butanoic acid marked the faecal MUO metabolome. CONCLUSIONS: Compared to MHO, MUO subset symptom picture is featured by specific differences in gut pro-inflammatory taxa and metabolites that could have a role in the progression to metabolically unhealthy status and developing of obesity-related cardiometabolic diseases. The approach is suitable to better explain the crosstalk existing among dysmetabolism-related inflammation, nutrient intake, lifestyle, and gut dysbiosis.

Predicting the Transition to Metabolically Unhealthy Obesity Among Young Adults With Metabolically Healthy Obesity in South Korea: Nationwide Population-Based Study.

BACKGROUND: Globally, over 39% of individuals are obese. Metabolic syndrome, usually accompanied by obesity, is regarded as a major contributor to noncommunicable diseases. Given this relationship, the concepts of metabolically healthy and unhealthy obesity, considering metabolic status, have been evolving. Attention is being directed to metabolically healthy people with obesity who have relatively low transition rates to noncommunicable diseases. As obesity rates continue to rise and unhealthy behaviors prevail among young adults, there is a growing need for obesity management that considers these metabolic statuses. A nomogram can be used as an effective tool to predict the risk of transitioning to metabolically unhealthy obesity from a metabolically healthy status. OBJECTIVE: The study aimed to identify demographic factors, health behaviors, and 5 metabolic statuses related to the transition from metabolically healthy obesity to unhealthy obesity among people aged between 20 and 44 years and to develop a screening tool to predict this transition. METHODS: This secondary analysis study used national health data from the National Health Insurance System in South Korea. We analyzed the customized data using SAS (SAS Institute Inc) and conducted logistic regression to identify factors related to the transition from metabolically healthy to unhealthy obesity. A nomogram was developed to predict the transition using the identified factors. RESULTS: Among 3,351,989 people, there was a significant association between the transition from metabolically healthy to unhealthy obesity and general characteristics, health behaviors, and metabolic components. Male participants showed a 1.30 higher odds ratio for transitioning to metabolically unhealthy obesity than female participants, and people in the lowest economic status were also at risk for the transition (odds ratio 1.08, 95% CI 1.05-1.1). Smoking status, consuming >30 g of alcohol, and insufficient regular exercise were negatively associated with the transition. Each relevant variable was assigned a point value. When the nomogram total points reached 295, the shift from metabolically healthy to unhealthy obesity had a prediction rate of >50%. CONCLUSIONS: This study identified key factors for young adults transitioning from healthy to unhealthy obesity, creating a predictive nomogram. This nomogram, including triglycerides, waist circumference, high-density lipoprotein-cholesterol, blood pressure, and fasting glucose, allows easy assessment of obesity risk even for the general population. This tool simplifies predictions amid rising obesity rates and interventions.

Follistatin as a Potential Biomarker for Identifying Metabolically Healthy and Unhealthy Obesity: A Cross-Sectional Study.

Metabolically healthy obesity (MHO) refers to obese individuals with a favorable metabolic profile, without severe metabolic abnormalities. This study aimed to investigate the potential of follistatin, a regulator of metabolic balance, as a biomarker to distinguish between metabolically healthy and unhealthy obesity. This cross-sectional study included 30 metabolically healthy and 32 metabolically unhealthy individuals with obesity. Blood samples were collected to measure the follistatin levels using an enzyme-linked immunosorbent assay (ELISA). While follistatin did not significantly differentiate between metabolically healthy (median 41.84 [IQR, 37.68 to 80.09]) and unhealthy (median 42.44 [IQR, 39.54 to 82.55]) individuals with obesity (p = 0.642), other biochemical markers, such as HDL cholesterol, triglycerides, C-peptide, and AST, showed significant differences between the two groups. Insulin was the most significant predictor of follistatin levels, with a coefficient of 0.903, followed by C-peptide, which exerted a negative influence at -0.624. Quantile regression analysis revealed nuanced associations between the follistatin levels and metabolic parameters in different quantiles. Although follistatin may not serve as a biomarker for identifying MHO and metabolically unhealthy obesity, understanding the underlying mechanisms that contribute to metabolic dysfunction could provide personalized strategies for managing obesity and preventing associated complications.

Clinical and molecular profiling of human visceral adipose tissue reveals impairment of vascular architecture and remodeling as an early hallmark of dysfunction.

Adipose tissue dysfunction is more related to insulin resistance than body mass index itself and an alteration in adipose tissue function is thought to underlie the shift from metabolically healthy to unhealthy obesity. Herein, we performed a clustering analysis that revealed distinct visceral adipose tissue gene expression patterns in patients with obesity at distinct stages of metabolic dysregulation. We have built a cross-sectional cohort that aims at reflecting the evolution of the metabolic sequelae of obesity with the main objective to map the sequential events that play a role in adipose tissue dysfunction from the metabolically healthy (insulin-sensitive) state to several incremental degrees of metabolic dysregulation, encompassing insulin resistance establishment, pre-diabetes, and type 2 diabetes. We found that insulin resistance is mainly marked by the downregulation of adipose tissue vasculature remodeling-associated gene expression, suggesting that processes like angiogenesis and adaptative expansion/retraction ability suffer early dysregulation. Prediabetes was characterized by compensatory growth factor-dependent signaling and increased response to hypoxia, while type 2 diabetes was associated with loss of cellular response to insulin and hypoxia and concomitant upregulation of inflammatory markers. Our findings suggest a putative sequence of dysregulation of biological processes that is not linear and has multiple distinct phases across the metabolic dysregulation process, ultimately culminating in the climax of adipose tissue dysfunction in type 2 diabetes. Several studies have addressed the transcriptomic changes in adipose tissue of patients with obesity. However, to the best of our knowledge, this is the first study unraveling the potential molecular mechanisms associated with the multi-step evolution of adipose tissue dysfunction along the metabolic sequelae of obesity.

Cardiorespiratory fitness and submaximal exercise dynamics in normal-weight obesity and metabolically healthy obesity.

PURPOSE: Cardiorespiratory fitness (CRF) is critical for cardiovascular health. Normal-weight obesity (NWO) and metabolically healthy obesity (MHO) may be at increased risk for cardiovascular disease, but a comparison of CRF and submaximal exercise dynamics against rigorously defined low- and high-risk groups is lacking. METHODS: Four groups (N = 40; 10/group) based on body mass index (BMI), body fat %, and metabolic syndrome (MetS) risk factors were recruited: healthy controls (CON; BMI 18.5-24.9 kg/m2, body fat < 25% [M] or < 35% [F], 0-1 risk factors), NWO (BMI 18.5-24.9 kg/m2, body fat ≥ 25% [M] or ≥ 35% [F]), MHO (BMI > 30 kg/m2, body fat ≥ 25% [M] or ≥ 35% [F], 0-1 risk factors), or metabolically unhealthy obesity (MUO; BMI > 30 kg/m2, body fat ≥ 25% [M] or ≥ 35% [F], 2 + risk factors). All participants completed a V ˙ O2peak test on a cycle ergometer. RESULTS: V ˙ O2peak was similarly low in NWO (27.0 ± 4.8 mL/kg/min), MHO (25.4 ± 6.7 mL/kg/min) and MUO (24.6 ± 10.0 mL/kg/min) relative to CON (44.2 ± 11.0 mL/kg/min) when normalized to total body mass (p's < 0.01), and adjusting for fat mass or lean mass did not alter these results. This same differential V ˙ O2 pattern was apparent beginning at 25% of the exercise test (PGroup*Time < 0.01). CONCLUSIONS: NWO and MHO had similar peak and submaximal CRF to MUO, despite some favorable health traits. Our work adds clarity to the notion that excess adiposity hinders CRF across BMI categories. CLINICALTRIALS: gov registration: NCT05008952.

Differences in metabolic characteristics between Metabolically Healthy Obesity (MHO) and Metabolically Unhealthy Obesity (MUO) in weight reduction therapy.

Metabolically Healthy Obesity (MHO) is generally recognized as the absence of any metabolic disorders and cardiovascular diseases, including type 2 diabetes, dyslipidemia, and hypertension, in obese individuals; however, it is not clearly defined. Therefore, the present study investigated differences in metabolic characteristics between individuals with MHO and Metabolically Unhealthy Obesity (MUO) during weight reduction therapy. The key factors defining MHO and the importance of weight reduction therapy for MHO were also examined. Cohort data from the Japan Obesity and Metabolic Syndrome (JOMS) study were analyzed. Subjects were divided into the MHO (n = 25) and MUO (n = 120) groups. Prior to weight reduction therapy, serum adiponectin levels were significantly higher in the MHO group than in the MUO group. Serum adiponectin levels also negatively correlated with the area of subcutaneous adipose tissue (SAT) and Homeostasis model assessment (HOMA)-R in the MHO group, but not in the MUO group. Collectively, the present results suggest the importance of adiponectin for maintaining metabolic homeostasis in the MHO group. On the other hand, no significant differences were observed in inflammatory markers between the MHO and MUO groups, suggesting the presence of chronic inflammation in both groups. Furthermore, a positive correlation was noted between changes in serum cystatin C levels and waist circumference in the MHO group, which indicated that despite the absence of metabolic disorders, the MHO group exhibited anti-inflammatory responses during weight reduction therapy. These results underscore the significance of weight reduction even for individuals with MHO.

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

BACKGROUND: The 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. METHODS: The 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). RESULTS: The 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. CONCLUSIONS: The 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 REGISTRATION: The 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.

Adherence to the Mediterranean diet as a possible additional tool to be used for screening the metabolically unhealthy obesity (MUO) phenotype.

BACKGROUND: The terms metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) categorize subjects with obesity based on the presence or absence of cardio-metabolic risk factors. Detecting MUO phenotype is crucial due to the high risk of cardio-metabolic complications, requiring tailored and intensive follow-up. However, diagnosing MUO is time-consuming and costly. Thus, we aimed to investigate the role of Mediterranean diet (MD) in determining MHO/MUO phenotypes and whether adherence to MD could serve as an additional screening tool for MUO phenotype. METHODS: The study population of this cross-sectional observational study consisted of 275 subjects with obesity. We assessed their lifestyle habits (physical activity and smoking habits), anthropometric measurements (weight, height, waist circumference, body mass index), blood pressure, metabolic parameters, inflammatory marker (high sensitivity C reactive protein levels), adherence to MD (by PREvención con DIetaMEDiterránea (PREDIMED) questionnaire), and MHO/MUO phenotypes. RESULTS: The study included 275 individuals with obesity (256F/19M; 34.0 ± 10.5 years; BMI 38.3 ± 5.95 kg/m2). Among them, 114 (41.5%) exhibited MHO phenotype, while 161 (58.5%) had MUO phenotype. MHO phenotype exhibited favorable anthropometric and cardio-metabolic profiles, characterized by lower waist circumference (p < 0.001), BMI (p < 0.001), insulin resistance (p < 0.001), blood pressure (p < 0.001), inflammation (p < 0.001), and lipid levels (p < 0.001) compared to MUO phenotype. Notably, we found that MHO phenotype had higher adherence to MD (p < 0.001) and consumed more extra virgin olive oil (EVOO) (p < 0.001), vegetables (p < 0.001), fruits (p < 0.001), legumes (p = 0.001), fish (p < 0.001), wine (p = 0.008), and nuts (p = 0.001), while reporting lower intake of red/processed meats (p < 0.001), butter, cream, margarine (p = 0.008), soda drinks (p = 0.006), and commercial sweets (p = 0.002) compared to MUO phenotype. Adherence to MD (p < 0.001) and EVOO (p = 0.015) intake were identified as influential factors in determining the presence of MUO/MHO phenotypes. Furthermore, a PREDIMED score < 5 proved to be the most sensitive and specific cut-point value for predicting the presence of MUO phenotype (p < 0.001). CONCLUSION: High adherence to MD was associated with MHO phenotype. Moreover, we suggest that a specific cut-off of the PREDIMED score could be an indicator to discriminate patients with MUO/MHO phenotypes and therefore help in identifying patients at higher cardiovascular risk who will require specific dietary intervention.

Evidence for proton-pump inhibitor (PPI)-associated dysbiosis in metabolically unhealthy obesity.

Obesity adversely impacts millions of American adults by predisposing them to significant health risks and further complications. Obesity is differentiated into two groups: metabolically healthy and metabolically unhealthy. In contrast to metabolically healthy counterparts, obese individuals who are metabolically unhealthy display hallmark symptoms of metabolic syndrome (e.g., hypertension, dyslipidemia, hyperglycemia, abdominal obesity). Gastroesophageal reflux disease (GERD) commonly occurs in all obese populations, as do poor dietary habits. Proton-pump inhibitors (PPIs), due to their wide availability, are most often used to treat GERD-related heartburn and other symptoms. Here, we review the evidence on how poor diet as well as short- and long-term use of PPIs adversely affect the gastrointestinal microbiota to cause dysbiosis. Key components of dysbiosis-induced metabolically unhealthy obesity (MUO) associated with PPI use include "leaky gut," systemic low-grade inflammation, and reduced amounts of short-chain fatty acids (SCFAs) such as butyrate that promote metabolic health. The benefit of using probiotics to mitigate PPI-induced dysbiosis and MUO is also discussed.

Insulin Resistance Is the Main Characteristic of Metabolically Unhealthy Obesity (MUO) Associated with NASH in Patients Undergoing Bariatric Surgery.

(1) Background: Metabolically healthy obesity (MHO) is a concept that applies to obese patients without any elements of metabolic syndrome (metS). In turn, metabolically unhealthy obesity (MUO) defines the presence of elements of metS in obese patients. The components of MUO can be divided into subgroups regarding the elements of inflammation, lipid and glucose metabolism and cardiovascular disease. MUO patients appear to be at greater risk of developing non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) compared to MHO patients. The aim of this study was to evaluate the influence of different MUO components on NAFLD and NASH in patients with morbid obesity undergoing bariatric surgery. (2) Methods: 141 patients undergoing bariatric surgery from September 2015 and October 2021 at RWTH Aachen university hospital (Germany) were included. Patients were evaluated pre-operatively for characteristics of metS and MUO (HbA1c, HOMA, CRP, BMI, fasting glucose, LDL, TG, HDL and the presence of arterial hypertension). Intraoperatively, a liver biopsy was taken from the left liver lobe and evaluated for the presence of NAFLD or NASH. In ordinal regression analyses, different factors were evaluated for their influence on NAFLD and NASH. (3) Results: Mean BMI of the patients was 52.3 kg/m2 (36-74.8, SD 8.4). Together, the parameters HbA1c, HOMA, CRP, BMI, fasting glucose, LDL, TG, HDL and the presence of arterial hypertension accounted for a significant amount of variance in the outcome, with a likelihood ratio of χ2 (9) = 41.547, p < 0.001, for predicting the presence of NASH. Only HOMA was an independent predictor of NASH (B = 0.102, SE = 0.0373, p = 0.007). Evaluation of steatosis showed a similar trend (likelihood ratio χ2 (9) = 40.272, p < 0.001). Independent predictors of steatosis were HbA1c (B = 0.833, SE = 0.343, p = 0.015) and HOMA (B = 0.136, SE = 0.039, p < 0.001). (4) Conclusions: The above-mentioned model, including components of MUO, was significant for diagnosing NASH in patients with morbid obesity undergoing bariatric surgery. Out of the different subitems, HOMA independently predicted the presence of NASH and steatosis, while HbA1c independently predicted steatosis and fibrosis. Taken together, the parameter of glucose metabolism appears to be more accurate for the prediction of NASH than the parameters of lipid metabolism, inflammation or the presence of cardiovascular disease.

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.

Prevalence and Predictive Clinical Characteristics of Metabolically Healthy Obesity in Obese Children and Adolescents.

BACKGROUND: The increasing prevalence of childhood obesity and accompanying comorbidities all over the world constitutes one of the most important public health problems of the changing world. The frequency and causes of the metabolically healthy obesity (MHO) phenotype in children is not clear. OBJECTIVE: The objective is to determine the prevalence of the MHO phenotype in obese Turkish children and adolescents and to identify clinical and biochemical indicators for this phenotype. METHODS: Eight hundred forty-seven obese children and adolescents, aged 3-18 years with BMI-SDS >+2 SD from the obesity outpatient clinic were included. Demographic, anthropometric, and physical examination information was collected from patient medical files. In addition, obesity-related comorbidities and results of laboratory tests were obtained. For study purposes, obese patients with no cardiometabolic risk factors were accepted as MHO, and those with ≥1 cardiometabolic risk factor were considered metabolically unhealthy obese (MUO). MHO was defined according to Damanhoury's criteria. RESULTS: Out of 847 children (mean age 10.6±3.4 years) who met the study criteria, 289 (34.1%) were diagnosed with MHO. Being younger, prepubertal, having relatively low BMI, low waist/hip ratio, low insulin resistance (HOMA-IR) index, high high-density lipoprotein, low triglyceride, low fasting insulin and glucose levels, low uric acid and low alanine transaminase (ALT) levels were associated with MHO. CONCLUSIONS: The MHO phenotype was present in just over a third of this obese pediatric cohort. The most important factors associated with MHO; age, waist-hip ratio, and BMI were determined.

Genetic Background of Metabolically Healthy and Unhealthy Obesity Phenotypes in Hungarian Adult Sample Population.

A specific phenotypic variant of obesity is metabolically healthy (MHO), which is characterized by normal blood pressure and lipid and glucose profiles, in contrast to the metabolically unhealthy variant (MUO). The genetic causes underlying the differences between these phenotypes are not yet clear. This study aims to explore the differences between MHO and MUO and the contribution of genetic factors (single nucleotide polymorphisms-SNPs) in 398 Hungarian adults (81 MHO and 317 MUO). For this investigation, an optimized genetic risk score (oGRS) was calculated using 67 SNPs (related to obesity and to lipid and glucose metabolism). Nineteen SNPs were identified whose combined effect was strongly associated with an increased risk of MUO (OR = 1.77, p < 0.001). Four of them (rs10838687 in MADD, rs693 in APOB, rs1111875 in HHEX, and rs2000813 in LIPG) significantly increased the risk of MUO (OR = 1.76, p < 0.001). Genetic risk groups based on oGRS were significantly associated with the risk of developing MUO at a younger age. We have identified a cluster of SNPs that contribute to the development of the metabolically unhealthy phenotype among Hungarian adults suffering from obesity. Our findings emphasize the significance of considering the combined effect(s) of multiple genes and SNPs in ascertaining cardiometabolic risk in obesity in future genetic screening programs.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Prevalence of liver cirrhosis based on the metabolic health and weight criteria: Report from the Korea National Health and Nutrition Examination Survey (KNHANES) data analysis.

INTRODUCTION AND OBJECTIVES: Recent studies have proposed two distinctive types of obesity, metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO), based on various physiological factors. This study sought to explore the relationship between the metabolic obesity types and the incidence of liver cirrhosis (LC) in a large nationally-representative population. MATERIALS AND METHODS: Data on 27,629 adults with MHO or MUHO, were analyzed from the Korea National Health and Nutrition Examination Survey (KNHANES) obtained from 2015 through 2019. Four categories of metabolic health and weight (MHW) were generated for analysis: (1) MHO, (2) MUHO, (3) Metabolically unhealthy normal weight (MUHNW), and (4) Metabolically healthy normal weight (MHNW). Statistical analyzes were performed with univariate and multivariate logistic regression. RESULTS: The prevalence of LC did not show statistically significant differences among the MHW categories: 0.5% in MHO, 0.4% in MUHO, 0.2% in MHNW, and 0.3% in MUHNW. The unadjusted analysis showed a significant association between self-reported LC and MUHO, but this association was not evident in the adjusted analysis. In the adjusted analysis of the prevalence of laboratory LC, a significant association emerged in the MUHO group, followed in descending order of magnitude by the MHO and MUHNW groups. A favorable fasting blood glucose level was the only factor associated with increased prevalence of reported LC in MUHO. CONCLUSIONS: The study demonstrated a difference in the prevalence of LC between MHO and MUHO. Our study concludes that the MHO phenotype is a transient status with regard to metabolic abnormalities, and caution is necessary when evaluating MHO.

Dietary determinants of healthy/unhealthy metabolic phenotype in individuals with normal weight or overweight/obesity: a systematic review.

Objectives: Nutritional factors are amongst the major determinants in the onset and development of obesity and metabolic complications. Nevertheless, the dietary determinants of metabolic health are not completely elucidated. The aim of this systematic review is to investigate nutritional and dietary factors that may contribute to metabolic heterogeneity in individuals with obesity or normal weight. Methods: A literature search was performed in PubMed, Scopus, EMBASE, and google scholar databases until August 2021, to locate studies that examined metabolic health and its association with intakes of specific foods or food groups, nutrient intakes or status, as well as adherence to certain dietary patterns. Two researchers had independently screened titles and abstracts, examined full-text studies, conducted data extraction, and evaluated their quality using the Newcastle-Ottawa Scale. Results: Twenty-seven studies, with a total of 39518 subjects, were included. Of these studies, 11 articles evaluated the association between different dietary patterns and metabolic phenotypes, while 15 had investigated the association of single food/nutrients intakes or nutrient status with metabolic phenotype, and one paper evaluated the association of dietary inflammatory index with metabolic health. The findings of these studies propose that healthy dietary patterns such as the Mediterranean pattern, Dietary Approaches to Stop Hypertension, and population-derived patterns (such as the "Healthy" and "Fruit and vegetable" patterns) were associated with higher odds of the metabolically healthy phenotype. Higher intakes of fruits, vegetables, dairy products, coffee/tea, vitamin D, magnesium, and flavonoids, were suggested to lower the risk of developing metabolically unhealthy phenotype, while, higher consumption of saturated fat, carbohydrate and sugar-sweetened beverages, fast foods, organ meats, and a pro-inflammatory diet increased the risk. Conclusion: Results from published studies, which were mostly cross-sectional, suggest that higher adherence to unhealthier dietary patterns, characterized by the consumption of refined and processed foods, was associated with a lower likelihood of having a healthy metabolic phenotype, while the opposite was observed for healthier dietary patterns. Findings may be used in developing nutritional strategies aimed at improving metabolic health in the population.

Corrigendum: Sex differences in metabolically healthy and metabolically unhealthy obesity among Chinese children and adolescents.

[This corrects the article DOI: 10.3389/fendo.2022.980332.].