Anthropometric indices of obesity as predictors of high blood pressure among school children

BackgroundMetabolic syndrome (MetS), a condition of metabolic disorders, is now causing large disease burden around the world. This study aimed to update the prevalence of MetS in Jiangsu Province of China and evaluate the predicting value of five anthropometric measures including waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), a body shape index (ABSI) and body roundness index (BRI) in MetS.Methods8040 participants from 12 survey sites were enrolled into this cross-sectional study by multi-stage stratified cluster random sampling method from 2014 nutrition and diet investigation project in Jiangsu Province. The transformation of sex-specific z-score made the comparison meaningful when conducting the logistic analysis between anthropometric indices and MetS. The abilities of anthropometric indices to predict MetS were evaluated by the receiver operating characteristic curve (ROC). Delong test was applied to compare area under different ROC curves.ResultsThe prevalence of MetS in Jiangsu Province was 35.2% and the standardized prevalence was 34.8%. WC, BMI, WHtR, ABSI and BRI z-scores were positively related to MetS and its components. WC, WHtR and BRI z-score had stronger associations with MetS than BMI and ABSI in both male and female population. WC, WHtR and BRI had larger area under ROC curve than BMI and ABSI in male and female. WC in men had the largest area under the ROC curve, significantly higher than the other four measures of BMI, WHtR, ABSI and BRI (Z value = 9.08, 2.88, 16.73, 2.75 respectively). Among women, WC, WHtR and BRI had larger area under the ROC curve than BMI and ABSI, but the area under the WC, WHtR and BRI curve were not significantly different after the pairwise comparison by the Delong test. The optimal cut-off values of WC, WHtR and BRI for predicting MetS was 85.25 cm, 0.52 and 3.61 in male, 80.05 cm, 0.51 and 3.83 in female.ConclusionMetS has become one of the major chronic diseases in Jiangsu Province. WC was better than other four indices in predicting MetS among male population in Jiangsu. WC, WHtR and BRI had superior abilities than BMI/ABSI in predicting MetS among female population.

To investigate the association between various obesity indices and the risk of developing microvascular complications in adult patients with Type 2 diabetes(T2DM), using cohort data derived from Yinzhou District Health Big Data Platform of China. This study included adult patients with type 2 diabetes(T2DM) who were enrolled between January 1, 2008, and December 31, 2013, in Yinzhou District, Ningbo, and did not have any microvascular complications at baseline. Data collection encompassed demographic characteristics, lifestyle behaviors, laboratory test result, and physical examination findings, obtained at both baseline and during follow-up periods through structured epidemiological surveys and clinical assessments. Various obesity indices were calculated, including body mass index(BMI), waist-to-height ratio(WHtR), a body shape index(ABSI) and body roundness index(BRI). We also computed the coefficients of variation for these obesity indices during the follow-up period. A Cox proportional hazards regression model was used to analyze the association between obesity indices at baseline and follow-up, and the risk of developing microvascular complications. Additionally, receiver operating characteristic(ROC) curves were used to analyze the predictive efficacy of the coefficients of variation for BMI, WHtR, ABSI and BRI in relation to microvascular complications, and the areas under the curve(AUCs) were calculated. A total of 27 635 patients with type 2 diabetes(T2DM) were included, contributing to 153 717 person-years of follow-up. During this period, 12 969 new cases of microvascular complications were identified, resultsing in an incidence rate of 84.37 cases per 1000 person-years. Patients were categorized into two groups based on the occurrence of complications: those with microvascular complications and those without. There was no significant difference in blood glucose levels between the two groups at baseline. After adjusting for sociodemographic characteristics, laboratory indicators, and potential confounders such as a history of hypertension and hyperlipidemia, it was found that only the WHtR(HR=1.027, 95%CI 1.008-1.046), ABSI(HR=1.035, 95%CI 1.018-1.053) and BRI(HR=1.030, 95%CI 1.011-1.049) were independently associated with the risk of microvascular complications at baseline, while waist circumference(HR=1.010, 95%CI 0.992-1.029) and BMI(HR=0.985, 95%CI 0.967-1.002) were not significantly related(P>0.05). During the follow-up period, the coefficients of variation for all obesity indices were independently associated with an increased risk of microvascular complications. Among them, abdominal obesity indices, such as waist circumference(HR=0.063, 95%CI 1.057-1.069), WHtR(HR=1.060, 95%CI 1.054-1.066), and ABSI(HR=1.062, 95%CI 1.058-1.066), were most strongly linked to the risk of microvascular complications. Further stratified analysis based on baseline BMI revealed that the variability in abdominal obesity indices was more strongly associated with microvascular complications in patients with normal and overweight BMI compared to those with obesity. Specifically, the following result were observed: waist circumference(HR_(normal BMI)=1.074, HR_(overweight)=1.059, HR_(obesity)=1.041; P<0.01), WHtR(HR_(normal BMI)=1.069, HR_(overweight)=1.059, HR_(obesity)=1.037; P<0.01), ABSI(HR_(normal BMI)=1.065, HR_(overweight)=1.067, HR_(obesity)=1.038; P<0.01), BRI(HR_(normal BMI)=1.023, HR_(overweight)=1.020, HR_(obesity)=1.011; P<0.01). Additionally, to further explore the predictive value of various obesity indices for microvascular complications in type 2 diabetes mellitus(T2DM), we conducted stratified analyses based on sex and age(using 60 years as the cutoff). WHtR showed similar predictive performance between men(AUC = 0.794) and women(AUC=0.789). However, WHtR demonstrated stronger predictive ability in individuals over 60 years old(AUC = 0.803) compared to those aged 60 years or younger(AUC = 0.777). ABSI exhibited a higher predictive value in men(AUC = 0.752) than in women(AUC = 0.730), and again, the index performed better in the older population(AUC = 0.761) than in the younger group(AUC = 0.725). Similarly, BRI demonstrated comparable performance between sexes [men(AUC = 0.796) and women(AUC = 0.791)] with the highest predictive accuracy seen in participants over 60 years(AUC = 0.806). By contrast, BMI showed relatively lower predictive power across all subgroups. Specifically, the AUC values for BMI were 0.744 in men and 0.714 in women, 0.714 in those aged 60 years or below and 0.748 in those above 60 years. Increased baseline abdominal obesity indices(WHtR, ABSI and BRI) and higher variability in obesity indices during follow-up are strongly associated with increased risks of microvascular complications in T2DM patients. In individuals with normal BMI, higher variability in abdominal obesity indices is positively correlated with the risk of microvascular complications. Furthermore, the variability in abdominal obesity indices(WHtR, ABSI and BRI) provides better predictive ability for microvascular complications compared to general obesity indices(BMI), especially in male patients and those aged over 60.

BackgroundVarious anthropometric indices can be used to estimate obesity, and it is important to determine which one is the best in predicting the risk of coronary heart disease (CHD) and to define the optimal cut-off point for the best index.MethodsThis cross-sectional study investigated a consecutive sample of 11,247 adults, who had lived in rural areas of China and were older than 35 years of age. Eight obesity indices, including the body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), abdominal volume index (AVI), body adiposity index (BAI), body roundness index (BRI) and a body shape index (ABSI) were investigated. The risk of CHD was evaluated by the 10-year coronary event risk (Framingham risk score). Receiver operating characteristic (ROC) curve analyses were used to evaluate the predictive ability of the obesity indices for CHD risk.ResultsOf the whole population, 3636 (32.32%) participants had a risk score higher than 10%. Those who suffered medium or high CHD risk were more likely to have higher mean anthropometric indices, except for BMI in males. In the multivariate-adjusted logistic regression, all these anthropometric measurements were statistically associated with CHD risk in males. After adjusting for all the possible confounders, these anthropometric measurements, except for ABSI, remained as independent indicators of CHD risk in females. According to the ROC analyses, ABSI provided the largest area under the curve (AUC) value in males, and BMI showed the lowest AUC value, with AUC varying from 0.52 to 0.60. WHtR and BRI provided the largest AUC value in female, and similarly, BMI showed the lowest AUC value, with AUC varying from 0.59 to 0.70. The optimal cut-off values were as follows: WHtR (females: 0.54), BRI (females: 4.21), and ABSI (males: 0.078).ConclusionsABSI was the best anthropometric index for estimating CHD risk in males, and WHtR and BRI were the best indicators in females. Males should maintain an ABSI of less than 0.078, and females should maintain a WHtR of less than 0.54 or a BRI of less than 4.21.

Currently, a growing number of research studies have shown a positive association between obesity and erectile dysfunction, while traditional anthropometric measures, such as BMI, have limited ability to assess the risk of erectile dysfunction. Therefore, this study aimed to investigate the association between the new anthropometric index and erectile dysfunction. A study involving 3594 participants from the National Health and Nutrition Examination Survey was conducted. The study calculated various anthropometric indices such as waist circumference (WC), waist-to-height ratio (WtHR), body mass index (BMI), a body shape index (ABSI), conicity index (CI), and body roundness index (BRI). The relationship between anthropometric indices and erectile dysfunction (ED) was investigated using multivariate logistic regression and restricted cubic splines (RCS). Interaction analysis was conducted on subgroups to confirm the findings. Additionally, the efficacy of various anthropometric indicators in predicting the risk of erectile dysfunction was assessed using the receiver operating characteristic curve (ROC). After adjusting for potential confounding factors, we identified a positive and independent correlation between erectile dysfunction (ED) and all other anthropometric measures except for BMI. Additionally, the risk of ED increased by 49% and 42% for each standard deviation increment in ABSI and CI, respectively. Dose-response curve analysis demonstrated that WC, BMI, WtHR, and CI displayed a non-linear correlation with the risk of ED. The subgroup analysis revealed that individuals classified as White, who had higher levels of WC, ABSI, and CI, were more susceptible to erectile dysfunction compared to people from other races. ROC analysis showed that ABSI was superior in detecting erectile dysfunction (area under the curve: 0.750; 95% CI 0.732-0.768; optimal cutoff value: 0.083) as compared to other indices. The combination of obesity defined by BMI and other anthropometric measures showed that higher ABSI and CI levels were positively associated with the prevalence of erectile dysfunction, independent of BMI (P < .001). In this study, anthropometric indicators including ABSI, BRI, WtHR, CI, and WC were positively associated with erectile dysfunction. To improve the prevention and treatment of this condition, it is recommended that new anthropometric indicators receive greater consideration.

BackgroundCardiometabolic multimorbidity (CMM) and obesity represent two major health problems. The relationship between adiposity indices and CMM, however, remains understudied. This study aimed to investigate the associations of body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), a body shape index (ABSI), body roundness index (BRI), and conicity index (CI) with CMM among Chinese adults.MethodsData of 101,973 participants were collected from a population-based screening project in Southern China. CMM was defined as having two or more of the following diseases: coronary heart disease, stroke, hypertension, and diabetes. The relationship between the six adiposity indices and CMM was investigated by multivariate logistic regression and restricted cubic splines. Receiver operator characteristic curve, C-statistic and net reclassification index were used to estimate the discriminative and incremental values of adiposity indices on CMM.ResultsLogistic regression models showed the six adiposity indices were all significantly associated with the odds of CMM with non-linear relationships. For per SD increment, WC (Odds ratio [OR]: 1.66; 95% confidence interval (CI): 1.62–1.70) and WHtR (OR, 1.61; 95% CI, 1.58–1.65) were more significantly associated with a higher prevalence of CMM than BMI (OR, 1.55; 95% CI, 1.52–1.58) (all P < 0.05). In addition, WC, WHtR, and BRI displayed significantly better performance in detecting CMM compared with BMI (all P < 0.05). Their respective area under the curve (AUC) values were 0.675 (95% CI: 0.670–0.680), 0.679 (95% CI: 0.675–0.684), and 0.679 (95% CI: 0.675–0.684), while BMI yielded an AUC of 0.637 (95% CI: 0.632–0.643). These findings hold true across all subgroups based on sex and age. When Adding WC, WHtR, or BRI to a base model, they all provided larger incremental values for the discrimination of CMM compared with BMI (all P < 0.05).ConclusionsAdiposity indices were closely associated with the odds of CMM, with WC and WHtR demonstrating stronger associations than BMI. WC, WHtR, and BRI were superior to BMI in discriminative ability for CMM. Avoidance of obesity (especially abdominal obesity) may be the preferred primary prevention strategy for CMM while controlling for other major CMM risk factors.

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.

BackgroundPrevious studies have explored the associations between obesity and Parkinson’s disease (PD), often using body mass index (BMI) as the main metric. However, findings remain inconsistent. Anthropometric indices—quantitative measures of body shape, size, and fat distribution—offer alternative ways to assess adiposity. This study aimed to evaluate the associations between eight anthropometric indices and PD prevalence.MethodsData were obtained from the National Health and Nutrition Examination Survey (NHANES), conducted in the U.S. from 1999 to 2020. A total of 41,374 participants aged 20 years and older were included, among whom 354 were diagnosed with PD. Eight anthropometric indices were analyzed: waist-to-weight index (WWI), conicity index (CI), a body shape index (ABSI), body roundness index (BRI), waist-to-height ratio (WHtR), BMI, waist circumference (WC), and weight (WT). Weighted multivariable logistic regression models were used to assess the association between these indices and PD. Restricted cubic spline (RCS) models were employed to examine dose–response relationships. Subgroup and sensitivity analyses were conducted to validate the robustness of the findings.ResultsSignificant differences were observed between the study groups, with positive and independent correlations identified between PD and all anthropometric measures, except BMI. After full adjustment, each 1-standard deviation increase in WWI, CI, ABSI, BRI, WHtR, WC, and WT was associated with an elevated PD risk by 34, 42, 36, 18, 21, 25, and 16%, respectively. RCS analysis revealed a linear relationship between CI, ABSI, BRI, WtHR, WC, WT, and PD prevalence, whereas WWI exhibited a nonlinear association. The subgroup and sensitivity analyses confirmed the consistency of these associations.ConclusionHigher values of several anthropometric indices, particularly the ABSI, WWI, and CI, were associated with increased PD prevalence. These findings highlight the potential role of fat distribution rather than overall adiposity in PD pathogenesis. Anthropometric measures may be valuable tools for early PD risk identification and targeted prevention strategies.

Hyperuricemia (HUA), a key risk factor for gout and cardiometabolic diseases, is underdiagnosed in elderly populations due to limited serum uric acid (SUA) testing in routine blood panels. Identifying noninvasive obesity-related indices for HUA could enhance early risk stratification, particularly in resource-constrained settings. This study evaluates the correlation and predictive performance of 6 obesity indices for HUA in Chinese elderly adults. A cross-sectional analysis of 8362 adults aged ≥ 65 years was conducted using 2021 health examination data from Guangdong, China. Multivariable logistic regression evaluated associations between obesity indices - Body Mass Index (BMI), Waist-to-Height Ratio (WHtR), Body Roundness Index (BRI), Weight-Adjusted-Waist Index (WWI), A Body Shape Index (ABSI), and Cardiometabolic Index (CMI) - and HUA. Receiver Operating Characteristic (ROC) curves with DeLong's test compared differences in Area Under the Curve (AUC). HUA prevalence was 48.62%, higher in females (53.07%) than males (43.20%). Adjusted for confounders, all indices showed significant associations: BMI (OR = 1.106, 95% CI = 1.090-1.121), WHtR (OR = 1.056 per 0.01-unit, 95% CI = 1.047-1.064), BRI (OR = 1.204, 95% CI = 1.173-1.236), WWI (OR = 1.185, 95% CI = 1.120-1.253), ABSI (OR = 1.083 per 0.01-unit, 95% CI = 1.002-1.171), and CMI (OR = 1.606, 95% CI = 1.488-1.735). CMI demonstrated the highest AUC (0.645, 95% CI = 0.633-0.656), significantly outperforming BMI, WHtR, BRI, WWI, and ABSI (DeLong's P < .001). WHtR (AUC = 0.621, 95% CI = 0.609-0.633) and BRI (AUC = 0.619, 95% CI = 0.608-0.631) showed comparable performance (P = .386). While CMI had superior predictive accuracy, its reliance on blood lipids limits practicality for routine screening. WHtR and BRI - requiring only waist circumference and height - offer optimal balance between performance and clinical feasibility, especially in primary care settings lacking laboratory infrastructure. These noninvasive tools could complement SUA testing to improve HUA risk assessment in elderly populations.

Metabolic syndrome (MSyn) is a considerable health concern in developing and developed countries, and it is a critical predictor of all-cause mortality. Obesity, specifically central obesity, is highly associated with MSyn incidence and development. In this study, seven anthropometric indices (Body Mass Index (BMI), Waist circumference (WC), Waist-to-Height Ratio (WHtR), A Body Shape Index (ABSI), Body Roundness Index (BRI), conicity index (CI), and the Visceral Adiposity Index (VAI)) were used to identify individuals with MSyn among the Jordanian population. These indices were assessed to identify their superiority in predicting the risk of MSyn. A total of 756 subjects (410 were male and 346 were female) were met between May 2018 and September 2019 and enrolled in this study. Height, weight, and waist circumferences were measured and BMI, WHtR, ABSI, BRI, CI, and VAI were calculated. Fasting plasma glucose level, lipid profile, and blood pressure were measured. Receiver-operating characteristic (ROC) curve was used to determine the discriminatory power of the anthropometric indices as classifiers for MSyn presence using the Third Adult Treatment Panel III (ATP III) definition. MSyn prevalence was 42.5%, and obese women and men have a significantly higher prevalence. BRI and WHtR showed the highest ability to predict MSyn (AUC = 0.83 for both indices). The optimal cutoff point for an early diagnosis of MSyn was > 28.4 kg/m2 for BMI, > 98.5 cm for WC, > 5.13 for BRI, > 0.09 m11/6 kg−2/3 for ABSI, > 5.55 cm2 for AVI, > 1.33 m3/2 kg−1/2 for CI, and > 0.59 for WHtR with males having higher cutoff points for MSyn early detection than females. In conclusion, we found that WHtR and BRI may be the best-suggested indices for MSyn prediction among Jordanian adults. These indices are affordable and might result in better early detection for MSyn and thereby may be helpful in the prevention of MSyn and its complications.

The strong association between obesity and chronic kidney disease (CKD) has been empirically validated, yet traditional measures like the Body Mass Index (BMI) fail to accurately assess the extent of obesity due to CKD's characteristics, such as reduced muscle mass and increased visceral fat. This study investigates the association between CKD and several anthropometric indices, including A Body Shape Index (ABSI), Body Roundness Index (BRI), Waist-to-Height Ratio (WHtR), and the Conicity Index (C-index), to determine their predictive capabilities. Based on the datasets from the National Health and Nutrition Examination Survey (NHANES) 2009-2018, weighted multivariable regression analyses were carried out to examine the independent relationship between two anthropometric indices and CKD. Also, subgroup analyses, restricted cubic spline regression (RCS), and receiver operating characteristic curve analysis were conducted for further data analyses. A total of 24,162 participants were enrolled in this study. After adjusting for confounding factors, ABSI, BRI, WHtR, and the C-index were significantly associated with an increased risk of CKD, while BMI was not. Height showed a protective effect against CKD. ABSI and the C-index demonstrated the highest areas under the curve (AUCs), indicating superior predictive capabilities compared to traditional measures like BMI and waist circumference (WC). Subgroup analyses revealed significant interactions between the anthropometric indices and factors such as age, disease status, dietary intake, and physical activity levels. This study highlights the significant associations between various anthropometric indices (including ABSI, BRI, WHtR, and C-index) and the risk of CKD. ABSI and the C-index demonstrated the strongest predictive capabilities for CKD, with the highest AUC values.

Body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) have long been important physical assessment indicators. In recent years a body shape index (ABSI) and body roundness index (BRI) have gained importance as new physical assessment indicators. However, it is unclear how these physical assessment indicators assess psychological symptoms in adolescents. The main purpose of this study was to determine the ability of BMI, WC, WHtR, ABSI, and BRI indicators to recognize psychological symptoms in Chinese adolescents and to examine whether ABSI and BRI are superior to the traditional BMI, WC, and WHtR. In this study, 42,472 (boys, 21,026, 49.5%) adolescents aged 12-18 years from six geographic regions in China were assessed for multiple physical assessment indicators (BMI, WC, WHtR, ABSI, BRI) and psychological symptoms from 2015 to 2016. One-way analysis of variance, Spearman's rank test, multifactor logistic regression analysis, and ROC analysis were used to analyze the validity of multiple physical assessment indicators to identify psychological symptoms. The prevalence of psychological symptoms among Chinese adolescents aged 12-18 years was 26.17%. The BMI, WC, WHtR, ABSI, and BRI of the adolescents were (20.19 ± 3.43) kg/m2, (69.68 ± 10.05) cm, (0.42 ± 0.06), (0.06 ± 0.01), and (2.05 ± 0.92), respectively. The results of Spearman's rank test showed a positive correlation (p < 0.001) between BMI, WC, WHtR, and BRI were positively associated with psychological symptoms (p < 0.001). The highest Spearman correlation coefficient was found between WC and psychological symptoms (r = 0.134, p < 0.001), and BMI was the lowest (r = 0.108, p < 0.001). Overall, the prevalence of psychological symptoms for all five physical assessment indicators (BMI, WC, WHtR, ABSI, and BRI) showed an increasing trend from Q1 to Q4 (p < 0.001). Overall, WC (AUC: 0.61, 95%CI: 0.61-0.62), WHtR (AUC: 0.61, 95%CI: 0.60-0.61), and BRI (AUC: 0.61, 95%CI: 0.60-0.61) were highly and identically predictive of psychological symptoms; ABSI was the least predictive of psychological symptoms (AUC: 0.51, 95%CI: 0.50 ~ 0.51). This study found that neither the ABSI nor the BRI was superior to BMI, WC, or WHtR in predicting psychological symptoms in Chinese adolescents. The ABSI was the least predictive of psychological symptoms in Chinese adolescents, whereas the BRI showed potential as a surrogate for WC and WHtR indicators for assessing psychological symptoms. This study provides additional help and suggestions for better identification of psychological symptoms in Chinese adolescents.

BackgroundThe Body Mass Index (BMI) has long been used as an anthropometric measurement. Waist circumference (WC) and waist-to-height ratio (WHtR) have been proposed as alternatives to BMI. Recently, two new anthropometric indices, the A Body Shape Index (ABSI) and Body Roundness Index (BRI) have been developed as possible improved alternatives to BMI and WC. The main research aim is to assess the capacity of the ABSI and BRI to identify subjects with diabetes mellitus (DM) and the secondary aim is to determine whether ABSI and/or BRI is superior to the traditional body indices (BMI, WC, and WHtR).Methods and ResultsThis cross-sectional study was conducted in the rural areas of northeast China from January 2012 to August 2013, and the final analysis included data obtained form 5253 men and 6092 women. 1182 participants (10.4 %) suffered from DM. Spearman rank test showed that BRI and WHtR showed the highest Spearman correlation coefficient for DM whereas ABSI showed the lowest. The prevalence of DM increased across quartiles for ABSI, BMI, BRI, WC and WHtR. A multivariate logistic regression analysis of the presence of DM for the highest quartile vs. the lowest quartile of each anthropometric measure, showed that the WHtR was the best predictor of DM (OR: 2.40, 95 % CI: 1.42–3.39 in men; OR: 2.67, 95 % CI: 1.60–3.74 in women, both P < 0.001), and the ABSI was the poorest predictor of DM (OR: 1.51, 95 % CI: 1.05–1.97 in men; OR: 1.55, 95 % CI: 1.07–2.04 in women, both P < 0.05). ABSI showed the lowest AUCs (AUC: 0.61, 95 % CI: 0.58–0.63 for men; AUC: 0.61, 95 % CI: 0.59–0.63 for women) for DM in both sexes, while BRI (AUC: 0.66, 95 % CI: 0.63–0.68 for men; AUC: 0.67, 95 % CI: 0.65–0.69 for women) had high AUCs for DM that equaled those of WHtR.ConclusionsOur results showed neither ABSI nor BRI were superior to BMI, WC, or WHtR for predicting the presence of DM. ABSI showed the weakest predictive ability, while BRI showed potential for use as an alternative obesity measure in assessment of DM.

Traditional anthropometric measures, including body mass index (BMI), are insufficient for evaluating gallstone risk. This study investigated the association between novel anthropometric indices and gallstone risk among 6,848 participants from the National Health and Nutrition Examination Survey in the United States. Measures calculated included weight (WT), BMI, waist circumference (WC), waist-to-height ratio (WtHR), conicity index (CI), A Body Shape Index (ABSI), Body Roundness Index (BRI), Abdominal Volume Index (AVI), and Weight-adjusted Waist Index (WWI). Logistic regression and smooth curve fitting assessed the relationships between these indices and gallstones, complemented by receiver operating characteristic (ROC) curve analysis to evaluate their discriminative power. The results indicated significant differences between study groups, with a positive and independent correlation identified between gallstones and all measures except ABSI. Specifically, per 1 SD increase in WC, WT, BMI, WtHR, and AVI was associated with a 57%, 59%, 52%, 53%, and 53% increased risk of gallstones, respectively. Dose-response analysis confirmed a positive correlation between these indices and gallstone risk. ROC analysis highlighted WtHR and BRI as having superior discriminative abilities (AUC = 0.6703). Further, among participants with a BMI < 30 kg/m2, elevated levels of WT, WtHR, CI, BRI, and WWI significantly increased the risk of gallstones (P < 0.001). Likewise, elevated BMI heightened the risk at low levels of WT, WC, WtHR, BRI, AVI, and CI (P < 0.001). This study supports the positive association between various anthropometric indicators and gallstones, recommending that newer anthropometric indices be considered more extensively to enhance gallstone prevention and treatment strategies.

ObjectiveTo compare the predictive performance of the percentage body fat (PBF), body mass index (BMI), waist circumference (WC), hip circumference (HC), waist–hip ratio (WHR), waist–height ratio (WHtR), a body shape index (ABSI), body roundness index (BRI), abdominal volume index (AVI), and conicity index (CI) for identifying hypertension.MethodsA cross-sectional study was conducted among 2,801 adults (1,499 men and 1,302 women) aged 18 to 81 in Ningbo, China. The receiver operator characteristic (ROC) analysis and multiple non-parametric Z tests were used to compare the areas under the curve (AUC). The maximum Youden's indices were used to determine the optimal cut-off points of 10 obesity-related indices (ORI) for hypertension risk.ResultsThe AUC of all the indices were statistically significant (P < 0.05). The AUC of all the indices in men and women were 0.67–0.73 and 0.72–0.79, respectively. Further non-parametric Z tests showed that WHR had the highest AUC values in both men [0.73 (95% CI: 0.70, 0.76)] and women (0.79 (95% CI: 0.75, 0.83)], and several central ORI (men: WHR, WC, BRI, AVI, and CI, 0.71–0.73; women: WC, WHR, and AVI, 0.77–0.79) were higher than general ORI (PBF and BMI, 0.68 in men; 0.72–0.75 in women), with adjusted P < 0.05. The optimal cut-off points for identifying hypertension in men and women were as follows: PBF (23.55%, 32.55%), BMI (25.72 kg/m2, 23.46 kg/m2), HC (97.59 cm, 94.82 cm), WC (90.26 cm, 82.78 cm), WHR (0.91, 0.88), WHtR (0.51, 0.55), ABSI (0.08 m7/6/kg2/3, 0.08 m7/6/kg2/3), BRI (4.05, 4.32), AVI (16.31 cm2, 13.83 cm2), and CI (1.23 m2/3/kg1/2, 1.27 m2/3/kg1/2). Multivariate logistic regression models showed that all indices were statistically significant (P < 0.05) with the adjusted ORs (per 1-SD increase) at 1.39–2.06 and ORs (over the optimal cut-off points) at 1.80–2.64.ConclusionsAll 10 ORI (PBF, BMI, HC, WC, WHR, WHtR, ABSI, BRI, AVI, and CI) can effectively predict hypertension, among which WHR should be recommended as the best predictor. Central ORI (WHR, WC, and AVI) had a better predictive performance than general ORIs (PBF and BMI) when predicting the risk of hypertension.