Biomarkers of Intestinal Permeability are Associated with Inflammation in Metabolically Healthy Obesity, but not Normal-Weight Obesity.

Systemic inflammation is reported in normal-weight obesity (NWO) and metabolically healthy obesity (MHO), which may be linked to their increased cardiovascular disease (CVD) risk. Yet, drivers of this inflammation remain unclear. We characterized factors known to influence inflammatory status (i.e., intestinal permeability, adipose tissue, diet quality, microbiota) - and their relationships with measured inflammation - in NWO and MHO, healthy controls (CON) and metabolically unhealthy obesity (MUO; N=80; n=20/group). Serum indicators of intestinal permeability and inflammation were assessed using ELISA and/or multiplex. Total, visceral, and percent body fat were measured with dual-energy X-ray absorptiometry (DXA). Fecal microbiota composition was assessed via 16S rRNA sequencing (n=9-10/group). For C-reactive protein (CRP), MUO > NWO > CON (p<0.0001). In MHO, CRP was intermediate - and similar to - both MUO and NWO. Lipopolysaccharide binding protein (LBP) and the ratio of LBP to soluble CD14 (sCD14) were higher in MHO and MUO versus CON/NWO (p's<0.0001). Across correlation and regression analyses, LBP consistently displayed the strongest relationships with CRP in the entire sample (r=0.78;ß = 0.57;p's<0.0001) and in MHO (r=0.74;p<0.01), but not NWO (r=0.37;p = 0.11). Shannon index was higher in CON compared to MUO (p<0.05) and inversely correlated with CRP in the full sample (r=-0.37;p<0.05). These data are consistent with the notion that intestinal permeability is associated with low-grade inflammation in MHO, which could be implicated in this population's reported CVD risk.

Phenotyping obesity: A focus on metabolically healthy obesity and metabolically unhealthy normal weight.

Over the past 4 decades, research has shown that having a normal body weight does not automatically imply preserved metabolic health and a considerable number of lean individuals harbour metabolic abnormalities typically associated with obesity. Conversely, excess adiposity does not always equate with an abnormal metabolic profile. In fact, evidence exists for the presence of a metabolically unhealthy normal weight (MUHNW) and a metabolically healthy obese (MHO) phenotype. It has become increasingly recognised that different fat depots exert different effects on the metabolic profile of each individual by virtue of their location, structure and function, giving rise to these different body composition phenotypes. Furthermore, other factors have been implicated in the aetiopathogenesis of the body composition phenotypes, including genetics, ethnicity, age and lifestyle/behavioural factors. Even though to date both MHO and MUHNW have been widely investigated and documented in the literature, studies report different outcomes on long-term cardiometabolic morbidity and mortality. Future large-scale, observational and population-based studies are required for better profiling of these phenotypes as well as to further elucidate the pathophysiological role of the adipocyte in the onset of metabolic disorders to allow for better risk stratification and a personalised treatment paradigm.

Genetic influences, lifestyle and psychosocial aspects in relation to metabolically healthy obesity and conversion to a metabolically unhealthy state.

AIMS: About 10%-30% of individuals with obesity are metabolically healthy, but the specific characteristics of the metabolically healthy obesity (MHO) phenotype remain unclear. We aimed to examine how physical activity, education, depressive symptoms and genetic predisposition to obesity differ between individuals with MHO and those with metabolically unhealthy obesity (MUO), and whether these factors predict stability in MHO or conversion to a metabolically unhealthy state. MATERIALS AND METHODS: We retrieved data on 9809 individuals with obesity from the Health and Retirement Study collected between 2006 and 2016. We compared how physical activity, education, depressive symptoms and a polygenic score for higher body mass index (BMI) (PGSBMI) differed cross-sectionally between MHO and MUO using logistic regression. We then examined if the same factors predict conversion to a metabolically unhealthy state over 4 years in individuals with MHO. RESULTS: Individuals with MHO had higher physical activity (odds ratio [OR] = 0.81), higher education (OR = 0.83) and lower depressive symptoms (OR = 1.14) compared to those with MUO but did not differ in the PGSBMI. The associations were slightly attenuated in mutually adjusted models. None of the factors were associated with conversion from MHO to a metabolically unhealthy state. However, a higher PGSBMI indicated 24% lower risk of conversion to a metabolically unhealthy state (p = 0.07). CONCLUSIONS: Physical activity, education and depressive symptoms differed between MHO and MUO, even when mutually adjusted for, but did not predict conversion from a metabolically healthy to unhealthy state. Although not statistically significant, the results indicated that those with genetically predicted high BMI are more likely to maintain MHO and not convert to a metabolically unhealthy state.

Metabolically healthy obesity and left ventricular geometric remodelling in Chinese children.

AIM: To investigate the association between metabolically healthy obesity (MHO) and left ventricular geometric remodelling in Chinese children. MATERIALS AND METHODS: This cross-sectional study used data from two population-based samples in China, including 2871 children aged 6-11 years. Weight status was defined based on body mass index according to the World Health Organization growth chart. Metabolic status was defined based on the 2018 consensus-based criteria proposed by Damanhoury et al. Obes Rev 2018;19:1476-1491 (blood pressure, lipids and glucose). Left ventricular geometric remodelling was determined as concentric remodelling, eccentric hypertrophy, and concentric hypertrophy. Multinomial logistic regression analysis was used to determine odds ratios (ORs) and 95% confidence intervals (CIs) for the association between categories of weight and metabolic status and left ventricular geometric remodelling. RESULTS: Compared with children with metabolically healthy normal weight, those with MHO had higher odds of left ventricular geometric remodelling, with adjusted ORs (95% CIs) of 2.01 (1.23-3.28) for concentric remodelling, 6.36 (4.03-10.04) for eccentric hypertrophy, and 17.07 (7.97-36.58) for concentric hypertrophy. Corresponding ORs (95% CIs) were 2.35 (1.47-3.75), 10.85 (7.11-16.55), and 18.56 (8.63-39.94), respectively, for children with metabolically unhealthy obesity. In contrast, metabolically unhealthy normal weight was not associated with higher odds of left ventricular geometric remodelling. Findings were consistent in sensitivity analyses that used different definitions of weight and metabolic status and left ventricular geometric remodelling. CONCLUSIONS: Children with MHO had higher odds of left ventricular geometric remodelling than their metabolically healthy normal weight counterparts. Our findings suggest MHO may not be a benign condition for cardiac health in children.

Body size, insulin sensitivity, metabolic health and risk of cardiovascular disease in Chinese adults: Insights from the China Cardiometabolic Disease and Cancer Cohort (4C) study.

AIMS: To assess the excess risk of cardiovascular disease (CVD) associated with different criteria for metabolic health, and the interplay of body size, insulin sensitivity and metabolic health with CVD risk. MATERIALS AND METHODS: We conducted a prospective study involving 115 638 participants from the China Cardiometabolic Disease and Cancer Cohort (4C) Study. Metabolic health was defined using three different definitions: (1) insulin sensitivity defined by homeostatic model assessment of insulin resistance index; (2) absence of metabolic syndrome according to the National Cholesterol Education Program Adult Treatment Panel III criteria; and (3) simultaneous absence of metabolic abnormalities (diabetes, hypertension, dyslipidaemia). The primary endpoint was a composite of incident CVD events comprising the first occurrence of myocardial infarction, stroke, heart failure, or cardiovascular death. RESULTS: During a mean 3.61-year follow-up period, obese individuals with insulin sensitivity (multivariable-adjusted hazard ratio [HR] 1.69, 95% confidence interval [CI] 1.37-2.08), or without metabolic syndrome (HR 1.46, 95% CI 1.13-1.89) still exhibited increased CVD risks, when compared to their normal-weight counterparts. Otherwise, those with obesity but simultaneous absence of metabolic abnormalities demonstrated similar CVD risk compared to normal-weight individuals (HR 0.91, 95% CI 0.53-1.59). CVD risk increased with the number of abnormalities across body mass index categories, regardless of insulin sensitivity. CONCLUSIONS: This study emphasizes the need for refined definitions of metabolic health and advocates for meticulous screening for metabolic abnormalities to reduce cardiovascular risks, even in individuals with normal weight and insulin sensitivity.

Frequency of prediabetes in individuals with increased adiposity and metabolically healthy or unhealthy phenotypes.

AIMS: To utilize the estimated glucose disposal rate (eGDR) index of insulin sensitivity, which is based on readily available clinical variables, namely, waist circumference, hypertension and glycated haemoglobin, to discriminate between metabolically healthy and unhealthy phenotypes, and to determine the prevalence of prediabetic conditions. METHODS: Non-diabetic individuals (n = 2201) were stratified into quartiles of insulin sensitivity based on eGDR index. Individuals in the upper quartiles of eGDR were defined as having metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW) or metabolically healthy obesity (MHO) according to their body mass index, while those in the lower quartiles were classified as having metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW) and metabolically unhealthy obesity (MUO), respectively. RESULTS: The frequency of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and IFG + IGT status was comparable among the MHNW, MHOW and MHO groups, while it increased from those with MUNW status towards those with MUOW and MUO status. As compared with participants with MHNW, the odds ratio of having IFG, IGT, or IFG + IGT was significantly higher in participants with MUOW and MUO but not in those with MUNW, MHOW and MHO, respectively. CONCLUSIONS: A metabolically healthy phenotype is associated with lower frequency of IFG, IGT, and IFG + IGT status across all body weight categories.

Predictive markers of metabolically healthy obesity in children and adolescents: can AST/ALT ratio serve as a simple and reliable diagnostic indicator?

This study aimed to estimate the prevalence of metabolically healthy obesity (MHO) according to two different consensus-based criteria and to investigate simple, measurable predictive markers for the diagnosis of MHO. Five hundred and ninety-three obese children and adolescents aged 6-18 years were included in the study. The frequency of MHO was calculated. ROC analysis was used to estimate the predictive value of AST/ALT ratio, waist/hip ratio, MPV, TSH, and Ft4 cut-off value for the diagnosis of MHO. The prevalence of MHO was 21.9% and 10.2% according to 2018 and 2023 consensus-based MHO criteria, respectively. AST/ALT ratio cut-off value for the diagnosis of MHO was calculated as ≥ 1 with 77% sensitivity and 52% specificity using Damanhoury et al.'s criteria (AUC = 0.61, p = 0.02), and 90% sensitivity and 51% specificity using Abiri et al.'s criteria (AUC = 0.70, p = 0.01). Additionally, using binomial regression analysis, only the AST/ALT ratio is independently and significantly associated with the diagnosis of MHO (p = 0.03 for 2018 criteria and p = 0.04 for 2023 criteria). CONCLUSION: The ALT/AST ratio may be a useful indicator of MHO in children and adolescents. WHAT IS KNOWN: • Metabolically healthy obesity refers to people who are obese but do not have any of the standard cardio-metabolic risk factors. • Metabolically healthy obesity is not entirely harmless; the metabolic characteristics of individuals with this phenotype are less favorable than those of healthy lean groups. Moreover, it is not a constant state, and there may be a transition to metabolically unhealthy phenotypes over time. WHAT IS NEW: • The prevalence of MHO is 21.9% and 10.2% according to 2018 and 2023 consensus-based metabolically healthy obesity criteria, respectively. • The ALT/AST ratio may be a useful indicator of metabolically healthy obesity in children and adolescents.

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.

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.

Dietary inflammatory index and its relationship with obesity phenotypes: a cross- sectional analysis from RaNCD cohort study.

PURPOSE: The potential dietary inflammatory index (DII) and the phenomenon of obesity have been linked in recent studies, but it is unclear whether this connection is dependent on metabolic status. Therefore, it was thought that this research would be useful in establishing the relationship between obesity phenotypes and DII. METHODS: The 5956 people who took part in the Ravansar non-communicable diseases (RaNCD) cohort research (MHNO) were put into four groups: metabolically unhealthy obesity (MUO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO), and metabolically healthy non-obesity. According to the International Diabetes Federation's criteria, MUO exhibits at least two metabolic disorders and have a body mass index of 30 kg/m2 or higher. DII was extracted from the participant's dietary consumption data. RESULTS: When possible confounders like age, gender, smoking, drinking alcohol, and exercise were taken into account, more adherence to DII was linked to a higher odds of MHO compared to MHNO (OR: 1.44; CI 95% 1.18, 1.75). Additionally, we discovered that greater adherence to DII was significantly related to higher odds for MUO compared to MHNO (OR: 1.67; CI 95% 1.3, 2.15). However, we found no association between adherence to DII and MUNO. CONCLUSIONS: Our findings indicated that greater adherence to DII was significantly associated with higher odds of MUO. However, it substantially increased the chances of both phenotypes of obesity. Level of evidence Level V-Cross-sectional observational study.

Metabolically healthy obesity and health risks - a review of meta-analyses.

OBJECTIVE: This article briefly summarizes the results of existing research on metabolically healthy obesity in the context of health risks. METHODS: The PubMed database was searched for relevant meta-analyses addressing metabolically healthy obesity in the context of health risks. RESULTS: We included a total of 17 relevant meta-analyses in this review. The results of the studied meta-analyses showed that metabolically healthy obesity may be only a transient condition associated with an increased risk of developing metabolic abnormalities in the future. People with obesity without metabolic abnormalities have an increased risk of type 2 diabetes, cardiovascular disease, cancer, chronic kidney disease, and depressive syndrome. In addition, all people with obesity are at risk of pathogenesis resulting from the mechanical stress caused by presence of abnormal adipose tissue, such as sleep apnoea syndrome or skin problems. CONCLUSION: Based on the results of meta-analyses, we recommend motivating all obese patients to change their lifestyle regardless of the presence of metabolic defects.

Metabolically healthy obesity is associated with higher risk of both hyperfiltration and mildly reduced estimated glomerular filtration rate: the role of serum uric acid in a cross-sectional study.

BACKGROUND: The impact of metabolically healthy obesity (MHO) on kidney dysfunction remains debatable. Moreover, few studies have focused on the early stages of kidney dysfunction indicated by hyperfiltration and mildly reduced eGFR. Thus, we aimed to investigate the association between the MHO and early kidney dysfunction, which is represented by hyperfiltration and mildly reduced estimated glomerular filtration rate (eGFR), and to further explore whether serum uric acid affects this association. METHODS: This cross-sectional study enrolled 1188 residents aged ≥ 40 years old from Yonghong Communities. Metabolically healthy phenotypes were categorized based on Adult Treatment Panel III criteria. Obesity was defined as body mass index (BMI) ≥ 25 kg/m2. Mildly reduced eGFR was defined as being in the range 60 < eGFR ≤ 90 ml/min/1.73m2. Hyperfiltration was defined as eGFR > 95th percentile after adjusting for sex, age, weight, and height. RESULTS: Overall, MHO accounted for 12.8% of total participants and 24.6% of obese participants. Compared to metabolically healthy non-obesity (MHNO), MHO was significantly associated with an increased risk of mildly reduced eGFR (odds ratio [OR] = 1.85, 95% confidence interval [CI] 1.13-3.01) and hyperfiltration (OR = 2.28, 95% CI 1.03-5.09). However, upon further adjusting for uric acid, the association between the MHO phenotype and mildly reduced eGFR was reduced to null. Compared with MHNO/non-hyperuricemia, MHO/non-hyperuricemia was associated with an increased risk of mildly reduced eGFR (OR = 2.04, 95% CI 1.17-3.58), whereas MHO/hyperuricemia was associated with an observably increased risk (OR = 3.07, 95% CI 1.34-7.01). CONCLUSIONS: MHO was associated with an increased risk of early kidney dysfunction, and the serum uric acid partially mediated this association. Further prospective studies are warranted to clarify the causality.

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.

Cardiovascular risk of metabolically healthy obesity in two european populations: Prevention potential from a metabolomic study.

BACKGROUND: A new definition of metabolically healthy obesity (MHO) has recently been proposed to stratify the heterogeneous mortality risk of obesity. Metabolomic profiling provides clues to metabolic alterations beyond clinical definition. We aimed to evaluate the association between MHO and cardiovascular events and assess its metabolomic pattern. METHODS: This prospective study included Europeans from two population-based studies, the FLEMENGHO and the Hortega study. A total of 2339 participants with follow-up were analyzed, including 2218 with metabolomic profiling. Metabolic health was developed from the third National Health and Nutrition Examination Survey and the UK biobank cohorts and defined as systolic blood pressure < 130 mmHg, no antihypertensive drugs, waist-to-hip ratio < 0.95 for women or 1.03 for men, and the absence of diabetes. BMI categories included normal weight, overweight, and obesity (BMI < 25, 25-30, ≥ 30 kg/m2). Participants were classified into six subgroups according to BMI category and metabolic healthy status. Outcomes were fatal and nonfatal composited cardiovascular events. RESULTS: Of 2339 participants, the mean age was 51 years, 1161 (49.6%) were women, 434 (18.6%) had obesity, 117 (5.0%) were classified as MHO, and both cohorts had similar characteristics. Over a median of 9.2-year (3.7-13.0) follow-up, 245 cardiovascular events occurred. Compared to those with metabolically healthy normal weight, individuals with metabolic unhealthy status had a higher risk of cardiovascular events, regardless of BMI category (adjusted HR: 3.30 [95% CI: 1.73-6.28] for normal weight, 2.50 [95% CI: 1.34-4.66] for overweight, and 3.42 [95% CI: 1.81-6.44] for obesity), whereas those with MHO were not at increased risk of cardiovascular events (HR: 1.11 [95% CI: 0.36-3.45]). Factor analysis identified a metabolomic factor mainly associated with glucose regulation, which was associated with cardiovascular events (HR: 1.22 [95% CI: 1.10-1.36]). Individuals with MHO tended to present a higher metabolomic factor score than those with metabolically healthy normal weight (0.175 vs. -0.057, P = 0.019), and the score was comparable to metabolically unhealthy obesity (0.175 vs. -0.080, P = 0.91). CONCLUSIONS: Individuals with MHO may not present higher short-term cardiovascular risk but tend to have a metabolomic pattern associated with higher cardiovascular risk, emphasizing a need for early intervention.

The risk for chronic kidney disease in metabolically healthy obese patients: A systematic review and meta-analysis.

BACKGROUND: Chronic kidney disease (CKD) is associated with obesity and metabolic syndrome. Nevertheless, the association of CKD with phenotype referred as metabolically healthy obese or overweight is unclear. In this this systematic review and meta-analysis, we investigate the relationships between obesity and CKD independent of metabolic syndrome by appraising published evidence in studies focusing on metabolically healthy obese people. MATERIALS AND METHODS: We performed a literature search through three databases Embase (Elsevier), the Cochrane Central Register of Controlled Trials (Wiley) and PubMed/Medline Web of Science up to March 2022 with the following terms: "chronic kidney disease", "kidney function", "obesity", "metabolic syndrome", "metabolically healthy obesity", "metabolically healthy overweight". Metabolically unhealthy was defined an individual having at least 3 of the following: abdominal obesity, high blood pressure, hypertriglyceridemia, low HDL cholesterol and hyperglycaemia. We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for reporting. Prospective, retrospective, randomized and nonrandomized studies fitting the search criteria were included in our results. RESULTS: Our final analysis included 16 studies with a total number of 4.965.285 participants. There is considerable heterogeneity in terms of study design, participant characteristics and number of participants across individual studies. In comparison to healthy normal weight patients, the risk was progressively higher in overweight (RR 1.29, 95% CI 1.27 to 1.32, p < 0.001) and obese patients (RR 1.47, 95% CI 1.31 to 1.65, p < 0.001). CONCLUSION: Metabolically healthy overweight and obese individuals have higher risk of CKD compared to individuals without weight excess.

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.

Association between the expression of TLR4, TLR2, and MyD88 with low-grade chronic inflammation in individuals with metabolically healthy obesity.

BACKGROUND: Among the Toll-like receptors (TLR) that are dependent of myeloid response protein (MyD88), the TLR4 and TLR2 are directly associated with low-grade chronic inflammation; however, they are not been investigated in subjects with metabolically healthy obesity (MHO). Thus, the objective of this study was to determine the association between the expression of TLR4, TLR2, and MyD88 with low-grade chronic inflammation in individuals with MHO. METHODS AND RESULTS: Men and women with obesity aged 20 to 55 years were enrolled in a cross-sectional study. Individuals with MHO were allocated into the groups with and without low-grade chronic inflammation. Pregnancy, smoking, alcohol consumption, intense physical activity or sexual intercourse in the previous 72 h, diabetes, high blood pressure, cancer, thyroid disease, acute or chronic infections, renal impairment, and hepatic diseases, were exclusion criteria. The MHO phenotype was defined by a body mass index (BMI ≥ 30 kg/m2) plus one or none of the following cardiovascular risk factors: hyperglycemia, elevated blood pressure, hypertriglyceridemia, and low high-density lipoprotein cholesterol. A total of 64 individuals with MHO were enrolled and allocated into the groups with (n = 37) and without (n = 27) inflammation. The multiple logistic regression analysis indicated that TLR2 expression is significantly associated with inflammation in individuals with MHO. In the subsequent analysis adjusted by BMI, TLR2 expression remained associated with inflammation in individuals with MHO. CONCLUSION: Our results suggest that overexpression of TLR2, but not TLR4 and MyD88, is associated with low-grade chronic inflammation in subjects with MHO.

The Association between Cardio-metabolic and hepatic indices and anthropometric measures with metabolically obesity phenotypes: a cross-sectional study from the Hoveyzeh Cohort Study.

BACKGROUND: This study aimed to compare the cardio-metabolic, anthropometric, and liver function indices among metabolic obesity phenotypes. METHODS: In this cross-sectional study, which was carried out in Hoveyzeh, Khuzestan Province, Iran, a total of 7,464 individuals (male: 2859, female: 4605), were recruited and classified into four groups, based on Body Mass Index (obese, BMI ≥ 30 kg/m2; non-obese, BMI = 18.5-29.9 kg/m2) and the National Cholesterol Education Program and Adult Treatment Panel (NCEP ATP) III criteria (Healthy group, ≤ 1 of the criteria; Unhealthy group, ≥ 2 of the criteria), as follows: Metabolically Healthy Non-Obese (MHNO, 28.14%), Metabolically Unhealthy Non-Obese (MUNO, 33.06%), Metabolically Healthy Obese (MHO, 6.54%), and Metabolically Unhealthy Obese (MUO, 32.26%). Anthropometric indices (Waist/Hip Ratio (WHR), Waist/Height Ratio (WHtR), Body Adiposity Index (BAI), Visceral Adiposity Index (VAI), and Weight adjusted Waist Index (WWI)), cardio-metabolic indices (Atherogenic Index of Plasma (AIP), Lipid Accumulation Product (LAP), Cardio-Metabolic Index (CMI), Lipoprotein Combine Index (LCI), Triglyceride-Glucose (TyG), TyG-BMI, TyG-WC, and Thrombolysis In Myocardial Infarction (TIMI) risk index), and hepatic indices (Hepatic Steatosis Index (HSI) and ALD/NAFLD index (ANI)) were calculated and compared between the groups. RESULTS: WHR,VAI, AIP, LAP, CMI, LCI, TyG, and TIMI risk index values were significantly higher in the MUNO phenotype compared to the MHO phenotype (WHR: 0.97 vs. 0.95; VAI: 3.16 vs. 1.33; AIP: 0.58 vs. 0.25; LAP: 78.87 vs. 55.79; CMI: 2.69 vs. 1.25; LCI: 27.91 vs. 12.11; TyG: 9.21 vs. 8.41; TIMI: 18.66 vs. 15.63; p < 0.001). The highest and lowest HSI and ANI values were detected in the MUO phenotype. After adjustment for age, sex, physical activity, and years of education, VAI showed the highest Odds Ratio for MUNO (OR: 5.65; 95% CI: 5.12, 6.24) and MUO (OR: 5.40; 95% CI: 5.89, 5.95) compared to the MHNO phenotypes (p < 0.001). The ANI indices was associated with a reduced risk of MUO (OR: 0.76; 95% CI: 0.75-0.78), MUNO (OR: 0.88; 95% CI: 0.87-0.90), and MHO (OR: 0.79; 95% CI: 0.77-0.81) phenotypes (p < 0.001). CONCLUSION: MUNO phenotype was exposed to a higher risk of cardiovascular disease compared to the MHO phenotype. VAI was found to be the optimal index for cardiovascular risk assessment.

Inflammation biomarkers and inflammatory genes expression in metabolically healthy obese patients.

BACKGROUND AND AIMS: Obesity without metabolic alterations (Metabolically Healthy Obesity, MHO) is a condition with a risk of death and cardiovascular disease lower than that of obesity associated with metabolic alterations (Metabolically Unhealthy Obesity, MUO) and similar to that of healthy non obese individuals. Inflammation is considered as a key risk factor mediating the adverse health outcomes in obesity. METHODS AND RESULTS: We compared circulating levels of thirteen major cytokines and adipokines and the expression profiles of fifteen pro-inflammatory and two anti-inflammatory genes in visceral and subcutaneous adipose tissue in a series of 16 MHO patients and in 32 MUO patients that underwent bariatric surgery. MHO was defined according to the most applied definition in current literature. Serum levels of a large set of major cytokines and adipokines did not differ between MHO and MUO patients (p ≥ 0.15). Analyses of the expression profile of pro-inflammatory and anti-inflammatory genes in subcutaneous and visceral adipose tissue failed to show differences between MHO and MUO patients (p ≥ 0.07). Sensitivity analyses applying two additional definitions of MHO confirmed the results of the primary analysis. CONCLUSION: In a series of metabolically healthy obese patients neither circulating levels of major cytokines and adipokines nor the gene expression profile of a large set of pro-inflammatory and anti-inflammatory genes in subcutaneous and visceral fat differed from those in metabolically unhealthy obese patients.

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.