Peripheral vascular disease

The natural histories of obesity subphenotypes are incompletely delineated. To investigate dynamic changes in obesity subphenotypes and associations with outcomes. Framingham Offspring Cohort participants (n = 4291) who attended the examination cycles 2 (1979 to 1983) to 7 (1998 to 2001), which included 26,508 participant observations. Obesity subphenotypes [metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy nonobese (MUNO), and metabolically unhealthy obese (MUO)] were ascertained based on metabolic health (<2 Adult Treatment Panel III criteria). The outcomes were subclinical cardiovascular disease (CVD), incident diseases [diabetes, hypertension, chronic kidney disease (CKD), CVD], and all-cause mortality. At baseline, 4% and 31% of participants exhibited the MHO and MUNO subphenotypes, respectively. Four-year probability of MHO participants becoming MUO was 43% in women and 46% in men. Compared with MHNO, MHO participants had 1.28-fold (95% CI, 0.85 to 1.93) and 1.92-fold (95% CI, 1.38 to 2.68) higher odds of subclinical CVD and coronary artery calcification, respectively; corresponding values for MUNO were 1.95 (1.54 to 2.47) and 1.92 (1.38 to 2.68). During follow-up (median of 14 years), 231 participants developed diabetes, 784 hypertension, 423 CKD, 639 CVD, and 1296 died. Compared with MHNO, MHO conferred higher risks of diabetes [hazard ratio (HR), 4.69; 95% CI, 2.21 to 9.96] and hypertension (HR, 2.21; 95% CI, 1.66 to 2.94). Compared with MUO, MHO conferred lower risks of diabetes (0.21; 0.12 to 0.39), CVD (0.64; 0.43 to 0.95), and CKD (0.44; 0.27 to 0.73), but similar hypertension, cardiovascular mortality, and overall mortality risks. Over time, most MHO participants developed metabolic abnormalities and clinical disease. The MHO subphenotype is a harbinger of future risk.

ObjectiveThe relationship between obesity and hyperuricemia has been demonstrated by many studies. However, whether or to what extent metabolic condition influents the association between obesity and hyperuricemia was not clear. Here, we aimed to examine the association between obese-metabolic phenotype and hyperuricemia in a large sample of Chinese adults.MethodsAccording to BMI and metabolic syndrome, obese-metabolic phenotype was defined as metabolically unhealthy obesity (MUO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO) and metabolically healthy non-obesity (MHNO)in the Tianning cohort (N=5072). We conducted a cross-sectional analysis between obese-metabolic phenotype and hyperuricemia, followed by a Mendelian Randomization analysis using GWAS summary data to confirm the causality between uric acid and BMI.ResultsThe average level of serum UA showed 41.87-higher μmol/L in participants with MHO (β=41.87, P<0.001) and 63.18-higher μmol/L in participants with MUO (β=63.18, P<0.001), compared to those with MHNO. Compared to participants with MHNO, those with MUO had the highest likelihood to have hyperuricemia (OR=4.56, P<0.001), followed by those with MHO (OR=3.32, P<0.001). Mendelian randomization analysis indicated that uric acid was more likely to be a consequence of BMI (β=0.059, P=6.54×10−154).ConclusionMUO, in comparison with MHO, was significantly associated with hyperuricemia in Chinese adults.

BackgroundChina has the world’s highest rail transportation network density, and the prevalence of obesity among railway workers in China is more than twice that of adults in the world. Carotid artery plaque (CAP) is a simple and noninvasive predictor of early atherosclerosis, while the association between different obese phenotypes and CAP risk among Chinese male railway drivers is unclear.MethodsThis cross-sectional study was performed among 8,645 Chinese male railway drivers. Obese phenotypes were assessed based on the obesity status (the body mass index ≥ 28 kg/m2 as obesity vs. < 28 kg/m2 as non-obesity) and metabolic status (metabolically healthy vs. metabolically unhealthy). Metabolically unhealthy was defined as the presence of at least one dysfunction, including elevated blood pressure, elevated fasting blood glucose, elevated triglyceride, and reduced high-density-lipoprotein cholesterol. Four obese phenotypes were defined based on the body mass index and metabolic status, i.e., metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), and metabolically unhealthy non-obesity (MUNO). Multivariable logistic regression was employed to estimate the association between different obese phenotypes and the risk of CAP. Subgroup analysis was performed to examine the variation of the association by age, circadian rhythm disorders, and history of smoking and drinking.ResultsThe prevalence of CAP among male railway drivers in MHO, MUO, MUNO, and MHNO was 8.75%, 18.67%, 17.82%, and 5.36%, respectively. Compared to those with MHNO, an increased risk for CAP was observed among those with MHO (OR = 2.18, 95% CI: 0.82, 5.10), MUO (OR = 1.78, 95% CI:1.44, 2.21), and MUNO (OR = 2.20, 95% CI: 1.67, 2.89). The subgroup analysis showed that both of the metabolically unhealthy groups (MUNO and MUO) aged < 45 years were prone to a higher risk of CAP (for the MUNO group, OR = 4.27, 95% CI:2.71, 7.10; for the MUO group, OR = 4.00, 95%CI: 2.26, 7.17).ConclusionThe obese phenotypes are associated with CAP risk in male railway drivers, especially those with metabolically unhealthy conditions aged < 45 years.

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

BackgroundObesity is a heterogeneous condition. While Metabolically Healthy Obesity (MHO) is often considered to carry a lower risk of metabolic complications than Metabolically Unhealthy Obesity (MUO), findings on their differences remain inconsistent. This study examines for the first time the metabolic differences across obesity phenotypes in Saudi adults, addressing a literature gap in understudied populations with high obesity prevalence.Materials and methodsThis cross-sectional study included 450 Saudi adults classified as MHO, MUO, or metabolically healthy normal weight (MHNW) based on BMI (≥ 30 kg/m²) and the presence of ≥ 3 metabolic abnormalities per National Cholesterol Education Program (NCEP) criteria. Anthropometric, metabolic, and inflammatory parameters were assessed. Age- and sex-adjusted logistic regression analyses were used to compare phenotypes.ResultsMUO individuals showed significantly higher adiposity indices, insulin resistance (HOMA-IR), and inflammatory markers compared to MHO individuals (p-values < 0.001). MHO individuals also differed significantly from MHNW individuals. Adiponectin levels were comparable across groups; however, the Adiponectin/Leptin ratio (adpn/lep) was lowest in the MUO group, indicating adipose tissue dysfunction. MUO individuals had increased odds of elevated HOMA-IR (OR = 5.76, p < 0.001) and CRP (OR = 2.64, p = 0.005) compared to MHO. While insulin resistance did not differ significantly between MHO and MHNW, MHO participants had higher odds of a low adpn/lep ratio (OR = 2.87, p < 0.005).ConclusionMUO individuals exhibited greater metabolic risk than MHO, with marked insulin resistance and inflammation. However, the presence of adipokine imbalance and elevated inflammatory markers in MHO suggests that ‘metabolic health’ in obesity may be relative rather than absolute.

Obesity and metabolic syndrome are risk factors for colorectal neoplasia (CRN). However, the association between metabolically healthy obese (MHO) or metabolically unhealthy non-obese (MUNO) status and the risk of CRN remains unclear. We aimed to elucidate the association between MHO or MUNO status and the risk of CRN. A total of 139,023 asymptomatic subjects who underwent a primary screening colonoscopy were categorized into 4 groups according to obesity and metabolic status: metabolically healthy non-obese (MHNO), MHO, MUNO, and metabolically unhealthy obese (MUO). Mean participant age was 41.0years, and the proportion of men was 65.3%. Among men, the risk of overall CRN increased in MHO (adjusted odds ratio [AOR] 1.22, 95% confidence intervals [CI] 1.12-1.33), MUNO (AOR 1.25, 95% CI 1.18-1.31), and MUO groups (AOR 1.47, 95% CI 1.40-1.54) compared with the MHNO group, whereas the risk of advanced CRN (ACRN) increased in MUNO (AOR 1.16, 95% CI 1.002-1.33) and MUO groups (AOR 1.49, 95% CI 1.31-1.70), but not in the MHO group (AOR 0.92, 95% CI 0.70-1.21). Moreover, among non-obese men, the risk of overall CRN and ACRN linearly increased with an increasing number of metabolic abnormalities. However, among women, only the MUO group had an increased risk of overall CRN (AOR 1.34, 95% CI 1.21-1.47) and no other significant associations were observed. Poor metabolic health, regardless of obesity, is an independent risk factor for CRN in men. Our results suggest that men with metabolic abnormalities should be considered as a high-risk group for colorectal cancer, even if they are not obese.

BackgroundThis study examined sex-specific differences in physical health-related quality of life (HRQoL) across subgroups of metabolic health and obesity. We specifically asked whether (1) obesity is related to lower HRQoL independent of metabolic health status and potential confounders, and (2) whether associations are similar in men and women.MethodsWe used cross-sectional data from the German Health Interview and Examination Survey 2008–11. Physical HRQoL was measured using the Short Form-36 version 2 physical component summary (PCS) score. Based on harmonized ATPIII criteria for the definition of the metabolic health and a body mass index ≥ 30 kg/m2 to define obesity, individuals were classified as metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). Sex-specific analyses including multivariable linear regression analyses were based on PCS as the dependent variable, metabolic health and obesity category as the independent variable with three categories and MHNO as the reference, and age, education, lifestyle and comorbidities as confounders.ResultsThis study included 6860 participants (3298 men, 3562 women). Compared to MHNO, all other metabolic health and obesity categories had significantly lower PCS in both sexes. As reflected by the beta coefficients [95% confidence interval] from bivariable linear regression models, a significant inverse association with PCS was strongest for MUO (men: −7.0 [−8.2; −5.8]; women: −9.0 [−10.2; −7.9]), intermediate for MUNO (men: −4.2 [−5.3; −3.1]; women: −5.6 [−6.8; −4.4]) and least pronounced for MHO (men: −2.2 [−3.6; −0.8]; women −3.9 [−5.4; −2.5]). Differences in relation to MHNO remained statistically significant for all groups after adjusting for confounders, but decreased in particular for MUNO (men:–1.3 [−2.3; −0.3]; women: −1.5 [−2.7; −0.3].ConclusionsObesity was significantly related to lower physical HRQoL, independent of metabolic health status. Potential confounders including age, educational status, health-related behaviors, and comorbidities explained parts of the inverse relationship. Associations were evident in both sexes and consistently more pronounced among women than men.

Objectives: To assess the prevalence of metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) using different definitions. Methods: The data were taken from a nation-wide survey on hypertension in China conducted in 2013–2015. Population-based sample of 4757 adults (male 51.1%) aged 35 years and older in Beijing provided data on BMI and metabolic status. Obesity was defined as BMI ≥28 kg/m2, and definitions of metabolic abnormalities were from 5 sets of metabolic syndrome or cardiometabolic abnormalities including the Adult Treatment Panel III (ATP-III), Karelis, Wildman, Chinese Diabetes Society (CDS) and homeostasis model assessment (HOMA) index. Results: The prevalence of obese was 24.3% (1155/4757). The highest prevalence of MHO (13.6%, 95% CI 11.0–16.2) was obtained from the HOMA criteria, followed by CDS, ATP-III, and Wildman. The lowest were from Karelis criteria (4.2%, 95% CI 1.4–7.0). The prevalence of MUO from the highest to the lowest were ranged on the contrary to MHO, the highest was Karelis (20.1%, 95% CI 17.6–22.6) and the lowest was HOMA (10.6%, 95% CI 7.9–13.3). The prevalence of MHO in criteria of ATP-III, Wildman, CDS and HOMA increased with age and reached the peak at age 45 years, then declined and slightly rose after age 80 except for Wildman criteria. The age-specific prevalence of MUO increased with age before 60–64 years and then declined after age 65 regardless of which criteria used. After age 84, the prevalence defined by the criteria of ATP-III and CDS kept on declining continuously, while an increment was observed in the prevalence defined by Karelis, Wildman and HOMA. Conclusion: The variations in the prevalence of both MHO and MUO were magnitude, and this may due to the heterogeneity of the definitions of metabolic disorder. Large cohort studies are needed in the future.

Background An understanding of the interplay between mental illnesses and metabolic disorders is crucial. At present, in Nigeria, studies on coexistence of these conditions are scarce. Therefore, this study was carried out to determine the prevalence of obesity, metabolically healthy obesity (MHO) and metabolic syndrome (MS) in adults with major mental illnesses. Materials and methods One hundred and twenty four patients with schizophrenia, depression and bipolar disorder were recruited into this cross-sectional study. Blood pressure and anthropometric indices were obtained using standard methods. After an overnight fast, plasma glucose levels and lipid profile were determined. MS was diagnosed using the Joint Interim Statement. MHO was defined as overweight/obesity with less than or equal to one MS risk factor, whereas metabolically unhealthy obesity (MUO) was defined as overweight/obesity with greater than or equal to two MS risk factors. Results More than half (55.6%) of the patients had normal body weight. The prevalence of overweight, obesity and MS was 25.8, 18.5 and 20.2%, respectively. Similarly, the prevalence of MHO and MUO among the overweight/obese patients was 21.8 and 78.2%, respectively. MUO was more prevalent in patients with schizophrenia compared with patients with depression and bipolar disorder. Low HDL and central obesity were the most common components of MS in the study participants. Conclusion It could be concluded from this study that metabolic disorders are not uncommon in Nigerians with major mental illness. Therefore, early identification of patients with metabolic alteration and introduction of preventive measures might forestall further cardiometabolic deterioration, especially in patients with schizophrenia.

Mitochondrial epigenetic changes and progression from metabolically healthy obesity to metabolically unhealthy obesity: a cross-sectional study

Obesity and metabolic abnormalities are independently and interactively associated with colorectal adenoma (CRA). This study aimed to investigate the association between different metabolic obesity phenotypes and CRA, and to assess whether this relationship is influenced by stratification based on age and sex. A total of 2042 subjects were enrolled in this study. The patients were classified into four metabolic obesity phenotypes based on their body mass index (BMI) and metabolic status, including metabolically healthy non-obesity (MHNO), metabolically unhealthy non-obesity (MUNO), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). Multiple logistic regression analysis and further subgroup analysis based on sex and age were performed to evaluate the association between the metabolic obesity phenotypes and the occurrence of CRA. In the overall population, CRA was detected at significantly higher rates in both the MUNO and MUO phenotypes compared to the MHNO and MHO phenotypes. After adjusting for confounders, in the overall population, the MUNO (OR = 1.353, 95% CI: 1.099-1.666, P < 0.05) and MUO (OR = 1.558, 95% CI: 1.111-2.187, P < 0.05) phenotypes were identified as risk factors for the development of CRA compared to the MHNO phenotype. Subgroup analysis based on sex revealed that in female subjects, the MUNO phenotype (OR 1.694; 95% CI 1.243-3.308, p < 0.01) exhibited significantly elevated risks of CRA compared to those in the MHNO phenotype, whereas the MHO phenotype showed no significant differences. Furthermore, in the male subjects, no statistically significant differences were observed among the four groups. Subgroup analysis based on age revealed that under 60 years with the MUNO (OR = 1.648, 95% CI: 1.252-2.169, P < 0.01) phenotype and over 60 years with MUO (OR = 2.301, 95% CI: 1.252-4.348, P < 0.01) phenotype, had significantly increased risks of CRA. Metabolically unhealthy phenotypes are associated with a higher risk of CRA incidence. Clinical screening for CRA should focus on metabolically unhealthy subjects.

Metabolically healthy obese (MHO) individuals generally show better cardiovascular prognosis compared with metabolically unhealthy counterparts, which may be related to different patterns of target organ damage (TOD). We aimed to investigate the patterns of TOD related to obesity and metabolic unhealthiness. A total of 659 Korean adults (mean age, 60.0 ± 11.8 years; male, 51.1%) undergoing health examinations were stratified into four groups according to obesity (body mass index ≥25.0 kg/m2) and metabolic healthiness (meeting ≤1 criteria of metabolic syndrome excluding abdominal circumference): metabolically healthy nonobese (MHNO), metabolically unhealthy nonobese (MUNO), MHO, and metabolically unhealthy obese (MUO). Four measures of TOD were evaluated: arterial stiffness, renal dysfunction, left ventricular (LV) diastolic dysfunction, and LV hypertrophy (LVH). In multivariable analyses, compared with the MHNO group, the MHO group showed 2.31 times higher odds for LVH, whereas, the MUNO group showed 3.14 and 6.28 times higher odds for increased arterial stiffness and renal dysfunction, respectively. Metabolic unhealthiness was associated with increased arterial stiffness [odds ratio (OR) 2.73; confidence interval (95% CI) 1.72-4.34], renal dysfunction (OR 4.02; 95% CI 1.54-10.49), and LV diastolic dysfunction (OR 2.28; 95% CI 1.14-4.55). Meanwhile, obesity showed weaker association with LVH and LV diastolic dysfunction, and was not associated with increased arterial stiffness and renal dysfunction in multivariable analyses. Metabolic unhealthiness shows more association with TOD than obesity, which may contribute to the higher risk of cardiometabolic abnormalities in MUNO compared with MHO.

Background: This study investigated the association of four metabolic obesity phenotypes with incident coronary artery disease and stroke in a large-scale, community population-based, prospective Korean cohort observed for over 10 years. Methods: The study participants included 7374 adults aged 40-69 years, drawn from the Korean Genome and Epidemiology Study. Participants with different metabolic obesity phenotypes were categorized according to body weight and metabolic health status into four groups: metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy nonobese (MUHNO), and metabolically unhealthy obese (MUHO). Combined cardiovascular events were defined as coronary artery disease and stroke. We used multivariate Cox proportional hazards regression models to prospectively assess hazard ratios (HRs) with 95% confidence intervals (CIs) for incident coronary artery disease or stroke over 10 years after the baseline survey. Results: During the follow-up period, newly developed coronary artery disease, stroke, and combined cardiovascular events were diagnosed in 151 (2.0%), 137 (1.9%), and 283 (3.8%) participants, respectively. After adjusting for confounding variables, the HRs (95% CIs) for incident combined cardiovascular events were 1.81 (1.34-2.46) in the MUHO group, 1.29 (0.92-1.81) in the MUHNO group, and 1.21 (0.81-1.79) in the MHO group compared with those in the MHNO group. Conclusions: This study revealed distinct risks associated with four metabolic obesity phenotypes concerning incident coronary artery disease and stroke. After adjusting for potential confounding variables, the results indicated that MUHO, but not MUHNO or MHO, showed a higher risk of developing coronary artery disease and stroke than MHNO.

Background: Obesity without metabolic syndrome is known as metabolically healthy obesity (MHO). The influence of MHO on apnea severity and sleep parameters in patients with obstructive sleep apnea (OSA) remains unknown. To determine whether MHO affect the apnea severity and sleep parameters, such as apnea-hypopnea index (AHI) and arousal index, in OSA patients with non-obesity, MHO, and metabolically unhealthy obesity (MUHO). Methods: A total of 266 men with OSA, aged between 20 and 65 years, were enrolled in this single-center, retrospective study. After excluding patients on hypnotics, antidepressants, or anticonvulsants, a total of 180 patients were included. The clinical data, AHI, and arousal index were examined in OSA patients with non-obesity, MHO, and MUHO. Results: The arousal index showed a significant difference in patients with MHO compared to those with MUHO (MHO vs MUHO: 28.6 ± 15.5 vs. 40.2 ± 21.1, respectively, p<0.01). There was no significant difference in the arousal index in OSA patients between the non-obese and MHO groups. AHI indicated a significant increase in patients with MHO compared to non-obesity (non-obesity vs MHO: 22.5 ± 13.7 vs. 38.4 ± 22.8, respectively, p<0.01). Conclusions: In OSA patients with MHO and MUHO, there were no significant differences in apnea severity, but the arousal index in OSA patients with MHO was lower than MUHO. The results demonstrate that the arousals during sleep may be due to the simple correlation with apnea appearance and metabolically unknown factors.

Obese individuals who have little or no metabolic syndrome components are proposed to be "metabolically healthy obese (MHO)". This study aim to evaluate the prevalence of MHO and examine the predictors associated with MHO in a multi-ethnic Asian cohort of severely obese children. This study included a cross-sectional cohort of 406 Chinese, Malay and Indian children aged 5-20 years old with BMI for age ≥ 97th percentile. Metabolic syndrome (MS) and metabolic health (MH) definitions based on the presence or absence of metabolic abnormalities (High triglycerides, low HDL cholesterol, elevated blood pressure and high glucose) were used to define MHO in the cohort. The prevalence of MHO is 63.5% by MS definition and 22.4% by MH definition. Maternal healthy metabolic status (OR: 2.47), age (OR: 0.83, 0.80), paternal obesity (OR: 0.48, 0.53), Malay (OR: 1.97) and Indian ethnicity (OR: 6.38, 3.21) (compared to Chinese ethnicity) are independent predictors for MHO phenotype based on different MHO definitions. Adiposity measures are not associated with MHO phenotype, but instead younger age, maternal healthy metabolic status, absence of paternal obesity, Malay and Indian ethnicity are independent predictors for MHO phenotype in a multi-ethnic Asian cohort of severely obese children. The prevalence of metabolically healthy obese (MHO) in our multi-ethnic Asian cohort of severely obese children is 63.5% and 22.4%, respectively, based on different MHO definitions. Adiposity measures are not associated with the MHO phenotype. There are other factors that contribute to the metabolic phenotype in obese individuals. Younger age, maternal healthy metabolic status, absence of paternal obesity, Malay and Indian ethnicity are independent predictors for MHO phenotype. Parental influence is important in predicting metabolic health in obese individuals.