Metabolically Healthy Obese Research Articles

Comparison of Esophageal Dysmotility and Reflux Burden in Patients with Different Metabolic Obesity Phenotypes Based on High-Resolution Impedance Manometry and 24-h Impedance-pH

Introduction: The relationship between the metabolically healthy obesity (MHO) phenotype and the occurrence of gastroesophageal reflux disease (GERD) and inefficient esophageal motility (IEM) is still unclear. Thus, we assessed the association between different metabolic obesity phenotypes and GERD and IEM using empirical data. Methods: We collected clinical and test data of 712 patients, including 24-h multichannel intraluminal impedance-pH (24-h MII-pH) monitoring, high-resolution manometry (HRM), and endoscopy. We divided 567 individuals into four categories according to their metabolic obesity phenotype: metabolically unhealthy non-obesity (MUNO), metabolically unhealthy obesity (MUO), metabolically healthy non-obesity (MHNO), and MHO. We compared differences in the 24-h MII-pH monitoring, HRM, and endoscopy findings among the four metabolic obesity phenotypes. Results: Patients with the MUNO, MHO, or MUO phenotype showed a greater risk of IEM and GERD (pathologic acid exposure time [AET] >6%) compared with patients with the MHNO phenotype. Regarding the HRM results, patients with the MHNO or MUNO phenotype had a lower integrated relaxation pressure, esophageal sphincter pressure, and esophagogastric junction contractile integral, and more ineffective swallows than patients with the MHO or MUO phenotype (p < 0.05). In terms of 24-h MII-pH, patients with the MHO or MUO phenotype had a higher total, upright, and supine AET; a higher total number of reflux episodes (TRs); and a lower mean nocturnal baseline impedance and post-reflux swallow-induced peristaltic wave index compared with those with the MHNO or MUNO phenotype (all p < 0.05). Considering the odds ratio of 19.086 (95% confidence interval 6.170–59.044) for pathologic AET and 3.659 (95% confidence interval 1.647–8.130) for IEM, patients with the MUO phenotype had the greatest risk after adjusting for all confounding variables. Conclusion: Obesity and metabolic disorders increase the risk of GERD and IEM. Obesity has a greater impact on esophageal dysmotility and pathologic acid exposure than metabolic diseases.

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

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. This study aimed to investigate the association of MHO status with arteriosclerosis-CVD (ASCVD) risk in Chinese under new diagnostic criteria. 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. 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. 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.

Association of dietary behavior patterns of middle-aged and older adults with their obesity metabolic phenotype: a cross-sectional study

BackgroundMiddle-aged and elderly individuals are the most susceptible groups for metabolic diseases, with their dietary behaviors being significant influencing factors. Exploring the association between overall dietary behaviors and obesity metabolic phenotypes is crucial for early prevention and control of chronic diseases, precision treatment and personalized interventions.MethodsWe conducted a cross-sectional study of 15,160 middle-aged and older adults between June 2019 and August 2021 to collect information on their body mass index (BMI), biochemical indices and disease history. The population was classified into four categories by the criteria of obesity metabolic phenotypes: metabolically healthy non-obesity (MHNO), metabolically unhealthy non-obesity (MUNO), metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). Scores were calculated based on compliance with healthy eating behavior patterns (appropriate or light dietary taste, moderately soft and hard food, slightly hot food temperature, medium or slow eating speed, daily intake of dietary supplements and eating with others), and the population was categorized into subgroups 0–2 (did not meet and met only 1 or 2), 3–4 (met 3 or 4), 5–6 (met 5 or 6). The relationship between dietary behavior patterns and different obesity metabolic phenotypes in middle-aged and elderly people were analyzed by multi-categorical logistic regression model.ResultsCompared with the 5–6 subgroup, the dietary behavior patterns of 0–2 and 3–4 scores were risk factors for MUNO, MHO and MUO (P < 0.05), and the lower the scores of the dietary behavior patterns, the higher the multiplicity of the occurrence of MUNO, MHO and MUO, especially for females and adults between 45–60 years old. Appropriate or light dietary taste, moderately soft and hard food, and slightly hot food temperature were protective factors for MUNO and MUO (P < 0.05); medium or slow eating speed and daily intake of dietary supplements were protective factors for MUNO, MHO and MUO (P < 0.05).ConclusionDietary behavior patterns in middle-aged and older adults are associated with different obesity metabolic phenotypes, and healthy dietary behaviors may be beneficial for the prevention and control of MUNO, MHO and MUO.

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.

Association betweentransitions in metabolic health and colorectal cancer across categories of body size phenotype: a prospective cohort study.

We aimed to investigate the associations of changes in metabolic health across categories of body size phenotype with the risk of colorectal cancer in a community-based prospective cohort. In the current study, a total of 70,987 participants were included. Changes in metabolic health across categories of body size phenotype were assessed between the health examination for the first time in the years 2006 through 2009 and a 2010/2011 health examination. A multivariate Cox proportional hazards model was used to assess the associations of changes in metabolic health across body size phenotype categories with risk of colorectal cancer. During the median follow-up time of 11.04 years, 428 (0.60%) participants developed colorectal cancer. Compared with metabolically healthy normal-weight (MHNW) participants who remained MH, the risk of colorectal cancer was increased by 144% (95% CI: 1.21-4.95) for participants with metabolically healthy obesity (MHO) who converted to a metabolically unhealthy (MU) phenotype. Participants who were MU at baseline were still at increased risk of colorectal cancer, regardless of obesity status. The MHO phenotype was a dynamic status over time, and converting to MU during follow-up and being initially MU were associated with having an increased risk of colorectal cancer, regardless of degree of obesity and body size phenotype.

Comparison of cardiovascular disease risk association with metabolic unhealthy obesity identified by body fat percentage and body mass index: Results from the 1999-2020 National Health and Nutrition Examination Survey.

Cardiovascular disease (CVD) risk among individuals across different categories of metabolic obesity phenotypes is controversial. The study used body fat percentage (BFP) or body mass index (BMI) to categorize obese status and to investigate the association between metabolic obesity phenotypes and CVD risk in a nationally representative population. This cross-sectional study included 49463 adult participants in National Health and Nutrition Examination Survey from 1999 to 2020. Metabolic healthy status was defined by the absence of metabolic syndrome according to the revised National Cholesterol Education Program Adult Treatment Group definition. Obesity was identified by BFP, assessed by dual-energy X-ray absorptiometry scan, and BMI. The primary outcome was CVD prevalence. The multivariable logistic regression model and restricted cubic spline analyses were used to examine the associations between metabolic obesity phenotypes and the risk of CVD. Among 49463 adult participants, 32.12% were metabolically unhealthy, 34.10% were overweight, 37.94% were obese; and 8.41% had CVD. Compared with metabolic healthy normal weight, metabolic healthy obesity, and metabolic unhealthy normal weight/overweight/obesity were all associated with increased CVD risk with adjusted odds ratios (95% confidence intervals) of 1.45 (1.14-1.85), 2.80(1.53-5.11), 2.55(1.88-3.47), and 2.96(2.18-4.02), respectively. Nonlinear dose-response relationships between BFP and CVD were observed both in metabolically healthy and unhealthy participants (both P for non-linearity<0.0001). When obesity was defined with BMI, there were a similar prevalence of obesity, and similar associations between metabolic obesity phenotypes and CKD risks. Metabolic healthy and unhealthy obesity were both associated with higher risks of CVD, whether using BFP or BMI to define obese status. It suggests that metabolic obesity phenotype is a risk factor for CVD.

Cushing’s syndrome screening with the 1-mg dexamethasone suppression test in metabolically healthy and unhealthy obesity phenotypes

BackgroundThe ongoing debate regarding the need for screening Cushing’s syndrome (CS) in patients with obesity continues. The objectives of this study were to establish the prevalence of CS in the population with obesity and assess how metabolic health status influences cortisol levels following the 1 mg dexamethasone suppression test (DST).MethodsThis retrospective study included 1008 patients with obesity who underwent screening with the 1 mg DST for CS. These patients were categorized into two groups as metabolically healthy obesity (MHO) and unhealthy obesity (MUO).ResultsOut of the 1008 patients, 779 (77.3%) belonged to the MUO group. Within the entire study cohort, 12 (1.2%) patients exhibited a cortisol level of ≥ 1.8 after the 1 mg DST. Cortisol levels following the 1 mg DST were also significantly higher in the MUO group than in the MHO group (p = 0.001). Among these 12 patients, 11 were presenting a MUO phenotype. Hypercortisolism was definitively diagnosed in two patients, resulting in an overall prevalence of 0.2%. The 1 mg DST demonstrated a specificity of 99% and 100% sensitivity for screening for CS.ConclusionsWhile the 1 mg DST is a practical screening test for CS with high specificity in obesity, the number of CS cases detected remains relatively low. Therefore, it may be more reasonable and applicable to screen patients with MUO phenotype rather than all individuals with obesity.

Association between metabolic obesity phenotypes and the risk of developing prostate cancer: a propensity score matching study based on Xinjiang.

Obesity-induced metabolic dysfunction increases the risk of developing tumors, however, the relationship between metabolic obesity phenotypes and prostate cancer (PCa) remains unclear. The term metabolic obesity phenotypes was introduced based on metabolic status and BMI categories. Participants were categorized into four groups: metabolically healthy nonobesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy nonobesity (MUNO), and metabolically unhealthy obesity (MUO). Propensity score matching was conducted based on age, ethnicity, marriage, etc. Univariate and multivariate conditional logistic regression analyses were used to assess the relationship between metabolic obesity phenotypes, metabolic risk factors, and PCa. Sensitivity analysis was performed to verify the robustness of the results. After propensity score matching among 564 PCa patients and 1418 healthy individuals, 209 were selected for each of the case and control groups. There were no statistically significant differences in the basic characteristics between the two groups. Univariate and multivariate conditional logistic regression suggested that the risk of developing PCa in both MHO and MUO individuals was higher than in MHNO individuals. Specifically, the risk of developing PCa in MHO individuals was 2.166 times higher than in MHNO individuals (OR=2.166, 95%CI: 1.133-4.139), and the risk in MUO individuals was is 2.398 times higher than in MHNO individuals(OR=2.398, 95%CI:1.271-4.523). Individuals with hyperglycemia and elevated triglycerides also had a higher risk of developing PCa (hyperglycemia:OR=1.488, 95%CI: 1.001-2.210; elevated triglycerides: OR=2.292, 95%CI: 1.419-3.702). Those with more than or equal to three metabolic risk factors had an increased risk of PCa (OR=1.990, 95%CI: 1.166-3.396). Sensitivity analysis indicated an increased risk of PCa in MUO individuals compared to MHNO individuals. In this retrospective study, individuals with MHO and MUO had a higher risk of developing PCa.

Prevalence and factors associated with unhealthy metabolic status according to body mass index: analysis of a national nutritional survey

IntroductionAlthough obesity substantially influences public health owing to related comorbidities, it has been discovered that the incidence of such issues is not directly related to obesity but to the patient’s unhealthy metabolic status (MUS) independent of the body mass index (BMI).ObjectivesTo describe the prevalence of UMS overall and according to BMI and determine the factors associated with it.MethodsA cross-sectional analytical study was used based on the analysis of secondary databases called the Life Stage Nutritional Surveillance Survey (VIANEV). Participants were selected in two stages, finally obtaining 885 participants. UMS was defined based on the criteria of the Adult Treatment Panel III used to define metabolic status in a set of 5 parameters, if the subject presented two or more alterations it was considered UMS. Six groups were formed according to BMI: metabolically healthy, average weight (MHNW) and unhealthy (MUNW), metabolically healthy, overweight (MHOW) and unhealthy (MUOW), metabolically healthy, obese (MHO) and unhealthy (MUO).).ResultsThe total prevalence of UMS was 73.11%, with MUNW, MUOW, and MUO being 47.90%, 80.34%, and 96.44%, respectively. Only 5.31% did not present any metabolic alteration. The multivariable analysis found variations globally according to sex, age, marital status, geographical region, smoking habit, and altitude.ConclusionsA high prevalence of UMS was observed in Peru, indicating that BMI alone is not a sufficient indicator of metabolic status. These findings suggest that strategies should be prioritized to address the growing problem of UMS, considering the particularities of each subpopulation and using a multifaceted approach that addresses modifiable and non-modifiable risk factors.

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.

Neurological-related proteomic profiling in plasma of children with metabolic healthy and unhealthy overweight/obesity.

Children with overweight/obesity (OW/OB) exhibit poor cardiometabolic health, yet mechanisms influencing brain health remain unclear. We examined the differences in neurological-related circulating proteins in plasma among children with metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) and the association with metabolic syndrome markers. In this cross-sectional study, we included 84 Caucasian children (39% girls), aged 10.1 ± 1.1 years, from the ActiveBrains project (NCT02295072). A ninety-two-protein targeted approach using Olink's® technology was used. We identified distinct concentrations of CD38, LAIR2, MANF and NRP2 proteins in MHO compared with MUO. Moreover, individual metabolic syndrome (MS) markers were linked to nine proteins (CD38, CPM, EDA2R, IL12, JAMB, KYNU, LAYN, MSR1 and SMOC2) in children with OW/OB. These proteins play crucial roles in diverse biological processes (e.g., angiogenesis, cholesterol transport, nicotinamide adenine dinucleotide (NAD+) catalysis and maintenance of blood-brain barrier) related to brain health. Our proteomics study suggests that cardiometabolic health (represented by MHO/MUO or individual MS markers) is associated with the concentration in plasma of several proteins involved in brain health. Larger-scale studies are needed to contrast/confirm these findings, with CD38 standing out as a particularly noteworthy and robust discovery.

Cardiovascular disease among bariatric surgery candidates: coronary artery screening and the impact of metabolic syndrome

BackgroundObesity is known as a risk factor for cardiovascular disease (CVD). However, there is an absence of preoperative cardiac risk assessment in bariatric surgery candidates and the incidence of CVD among these high-risk patients is still unknown.MethodsA consecutive series of bariatric surgery candidates at two Chinese tertiary hospitals received coronary CT angiography or coronary angiography from 2017 to 2023. Patients were categorized as metabolically unhealthy obesity (MUO) and metabolically healthy obesity (MHO) based on the presence or absence of MetS. CVD was diagnosed based on the maximum intraluminal stenosis > 1% in any of the segments of the major epicardial coronary arteries. Obstructive CVD was defined as coronary stenosis ≥ 50%. Binary multivariable logistic regression was performed to analyze the association between CVD and metabolic status. The number of principal MetS components was categorized into zero (without glycemic, lipid, and BP components), one (with one of the components), two (with any two components), and three (with all components) to explore their association with CVD.ResultsA total of 1446 patients were included in the study. The incidence of CVD and obstructive CVD were 31.7% and 9.6%. Compared with MHO patients, MUO patients had a significantly higher incidence of mild (13.7% vs. 6.1%, P < 0.05), moderate (7.4% vs. 0.8%, P < 0.05), and severe CVD (3.1% vs. 0%, P < 0.05). Following complete adjustment, compared with zero or one component, two principal MetS components was found to be associated with a notable increase in the risk of CVD (OR 2.05, 95% CI 1.18–3.58, P < 0.05); three principal MetS components were observed to have a higher risk of CVD and obstructive CVD (OR 2.68, 95% CI 1.56–4.62, P < 0.001; OR 3.93, 95% CI 1.19–12.93, P < 0.05). Each increase in the number of principal MetS components correlated with a 1.47-fold (95% CI 1.20–1.81, P < 0.001) and 1.78-fold (95% CI 1.24–2.55, P < 0.05) higher risk of CVD and obstructive CVD, respectively.ConclusionThis study reported the incidence of CVD based on multicenter bariatric surgery cohorts. CVD is highly prevalent in patients with obesity, especially in MUO patients. Increased number of principal MetS components will significantly elevate the risk of CVD.

Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores.

East Asian populations exhibit a genetic predisposition to obesity, yet comprehensive research on these traits is limited. We conducted a genome-wide association study (GWAS) with 93,673 Korean subjects to uncover novel genetic loci linked to obesity, examining metrics such as body mass index, waist circumference, body fat ratio, and abdominal fat ratio. Participants were categorized into non-obese, metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) groups. Using advanced computational methods, we developed a multifaceted polygenic risk scores (PRS) model to predict obesity. Our GWAS identified significant genetic effects with distinct sizes and directions within the MHO and MUO groups compared with the non-obese group. Gene-based and gene-set analyses, along with cluster analysis, revealed heterogeneous patterns of significant genes on chromosomes 3 (MUO group) and 11 (MHO group). In analyses targeting genetic predisposition differences based on metabolic health, odds ratios of high PRS compared with medium PRS showed significant differences between non-obese and MUO, and non-obese and MHO. Similar patterns were seen for low PRS compared with medium PRS. These findings were supported by the estimated genetic correlation (0.89 from bivariate GREML). Regional analyses highlighted significant local genetic correlations on chromosome 11, while single variant approaches suggested widespread pleiotropic effects, especially on chromosome 11. In conclusion, our study identifies specific genetic loci and risks associated with obesity in the Korean population, emphasizing the heterogeneous genetic factors contributing to MHO and MUO.

Cardiometabolic Health in Adolescents with Obesity: The Role of Protein Intake, Diet Quality, and Physical Activity.

Background: Although adolescents with obesity have an increased risk of cardiometabolic disease, a subset maintains a healthy cardiometabolic profile. Unhealthy lifestyle behaviors may determine cardiometabolic risk. We aimed to characterize the lifestyle behaviors of adolescents with obesity, compare differences between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO), and assess associations between lifestyle behaviors and cardiometabolic profiles. Methods: Participants aged 10-18 years with body mass index (BMI) ≥ 95th percentile were included. Dietary intake (DI) was estimated from 3-day food records, and diet quality (DQ) was assessed using the Healthy Eating Index-Canadian Adaptation. Physical activity (PA), body composition, anthropometrics, blood markers, and blood pressure (BP) were objectively measured. MUO was defined as having high triglycerides, BP, glucose, or low high-density lipoprotein. Regression analyses were performed between lifestyle behaviors and cardiometabolic markers. Results: Thirty-nine participants (BMI z-score 2.8 [2.5-3.5], age 12.5 [10.9-13.5] years, 56.4% female) were included. A high proportion of participants failed to meet lifestyle recommendations, particularly for DQ (94.7%, n = 36), fiber (94.7%, n = 36), and PA (90.9%, n = 30). No differences in lifestyle behaviors were found between MUO (59.0%, n = 22) and MHO (41.0%, n = 16). Protein intake was negatively associated with BMI and waist circumference z-scores, fat mass index, insulin resistance, low-density lipoprotein, and C-reactive protein, whereas higher DQ was associated with lower C-reactive protein. Higher light PA levels were associated with lower total cholesterol and triglycerides. Conclusion: Adolescents with either MUO or MHO displayed low adherence to DQ, DI, and PA recommendations; no differences in lifestyle behaviors were found. Protein intake, DQ, and PA were associated with a healthier cardiometabolic profile.

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.

Characteristics of the epicardial adipose tissue measured by low-dose chest computed tomography according to the metabolic health/obesity status

BackgroundEpicardial adipose tissue (EAT) is associated with the development of cardiovascular disease and long-term survival. This study aimed to assess the characteristics of EAT according to the metabolic health and obesity status using low-dose chest computed tomography (CT). MethodsA total of 1074 asymptomatic adults who underwent a medical health check-up were enrolled. Subjects were categorized into the following four groups according to the metabolic health and obesity status: metabolically healthy non-obese (MHNO); metabolically unhealthy non-obese (MUNO); metabolically healthy obese (MHO); and metabolically unhealthy obese (MUO). EAT on low-dose chest CT was measured by using automatic, quantitative measurement software. ResultsMUO showed the highest EAT volume and lowest EAT radiodensity in comparison with MHNO (p < 0.001). The MUNO (n = 70), MHO (n = 259), and MUO (n = 231) groups had increased EAT volume (β [95 % CI], 37.65 [23.11,52.18], 56.79 [47.56,66.02], 84.85 [74.59,95.11] respectively, all p < 0.001), decreased EAT radiodensity (β [95 % CI], − 3.22 [− 4.59,− 1.85], − 4.48 [− 5.30,− 3.66], − 6.03 [− 6.90,− 5.16] respectively, all p < 0.001) in comparison with the MHNO (n = 514) group by using multivariable linear regression models. ConclusionsBoth metabolic abnormalities and obesity were closely associated with EAT characteristics. Characteristics of EAT are similar in MHO and MUNO. This finding suggests that MHO is not a favorable condition in terms of cardiac health, as assessed by the characteristics of EAT. The combination of obesity and metabolically unhealthy status has a synergistic adverse effect on EAT. Measurement of EAT could be a useful imaging biomarker for evaluation of an individual's metabolic health/obesity status.

Metabolically healthy obesity: from epidemiologyand mechanisms to clinical implications.

The concept of metabolic health, particularly in obesity, has attracted a lot of attention in the scientific community, and is being increasingly used to determine the risk of cardiovascular diseases and diabetes mellitus-related complications. This Review assesses the current understanding of metabolically healthy obesity (MHO). First, we present the historical evolution of the concept. Second, we discuss the evidence for and against its existence, the usage of different definitions of MHO over the years and the efforts made to provide novel definitions of MHO. Third, we highlight epidemiological data with regard to cardiovascular risk in MHO, which is estimated to be moderately elevated using widely used definitions of MHO when compared with individuals with metabolically healthy normal weight, but potentially not elevated using a novel definition of MHO. Fourth, we discuss novel findings about the physiological mechanisms involved in MHO and how such knowledge helps to identify and characterize both people with MHO and those with metabolically unhealthy normal weight. Finally, we address how the concept of MHO can be used for risk stratification and treatment in clinical practice.

Metabolic obesity phenotypes and all-cause mortality among the Chinese oldest-old population: a prospective cohort study.

The obesity paradox has been reported among older adults. However, whether the favorable effect of obesity is dependent on metabolic status remains largely unknown. We aimed to explore the association of metabolic obesity phenotypes and their changes with all-cause mortality among the Chinese oldest-old population. This prospective cohort study included 1207 Chinese oldest old (mean age: 91.8 years). Metabolic obesity phenotypes were determined by central obesity and metabolic status, and participants were classified into metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), metabolically healthy non-obesity (MHN), and metabolically unhealthy non-obesity (MUN). The hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated by Cox regression models. During 5.3 years of follow-up, 640 deaths were documented. Compared with non-obesity, obesity was associated with a decreased mortality risk among participants with metabolically healthy (HR, 0.75; 95% CI, 0.63-0.91) while this association was insignificant among metabolically unhealthy. Compared to MHO, MHN (HR, 1.27; 95% CI, 1.06-1.53) and MUN (HR, 1.49; 95% CI, 1.10-2.02) were significantly associated with an increased mortality risk. Compared to those with stable MHO, those transited from MHO to MUO demonstrated a higher mortality risk (HR, 1.81; 95% CI, 1.06-3.11). MHO predicts better survival among the Chinese oldest-old population. These findings suggest that ensuring optimal management of metabolic health is beneficial and taking caution in weight loss based on the individual body weight for the metabolically healthy oldest-old adults.

Physical activity in relation to metabolic health and obesity: The Feel4Diabetes study.

To examine physical activity levels in association with metabolic health and estimate the stability of metabolically healthy obese (MHO) phenotypes over a 2-year period. In total, 2848 men and women from families at risk of the development of diabetes were recruited. Participants were classified as obese or non-obese and metabolic health was defined using five existing definitions. Physical activity was estimated with the International Physical Activity Questionnaire and pedometers. Prevalence of the MHO phenotype varied among definitions (0% to 20.2%). Overall, the MHO were more active than the metabolically unhealthy obese (MUO). Daily sitting hours (odds ratio [OR] = 1.055, 95% confidence interval [CI]: 1.009-1.104) and daily steps (per 500; OR = 0.934, 95% CI: 0.896-0.973) were remarkable predictors of metabolic health in individuals with obesity; and likewise, in individuals without obesity. After 2 years, 44.1% of baseline MHO adults transitioned to MUO, while 84.0% of the MUO at baseline remained at the same phenotype. Although physical activity was not a major determinant in phenotype transitioning, daily steps were associated with the maintenance of metabolic health over time in the non-obese group. A universally accepted definition for MHO is needed. Being physically active can contribute to a metabolically healthy profile even in the presence of obesity; still, MHO is a transient condition and physical activity alone may not be an adequate factor for its maintenance.

Association between metabolically healthy obesity and risk of dementia: A systematic review and meta‐analysis

AbstractBackgroundSome studies suggest that the metabolic alterations linked to obesity in humans may increase the likelihood of developing dementia. However, there are currently no existing meta‐analyses exploring the possible occurrence of dementia in patients with metabolically healthy obesity (MHO). In the present study, we aimed to investigate a potential correlation between individuals with MHO and the likelihood of developing dementia.MethodOur research focused on searching three databases to locate pertinent studies starting from January 10, 2023. We used sensitivity analysis, the Egger's test, and a visual funnel plot to separately assess the reliability of the research results and publication bias. We conducted this study according to a pre‐established protocol in the PROSPERO registry (CRD42023449459).ResultsWe incorporated three potential cohort investigations. Individuals with good metabolic health and obesity showed a decreased likelihood of developing dementia compared with those who were metabolically healthy but not obese (hazard ratio (HR) = 0.79; 95% confidence interval (CI) 0.68–0.92). In contrast, individuals who were metabolically unhealthy and nonobese had an increased likelihood of developing dementia (HR = 1.33; 95% CI 1.25–1.42), whereas the population with metabolically unhealthy obesity did not exhibit a notable association with dementia onset (HR = 1.06; 95% CI 0.83–1.37).ConclusionAccording to our meta‐analysis, patients with MHO had a reduced likelihood of developing general dementia and Alzheimer disease. Maintaining a well‐functioning metabolism can provide advantages in reducing the occurrence of dementia.