This study aimed to elucidate the correlation of Visceral Adiposity Index (VAI) with prostate cancer (PCa) among men aged 40 years and older in the United States.

Analysis included multivariate linear and logistic regression, smoothing curve fitting, and threshold effect evaluation using 2003-2010 National Health and Nutrition Examination Survey (NHANES) data. The stability of this relationship across demographic groups was assessed via subgroup analyses and interaction tests.

Among 2,768 participants, those with elevated VAI displayed lower total prostate-specific antigen (tPSA) levels and reduced PCa risk. Each VAI unit elevation corresponded to a 0.075 ng/mL tPSA reduction [-0.075 (-0.145, -0.005)] and 18.8% PCa risk reduction [0.812 (0.687, 0.960)]. Top-quartile VAI individuals exhibited 0.282 ng/mL reduced tPSA [-0.282 (-0.557, -0.007)] and 49.7% reduced PCa risk [0.503 (0.282, 0.896)] relative to bottom-quartile counterparts. This inverse relationship was more pronounced in men ≥70 years. Moreover, VAI-tPSA in other races demonstrated a U-shaped pattern, with a 2.09 inflection point. At the same time, a Mexican American subgroup exhibited an inverted U-shape for VAI and PCa risk, with a 1.42 inflection point.

In men aged ≥70, VAI indicates an inverse PCa relationship. However, PSA-based PCa screening may be influenced in visceral-obese individuals aged <70.

Cardiovascular diseases such as coronary artery disease, myocardial infarction, and heart failure impact millions of people annually globally and are a major cause of disease and death. This study explores the predictive capabilities of novel metabolic indices (TyG, HOMA-IR, TG/HDL-C, and VAI) for major adverse cardiovascular events (MACE) and analyzes their relationships with diabetes and cardiovascular risks. Using data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2003 to 2018, we applied multiple machine learning algorithms to evaluate nine metabolic indicators including cholesterol levels, triglycerides, insulin, and waist circumference. Through cross-validation to validate model performance, the XGBoost algorithm demonstrated the most accurate performance in predicting cardiovascular outcomes, particularly for diseases like angina and heart failure. Additionally, SHAP value analysis confirmed the critical roles of waist circumference and METS-IR in predicting adverse cardiovascular events. Furthermore, we employed 100 machine learning algorithms to calculate the AUC values of metabolic indicators in predicting AP, CHD, HF, and MI, showing that METS-IR had the greatest contribution in these aspects. This research highlights the significance of metabolic indices in stratifying cardiovascular risks and presents potential avenues for targeted preventive strategies.

Obesity is increasingly recognized among people with type 1 diabetes mellitus (T1DM). Little is known about the body composition of adolescents with T1DM and its metabolic outcomes. Hence, this study assessed the body composition of adolescents with T1DM and its relationship with glycaemic control, insulin resistance and vascular complications.

One hundred twenty adolescents with T1DM were assessed for anthropometric measures, insulin therapy, bioelectrical impedance analysis (BIA), fasting lipids, glycated haemoglobin, with estimated glucose disposal rate (eGDR) calculation. Regional body fat quantification was performed via Magnetic resonance imaging (MRI) 3-T.

Thirty-three adolescents with T1DM were overweight (27.5%), and 8 were obese (6.6%). Adolescents with T1DM having insulin resistance were found to have significantly higher BMI z score, total body fat %, and visceral/subcutaneous fat % than those without insulin resistance (p < 0.05). Moreover, adolescents with T1DM having microvascular complications showed significantly higher total fat % and visceral/subcutaneous fat % than those without microvascular complications (p < 0.05). Visceral fat % and visceral/subcutaneous fat ratio were positively correlated with waist/hip ratio, eGDR and LDL level (p < 0.05). Waist/hip ratio and eGDR were the most significant independent variables associated with visceral fat % and visceral/subcutaneous fat ratio among adolescents with T1DM using multivariate regression analysis.

Overweight and visceral adiposity are frequently encountered among the studied adolescents with T1DM. Visceral adiposity is associated with insulin resistance, hyperlipidaemia and microvascular complications among adolescents with T1DM independent of glycaemic control and insulin dosage.

Central obesity demonstrated an association with stroke, while the association between visceral fat accumulation and stroke required further investigation. We aimed to evaluate the association of Chinese visceral adiposity index (CVAI) and its dynamic change patterns with stroke risk.

A total of 9052 individuals from a nationally representative cohort were included. The main outcome was a self-reported physician's diagnosis of stroke. Multivariable Cox and logistic regression models were employed to evaluate the association of the exposure with stroke. The shape of the association was explored using restricted cubic splines. The mean age of the study population was 60.0 ± 9.5 years, and the median follow-up period was 7 years. A linear dose-response association between baseline CVAI and stroke risk was observed. Compared with the lowest CVAI quartile, those in the second, third, and fourth quartiles had 1.62, 1.72, and 2.08 times higher risk, respectively. Additionally, each standard deviation increase in CVAI induced 23 % increased risk of stroke. Among the five CVAI change patterns, the low-stable pattern carried a greatest protective effect against stroke (OR = 0.35, 95 % CI: 0.21-0.58); the decreasing pattern exhibited greater risk reduction compared to the moderate and the increasing patterns (with reference to the persistent-high pattern). In terms of identifying high-risk individuals of stroke, CVAI outperformed other obesity indices.

Higher baseline CVAI and a persistent-high pattern were associated with increased risk of incident stroke. Maintaining a low level of CVAI or reducing it may have potential benefits for stroke prevention.

Numerous cohort studies have consistently shown a significant link between obesity and an increased risk of gallstones. However, body mass index (BMI) alone may not fully capture the complexity of obesity. This study aimed to analyze the associations between different obesity indexes and the incidence of gallstones, using a competing risk model.

A total of 459,523 UK Biobank participants without gallstones at baseline were analyzed. Cox proportional hazards models, competing risk models, and restricted cubic spline were performed to assess the longitudinal associations between 11 obesity indexes including a body shape index (ABSI), body adiposity index (BAI), body roundness index (BRI), conicity index (ConI), cardiometabolic index (CMI), hip circumference (HC), visceral adiposity index (VAI), lipid accumulation product (LAP), waist circumference (WC), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR) apart from BMI and gallstone occurrence risk, respectively.

Over a follow-up period of 12.54 years, a total of 14,951 participants developed gallstones. After adjusting for confounding factors, competing risk regression analyses revealed that BMI, BRI, BAI, confidence interval, CMI, HC, LAP, VAI, WC, and WHtR were all positively associated with an increased risk of gallstones. ABSI and WHR showed a "J-shaped" association with the incidence of gallstones, suggesting a nonlinear relationship. Sensitivity analyses confirmed the consistency of the results even after excluding participants who developed gallstones within 1 year of follow-up.

Different dimensions of obesity are all significantly associated with an increased risk of gallstones. These findings highlight the importance of managing obesity, including both overall body weight and abdominal fat, to prevent the occurrence of gallstones.

The visceral adiposity index is a valuable tool for assessing visceral fat accumulation. However, its non-linear association with prediabetes and diabetes requires further elucidation. Therefore, we aim to clarify the intricate interplay between the visceral adiposity index and these dysglycemic conditions.

The National Health and Nutrition Examination Survey database from 1999 to 2018 was utilized to analyze health data from 24,072 participants. A multivariate logistic regression model was employed to evaluate the independent association between the visceral adiposity index and prediabetes and diabetes while considering potential confounding factors. Generalized additive models were used to identify any non-linear relationships by fitting smooth curves. Additionally, a stratified analysis based on different baseline characteristics was conducted, along with an interactive analysis.

After accounting for all relevant variables, individuals in the lowest quartile of the visceral adiposity index had a notably diminished likelihood of progressing to prediabetes and diabetes when compared with those in the other three quartiles. The odds ratios and 95% confidence intervals were as follows: 1.37 (1.23, 1.53), 1.87 (1.65, 2.12), and 2.80 (2.33, 3.37). More importantly, a non-linear association was observed between the visceral adiposity index and prediabetes and diabetes, with a threshold identified at 2.10.

There exists a notable and positive association between the visceral adiposity index and prediabetes and diabetes, displaying non-linear attributes in this evaluation of the relationship. Risk assessment and early prevention strategies targeting the maintenance of low levels of visceral adiposity index may substantially diminish the likelihood of developing prediabetes and diabetes.

This research is aimed at investigating the association between the cardiometabolic index (CMI) and depressive symptoms in Chinese population of middle and older age, using data derived from the CHARLS study.

Using data from 7,800 participants in the 2011-2012 wave of the CHARLS cohort, this cross-sectional analysis concentrated on examining the association between CMI and depressive symptoms, assessed through CESD-10 scores. The study utilized multivariate logistic regression, multiple linear regression, and restricted cubic spline (RCS) models to investigate the link between CMI and depression, with subgroup analyses offering further insights. Sensitivity analyses included propensity score matching and data from 8,457 participants in the 2015-2016 CHARLS wave.

In fully adjusted models, higher CMI was significantly associated with an elevated risk of depression, with participants having a CMI ≥ 0.594 showing a 162% higher risk compared to those with lower CMI. The RCS analysis identified a threshold at CMI = 0.594, where participants with CMI ≥ 0.594 had a 162% elevated possibility of depression in comparison to those with CMI < 0.594 [OR = 2.62, 95% CI: 2.36-2.91]. Sensitivity analyses, including propensity score matching and data from the 2015-2016 CHARLS wave, confirmed the robustness of the findings.

Our analysis demonstrates that elevated CMI levels are independently correlated with a heightened likelihood of experiencing depressive symptoms, highlighting the significance of metabolic interventions in mitigating depressive tendencies in middle-aged and older individuals.

Background/Objectives: An increase in body fat is linked to abnormalities in energy metabolism. We aimed at determining cardiometabolic risk in Algerian participants with obesity alone and with or without type 2 diabetes. The study measured the concentrations of circulating adipocytokines (leptin, adiponectin, resistin), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) to identify and examine how imbalances in adipocytokines may affect the parameters of cardiometabolic health. Methods: Algerian participants (n = 300) were recruited and divided into three groups: control, obese, and type 2 diabetics (with two sub-groups: with and without obesity). Insulin resistance was evaluated using HOMA-IR, while ELISA was used to measure adipocytokines. Atherogenic index in plasma (AIP), adiponectin-leptin ratio (ALR), and visceral adiposity index (VAI) were also assessed. One-way ANOVA was used to compare obesity and diabetes groups to the control one (p < 0.05). Logistic regression analysis was conducted to strengthen the robustness of statistical correlations. Results: Participants with reduced adiponectin-leptin ratio (ALR) and elevated levels of resistin, TNF-α, and IL-6 are found to be at higher risk of cardiovascular diseases. An imbalance in adipocytokine levels is caused by a decrease in adiponectin concentrations, and an increase in pro-inflammatory adipocytokines that maintain and exacerbate energy imbalance and induces hyperinsulinemia, exposing individuals to a high risk of cardiovascular diseases. Conclusions: Given that ALR is a functional biomarker of inflammation, insulin resistance, and adipose tissue dysfunction, targeting ALR could potentially be a therapeutic approach to coping with obesity-related cardiometabolic risks. Mediterranean diet, weight loss, and increased physical activity can be key components to promote healthy adipose tissue through the increase in ALR.

The aim of this study is to assess the individual and joint associations of variability in multiple cardiometabolic parameters with mortality risk across older populations.

A total of 51,551 Chinese elderly participants (aged ≥60 years) with ≥3 measurements of systolic blood pressure, visceral adiposity index, fasting blood glucose, and low-density lipoprotein cholesterol during 2018-2022 were included. Variability metrics included SD, coefficient of variation, average real variability, and variability independent of the mean (used in primary analysis). Participants were classified on the basis of the number of high-variability (highest quartile of variability) parameters into 4 categories: with 0, 1, 2, and 3-4 high-variability cardiometabolic parameters. Cox regression analyses were performed in 2024. Findings were then externally validated using the Health and Retirement Study (Waves 8-15).

Higher systolic blood pressure, visceral adiposity index, fasting plasma glucose, and low-density lipoprotein cholesterol variability were associated with greater all-, cardiovascular-, and other-cause mortality risk. Compared with those of subjects with no high-variability parameters measured as the variability independent of the mean, the hazard ratios (95% CI) of all-cause mortality were 1.30 (1.16, 1.44) for 1 parameter, 1.86 (1.66, 2.09) for 2 parameters, and 2.02 (1.75, 2.32) for 3-4 parameters. Consistent results were noted for cardiovascular-, cancer-, and other-cause mortality using other variability indices and in various sensitivity and subgroup analyses. These associations were validated in the Health and Retirement Study (n=1,991).

Increased variability in cardiometabolic parameters is associated with elevated risks of all-cause and cause-specific mortality among older adults in China. Reducing variability of these parameters could serve as a target to increase life expectancy in older populations.

Visceral adipose tissue has been indicated closely connected with the severity of acute pancreatitis (AP). Visceral adiposity index (VAI) is a mathematical model that consists of waist circumference, body mass index, triglyceride and high-density lipoprotein cholesterol, which has been demonstrated to be a better indicator of visceral fat than other traditional indices. This study aimed to evaluate the relationship between VAI and the severity of AP.

A retrospective analysis was conducted on a cohort of 1174 patients diagnosed with AP. These patients were categorized into two groups based on their VAI values: the normal VAI (NVAI) group and the elevated VAI (EVAI) group.

The EVAI group were much younger, mainly male and had higher incidence of severe acute pancreatitis (SAP) compared with the NVAI group (p < 0.001). The EVAI group developed higher incidences of persistent respiratory failure, acute peripancreatic fluid collection (APFC) and acute necrotic collection (ANC). The VAI level and the percentage of EVAI showed an increasing trend with the severity of AP (p < 0.001). EVAI was the most independent risk factor for persistent respiratory failure (OR = 6.405, 95% CI 2.317-17.705), APFC (OR = 2.093, 95% CI 1.255-3.578) and ANC (OR = 4.910, 95% CI 1.736-13.887).

EVAI was strongly related to the severity of AP. It was the most independent risk factor of persistent respiratory failure, APFC and ANC.

The risk factors for type 2 diabetes mellitus (T2DM) have been increasingly researched, but the lack of systematic identification and categorization makes it difficult for clinicians to quickly and accurately access and understand all the risk factors, which are categorized in this paper into five categories: Social determinants, lifestyle, checkable/testable risk factors, history of illness and medication, and other factors, which are discussed in a narrative review. Meanwhile, this paper points out the problems of the current research, helps to improve the systematic categorisation and practicality of T2DM risk factors, and provides a professional research basis for clinical practice and industry decision-making.

The Chinese Visceral Adiposity Index (CVAI), a measure of visceral adiposity dysfunction, is used to assess visceral fat (VFA) malfunction. This research was performed to evaluate the relationship between CVAI and serum uric acid levels in type 2 diabetes mellitus (T2DM) patients.

A total of 2268 patients with T2DM were enrolled in this study. We collected the general clinical information of patients, measured the basic anthropometric indicators, tested glycolipid metabolism and biochemical indicators, and measured the visceral and subcutaneous fat area with bioelectrical impedance technology. According to the quartiles of the CVAI, the T2DM patients were classified into four groups: group A (CVAI ≤ 94.43), group B (94.43

Results

Among quartiles of CVAI, as CVAI increased, the proportion of patients with HUA gradually increased. The correlation analysis showed that the majority of basal measures, glycolipid metabolism and biochemical indicators were positively correlated with CVAI. By comparison, the level of CVAI in the HUA group was significantly higher than non-HUA group. Meanwhile, through using the ROC curve, our study observed the more predictive value of CVAI than other obesity indicators for T2DM with HUA.

Conclusion

CVAI is a simple but effective indicator, which is significantly correlated with HUA in T2DM and can reflect the incidence of HUA in T2DM patients. As CVAI increased, the risk of HUA in T2DM patients increased. Therefore, we should pay more attention to the application of CVAI in T2DM.

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We investigated the correlation between Triglyceride-glucose (TyG) index and cardiovascular disease (CVD) mortality in type 2 diabetes mellitus (T2DM) population across different obesity classications using a cohort study. We analyzed 7867 T2DM participants from the National Health and Nutrition Examination Survey 1999-2018, categorizing them into obese or non-obese group by body mass index (BMI) and waist circumference (WC). Cox regression models were used to estimate the correlation between TyG index and CVD mortality risk, comparing the results across the two obesity classifications. Over a 9.1-year follow-up, 691 CVD deaths occurred. Among non-obese T2DM participants (BMI-defined), the hazard ration for CVD mortality was 1.73 in the fourth quartile group of TyG index compared with the first quartile group. Conversely, among obese T2DM participants (WC-defined), the fourth quartile group of TyG index held a 1.51-fold risk of CVD mortality compared with the first quartile group. The association between obesity and higher CVD risk was observed in WC-defined obesity but not in BMI-defined obesity. A totally opposite relationship appeared between TyG index and CVD mortality based on how obesity was defined using BMI or WC in the T2DM participants, suggesting a reevaluation of BMI's accuracy in predicting mortality risk.

This study aimed to explore the synergistic effect of lipid accumulation product (LAP) and visceral adiposity index (VAI) on diabetes risk, and to evaluate the potential associations of these novel metabolic markers with diabetes. The current cross-sectional survey utilised data from the 2015-2018 National Health and Nutrition Examination Survey (NHANES). The relationship between LAP and VAI levels and diabetes was examined using multiple logistic regression analysis. Moreover, threshold effects analysis and smoothed curve fitting were used as analytical techniques. The diabetes group exhibited significantly higher LAP (90.1 ± 84.1) and VAI (2.8 ± 2.8) levels compared to the non-diabetes group (p < 0.0001).After adjusting for confounding factors, LAP (OR = 1.01, 95% CI: 1.00-1.01, p < 0.0001) and VAI (OR = 1.22, 95% CI: 1.16-1.28, p < 0.0001) were independently associated with diabetes risk. The interaction term (LAP x VAI) showed a significant synergistic effect (OR = 1.01, 95% CI: 1.00-1.07, p = 0.0042).Diabetes risk significantly increased when LAP was below 97.70 (OR = 1.03, 95% CI: 1.02-1.03, p < 0.0001) and when VAI was below 3.76 (OR = 1.88, 95% CI: 1.69-2.08, p < 0.0001). According to this study, LAP and VAI are independent predictors of diabetes risk and exhibit a significant synergistic effect. Combining these indices may improve the accuracy of diabetes screening.

The objective of this investigation was to explore the correlation between the visceral adiposity index (VAI) and depression, and to analyze how type 2 diabetes mellitus (T2DM) may influence this relationship.

This study included data of 12,378 participants sourced from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Utilizing multivariate logistic regression and restricted cubic spline (RCS) regression, we examined the correlation between VAI and depression. Additionally, we investigated the interactive and mediating effects of T2DM on the association between VAI and depression.

Controlling for all potential confounders, the Ln logarithmic transformation of VAI showed a significant positive correlation with depression [odds ratio (OR) = 1.16, 95 % confidence interval (CI): 1.01-1.35, P = 0.041]. T2DM exhibited a notable interaction effect on the relationship connecting lnVAI and depression (P for interaction = 0.013). Specifically, the T2DM group exhibited a notable positive correlation between lnVAI and depression (OR = 1.46, 95 % CI: 1.17-1.82, P < 0.001), whereas such correlation didn't reach statistical significance within the non-T2DM group. The RCS model revealed a J-shaped nonlinear link between lnVAI and depression, with an inflection point value of 0.052. Mediation analysis indicated that diabetes accounted for 8.0 % of the correlation between lnVAI and depression. Furthermore, sensitivity analysis confirmed the consistency of these findings.

A J-shaped nonlinear dose-response relationship was observed between lnVAI and depression among American adults, with a threshold of 0.052. T2DM not only served as a mediator between the two variables but also modified their association.

To analyze the relationship between Chinese Visceral Adiposity Index (CVAI) and stroke in Pudong New Area, and to provide a scientific basis for the prevention and treatment of this condition.

Based on the Follow-up Cohort Program of Chronic Disease Risk Factors in Pudong New Area, a total of 7,194 residents from 12 townships and 35 village committees or neighborhood committees were selected. The cohort data in 2016 served as the baseline, and a follow-up was conducted on 5462 individuals from 2019 to 2020.

In the analysis of stroke incidence, 5,462 subjects were included, with 616 stroke events recorded. The incidence density of stroke was 33.41 per 1000 person-years, while the China standard rate and the world standard rate were 13.78 and 12.52 per 1000 person-years, respectively. The incidence density of stroke rose with increasing quartiles of CVAI. After adjusting for potential confounders, the risk of stroke for males and females with CVAI ≥ 132.11 was 2.492 times (95% CI: 1.457 ∼ 4.260) and 1.759 times (95% CI: 1.070-2.891), respectively, compared to the groups with CVAI < 81.91. Among the eight obesity indicators, including CVAI, Visceral Adiposity Index (VAI), Body Mass Index (BMI), Waist Circumference (WC), Waist Hip Ratio (WHR), Waist Height Ratio (WHtR), Body Adiposity Index (BAI), A Body Shape Index (ABSI),and Body Roundness Index (BRI), CVAI has the highest diagnostic accuracy for female stroke using Receiver Operator Characteristic (ROC) curves (the Area under the ROC curve/AUC = 0.619). Subgroup analysis showed that smoking and dyslipidemia significantly modified the association between CVAI and stroke risk in females (interaction P < 0.05).

CVAI is identified as a significant risk factor for stroke and serves as a valuable predictor of stroke, particularly in females.

The visceral adiposity index (VAI) is a marker of visceral fat accumulation and metabolic dysfunction, but there is limited evidence of its association with cancer. The objective of this study was to investigate associations between the VAI and both incident cancer at 23 sites and all-cause cancer.

In total, 385,477 participants (53.3% women; mean age, 56.3 years) from the UK Biobank prospective cohort were included in this study. The median follow-up was 8.2 years (interquartile range, 7.3-8.9 years). The VAI was calculated using formula the published by Amato et al. and was categorized into sex-specific tertiles. Twenty-four incident cancers were the outcomes. Cox proportional hazard models were adjusted for sociodemographics, lifestyle factors, and multimorbidity counts.

Over the follow-up period, 47,882 individuals developed cancer. In the fully adjusted models, the VAI was associated with a higher risk of six cancer sites. Individuals in the highest tertile, compared with those in the lowest tertile, had higher risks of uterine (hazard ratio [HR], 2.09; 95% confidence interval [CI], 1.76-2.49), gallbladder (HR, 1.83; 95% CI, 1.26-2.66), kidney (HR, 1.39; 95% CI, 1.18-1.64), liver (HR, 1.25; 95% CI, 1.00-1.56), colorectal (HR, 1.14; 95% CI, 1.05-1.24), and breast (HR, 1.11; 95% CI, 1.03-1.19) cancers and of all-cause cancer (HR, 1.05). There was no evidence of a nonlinear association between the VAI and cancer risk.

The VAI was associated with six cancer sites and with all-cause cancer. The prognostic and etiologic roles of visceral fat accumulation and dysfunction in cancer warrant further research.

The present study leverages the comprehensive data from the National Health and Nutrition Examination Survey (NHANES) to examine the Influencing factors of sarcopenia (SA) and sarcopenic obesity (SO). The investigation is designed to a non-invasive, cost-effective, and convenient method that is applicable to the adult population, enabling the accurate and simultaneous detection of risks associated with SA and SO. Furthermore, this research will evaluate the critical values of effective anthropometric indicators, providing early warning for risk management in self-health care and offering valuable insights for subsequent research and clinical practice. The data pertaining to NHANES participants were meticulously selected from the databases of six cycles, spanning from 2001 to 2004 and 2011 to 2018. Utilizing the diagnostic criteria established by the American Foundation for the National Institutes of Health (FNIH), anthropometric measurement data were extracted to construct composite indices. These indices were then cross-referenced with diagnostic assessments from dual-energy X-ray absorptiometry and bioelectrical impedance analysis to examine the correlations between various metrics and the incidence of diseases. R software (version 4.3.3) was used for analysis, and the primary analytical methods employed included logistic regression, restricted cubic splines (RCS), and the Receiver Operating Characteristic (ROC) curve analysis (AUC). Sarcopenia and sarcopenic obesity are commonly observed in individuals within the middle-aged and elderly demographics. The prevalence of these conditions is higher among middle-aged men of a given age when contrasted with women at the age of 40. All anthropometric indexes demonstrated a positive correlation with the onset of SA and SO, with the association with waist-to-height ratio (WHtR) showcasing a heightened strength subsequent to the adjustment for all covariates. The predictive models of all ROC curves performed commendably, particularly with the body roundness index and WHtR forecasting models exhibiting superior performance, the area under the AUC curve is 0.87 (95 CI% 0.85, 0.88) and 0.86 (95 CI% 0.85, 0.88), respectively. The RCS curve delineated a distinctive J-shaped distribution for each physical index in concurrence with SA and SO, signifying an optimal value at which the incidence of these conditions is minimal; conversely, deviations from this optimal value entailed an escalated risk of disease. Diverse anthropometric index metrics bear a strong correlation with adult onset of sarcopenia and sarcopenic obesity, each displaying commendable predictive capability. Notably, the body roundness index and waist-to-height ratio may harbor heightened potential as indicative anthropometric indexes. Furthermore, the dose-effect relationship analysis inferred that the lowest disease risk is manifested among individuals with specific index profiles, thereby advocating for autonomous health monitoring to promote physical activity and bolster nutrient intake, thus mitigating the risk of sarcopenia and sarcopenic obesity.

Metabolic syndrome (MetS) is an indicator and diverse endocrine syndrome that combines different metabolic defects with clinical, physiological, biochemical, and metabolic factors. Obesity, visceral adiposity and abdominal obesity, dyslipidemia, insulin resistance (IR), elevated blood pressure, endothelial dysfunction, and acute or chronic inflammation are the risk factors associated with MetS. Abdominal obesity, a hallmark of MetS, highlights dysfunctional fat tissue and increased risk for cardiovascular disease and diabetes. Insulin, a vital peptide hormone, regulates glucose metabolism throughout the body. When cells become resistant to insulin's effects, it disrupts various molecular pathways, leading to IR. This condition is linked to a range of disorders, including obesity, diabetes, fatty liver disease, cardiovascular disease, and polycystic ovary syndrome. Atherogenic dyslipidemia is characterized by three key factors: high levels of small, low-dense lipoprotein (LDL) particles and triglycerides, alongside low levels of high-density lipoprotein (HDL), the "good" cholesterol. Such a combination is a major player in MetS, where IR is a driving force. Atherogenic dyslipidemia contributes significantly to the development of atherosclerosis, which can lead to cardiovascular disease. On top of that, genetic alteration and lifestyle factors such as diet and exercise influence the complexity and progression of MetS. To enhance our understanding and consciousness, it is essential to understand the fundamental pathogenesis of MetS. This review highlights current advancements in MetS research including the involvement of gut microbiome, epigenetic regulation, and metabolomic profiling for early detection of Mets. In addition, this review emphasized the epidemiology and fundamental pathogenesis of MetS, various risk factors, and their preventive measures. The goal of this effort is to deepen understanding of MetS and encourage further research to develop effective strategies for preventing and managing complex metabolic diseases.

Frailty poses a considerable public health challenge because of its association with negative health consequences. Although obesity is recognized as a contributor to frailty, conventional measures fail to adequately account for the effects of visceral adiposity. The study aimed to investigate the associations between the visceral adiposity index (VAI) or lipid accumulation product (LAP) and frailty.

This study used data from the National Health and Nutrition Examination Survey (NHANES), which included 5,279 participants aged ≥ 20 years. The VAI and LAP were calculated via recognized formulas, and frailty was evaluated via a deficit accumulation approach. We employed logistic regression and restricted cubic splines to assess the associations among LAP, VAI and frailty.

Out of 5,279 participants, 1,836 individuals were categorized as frail. According to the fully adjusted models, the highest VAI and LAP values were significantly associated with frailty, with adjusted ORs of 1.84 (95% CI: 1.40-2.42) and 2.47 (95% CI: 1.89-3.24), respectively, compared with the lowest values. A nonlinear relationship was identified between the LAP and frailty, with an inflection point of 1.589 (ln-transformed), whereas the VAI was linearly associated with frailty. Sensitivity analyses confirmed the robustness of these associations.

The VAI and LAP are significantly related to frailty, highlighting the importance of visceral adiposity in frailty risk. These results increase the understanding of the metabolic underpinnings of frailty and may guide the development of targeted prevention strategies.

The prevalence of pain has increased with the increase of obesity, and finding indicators to predict pain risk has become an urgent need. BMI, WC, and WHtR have the potential to be excellent predictors. However, the association of these obesity indicators with various pains remains unclear.

This longitudinal cohort study included 2155 pain-free participants (mean age = 68.5 years, standard deviation [SD] = 8.6) from the English Longitudinal Study of Ageing (ELSA). BMI, WC, and WHtR were measured during nurse visits at waves 0, 2, and 4. The Group-Based Trajectory Model (GBTM) was used to identify optimal trajectories for BMI, WC, and WHtR. Self-reported pain at four anatomical sites (lower back, hip, knee and total pain) was assessed at Waves 4 to 9. Cox proportional hazards models were employed to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between obesity indices and pain risk.

Over a median follow-up period of 7.49 years, we observed 1,059 incident cases of all-cause pain. After adjusting for confounders, participants in the High-Stable WC group had a 30% higher risk of experiencing pain (HR: 1.30, 95 % CI: 1.05-1.60, P = 0.014), while those in the High-Stable WHtR group had a 29% higher risk (HR: 1.29, 95% CI: 1.06-1.56, P = 0.010) compared to the Low-Stable group. High-stable trajectories for BMI, WC, and WHtR were also associated with an elevated risk of back, hip, and knee pain.

This study identifies long-term obesity indices as significant predictors of pain, suggesting the importance of monitoring these measures for effective clinical risk assessments. Further research is needed to explore the underlying mechanisms of these associations.

This study aims to discover the association between serum osteocalcin, the Chinese visceral adiposity index (CVAI), and atherosclerotic cardiovascular disease (ASCVD) risk, and their impact on arterial stiffness in T2DM patients. We included 639 T2DM patients aged 30 and older who received the assessment of ASCVD risk using the China-PAR equation, Osteocalcin and arterial stiffness in this cross-sectional study. We found that osteocalcin and CVAI as independent risk factors for both medium-high-risk ASCVD (osteocalcin: men, OR,0.96, 95% CI 0.92, 1.00; women, OR, 0.93, 95% CI 0.8, 1.08, respectively)(CVAI: men, OR,1.01,95% CI 1.00,1.02; women: OR, 1.08, 95% CI 1.02,1.14, respectively) and arterial stiffness (osteocalcin: men, OR, 0.98, 95% CI 0.94,1.01; women, OR, 0.98, 95% CI 0.90,1.06, respectively)(CVAI: men, OR,1.0, 95% CI 0.99,1.01; women, OR, 1.02, 95% CI 1.00,1.04, respectively) in both men and women patients with T2DM. Combining osteocalcin levels and CVAI improved the prediction accuracy of arterial stiffness in men patients with T2DM (difference of AUC(Model 4 vs. Model 1):1.5%, NRI: 0.06 [0.0,0.4]). All P-values were < 0.05. The results suggested that osteocalcin levels and CVAI are independent risk factors for ASCVD risk and arterial stiffness in T2DM. Combining osteocalcin and CVAI can enhance the early detection of atherosclerosis through male patients with T2DM.

This study aims to investigate the association of Chinese visceral adiposity index (CVAI) with incident hyperuricemia (HUA).

We included 5186 adults aged ≥45 years from China Health and Retirement Longitudinal Study. Modified Poisson regression model was used to estimate the relative risks (RRs) of incident HUA associated with baseline CVAI, and logistic model was used to estimate the odds ratios (ORs) of HUA for CVAI change. Restricted cubic splines analysis was adopted to model the dose-response associations. The area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the predictive value of CVAI. During 4-year follow-up, a total of 510 (9.8%) HUA cases were identified. The RRs (95%CIs) of incident HUA were 3.75 (2.85-4.93) for quartile 4 versus quartile 1 and 1.56 (1.45-1.69) for per-standard deviation increase in baseline CVAI. For the analyses of CVAI change, compared with stable group, participants in decreased group had 34% lower risk (OR 0.66, 95%CI 0.49-0.87) and those in increased group had 35% (1.35, 1.03-1.78) higher risk of HUA. Linear associations of baseline CVAI and its change with HUA were observed (Pnonlinear >0.05). Besides, the AUC value for HUA was 0.654 (0.629-0.679), which was higher than other five obesity indices.

Our study found linear associations between baseline CVAI and its change and risk of HUA. CVAI had the best predictive performance in predicting incident HUA. These findings suggest CVAI as a reliable obesity index to identify individuals with higher HUA risk.

The objective of this study is to investigate the association between diabetic kidney disease (DKD) and various adiposity indexes, including the visceral adiposity index (VAI), lipid accumulation product index (LAPI), visceral fat area (VFA), and subcutaneous fat area (SFA) in type 2 diabetes mellitus (T2DM) patients. 1176 T2DM patients was stratified into normoalbuminuria (NO), microalbuminuria (MI), and macroalbuminuria (MA) groups based on their urinary albumin-creatinine ratio (UACR) levels. To analyse the correlation between DKD and VAI, LAPI, VFA, and SFA. Multiple linear, restricted cubic spline (RCS), subgroup analyses, and multinomial logistic regression were employed. After adjusting for confounding variables, UACR levels were positively associated with VAI, LAPI, and VFA. RCS curves demonstrated a J-shaped dose-response relationship between VAI and LAPI levels with UACR levels, while a linear correlation was observed between UACR levels and VFA. Using the NO and MI as reference groups, the MA group was analysed as the observational group. DKD severity was positively associated with VAI, LAPI and VFA. When evaluating DKD prognostic risk, with the low-risk and medium-risk groups serving as reference categories, a significant positive correlation was identified with prognostic risk and VAI, LAPI, and VFA in the high-risk or very high-risk groups. In patients with T2DM, DKD severity and prognostic risk were positively correlated with VAI, LAPI, and VFA levels.

The correlation between visceral obesity index (VAI) and diabetes and accuracy of early prediction of diabetes are still controversial.

This study aims to review the relationship between high level of VAI and diabetes and early predictive value of diabetes.

The databases of PubMed, Cochrane, Embase, and Web of Science were searched until October 17, 2023.

After adjusting for confounding factors, the original study on the association between VAI and diabetes was analyzed.

We extracted odds ratio (OR) between VAI and diabetes management after controlling for mixed factors, and the sensitivity, specificity, and diagnostic 4-grid table for early prediction of diabetes.

Fifty-three studies comprising 595 946 participants were included. The findings of the meta-analysis elucidated that in cohort studies, a high VAI significantly increased the risk of diabetes mellitus in males (OR = 2.83 [95% CI, 2.30-3.49]) and females (OR = 3.32 [95% CI, 2.48-4.45]). The receiver operating characteristic, sensitivity, and specificity of VAI for early prediction of diabetes in males were 0.64 (95% CI, .62-.66), 0.57 (95% CI, .53-.61), and 0.65 (95% CI, .61-.69), respectively, and 0.67 (95% CI, .65-.69), 0.66 (95% CI, .60-.71), and 0.61 (95% CI, .57-.66) in females, respectively.

VAI is an independent predictor of the risk of diabetes, yet its predictive accuracy remains limited. In future studies, determine whether VAI can be used in conjunction with other related indicators to early predict the risk of diabetes, to enhance the accuracy of prediction of the risk of diabetes.

The relationship between changes in Chinese visceral adiposity index (CVAI) and cardiometabolic diseases (CMD) in middle-aged and elderly individuals remains unclear. This study aimed to explore whether changes in the CVAI were associated with CMD incidence.

This study included 3,243 individuals aged over 45 years from the China Health and Retirement Longitudinal Study. The exposures were changes in the CVAI and cumulative CVAI from 2012 to 2015. Changes in the CVAI were classified using K-means clustering analysis, and the cumulative CVAI was calculated as follows: (CVAI2012 + CVAI2015)/2 × time (2015-2012). Multivariable logistic regression models were used to assess the relationship between different CVAI change classes and CMD incidence. Restricted cubic splines regression was used to assess the dose-response relationship between cumulative CVAI and CMD incidence. To investigate the relationship between combined exposure to each component of CAVI and CMD incidence, a weighted quantile sum regression analysis was employed.

During the 5 years of follow-up, 776 (24%) incident CMD cases were identified. Changes in CVAI and cumulative CVAI were independently and positively associated with CMD. After adjusting for potential confounders, compared with Class 1, the adjusted ORs (95% CIs) for incident CMD were 1.18 (0.90-1.57) for Class 2, 1.40 (1.03-1.92) for Class 3, and 1.56 (1.04-2.34) for Class 4. When cumulative CVAI was categorized into quartiles, compared with Q1, the adjusted ORs (95% CIs) for incident CMD were 1.30 (1.00-1.70) for Q2, 1.34 (1.01-1.79) for Q3, and 1.63 (1.15-2.31) for Q4. In addition, cumulative CVAI in the overall population exhibited a linear association with CMD (Poverall = 0.012, Pnon-linearity = 0.287), diabetes (Poverall = 0.022, Pnon-linearity = 0.188), and stroke (Poverall = 0.002, Pnon-linearity = 0.978), but showed no significant association with heart disease (Poverall = 0.619, Pnon-linearity = 0.442).

Participants with higher baseline CVAI level and a change of elevating CVAI level may suffer an increased incidence of CMD. Furthermore, our findings elucidate the underlying mechanisms of the CVAI by highlighting TG as the primary contributor to the observed associations. Long-term CVAI monitoring is of significant importance for early identification and prevention of CMD, with significant implications for clinical practice.

The Lipid Accumulation Product (LAP) is a measure that indicates excessive fat accumulation in the body. LAP has been the focus of research in epidemiological studies aimed at forecasting chronic and metabolic diseases. This study aimed to evaluate the association between LAP and type 2 diabetes mellitus (T2DM) among adults in western Iran.

The study involved 9,065 adults who participated in the initial phase of the Ravansar non-communicable diseases study (RaNCD) cohort. To investigate the association between LAP and T2DM, multiple logistic regressions were employed. Additionally, the receiver operating characteristic (ROC) curve was used to evaluate LAP's predictive ability concerning T2DM.

The participants had an average age of 47.24 ± 8.27 years, comprising 49.30% men and 50.70% women. The mean LAP was 53.10 ± 36.60 for the healthy group and 75.51 ± 51.34 for the diabetic group (P < 0.001). The multiple regression analysis revealed that the odds of T2DM in the second quartile of LAP were 1.69 (95% CI: 1.25, 2.29) times greater than in the first quartile. Furthermore, the odds in the third and fourth quartiles were 2.67 (95% CI: 2.01, 3.55) and 3.73 (95% CI: 2.83, 4.92) times higher, respectively. The ROC analysis for predicting T2DM showed that the LAP index had an area under the curve (AUC) of 0.66 (95% CI: 0.64, 0.68).

A strong association was identified between elevated LAP levels and T2DM in the adult population of western Iran. LAP is recommended as a potential tool for screening diabetes susceptibility.

To investigate the associations of conicity index (C-index) and relative fat mass (RFM) with incident type 2 diabetes mellitus (T2DM) among adults in China.

A total of 10 813 participants aged over 18 years in Shenzhen Longhua district were enrolled in a follow-up study conducted from 2018 to 2022. The participants were categorized based on quartiles (Q) of C-index and RFM. The Cox proportional hazards model was performed to examine the relationships between C-index, RFM and the risk of T2DM.

After adjusting for potential confounding factors, including age, sex, occupation, marital status, education level, smoking status, alcohol consumption, physical exercise, hypertension status, fasting blood glucose (FBG) and total cholesterol (TC), both C-index and RFM showed positive and independent associations with risk of T2DM. The multivariable-adjusted hazard ratios (95% confidence intervals) for T2DM risk in participants in C-index Q3 and Q4 compared with those in C-index Q1 were 1.50 (1.12, 2.02) and 1.73 (1.29, 2.30), and 1.94 (1.44, 2.63), 3.18 (1.79, 5.64), 4.91 (2.68, 9.00) for participants in RFM Q2, Q3 and Q4 compared with RFM Q1. These differences were statistically significant (all p < 0.05).

C-index and RFM are strongly associated with new-onset T2DM and could be used to identify the risk of diabetes in large-scale epidemiological studies.

Screening and treatment of dysglycemia (prediabetes and diabetes) represent significant challenges in advancing the Healthy China initiative. Identifying the crucial factors contributing to dysglycemia in urban-rural areas is essential for the implementation of targeted, precise interventions.

Data for 26,157 adults in Fujian Province, China, were collected using the Social Factors Special Survey Form through a multi-stage random sampling method, wherein 18 variables contributing to dysglycemia were analyzed with logistic regression and the random forest model.

Investigating urban-rural differences and critical factors in dysglycemia prevalence in Fujian, China, with the simultaneous development of separate predictive models for urban and rural areas.

The detection rate of dysglycemia among adults was 35.26%, with rates of 34.1% in urban areas and 35.8% in rural areas. Common factors influencing dysglycemia included education, age, BMI, hypertension, and dyslipidemia. For rural residents, higher income (OR = 0.80, 95% CI [0.74, 0.87]), average sleep quality (OR = 0.89, 95% CI [0.80, 0.99]), good sleep quality (OR = 0.89, 95% CI [0.80, 1.00]), and high physical activity (PA) (OR = 0.87, 95% CI [0.79, 0.96]) emerged as protective factors. Conversely, a daily sleep duration over 8 hours (OR = 1.46, 95% CI [1.03, 1.28]) and middle income (OR = 1.12, 95% CI [1.03, 1.22]) were specific risk factors. In urban areas, being male (OR = 1.14, 95% CI [1.02, 1.26]), cohabitation (OR = 1.18, 95% CI [1.02, 1.37]), and central obesity (OR = 1.35, 95% CI [1.19, 1.53]) were identified as unique risk factors. Using logistic regression outcomes, a random forest model was developed to predict dysglycemia, achieving accuracies of 75.35% (rural) and 76.95% (urban) with ROC areas of 0.77 (rural) and 0.75 (urban).

This study identifies key factors affecting dysglycemia in urban and rural Fujian residents, including common factors such as education, age, BMI, hypertension, and dyslipidemia. Notably, rural-specific protective factors are higher income and good sleep quality, while urban-specific risk factors include being male and central obesity. These findings support the development of targeted prevention and intervention strategies for dysglycemia, tailored to the unique characteristics of urban and rural populations.

Current research offers limited clarity on the correlation between waist circumference and chronic pain prevalence.

This investigation seeks to elucidate the potential relationship between waist circumference and chronic pain and their causal association.

An observational study was conducted, leveraging data from the National Health and Nutrition Examination Survey (NHANES) collected between 2001 and 2004. The multivariable logistic regression was used to assess the relationship between waist circumference and chronic pain. Furthermore, a meta-analysis of Mendelian Randomization (MR) was applied to explore a causal relationship between waist circumference and pain.

The observational study, post multivariable adjustment, indicated that an increase in waist circumference by 1 dm (decimeter) correlates with a 14% elevation in chronic pain risk (Odds Ratio [OR] = 1.14, 95% Confidence Interval [CI]: 1.04-1.24, p = 0.01). Moreover, the meta-analysis of MR demonstrated that an increased waist circumference was associated with a genetic predisposition to pain risk (OR = 1.14, 95%CI: 1.06-1.23, p = 0.0007).

Observational analysis confirmed a significant relationship between increased waist circumference and the incidence of chronic pain, and results based on MR Study identified increased waist circumference as potentially causal for pain.

Relationships between the visceral adiposity index (VAI) and type 2 diabetes mellitus (T2DM) have been underexplored. The purpose of this study is to explore association between VAI and T2DM in adults in the US. Based on the National Health and Nutrition Examination Survey 2007-2018, 11,214 participants aged 20 years or older were included in a cross-sectional study. Multifactorial logistic regression analysis and smoothed curve fitting analysis were performed to investigate links between VAI and the prevalence of T2DM, as well as the stability and incidence between subgroups. In a fully adjusted continuous model, the aggregate population risk of T2DM increased 0.43-fold with each 1-unit increase in VAI [odds ratio (OR) = 1.43; 95% confidence interval (CI) 1.35-1.50]. In the fully adjusted categorical model with VAI scores stratified by quartiles, results showed a higher T2DM advantage among participants in the second, third, and fourth quartiles (Q2: OR 1.35, 95% CI 1.06-1.71; Q3: OR 2.46, 95% CI 1.95-3.11; Q4: OR 4.42, 95% CI 3.55-05.50). Compared with Q1, the prevalence of T2DM in the total population increased 3.42-fold in Q4. The above results indicated that VAI was positively associated with the prevalence of T2DM, which was consistent and nonlinear with the smoothed curve-fitting analysis (P for non-linear = 0). Subgroup analyses after adjusting for covariates showed that keeping with the overall population results, it also was found that there was an interaction between sex and hypertension in the subgroups. VAI was positively associated with the prevalence of T2DM and was more prevalent in women, non-hypertensive than in men, hypertensive populations.

Metabolic syndrome (MetS) is closely associated with adverse cardiometabolic outcomes. The objective of this study was to identify practical methods that could enable the effective identification of MetS based on anthropometric indices. The basis of our study involved retrospective database obtained from routine medical prophylactic examinations. This was a cross-sectional study on the health status of male workers employed in hazardous working conditions at industrial enterprises in the Ural region conducted in 2019. A total of 347 male workers employed under hazardous working conditions were investigated. The presence of MetS was established by a healthcare professional in accordance with the guidelines of the International Diabetes Federation (IDF). Simple linear regression was used to evaluate the associations between anthropometric indices and MetS incidence. Logistic regression was used to determine the odds ratios of MetS in relation to increases in anthropometric indices. ROC curves were calculated to compare the ability of each anthropometric index to predict MetS and to determine the diagnostic thresholds of the indicators considered. According to the IDF criteria, 36.3% of the workers had MetS. A direct relationship was found between the individual components of MetS and the anthropometric indices studied. The highest OR was shown by the Body Roundness Index (BRI) of 2.235 (95% CI 1.796-2.781). For different age quartiles, the optimal cut-off values for predicting MetS were as follows: BRI, 4.1-4.4 r.u.; body shape index (ABSI), 0.080-0.083 m11/6 kg-2/3; and lipid accumulation product (LAP), 49.7-70.5 cm mmol/l. The most significant associations with MetS were observed where the values were greater than these cut-off points (Se = 97.4%). The results of this study demonstrated the rapid use of new anthropometric indicators, which have shown good predictive ability and are quite easy to use.

Insulin resistance (IR) is a pivotal pathogenic component of metabolic diseases. It is crucial to identify convenient and reliable indicators of insulin resistance for its early detection. This study aimed at assessing the predictive ability of seven novel obesity and lipid-related indices.

A total of 5,847 female and 3,532 male healthy subjects were included in the study. The triglyceride glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), lipid accumulation products (LAP), body roundness index (BRI), body adiposity index (BAI), and visceral adiposity index (VAI) were measured and calculated using the established formulae. IR was diagnosed using the homeostatic model assessment of insulin resistance (HOMA-IR) index over the third quantile.

The levels of all seven lipid-related indices were significantly higher in subjects with higher HOMA-IR values than in those with lower HOMA-IR values. These indices displayed moderate to high effectiveness [receiver operating characteristic (ROC) curve-area under the curve (AUC) > 0.6] in predicting IR. Among them, TyG-BMI (AUC: 0.729), LAP (AUC: 0.708), and TyG-WC (AUC: 0.698) showed the strongest association with HOMA-IR. In the female population, the AUC for TyG-BMI, LAP, and TyG-WC in predicting IR was 0.732, 0.705, and 0.718, respectively. Logistic regression analysis showed the optimal cut-off values of those indicators in predicting IR as follows: TyG-BMI: male subjects - 115.16 [odds ratio (OR) = 6.05, 95% CI: 5.09-7.19], female subjects - 101.58 (OR = 4.55, 95% CI: 4.00-5.16); LAP: male subjects - 25.99 (OR = 4.53, 95% CI: 3.82-5.38), female subjects - 16.11 (OR = 3.65, 95% CI: 3.22-4.14); and TyG-WC: male subjects - 409.43 (OR = 5.23, 95% CI: 4.48-6.24), female subjects - 342.48 (OR = 4.07, 95% CI: 3.59-4.61).

TyG-index-related parameters and LAP appear to be effective predictors of IR in the Chinese population. Specifically, TyG-BMI may be the most appropriate predictor of IR.

It is unclear how the Chinese Visceral Adiposity Index (cVAI) relates to metabolic dysfunction-associated fatty liver disease (MAFLD) and alanine aminotransferase (ALT) in nonobese individuals. In this study, we evaluated the ability of the cVAI to predict MAFLD and elevated ALT in nonobese participants.

This cross-sectional study recruited 541 nonobese subjects from March 2019 to January 2022 with the age range of 18-80 years. Hepatic steatosis was diagnosed by ultrasound. Participants were divided into four groups according to cVAI quartiles. To assess the associations between cVAI and MAFLD and elevated ALT, multivariate logistic regression was used. Receiver operating characteristic (ROC) curves were generated to evaluate the ability of the cVAI to predict MAFLD and elevated ALT.

Compared to the group with the lowest cVAI, the group with the highest cVAI was positively associated with nonobese MAFLD [16.173 (4.082-64.073), P < 0.001] and elevated ALT [8.463 (2.859-25.049), P < 0.001]. The area under the ROC curve (AUC) of the cVAI was greater than that of WC, waist-to-height ratio, or BMI for predicting nonobese MAFLD in the male, female, > 38 and ≤ 38 years old subgroups (P < 0.05), respectively. In addition, the ability of the cVAI to predict MAFLD was better in females, young individuals, and individuals with a higher education level (P < 0.05). The cVAI also had good predictive ability for elevated ALT levels [0.655 (0.602-0.708)], particularly in females, young people, and highly educated participants. Furthermore, the cVAI was strongly positively correlated with the liver fibrosis score (P < 0.05) and was also a strong indicator of concomitant metabolic syndrome in nonobese MAFLD patients [AUC = 0.688 (0.612-0.763)].

The cVAI was strongly related to nonobese MAFLD and elevated ALT. The cVAI may be a reliable and accessible predictor of nonobese MAFLD and elevated ALT.

Individuals with hyperinsulinemia may initially not meet any diagnostic criteria for metabolic syndrome, though displaying a higher risk of cardiovascular complications combined with obesity, diabetes, and hypertension.

The main objective of our study was to assess the diagnostic accuracy of various cardiovascular risk indices in hyperinsulinemic children and adolescents; a secondary objective was to estimate the optimal cut-offs of these indices.

This retrospective single-center study was conducted on 139 patients aged 12.1 ± 2.9 years, managed for hyperinsulinism.

We found statistically significant differences in homeostasis model assessment of insulin resistance index (HOMA-IR), triglyceride glucose index (TyG), TyG-body mass index, visceral adiposity index, lipid accumulation product index, fatty liver index, and hepatic steatosis index. At the linear logistic regression assessment, we found that insulin growth factor-1 (IGF-1), HOMA-IR, and ALT/AST ratio were independently associated with confirmed hyperinsulinism. At the multivariate analysis, IGF-1 levels over 203 ng/mL and HOMA-IR higher than 6.2 were respectively associated with a 9- and 18-times higher odds ratio for hyperinsulinism. The other investigated parameters were not significantly related to hyperinsulinism, and could not predict either the presence of hyperinsulinemia or a subsequent cardiovascular risk in our patients.

Commonly used indices of cardiovascular risk in adults cannot be considered accurate in confirming hyperinsulinism in children, with the exception of HOMA-IR. Further studies are needed to verify the usefulness of specific cardiovascular risk indices in hyperinsulinemic children and adolescents.

To investigate the screening and predicting functions of obesity- and lipid-related indices for type 2 diabetes (T2D) in middle-aged and elderly Chinese, as well as the ideal predicted cut-off value. This study's data comes from the 2011 China Health and Retirement Longitudinal Study (CHARLS). A cross-sectional study design was used to investigate the relationship of T2D and 13 obesity- and lipid-related indices, including body mass index (BMI), waist circumference (WC), waist-height ratio (WHtR), visceral adiposity index (VAI), a body shape index (ABSI), body roundness index (BRI), lipid accumulation product (LAP), conicity index (CI), Chinese visceral adiposity index (CVAI), triglyceride- glucose index (TyG index) and its correlation index (TyG-BMI, TyG-WC, TyG-WHtR). The unadjusted and adjusted correlations between 13 indices and T2D were assessed using binary logistic regression analysis. The receiver operating characteristic curve (ROC) was used to determine the usefulness of anthropometric indices for screening for T2D and determining their cut‑off value, sensitivity, specificity, and area under the curve (AUC). The study comprised 9488 people aged 45 years or above in total, of whom 4354 (45.89%) were males and 5134 (54.11%) were females. Among them were 716 male cases of T2D (16.44%) and 870 female cases of T2D (16.95%). A total of 13 obesity- and lipid-related indices were independently associated with T2D risk after adjusted for confounding factors (P < 0.05). According to ROC analysis, the TyG index was the best predictor of T2D among males (AUC = 0.780, 95% CI 0.761, 0.799) and females (AUC = 0.782, 95% CI 0.764, 0.799). The AUC values of the 13 indicators were higher than 0.5, indicating that they have predictive values for T2D in middle-aged and elderly Chinese. The 13 obesity- and lipid-related indices can predict the risk of T2D in middle‑aged and elderly Chinese. Among 13 indicators, the TyG index is the best predictor of T2D in both males and females. TyG-WC, TyG-BMI, TyG-WHtR, LAP, and CVAI all outperformed BMI, WC, and WHtR in predicting T2D.

Higher fat-to-muscle mass ratio (FMR) is reported to be a risk factor for various diseases, including type 2 diabetes and cardiovascular diseases, and mortality. Although this association suggests that reducing FMR may help to prevent certain diseases and mortality, the relationship between FMR and lifestyle factors is unclear. Therefore, we performed a cross-sectional study with the aim to elucidate this relationship. This cross-sectional study included 1518 healthy Japanese adults aged 30 to 64 years. We measured FMR in the whole body, arms, legs, and trunk and assessed various lifestyle factors. Then, we performed forced entry multiple regression analyses for FMR with the following variables: sex, age, physical activity, dietary intake, sleep quality, cigarette smoking, stress levels, and body mass index. As a result, whole-body and regional FMRs were correlated with female sex (β = 0.71); age (β = 0.06); physical activity (β = - 0.07); dietary intake of protein (β = - 0.12), carbohydrate (β = 0.04), sodium (β = 0.13), and fiber (β = - 0.16); and body mass index (β = 0.70). The results suggest that in the Japanese middle-aged population, low FMR is associated with certain lifestyle factors, i.e. higher physical activity and a diet with higher protein and fiber and lower carbohydrate and sodium, independent of age, sex, and body mass index.