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.

Reverse epidemiology of obesity paradox: Fact or fiction?

Obesity paradox refers to the clinical observation that when acute cardiovascular decompensation occurs, patients with obesity may have a survival benefit. This apparently runs counter to the epidemiology of obesity, which may increase the risk for non-communicable diseases (NCDs). The scientific community is split on obesity paradox, with some supporting it, while others call it BMI paradox. This review: (a) defines the obesity paradox, and its proposed role in overall mortality in NCDs; (b) delineates evidence for and against obesity paradox; (c) presents the importance of using different indices of body mass to assess the risk in NCDs; (d) examines the role of metabolically healthy obesity in obesity paradox, and emerging importance of cardio-respiratory fitness (CRF) as an independent predictor of CVD risk and all-cause mortality in patients with/without obesity. Evidence suggests that the development of obesity and insulin resistance are influenced by genetic (or ethnic) make up and dietary habits (culture) of the individuals. Hence, this review presents lean diabetes, which has higher total CVD and non-CVD mortality as compared to diabetics with obesity and the possibility of maternal factors programming cardiometabolic risk during fetal development, which may lead to a paradigm shift in our understanding of obesity.

Association of Initial Muscle Fitness with Weight Loss and Metabolically Healthy Status in Children and Adolescents with Obesity: A Retrospective Study.

This retrospective study aimed to investigate the association of initial muscular fitness (MF) with weight loss and metabolic health status in 282 children and adolescents with obesity during 3 to 4 weeks of diet- and exercise-based interventions. Metabolically healthy obesity (MHO) definitions established in 2023 and MF standards based on the 2021 Chinese children's grip strength grading were applied. The proportion of metabolically unhealthy obesity (MUO) was higher in the high MF group than in their low MF counterparts at baseline. After the intervention, neither group transitioned from MUO to MHO due to the high frequency of low HDL-C. High MF females showed a higher percentage of high systolic blood pressure (SBP) than low MF females before and after intervention. High MF males exhibited greater improvements in waist circumference, hip circumference, waist-hip ratio, triglycerides, total cholesterol, and LDL-C than low MF males. The benefits of weight loss and blood lipids obtained by males are more evident than those obtained by females under the same MF level. Thus, attention should be paid to females during weight loss regardless of MF levels. Precision therapy should prioritize the management of blood pressure and avoid excessive reduction in HDL-C levels to sustain metabolic health.

Relationship between Body Adiposity Indices and Reversal of Metabolically Unhealthy Obesity 6 Months after Roux-en-Y Gastric Bypass.

The factors determining the reversal of metabolically unhealthy obesity (MUO) to metabolically healthy obesity (MHO) after Roux-en-Y gastric bypass (RYGB) are not completely elucidated. The present study aims to evaluate body adiposity and distribution, through different indices, according to metabolic phenotypes before and 6 months after RYGB, and the relationship between these indices and transition from MUO to MHO. This study reports a prospective longitudinal study on adults with obesity who were evaluated before (T0) and 6 months (T1) after RYGB. Bodyweight, height, waist circumference (WC), BMI, waist-to-height ratio (WHR), total cholesterol (TC), HDL-c, LDL-c, triglycerides, insulin, glucose, HbA1c and HOMA-IR were evaluated. The visceral adiposity index (VAI), the conicity index (CI), the lipid accumulation product (LAP), CUN-BAE and body shape index (ABSI) were calculated. MUO was classified based on insulin resistance. MUO at T0 with transition to MHO at T1 formed the MHO-t group MHO and MUO at both T0 and T1 formed the MHO-m and MUO-m groups, respectively. At T0, 37.3% of the 62 individuals were classified as MHO and 62.7% as MUO. Individuals in the MUO-T0 group had higher blood glucose, HbA1c, HOMA-IR, insulin, TC and LDL-c compared to those in the MHO-T0 group. Both groups showed significant improvement in biochemical and body variables at T1. After RYGB, 89.2% of MUO-T0 became MHO (MHO-t). The MUO-m group presented higher HOMA-IR, insulin and VAI, compared to the MHO-m and MHO-t groups. CI and ABSI at T0 correlated with HOMA-IR at T1 in the MHO-t and MHO-m groups. CI and ABSI, indicators of visceral fat, are promising for predicting post-RYGB metabolic improvement. Additional studies are needed to confirm the sustainability of MUO reversion and its relationship with these indices.

A Bayesian network analysis of the probabilistic relationships between metabolically healthy obesity and cardiovascular disease risk under new diagnostic criteria.

BACKGROUND: The relationship between metabolically healthy obesity (MHO) and cardiovascular disease (CVD) risk remains debated. The critical point may be the lack of consensus on MHO's definition and diagnostic criteria. OBJECTIVE: This study aimed to investigate the association of MHO status with arteriosclerosis-CVD (ASCVD) risk in Chinese under new diagnostic criteria. METHODS: Participants aged 35-79 in the 2009 China Health and Nutrition Survey cohort were included. The 10-year ASCVD risk was predicted by the prediction for ASCVD risk in China, and participants with a predicted risk of ⩾ 10% were classified into the high-risk group. The Bayesian network (BN) models were constructed to characterize the multivariable probabilistic connections between metabolically obesity phenotypes and ASCVD risk. RESULTS: The 10-year ASCVD risk score and the proportion of individuals at ASCVD high risk were significantly different between metabolically obesity phenotypes (P< 0.001). BN reasoning results showed that MHO individuals were not significantly associated with a 10-year ASCVD risk. Among metabolically unhealthy individuals, the conditional probability of high ASCVD risk increased with the Body Mass Index (BMI), with the conditional probability of high ASCVD risk was 24.63% (95% CI: 22.81-26.55%), 32.97% (95% CI: 30.75-35.27%) and 40.2% (95% CI: 36.64-43.86%) for metabolically unhealthy normal weight (MUNW), metabolically healthy overweight weight (MHOW), and metabolically unhealthy obesity (MUO) group, respectively. Subgroup analysis showed that MHO individuals were at increased risk of CVD compared with metabolically healthy normal weight (MHNW) individuals only in females. CONCLUSION: These results showed that there was no significant increase in ASCVD risk of MHO phenotype based on the new diagnostic criteria, suggesting that MHO is in a relatively healthy state.

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.

Preoperative nonalcoholic steatohepatitis and resolution of metabolic comorbidities after bariatric surgery.

BACKGROUND: Most patients undergoing bariatric surgery demonstrate elements of the metabolic syndrome (MetS) and can therefore be diagnosed with metabolically unhealthy obesity (MUO). Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) as hepatic manifestations of the MetS occur in many patients with obesity, but their leverage on postoperative improvement to Metabolic Health (MH), defined as absence of any metabolic comorbidity, remains unclear. OBJECTIVES: The aim of this study was to assess the influence of liver health status, operative procedure, and sex on postoperative switch from a MUO to an MH phenotype. Secondary objective was weight loss to MH. SETTING: University Hospital, Germany. METHODS: Patients who underwent either Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) at our obesity surgery center were included in this retrospective study. Liver biopsy was taken and evaluated for presence of NAFLD/NASH. For diagnosis of MH, blood pressure and laboratory values referring to the MetS were assessed preoperatively and at 3, 6, 12, and 24 months' postoperatively. RESULTS: One hundred thirty-three patients (73% female) with a mean body mass index of 52.0 kg/m2 and mean age of 43 years were included in this study. A total of 55.6% underwent RYGB and 44.4% underwent SG. NAFLD was found in 51.1% of patients and NASH in 33.8%. All patients were diagnosed MUO at baseline. Postoperatively, 38.3% patients (n = 51) switched to a MH condition. Mean time to MH was 321 days and mean excess body mass index loss to MH was 63.8%. There were no differences regarding liver health status, operative procedure, or sex. CONCLUSIONS: Bariatric surgery can resolve MUO independent of liver health status, operative procedure, and sex. However, patients should be closely monitored to ensure sustainable long-term outcomes following the switch to the MH condition.

Circulating proteomic profiles in women with morbid obesity compared to normal-weight women.

In this study, we aimed to evaluate circulating proteomic levels in women with morbid obesity (MO) compared to normal-weight (NW) women. Moreover, we have compared the proteomic profile between women with metabolically healthy (MH) MO and those with type 2 diabetes mellitus (T2DM). The study included 66 normal-weight (NW) women and 129 women with MO (54 MH and 75 with T2DM). Blood samples were processed for proteomics, involving protein extraction, quantification, digestion with peptide labelling and Nano (liquid chromatography (LC)-(Orbitrap) coupled to mass/mass spectrometry (MS/MS) analysis. Statistical analyses were performed. We identified 257 proteins. Women with MO showed significantly increased levels of 35 proteins and decreased levels of 45 proteins compared to NW women. Enrichment analysis of metabolic pathways revealed significant findings. Women with MO have an altered proteomic profile compared to normal-weight women, involving proteins significantly related to chylomicron assembly, complement cascade, clotting pathways and the insulin growth factor system. Regarding women with MO and T2DM compared to MHMO women, the proteomic profile shows alterations in mostly the same pathways associated with obesity. These findings confirmed in previous reports can help us better understand the pathophysiology of obesity and associated diseases. SIGNIFICANCE: Women with morbid obesity (MO) exhibit substantial proteomic alterations compared to normal-weight (NW) women, involving 80 proteins. These alterations are linked to significant metabolic pathways, including chylomicron assembly, complement cascade, clotting pathways and the insulin growth factor system. Additionally, women with MO and type 2 diabetes mellitus (T2DM) compared to metabolically healthy MO women share similar proteomic changes than the first comparison. These findings enhance our understanding of the pathophysiology of obesity and associated diseases, offering potential targets for therapeutic intervention.

Association of serum calcium and metabolically healthy obese in US adults: a cross-sectional study.

OBJECTIVES: The relationship between serum calcium and occurrence of MHO (metabolically healthy obesity) and MUNO (metabolically unhealthy non-obesity) remains unclear, and distinguishing these two phenotypes is difficult within primary healthcare units. This study explores that relationship. METHODS: This survey included 28590 adults from the National Health and Nutrition Examination Survey (NHANES) 2001-2018. Obesity phenotypes were categorized based on BMI and presence or absence of metabolic syndrome components. Weighted multivariate logistic regression analyses were used to assess the association between serum calcium levels and the obesity phenotype. Restricted cubic spline analysis characterized dose-response relationships, and stratified analyses explored these relationships across sociodemographic and lifestyle factors. RESULTS: The overall prevalence of MHO and MUNO were 2.6% and 46.6%, respectively. After adjusting for covariates, serum calcium exhibited a negative association with MHO [OR (95%): 0.49 (0.36,0.67), p < 0.001], while exhibiting a positive association with MUNO [OR (95%): 1.48 (1.26,1.84), p < 0.001]. Additionally, we found a non-linear association between serum calcium levels and the incidences of MHO and MUNO. Stratified analyses demonstrated a strong negative correlation between serum calcium levels and MHO occurrence across various subgroups. There was no significant interaction between calcium and stratified variables except sex; the association between calcium and the occurrence of MHO was remarkable in female patients. Meanwhile, the predictive ability of serum calcium level for the occurrence of MUNO among all patients was consistent across various subgroups. There was a significant interaction between calcium level and stratified variables based on age, sex, race, and smoking status; the association was remarkable in older (≥ 40 years old), white, none or less smoking, and female patients. CONCLUSIONS: A significant correlation was identified between serum calcium levels and MHO or MUNO. The findings suggest that serum calcium levels may serve as an indicator for more accurate assessment and diagnosis of MUNO and MHO, especially among individuals with abdominal obesity.

Serum calcium levels exhibited an inverse relationship with metabolically healthy obesity (MHO) and a positive relationship with metabolically unhealthy non-obese (MUNO).A nonlinear association exists between serum calcium levels and the incidence of both MHO and MUNO.Serum calcium has the potential to enhance evaluation and screening for MUNO or MHO in the general US adult population.

Irruption of Network Analysis to Explain Dietary, Psychological and Nutritional Patterns and Metabolic Health Status in Metabolically Healthy and Unhealthy Overweight and Obese University Students: Ecuadorian Case.

BACKGROUND: The association between dietary nutritional patterns, psychological factors, and metabolic health status has not been investigated in university students. There are studies that include numerous variables to test hypotheses from various theoretical bases, but due to their complexity, they have not been studied in combination. The scientific community recognizes the use of Gaussian graphical models (GGM) as a set of novel methods capable of addressing this. OBJECTIVE: To apply GGMs to derive specific networks for groups of healthy and unhealthy obese individuals that represent nutritional, psychological, and metabolic patterns in an Ecuadorian population. METHODOLOGY: This was a quantitative, non-experimental, cross-sectional, correlational study conducted on a sample of 230 obese/overweight university students, selected through a multi-stage random sampling method. To assess usual dietary intake, a Food Frequency Questionnaire (FFQ) was used; to evaluate psychological profiles (anxiety, depression, and stress), the DASS-21 scale was employed; blood pressure and anthropometric data were collected; and insulin levels, lipid profiles, and glucose levels were determined using fasting blood samples. The International Diabetes Federation (IDF) criteria were applied to identify metabolically healthy and unhealthy individuals. Statistical analysis relied on univariate methods (frequencies, measures of central tendency, and dispersion), and the relationships were analyzed through networks. The Mann-Whitney U test was used to analyze differences between groups. RESULTS: In metabolically unhealthy obese individuals, GGMs identified a primary network consisting of the influence of waist circumference on blood pressure and insulin levels. In the healthy obese group, a different network was identified, incorporating stress and anxiety variables that influenced blood pressure, anthropometry, and insulin levels. Other identified networks show the dynamics of obesity and the effect of waist circumference on triglycerides, anxiety, and riboflavin intake. CONCLUSIONS: GGMs are an exploratory method that can be used to construct networks that illustrate the behavior of obesity in the studied population. In the future, the identified networks could form the basis for updating obesity management protocols in Primary Care Units and supporting clinical interventions in Ecuador.

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 control subjects (CON), and metabolically unhealthy obesity (MUO; N = 80; n = 20/group). Serum indicators of intestinal permeability and inflammation were assessed by 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 vs. CON/NWO (P < 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 < 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 with 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.NEW & NOTEWORTHY This is the first study to our knowledge to examine biomarkers of intestinal permeability in normal-weight obesity and one of few assessing microbiota compositions in this population. Additionally, we report that individuals with metabolically healthy obesity and metabolically unhealthy obesity displayed similar evidence of intestinal permeability, which was more strongly associated with systemic inflammation than total and visceral adipose tissue mass.

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.

Trends and characteristics of the metabolically healthy obese phenotype in an Arab population.

The metabolically healthy obesity (MHO) phenotype represents a complex and distinctive trait, the trends and characteristics of which remain unknown in the Saudi Arabian adult population. The present study aims to fill that gap. A combined total of 10,220 Saudi adults from 2 independent cohorts [2008-2019, N = 7,896 (2,903 males and 4,993 females), and 2021-2023, N = 2,324 (830 males and 1,494 females)] aged 19-70 years old was screened, of whom 9,631 (3,428 males and 6,203 females) were included. Anthropometric data were measured, and fasting blood samples were collected to assess glucose, lipids, adipocytokines and inflammatory markers using routine methods and commercially available assays. Obesity was defined as a body mass index (BMI) ≥30 kg/m2. Screening for MHO was done using the empiric definition proposed by Zembic and colleagues and the by the National Cholesterol Education Program's Adult Treatment Panel III (NCEP ATPIII). Of the 3,949 (41.0%) participants with obesity, 33.4% (95% confidence interval, CI, 32-35) were considered MHO using the empiric definition, and 32.8% (95% CI, 31-34) using NCEP-ATPIII. The overall age and gender adjusted prevalence of MHO in the Saudi adult population was 31.6% (95% CI, 30-33) and 30.1% (29-31) by the two definitions, respectively. Females had a higher age-adjusted prevalence of MHO than males (OR = 1.22, 95% CI 1.1-1.4, p = 0.009) as per the ATPIII criteria. MHO prevalence substantially increased over time from 2008 to 2023 (p < 0.001) for both definitions. Circulating leptin levels and insulin resistance were significantly higher in the MUO group than the MHO group independent of the definition used, suggesting the presence of a more severe form of leptin resistance in the MUO group which may explain the worse cardiometabolic profile as compared to the MHO group. In summary, the study highlights the first time the characteristics and trends of the MHO phenotype among Saudi Arabian adults. The pluripotent effects of leptin and its resistance may be central to MHO's progression, or lack thereof, to the MUO phenotype, and this needs further investigation.

The prevalence of metabolically healthy obesity and its transition into the unhealthy state: A 5-year follow-up study.

People with metabolically healthy obesity (MHO) are at risk of developing cardiometabolic diseases. We investigated the prevalence of MHO and factors influencing its transition into a metabolically unhealthy state (MUS). This study was conducted as part of the Kerman Coronary Artery Disease Risk Factor Study (KERCADRS). From 2014 to 2018, 9997 people were evaluated. The obesity and metabolic status of the MHO participants were re-examined after 5 years of their initial participation in the study. Out of 347 MHO, 238 individuals were accessed at follow-up. Twenty-nine (12.2%) had metabolic unhealthy normal weight (MUNW), 169 (71.0%) had metabolic unhealthy obesity (MUO), and the others had healthy metabolic state. Among age, total cholesterol, diastolic blood pressure and triglyceride (TG) variables, the baseline serum TG level was associated with a significant increase in the risk of developing MUS during 5 years (p <.05). The TG level optimal cut-off point for predicting the development into MUS was 107 mg/dL with 62.1% sensitivity and 77.5% specificity (AUC = 0.734, p <.001). A high percentage of MHO people transit into MUS during 5 years. A TG level higher than 107 mg/dL can help to identify people at a higher risk of developing into MUS.

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.

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.

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.

Rising Trends in Metabolically Healthy Obesity in Cancer Patients and Its Impact on Cardiovascular Events: Insights from a Contemporary Nationwide Analysis in the USA (2016-2020).

Background: Obesity or overweight raises the risk of developing 13 types of cancer, representing 40% of all cancers diagnosed in the United States annually. Given the ongoing debate surrounding the impact of metabolically healthy obesity (MHO) on cardiovascular outcomes, it is crucial to comprehend the incidence of Major Adverse Cardiovascular and Cerebrovascular Events (MACCEs) and the influence of MHO on these outcomes in cancer patients. Methods: Data of hospitalized cancer patients with and without obesity were analyzed from the National Inpatient Sample 2016-2020. Metabolically healthy patients were identified by excluding diabetes, hypertension, and hyperlipidemia using Elixhauser comorbidity software, v.2022.1. After that, we performed a multivariable regression analysis for in-hospital MACCEs and other individual outcomes. Results: We identified 3,111,824 cancer-related hospitalizations between 2016 and 2020. The MHO cohort had 199,580 patients (6.4%), whereas the MHnO (metabolically healthy non-obese) cohort had 2,912,244 patients (93.6%). The MHO cohort had a higher proportion of females, Blacks, and Hispanics. Outcomes including in-hospital MACCEs (7.9% vs. 9.5%; p < 0.001), all-cause mortality (6.1% vs. 7.5%; p < 0.001), and acute myocardial infarction (AMI) (1.5% vs. 1.6%; p < 0.001) were lower in the MHO cohort compared to the MHnO cohort. Upon adjusting for the baseline characteristics, the MHO group had lower odds of in-hospital MACCEs [adjusted odds ratio (AOR) = 0.93, 95% CI (0.90-0.97), p < 0.001], all-cause mortality [AOR = 0.91, 95% CI (0.87-0.94); p < 0.001], and acute ischemic stroke (AIS) [AOR = 0.76, 95% CI (0.69-0.84); p < 0.001], whereas there were higher odds of acute myocardial infarction (AMI) [AOR = 1.08, 95% CI (1.01-1.16); p < 0.001] and cardiac arrest (CA) [AOR = 1.26, 95% CI (1.01-1.57); p = 0.045] in the MHO cohort compared to the MHnO cohort. Conclusions: Hospitalized cancer patients with MHO exhibited a lower prevalence of in-hospital MACCEs than those with MHnO. Additional prospective studies and randomized clinical trials are imperative to validate these findings, particularly in stratifying MHO across various cancer types and their corresponding risks of in-hospital MACCEs.

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.