Prognostic value of abdominal obesity indicators for all-cause mortality in familial hypercholesterolemia.

This study aims to compare the association of hypertension plus hyperuricemia (HTN-HUA) with seven anthropometric indexes. These include the atherogenic index of plasma (AIP), lipid accumulation product (LAP), visceral adiposity index (VAI), triglyceride-glucose index (TyG), body roundness index (BRI), a body shape index (ABSI), and the cardiometabolic index (CMI). Data was procured from the National Health and Nutrition Examination Survey (NHANES), which recruited a representative population aged 18 years and above to calculate these seven indexes. Logistic regression analysis was employed to delineate their correlation and to compute the odds ratios (OR). Concurrently, receiver operating characteristic (ROC) curves were utilized to evaluate the predictive power of the seven indexes. A total of 23,478 subjects were included in the study. Among these, 6,537 (27.84%) were patients with HUA alone, 2,015 (8.58%) had HTN alone, and 2,836 (12.08%) had HTN-HUA. The multivariate logistic regression analysis showed that the AIP, LAP, VAI, TyG, BRI, ABSI, and CMI were all significantly associated with concurrent HTN-HUA. The OR for the highest quartile of the seven indexes for HTN-HUA were as follows: AIP was 4.45 (95% CI 3.82-5.18), LAP was 9.52 (95% CI 7.82-11.59), VAI was 4.53 (95% CI 38.9-5.28), TyG was 4.91 (95% CI 4.15-5.80), BRI was 9.08 (95% CI 7.45-11.07), ABSI was 1.71 (95% CI 1.45 -2.02), and CMI was 6.57 (95% CI 5.56-7.76). Notably, LAP and BRI demonstrated significant discriminatory abilities for HTN-HUA, with area under the curve (AUC) values of 0.72 (95% CI 0.71 - 0.73) and 0.73 (95% CI 0.72 - 0.74) respectively. The AIP, LAP, VAI, TyG, BRI, ABSI, and CMI all show significant correlation with HTN-HUA. Notably, both LAP and BRI demonstrate the capability to differentiate cases of HTN-HUA. Among these, BRI is underscored for its effective, non-invasive nature in predicting HTN-HUA, making it a superior choice for early detection and management strategies.

To investigate the relation between obesity-related indices and mild cognitive impairment (MCI) in elderly patients with type 2 diabetes (T2D). A total of 597 eligible elderly patients with T2D were included in this retrospective study. All patients were divided into MCI group and normal cognitive group based on neuropsychological assessment. Twelve obesity-related indices were calculated, including body mass index (BMI), waist-hip ratio (WHR), waist-to-height ratio (WHtR), lipid accumulation product (LAP), body roundness index (BRI), conicity index (CI), visceral adiposity index (VAI), body adiposity index (BAI), abdominal volume index (AVI), a body shape index (ABSI), triglyceride glucose (TyG) index and cardiometabolic index (CMI). Multivariate logistic regression analysis, tests for trend and restricted cubic splines were used to assess the relationships between the tests for trend and MCI in elderly patients with T2D. Receiver operating characteristic (ROC) curves and areas under the curves (AUC) were used to assess the performance and predictive ability of the obesity-related indices for identifying MCI in elderly patients with T2D. Multivariate logistic regression showed that elevated BMI, WHR, WHtR, LAP, BRI, CI, VAI, AVI, TyG index, and CMI were associated with an increased risk of MCI in elderly T2D patients after adjusting for potential confounders (all P<0.05). In addition, TyG index, LAP, CMI, VAI, AVI, WHR, WHtR, BRI, and CI had negative correlations with Montreal Cognitive Assessment (MoCA) scores (all P<0.05). There was a significant linear trend between the levels of BMI (P for trend = 0.004, P for non-linearity = 0.637), WHR (P for trend = 0.006, P for non-linearity = 0.430), WHtR (P for trend <0.001, P for non-linearity = 0.452), BRI (P for trend <0.001, P for non-linearity = 0.252), AVI ( P for trend <0.001, P for non-linearity = 0.944), and TyG index (P for trend <0.001, P for non-linearity = 0.514) and risk of MCI in elderly patients with T2D after adjusting for potential confounders. There was a nonlinear association between LAP, VAI or CMI and risk of MCI in elderly patients with T2D (all P for non-linearity < 0.001). CMI had the greatest AUC (AUC = 0.682), followed by VAI (AUC = 0.679), TyG index (AUC = 0.673), LAP (AUC = 0.669), AVI (AUC = 0.580), WHtR and BRI (AUC = 0.575), BMI (AUC = 0.560), CI (AUC = 0.556), WHR (AUC = 0.554), BAI (AUC = 0.547), and ABSI (AUC = 0.536). Elevated obesity-related indices, particularly CMI, VAI, TyG index and LAP, which displayed the higher predictive power, were instrumental in forecasting and evaluating MCI in elderly T2D patients. These findings may provide clues for future studies exploring early diagnostic biomarkers and treatment of MCI in elderly T2D patients.

Metabolic syndrome is a common condition among middle-aged and elderly people. Recent studies have reported the association between obesity- and lipid-related indices and metabolic syndrome, but whether those conditions could predict metabolic syndrome is still inconsistent in a few longitudinal studies. In our study, we aimed to predict metabolic syndrome by obesity- and lipid-related indices in middle-aged and elderly Chinese adults. A national cohort study that consisted of 3,640 adults (≥45 years) was conducted. A total of 13 obesity- and lipid-related indices, including body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), conicity index (CI), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), a body shape index (ABSI), body roundness index (BRI), and triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-WC, and TyG-WHtR), were recorded. Metabolic syndrome (MetS) was defined based on the criteria of the National Cholesterol Education Program Adult Treatment Panel III (2005). Participants were categorized into two groups according to the different sex. Binary logistic regression analyses were used to evaluate the associations between the 13 obesity- and lipid-related indices and MetS. Receiver operating characteristic (ROC) curve studies were used to identify the best predictor of MetS. A total of 13 obesity- and lipid-related indices were independently associated with MetS risk, even after adjustment for age, sex, educational status, marital status, current residence, history of drinking, history of smoking, taking activities, having regular exercises, and chronic diseases. The ROC analysis revealed that the 12 obesity- and lipid-related indices included in the study were able to discriminate MetS [area under the ROC curves (AUC > 0.6, P < 0.05)] and ABSI was not able to discriminate MetS [area under the ROC curves (AUC < 0.6, P > 0.05)]. The AUC of TyG-BMI was the highest in men, and that of CVAI was the highest in women. The cutoff values for men and women were 187.919 and 86.785, respectively. The AUCs of TyG-BMI, CVAI, TyG-WC, LAP, TyG-WHtR, BMI, WC, WHtR, BRI, VAI, TyG index, CI, and ABSI were 0.755, 0.752, 0.749, 0.745, 0.735, 0.732, 0.730, 0.710, 0.710, 0.674, 0.646, 0.622, and 0.537 for men, respectively. The AUCs of CVAI, LAP, TyG-WC, TyG-WHtR, TyG-BMI, WC, WHtR, BRI, BMI, VAI, TyG-index, CI, and ABSI were 0.687, 0.674, 0.674, 0.663, 0.656, 0.654, 0.645, 0.645, 0.638, 0.632, 0.607, 0.596, and 0.543 for women, respectively. The AUC value for WHtR was equal to that for BRI in predicting MetS. The AUC value for LAP was equal to that for TyG-WC in predicting MetS for women. Among middle-aged and older adults, all obesity- and lipid-related indices, except ABSI, were able to predict MetS. In addition, in men, TyG-BMI is the best indicator to indicate MetS, and in women, CVAI is considered the best hand to indicate MetS. At the same time, TyG-BMI, TyG-WC, and TyG-WHtR performed better than BMI, WC, and WHtR in predicting MetS in both men and women. Therefore, the lipid-related index outperforms the obesity-related index in predicting MetS. In addition to CVAI, LAP showed a good predictive correlation, even more closely than lipid-related factors in predicting MetS in women. It is worth noting that ABSI performed poorly, was not statistically significant in either men or women, and was not predictive of MetS.

The purpose of this study is to explore the association between adiposity indices and blood lipid indices and prediabetes. We compare the predictive value of new adiposity indices and traditional adiposity indices and blood lipid indices in the diagnosis of prediabetes. This is a prospective cohort study of 7953 participants. The follow-up time was 3years. The eight adiposity indices included the following: body mass index (BMI), waist circumference (WC), body roundness index (BRI), A Body Shape Index (ABSI), visceral adiposity index (VAI), lipid accumulation product (LAP), fatty liver index (FLI), and triglyceride-to-glucose fasting index (TyG), as well as four blood lipid indices as follows: total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL-C), and low-density lipoprotein (LDL-C).The association between adiposity indices and blood lipid indices for diagnosis of prediabetes was estimated using a logistic regression model to obtain the odds ratio (OR) and its 95% confidence interval (CI). We calculated the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis to measure the predictive value of adiposity indices and blood lipid indicators for the diagnosis of prediabetes in the general population stratified by gender. The median age of the participants was 56years old, men accounting for 35.3% of the final group. After adjusting for confounding factors, association of BMI, BRI, VAI, LAP, TyG, TC, TG, and LDL-C with prediabetes status was assessed at both baseline and follow-up. TyG (AUC, overall: 0.677 (95% CI, 0.665, 0.689), male: 0.645 (95% CI, 0.624-0.667), and female: 0.693 (95% CI, 0.678-0.708)) have better diagnostic value for prediabetes than VAI, LAP, FLI, TC, TG, HDL-C, and LDL-C. The predictive value of the combination of TyG, BRI, VAI, and TG significantly improves the power of any single index in the diagnosis of prediabetes. The AUC and corresponding 95% CI of TyG, BRI, VAI, and TG and the combination of these four indicators to diagnose prediabetes were 0.677 (0.665, 0.689), 0.630 (0.617, 0.643), 0.618 (0.606, 0.631), 0.622 (0.609, 0.635), and 0.728 (0.716, 0.739), respectively. Among the eight adiposity indices and four blood lipid indices evaluated in the study, TyG had the highest diagnostic value for prediabetes in isolated indexes, and the combination of TyG, BRI, VAI, and TG significantly improved the diagnostic value for prediabetes of any single indicator.

We aimed to compare the predictive ability of the anthropometric indices reflecting general, central and visceral obesity for identification of metabolic syndrome (MetS) in maintenance hemodialysis (MHD) patients. A multicenter, cross-sectional study that consisted of 1603 adult MHD patients (54.6 ± 16years) was conducted in Guizhou Province, Southwest China. Eight anthropometric obesity indexes including body mass index (BMI), waist circumference (WC), waist-height ratio (WHtR), conicity index (Ci) and visceral adiposity index (VAI), lipid accumulation product (LAP), a body shape index (ABSI) and body roundness index (BRI) were recorded. MetS was defined based on the criteria of the International Diabetes Federation. Participants were categorized into four groups according to quartiles of different obesity indices. Binary logistic regression analyses were used to evaluate the associations between the eight obesity parameters and MetS. Receiver operator curve (ROC) analyses were used to identify the best predictor of MetS. The eight anthropometric obesity indexes were independently associated with MetS risk, even after adjustment for age, sex, educational status and history of smoking. The ROC analysis revealed that all the eight obesity indices included in the study were able to discriminate MetS [all area under the ROC curves (AUCs) > 0.6, P < 0.05]. LAP showed the highest AUC and according to the maximum Youden indexes, the cut off values for men and women were 27.29 and 36.45, respectively. The AUCs of LAP, VAI, ABSI, BRI, WC, WHtR, Ci and BMI were 0.88, 0.87, 0.60, 0.78, 0.79, 0.78, 0.69 and 0.76 for men, and 0.87, 0.85, 0.65, 0.79, 0.81, 0.79, 0.73 and 0.76 for women, respectively. There was no significant difference in the AUC value between LAP and VAI, BRI/WHtR and BMI in men and between BRI/WHtR and BMI in women. The AUC value for WHtR was equal to that for BRI in identifying MetS. Visceral obesity marker LAP followed by VAI was the most effective predictor of MetS while ABSI followed by CI was the weakest indicator for the screening of MetS in MHD patients. BRI could be an alternative obesity measure to WHtR in assessment of MetS. LAP may be a simple and useful screening tool to identify individuals at high risk of MetS particularly in middle-aged and elderly Chinese MHD patients. Level V, descriptive study.

Introduction: Obesity has been established as a significant risk factor for rapid kidney function decline (RKFD) and chronic kidney disease (CKD). However, the comparative prognostic value of various obesity-related indices in predicting RKFD and CKD remains inadequately elucidated. The objective of this study was to explore the correlations between ten obesity-related indices: body mass index (BMI), Chinese visceral adiposity index (CVAI), waist-to-height ratio, visceral adiposity index (VAI), body roundness index (BRI), a body shape index (ABSI), lipid accumulation product (LAP), waist triglyceride index (WTI), relative fat mass (RFM), and conicity index (C-index) and RKFD and CKD. Methods: This retrospective longitudinal cohort study leveraged data sourced from the China Health and Retirement Longitudinal Study (CHARLS). Multivariate logistic regression models with covariate adjustment were employed to assess independent associations between obesity-related indices and clinical outcomes. Restricted cubic spline (RCS) regression analyses were performed to characterize potential nonlinear relationships. Predictive performance was quantified through receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) comparisons. Results: A total of 1,620 participants were enrolled in this study. Among them, 109 participants developed RKFD, and 60 progressed to CKD. Adjusted logistic regression revealed significant positive associations between CVAI, VAI, LAP, WTI, and RKFD risk, while BRI and C-index demonstrated per standard deviation increases associated with CKD progression. RCS curve analysis demonstrated that CVAI and LAP exhibited a nonlinear relationship with the risk of RKFD, while VAI and WTI had a linear relationship. Moreover, the C-index had a nonlinear relationship with the risk of CKD, whereas BRI had a linear relationship. ROC analysis revealed WTI as the superior RKFD predictor and ABSI as the optimal CKD progression indicator among the evaluated obesity-related indices. Conclusion: This study comprehensively investigated the associations between ten obesity-related indices and both RKFD and CKD. Our findings indicated that CVAI, VAI, LAP, and WTI were associated with RKFD, with WTI exhibiting the highest predictive value. Furthermore, BRI and C-index were associated with CKD, with ABSI demonstrating the highest predictive value for the progression to CKD.

This article explored the relationship between anthropometric indices and hyperuricemia in Chinese adults. The ability of each anthropometric index to predict hyperuricemia was also compared in this article. This is a cross-sectional study containing 69,842 samples from 31 provinces and cities in China. Anthropometric indices included body mass index (BMI), waist circumference (WC), a body shape index (ABSI), body roundness index (BRI), waist-to-height ratio (WHtR), lipid accumulation product (LAP), visceral adiposity index (VAI), triglyceride-glucose index (TyG), waist circumference-triglyceride index (WTI), and weight-adjusted waist index (WWI). The survey data obtained were disaggregated and analyzed according to sex and age. BMI, WC, BRI, WHtR, LAP, VAI, TyG, WTI, and WWI were all significantly associated with hyperuricemia (P < 0.001). In the total population, WTI (AUC 0.7015, P < 0.001) had the highest predictive power, and WWI (AUC 0.5417, P < 0.001) had the lowest. In addition, after dividing the male and female populations, LAP (AUC 0.6571, P < 0.001 for men; AUC 0.7326, P < 0.001 for women) had the highest predictive power among both men and women. The ABSI (AUC 0.5189, P < 0.001 for men; AUC 0.5788, P < 0.001 for women) had the lowest predictive power among both men and women. BMI, WC, BRI, WHtR, LAP, VAI, TyG, and WTI were positively correlated with the risk of hyperuricemia and serum uric acid concentrations in both sexes. Among the general population, WTI had the highest predictive power. After dividing the population by sex, LAP had the highest predictive power in both men and women. Key Points • Anthropometric indices are highly correlated with hyperuricemia. Waist circumference-triglyceride index (WTI) is first found to be associated with hyperuricemia, and it has high predictive power. • The predictive power of anthropometric indices for hyperuricemia is more useful in women. • The restricted cubic splines visually shows the ratio of anthropometric indices to hyperuricemia ratio and the patient's serum uric acid concentration.

Type 2 diabetes mellitus (DM) is an increasing global health issue. Peripheral artery occlusive disease (PAOD) is a common complication of diabetes, and it is a complex and costly disease. The association between type 2 DM and obesity is well known, however, the relationship between obesity and PAOD in patients with type 2 DM has yet to be elucidated. Therefore, the aim of this study was to examine associations between obesity-related indices and PAOD in patients with type 2 DM. A total of 1872 outpatients with type 2 DM were recruited from two hospitals in southern Taiwan. An ankle–brachial index (ABI) < 0.9 in either leg was considered to indicate the presence of PAOD. The following obesity-related indices were investigated: conicity index (CI), waist–hip ratio (WHR), body roundness index (BRI), waist-to-height ratio (WHtR), abdominal volume index, a body shape index (ABSI), visceral adiposity index (VAI), lipid accumulation product (LAP), body adiposity index, body mass index and triglyceride–glucose index. Overall, 4.1% of the enrolled patients had an ABI < 0.9. High values of the following obesity-related indices were significantly associated with a low ABI: WHtR (p = 0.045), VAI (p = 0.003), CI (p = 0.042), BRI (p = 0.021) and ABSI (p = 0.043). Furthermore, WHR (area under the curve (AUC) = 0.661), CI (AUC = 0.660) and LAP (AUC = 0.642) had the best performance (all p < 0.001) to predict PAOD. In conclusion, high WHtR, BRI, CI, VAI and BAI values were associated with a low ABI in the enrolled patients, and WHR, CI and LAP were the most powerful predictors of PAOD.

ABSTRACTObjective We examined the accuracy of novel anthropometric indices in predicting the progression of prediabetes to diabetes.Subjects and methods This study was performed on the pre-diabetic sub-population from Isfahan Cohort Study (ICS). Participants were followed up from 2001 to 2013. During every 5-year follow-up survey, patients’ data regarding the incidence and time of incidence of diabetes were recorded. We evaluated the association between the risk of developing diabetes and novel anthropometric indices including: visceral adiposity index (VAI), lipid accumulation products (LAP), deep abdominal adipose tissue (DAAT), abdominal volume index (AVI), A body shape index (ABSI), body roundness index (BRI) and weight-adjusted waist index (WWI). We categorized the indices into two groups according to the median value of each index in the population. We used Cox regression analysis to obtain hazard ratios (HR) using the first group as the reference category and used receiver operating characteristics (ROC) curve analysis for comparing the predictive performance of the indices.Results From 215 included subjects, 79 developed diabetes during the 13-year follow-up. AVI, LAP, BRI, and VAI indicated statistically significant HR in crude and adjusted regression models. LAP had the greatest association with the development of diabetes HR = 2.18 (1.36-3.50) in multivariable analysis. ROC curve analysis indicated that LAP has the greatest predictive performance among indices (area under the curve = 0.627).Conclusion Regardless of baseline confounding variables, prediabetic patients with a higher LAP index may be at significantly higher risk for developing diabetes.

Introduction: the increasing prevalence of metabolic syndrome draws attention to the importance of detecting metabolic syndrome with practical methods in the early period. Objectives: to compare anthropometric measurements and indexes for prediction of metabolic syndrome (MetS) in adults. Methods: the study was conducted with adults classified as MetS (n = 92) and a control group (n = 137) according to the International Diabetes Federation. Anthropometric measurements, visceral adiposity index (VAI), dysfunctional adiposity index (DAI), A body shape index (ABSI), lipid accumulation product (LAP), body roundness index (BRI), glucose, lipid biomarkers, and blood pressure (BP) levels were compared. A ROC analysis was performed. Results: MetS frequency was determined to be 40.2 % (n = 92). All biochemical parameters except high-density lipoprotein-cholesterol and BP levels, all anthropometric measurements, and all index values except ABSI of the MetS group were higher than in the control group (p < 0.001). DAI had the highest discriminatory ability for MetS (AUC = 0.921). While the discriminatory ability of LAP was slightly lower (AUC = 0.915), ABSI had the lowest ability for MetS (AUC = 0.606). Conclusion: according to the study findings, MetS was found in almost half of individuals, and the LAP index and DAI can be used as predictive tools for early detection of MetS.

BackgroundNormal-weight maintenance hemodialysis (MHD) patients with abdominal obesity exhibited a more proatherogenic profile than overweight and obesity patients with abdominal obesity, highlighting the importance of early identification of metabolically unhealthy nonobese (MUNO) in this population. Visceral fat accumulation plays a crucial role in the development of MUNO. Lipid accumulation product (LAP), visceral adiposity index (VAI) have been proved as reliable visceral obesity markers. The Chinese visceral adiposity index (CVAI) and a body shape index (ABSI) are newly discovered indexes of visceral obesity and have been reported to be associated with multiple metabolic disorders. There are limited studies investigating the associations between different visceral obesity indices and risk of MUNO, especially in hemodialysis patients. Moreover, no general agreement has been reached to date regarding which of these obesity indices performs best in identifying MUNO. We aimed to investigate the prevalence of MUNO in MHD patients and compare the associations between different adiposity indices (CVAI, ABSI,VAI, LAP, body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHtR)) with MUNO risk in this population.MethodsWe conducted a multi-center cross-sectional study in Guizhou Province, Southwest China. 1302 nonobese adult MHD patients were included in our study. MUNO was defined as being nonobese and having the presence of > = 2 components of metabolic syndrome (MetS). Nonobese was defined as BMI less than 25 kg/m2. VAI, LAP, CVAI, ABSI, BMI, WC and WHtR were calculated. Logistic regression analyses and receiver operator curve (ROC) analyses were performed.Results65.6% participants were metabolically unhealthy. The ROC curve analysis demonstrated that of the seven obesity indices tested, the VAI (AUC 0.84 for women and 0.79 for men) followed by LAP (AUC 0.78 for women and 0.72 for men) had the highest diagnostic accuracy for MUNO phenotype while ABSI exhibited the lowest AUC value for identifying MUNO phenotypeConclusionsMetabolically unhealthy is highly prevalent in nonobese MHD patients. VAI and LAP outperformed CVAI in discriminating MUNO in MHD patients. Though ABSI could be a weak predictor of MUNO, it is not better than WHtR, WC and BMI.

Depressive symptom is a serious mental illness often accompanied by physical and emotional problems. The prevalence of depressive symptom in older adults has become an increasingly important public health priority. Our study used cardiometabolic indicators to predict depressive symptom in middle-aged and older adults in China. The data came from the China Health and Retirement Longitudinal Study 2011 (CHARLS2011), which was a cross-sectional study. The analytic sample included 8,942 participants aged 45 years or above. The study evaluated the relationship between cardiometabolic indicators and depression by measuring 13 indicators, including body mass index (BMI), waist circumference, waist-height ratio (WHtR), conicity index, visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), a body shape index (ABSI), body roundness index (BRI), triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-waist circumference, TyG-WHtR). Binary logistic regression analysis was used to examine the association between thirteen cardiometabolic indicators and depressive symptom. In addition, the receiver operating characteristic (ROC) curve analysis and area under curve (AUC) were used to evaluate the predictive anthropometric index and to determine the optimum cut-off value. The study included 8,942 participants, of whom 4,146 (46.37%) and 4,796 (53.63%) were male and female. The prevalence of depressive symptom in mid-aged and older adults in China was 41.12% in males and 55.05% in females. The results revealed that BMI [AUC = 0.440, 95%CI: 0.422-0.457], waist circumference [AUC = 0.443, 95%CI: 0.425-0.460], WHtR [AUC = 0.459, 95%CI: 0.441-0.476], LAP [AUC = 0.455, 95%CI: 0.437-0.472], BRI [AUC = 0.459, 95%CI: 0.441-0.476], CVAI [AUC = 0.449, 95%CI: 0.432-0.467], TyG-BMI [AUC = 0.447, 95%CI: 0.429-0.465], and TyG-waist circumference [AUC =0.452, 95%CI: 0.434-0.470] were weak predictors of depressive symptom (p < 0.05) in males. In females, BMI [AUC = 0.470, 95%CI: 0.453-0.486], LAP [AUC = 0.484, 95%CI: 0.467-0.500], TyG-BMI [AUC = 0.470, 95%CI: 0.454-0.487], and TyG-waist circumference [AUC =0.481, 95%CI: 0.465-0.498] were weak predictors of depressive symptom (p < 0.05). On the other side, VAI, ABSI, conicity index and TyG index could not predict depressive symptom in middle-aged and older adults. Most cardiometabolic indicators have important value in predicting depressive symptom. Our results can provide measures for the early identification of depressive symptom in middle-aged and older adults in China to reduce the prevalence of depressive symptom and improve health.

Introduction Insufficient and excessive sleep have both been linked to increased cardiovascular and all-cause mortality. Whilst the relationship between sleep duration and traditional lipid indices are well described, the connection to novel lipid and anthropometric indices remains unclear. This study examines these associations using the National Health and Nutrition Examination Survey (NHANES). Methods Data from 9847 adults from NHANES 2005-2020, excluding those with major cardiovascular disease and cancer, were analysed. Sleep duration was categorized as insufficient (&amp;lt;7hours), normal (7-8hours), and excessive (&amp;gt;8 hours). Self-reported sleep disturbance was documented. Novel indices included non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR), Triglyceride to HDL-Cholesterol (TG/HDL), Triglyceride-glucose index (TyG), Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), Conicity Index (CI), Body-roundness index (BRI), A body shape index (ABSI), and Weight-adjusted waist index (WWI). Generalized additive models (GAM) with spline smoothing and threshold analysis assessed non-linear associations, adjusting for confounders. Weighted multivariate linear regression evaluated linear associations. Results Insufficient sleep was associated with higher TyG combined with waist-to-height ratio (TyG-WHtR) (p=.003). Excessive sleep was linked to higher TyG-WHtR, CI, BRI, ABSI, and WWI (p&amp;lt;.001). Sleep disturbance was associated with elevated TyG-WHtR, TyG-WC, LAP, CI, BRI, ABSI, and WWI (p&amp;lt;.001). Threshold analysis confirmed significant changes in several indices, emphasising the impact of both insufficient and excessive sleep. Discussion Insufficient sleep duration, excessive sleep duration, and sleep disturbance, are associated with adverse lipid and anthropometric profiles indicating increased cardiometabolic risk. Optimising sleep duration and minimising sleep disturbance could mitigate these risks.

ObjectivesInsufficient or excessive sleep and dyslipidemia are significant cardiovascular risk factors. Whilst the relationship between sleep duration and traditional lipid indices are well described, the connection to novel lipid and anthropometric indices remains unclear. This study examines these associations using National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2020.MethodsThis cross-sectional study analyzed data from 9847 adults from NHANES 2005–2020, excluding those with major cardiovascular disease and cancer. Sleep duration was categorized as insufficient (< 7 h), normal (7-8 h), and excessive (> 8 h). Self-reported sleep disturbance was documented. Novel indices included non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR), Triglyceride to HDL-Cholesterol (TG/HDL), Triglyceride-Glucose (TyG) Index, Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), Conicity Index (CI), Body-Roundness Index (BRI), A Body Shape Index (ABSI), and Weight-adjusted waist index (WWI). Generalized additive models (GAMs) with spline smoothing and threshold analysis assessed nonlinear associations, adjusting for confounders. Weighted multivariate linear regression evaluated linear associations.ResultsInsufficient sleep was associated with higher TyG combined with waist-to-height ratio (TyG–WHtR) (p = 0.003). Excessive sleep was linked to higher TyG–WHtR, CI, BRI, ABSI, and WWI (p < 0.001). Sleep disturbance was associated with elevated TyG–WHtR, TyG–WC, LAP, CI, BRI, ABSI, and WWI (p < 0.001). Threshold analysis confirmed significant changes in several indices, emphasizing the impact of both insufficient and excessive sleep.ConclusionsInsufficient, excessive sleep duration and sleep disturbance are associated with adverse lipid and anthropometric profiles, indicating increased cardiometabolic risk. Optimal sleep duration and addressing sleep disturbance could mitigate these risks. Further research is needed to understand the underlying mechanisms.

While novel adiposity indices such as the body roundness index (BRI), a body shape index (ABSI), the visceral adiposity index (VAI), and the Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) index outperform body mass index (BMI) in predicting cardiovascular disease (CVD) risk, their long-term trajectories remain unstudied in Iran. We investigated the patterns of the BRI/ABSI/VAI/CUN-BAE index and their associations with the risk of CVD, all-cause mortality, and specific-cause mortality among Iranian adults. This prospective cohort study analyzed 11,394 Iranian adults (55.5% women, mean age 41.2 ± 15.0 years) from the Tehran Lipid and Glucose Study (1999–2018). Latent class growth mixture modeling identified trajectories of adiposity indices over a median of 18.0 years. Cox models assessed associations between the trajectories and incident CVD (n = 728), all-cause mortality (n = 532), and cause-specific mortality, adjusting for various socio-demographic, lifestyle, and metabolic confounders. Three distinct trajectories (low, moderate, and high-increase) emerged for all indices, with the high-increasing trajectories showing the strongest associations for CVD risk, the CUN-BAE index (HR: 2.45, 95%CI: 1.73–3.49), BRI (HR: 2.12, 95%CI: 1.65–2.73), ABSI (HR: 2.02, 95%CI: 1.38–2.95), and VAI (HR: 1.92, 95% CI: 1.49–2.49). Besides, the high-increase in BRI was associated with a higher risk of all-cause mortality (HR: 1.47, 95% CI: 1.10–1.96) and cardiovascular mortality (HR: 3.25, 95% CI: 1.85–5.69) compared with the low-increase group. There was no relationship between trajectory in the CUN-BAE index, VAI, and ABSI and risk of all-cause and cause-specific mortality. Notably, no significant associations were observed between any adiposity index trajectories and cancer mortality. Longitudinal trajectories of adiposity indices particularly BRI strongly predict CVD and mortality risks in Iranian adults. These findings support the incorporation of dynamic adiposity measures into clinical risk stratification.