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Background and Aim: Obesity is association with elevated risks of erosive esophagitis (EE), and metabolic abnormalities play crucial roles in its development. The aim of the study was to assess the association between metabolic obesity phenotypes and the risk of EE. Methods: This retrospective study enrolled 11,599 subjects who had undergone upper gastrointestinal endoscopy at the First Affiliated Hospital of Dalian Medical University from January 1, 2008, to December 31, 2023. The enrolled individuals were grouped into four cohorts based on their metabolic health and obesity profiles, namely, metabolically healthy non-obesity (MHNO; n=2134, 18.4%), metabolically healthy obesity (MHO; n=1736, 15.0%), metabolically unhealthy non-obesity (MUNO; n=4290, 37.0%), and metabolically unhealthy obesity (MUO; n=3439, 29.6%). The relationships of the different phenotypes of metabolic obesity with the risks of developing EE in the different sexes and age groups were investigated by multivariate logistic regression analysis. Results: The MUNO, MHO, and MUO cohorts exhibited elevated risks of developing EE than the MHNO cohort. The confounding factors were adjusted for, and the findings revealed that the MUO cohort exhibited the greatest risk of EE, with odds ratios (ORs) of 5.473 (95% CI: 4.181-7.165) and 7.566 (95% CI: 5.718-10.010) for males and females, respectively. The frequency of occurrence of EE increased following an increase in proportion of metabolic risk factors. Subgroup analyses showed that the individuals under and over 60 years of age in the MHO, MUNO, and MUO cohorts exhibited elevated risks of developing EE. Further analysis suggested that obesity has a stronger influence on the risks of developing EE compared to metabolic disorders. Conclusion: Metabolic disorders and obesity are both related with an elevated risk of EE, in which obesity has a potentially stronger influence. Clinical interventions should target both obesity and metabolic disorders to reduce EE risk.

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OBJECTIVE: This study aimed to explore obesity phenotypes and investigate their association with dietary patterns. METHODS: Data were obtained from the baseline survey conducted in the China Multi-Ethnic Cohort Study from July 2018 to August 2019. All participants with a body mass index of at least 24 kg/m2 were enrolled and underwent a questionnaire survey, physical examination, and clinical laboratory tests. A two-step cluster analysis was employed to classify the participants into phenotypes. Dietary information was collected using the food frequency questionnaire, and principal component analysis was conducted to identify distinct dietary patterns. RESULTS: We analyzed the data of 8757 participants. They were categorized based on demographic characteristics, biochemical indicators, and anthropometric measurements into two distinct clusters identified as metabolically healthy obesity and metabolically unhealthy obesity (MUO). Key predictors included serum uric acid, sex, and diastolic blood pressure. Subgroup analysis by sex identified three distinct clusters within both male and female participants. The MUO group had the highest prevalence of a range of chronic noncommunicable diseases. The analysis uncovered three unique dietary patterns among participants classified as the premium protein, rice-oil-red meat, and oil-salt patterns. Notably, the MUO subgroup demonstrated significantly higher factor scores for both the rice-oil-red meat and oil-salt patterns. CONCLUSIONS: Obesity phenotypes are closely related to metabolic and demographic characteristics, with serum uric acid being a significant factor in categorizing the metabolic states of obesity. The rice-oil-red meat and oil-salt patterns may be related to the metabolic status of individuals with obesity.

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OBJECTIVE: The aim of this study was to investigate the combined effect of handgrip strength (HGS) and obesity phenotype on the risk of stroke in Chinese middle-aged and elderly people. METHODS: The data was used from the China Health and Retirement Longitudinal Study (CHARLS). Middle-aged and older adults who participated in surveys between 2011 and 2018 were included in the study. They were divided into 4 different types of obesity phenotypes based on obesity and metabolic status: metabolically healthy non-overweight/obesity (MHNO), metabolically healthy overweight/obesity (MHO), metabolically abnormal non-overweight/obesity (MANO), and metabolically abnormal overweight/obesity (MAO). The HGS level was divided into low and high groups according to the median values. Cox proportional risk regression model was used to analyze the joint effect of HGS and obesity phenotype on the risk of stroke among participants. RESULTS: A total of 7904 participants aged 58.89±9.08 years were included in this study. After adjusting for potential confounders, high HGS&MHO (HR=1.86, 95 % CI=1.12-3.09), high HGS&MANO (HR=2.01, 95 %CI=1.42-2.86), high HGS&MAO (HR=2.01, 95 % CI=1.37-2.93), low HGS&MHNO (HR=1.57, 95 % CI=1.00-2.46), low HGS&MHO (HR=2.09, 95 % CI=1.29-3.38), low HGS&MANO (HR=2.02, 95 % CI=1.35-3.03), and low HGS&MAO (HR=2.48, 95 % CI=1.72-3.58) group had significantly higher risks of stroke than the high HGS&MHNO group. CONCLUSION: The coexistence of metabolically unhealthy and low HGS can synergistically increase the risk of stroke in Chinese middle-aged and elderly people.

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BACKGROUND: Although exposure to greenness has generally benefited human metabolic health, the association between greenness exposure and metabolic obesity remains poorly studied. We aimed to investigate the associations between residential greenness and obesity phenotypes and the mediation effects of air pollutants and physical activity (PA) level on the associations. METHODS: We used the baseline of the China Multi-Ethnic Cohort (CMEC) study, which enrolled 87,613 adults. Obesity phenotypes were defined based on obesity and metabolic status, including metabolically unhealthy obesity (MUO), non-obesity (MUNO), metabolically healthy obesity (MHO), and non-obesity (MHNO). Greenness exposure was measured as the 3-year mean values of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) within the 500-m buffer zones around the participants' residence. Multivariable logistic regression was used to estimate the associations between greenness and obesity phenotypes. Stratified analyses by age, sex, educational level, and urbanicity were performed to identify how the effect varies across different subgroups. Causal mediation analysis was used to examine the mediation effects of air pollutants and PA level. RESULTS: Compared with MHNO, each interquartile range (IQR) increase in greenness exposure was associated with reduced risks of MHO (ORNDVI [95% CI] = 0.87 [0.81, 0.93]; OREVI = 0.91 [0.86, 0.97]), MUO (ORNDVI = 0.83 [0.78, 0.88]; OREVI = 0.86 [0.81, 0.91]), and MUNO (ORNDVI = 0.88 [0.84, 0.91]; OREVI = 0.89 [0.86, 0.92]). For each IQR increase in both NDVI and EVI, the risks of MHO, MUO, and MUNO were reduced more in men, participants over 60 years, those with a higher level of education, and those living in urban areas, compared to their counterparts. Concentrations of particulate matter (PM) and PA level partially mediated the associations between greenness exposure and obesity phenotypes. CONCLUSIONS: Exposure to residential greenness was associated with decreased risks of MHO, MUO, and MUNO, which was mediated by concentrations of PM and PA level, and modified by sex, age, educational level, and urbanicity.

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Purpose: A bone turnover marker reflects bone bioactivity. The effects of metabolically healthy compared with metabolically unhealthy obesity phenotypes on bone metabolism are not well understood. The aim of the study was to evaluate differences of bone transformation indexes in these two obesity phenotypes. Patients and Methods: A total of 419 obese subjects were recruited, 64 with metabolically healthy obesity (MHO) and 351 with metabolically unhealthy obesity (MuHO). BTMs and clinical parameters were measured. Results: Bone metabolism indexes, including tartrate resistant acid phosphatase (TRACP, p < 0.05), ß carboxyl terminal peptide of collagen (ß-CTX, p < 0.01), and bone alkaline phosphatase (BAP, p < 0.01), were higher in subjects with MHO than MuHO, but parathyroid hormone (PTH) was lower (p < 0.05). The between-group difference in serum calcium was not significant. Low bone turnover activity was associated with significant hyperglycemia, insulin resistance, and body fat index (p < 0.05). Multivariate logistic regression found that TRACP, ß-CTX, and BAP were independently associated with the presence of MHO. Receiver operating characteristic curve analysis found that the maximum area under the curve value for the definition of MHO was (0.8221) and was obtained when sex, age, body mass index (BMI), TRACP, ß-CTX and BAP were included simultaneously, resulting in a sensitivity of 81.25% and specificity: 72.3%. Conclusion: The MHO group had significantly increased circulating TRACP and ß-CTX compared with the MuHO group and BAP levels were within the physiological range. Obesity with the metabolically healthy phenotype had slightly increased bone turnover activity that may be an early compensatory response of skeletal metabolism to the increased BMI.

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BACKGROUND AND AIMS: Although the associations of vitamin D with obesity and metabolic abnormalities have been reported, the role of vitamin D in the transition of obesity phenotype remains unclear but is highly desired since it is crucial to identify potential methods for obesity management. Therefore, we aimed to investigate the relationship between vitamin D and the risk for metabolically unhealthy obesity (MUO) or metabolically healthy obesity (MHO) in metabolically healthy children with 2 years of follow-up. METHODS: Data were collected from a population-based cohort consisting of 6424 metabolically healthy children aged 6-16 years at baseline. Metabolic abnormalities including hypertension, high triglycerides (TG), low high-density lipoprotein cholesterol (HDL-C), hyperglycemia, and hyperuricemia were assessed both at baseline and follow-up. Baseline serum 25-hydroxyvitamin D (25[OH]D) concentrations were measured as exposure. The obesity phenotype transition was evaluated by weight status with the combination of metabolic health status from baseline to follow-up. RESULTS: During a 2-year follow-up, 889 (13.8 %) incident MUO cases occurred. For participants with obesity, each 10 nmol/L increment in 25(OH)D concentrations was associated with a 21 % (95%CI: 13 %∼43 %) and a 7 % (95%CI: 1 %∼14 %) decreased risk in high TG and hyperuricemia, respectively. A 51 % (95%CI: 22 %∼69 %) lower risk of MUO was observed in participants with sufficient vitamin D levels (≥50 nmol/L) compared to those with vitamin D deficiency (<30 nmol/L). Besides, among children who were MHO at baseline, those with sufficient vitamin D levels (≥50 nmol/L) were more likely to transition to metabolically healthy normal weight (MHNW) than vitamin D deficient individuals (<30 nmol/L). CONCLUSIONS: Vitamin D may prevent the development of MUO and help increase the transition from MHO to MHNW. The findings highlight that vitamin D might be an effective nutrient for obesity management.

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Background: Non-alcoholic fatty liver disease (NAFLD), especially lean NAFLD is associated with an increased risk of atherosclerotic cardiovascular disease (CVD). It is not currently known which clinical phenotypes of NAFLD contribute most to individual subclinical atherosclerosis risk. We examined the relationship between body mass index (BMI), the metabolically healthy status, and subclinical atherosclerosis in the NAFLD population. Methods: Data from asymptomatic NAFLD subjects who participated in a routine health check-up examination were collected. Participants were stratified by BMI (cutoff values: 24.0-27.9 kg/m2 for overweight and ≥28.0 kg/m2 for obesity) and metabolic status, which was defined by Adult Treatment Panel III criteria. Subclinical atherosclerosis was evaluated by brachial-ankle pulse wave velocity (baPWV) in 27,738 participants and by carotid plaque in 14,323 participants. Results: Within each BMI strata, metabolically unhealthy subjects had a significantly higher prevalence of subclinical atherosclerosis than metabolically healthy subjects, whereas fewer differences were observed across subjects within the same metabolic category. When BMI and metabolic status were assessed together, a metabolically unhealthy status was the main contributor to the association of clinical phenotypes with the subclinical atherosclerosis burden (all p < 0.001). When BMI and metabolic abnormalities were assessed separately, the incidence of subclinical disease did not increase across BMI categories; however, it increased with an increase in the number of metabolic abnormalities (0, 1, 2 and ≥3). Conclusion: A metabolically healthy status in NAFLD patients was closely correlated with subclinical atherosclerosis, beyond that of the BMI-based obesity phenotype. The application of metabolic phenotyping strategies could enable more precise classification in evaluating cardiovascular risk in NAFLD.

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Objectives: Various diseases are associated with obesity and metabolism. We sought to investigate the risk of cardiovascular disease (CVD) in diverse metabolic obesity phenotypes. Methods and Results: A prospective observational study of 1517 participants ≥25 years of age without CVD at baseline was conducted. Participants were categorized into four groups based on the condition of central obesity and metabolic health status: metabolically healthy normal weight, metabolically healthy obesity (MHO), metabolically unhealthy normal weight, and metabolically unhealthy obese (MUO). A multivariate Cox regression analysis was used to analyze the relationship between different obesity phenotypes and CVD. During 14830.49 person-years of follow-up, there were 244 incident cases of CVD. Of the 1517 participants, 72 (4.75%) and 812 (53.53%) were classified as having MHO and MUO, respectively. MHO and MUO had a tendency toward a higher risk of CVD [adjusted hazard ratios (HRs) = 1.49, 95% confidence interval (CI): 1.11-2.02 and HR = 1.25, 95% CI: 1.00-1.55, respectively] based on the waist circumference criterion. Conclusion: MHO and MUO can increase the risk of CVD.

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BACKGROUND: Existing evidence suggests a close link among high levels of serum urate (SU), obesity and carotid atherosclerosis. The aim of the present study was to evaluate the interrelations between SU levels and carotid atherosclerosis in subjects with different obesity phenotypes. METHODS: In this study, a total of 2076 subjects (mean age 48.1 ± 13.1 years; 1307 women) were recruited: 59 with general obesity, 616 with central obesity, 715 with mixed (general-central) obesity and 686 non-obese. Anthropometric measurements, vascular risk factors, blood biochemistry analysis (including SU levels), and carotid ultrasound were performed. Ultrasound assessment included evaluation of intima-media thickness (IMT) and plaque characteristics, including number, total area and type (vulnerable vs. stable) of plaques. RESULTS: After adjustment for potential confounders, the highest levels of SU were observed in subjects with mixed obesity, followed by subjects with central obesity, general obesity and the non-obese (309.4 ± 82.2 vs. 301.2 ± 73.1 vs. 272.9 ± 61.8 vs. 234.2 ± 59.8 µmol/L, respectively; F = 149.2, post hoc p < 0.001). Similarly, subjects with mixed and central obesity presented higher values of IMT compared to subjects with general obesity and the non-obese (0.68 ± 0.16 vs. 0.67 ± 0.16 vs. 0.62 ± 0.14 vs. 0.57 ± 0.13 mm, respectively; F = 54.2, post hoc p < 0.001). No difference in number, total area and type of plaques among obesity groups were attested (all p > 0.05). Significantly higher IMT values were observed in subjects with increased SU levels compared to subjects with normal SU levels (0.70 ± 0.10 vs. 0.62 ± 0.14 mm, p = 0.02) only within the central obesity group. Increasing levels of SU were associated with a higher frequency of increased IMT only in subjects with central obesity (OR 1.033, 95% CI 1.025-1.041). Similarly, SU levels yielded a satisfactory performance in detecting subjects with increased IMT (AUC 0.65, 95% CI 0.50-0.73, subjects with carotid plaques (0.62, 95% CI 0.55-0.68) and subjects with vulnerable plaque types (0.68, 0.59-0.76) only within the central obesity group. CONCLUSIONS: Among the studied obesity types, the association between SU levels and markers of carotid atherosclerosis was of particular significance in subjects with central obesity.

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BACKGROUND: Mounting evidence suggests a pivotal role for the gut microbiome in energy disequilibrium characteristic of obesity. The clinical utility of microbial profiling for the distinction between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) remains ill-defined. We aim to probe microbial composition and diversity in young adult Saudi females with MHO and MUO. This observational study included anthropometric and biochemical measurements and shotgun sequencing of stool DNA for 92 subjects. α- and ß-diversity metrics were calculated to determine the richness and variability in microbial communities, respectively. Results showed that Bacteroides and Bifidobacterium merycicum were less abundant in MUO compared to healthy and MHO groups. BMI was negatively correlated with B. adolescentis, B. longum, and Actinobacteria in MHO, while being positively correlated with Bacteroides thetaiotaomicron in both MHO and MUO. Positive correlations between waist circumference and B. merycicum and B. thetaiotaomicron were observed in MHO and MUO, respectively. Compared to MHO and MUO groups, higher α-diversity was detected in healthy individuals who also had higher ß-diversity compared to those with MHO. We conclude that modulation of the gut microbiome cohorts through prebiotics, probiotics, and fecal microbiota transplantation may be a promising preventive and therapeutic approach to obesity-associated disease.

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AIMS: We investigated whether thyroid function could be associated with obesity phenotypes amongst euthyroid individuals. MATERIALS AND METHODS: A cross-sectional analysis was conducted among healthy, euthyroid subjects. The study participants were chosen from the Tehran Thyroid Study (TTS). We analyzed 2988 euthyroid adults and classified them into four obesity phenotype groups: metabolically healthy normal weight (MHNW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy obese (MUO). The statistical differences between thyroid hormones between various obesity phenotypes according to age and sex was compared using analysis of covariance (ANCOVA). RESULTS: It was found that MHNW participants had higher levels of FT4 when compared with metabolically healthy or unhealthy obese subjects (P < 0.001), even after adjustment for the confounding variables. No difference was observed in the levels of TSH (P = 0.260) among obesity phenotypes. In the subgroup analysis according to the age, a significant difference was observed in the level of FT4 only in subjects with age < 55 years (P = 0.001). However, analyzing men and women separately did not show a significant difference in the FT4 level among obesity phenotypes (P > 0.05). CONCLUSION: "Metabolically abnormality" was independently related to low normal FT4 levels in overweight/obese euthyroid individuals. There is a need for further research to understand how low FT4 levels are linked to metabolically unhealthy states in euthyroid individuals.

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The univariate obesity-brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist-height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing different obesity phenotypes indicated that the obesity-brain associations varied according to the type of measure of obesity. The shared structure among resting-state functional connectivity patterns revealed reproducible neuroimaging biomarkers of obesity, primarily comprising the connectomes within the visual cortex and between the visual cortex and inferior parietal lobule, visual cortex and orbital gyrus, and amygdala and orbital gyrus, which further suggested that the dysfunctions in the perception, attention and value encoding of visual information (e.g. visual food cues) and abnormalities in the reward circuit may act as crucial neurobiological bases of obesity. The recruitment of multiple obesity phenotypes is indispensable in future studies seeking reproducible obesity-brain associations.

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OBJECTIVE: Birth weight (BW) and sibling's status are two important indicators of early intrauterine environment and subsequent living environment, but no evidence has emerged on their joint associations on metabolic obesity phenotype. To determine the joint associations between BW and single-child status with childhood metabolic obesity phenotype was our purpose. METHODS: A cross-sectional assessment of children and adolescents aged 7-18 years was performed in Chinese seven provinces in 2013. We obtained anthropometric, blood pressure and biochemical measurements, and distributed questionnaires covering demographic, neonatal and lifestyle characteristics. The metabolic obesity phenotype was defined by 2018 consensus-based criteria. Logistic regression and restricted cubic spline models were applied to evaluate the associations of BW and metabolic obesity phenotype, and estimate the multiplicative interactions and the combined associations of BW and single-child status with metabolic obesity phenotype. RESULTS: Of enrolled 12 346 children and adolescents, the prevalence of metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) was 1.96% and 3.03%. There were 8.95% and 4.03% children with high BW or low BW, and 67.55% did not have siblings. High BW was positively associated with MHO (OR = 1.94, 95%CI = 1.28-2.94). Single-child also had increased odds of MHO and MUO (p < 0.05), and it had joint associations with high BW showing 0.85- to 2.58-fold higher odds of MUO and MHO. CONCLUSIONS: High BW and single-child status have joint positive associations with the subsequent odds of MHO and MUO, which should be jointly prevented through earlier screening and subsequent preventive strategies.

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Purpose: The association between metabolic phenotypes and thyroid function has not yet been established; therefore, this study examined whether different metabolic phenotypes are associated with the development of hypothyroidism. Methods: Study participants were selected from the Tehran Thyroid Study (TTS). A total of 3338 euthyroid adults were included and categorized into four obesity phenotype groups: metabolically healthy normal weight (MHNW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy obese (MUO). The participants were assessed at baseline and during three follow-up studies at three-year intervals. Multiple logistic regression analysis was used to examine the relationship between metabolic phenotypes and the development of hypothyroidism. Results: In the total population, the chi-square test was only significant (P=0.008) in 3rd year with a higher prevalence of hypothyroidism in the MUNW phenotype, followed by MHO, MUO, and MHNW. Moreover, in the 3rd and 9th years of follow-up, hypothyroidism was more prevalent in MUO only in male subjects (P=0.002 and 0.035, respectively). In the unadjusted model, the MHO phenotype increased the odds of hypothyroidism compared with the MHNW phenotype (OR=1.51; 95% CI=1.04, 2.18; P-value=0.031). After adjusting for confounding factors, the odds of hypothyroidism were higher in the MUNW (OR=1.86; 95% CI=1.17, 2.96; P-value=0.008), MHO (OR=1.71; 95% CI=1.09, 2.67; P-value=0.018), and MUO (OR=1.64; 95% CI=1.03, 2.62; P-value=0.036) phenotypes than in the MHNW group. The MUNW phenotype increased the risk of hypothyroidism compared to MHNW, only in males. However, in females, the MHO phenotype increased the risk of hypothyroidism compared to MHNW. Conclusion: Both obesity and metabolic abnormalities are associated with hyperthyroidism. Healthy metabolic and weight maintenance were associated with a lower risk of hypothyroidism in males and females.

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Purpose: The visceral adiposity index (VAI) and lipid accumulation product (LAP) are useful for assessing visceral obesity. However, these indices were developed for Caucasians, and it is necessary to confirm whether the VAI and LAP are appropriate indicators for identifying obesity phenotypes in Asians. This study investigated whether the VAI and LAP are effective indicators for diagnosing four obesity phenotypes in South Korean adults. Patients and Methods: This cross-sectional study enrolled 23,310 adult participants (age ≥20 years) who had undergone a health checkup at a general hospital in Gyeonggi-do, South Korea from January 2017 to December 2020. VAI and LAP were calculated based on the presented mathematical model according to sex. According to the metabolic health status and presence or absence of obesity, the obesity phenotypes were classified into 4 groups: metabolically healthy non-obese (N=14,240, 61.1%), metabolically unhealthy non-obese (N=477, 2.0%), metabolically healthy obese (MHO; N=6796, 29.2%), and metabolically unhealthy obesity (MUO; N=1797, 7.7%). Results: The receiver operating characteristics curve analysis showed VAI best predicted MUO among the four obesity phenotypes, whereas the LAP showed excellent discriminating ability for the MUO group (area under the curve 0.877, 0.849, and 0.921 and 0.923, 0.907, and 0.954 for all participants, men, and women, respectively). The optimal VAI cutoff values for identifying the MUO group were 1.83 in men and 1.58 in women, and the optimal cutoff values for the LAP were 41.45 in men and 23.83 in women, with a higher value for men. After adjusting for potential confounding factors, the VAI and LAP were associated with an increased risk in the MHO and MUO groups among the obesity phenotypes in both sexes. Conclusion: In South Korean adults, the VAI and LAP are closely related to the MUO phenotype in both sexes and are effective indices for predicting the MUO phenotype.

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BACKGROUND: The present definition of obesity based on body mass index (BMI) is not accurate and effective enough to identify hospitalized patients with a heavier burden, especially for postmenopausal hospitalized patients concomitant with osteoporosis. The link between common concomitant disorders of major chronic diseases such as osteoporosis, obesity, and metabolic syndrome (MS) remains unclear. Here, we aim to evaluate the impact of different metabolic obesity phenotypes on the burden of postmenopausal hospitalized patients concomitant with osteoporosis in view of unplanned readmissions. METHODS: Data was acquired from the National Readmission Database 2018. The study population was classified into metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) patients. We estimated the associations between metabolic obesity phenotypes and 30- and 90-day unplanned readmissions. A multivariate Cox Proportional Hazards (PH) model was used to assess the effect of factors on endpoints, with results expressed as HR and 95% CI. RESULTS: The 30-day and 90-day readmission rates for the MUNO and MUO phenotypes were higher than that of the MHNO group (all p < 0.05), whereas no significant difference was found between the MHNO and MHO groups. For 30-day readmissions, MUNO raised the risk mildly (hazard ratio [HR] = 1.110, p < 0.001), MHO had a higher risk (HR = 1.145, p = 0.002), and MUO further elevated this risk (HR = 1.238, p < 0.001). As for 90-day readmissions, both MUNO and MHO raised the risk slightly (HR = 1.134, p < 0.001; HR = 1.093, p = 0.014, respectively), and MUO had the highest risk (HR = 1.263, p < 0.001). CONCLUSIONS: Metabolic abnormalities were associated with elevated rates and risks of 30- or 90-day readmission among postmenopausal hospitalized women complicated with osteoporosis, whereas obesity did not seem to be innocent, and the combination of these factors led to an additional burden on healthcare systems and individuals. These findings indicate that clinicians and researchers should focus not only on weight management but also metabolism intervention among patients with postmenopausal osteoporosis.

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CONTEXT: There is little evidence regarding the joint effect of serum adipocyte fatty acid binding protein (A-FABP) levels and obesity phenotype on the risk of cardiovascular events. OBJECTIVE: To explore the association between serum A-FABP levels and obesity phenotype defined by fat percentage (fat%) and visceral fat area (VFA), and their joint impact on incident cardiovascular events. METHODS: A total of 1345 residents (579 men and 766 women) without previous cardiovascular diseases at baseline, with body composition and serum A-FABP data available, were included. A bioelectrical impedance analyzer and magnetic resonance imaging were used to assess fat% and VFA, respectively. RESULTS: During a mean follow-up of 7.6 years, 136 cases of cardiovascular events (13.9 per 1000 person-years) occurred. Per 1-unit increase in loge-transformed A-FABP levels was associated with an increase in cardiovascular events risk (hazard ratio [HR] 1.87, 95% CI 1.33-2.63). The highest tertiles of fat% and VFA levels were related to higher risks of cardiovascular events (fat%: HR 2.38, 95% CI 1.49-3.81; VFA: HR 1.79, 95% CI 1.09-2.93). The association between A-FABP levels and cardiovascular events was more pronounced in participants with low fat%, regardless of VFA levels. The joint effect of high A-FABP levels and obesity resulted in a greater risk of cardiovascular events. CONCLUSION: Serum A-FABP levels were significantly associated with the risk of cardiovascular events, and this pattern of association was more prominent among the population with low fat%, which was independent of VFA.

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OBJECTIVE: To analyze the relationship between the biochemical markers of liver metabolism in different stages of Metabolic Associated Fatty Liver Disease (MAFLD) according to the obesity phenotype. METHODOLOGY: This is a cross-sectional study with individuals with class III obesity classified according to the obesity phenotypes proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. Biochemical and anthropometric variables were analyzed according to the staging of MAFLD and obesity phenotype. RESULTS: A total of 50 subjects with MAFLD, 62% (n = 31) with steatosis and 38% (n = 19) with steatohepatitis without fibrosis; 36% were classified as metabolically healthy obesity (MHO) and 64% as metabolically unhealthy obesity (MUHO), respectively. Mean values of alkaline phosphatase were 85.44 ± 27.27 vs. 61.92 ± 17.57 (p = 0.006); gamma-glutamyl transpeptidase, 25.77 ± 15.36 vs. 30.63 ± 19.49 (p = 0.025); and albumin, 3.99 ± 0.34 vs. 4.24 ± 0.23 (p = 0.037), were lower and statistically significant in the MHO group with steatosis. The results show when considering individuals with IR, only AP is a predictor of unhealthy phenotype (B-0.934, 0.848- 1.029, p = 0.031). CONCLUSION: MHO individuals with steatosis present lower severe changes related to markers of liver damage and function and AP is considered the predictor of MUHO phenotype.

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OBJECTIVES: The purpose of the current study was to examine the association between poor sleep health (ie, excessive daytime sleepiness), obesity sequelae (ie, percent overweight and serum leptin levels), and addictive-like eating behaviors, an obesity phenotype, in a sample of Black/African American (B/AA) adolescents. DESIGN: The current study analyzed archival baseline data from a sample of B/AA adolescents with obesity enrolled in a sequential randomized clinical trial. SETTING: Data were collected in the participants' homes by trained research assistants unaware of treatment condition. PARTICIPANTS: A sample of 181 B/AA adolescents with obesity between the ages of 12 and 16 years (M = 14.26, SD = 1.46) and having a body mass index (BMI) above the 95th percentile for age and gender were sampled. MEASUREMENTS: Self-report measures included the Cleveland Adolescent Sleepiness Questionnaire and the Yale Food Addiction Scale-Children (YFAS-C). Anthropomorphic data and blood samples were used to determine BMI and serum leptin levels, respectively. RESULTS: Excessive daytime sleepiness was positively correlated with YFAS-C symptom count, r = 0.295, P < .001. Serum leptin levels and percent overweight were both positively correlated with each other, r = 0.445, P < .001 and with YFAS-C symptom count, r = 0.215, P = .006. After controlling for age and gender, results supported an indirect effect from daytime sleepiness to both serum leptin levels (estimate = 2.210, SE = 0.932, P = .018) and percent overweight (estimate = 2.817, SE = 1.415, P = .046) through YFAS-C symptom count. CONCLUSIONS: Culturally informed interventions on eating behaviors (ie, addictive-like eating) when excessive daytime sleepiness is reported are needed. Early intervention may help prevent the onset or worsening of obesity among adolescents.

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Objective: To assess the association between early life exposure to famine and the risk of metabolic obesity phenotypes among adults in middle age. Methods: The study selected two comparison groups. Comparison A consisted of a non-exposed group born between 1963-1965 from the China Health and Retirement Longitudinal Study (CHARLS) 2015 wave (N=862) and a fetal-exposed group born between 1959-1961 from the 2011 wave (N=507). Comparison B consisted of an early childhood-exposed group born between 1955-1957 from the 2011 wave (N=830) and a fetal-exposed group born between 1959-1961 from the 2015 wave (N=552). Multivariable logistic regressions were conducted to explore the associations between different periods of famine exposure and obesity, metabolic health status, and metabolic obesity phenotypes, with stratification by sex. Results: Compared with the non-exposed group, participants exposed to famine in the fetal period had a significantly lower risk of overweight/obesity (OR: 0.78, 95%CI: 0.63-0.97) and a higher risk of metabolically unhealthy status (OR: 1.73, 95%CI: 1.34-2.23) and metabolically unhealthy non-obesity (MUNO) (OR: 2.12, 95%CI: 1.46-3.08) at the age of 50-52 years. In the sex-stratified analysis, males exposed to famine in the fetal period had a significantly lower risk of overweight/obesity (OR: 0.59, 95%CI: 0.43-0.80) and metabolically healthy obesity (MHO) (OR: 0.56, 95%CI: 0.37-0.85), while such associations were not found in females. Compared with the early childhood exposure group, participants in the fetal exposure group had a significantly lower risk of metabolic unhealthy status (OR: 0.65, 95%CI: 0.51-0.85) and MUNO (OR: 0.50, 95%CI: 0.35-0.72). Those associations were observed in both males and females. Conclusion: Exposure to famine in early life increased the risk of metabolically unhealthy status in adulthood. Different metabolic subtypes should be identified at an early stage and followed by classification, intervention, and treatment.

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