Body Adiposity Index Research Articles

Evidence of association between gender and physical fitness in physically active people in Colombia

Abstract Background Physical Condition (PC) is an important health marker (Colley, 2019), as a predictor of morbidity and mortality from cardiovascular diseases, among other causes (Cruz, 2018). Aim To perform association tests between gender (G) and CP in physically active people in Colombia. This study is observational cross-sectional, 1,503 people participated, 1,019 Women (W) (68.6%) and 485 Men (M) (31.4%) in Colombia, mean age 52±16.2 in W and 42±17.8 in M. Data were analyzed with Rstudio v2022.12.0. The statistical protocol was: 1- Empirical distribution. 2- Normality tests with Kolmogorov-Smirnov. 3- Tests of independence with Chi-square (χ²). The mean (X¯), percentage (%) and standard deviation (±) were taken into account. PC was measured in body composition variables; Body Mass Index (BMI), Body Adiposity Index (BAI) and Abdominal Circumference (AC), Grip Strength (GS) and Maximal Oxygen Consumption (Vo2max). Results In W, 38.3% (27.2±1.37) were overweight (Ow) and 19.1% (33.0±2.83) obese (Ob), 69.6% (90.9±8.14) cardiovascular risk (CVR) for AC and 50.4% (32±4.73) risk for BAI. Regarding GS 43.4% (15.8±6.38) is weak and 54.6% (27.7±4.98) Vo2max is low. In M 35.6% (26.9±1.48) presented Ow and 10.6% (32.7±2.39) in Ob, 39.7% (98.4±6.8) with Rcv by AC and 24.3% (29.7±3.45) at risk by BAI. As for GS 41.5% (27.1±6.39) are weak and 32.3% (33.3±6.33) under Vo2max. According to independence tests with χ² there is association between the G and BMI (χ²=1452.1, df = 1427, p-value=0. 315), G and AC (χ²=394.9, df = 382, p-value=0. 313), G and Vo2max (χ²=155.5, df = 6, p-value<2. 2e-16), G and BAI (χ²=2970.3, df = 1502, p-value<2.2e-16), G and GS (χ²=6015.5, df = 1502, p-value<2.2e-16). This project has been approved by the bioethics committee. Conclusions Test of independence with (χ²), being a non-homogeneous sample, there is a strong association between G and PC with BMI, BAI, AC, GS and Vo2max, each variable depending on the others, evidencing higher Rcv in women with respect to men. Key messages • Several studies consider Physical Condition (PC) is an important health marker (Colley, 2019), as a predictor of morbidity and mortality of cardiovascular disease, among other causes (Cruz, 2018). • There is a strong association between G and the levels of BMI, BAI, AC, SG and Vo2max variables, each variable depending on the others, showing a higher Rcv in women compared to men.

Associations between diabesity and mortality over 7 years: a Maltese population-level case study

Abstract Introduction Diabesity presents a growing global epidemic. Demographic and socioeconomic associations have been documented, yet, the literature on mortality risk associated with diabesity is scarce. The aim of this study was to assess whether diabetes and/or diabesity increas the risk of all-cause mortality across a period of 7 years at a population level. Methods A nation-wide health examination survey in Malta was conducted between 2014 and 2016. This collected sociodemographic information, bio-anthropometric measurements (waist circumference, body mass index [BMI], body adiposity index [BAI]) and blood samples. Mortality information was acquired in 2021 by cross-referencing with the national mortality register. Statistical analysis was conducted using chi-square tests and Cox proportional hazard models. Results A mortality rate of 1.67% was observed (n = 3947), with diabetes and obesity being responsible for 6.06% and 3.03% of deaths, respectively. Examining the deceased individuals alongside their baseline characteristics (2014-2015) revealed that 25.76% (CI95%: 16.75 - 37.44) had diabetes, and 30.30% (CI95%: 20.55 - 42.22) were obese. Diabetes was associated with significant mortality risk (HR: 2.65 CI95%: 1.20 - 5.85; p = 0.02), while diabesity showed borderline significance (p = 0.06). After adjustment for potential confounding factors the mortality risk linked to diabetes remained significant and BAI was the only anthropometric measurement positively associated with mortality. Conclusions These findings emphasise the urgency for comprehensive public health policies targeting the concurrent prevention and management of diabetes and obesity at a population level. Our results also indicate that incorporating body adiposity index (BAI) into risk assessment protocols may provide valuable insights into mortality risk prediction and inform tailored interventions. Key messages • Diabetes and a high BAI score were significantly associated with mortality in the Maltese population, while diabesity had moderate significance, emphasising the urgency of tackling this epidemic. • These findings call for broad health policies to address diabesity at a population level and suggest integrating BAI into risk assessment for better mortality prediction and tailored interventions.

Association between fitness levels and physical activity in apparently healthy Colombian men

Abstract Physical condition (PC) such as strength and endurance favor functional independence and disease prevention (Melo, 2014). Objective: To characterize the levels of Physical Activity (PA) and CP in apparently healthy men who practice PA in different settings in Bogotá-Colombia. This study is observational cross-sectional, 485 Men (M) participated, mean age 42±17.8 years. To measure the levels PA the IPAQ (International Physical Activity Questionnaires) was applied. The Confidence Interval (CI) and Standard Error (SE) were taken into account. For PA levels, data were analyzed with Rstudio v2022.12.0, with empirical distribution tests, Kolmogorov-Smirnov normality and independence with Chi-Square (χ²), in addition, mean (X¯), percentage (%) and standard deviation (±). 90.6% of the H met the overall PA recommendations (Leisure Time+Active Transportation) with 95% CI (88.0-93.0) and SE (0.012). The PC variables such as; Body Mass Index (BMI), Body Adiposity Index (BAI), Abdominal Circumference (AC), Grip Strength (GS) and Maximal Oxygen Consumption (Vo2max). The results showed that 35.6% (26.9±1.48) of H were Overweight (Ow) and 10.6% (32.7±2.39) were Obese (Ob), 39.7% (98.4±6.8) with Cardiovascular Risk (Rcv) by AC and 24.3% (29.7±3.45) with risk by BAI. As for GS 41.5% (27.1±6.39) is weak and 32.3% (33.3±6.3) is low Vo2max. According to independence tests with χ² there is association between the G and BMI (χ²=1452.1, df = 1427, p-value=0. 315), G and AC (χ²=394.9, df = 382, p-value=0. 313), G and Vo2max (χ²=155.5, df = 6, p-value<2.2e-16), G and BAI (χ²=2970.3, df = 1502, p-value<2.2e-16), G and GS (χ²=6015.5, df = 1502, p-value<2.2e-16). This project was approved by the bioethics committee. Conclusions 149 men corresponding to 30.7% of the sample present an increased possibility of Rcv, despite compliance with global PA recommendations. It is recommended that PA programs reinforce the improvement and promotion of PC to prevent possible Rcv. Key messages • The capacities that define physical fitness (FC) such as strength and endurance favor functional independence and disease prevention (Melo, 2014). • Apparently healthy men in Colombia present a higher possibility of Cardiovascular Risk due to low levels of physical fitness, despite compliance with global BP recommendations.

Contribution of body adiposity index and conicity index in prediction of metabolic syndrome risk and components

Background and aimsBody adiposity index (BAI) and conicity index have been known as useful measures in predicting cardio-metabolic diseases. This study aimed to evaluate the predictive potential of BAI and conicity index for the risk of metabolic syndrome (MetS) in comparison with body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR). MethodsIn this cross-sectional study, 174 adults (87 with MetS and 87 healthy individuals) were recruited from a medical weight loss center. Anthropometric parameters, systolic and diastolic blood pressures (SDP and DBP), lipid profile, and fasting blood sugar (FBS) were measured. ResultsAll anthropometric parameters were significantly higher in subjects with MetS than in healthy subjects. Both in MetS and healthy subjects, females had significantly higher BAI and BMI than males. In the fully adjusted model, the odds of MetS increased for each unit increase in BAI by 27 % (p = 0.001), in BMI by 33 % (p = 0.001), in WC by 13 % (p < 0.001), and in HC by 9 % (p = 0.005). ROC curve analysis showed that all the anthropometric parameters displayed clinical importance in predicting MetS, but WHR had the largest area under the curve (AUC) in total, male, and female patients. In participants with MetS, the conicity index was negatively correlated with FBS; BAI was positively associated with HDL level. ConclusionAll studied anthropometric parameters had acceptable accuracy for predicting MetS. Traditional parameters, particularly the WHR, exhibited a higher predictive power concerning MetS. The results underscore the reliability of conventional anthropometric measures in clinical and epidemiological settings.

Obesity-related indices as predictors of lower extremity arterial disease in type 2 diabetes mellitus.

The aim of this study was to investigate the relationship between obesity and lower extremity arterial disease (LEAD) in patients with type 2 diabetes mellitus (T2DM). This retrospective study included 1821 patients with type 2 diabetes: 364 patients with LEAD and 1457 patients without LEAD. The patients were divided into training and internal test cohorts in a 7:3 ratio. LASSO regression analysis was used in the training cohort to filter relevant variables. Univariate and multivariate regression analyses were conducted to assess independent risk factors. A diagnostic nomogram was constructed and its discrimination was evaluated using the area under the ROC curve (AUC). The consistency was assessed using a calibration plot. The clinical application of the nomogram was evaluated by performing a decision curve analysis (DCA) and validated by an internal test cohort of the training cohorts. The LEAD group exhibited significantly higher values in obesity-related indices compared to the non-LEAD group, including waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), conicity index (CI), body adiposity index (BAI), and abdominal volume index (AVI). Multivariate analysis identified BMI, CI, BAI, and other parameters as independent risk factors for LEAD. A nomogram was constructed, and the AUC value of the nomogram was 0.746 in the training cohort and 0.663 in the internal test cohort. Obesity-related indices are associated with LEAD in patients with T2DM. Therefore, it is important to manage waist circumference and weight to reduce the risk of LEAD in patients with T2DM.

The Different Predictive Effects of Multiple Body Fat Indexes on Metabolic Dysfunction-Associated Fatty Liver Disease.

The aim of this study was to comprehensively compare the predictive effect of 10 body fat indexes on MAFLD in different sex, age and BMI subgroups. A total of 5403 physical examination data were included and divided into the MAFLD group (N=2632) and non-MAFLD group (N=2771). The differences and correlation of 10 promising indicators between the two groups were compared, including fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation product (LAP), visceral fat index (VAI), cardiometabolic index (CMI), body adiposity index (BAI), and triglyceride-glucose index (TyG), waist circumference index (WC), body mass index (BMI), waist to height ratio (WHtR). Logistic regression was used to analyze the risk of MAFLD under different adjustment conditions. The operating characteristic curve of different genders, BMI levels and age subgroups was plotted. Male gender, smoking, alcohol drinking, and higher age are risk factors for MAFLD. In addition to BAI, the other 9 indicators had a high correlation with MAFLD, the area under the curve (AUC) value was >0.7, and the prediction effect was better in females, BMI<24 kg/m2, age <35 years subgroup, among which FLI (AUC: 0.912, 95% CI: 0.905-0.920), LAP (AUC: 0.894, 95% CI: 0.8866-0.903), and HSI (AUC: 0.881, 95% CI: 0.872-0.890) have better prediction effects. Our study confirmed the accuracy of body fat-related indexes in predicting MAFLD in people of different sexes, ages, and BMI levels. Among them, FLI, LAP and HSI have high predictive value and can be utilized as simple and cost-effective tools for screening MAFLD in clinical settings.

Clinical utility of novel anthropometric indices in identifying type 2 diabetes mellitus among South African adult females.

To assess the clinical utility of novel anthropometric indices and other traditional anthropometric indices in identifying the risk of type 2 diabetes mellitus (T2D) among South African adult females. In the first South African National Health and Nutrition Examination Survey (SANHANES-1), traditional [body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR)] and novel [a-body shape index (ABSI), abdominal volume index (AVI), body adiposity index (BAI), body roundness index (BRI), conicity index (CI), and Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE)] anthropometric indices were assessed. T2D was diagnosed using glycated haemoglobin (HbA1c) ≥ 6.5% among participants without known T2D. Basic statistics and multiple regression analyses were explored the association between anthropometric indices and newly diagnosed T2D. Receiver operating characteristic (ROC) curve analysis was used to measure the predictive ability of both traditional and novel indices. Among 2 623 participants, 384 (14.6%) had newly diagnosed T2D. All anthropometric indices mean values were significantly higher among participants with T2D (most p < 0.001). Higher mean values increased T2D odds e.g., in the model adjusted for age, employment, residence, and population group, odds ratio (OR) and 95% confidence interval (CI) for T2D with some of anthropometric indices were: 1.86 (1.60-2.15) for WC, 1.84 (1.59-2.13) for WHtR, 1.73 (1.51-1.99) for AVI, 1.71 (1.49-1.96) for BRI and 1.86 (1.57-2.20) for CUN-BAE. The top quartile for all indices had the highest T2D odds (p < 0.05). These outcomes were the highest for WC, AVI, and CUN-BAE and remained so even after removing the confounding effects of age, employment, population group, and residence. Based on the ROC analysis, none of the anthropometrical indices performed excellently (i.e., had an area under the curve [AUC] > 0.80). The WC, WHtR, AVI, BRI, and CUN-BAE, however, performed acceptably (AUCs 0.70-0.79), while also exhibiting corresponding cutoff values of 86.65cm, 0.57, 15.52, 3.83, and 38.35, respectively. The data shows that traditional and novel anthropometric indices similarly identifying newly diagnosed T2D among adult South African females. We recommend the continuing the use of traditional indices, as they are affordable and easy to use in our setting.

HTLV1 infection and long term association with liver function and lipid indices; 10 years’ follow-up

BackgroundHuman T-cell leukemia virus type 1 (HTLV-1) is a well-known retrovirus, particularly prevalent in northeastern Iran, where it is associated with a range of disorders, including liver dysfunction. Previous studies have demonstrated that HTLV-1 infection can alter lipid profiles, yet no research has examined lipid indices and liver function tests in these patients in the long term.MethodsThis data is part of the Mashhad stroke and heart atherosclerotic disorder (MASHAD) study. A total of 1116 participants were randomly selected, including 837 healthy individuals and 279 HTLV-1-infected patients. Following a 10-year follow-up period, Serum levels of liver enzymes were measured. Lipid indices such as the Atherogenic Index of Plasma (AIP), Body Adiposity Index (BAC), Castelli risk index (CRI-I, CRI-II), Lipid Accumulation Product (LAP), Visceral Adiposity Index (VAI), Triglyceride-glucose index (TyG), and Triglyceride and HDL-C Ratio (THR) were calculated.ResultsMultivariable-adjusted regression analysis demonstrated a significant coefficient for the Visceral Adiposity Index (VAI) in HTLV-infected patients compared to healthy controls (B: -0.014, 95% CI: -0.02, 0.00, p = 0.046). However, no significant differences were observed in other lipid indices between HTLV-infected patients and healthy individuals. Regarding liver enzymes, significant variations were noted in HTLV-infected patients compared to healthy controls: Aspartate Aminotransferase (AST) (B: 2.978, 95% CI: 1.34, 4.61, p < 0.001), Alanine Aminotransferase (ALT) (B: 3.687, 95% CI: 1.59, 5.78, p = 0.001), Alkaline Phosphatase (ALP) (B: 18.232, 95% CI: 6.81, 29.65, p = 0.002), and Gamma-Glutamyl Transferase (GGT) (B: 3.714, 95% CI: 0.18, 7.24, p = 0.039).ConclusionIndividuals with HTLV-1 infection exhibit reduced VAI but elevated levels of liver enzymes such as AST, ALT, ALP, and GGT, indicating liver damage. These findings emphasize the virus’s involvement in liver pathology. Also, HTLV-I is associated with reduced visceral fat tissue.

Relationship between Body Adiposity Indices and Reversal of Metabolically Unhealthy Obesity 6 Months after Roux-en-Y Gastric Bypass.

The factors determining the reversal of metabolically unhealthy obesity (MUO) to metabolically healthy obesity (MHO) after Roux-en-Y gastric bypass (RYGB) are not completely elucidated. The present study aims to evaluate body adiposity and distribution, through different indices, according to metabolic phenotypes before and 6 months after RYGB, and the relationship between these indices and transition from MUO to MHO. This study reports a prospective longitudinal study on adults with obesity who were evaluated before (T0) and 6 months (T1) after RYGB. Bodyweight, height, waist circumference (WC), BMI, waist-to-height ratio (WHR), total cholesterol (TC), HDL-c, LDL-c, triglycerides, insulin, glucose, HbA1c and HOMA-IR were evaluated. The visceral adiposity index (VAI), the conicity index (CI), the lipid accumulation product (LAP), CUN-BAE and body shape index (ABSI) were calculated. MUO was classified based on insulin resistance. MUO at T0 with transition to MHO at T1 formed the MHO-t group MHO and MUO at both T0 and T1 formed the MHO-m and MUO-m groups, respectively. At T0, 37.3% of the 62 individuals were classified as MHO and 62.7% as MUO. Individuals in the MUO-T0 group had higher blood glucose, HbA1c, HOMA-IR, insulin, TC and LDL-c compared to those in the MHO-T0 group. Both groups showed significant improvement in biochemical and body variables at T1. After RYGB, 89.2% of MUO-T0 became MHO (MHO-t). The MUO-m group presented higher HOMA-IR, insulin and VAI, compared to the MHO-m and MHO-t groups. CI and ABSI at T0 correlated with HOMA-IR at T1 in the MHO-t and MHO-m groups. CI and ABSI, indicators of visceral fat, are promising for predicting post-RYGB metabolic improvement. Additional studies are needed to confirm the sustainability of MUO reversion and its relationship with these indices.

The variations in health cost based on the traditional obesity parameters among patients with coronary artery diseases undergoing cardiac catheterization

BackgroundIn the literature, obesity has been correlated with coronary artery diseases (CADs) and high health costs. This study aimed to investigate the relationships between obesity parameters and the health costs among patients with CADs undergoing cardiac catheterization.MethodA secondary data analysis was done for an original study. The original study was conducted among 220 hospitalized patients undergoing cardiac catheterization from two main hospitals located in the Middle and Northern regions of Jordan. Bivariate Pearson’s correlation and forward linear regression analysis were calculated in this study.ResultsThe average health cost for the participants was 1,344 JOD (1,895.63 USD). A significant positive moderate correlation (r = 0.4) was found between hip circumference (HC) and health cost. There were significant positive weak correlations between low-density lipoprotein (LDL), triglycerides, high-sensitivity C-reactive protein (HS-CRP), hemoglobin A1c (HbA1c), and depression, and the health cost (correlation coefficient 0.17, 0.3, 0.29, 0.22 and 0.17, respectively. HC, waist circumference (WC), waist-height ratio (WHtR), waist-hip ratio (WHR), and body adiposity index (BAI) were significantly associated with health costs among male participants. In contrast, among females, none of the obesity parameters was significantly associated with health costs. The forward regression analysis illustrated that an increase of HC by 3.9 cm (β (0.292) * SD (13.4)) will increase the health cost by 1 JOD (0.71 USD). The same analysis revealed that HS-CRP increased by 0.4 mg/dl (β (0.258)*SD (1.43)), or triglycerides increased by 8.3 mg/dl (β (0.241)* SD (34.3)), or depression score increased by 0.32 score (β (0.137)* SD (2.3)), or total cholesterol increased by 4 mg/dl (β (0.163)* SD (24.7)), the health cost will increase by one JOD (0.71 USD).ConclusionHealthcare providers, including nurses, should significantly consider these factors to reduce the health costs for those at-risk patients by providing the appropriate healthcare on time.

Anthropometric indices in predicting 10-year cardiovascular risk among males and females aged 40–74 years in south and southeast Asia: analysis of 12 WHO STEPS survey data

The relevance of anthropometric indices in predicting cardiovascular disease (CVD) or CVD risk factors is established across different countries, particularly in the high-income countries. However, past studies severely lacked representation from the south and southeast Asian countries. The main aim of this study was to determine the performance of conventional and new anthropometric indices to best predict 10-year cardiovascular disease (CVD) risk in south Asian and southeast Asian populations. The present study examined data from 14,532 participants in three south Asian and 13,846 participants (all aged between 40 and 74 years) in six southeast Asian countries, drawn from twelve cross-sectional studies (WHO STEPwise approaches to NCD risk factor surveillance [STEPS] survey data from 2008 to 2019). A Predictive performance of ten anthropometric indices were examined for predicting 10-year CVD risk ≥ 10% (CVD-R ≥ 10%). The 10-year CVD-R ≥ 10% was calculated by utilising the WHO CVD risk non-laboratory-based charts. Receiver operating characteristic (ROC) curve analysis was used to identify the optimal anthropometric index. Among the ten anthropometric indices, a body shape index (ABSI), body adiposity index (BAI), body roundness index (BRI), hip index (HI), and waist-height ratio (WHtR) performed best in predicting 10-year CVD risk among south Asian males and females. Improved performances were found for ABSI, BRI, conicity index (CI), WHtR, and waist-hip ratio (WHR) for 10-year CVD-R ≥ 10% predictions among southeast Asian males. Contrastingly, among southeast Asian females, ABSI and CI demonstrated optimal performance in predicting 10-year CVD-R ≥ 10%. The performance of anthropometric indices in predicting CVD risk varies across countries. ABSI, BAI, BRI, HI, and WHtR showed better predictions in south Asians, whereas ABSI, BRI, CI, WHtR, and WHR displayed enhanced predictions in southeast Asians. None.

Gender Differences in the Predictive Value of Obesity Indices for Insulin Resistance in Adult Mexican Individuals.

Obesity-linked insulin resistance (IR) is an important risk factor for metabolic diseases, and anthropometric indices are commonly used for risk assessment. The study aimed to assess possible differences between women and men in the predictive value and association of nine obesity indices with IR, as assessed by HOMA-IR, in a nondiabetic adult population. The cross-sectional study included individuals recruited from a hospital in Mexico City. Indices evaluated were waist circumference (WC), hip circumference (HC), body mass index (BMI), waist-to-hip ratio, waist-to-height ratio, visceral adiposity index, body adiposity index (BAI), relative fat mass (RFM), and conicity index (CI). Fasting plasma glucose and insulin were measured to calculate HOMA-IR. Correlation analysis was performed between obesity indices and HOMA-IR. Receiver operating characteristics curve analyses were performed to determine predictive accuracy and cut-off values of obesity indices for IR. A binary logistic regression (BLR) analysis with OR calculation was performed to determine the strength of association with HOMA-IR. We included 378 individuals (59% females, mean age 46.38 ±12.25 years). The highest Pearson coefficient value was observed for BMI among women, while among men, the highest values were found for BMI and BAI. WC among women, and BAI and RFM among men showed the highest sensitivity, while the highest specificity was observed for WHR among women and WC among men with respect to insulin prediction. In the adjusted BLR model, BMI, WC, and WHR among women and WC and RFM and BAI among men were independently associated with IR, showing the highest odds ratio (OR). In Mexican adults, WC, WHR, RFM and BAI could be complementary tools for BMI in screening for IR.

Comparison of obesity indicators for predicting cardiovascular risk factors and multimorbidity among the Chinese population based on ROC analysis

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

Significance of measuring anthropometric and atherogenic indices in patients with polycystic ovary syndrome

BackgroundPolycystic ovary syndrome (PCOS) is a prevalent hormonal disorder affecting 5–15% of women of reproductive age, characterized by ovulatory dysfunction, hyperandrogenism, and polycystic ovarian morphology. PCOS is associated with metabolic disturbances such as dyslipidemia, insulin resistance (IR), and an increased risk of type 2 diabetes (T2DM) and cardiovascular disease.ObjectiveThe aim of this study is to apply new anthropometric indices [body adiposity index (BAI), visceral adiposity Index (VAI), lipid accumulation product (LAP), body roundness index (BRI), a body shape index (ABSI)] and new atherogenic indices [Castelli index-I, Castelli index-II, atherogenic risk of plasma (AIP), atherogenic coefficient (AC), lipoprotein combined index (LCI), triglycerides to high-density lipoprotein cholesterol (TG/HDL-C) ratio, metabolic score for insulin resistance (METS-IR), triglyceride glucose (TyG) index, triglyceride glucose-dody mass (TyG-BMI) index, triglyceride glucose-waist circumference (TyG-WC) index] metabolic score of insulin resistance to patients with PCOS.MethodsA retrospective analysis was conducted on 248 women diagnosed with PCOS based on the 2003 Rotterdam criteria. Anthropometric measurements, biochemical parameters, and atherogenic indices were collected from patient records. Statistical analyses were performed using Statistical Package for the Social Sciences software version 28.0.ResultsSignificant correlations were found between fasting glucose and various anthropometric indices, such as Body mass index (BMI), waist-height ratio (WHtR), and BAI, indicating a link between adiposity and glucose metabolism in PCOS. Atherogenic indices like Castelli’s risk indices, AIP, and AC showed positive correlations with glucose and insulin levels, reinforcing their role in assessing cardiovascular risk. Novel indices such as METS-IR and TyG demonstrated strong correlations with both glucose and insulin profiles, highlighting their potential as reliable markers for IR and cardiometabolic risk.ConclusionThe study underscores the importance of using a range of anthropometric and atherogenic indices for comprehensive metabolic assessment in women with PCOS. Indices like METS-IR and TyG offer valuable insights into insulin sensitivity and cardiovascular risk, potentially aiding in better management and prognosis of PCOS.

Telomere length in young and middle-aged patients: relationship with cardiovascular risk factors

Aim. To evaluate the relationship between leukocyte telomere length (LTL) and cardiovascular risk factors in young and middle-aged patients without cardiovascular disease (CVD).Material and methods. This cross-sectional study involved 450 patients aged 30 [21;42] years. Risk factors and possible exclusion criteria was assessed through questionnaires and anthropometric examination. In all subjects, glucose concentration and lipid profile were assessed using the CardioChekPA express analyzer (USA, 2017), followed by calculation of integral metabolic indices: visceral adiposity index (VAI), body fat percentage, body adiposity index (BAI), lipid accumulation product (LAP). LTL was measured in whole blood samples using fluorimetry (Qubit 4 Singapore 2020) and reverse transcription polymerase chain reaction (QIAamp Blood Mini Kit, German 2022). Statistical analysis was carried out using the Statistica 10 program.Results. The prevalence of the studied risk factors in the main group corresponded to the general population. According to the correlation analysis, LTL was associated with age (r=-0,26; p&lt;0,05), smoking (r=-0,35; p&lt;0,05), obesity (r=-0,19; p&gt;0,05), neck circumference (NC) (r=-0,53; p&lt;0,05), diastolic blood pressure (r=-0,31; p&lt;0,05), cholesterol (r=-0,64; p&lt;0,05), high-density lipoproteins (HDL) (r=0,59; p&lt;0,05) and low-density lipoproteins (r=-0,52; p&lt;0,05), triglycerides (r=-0,46; p&lt;0,05), glucose (r=-0,33; p&lt;0,05), LAP (r=-0,4; p&lt;0,05), VAI (r=-0,57; p&lt;0,05) and BAI (r=-0,32; p&lt;0,05). According to the multivariate regression analysis, LTL was associated with age (B=-0,04, Std. Err. of B=0,02, p=0,03), smoking (B=-0,87, Std. Err. of B=0,26, p=0,001), NC (B=-0,23, Std. Err. of B=0,07, p=0,001), total cholesterol levels (B=-0,37, Std. Err. of B=0,87, p&lt;0,001), HDL (B=0,59, Std. Err. of B=0,24, p=0,018), LAP (B=-0,01, Std. Err. of B=0,02, p&lt;0,011), VAI (B=-0,37, Std. Err. of B=0,16, p=0,025).Conclusion. LTL is interconnected with cardiovascular risk factors, which determines the significance of their participation in CVD development and biological aging in young and middle-aged people.

Validity of Different Anthropometric Indices in the Assessment of Body Composition of Children and Adolescents with Type 1 Diabetes

Background: The prevalence of overweight and obesity in young people with type 1 diabetes (T1D) has been increasing despite current advancements in the management of T1D. Objectives: This study aimed to assess the prevalence of overweight/obesity using bioelectrical impedance analysis (BIA) in children and adolescents with T1D and evaluate the validity of new anthropometric indices, which have been recommended for defining overweight and obesity. Methods: A cross-sectional study was performed on subjects with T1D between the ages of 5 and 21 years. The patients' anthropometric measurements were obtained, and Body Mass Index (BMI), waist/height ratio (WHtR), Tri-Ponderal Mass Index (TMI), Body Adiposity Index (BAI), Body Shape Index (ABSI), Body Roundness Index (BRI), Abdominal Volume Index (AVI), and Conicity Index (CI) were calculated. The percentage of body fat (PBF) was measured with BIA. Results: Our study included 133 subjects (48.1% male, 77.4% pubertal) with a median age of 14.0 years (11.3 - 16.5) and a median duration of T1D of 4.5 years (1.5 - 7.3). The prevalence of overweight/obesity was 21.8% according to BMI-SDS and 30.1% according to BIA. BMI-SDS, WC, WHtR, TMI, AVI, BRI, and BAI were effective methods in differentiating overweight/obesity. BMI-SDS demonstrated a sensitivity of 78.79%, a positive predictive value of 72.22%, a negative predictive value of 89.71%, and an AUC of 0.895 at a cut point of 0.63, highlighting its effectiveness in distinguishing overweight/obesity. Conclusions: In children and adolescents with T1D, the prevalence of obesity/overweight identified using BIA was significantly higher compared to BMI-SDS. When reference methods are unavailable, BMI-SDS is the optimal anthropometric method for determining overweight/obesity in this population, independent of sex and puberty. These findings suggest that BMI-SDS can be a reliable alternative for body composition assessment, ensuring accurate identification and management of overweight/obesity in this population.

Comparison of novel and traditional anthropometric indices in Eastern-China adults: which is the best indicator of the metabolically obese normal weight phenotype?

BackgroundPeople with the metabolically obese normal weight (MONW) phenotype have been confirmed to significantly increase the risk of unfavorable health consequences. This study aimed to investigate the relationships between traditional and novel anthropometric indices with the MONW phenotype and compare the predictive ability of different anthropometric indices in identifying individuals with the MONW phenotype.MethodsThis cross-sectional study involved a total of 26,332 participants aged 18 years or older with a normal weight from Nanjing, China. Sociodemographic information, biochemical measurements, and anthropometric indices were collected. The novel body fat anthropometric indices included body shape index (ABSI), body roundness index (BRI), abdominal volume index (AVI), weight-adjusted-waist index (WWI), body adiposity index (BAI), conicity index (CI), waist-hip-height ratio (WHHR), as well as traditional indices such as waist circumference (WC), hip circumference (HC), body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR).The prevalence ratio (PR) from modified poisson regression and area under the receiver-operating characteristic curve (AUC) were conducted to compare the association and predictive capacity of different obesity indicators for the MONW phenotype. All analyses were stratified by sex.ResultsModified poisson regression analyses revealed that weight, WC, HC, BMI, WHR, WHtR, ABSI, BRI, AVI, WWI, BAI, CI, and WHHR were independently associated with higher risk of the MONW phenotype, regardless of whether they were treated as a continuous or categorical variable (P < 0.05). Notably, BRI demonstrated the strongest association in both men (highest quartile VS lowest quartile; PR = 3.14, 95%CI, 2.49, 3.96; P < 0.001) and women (PR = 4.63, 95%CI, 3.81, 5.62; P < 0.001). Receiver operating characteristic analysis indicated that AUC for the different anthropometric indices ranged from 0.50 to 0.80. BRI and WHtR had the largest AUC in both males (both AUC = 0.733; 95% CI, 0.717, 0.750) and females (both AUC = 0.773; 95% CI, 0.761, 0.786). The optimal cut-off points for BRI, determined by maximizing the Youden’s index, were 3.102 (sensitivity: 63.2%, specificity: 36.2%) in males and 3.136 (sensitivity: 68.9%, specificity: 44.2%) in females. Moreover, BRI and WHtR exhibited the highest diagnostic accuracy in younger age groups, specifically those aged 18–34 in both sexes.ConclusionsBRI emerged as the optimal predictor and independent determinant of the MONW phenotype, regardless of gender. This association was particularly pronounced in young individuals.

Community childhood obesity assessment in elementary school, anthropometric indices as screening tools: a community cross-sectional study in Indonesia

BackgroundRepresentative anthropometric epidemiological data are needed to formulate screening and intervention methods to prevent obesity in children. This study aims to conduct community childhood obesity assessment in elementary school based...

Novel genomic variants related to visceral adiposity index (VAI) and body adiposity index (BAI) in Indian sib-pairs.

Obesity is among the leading public health threats globally. Over the last few years, visceral adiposity index (VAI), and body adiposity index (BAI), derived from anthropometric, and biochemical measures, have gained importance as a measure of obesity. However, unlike other common indices like body mass index, and waist circumference, the genetic predisposition of VAI, and BAI under-examined. 2265 sib-pairs from Indian Migration Study were used for examining the association of genetic variants from the Cardio-Metabochip array with VAI, and BAI. Mixed linear regression models were run, and all inferences were based on the within-sib component of the Fulker's association models. Gene-environment/lifestyle interaction analyses were also undertaken. rs6659428 at LOC400796 | SEC16B (β = 0.26, SE = 0.05), and rs7611535 at DRD3 | LOC645180 (β = 0.18, SE = 0.04) were associated with VAI at suggestive significance value of <8.21 × 10-6. For BAI, rs73300702 at JAZF1-AS1 (β = 0.27, SE = 0.06), was the top hit at p value < 8.21 × 10-6. Further, rs6659428 showed marginal effect modification with rural/urban location (β = 0.26, SE = 0.13, p value = 0.047), and rs73300702 with physical activity (β = -0.29,SE = 0.14, p value = 0.034). We report three novel genetic loci for VAI, and BAI in Indians that are important indicators of adiposity. These findings need to be replicated and validated with larger samples from different ethnicities. Further, functional studies for understanding the biological mechanisms of these adiposity indices need to be undertaken to understand the underlying pathophysiology.

Triglyceride glucose-related indexes and lipid accumulation products-reliable markers of insulin resistance in the Chinese population.

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.