Conversions between metabolically unhealthy and healthy obesity from midlife to late-life.

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.

Metabolically healthy obesity: Misleading phrase or healthy phenotype?

Obesity is a heterogenous condition with multiple different phenotypes. Among these a particular subtype exists named as metabolically healthy obesity (MHO). MHO has multiple definitions and its prevalence varies according to study. The potential mechanisms underlying the pathophysiology of MHO include the different types of adipose tissue and their distribution, the role of hormones, inflammation, diet, the intestinal microbiota and genetic factors. In contrast to the negative metabolic profile associated with metabolically unhealthy obesity (MUO), MHO has relatively favorable metabolic characteristics. Nevertheless, MHO is still associated with many important chronic diseases including cardiovascular disease, hypertension, type 2 diabetes, chronic kidney disease as well as certain types of cancer and has the risk of progression into the unhealthy phenotype. Therefore, it should not be considered as a benign condition. The major therapeutic alternatives include dietary modifications, exercise, bariatric surgery and certain medications including glucagon-like peptide-1 (GLP-1) analogs, sodium-glucose cotransporter-2 (SGLT-2) inhibitors and tirzepatide. In this review, we discuss the significance of MHO while comparing this phenotype with MUO.

Risk of Heart Failure between Different Metabolic States of Health and Weight: A Meta-Analysis of Cohort Studies.

We conducted a systematic review of cohort studies comparing the risk of heart failure in people with differing metabolic health and obesity statuses. We searched three electronic databases (PubMed, Web of Science, Scopus), where the studies of the relationships of metabolic health and obesity statuses with heart failure were included. Fixed-effects or random-effects models were used to estimate the summary relative risks [RRs]. Ten cohort studies were selected. Compared with individuals with normal metabolic health and body mass, the pooled RRs (95% confidence intervals) for heart failure were 1.23 (1.17, 1.29) for metabolic healthy overweight individuals, 1.52 (1.40, 1.64) for metabolic healthy individuals with obesity, 1.56 (1.30, 1.87) for metabolically unhealthy normal-weight individuals, 1.75 (1.55, 1.98) for metabolically unhealthy overweight individuals, and 2.28 (1.96, 2.66) for metabolic unhealthy individuals with obesity. A sensitivity analysis suggested that no single study had a substantial effect on the results. The Egger's and Begg's tests showed no evidence of publication bias. People with overweight or obesity were at a higher risk of heart failure, even if metabolically healthy. In addition, compared with metabolically healthy normal-weight individuals; metabolically unhealthy normal-weight individuals, and those with overweight or and obesity, were at higher risk of heart failure.

The metabolic phenotype of the patient influences the reduction in carotid intima-media thickness achieved following metabolic surgery.

OBJECTIVES: To determine whether metabolic phenotype is associated with the change in carotid intima-media thickness (CIMT) in patients undergoing bariatric /metabolic surgery (BMS). METHODS: We performed a case-control study of BMS candidates who had metabolically unhealthy obesity (MUO) or metabolically healthy obesity (MHO). We measured the change in CIMT during the 9 months following BMS. The plasma tumor necrosis factor-α, interleukin-1ß, adiponectin, leptin, nitric oxide (NO), vascular endothelial growth factor A (VEGF-A), and malondialdehyde concentrations were determined, adipocyte area was measured histologically, and adipose tissue area was estimated using computed tomography. RESULTS: Fifty-six patients (mean age 44.5 years, mean body mass index 44.9 kg/m2, 53% women, and 53% had MUO) were studied. Nine months following BMS, the MUO phenotype was not associated with a significant reduction in CIMT, and that of the MHO group was larger. In addition, fewer participants achieved a 10% reduction in CIMT in the MUO group. A CIMT reduction was associated with lower VEGF-A and NO in the MUO group, while that in the MHO group was associated with a higher NO concentration. CONCLUSION: The metabolic phenotype of patients may influence their change in CIMT following BMS, probably through circulating vasodilatory and pro-inflammatory molecules.

Ferritin, transferrin, and transferrin receptor in relation to metabolic obesity phenotypes: Findings from the China Health and Nutrition Survey.

Background: This study aimed to explore the relationship between iron markers and metabolic obesity phenotypes and the role of age. Methods: Data were from the China Health and Nutrition Survey 2009. Metabolic obesity phenotypes included metabolically healthy with normal weight (MHNW), metabolically unhealthy with normal weight (MUNW), metabolically healthy with overweight/obesity (MHO), and metabolically unhealthy with overweight/obesity (MUO). Iron markers including ferritin, transferrin, and soluble transferrin receptor were calculated as Log and quartered. The linear regression and multinomial logistic regression were used to explore the association of iron markers with age and metabolic obesity phenotypes, respectively. Results: Ferritin was linearly related with age, with ß (95% confidence interval, CI) of 0.029 (0.027 to 0.032) and -0.005 (-0.007 to -0.002) for women and men. Transferrin was negatively associated with age in both men and women (ß < -0.011). Furthermore, compared with participants in the quartile 1 ferritin group, those in the quartile 4 had increased odds of MUNW, MHO, and MUO, with odds ratio and 95% confidence interval (OR, 95% CI) of 3.06 (2.20 to 4.25), 1.66 (1.35 to 2.05), and 5.27 (4.17 to 6.66). Transferrin showed similar relationships with MUNW, MUO, and MHO; whereas transferrin receptor showed no significance. We also found joint associations of ferritin and transferrin with MUNW, MUO, and MHO. The interactive effect of ferritin and transferrin on MUO was significant (P = 0.015). Conclusion: Increased ferritin and transferrin were associated with MUNW, MHO, and MUO. Age should be considered when investigating iron.

Dietary and lifestyle indices for hyperinsulinemia with the risk of obesity phenotypes: a prospective cohort study among Iranian adult population.

BACKGROUND: Previous studies have cited insulin-related disorders, including hyperinsulinemia, as one of the main causes of obesity risk and metabolic disorders. We aimed to investigate the association of the Empirical Dietary Index for Hyperinsulinemia (EDIH) and Empirical Lifestyle Index for Hyperinsulinemia (ELIH) with the risk of obesity phenotypes among Iranian adults. METHODS: Present study was conducted on 2705 subjects, including 1604 metabolically healthy normal weights (MHNW) and 1101 metabolically healthy obesity (MHO) individuals. Obesity phenotypes, including MHNW, MHO, metabolically unhealthy normal weights (MUNW), and metabolic unhealthy obesity (MUO), were determined using the criteria of the Joint International statement (JIS) for metabolic syndrome. Dietary intake data from the previous year was gathered using a food frequency questionnaire. Cox proportional hazard regression was used to estimate the hazard ratio and 95% confidence intervals (HRs and 95% CIs) of obesity phenotypes incident across tertiles of EDIH and ELIH scores. RESULTS: The mean ± SD of age and BMI of all participants were 33.5 ± 12.2 years and 24.3 ± 3.8 kg/m2, respectively. In the multivariable-adjusted model, a higher ELIH score was associated with a greater risk for incidence of MUO (HR: 3.47, 95%CI: 2.54-4.74; Ptrend = < 0.001) and MHO (HR: 3.61, 95%CI: 2.73-4.77; Ptrend = < 0.001). Also, a higher score of EDIH was related to an increased risk of MUO incidence (HR: 1.35, 95%CI: 1.02-1.79; P for trend = 0.046). However, there was no significant association between a higher score of EDIH and the risk of MHO. CONCLUSION: Our findings revealed that a high insulinemic potential of diet and lifestyle, determined by EDIH and ELIH indices, may be related to an increase in the simultaneous occurrence of obesity with metabolic disorders in Iranian adults.

Serum proteome profiling reveals differentially expressed proteins between subjects with metabolically healthy obesity and nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is the leading risk factor for common chronic liver disease and is often regarded as a prevalent metabolic disorder tightly associated with obesity. However, the existence of metabolically healthy obesity (MHO) indicates that some important factors may participate in protecting individuals with MHO free of NAFLD, even with excessive adiposity. To explore factors independent of obesity that may be involved in the occurrence of NAFLD, we performed an iTRAQ-based proteomic study to identify proteins differentially expressed in serum between NAFLD and MHO subjects. Compared with the MHO group, ten proteins were upregulated and five were downregulated significantly in the NAFLD group. Gene Ontology analysis indicated significant changes in the immune response and triglyceride metabolism-related pathways between MHO and NAFLD. We further validated three candidates markedly dysregulated in NAFLD by Western blotting and ELISA, including two upregulated proteins (afamin and apolipoprotein H) and one downregulated protein (apolipoprotein C-1). Detection of serum apolipoprotein H levels in a large-scale cohort with MHO and different stages of NAFLD indicated that apolipoprotein H may be a potential blood biomarker for distinguishing NAFLD from MHO and an independent risk factor for predicting NAFLD.

Metabolic phenotypes and risk of colorectal cancer: a systematic review and meta-analysis of cohort studies.

BACKGROUND: The association of obesity with colorectal cancer (CRC) may vary depending on metabolic status. OBJECTIVE: This meta-analysis aimed to investigate the combined impacts of obesity and metabolic status on CRC risk. METHODS: The Scopus, PubMed, and web of sciences databases were systematically searched up to Jun 2021 to find all eligible publications examining CRC risk in individuals with metabolically unhealthy normal-weight (MUHNW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUHO) phenotypes. RESULTS: A total of 7 cohort studies with a total of 759,066 participants were included in this meta-analysis. Compared with healthy normal-weight people, MUHNW, MHO, and MUHO individuals indicated an increased risk for CRC with a pooled odds ratio of 1.19 (95% CI = 1.09-1.31) in MUHNW, 1.14 (95% CI = 1.06-1.22) in MHO, and 1.24 (95% CI = 1.19-1.29) in MUHO subjects. When analyses were stratified based on gender, associations remained significant for males. However, the elevated risk of CRC associated with MHO and MUHO was not significant in female participants. CONCLUSIONS: The individuals with metabolic abnormality, although at a normal weight, have an increased risk for CRC. Moreover, obesity is associated with CRC irrespective of metabolic status.

Transition patterns of metabolism-weight phenotypes over time: A longitudinal study using the multistate Markov model in China.

Background: A change in weight or metabolic status is a dynamic process, yet most studies have focused on metabolically healthy obesity (MHO) and the transition between MHO and metabolically unhealthy obesity (MUO); therefore, they have not fully revealed the nature of all possible transitions among metabolism-weight phenotypes over the years. Methods: This was a longitudinal study based on a retrospective health check-up cohort. A total of 9,742 apparently healthy individuals aged 20-60 years at study entry were included and underwent at least two health check-ups. Six metabolism-weight phenotypes were cross-defined by body mass index (BMI) categories and metabolic status as follows: metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), MHO, metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and MUO. A multistate Markov model was used to analyse all possible transitions among these phenotypes and assess the effects of demographic and blood indicators on the transitions. Results: The transition intensity from MUNW to MHNW was the highest (0.64), followed by the transition from MHO to MUO (0.56). The greatest sojourn time appeared in the MHNW state (3.84 years), followed by the MUO state (2.34 years), and the shortest sojourn time appeared in the MHO state (1.16 years). Transition intensities for metabolic improvement gradually decreased with BMI level as follows: 0.64 for MUNW to MHNW, 0.44 for MUOW to MHNW, and 0.27 for MUO to MHO; however, transition intensities for metabolic deterioration, including MHNW to MUNW, MHOW to MUOW, and MHO to MUO, were 0.15, 0.38, and 0.56, respectively. In the middle-aged male group, elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), and uric acid (UA) increased the risk of deterioration in weight and metabolic status and decreased the possibility of improvement. Conclusion: Maintaining a normal and stable BMI is important for metabolic health. More attention should be given to males and elderly people to prevent their progression to an unhealthy metabolic and/or weight status. MHO is the most unstable phenotype and is prone to convert to the MUO state, and individuals with abnormal ALT, AST and UA are at an increased risk of transitioning to an unhealthy weight and/or metabolic status; therefore, we should be alert to abnormal indicators and MHO. Intervention measures should be taken early to maintain healthy weight and metabolic status.

Association of metabolic health phenotypes, obesity, and hepatocellular carcinoma risk.

BACKGROUND: The obesity and hepatocellular carcinoma (HCC) risk association may differ by individuals' metabolic health status. AIM: To investigate the association between obesity categories and HCC risk among individuals with different metabolic health phenotypes. METHODS: A case-control study among 518 HCC cases and 1,036 frequency-matched controls was conducted. Body mass index (BMI) was assessed before diagnosis. Pre-diagnosis data on dyslipidemia, hypertension, and diabetes were used to categorize participants as metabolically healthy or metabolically unhealthy. Participants were further categorized into metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and metabolically unhealthy obese (MHO). We used logistic regression to calculate multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Being overweight (OR=1.68, 95%CI=1.21-2.34) or obese (OR=1.49, 95%CI=1.11-1.89) was associated with higher HCC risk. Among metabolically healthy participants, no association was found between being overweight or obese and HCC risk. However, among the metabolically unhealthy participants, being overweight (OR=1.89, 95%CI=1.31-2.72) or obese (OR=1.50, 95%CI=1.07-2.09) was associated with higher HCC risk. Compared to the MHNW phenotype, no association was found between the MHOW and MHO phenotypes and HCC risk, but the MUNW (OR=1.94, 95%CI=1.09-3.43), MUOW (OR=3.78, 95%CI=2.15-6.65), and MUO (OR=2.93, 95%CI=1.70-5.05) phenotypes were associated with higher HCC risk. CONCLUSION: The association between BMI and HCC appears to be restricted to individuals with underlying metabolic abnormalities.

The association between a metabolically healthy overweight/obesity phenotype and markers of inflammation among Chinese children and adolescents aged 10-18 years.

OBJECTIVES: The association between metabolically healthy overweight/obesity (MHO) and inflammatory markers remains controversial. The aim of the present study was to describe the prevalence of different metabolic phenotypes and to examine the relationship of different metabolic phenotypes with inflammatory markers among Chinese children and adolescents. METHODS: The study included 1,125 children and adolescents aged 10-18 years using a cross-sectional survey, and all subjects were classified into four groups based on a combination of BMI and metabolic status. In addition, the inflammatory markers we measured were high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6). RESULTS: The prevalence of metabolically healthy with normal-weight (MHNW), MHO, metabolically unhealthy with normal-weight (MUNW), and metabolically unhealthy overweight/obesity (MUO) phenotypes was 38.76, 7.11, 38.67 and 15.47%, respectively. The results of logistic regression analysis showed that the MHO was associated with the z scores of hs-CRP in Chinese children and adolescents (OR=0.57, 95% CI: 0.39-0.83). Meanwhile, multivariate adjusted regression analysis showed that the relationship between hs-CRP and MHO among the overweight/obese was consistent with the results above, but among the normal-weight, only the highest quartile of TNF-α could increase the risk of MUNW (OR=1.65, 95% CI: 1.09-2.52). CONCLUSIONS: MHO phenotypes were not common in Chinese children and adolescents. Individuals with MHO had a more beneficial hs-CRP profile than those with MUO.

Biochemical predictors of metabolically unhealthy obesity in children and adolescents.

BACKGROUND: Children and adolescents with obesity can now be classified according to metabolic profile, as those with metabolically healthy obesity (MHO) and those with metabolically unhealthy obesity (MUO). We aimed to determine the prevalence of MUO and identify its biochemical predictors in pediatric patients with obesity. METHODS: We evaluated the medical records of 187 boys and girls with obesity. The children were divided into MHO and MUO groups, and anthropometric and biochemical parameters were assessed. Oral glucose tolerance test (OGTT) was used to identify impaired glucose regulation and hyperinsulinism, and binary logistic regression analysis was used to determine predictors of MUO in children with obesity. RESULTS: Of the 187 children, MUO was found in 71.7% (n=134) and MHO in 28.3% (n=53); those in the MHO group were younger than those in the MUO group. Blood pressure, triglyceride, total cholesterol, and uric acid levels were significantly higher in the MUO group than in the MHO group. Further, the MUO group exhibited a significantly higher level of insulin resistance (p<0.05) than the MHO group. Serum levels of uric acid and homeostasis model assessment of insulin resistance index (HOMA-IR) were confirmed as biochemical predictors of the MUO phenotype in children with obesity. CONCLUSIONS: The ratio of MUO in children with obesity was relatively high; further, serum levels of uric acid and HOMA-IR can be used as biochemical predictors of MUO.

Wrist circumference as a novel predictor of transition from metabolically healthy to unhealthy phenotype in overweight/obese adults: a gender-stratified 15.5-year follow-up.

BACKGROUND: Individuals with transition from metabolically healthy overweight/obese (MHO) to metabolically unhealthy overweight/obese (MUO) phenotype are significantly predisposed to greater risks of cardiovascular events compared to those with a persistent MHO phenotype. The aim of this study was to evaluate the predictive performance of wrist circumference for this transition in adults over a 15.5-year follow-up. METHODS: We included 309 males and 821 females with the age of ≥18 years old, body mass index ≥25 kg/m2, and metabolically healthy status according to the criteria of the Joint Interim Statement. The incidence of MUO phenotype was evaluated for each gender, across tertiles wrist circumference, using Cox-proportional hazard models. RESULTS: The overall rate of transition from MHO to MUO phenotype was 87.1% in males and 77.5% in females. The hazard ratios (HRs) with 95% CI across second and third tertiles of wrist circumference were 0.89 (0.64-1.24) and 1.31 (0.99-1.73) in men (P for trend =0.027); and 1.34 (1.09-1.66) and 1.61 (1.30-2.00) in women (P for trend <0.001), respectively. After multivariable adjustment, HRs across second and third tertiles of wrist circumference were 0.92 (0.64-1.32) and 1.18 (0.83-1.67) in males (p for trend =0.352), and 1.32 (1.05-1.65) and 1.34 (1.06-1.96) in females (p for trend =0.025), respectively. CONCLUSIONS: Wrist circumference significantly predicts the transition from MHO to MUO phenotype in adults of both genders. However, it is an independent predictor of the transition only in females. Future studies are warranted to clarify the role of wrist circumference mechanisms on metabolic risk deterioration.

Metabolically Healthy Obesity (MHO) vs. Metabolically Unhealthy Obesity (MUO) Phenotypes in PCOS: Association with Endocrine-Metabolic Profile, Adherence to the Mediterranean Diet, and Body Composition.

Obesity and obesity-related low-grade inflammation are common findings in polycystic ovary syndrome (PCOS), the most common endocrine-metabolic disorder-affecting women in reproductive age. The terms metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) have been introduced to define individuals with obesity in whom cardio-metabolic risk factors are absent or present, respectively. To date, evidence investigating differences in body composition and adherence to the Mediterranean diet (MD) between MHO and MUO-PCOS women are lacking. Aim of this study was to better characterize the determinants of the metabolic health status in PCOS patients with obesity according to MHO and MUO phenotypes by evaluating endocrine-metabolic profile, inflammatory status, adherence to the MD, and body composition. The study population consisted of 94 treatment-naïve women with PCOS and obesity (BMI = 38.23 ± 6.62 kg/m2 and age = 24.12 ± 3.68 years). Compared PCOS MHO with PCOS MUO patients, the latter had higher levels of high-sensitivity C-reactive protein (hs-CRP) (p < 0.001), testosterone (p < 0.001), and insulin (p < 0.001), worse metabolic parameters, and higher Homeostatic Model Assessment of Insulin Resistance (HoMA-IR), Visceral Adiposity Index (VAI), and Fatty liver Index (FLI) (p < 0.001). Furthermore, PCOS MUO patients had lower adherence to the MD (p < 0.001) in spite of the same total energy intake (p = 0.102) as compared to PCOS MHO. The presence of MUO was associated with highest hs-CRP levels (OR = 1.49, p < 0.001), more severe hyperandrogenism and cardio-metabolic indices (p < 0.001). On the contrary, being PCOS MUO was associated with lower adherence to the MD (OR = 0.28, p < 0.001), and smaller PhAs (OR = 0.04, p < 0.001). Using a regression linear analysis model PREDIMED score entered at the first step (p < 0.001), followed by VAI (p < 0.001), and FLI (p = 0.032) in this analysis. At ROC analysis, a PREDIMED score of ≤4 (p < 0.001, AUC 0.926) could serve as a threshold for a significantly increased risk of presence the MUO-PCOS phenotype. To the best of our knowledge, this is the first study that characterized MHO and MUO-PCOS women on the basis of their adherence to the MD, body composition, and cardio-metabolic indices, providing evidence of the usefulness of adjunctive diagnostic parameters to better differentiate the MHO/MHO phenotypes in this cohort of PCOS patients with obesity.

Precisión diagnóstica del índice de masa triponderal (kg/m3) para identificar el fenotipo de riesgo metabólico en pacientes obesos / Diagnostic accuracy of the tri-ponderal mass index in identifying the unhealthy metabolic obese phenotype in obese patients

INTRODUCCIÓN: El fenotipo obeso metabólicamente sano (FOMS) define a los pacientes obesos que tienen preservada la sensibilidad a la insulina y que no presentan complicaciones metabólicas. Este fenotipo se asocia a menor riesgo de padecer enfermedad cardiovascular y diabetes tipo2 en la edad adulta. OBJETIVOS: Determinar la prevalencia del FOMS y del fenotipo obeso con riesgo metabólico (FORM) en una cohorte de niños y adolescentes obesos y establecer la capacidad predictiva del índice de masa triponderal (IMT) y de otros parámetros antropométricos para identificar a estos pacientes. PACIENTES Y MÉTODOS: Estudio transversal de 239 pacientes (125varones) obesos de 8 a 18años de edad. El 45,9% presentan obesidad grado3. Se utilizan las curvas ROC para buscar el mejor punto de corte para: IMT, índice de masa corporal (IMC), valor z-score del IMC (zsIMC) e índice cintura/talla (ICT). Componentes FOMS: glucemia plasmática, triglicéridos plasmáticos, colesterol HDL y presión arterial. RESULTADOS: La prevalencia del FORM en nuestra cohorte es del 62,4%, sin que se observen diferencias entre sexos, incrementándose con la edad y con el grado de obesidad. El IMT tiene una sensibilidad de 75,8 y una especificidad de 42,2 para identificar los pacientes FORM. El mejor punto de corte para el IMT es 18,7kg/m3, para el IMC 30,4kg/m2, para el zsIMC +3,5DE y para el ICT 0,62. CONCLUSIONES: La precisión diagnóstica del IMT para identificar niños y adolescentes con riesgo metabólico es similar al IMC y al ICT. No obstante, su cálculo es más sencillo y además facilita y simplifica la categorización del grado de obesidad en ambos sexos

INTRODUCTION: The metabolically healthy obese (MHO) phenotype defines obese patients who have preserved insulin sensitivity and absence of metabolic complications. This phenotype is associated with a lower risk of cardiovascular disease and type2 diabetes in adulthood. OBJECTIVES: To determine the prevalence of MHO and the metabolically unhealthy obesity (MUO) phenotype in a cohort of obese children and adolescents and to establish the predictive capacity of the tri-ponderal mass index (TMI) and other anthropometric parameters in order to identify these patients. PATIENTS AND METHODS: A cross-sectional study was conducted on 239 obese patients (125 males) from 8 to 18 years of age. Grade3 obesity was present in 45.9% of the patients. ROC curves were used to find the best cut-off point for: TMI, body mass index (BMI), BMI z-score (BMIzs), and waist/height index (WHI). MHO components: plasma blood glucose, plasma triglycerides, HDL-cholesterol, and blood pressure. RESULTS: The prevalence of MUO in the study cohort was 62.4%. No differences between genders were observed, and it was increasing with the age and obesity degree. The TMI has a sensitivity of 75.8 and a specificity of 42.2 to identify the MUO patients. The best cut-off point for TMI is 18.7 kg/m3, for BMI it was 30.4 kg/m2, for BMIzs + 3.5SD, and 0.62 for WHI. CONCLUSIONS: The diagnostic accuracy of TMI in identifying obese adolescents with metabolic risk was similar to BMI and WHI. However, the TMI is much simpler to use and simplifies the categorization of the obesity in both genders

Dysregulation of endocannabinoid concentrations in human subcutaneous adipose tissue in obesity and modulation by omega-3 polyunsaturated fatty acids.

Obesity is believed to be associated with a dysregulated endocannabinoid system which may reflect enhanced inflammation. However, reports of this in human white adipose tissue (WAT) are limited and inconclusive. Marine long-chain omega-3 polyunsaturated fatty acids (LC n-3 PUFAs) have anti-inflammatory actions and therefore may improve obesity-associated adipose tissue inflammation. Therefore, fatty acid (FA) concentrations, endocannabinoid concentrations, and gene expression were assessed in subcutaneous WAT (scWAT) biopsies from healthy normal weight individuals (BMI 18.5-25 kg/m2) and individuals living with metabolically healthy obesity (BMI 30-40 kg/m2) prior to and following a 12-week intervention with 3 g fish oil/day (1.1 g eicosapentaenoic acid (EPA) + 0.8 g DHA) or 3 g corn oil/day (placebo). WAT from individuals living with metabolically healthy obesity had higher n-6 PUFAs and EPA, higher concentrations of two endocannabinoids (anandamide (AEA) and eicosapentaenoyl ethanolamide (EPEA)), higher expression of phospholipase A2 Group IID (PLA2G2D) and phospholipase A2 Group IVA (PLA2G4A), and lower expression of CNR1. In response to fish oil intervention, WAT EPA increased to a similar extent in both BMI groups, and WAT DHA increased by a greater extent in normal weight individuals. WAT EPEA and docosahexaenoyl ethanolamide (DHEA) increased in normal weight individuals only and WAT 2-arachidonyl glycerol (2-AG) decreased in individuals living with metabolically healthy obesity only. Altered WAT fatty acid, endocannabinoid, and gene expression profiles in metabolically healthy obesity at baseline may be linked. WAT incorporates n-3 PUFAs when their intake is increased which affects the endocannabinoid system; however, effects appear greater in normal weight individuals than in those living with metabolically healthy obesity.

Metabolically healthy obesity: is there a link with polyunsaturated fatty acid intake and status?

The aim of this study was to compare dietary intake and status of polyunsaturated fatty acids (PUFA) in plasma and erythrocyte phospholipids metabolically healthy and unhealthy, and obese and nonobese persons. Metabolic health status in 171 participants was defined according to criteria for metabolic syndrome. Obese and nonobese metabolically unhealthy persons (MUHO and MUHNO) had higher energy intake of n-6 PUFA (7.82 ± 1.03 and 7.49 ± 0.86) and lower intake of n-3 PUFA (0.60 ± 0.12 and 0.62 ± 0.11) compared to obese and nonobese metabolically healthy persons (MHO and MHNO) (5.92 ± 0.63 and 5.72 ± 0.67; 1.20 ± 0.07 and 1.22 ± 0.09, respectively) and a higher n-6/n-3 PUFA ratio. The plasma level of n-6 PUFA was lower in the MUHO and MUHNO groups (38.49 ± 3.71 and 38.53 ± 2.19) compared to MHNO (40.90 ± 2.43), while n-3 PUFA status was lower in obese than in nonobese persons (3.58 ± 0.79 and 3.50 ± 1.02 vs. 4.21 ± 0.80 and 4.06 ± 1.15). The MHO group had a higher eicosapentaenoic/arachidonic acid ratio and estimated desaturase (SCD16, D6D) and elongase activity in plasma phospholipids compared to MHNO. The low intake of n-3 PUFA is directly associated with metabolic risk factors. These results indicated that obesity is closely associated with low levels of n-3 PUFA in plasma phospholipids, suggesting that dietary modifications including n-3 PUFA supplementation appear to be suitable therapeutic strategy in obese persons.

Epigenetic approach in obesity: DNA methylation in a prepubertal population which underwent a lifestyle modification.

BACKGROUND: Metabolically healthy obesity (MHO) is a considerably controversial concept as it is considered a transitory condition towards the development of different pathologies (type 2 diabetes, insulin resistance, or cardiovascular disease). MHO is closely related to lifestyle and environmental factors. Epigenetics has become an essential biological tool to analyze the link between obesity and metabolic status. The aim of this study was to determine whether MHO status is conditioned by the DNA methylation (DNAm) of several genes related to lipid metabolism (lipoprotein lipase, retinoid X receptor alpha, liver X receptor, stearoyl-CoA desaturase, sterol regulatory element binding factor 1), and inflammation (LEP) in peripheral blood mononuclear cells (PBMCs) from 131 prepubertal subjects with MHO phenotype after lifestyle modifications with personalized Mediterranean diet (MedDiet) combined with a physical activity (PA) program. RESULTS: The DNAm of all studied genes were significantly modified in the population after 12 months of lifestyle modifications (MedDiet and PA). In addition, associations were found between the DNAm studies and BMI, homeostatic model assessment of insulin resistance, monounsaturated fatty acid and polyunsaturated fatty acid, moderate-vigorous PA, fat mass, and adherence to MedDiet. CONCLUSIONS: It was found that DNAm of genes related to lipid metabolism and inflammation are also present in childhood and that this methylation profile can be modified by interventions based on MedDiet and PA.

The association between transition from metabolically healthy obesity to metabolic syndrome, and incidence of cardiovascular disease: Tehran lipid and glucose study.

Considering that the data available on the cardiovascular (CV) risk of metabolically healthy obesity phenotype, and the effect of transition to an unhealthy status are inconsistent, the aim of this study was to investigate the possible role of transition to unhealthy status among metabolically healthy overweight/obese (MHO) subjects on CVD incidence over a median follow-up of 15.9 years. In this large population-based cohort, 6758 participants (41.6% men) aged ≥ 20 years, were enrolled. Participants were divided into 4 groups based on their obesity phenotypes and follow-up results, including persistent metabolically healthy normal weight (MHNW), persistent MHO, transitional MHO and metabolically unhealthy overweight/obese (MUO). Metabolic health was defined as not having metabolic syndrome based on the Joint Interim Statement (JIS) criteria. Multivariable adjusted hazard ratios (HRs) were calculated for cardiovascular events. During follow-up, rate of CVD Incidence per 1000 person-years were 12 and 7 in males and females, respectively. Multivariable adjusted HRs (CI 95%) of CVD incidence among males and females were 1.37 (.78-2.41) and .85 (.34-2.15) in persistent MHO group, 1.55 (1.02-2.37) and .93 (.41-2.12) in transitional MHO group and 2.64 (1.89-3.70) and 2.65 (1.24-5.68) in MUO group. Our findings showed that CVD risk did not increase in the persistent MHO phenotype over a 15.9-year follow-up in both sexes. However, transition from MHO to MUO status during follow-up increased the CVD risk just in male individuals. Further studies are needed to provide conclusive evidence in favor of benign nature of transitional MHO phenotype in females.

Exploring Therapeutic Targets to Reverse or Prevent the Transition from Metabolically Healthy to Unhealthy Obesity.

The prevalence of obesity and obesity-related metabolic comorbidities are rapidly increasing worldwide, placing a huge economic burden on health systems. Excessive nutrient supply combined with reduced physical exercise results in positive energy balance that promotes adipose tissue expansion. However, the metabolic response and pattern of fat accumulation is variable, depending on the individual's genetic and acquired susceptibility factors. Some develop metabolically healthy obesity (MHO) and are resistant to obesity-associated metabolic diseases for some time, whereas others readily develop metabolically unhealthy obesity (MUO). An unhealthy response to excess fat accumulation could be due to susceptibility intrinsic factors (e.g., increased likelihood of dedifferentiation and/or inflammation), or by pathogenic drivers extrinsic to the adipose tissue (e.g., hyperinsulinemia), or a combination of both. This review outlines the major transcriptional factors and genes associated with adipogenesis and regulation of adipose tissue homeostasis and describes which of these are disrupted in MUO compared to MHO individuals. It also examines the potential role of pathogenic insulin hypersecretion as an extrinsic factor capable of driving the changes in adipose tissue which cause transition from MHO to MUO. On this basis, therapeutic approaches currently available and emerging to prevent and reverse the transition from MHO to MUO transition are reviewed.