BACKGROUND: The fat-to-muscle mass ratio (FMR), integrating the antagonistic effects of fat and muscle mass, has been suggested as a valuable indicator to assess cardiometabolic health independent of overall adiposity. However, the specific associations of total and regional FMR with cardiometabolic risk are poorly understood. We aimed to examine sex-specific associations of total and regional FMR with single and clustered cardiometabolic risk factors (CRFs).
METHODS: 13,505 participants aged 20 years and above were included in the cross-sectional study. Fat mass and muscle mass were assessed using a bioelectrical impedance analysis device. FMR was estimated as fat mass divided by muscle mass in corresponding body parts (whole body, arm, leg, and trunk). Clustered CRFs was defined as the presence of two or more risk factors, including hypertension, elevated blood glucose, dyslipidemia, insulin resistance (IR), and hyperuricemia. IR was assessed by the triglyceride glucose (TyG) index. Multivariable logistic regression models were applied to explore the associations of FMR in the whole body and body parts with single and clustered CRFs.
RESULTS: The odds ratios (ORs) increased significantly for all single and clustered CRFs with the per quartile increase of total and regional FMR in both sexes (P for trend < 0.001), following adjustment for confounders. Among the regional parts, FMRs of the legs presented the strongest associations for clustered CRFs in both men and women, with adjusted OR of 8.54 (95% confidence interval (CI): 7.12-10.24) and 4.92 (95% CI: 4.24-5.71), respectively. Significant interactions (P for interaction < 0.05) were identified between age and FMRs across different body parts, as well as between BMI status and FMRs in different regions for clustered CRFs. Restricted cubic splines revealed significant non-linear relationships between FMRs of different body parts and clustered CRFs in both sexes (P for nonlinear < 0.05).
CONCLUSIONS: FMRs in the whole body and different regions were significantly associated with single and clustered CRFs in the general Chinese population. The association between FMR and clustered CRFs was more pronounced in youngers than in the elderly.

To date, the best obesity-related indicators (ORIs) for predicting hypertension, dyslipidaemia, Type 2 diabetes mellitus (T2DM) and multimorbidity are still controversial. This study assessed the ability of 17 ORIs [body mass index (BMI), body fat percentage (BF%), c-index, Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), waist-hip ratio (WHR), waist-to-height ratio (WHtR), body roundness index (BRI), abdominal volume index (AVI), triglyceride glucose index (TYG), lipid accumulation product (LAP), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), waist triglyceride index (WTI) and cardiometabolic index (CMI)] to predict hypertension, dyslipidemia, T2DM, and multimorbidity in populations aged 40-69 years. From November 2017 to December 2022, 10,432 compliant residents participated in this study. Receiver operating characteristic curves were used to assess the ability of ORIs to predict target diseases across the whole population and genders. The DeLong test was used to analyse the heterogeneity of area under curves (AUCs). Multivariable logistic regression was used to analyse the association of ORIs with hypertension, dyslipidaemia, T2DM, and multimorbidity. The prevalence of hypertension, dyslipidaemia, T2DM, and multimorbidity was 67.46%, 39.36%, 12.54% and 63.58%, respectively. After excluding ORIs associated with the target disease components, in the whole population, CVAI (AUC = 0.656), BMI (AUC = 0.655, not significantly different from WC and AVI), CVAI (AUC = 0.645, not significantly different from LAP, CMI, WHR, and WTI), and TYG (AUC = 0.740) were the best predictor of hypertension, dyslipidemia, T2DM, and multimorbidity, respectively (all P < 0.05). In the male population, BF% (AUC = 0.677), BMI (AUC = 0.698), CMI (AUC = 0.648, not significantly different from LAP and CVAI), and TYG (AUC = 0.741) were the best predictors (all P < 0.05). In the female population, CVAI (AUC = 0.677), CUN-BAE (AUC = 0.623, not significantly different from BF%, WC, WHR, WHtR, BRI and BMI), CVAI (AUC = 0.657, not significantly different from WHR), TYG (AUC = 0.740) were the best predictors (all P < 0.05). After adjusting for all covariates, all ORIs were significantly associated with hypertension, dyslipidaemia, T2DM, and multimorbidity (all P < 0.05), except for ABSI and hypertension and BAI and T2DM, which were insignificant. Ultimately, after considering the heterogeneity of prediction of ORIs among different populations, for hypertension, BF% was the best indicator for men and CVAI for the rest of the population. The best predictors of dyslipidaemia, T2DM, and multimorbidity were BMI, CVAI and TYG, respectively. Screening for common chronic diseases in combination with these factors may help to improve the effectiveness.

The metabolic changes that occur during the early post-partum period in dairy cows can indeed lead to an imbalance in energy utilization, resulting in the production of excessive ketone bodies. This can have detrimental effects on the cow\'s health and milk production, leading to economic losses for dairy producers. The release of non-esterified fatty acids into the blood due to increased lipolysis is a key factor in the development of ketosis. Abdominal adiposity is a key factor on these outcomes in modern dairy cows. The redirection of energy and glucose towards lactose synthesis and milk yield leaves a deficit of gluconeogenic precursors, leading to the conversion of acetyl-CoA into ketone bodies instead of entering the Krebs cycle. These ketone bodies, including acetone, acetoacetate and β-hydroxybutyrate, accumulate in the blood and can be detected in various bodily fluids, such as urine, blood and milk, allowing for diagnostic testing. Prevention is indeed crucial in managing ketosis in dairy cattle. Supplementation of propylene glycol in the diet or the use of monensin, either in the diet or in the form of a slow-release bolus, can help prevent the occurrence of ketosis. However, avoiding high body condition (subcutaneous fat) and excessive abdominal adiposity during the dry period and parturition plus an adequate cow comfort are fundamental tasks to avoid ketosis and related disorders. These interventions aim to provide additional energy sources or enhance the cow\'s ability to utilize energy efficiently, thus reducing the reliance on excessive lipolysis and ketone body production.

OBJECTIVE: The causal relationship between life course adiposity with metabolic dysfunction-associated steatotic liver disease (MASLD) is ambiguous. We aimed to investigate whether there is an independent genetic causal relationship between body size at various life course and MASLD.
METHODS: We performed univariable and multivariable Mendelian randomization (MR) to estimate the causal effect of body size at different life stages on MASLD (i.e., defined by the clinical comprehensive diagnosis from the electronic health record [HER] codes [ICD9/ICD10] or diagnostic phrases), including birthweight, childhood body mass index (BMI), adult BMI, waist circumference (WC), waist-to-hip ratio (WHR), body fat percentage (BFP).
RESULTS: In univariate analyses, higher genetically predicted lower birthweight (ORIVW = 0.61, 95%CI, 0.52 to 0.74), Childhood BMI ( ORIVW = 1.37, 95%CI, 1.12 to 1.64), and adult BMI (ORIVW = 1.41, 95%CI, 1.27 to 1.57) was significantly associated with subsequent risk of MASLD after Bonferroni correction. The MVMR analysis demonstrated compelling proof that birthweight and adult BMI had a direct causal relationship with MASLD. However, after adjusting for birthweight and adult BMI, the direct causal relationship between childhood BMI and MASLD disappeared.
CONCLUSIONS: For the first time, this MR elucidated new evidence for the effect of life course adiposity on MASLD risk, providing lower birthweight and duration of obesity are independent risk factors for MASLD. Our findings indicated that weight management during distinct time periods plays a significant role in the prevention and treatment of MASLD.

BACKGROUND: Glucose metabolic disorder is associated with the risk of heart failure (HF). Adiposity is a comorbidity that is inextricably linked with abnormal glucose metabolism in older individuals. However, the effect of adiposity on the association between glucose metabolic disorder and HF risk, and the underlying mechanism remain unclear.
METHODS: A total of 13,251 participants aged ≥ 60 years from a cohort study were categorized into euglycemia, prediabetes, uncontrolled diabetes, and well-controlled diabetes. Adiposity was assessed using body mass index (BMI), waist-to-hip ratio (WHR), and visceral fat area (VFA). Adiposity-associated metabolic activities were evaluated using adiponectin-to-leptin ratio (ALR), homeostatic model assessment of insulin resistance (HOMA-IR), and triglyceride-glucose index (TyG). The first occurrence of HF served as the outcome during the follow-up period.
RESULTS: A total of 1,138 participants developed HF over the course of an average follow-up period of 10.9 years. The rate of incident HF occurrence was higher in prediabetes, uncontrolled diabetes, and well-controlled diabetes participants compared to that in euglycemia participants. However, the high rates were significantly attenuated by BMI, VFA, and WHR. For WHR in particular, the hazard ratio for incident HF was 1.18 (95% confidence interval (CI): 1.03, 1.35, Padj.=0.017) in prediabetes, 1.59 (95% CI: 1.34, 1.90, Padj.<0.001) in uncontrolled diabetes, and 1.10 (95% CI: 0.85, 1.43, Padj.=0.466) in well-controlled diabetes. The population attributable risk percentage for central obesity classified by WHR for incident HF was 30.3% in euglycemia, 50.0% in prediabetes, 48.5% in uncontrolled diabetes, and 54.4% in well-controlled diabetes. Adiposity measures, especially WHR, showed a significant interaction with glucose metabolic disorder in incident HF (all Padj.<0.001). ALR was negatively associated and HOMA-IR and TyG were positively associated with BMI, WHR, VFA, and incident HF (all Padj.<0.05). ALR, HOMA-IR, and TyG mediated the associations for BMI, WHR and VFA with incident HF (all Padj.<0.05).
CONCLUSIONS: Adiposity attenuated the association of glucose metabolic disorder with incident HF. The results also showed that WHR may be an appropriate indicator for evaluating adiposity in older individuals. Adiposity-associated metabolic activities may have a bridging role in the process of adiposity attenuating the association between glucose metabolic disorder and incident HF.
BACKGROUND: retrospectively registered number: ChiCTR-EOC-17,013,598.

BACKGROUND: Previous studies have revealed the effects of different physical activity (PA) types on visceral adipose tissue (VAT) accumulation in individuals with overweight/obesity. However, the independent association (especially the dose-response relationship) between PA and VAT in individuals with and without overweight/obesity remains less explored. Visceral adiposity index (VAI), calculated from waist circumference, body mass index (BMI), triglyceride and high-density lipoprotein cholesterol, is a novel indicator of VAT. This study aims to elucidate the association between PA and VAI in participants with and without overweight/obesity.
METHODS: Participants who are overweight or obese and with complete data on VAI, PA, and other essential covariates from the National Health and Nutrition Examination Survey (NHANES) database (2015-2018) were included in this study. PA was evaluated by the PA questionnaire and converted into metabolic equivalent task (MET) hours per week (MET-h/wk) based on the suggested MET scores. Multivariate linear regression models were used to identify the association between PA and VAI. Subgroup analyses, combined with interaction tests and restricted cubic spline (RCS) regression analyses, were utilized to explore the stability and nonlinearity of PA-VAI association, respectively.
RESULTS: A total of 4, 312 participants with complete data on PA and VAI was included in this study, with 3, 441 of them being overweight or obese. After adjusting for all potential covariates, increased PA was found to be significantly associated with remarkable lower level of VAI in all participants (β = -0.0004, P = 0.003), participants with (β = -0.0013, P = 0.012) and without (β = -0.0004, P = 0.003) overweight/obesity. Subgroup analyses and interaction tests revealed that the PA-VAI association was not modified by other covariates in individuals with overweight/obesity. Furthermore, RCS analyses revealed that PA was significantly, linearly and negatively associated with VAI in all participants, participants with and without overweight/obesity (all P < 0.05, all P for nonlinearity > 0.05). Noteworthily, as opposed to individuals without overweight/obesity, PA was significantly associated with lower VAI in participants with overweight/obesity after exceeding the threshold of 150 MET-h/wk.
CONCLUSIONS: Increased PA was significantly associated with lower level of VAI, but a higher level of PA (> 150 MET-h/wk) was needed to obtain significantly lower level of VAI in individuals with overweight/obesity.

BACKGROUND: Recent years have seen the emergence of numerous novel indicators for visceral obesity. This study investigates the potential correlation between the Chinese visceral adiposity index (CVAI) and hyperuricemia (HUA).
METHODS: This research, derived from a 2011 cross-sectional analysis in Dalian, China, employed restricted cubic spline (RCS) plots to identify inflection points. Subsequently, one-way and multifactorial logistic regression models were utilized, with HUA as the outcome variable. Additionally, subgroup analyses and interaction tests were conducted. Eventually, receiver operating characteristic (ROC) curves were calculated to assess the effectiveness of CVAI and other body composition indices in predicting HUA.
RESULTS: The study included 10,061 individuals, with a HUA prevalence of 14.25%. Significant relationships with HUA were observed for CVAI. RCS analysis revealed a J-shaped relationship between CVAI and HUA. Compared to those in the low CVAI category, HUA was notably associated with individuals in the high CVAI category in multifactorial logistic regression (OR = 2.661, 95% CI: 2.323, 3.047). Subgroup analyses demonstrated stronger relationships in women, participants without hypertension, and participants without diabetes. Additional modeling via ROC curves suggested that the CVAI may offer effective predictive value for HUA.
CONCLUSIONS: This study confirmed that an elevated CVAI elevates the risk of HUA in middle-aged and elderly populations in the Dalian community. The findings advance obesity prevention strategies that mitigate HUA risk and support healthcare initiatives for China\'s aging population.

Previous observational studies have identified a link between obesity, adiposity distribution, type 1 Diabetes Mellitus (T1DM), type 2 Diabetes Mellitus (T2DM), and the risk of pressure ulcers (PUs). However, the definitive causality between obesity and PUs, and potential DM mediators remains unclear. Univariable, multivariable, and mediation Mendelian randomization (MR) analyses were conducted to explore the mediating role of T1DM or T2DM in the association between obesity, adiposity distribution, and PUs. Instrumental variables for obesity and adiposity distribution, including Body Mass Index (BMI), waist circumference, hip circumference, trunk fat mass, whole body fat mass, trunk fat percentage, and body fat percentage, were selected from two genome-wide association studies (GWAS). In univariable MR analysis, BMI, hip circumference, and obesity were associated with PUs using inverse variance weighted (IVW) regression. These findings were further corroborated by the replication cohorts and meta-analysis (BMI: OR = 1.537, 95% CI = 1.294-1.824, p < 0.001; Hip circumference: OR = 1.369, 95% CI = 1.147-1.635, p < 0.001; Obesity: OR = 1.235, 95% CI = 1.067-1.431, p = 0.005), respectively. Even after adjusting for confounding factors such as T1DM and T2DM, BMI and hip circumference remained statistically significant in multivariable MR analyses. T2DM may mediate the pathogenesis of BMI-related (OR = 1.106, 95% CI = 1.054-1.160, p = 0.037) and obesity-related PUs (OR = 1.053, 95% CI = 1.034-1.973, p = 0.004). These findings provide insights for the prevention and treatment of PUs, particularly in patients with obesity or DM.

To investigate the association between the visceral adiposity index (VAI) and asthma using data from National Health and Nutrition Examination Survey 2003 to 2018 by a cross-sectional study. We explored the potential relationship between the VAI and asthma incidence via a cross-sectional study of the National Health and Nutrition Examination Survey from 2003 to 2018. Multiple logistic regression analysis, restricted cubic spline analysis and subgroup analysis were performed. Among the 80,312 participants, 1984 had been told by a doctor or other health professional, and 1142 still had asthma. With all confounders controlled, the VAI was positively associated with asthma incidence (odds ratios 1.04, 95% confidence interval: 1.01, 1.08). When comparing the second, third, and fourth VAI quartiles to the lowest quartile, the adjusted odds ratios (95% confidence intervals) for asthma risk were 1.02 (0.86, 1.21), 1.14 (0.96, 1.36), and 1.18 (1, 1.39), respectively (P for trend = .02). Subgroup analysis revealed no significant interaction effect among the subgroups (P > .05). The positive association was stronger in current asthma patients (odds ratios 1.13, 95% confidence interval: 1.03, 1.24). When comparing the second, third, and fourth VAI quartiles to the lowest quartile, the adjusted odds ratios for current asthma risk were 1.15 (0.81, 1.64), 1.29 (0.91, 1.84), and 1.51 (1.01, 2.24), respectively (P for trend .04). The restricted cubic spline regression analysis did not reveal a nonlinear correlation between the VAI and asthma or current asthma. Subgroup analysis revealed a significant interaction effect between age (P for interaction = .03) and diabetes status (P for interaction = .02). Except in the age ≥60 years, Less than high school, normal body mass index subgroup, VAI, and current asthma were positively correlated. A positive relationship between the VAI and asthma incidence was observed. In particular, there was a strong positive correlation between the VAI score and current asthma. According to the subgroup analysis, more attention should be given to individuals aged 40 to 59 years who have diabetes.

Nonalcoholic fatty liver disease (NAFLD) has recently been renamed metabolic dysfunction-associated fatty liver disease (MAFLD) by international consensus. Both MAFLD and osteoporosis are highly prevalent metabolic diseases. Recent evidence indicates that NAFLD increases the risk of low bone mineral density and osteoporosis, likely mediated by obesity. NAFLD has a close association with obesity and other metabolic disorders. Although obesity was previously thought to protect against bone loss, it now heightens osteoporotic fracture risk. This overview summarizes current clinical correlations between obesity, NAFLD, and osteoporosis, with a focus on recent insights into potential mechanisms interconnecting these three conditions. This study reviewed the scientific literature on the relationship between obesity, nonalcoholic fatty liver disease, and osteoporosis as well as the scientific literature that reveals the underlying pathophysiologic mechanisms between the three. Emerging evidence suggests obesity plays a key role in mediating the relationship between NAFLD and osteoporosis. Accumulating laboratory evidence supports plausible pathophysiological links between obesity, NAFLD, and osteoporosis, including inflammatory pathways, insulin resistance, gut microbiota dysbiosis, bone marrow adiposity, and alterations in insulin-like growth factor-1 signaling. Adiposity has important associations with NAFLD and osteoporosis, the underlying pathophysiologic mechanisms between the three may provide new therapeutic targets for this complex patient population.