Gut microbial ecosystems differ across metabolic and obesity phenotypes

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.

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.

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.

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

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.

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.

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.

Obesity and metabolic disorders were associated with increased risk of MM, a disease characterized by high risk of relapsing and require frequent hospitalizations. In this study, we conducted a retrospective cohort study to explore the association of metabolic obesity phenotypes with the readmission risk of MM. We analyzed 34,852 patients diagnosed with MM from the Nationwide Readmissions Database (NRD), a nationally representative database from US. Hospitalization diagnosis of patients were obtained using ICD-10 diagnosis codes. According to obesity and metabolic status, the population was divided into four phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). The patients with different phenotypes were observed for hospital readmission at days 30-day, 60-day, 90-day and 180-day. Multivariate cox regression model was used to estimate the relationship between obesity metabolic phenotypes and readmissions risk. There were 5,400 (15.5%), 7,255 (22.4%), 8,025 (27.0%) and 7,839 (35.6%) unplanned readmissions within 30-day, 60-day, 90-day and 180-day follow-up, respectively. For 90-day and 180-day follow-up, compared with patients with the MHNO phenotype, those with metabolic unhealthy phenotypes MUNO (90-day: P = 0.004; 180-day: P = < 0.001) and MUO (90-day: P = 0.049; 180-day: P = 0.004) showed higher risk of readmission, while patients with only obesity phenotypes MHO (90-day: P = 0.170; 180-day: P = 0.090) experienced no higher risk. However, similar associations were not observed for 30-day and 60-day. Further analysis in 90-day follow-up revealed that, readmission risk elevated with the increase of the combined factor numbers, with aHR of 1.068 (CI: 1.002-1.137, P = 0.043, with one metabolic risk factor), 1.109 (CI: 1.038-1.184, P = 0.002, with two metabolic risk factors) and 1.125 (95% CI: 1.04-1.216, P = 0.003, with three metabolic risk factors), respectively. Metabolic disorders, rather than obesity, were independently associated with higher readmission risk in patients with MM, whereas the risk elevated with the increase of the number of combined metabolic factors. However, the effect of metabolic disorders on MM readmission seems to be time-dependent. For MM patient combined with metabolic disorders, more attention should be paid to advance directives to reduce readmission rate and hospitalization burden.

We explored the joint effects of different metabolic obesity phenotypes on all-cause and disease-specific mortality risk among the American population. Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. Mortality outcome data were from mortality files linked to National Death Index record and follow-up information was up to December 31, 2019. 50,013 participants were finally included. Four metabolic obesity phenotypes were defined based on obesity and metabolic status: metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), metabolically healthy non-obese (MHNO), and metabolically unhealthy non-obese (MUNO). Population-weighted Cox proportional hazards models were used to explore the all-cause and disease-specific mortality risk of metabolic obesity phenotypes. The all-cause mortality risk of MUO and MUNO was significantly higher than MHNO. MUNO was associated with a significantly increased risk of death from heart disease (HR: 1.40, 95% CI 1.16–1.70), hypertension (HR: 1.68, 95% CI 1.34–2.12), diabetes (HR: 2.29, 95% CI 1.67–3.15), and malignant neoplasms (HR:1.29, 95% CI 1.09–1.53). Metabolic unhealth significantly increased the risk of all-cause mortality, regardless of obesity status. Among individuals with metabolic unhealthy status, obesity significantly reduced the risk of all-cause mortality (HR: 0.91, 95% CI 0.85–0.98). Our study highlights the importance of identifying and characterizing metabolic obesity phenotypes in obese and metabolically abnormal patients, as well as healthy adults. Comprehensive evaluation of obesity and metabolic status is necessary to adopt appropriate interventions and treatment measures and maximize patient benefit.

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.

Today, metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) are distinguished. Adipose and muscle tissues can determine the obese phenotype due to adipokine and myokine production. Gut microbial community is also involved in MHO. The study was aimed to reveal the features of adipokine and myokine levels and their association with the gut microbiome alpha diversity in patients with MHO and MUO. A total of 265 subjects were divided into two groups: healthy individuals and obese patients. The latter were divided into two subgroups: patients with MHO and patients with MUO. Body mass index, waist circumference, HOMA-IR, adipokine and myokine levels, gut microbiome taxonomic composition, alpha diversity indices were defined in all the surveyed individuals, lipid and carbohydrate metabolism was also assessed. Significant differences in the adipokine and myokine levels and their association with the gut microbiome diversity indicators were revealed in patients with different obese phenotypes. Patients with MHO and MUO showed significantly lower adiponectin levels (р &lt; 0.05) and significantly higher leptin and asprosin levels (р &lt; 0.05) than healthy individuals. Patients with MUO had lower adiponectin and leptin levels (p &lt; 0.05) than patients with MHO. Significantly higher FGF21 levels were observed in patients with MUO. Large-scale correlation analysis revealed the relationship between the glucose levels and the gut microbiome diversity indices that was missing in patients with MUO. This indicated the loss of the microbiota diversity effects on the blood glucose control in individuals with MUO, as well as different regulatory roles in the gut microbiome‒liver‒muscle/adipose tissue axes of individuals with MHO and MUO played by gut microbiota. The findings show the relationship between the gut microbiome diversity and the obese phenotype.

Peripheral vascular disease

BackgroundThis study was evaluated the association between obesity phenotypes and risk of lower torso musculoskeletal disorders including low back pain (LBP), low back stiffness (LBS), arthralgia, and joint stiffness in Ravansar non-communicable diseases (RaNCD) cohort study.MethodsIn this cross-sectional study, 6940 adults were examined for the presence of lower torso musculoskeletal disorders by a physician. Obesity phenotypes including metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) were defined based on the International Diabetes Federation, as well as, body mass index > 30 kg/m2. Metabolically unhealthy non-obesity (MUNO) phenotype was considered as unhealthy metabolic without obesity.ResultsThe prevalence of LBP, LBS, arthralgia, and joint stiffness in MHO, MUO, and MUNO were significantly higher than in healthy participants compared to obesity phenotypes. Logistic regression showed that MHO phenotype was significantly increased with risk of LBP (OR: 1.19, CI 95%: 1.01–1.41), LBS (OR: 1.44, CI 95%: 1.12–1.86), arthralgia (OR: 1.54, CI 95%: 1.33–1.78), and joint stiffness (OR: 1.84, CI 95%: 1.35–2.52). Moreover, MUO phenotype was positively associated with risk of LBS (OR: 1.46, CI 95%: 1.09–1.94) and arthralgia (OR: 1.66, CI 95%: 1.41–1.96). In addition, MUNO phenotype was associated with a higher risk of arthralgia (OR: 1.21, CI 95%: 1.06–1.37).ConclusionAll three phenotypes, MHO, MUO and MUNO were significantly increased the risk of arthralgia. However, MHO phenotype was significantly associated with a higher risk of all examined lower torso musculoskeletal disorders in the current study.

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.

Over 650 million adults are obese (body mass index ≥ 30 kg/m2) worldwide. Obesity is commonly associated with several comorbidities, including cardiovascular disease and type II diabetes. However, compiled estimates suggest that from 5 to 40% of obese individuals do not experience metabolic or cardiovascular complications. The existence of the metabolically unhealthy obese (MUO) and the metabolically healthy obese (MHO) phenotypes suggests that underlying differences exist in both tissues and overall systemic function. Macrophage accumulation in white adipose tissue (AT) in obesity is typically associated with insulin resistance. However, as plastic cells, macrophages respond to stimuli in their microenvironments, altering their polarization between pro- and anti-inflammatory phenotypes, depending on the state of their surroundings. The dichotomous nature of MHO and MUO clinical phenotypes suggests that differences in white AT function dictate local inflammatory responses by driving changes in macrophage subtypes. As obesity requires extensive AT expansion, we posit that remodeling capacity with adipose expansion potentiates favorable macrophage profiles in MHO as compared with MUO individuals. In this review, we discuss how differences in adipogenesis, AT extracellular matrix deposition and breakdown, and AT angiogenesis perpetuate altered AT macrophage profiles in MUO compared with MHO. We discuss how non-autonomous effects of remote organ systems, including the liver, gastrointestinal tract, and cardiovascular system, interact with white adipose favorably in MHO. Preferential AT macrophage profiles in MHO stem from sustained AT function and improved overall fitness and systemic health.