Cardiometabolic Index (CMI), Lipoprotein Combine Index (LCI), Conicity Index (CI), Weight-adjusted Waist Index (WWI), Waist-to-Hip-to-Height Ratio (WHHR), Body Surface Area (BSA) and the 10-year risk of hypertension: A machine learning approach in the Yazd Healthy Heart Project

Cardiometabolic Index (CMI), Lipoprotein Combine Index (LCI), Conicity Index (CI), Weight-adjusted Waist Index (WWI), Waist-to-Hip-to-Height Ratio (WHHR), Body Surface Area (BSA) and the 10-year risk of hypertension: A machine learning approach in the Yazd Healthy Heart Project | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cardiometabolic Index (CMI), Lipoprotein Combine Index (LCI), Conicity Index (CI), Weight-adjusted Waist Index (WWI), Waist-to-Hip-to-Height Ratio (WHHR), Body Surface Area (BSA) and the 10-year risk of hypertension: A machine learning approach in the Yazd Healthy Heart Project Parisa Peigan, Farnoosh Ghomi, Motahare Shabestari, Pedro Marques-Vidal, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7670443/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To evaluate and compare the power of novel indices in forecasting the 10-year risk of hypertension, and also to identify the most reliable predictor of hypertension using machine learning and conventional statistical techniques. Methodology: Data were obtained from 2,000 adults aged 20 to 74 years who were enrolled in the Yazd Healthy Heart Project and followed for 10 years. Participants underwent comprehensive assessments of anthropometric, biochemical, and lifestyle variables. The discriminative ability of each index was evaluated using Receiver Operating Characteristic (ROC) analysis, Cox regression models, and three machine learning algorithms: Random Forest, XGBoost, and LightGBM. Outcomes: The LCI and CMI demonstrated the strongest independent associations with hypertension (LCI: HR = 3.64, AUC = 0.72; CMI: HR = 2.93, AUC = 0.72). Among the machine learning models, XGBoost yielded the best predictive performance (AUC = 0.76, sensitivity = 76.0%, F1-score = 54.8%) and exhibited the smallest discrepancy between training and test results. Stratified analyses revealed that LCI was most predictive in middle-aged individuals, whereas CMI demonstrated greater predictive value in older adults. In contrast, the CI, WWI, and BSA lost statistical significance after multivariable adjustment. Conclusion The LCI and CMI outperformed traditional anthropometric measures in predicting hypertension across both conventional statistical analyses and machine learning models. Incorporating these indices into clinical screening protocols could enhance early detection and support more targeted prevention strategies. Hypertension Anthropometric indexes Public health Machine learning Figures Figure 1 Figure 2 Figure 3 Introduction Hypertension is a major global health concern, contributing significantly to cardiovascular morbidity and mortality ( 1 ). The World Health Organization (WHO) reports that approximately 1.28 billion adults aged 30–79 years worldwide have hypertension, with a significant proportion unaware of their condition ( 2 ). Traditional anthropometric indices, such as Body Mass Index (BMI) and waist circumference (WC), have been widely used to assess obesity-related hypertension risk ( 3 ). However, these measures often fail to account for fat distribution and metabolic variation, limiting their predictive accuracy ( 4 , 5 ). In recent years, novel anthropometric indices have been developed to provide a more comprehensive assessment of cardiometabolic risk ( 6 ). These include the Cardiometabolic Index (CMI), Conicity Index (CI), Weight-adjusted Waist Index (WWI), Waist-to-Hip-to-Height Ratio (WHHR), Body Surface Area (BSA), and Lipoprotein Combine Index (LCI), which aim to capture the complex interplay among body composition, fat distribution, and metabolic health ( 6 ). The advent of machine learning (ML) techniques has further enhanced the ability to analyze complex, multidimensional data, enabling the development of more accurate predictive models for hypertension ( 7 ). ML algorithms can identify intricate patterns and interactions among various risk factors, often surpassing traditional statistical methods in predictive performance ( 7 ). This study aims to compare the predictive efficacy of CMI, CI, WWI, WHHR, BSA, and LCI in forecasting the incidence of hypertension using machine learning approaches. By evaluating these indices within an ML framework, we seek to evaluate and compare the power of novel indices in forecasting the 10-year risk of hypertension, and also to identify the most reliable predictor of hypertension using machine learning and conventional statistical techniques, thereby informing targeted prevention and intervention strategies. Materials and methods Study Population This cohort study aims to Machine learning-driven evaluation of novel anthropometric indices for hypertension prediction. Data were obtained from the Yazd Healthy Heart Project (YHHP), a population-based epidemiological study conducted in Yazd, Iran, designed to monitor and investigate cardiovascular and metabolic disorders. In the baseline phase (2005–2006), 2,000 individuals aged 20 to 74 years were randomly selected from the urban population using a multistage cluster sampling method. A total of 100 clusters were identified, from which 20 families per cluster were chosen, and one adult was randomly selected from each family (1,000 men and 1,000 women). Trained personnel collected demographic, socioeconomic, and lifestyle information, as well as anthropometric measurements (weight, height, waist circumference), physical activity levels, dietary habits, smoking status, and biochemical markers, including fasting blood glucose, triglycerides, and HDL cholesterol. Blood samples were collected at both the baseline and follow-up phases. After 10 years (2015–2016), participants were re-invited for reassessment. Individuals with hypertension at baseline, those with missing key anthropometric or biochemical data, or those lost to follow-up were excluded from the final analysis. Inclusion and Exclusion Criteria Of the 2,000 individuals initially enrolled in the study, 36.1% were diagnosed with hypertension at baseline and were therefore excluded from further analysis. Among the 1,269 participants who were normotensive at baseline, 786 (61.9%) had complete data and were successfully followed up after 10 years. As shown in Fig. 1 , 40 .3% of these individuals developed hypertension during the follow-up period. Data collection Participants provided blood samples for various laboratory tests after an overnight fast. Serum glucose and triglyceride were assessed using Pars Azmoon kits, while a lipid profile, including total cholesterol, LDL, and HDL, was measured using Bionic kits. All tests were analyzed by a BT 3000 biochemical auto-analyzer. Blood pressure was measured twice using an automatic digital monitor while participants were seated, following 5 minutes of rest. Height was measured using a stadiometer mounted on a smooth, undamaged wall, with a margin of error of 0.5 cm. Participants were instructed to ensure their heels, hips, shoulders, and back were in contact with the wall, and to keep their heads in a horizontal position. Weight was measured in light clothing using a digital scale (Seca, Germany). During all phases of the study, weight was recorded to the nearest 0.1 kg using an Omron Karada Body Scan and Scale (Model BF511, Omron Co., Osaka, Japan). Hip and waist circumferences were measured using non-stretchable tape; hip measurements were taken at the widest part of the buttocks, and waist measurements were taken at the superior border of the iliac crest. Description of hypertension Hypertension was defined as a systolic blood pressure ≥ 140 mmHg, a diastolic blood pressure ≥ 90 mmHg, or ongoing treatment for hypertension. Blood pressure was measured twice on the participants' right arms while seated, using an automated digital monitor (Model M6 Comfort, Omron Co., Osaka, Japan). A five-minute interval separated the measurements, which were performed by nursing staff. Biochemical measurements Following an 8-hour fasting period, 12 mL blood samples were collected from the antecubital veins for serum isolation and biochemical analysis. Fasting blood sugar (FBS), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) were measured using a biochemical autoanalyzer (BT 3000, Italy) at the Afshar Hospital's medical laboratory in Yazd, Iran, by standard calibration procedures. Other Covariates This study used questionnaires to collect data on angina pectoris, smoking habits, physical activity levels (categorized according to the IPAQ), family history of early coronary artery disease (CAD), and demographics. Participants were classified based on smoking status, physical activity intensity (low, moderate, or intense), familial CAD history, and educational attainment (primary school, high school, or academic degree) and four age groups (≤ 40, 40–50, 50–60 and ≥ 60 years) were created. Definitions of six anthropometric indexes ( 8 – 10 ) Statistics analyses Anthropometric indices (CMI, LCI, CI, WWI, BSA, and WHHR) were categorized into sex-specific quartiles. Baseline demographic and clinical characteristics across quartiles were compared using one‐way analysis of variance for continuous variables (reported as mean ± SD) and chi‐square tests for categorical variables (expressed as percentages). Before ANOVA, the normality of residuals was verified by Shapiro-Wilk tests and homogeneity of variances by Levene’s test; where assumptions were violated, Kruskal–Wallis tests were substituted. Linear trends across increasing quartiles were assessed by including quartile medians as continuous predictors in trend tests, and post hoc pairwise differences were adjusted using Bonferroni correction. Missing data (< 5% per variable) were handled via multiple imputations by chained equations, and sensitivity analyses confirmed that inferences were robust to the imputation strategy. Receiver operating characteristic (ROC) curve analyses were performed to identify optimal cut‐off values for each index: areas under the curve (AUCs) with 95% confidence intervals were calculated by DeLong's method, and corresponding sensitivity, specificity, and Youden’s J statistics were determined for the overall cohort as well as stratified by sex. Bootstrap resampling (n = 1,000) was used to ensure stability of the optimal thresholds across repeated sampling. Associations between each anthropometric index and incident hypertension were quantified using Cox proportional hazards regression. The Cox model was employed here for prognostic modeling and variable selection for machine learning, not for causal inference .A crude model was first fitted, followed by Model 1 adjusted for age and sex, and Model 2 additionally adjusted for total cholesterol, LDL, HDL, family history of coronary artery disease, physical activity, smoking status, and educational attainment. Proportional hazard assumptions were evaluated via Schoenfeld residuals and time-dependent covariate tests, with no significant violations detected. Model discrimination and calibration were assessed using Harrell’s C‐index, respectively, demonstrating adequate fit. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated for each quartile, and tests for trend were conducted by modeling quartile medians continuously. All statistical analyses were carried out using R version 4.1.2 (packages ‘survival’, ‘rms’, and ‘pROC’), with two‐sided P < 0.05 considered indicative of statistical significance. Three state-of-the-art ensemble tree-based classifiers Random Forest, XGBoost, and LightGBM were employed to predict hypertension status from six continuous predictors (BSA, CI, WWI, WHHR, LCI, and CMI) ( 11 ). To ensure a consistent comparison, all machine learning models were trained on an identical set of all six indices. The final XGBoost model was selected based on its superior performance, and feature importance was objectively ranked using permutation importance, providing a fair assessment of each index's predictive value. The full dataset was partitioned into 75% training and 25% hold-out test sets, stratified by both outcome and gender to preserve class proportions; within each training fold, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to augment the minority class to 1:1 parity. Hyperparameter spaces for each algorithm were defined a priori and explored via randomized search over 20 parameter combinations, embedded in a five-fold stratified cross-validation loop (80% train, 20% validation per fold), thereby ensuring rigorous tuning and limiting overfitting across both training and independent test partitions. Each model was trained using all available CPU cores on a high-performance server (Intel Xeon, 32 GB RAM), and convergence was monitored via early stopping on validation log-loss. Discriminative performance metrics including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC-AUC), were computed on both training and test sets; XGBoost was selected as the optimal classifier, demonstrating the highest test ROC-AUC (0.76) and the smallest train–test gap (0.106). To interpret variable contributions, a cross-validated permutation importance procedure was implemented separately for male and female cohorts: for each predictor, the average absolute decrease in ROC-AUC induced by random permutation across five folds was calculated, and 1,000 bootstrap replicates were used to derive 95% confidence intervals. This approach yielded stable, rank-ordered importance measures that illuminate the most influential physiological indices and uncover sex-specific risk factor profiles. Results In the comparison of quartiles, significant differences (p < 0.001) were observed across all indicators, including age, blood pressure, fasting blood sugar, LDL cholesterol, total cholesterol, and educational level. ( Supplementary Table 1 ). Table 1 investigates the relationship between anthropometric adiposity indicators and the incidence of hypertension using three models: the crude model, Model 1 (adjusted for age and sex), and Model 2 (further adjusted for multiple factors). The initial crude analysis revealed significant positive associations between all indicators and hypertension risk (P for trend < 0.001 for all). After adjustment for age and sex in Model 1, these associations remained significant but were attenuated. Following additional adjustments in Model 2, only LCI (Q4 HR: 3.64; 95% CI: 2.28–8.81; P < 0.001; P for trend < 0.001) and CMI (Q4 HR: 2.93; 95% CI: 1.21–3.08; P < 0.001; P for trend < 0.001) maintained significant associations with hypertension, with LCI demonstrating the strongest and most consistent relationship. Table 1 Associations between anthropometric indices and hypertension based on crude and multivariate hazard ratios in the Yazd population. Anthropometric indicators of adiposity Crude model Model 1 Model 2 HR(95%CI) P -value HR(95%CI) P -value HR(95%CI) P -value CMI Q1 1.00 1.00 1.00 Q2 1.59 (0.97–2.60) 0.07 1.33 (0.82–2.19) 0.25 1.27 (0.87–2.09) 0.34 Q3 2.74 (1.74–4.33) < 0.001 2.14 (1.35–3.39) 0.001 1.93 (1.21–3.08) 0.006 Q4 4.46 (2.89–6.88) < 0.001 3.36 (2.16–5.21) < 0.001 2.93 (1.21–3.08) < 0.001 P for trend < 0.001 < 0.001 < 0.001 LCI Q1 1.00 1.00 1.00 Q2 2.08 (1.26–3.43) 0.004 1.79 (1.08–2.95) 0.02 1.81 (1.09-3.00) 0.02 Q3 3.12 (1.93–5.03) < 0.001 2.61 (1.61–4.21) < 0.001 2.63 (1.63–4.27) < 0.001 Q4 5.00 (3.16–7.89) < 0.001 3.68 (2.31–5.86) < 0.001 3.64 (2.28–8.81) < 0.001 P for trend < 0.001 < 0.001 < 0.001 CI Q1 1.00 1.00 1.00 Q2 1.07 (0.69–1.66) 0.77 0.88 (0.57–1.37) 0.57 0.81 (0.52–1.27) 0.36 Q3 2.00 (1.35–2.97) < 0.001 1.23 (0.81–1.88) 0.33 1.10 (0.72–1.68) 0.66 Q4 2.26 (1.54–3.33) < 0.001 1.19 (0.77–1.83) 0.43 1.02 (0.66–1.58) 0.93 P for trend < 0.001 0.21 0.57 WWI Q1 1.00 1.00 1.00 Q2 1.54 (1.01–2.36) 0.04 1.15 (0.74–1.78) 0.53 1.05 (0.67–1.63) 0.83 Q3 2.17 (1.45–3.24) < 0.001 1.35 (0.88–2.01) 0.17 1.15 (0.74–1.78) 0.54 Q4 2.37 (1.60–3.52) < 0.001 1.34 (0.84–2.13) 0.23 1.07 (0.66–1.73) 0.80 P for trend < 0.001 0.19 0.76 BSA Q1 1.00 1.00 1.00 Q2 1.29 (0.87–1.92) 0.21 1.36 (0.91–2.02) 0.14 1.33 (0.89–1.10) 0.16 Q3 1.09 (0.74–1.61) 0.66 1.28 (0.85–1.91) 0.24 1.21 (0.80–1.82) 0.37 Q4 2.17 (1.54–3.05) < 0.001 2.42 (1.67–3.52) < 0.001 2.13 (1.44–3.14) < 0.001 P for trend < 0.001 < 0.001 < 0.001 WHHR Q1 1.00 1.00 1.00 Q2 2.02 (1.31–3.12) 0.001 1.61 (1.04–2.49) 0.04 1.50 (0.97–2.34) 0.07 Q3 2.74 (1.82–4.13) < 0.001 2.03 (1.32–3.12) 0.001 1.71 (1.10–2.66) 0.02 Q4 2.86 (1.91–4.30) < 0.001 1.70 (1.07–2.71) 0.02 1.46 (0.91–2.35) 0.12 P for trend < 0.001 0.03 0.20 Model 1: adjusted for age and sex, Model 2: model 1 plus total cholesterol, LDL, HDL, family history of premature CAD, physical activity, and smoking, Education. Table 2 and Fig. 2 Cut-off values for six adiposity indices were derived by maximizing Youden’s J statistic, and diagnostic performance was evaluated by ROC-AUC (95% CI), sensitivity, and specificity. In the overall cohort, both the lipid accumulation product (LCI; AUC = 0.72, 95% CI 0.68–0.75) and cardiometabolic index (CMI; AUC = 0.72, 95% CI 0.68–0.75) outperformed other measures, achieving balanced sensitivity (80.8% and 74.9%, respectively) and specificity (50.8% and 58.0%). Traditional indices such as CI and WHHR yielded moderate AUCs (0.65 and 0.65), whereas BSA showed the weakest discrimination (AUC = 0.62). Sex-stratified analyses revealed that CMI retained superior predictive accuracy in men (AUC = 0.74) and women (AUC = 0.69), with optimal thresholds of 1.49 and 1.24, respectively. Notably, BSA failed to reach statistical significance in females (AUC = 0.52; P = 0.61), underscoring its limited utility for hypertension screening in this subgroup. All other indices demonstrated P < 0.001, indicating robust associations with hypertensive status. These findings support the preferential use of CMI and LCI and to a lesser extent WHHR in sex-specific risk stratification for hypertension. Table 2 Area Under the Receiver Operating Characteristic Curves and Optimal Cut-Off Points of Multiple Anthropometric Indices for Predicting Hypertension by Sex Indicator Youden Threshold ROCO1 Max Efficiency Sensitivity (%) Specificity (%) AUC (95% CI) P-value ALL CMI 1.36 1.54 4.50 74.90 58.00 0.72 (0.68, 0.75) < 0.001 LCI 11.05 14.48 39.18 80.80 50.80 0.72 (0.68, 0.75) < 0.001 CI 1.30 1.30 1.46 67.80 57.90 0.65 (0.61, 0.69) < 0.001 WWI 10.84 11.00 12.68 70.20 51.20 0.64 (0.60, 0.68) < 0.001 BSA 1.84 1.83 2.10 48.20 74.30 0.62 (0.58, 0.67) < 0.001 WHHR 0.53 0.54 0.65 75.70 47.40 0.65 (0.61, 0.68) < 0.001 Male CMI 1.36 1.49 2.76 76.80 61.40 0.74 (0.69, 0.79) < 0.001 LCI 16.92 14.44 33.96 62.70 71.00 0.73 (0.68, 0.78) < 0.001 CI 1.29 1.30 1.46 69.70 59.50 0.67 (0.62, 0.73) < 0.001 WWI 11.00 10.85 12.83 55.60 70.70 0.66 (0.61, 0.71) < 0.001 BSA 1.88 1.89 2.00 65.50 68.80 0.71 (0.66, 0.76) < 0.001 WHHR 0.51 0.53 0.90 83.80 44.90 0.65 (0.60, 0.70) < 0.001 Female CMI 1.24 1.54 4.65 77.90 52.20 0.69 (0.64, 0.74) < 0.001 LCI 11.05 14.48 61.42 80.50 50.30 0.70 (0.65, 0.76) < 0.001 CI 1.30 1.30 1.49 65.50 57.50 0.64 (0.58, 0.70) < 0.001 WWI 11.81 11.24 12.68 51.30 74.70 0.67 (0.61, 0.72) < 0.001 BSA 1.81 1.70 2.24 24.60 81.90 0.52 (0.45, 0.58) 0.61 WHHR 0.56 0.57 0.65 73.50 56.90 0.70 (0.64, 0.75) < 0.001 Supplementary Table 2 The p-values indicate the strength of the association between the anthropometric indices and high hypertension. Since the two indices, LCI and CMI, were associated with a higher risk of elevated hypertension compared to the other indices, we focused our age group comparisons specifically on these two. The significant trends observed in the 40–50 and 50–60 age groups underscore the importance of monitoring LCI and CMI as potential risk factors during middle age. The evaluation metrics summarized in Table 3 show that, although all three models excelled in the training data, their ability to generalize diverged. LightGBM achieved the highest in-sample ROC-AUC (0.898) and accuracy (80.8%) but displayed the largest train–test gap (0.149), pointing to some overfitting. By contrast, Random Forest (gap = 0.128) and XGBoost (gap = 0.106) maintained closer alignment between training and validation results. Most notably, XGBoost delivered the strongest out-of-sample performance test ROC-AUC of 0.760 and accuracy of 71.4% while preserving balanced sensitivity (76.0%) and F1-score (54.8%). Across models, recall on the test set ranged from 63.9% to 65.9%, whereas precision stayed modest (46.3–48.0%), indicating a tendency toward false-positive predictions. Taken together, these findings highlight XGBoost’s robustness and its suitability as the primary classifier for hypertension prediction in this cohort. Figure 3 illustrates the average decrease in ROC-AUC resulting from a random permutation of each feature across five stratified folds, with error bars denoting one standard deviation. As shown in Fig. 3 , LCI and WHHR are the most influential predictors of hypertension in women, whereas CMI and LCI drive model performance most strongly in men. These sex-specific importance patterns underline the need for tailored risk assessment using body composition measures. Table 3 Performance Metrics and ROC_AUC Train–Test Discrepancy for Ensemble Tree-Based Models in Hypertension Prediction Train–Test Gap (ROC_AUC) Specificity F1-score Recall precision Accuracy Machine learning Models Random Forest Train 77.9 56.9 79.4 66.2 88.1 0.128 Test 69.9 46.3 65.9 54.3 75.3 XGBoost Train 78.6 58.0 77.8 66.4 86.6 0.106 Test 71.4 48.0 64.3 54.8 76.0 LightGBM Train 80.8 61.4 79.5 69.3 89.8 0.149 Test 71.0 47.5 63.9 54.4 74.9 Discussion The current study aimed to investigate and compare the prognostic performance of six anthropometric and cardiometabolic indices CMI, CI, WWI, WHHR, BSA, and LCI in forecasting hypertension using a machine learning-based approach. The results not only reaffirmed the relationship between central obesity and hypertension but also revealed that indices incorporating metabolic parameters, particularly LCI and CMI, outperform purely anthropometric measures in predicting the incidence of hypertension. Obesity significantly contributes to hypertension through adipose tissue dysfunction, which leads to vascular inflammation, oxidative stress, and activation of the renin-angiotensin-aldosterone system ( 12 ). These processes involve altered renal sodium reabsorption, sympathetic nervous system activation, and endocrine changes, all of which affect intrarenal hemodynamics and may compress the kidneys ( 13 , 14 ). Given the complex nature of obesity-related hypertension, effective management requires individualized strategies that incorporate lifestyle modifications and appropriate antihypertensive medications ( 12 , 14 ). Hypertensive participants showed elevated levels of CMI, CI, WWI, WHHR, BSA, LCI, FBS, total cholesterol, LDL, and triglycerides. One of the key findings in this study was the consistently strong association of LCI with hypertension across all models, even after adjusting for a comprehensive set of confounding factors (HR: 3.64, 95% CI: 2.28–8.81, p < 0.001). This suggests that LCI, which integrates lipid profiles (total cholesterol, triglycerides, LDL, and HDL), may serve as a more sensitive indicator of metabolic dysfunction contributing to hypertension. Similarly, CMI, which reflects the interplay between dyslipidemia and central adiposity, also maintained its predictive strength, particularly among men AUC (0.74, 95% CI: 0.69–0.79, p < 0.001). These findings align with previous research highlighting the pivotal role of visceral fat and lipid abnormalities in the development of hypertension, insulin resistance, and endothelial dysfunction. Recent studies have highlighted the significant association between lipid profiles and hypertension risk. CMI is significantly associated with hypertension risk, with higher CMI levels observed in hypertensive participants ( 15 ). A large cross-sectional study revealed a positive, nonlinear association between CMI and the risk of cardiometabolic multimorbidity in hypertensive patients ( 16 ). Similarly, a nonlinear positive correlation was observed between CMI and the incidence of metabolic-associated fatty liver disease, with a critical threshold identified ( 17 ). In contrast, LCI has emerged as a potentially superior marker for identifying non-alcoholic fatty liver disease (NAFLD) compared to traditional lipid parameters ( 18 ). Additionally, elevated lipoprotein (a) [Lp (a)] levels, inflammation, oxidative stress, and chronic kidney disease have been identified as major risk factors for hypertension in non-diabetic patients ( 19 ). These findings underscore the importance of regularly monitoring blood pressure and lipid profiles in hypertensive patients to prevent cardiovascular disease and other comorbidities ( 20 ). In contrast, other indices such as CI, WWI, and BSA, although significant in crude models, lost their predictive value after multivariable adjustment. This attenuation suggests that while abdominal adiposity is relevant, it may not independently drive hypertension risk when metabolic factors are considered. WWI is designed to reflect central obesity by adjusting waist circumference for body weight. Some studies have found a positive association between higher WWI values and an increased risk of hypertension ( 21 – 23 ). a study analyzing data from the National Health and Nutrition Examination Survey (NHANES) indicated that elevated WWI is linked to a higher prevalence of hypertension ( 24 ). However, the predictive power of WWI diminishes when adjusted for confounding factors such as age, sex, and metabolic parameters (HR: 1.07, 95% CI: 0.66–1.73, p = 0.80). This suggests that while WWI captures aspects of central obesity, it may not fully account for the complex interplay between adiposity and metabolic dysfunction in the development of hypertension. CI assesses abdominal adiposity by considering waist circumference, weight, and height. However, research indicates that its predictive capability for hypertension is inferior to other indices of abdominal fatness, such as waist circumference, waist-to-hip ratio, and waist-to-stature ratio ( 25 , 26 ). According to a study by Richelsen et al. (1995), CI was found to be inappropriate for predicting metabolic risk profiles associated with abdominal fatness in non-obese men and performed considerably worse than other abdominal adiposity indices ( 27 ). which are consistent with the present study. This limitation arises because CI focuses solely on body shape and fat distribution, without accounting for metabolic health an essential factor in the development of hypertension ( 28 ). BSA is commonly used to standardize physiological measurements and drug dosages ( 29 – 31 ). Although some studies have investigated the association between BSA and hypertension, the evidence suggests that BSA alone is not a strong predictor of the condition ( 32 ). For instance, while BSA has shown a moderate correlation with systolic blood pressure in adolescents ( 33 ), it appears to be less effective than waist circumference or waist-to-height ratio in predicting hypertension among overweight and obese adults ( 34 ). Further research indicates that BSA does not significantly correlate with diastolic blood pressure in the supine position and demonstrates only a modest association when individuals are standing. This limited predictive capability may stem from BSA’s inability to distinguish between lean and fat mass or to reflect central fat distribution factors more strongly associated with hypertension risk ( 30 ). These findings were further supported by ROC analysis, which demonstrated that both the LCI and CMI exhibited the highest discriminative power, particularly among men, as reflected by the largest AUC values) AUC = 0.73), (AUC = 0.74) respectively. Notably, in females, LCI (AUC = 0.70, 95% CI: 0.65–0.76, p < 0.001) and WHHR (AUC = 0.70, 95% CI: 0.64–0.75, p < 0.001) showed comparable AUCs, suggesting that incorporating height into WHHR may provide a more gender-sensitive adjustment for body composition. Collectively, these results underscore the limitations of traditional metrics such as BMI and highlight the clinical value of adopting more refined indices that integrate both anthropometric and biochemical dimensions of cardiometabolic risk. From both pathophysiological and clinical perspectives, the superiority of the LCI and CMI likely stems from their capacity to capture atherogenic dyslipidemia an established hallmark of metabolic syndrome and a known contributor to increased arterial stiffness and vascular resistance. This underlying mechanism may explain their stronger association with hypertension compared to indices that assess only body size or shape. Given the widespread availability of lipid profile data, the calculation of LCI and CMI can be readily implemented in both primary care and public health screening programs ( 35 , 36 ). The analysis indicates that the 40–50-year age group for LCI is indeed associated with an increased risk of hypertension, particularly in the higher quartiles. For CMI, individuals aged 60 years and above (≥ 60) are also associated with an increased risk, especially in the highest quartile (Q4). These findings reinforce the notion that age and anthropometric indices play a critical role in hypertension risk. Wang et al. (2018) found that the CMI, lipid accumulation product (LAP), and body adiposity index (BAI) were independently associated with an increased risk of hypertension, particularly in the higher quartiles ( 37 ). Similarly, Aftab et al. (2023) observed a higher prevalence of hypertension in individuals aged 60 and above, and a higher prevalence of pre-hypertension in the 45–59 age group ( 38 ). These findings emphasize the need for lifestyle modifications and increased public awareness to prevent hypertension, particularly as people age. Predictive modeling relies on feature selection algorithms, particularly in healthcare, as they enhance model performance and help identify the most relevant variables ( 39 , 40 ). Combining multiple approaches can further improve their effectiveness ( 41 , 42 ). While statistical models offer greater interpretability, machine learning excels in predictive accuracy ( 43 ). By employing both methodologies, this study found that the LCI and the CMI are the most effective anthropometric markers of adiposity for predicting hypertension. Notably, above certain thresholds, LCI outperformed other indices in terms of predictive accuracy. This study systematically evaluates and compares anthropometric measures of adiposity, particularly the LCI and the CMI, for predicting hypertension, using a standardized diagnosis and a prospective community-based design to minimize bias. The consistency of findings across various methodologies reinforces the reliability of LCI. However, limitations include potential biases due to infrequent follow-up visits, reliance on a single baseline evaluation, and limited generalizability, as the study was conducted at a single institution focused on the Iranian population. Also, a further limitation is the 38% loss to follow-up over the 10-year period. However, a sensitivity analysis revealed that participants lost to follow-up had similar baseline levels of key metabolic parameters (blood pressure, lipids, and fasting glucose) and sex distribution compared to those who completed the study. While there were minor differences in some anthropometric measures, the overall similarity in critical cardiovascular risk profiles provides reassurance that the observed associations between the novel indices and hypertension are unlikely to be severely biased by the attrition. Conclusion This study highlights the superior prognostic performance of lipid-integrated indices, particularly LCI and CMI, in identifying the risk of hypertension. Their consistent performance across models and age groups underscores their clinical utility. LCI, in particular, demonstrated a strong and independent association with hypertension. These findings support the incorporation of metabolic indices into routine screening to improve the accuracy of hypertension prediction and prevention. Declarations Ethics approval and consent to participate The study was conducted by the guidelines of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Shahid Sadoughi University of Medical Sciences (Ethics Code: IR.SSU.REC.1403.187). Eligible individuals were enrolled after providing written informed consent. Data collection procedures ensured complete anonymity, and no personally identifiable information was included in the individual records. Competing interests The authors declare no competing interests. Supplementary Table 2 Risk of hypertension according to age-specific quartiles of anthropometric indices of obesity. Funding This study was conducted without any external funding. Author Contribution PP and FG wrote the paper. MS, PMV, HM, SMN and MS made substantial contributions to study design, intellectual direction, and revision of the drafting of the manuscript. MS and SMN made contributions to data collection. All authors read and approved the final manuscript. Acknowledgement We thank all study participants and the members of the survey teams at theYazd Cardiovascular Research Center and Afshar Hospital Clinical Research Development Center, Yazd, Iran Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Mugisha E, University Ix KI. Management and Therapeutic Intervention for Hypertension: A Comprehensive Review. 2024;9:12 – 6. Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International Society of Hypertension global hypertension practice guidelines. Hypertension. 2020;75(6):1334–57. Consultation W. Waist circumference and waist-hip ratio. 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Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy. Pertanika J Sci Technol. 2018;26(1). Pes B. Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains. Neural Comput Appl. 2020;32(10):5951–73. Rajula HSR, Verlato G, Manchia M, Antonucci N, Fanos V. Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment. Medicina. 2020;56(9):455. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":73307,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the enrollment of the study population.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7670443/v1/14b8fda095833e9a63fb85ba.jpg"},{"id":95806078,"identity":"4e44bd7e-d088-42d1-9720-b2c7dc1d2462","added_by":"auto","created_at":"2025-11-13 08:47:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122611,"visible":true,"origin":"","legend":"\u003cp\u003eFeature selection using the best-performing ensemble model A) Male, B) Female, and c) Total.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7670443/v1/004c6734fec9f37e35dd4cc3.jpg"},{"id":95805793,"identity":"c2502801-c69d-4c51-bd37-54adc1b16052","added_by":"auto","created_at":"2025-11-13 08:46:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71253,"visible":true,"origin":"","legend":"\u003cp\u003eCross-validated permutation importance (mean |ΔROC-AUC|) of six anthropometric indices for XGBoost models stratified by sex.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7670443/v1/0ccb7df58540777e5cea4524.jpg"},{"id":102048535,"identity":"461145e5-cc5d-492a-851d-7622a343a6c7","added_by":"auto","created_at":"2026-02-06 14:27:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1505083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7670443/v1/7f1da8b8-decb-44ef-81c5-1bb2d353286b.pdf"},{"id":95806038,"identity":"a8ef50be-ef4f-422c-9bdc-d4d048735d98","added_by":"auto","created_at":"2025-11-13 08:47:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40817,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7670443/v1/d43b5bd2a8f3bd97c736a051.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cardiometabolic Index (CMI), Lipoprotein Combine Index (LCI), Conicity Index (CI), Weight-adjusted Waist Index (WWI), Waist-to-Hip-to-Height Ratio (WHHR), Body Surface Area (BSA) and the 10-year risk of hypertension: A machine learning approach in the Yazd Healthy Heart Project","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension is a major global health concern, contributing significantly to cardiovascular morbidity and mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The World Health Organization (WHO) reports that approximately 1.28\u0026nbsp;billion adults aged 30\u0026ndash;79 years worldwide have hypertension, with a significant proportion unaware of their condition (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTraditional anthropometric indices, such as Body Mass Index (BMI) and waist circumference (WC), have been widely used to assess obesity-related hypertension risk (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, these measures often fail to account for fat distribution and metabolic variation, limiting their predictive accuracy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In recent years, novel anthropometric indices have been developed to provide a more comprehensive assessment of cardiometabolic risk (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese include the Cardiometabolic Index (CMI), Conicity Index (CI), Weight-adjusted Waist Index (WWI), Waist-to-Hip-to-Height Ratio (WHHR), Body Surface Area (BSA), and Lipoprotein Combine Index (LCI), which aim to capture the complex interplay among body composition, fat distribution, and metabolic health (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe advent of machine learning (ML) techniques has further enhanced the ability to analyze complex, multidimensional data, enabling the development of more accurate predictive models for hypertension (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). ML algorithms can identify intricate patterns and interactions among various risk factors, often surpassing traditional statistical methods in predictive performance (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study aims to compare the predictive efficacy of CMI, CI, WWI, WHHR, BSA, and LCI in forecasting the incidence of hypertension using machine learning approaches.\u003c/p\u003e\u003cp\u003eBy evaluating these indices within an ML framework, we seek to evaluate and compare the power of novel indices in forecasting the 10-year risk of hypertension, and also to identify the most reliable predictor of hypertension using machine learning and conventional statistical techniques, thereby informing targeted prevention and intervention strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThis cohort study aims to Machine learning-driven evaluation of novel anthropometric indices for hypertension prediction. Data were obtained from the Yazd Healthy Heart Project (YHHP), a population-based epidemiological study conducted in Yazd, Iran, designed to monitor and investigate cardiovascular and metabolic disorders. In the baseline phase (2005\u0026ndash;2006), 2,000 individuals aged 20 to 74 years were randomly selected from the urban population using a multistage cluster sampling method. A total of 100 clusters were identified, from which 20 families per cluster were chosen, and one adult was randomly selected from each family (1,000 men and 1,000 women). Trained personnel collected demographic, socioeconomic, and lifestyle information, as well as anthropometric measurements (weight, height, waist circumference), physical activity levels, dietary habits, smoking status, and biochemical markers, including fasting blood glucose, triglycerides, and HDL cholesterol. Blood samples were collected at both the baseline and follow-up phases. After 10 years (2015\u0026ndash;2016), participants were re-invited for reassessment. Individuals with hypertension at baseline, those with missing key anthropometric or biochemical data, or those lost to follow-up were excluded from the final analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eOf the 2,000 individuals initially enrolled in the study, 36.1% were diagnosed with hypertension at baseline and were therefore excluded from further analysis. Among the 1,269 participants who were normotensive at baseline, 786 (61.9%) had complete data and were successfully followed up after 10 years. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003e40\u003c/b\u003e.3% of these individuals developed hypertension during the follow-up period.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eParticipants provided blood samples for various laboratory tests after an overnight fast. Serum glucose and triglyceride were assessed using Pars Azmoon kits, while a lipid profile, including total cholesterol, LDL, and HDL, was measured using Bionic kits. All tests were analyzed by a BT 3000 biochemical auto-analyzer. Blood pressure was measured twice using an automatic digital monitor while participants were seated, following 5 minutes of rest. Height was measured using a stadiometer mounted on a smooth, undamaged wall, with a margin of error of 0.5 cm. Participants were instructed to ensure their heels, hips, shoulders, and back were in contact with the wall, and to keep their heads in a horizontal position. Weight was measured in light clothing using a digital scale (Seca, Germany). During all phases of the study, weight was recorded to the nearest 0.1 kg using an Omron Karada Body Scan and Scale (Model BF511, Omron Co., Osaka, Japan). Hip and waist circumferences were measured using non-stretchable tape; hip measurements were taken at the widest part of the buttocks, and waist measurements were taken at the superior border of the iliac crest.\u003c/p\u003e\n\u003ch3\u003eDescription of hypertension\u003c/h3\u003e\n\u003cp\u003eHypertension was defined as a systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, a diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or ongoing treatment for hypertension. Blood pressure was measured twice on the participants' right arms while seated, using an automated digital monitor (Model M6 Comfort, Omron Co., Osaka, Japan). A five-minute interval separated the measurements, which were performed by nursing staff.\u003c/p\u003e\n\u003ch3\u003eBiochemical measurements\u003c/h3\u003e\n\u003cp\u003eFollowing an 8-hour fasting period, 12 mL blood samples were collected from the antecubital veins for serum isolation and biochemical analysis. Fasting blood sugar (FBS), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) were measured using a biochemical autoanalyzer (BT 3000, Italy) at the Afshar Hospital's medical laboratory in Yazd, Iran, by standard calibration procedures.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eOther Covariates\u003c/h2\u003e\u003cp\u003eThis study used questionnaires to collect data on angina pectoris, smoking habits, physical activity levels (categorized according to the IPAQ), family history of early coronary artery disease (CAD), and demographics. Participants were classified based on smoking status, physical activity intensity (low, moderate, or intense), familial CAD history, and educational attainment (primary school, high school, or academic degree) and four age groups (\u0026le;\u0026thinsp;40, 40\u0026ndash;50, 50\u0026ndash;60 and \u0026ge;\u0026thinsp;60 years) were created.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDefinitions of six anthropometric indexes\u003c/strong\u003e\u003cp\u003e(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"724\" height=\"498\"\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStatistics analyses\u003c/h3\u003e\n\u003cp\u003eAnthropometric indices (CMI, LCI, CI, WWI, BSA, and WHHR) were categorized into sex-specific quartiles. Baseline demographic and clinical characteristics across quartiles were compared using one‐way analysis of variance for continuous variables (reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) and chi‐square tests for categorical variables (expressed as percentages). Before ANOVA, the normality of residuals was verified by Shapiro-Wilk tests and homogeneity of variances by Levene\u0026rsquo;s test; where assumptions were violated, Kruskal\u0026ndash;Wallis tests were substituted. Linear trends across increasing quartiles were assessed by including quartile medians as continuous predictors in trend tests, and post hoc pairwise differences were adjusted using Bonferroni correction. Missing data (\u0026lt;\u0026thinsp;5% per variable) were handled via multiple imputations by chained equations, and sensitivity analyses confirmed that inferences were robust to the imputation strategy. Receiver operating characteristic (ROC) curve analyses were performed to identify optimal cut‐off values for each index: areas under the curve (AUCs) with 95% confidence intervals were calculated by DeLong's method, and corresponding sensitivity, specificity, and Youden\u0026rsquo;s J statistics were determined for the overall cohort as well as stratified by sex. Bootstrap resampling (n\u0026thinsp;=\u0026thinsp;1,000) was used to ensure stability of the optimal thresholds across repeated sampling.\u003c/p\u003e\u003cp\u003eAssociations between each anthropometric index and incident hypertension were quantified using Cox proportional hazards regression. The Cox model was employed here for prognostic modeling and variable selection for machine learning, not for causal inference .A crude model was first fitted, followed by Model 1 adjusted for age and sex, and Model 2 additionally adjusted for total cholesterol, LDL, HDL, family history of coronary artery disease, physical activity, smoking status, and educational attainment. Proportional hazard assumptions were evaluated via Schoenfeld residuals and time-dependent covariate tests, with no significant violations detected. Model discrimination and calibration were assessed using Harrell\u0026rsquo;s C‐index, respectively, demonstrating adequate fit. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated for each quartile, and tests for trend were conducted by modeling quartile medians continuously. All statistical analyses were carried out using R version 4.1.2 (packages \u0026lsquo;survival\u0026rsquo;, \u0026lsquo;rms\u0026rsquo;, and \u0026lsquo;pROC\u0026rsquo;), with two‐sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered indicative of statistical significance.\u003c/p\u003e\u003cp\u003eThree state-of-the-art ensemble tree-based classifiers Random Forest, XGBoost, and LightGBM were employed to predict hypertension status from six continuous predictors (BSA, CI, WWI, WHHR, LCI, and CMI) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). To ensure a consistent comparison, all machine learning models were trained on an identical set of all six indices. The final XGBoost model was selected based on its superior performance, and feature importance was objectively ranked using permutation importance, providing a fair assessment of each index's predictive value. The full dataset was partitioned into 75% training and 25% hold-out test sets, stratified by both outcome and gender to preserve class proportions; within each training fold, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to augment the minority class to 1:1 parity. Hyperparameter spaces for each algorithm were defined a priori and explored via randomized search over 20 parameter combinations, embedded in a five-fold stratified cross-validation loop (80% train, 20% validation per fold), thereby ensuring rigorous tuning and limiting overfitting across both training and independent test partitions. Each model was trained using all available CPU cores on a high-performance server (Intel Xeon, 32 GB RAM), and convergence was monitored via early stopping on validation log-loss. Discriminative performance metrics including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC-AUC), were computed on both training and test sets; XGBoost was selected as the optimal classifier, demonstrating the highest test ROC-AUC (0.76) and the smallest train\u0026ndash;test gap (0.106). To interpret variable contributions, a cross-validated permutation importance procedure was implemented separately for male and female cohorts: for each predictor, the average absolute decrease in ROC-AUC induced by random permutation across five folds was calculated, and 1,000 bootstrap replicates were used to derive 95% confidence intervals. This approach yielded stable, rank-ordered importance measures that illuminate the most influential physiological indices and uncover sex-specific risk factor profiles.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn the comparison of quartiles, significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were observed across all indicators, including age, blood pressure, fasting blood sugar, LDL cholesterol, total cholesterol, and educational level. (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e investigates the relationship between anthropometric adiposity indicators and the incidence of hypertension using three models: the crude model, Model 1 (adjusted for age and sex), and Model 2 (further adjusted for multiple factors). The initial crude analysis revealed significant positive associations between all indicators and hypertension risk (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). After adjustment for age and sex in Model 1, these associations remained significant but were attenuated. Following additional adjustments in Model 2, only LCI (Q4 HR: 3.64; 95% CI: 2.28\u0026ndash;8.81; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CMI (Q4 HR: 2.93; 95% CI: 1.21\u0026ndash;3.08; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) maintained significant associations with hypertension, with LCI demonstrating the strongest and most consistent relationship.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations between anthropometric indices and hypertension based on crude and multivariate hazard ratios in the Yazd population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAnthropometric indicators of adiposity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCrude model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHR(95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eHR(95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eHR(95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd 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colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.61 (1.61\u0026ndash;4.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.63 (1.63\u0026ndash;4.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.00 (3.16\u0026ndash;7.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.68 (2.31\u0026ndash;5.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.64 (2.28\u0026ndash;8.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.07 (0.69\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88 (0.57\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.81 (0.52\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.00 (1.35\u0026ndash;2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.23 (0.81\u0026ndash;1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.10 (0.72\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.26 (1.54\u0026ndash;3.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.19 (0.77\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.02 (0.66\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWWI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.54 (1.01\u0026ndash;2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15 (0.74\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.05 (0.67\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.17 (1.45\u0026ndash;3.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.35 (0.88\u0026ndash;2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.15 (0.74\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.37 (1.60\u0026ndash;3.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.34 (0.84\u0026ndash;2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07 (0.66\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.29 (0.87\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.36 (0.91\u0026ndash;2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.33 (0.89\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.09 (0.74\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.28 (0.85\u0026ndash;1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.21 (0.80\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.17 (1.54\u0026ndash;3.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.42 (1.67\u0026ndash;3.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.13 (1.44\u0026ndash;3.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.02 (1.31\u0026ndash;3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.61 (1.04\u0026ndash;2.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.50 (0.97\u0026ndash;2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.74 (1.82\u0026ndash;4.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.03 (1.32\u0026ndash;3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.71 (1.10\u0026ndash;2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.86 (1.91\u0026ndash;4.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.70 (1.07\u0026ndash;2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.46 (0.91\u0026ndash;2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1: adjusted for age and sex, Model 2: model 1 plus total cholesterol, LDL, HDL, family history of premature CAD, physical activity, and smoking, Education.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Cut-off values for six adiposity indices were derived by maximizing Youden\u0026rsquo;s J statistic, and diagnostic performance was evaluated by ROC-AUC (95% CI), sensitivity, and specificity. In the overall cohort, both the lipid accumulation product (LCI; AUC\u0026thinsp;=\u0026thinsp;0.72, 95% CI 0.68\u0026ndash;0.75) and cardiometabolic index (CMI; AUC\u0026thinsp;=\u0026thinsp;0.72, 95% CI 0.68\u0026ndash;0.75) outperformed other measures, achieving balanced sensitivity (80.8% and 74.9%, respectively) and specificity (50.8% and 58.0%). Traditional indices such as CI and WHHR yielded moderate AUCs (0.65 and 0.65), whereas BSA showed the weakest discrimination (AUC\u0026thinsp;=\u0026thinsp;0.62). Sex-stratified analyses revealed that CMI retained superior predictive accuracy in men (AUC\u0026thinsp;=\u0026thinsp;0.74) and women (AUC\u0026thinsp;=\u0026thinsp;0.69), with optimal thresholds of 1.49 and 1.24, respectively. Notably, BSA failed to reach statistical significance in females (AUC\u0026thinsp;=\u0026thinsp;0.52; P\u0026thinsp;=\u0026thinsp;0.61), underscoring its limited utility for hypertension screening in this subgroup. All other indices demonstrated P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating robust associations with hypertensive status. These findings support the preferential use of CMI and LCI and to a lesser extent WHHR in sex-specific risk stratification for hypertension.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eArea Under the Receiver Operating Characteristic Curves and Optimal Cut-Off Points of Multiple Anthropometric Indices for Predicting Hypertension by Sex\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYouden Threshold\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eROCO1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax Efficiency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAUC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e58.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.72 (0.68, 0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.72 (0.68, 0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.65 (0.61, 0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWWI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.64 (0.60, 0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.62 (0.58, 0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e47.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.65 (0.61, 0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e76.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.74 (0.69, 0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e71.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.73 (0.68, 0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e59.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.67 (0.62, 0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWWI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.66 (0.61, 0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.71 (0.66, 0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e44.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.65 (0.60, 0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e52.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.69 (0.64, 0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.70 (0.65, 0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.64 (0.58, 0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWWI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.67 (0.61, 0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e81.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.52 (0.45, 0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.70 (0.64, 0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e The p-values indicate the strength of the association between the anthropometric indices and high hypertension. Since the two indices, LCI and CMI, were associated with a higher risk of elevated hypertension compared to the other indices, we focused our age group comparisons specifically on these two. The significant trends observed in the 40\u0026ndash;50 and 50\u0026ndash;60 age groups underscore the importance of monitoring LCI and CMI as potential risk factors during middle age.\u003c/p\u003e\u003cp\u003eThe evaluation metrics summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that, although all three models excelled in the training data, their ability to generalize diverged. LightGBM achieved the highest in-sample ROC-AUC (0.898) and accuracy (80.8%) but displayed the largest train\u0026ndash;test gap (0.149), pointing to some overfitting. By contrast, Random Forest (gap\u0026thinsp;=\u0026thinsp;0.128) and XGBoost (gap\u0026thinsp;=\u0026thinsp;0.106) maintained closer alignment between training and validation results. Most notably, XGBoost delivered the strongest out-of-sample performance test ROC-AUC of 0.760 and accuracy of 71.4% while preserving balanced sensitivity (76.0%) and F1-score (54.8%). Across models, recall on the test set ranged from 63.9% to 65.9%, whereas precision stayed modest (46.3\u0026ndash;48.0%), indicating a tendency toward false-positive predictions. Taken together, these findings highlight XGBoost\u0026rsquo;s robustness and its suitability as the primary classifier for hypertension prediction in this cohort. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the average decrease in ROC-AUC resulting from a random permutation of each feature across five stratified folds, with error bars denoting one standard deviation. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, LCI and WHHR are the most influential predictors of hypertension in women, whereas CMI and LCI drive model performance most strongly in men. These sex-specific importance patterns underline the need for tailored risk assessment using body composition measures.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerformance Metrics and ROC_AUC Train\u0026ndash;Test Discrepancy for Ensemble Tree-Based Models in Hypertension Prediction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTrain\u0026ndash;Test Gap (ROC_AUC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF1-score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eprecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMachine learning Models\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eRandom Forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTrain\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e66.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e88.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e54.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e75.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eXGBoost\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTrain\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e66.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e86.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e54.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e76.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eLightGBM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTrain\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e69.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e54.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e74.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study aimed to investigate and compare the prognostic performance of six anthropometric and cardiometabolic indices CMI, CI, WWI, WHHR, BSA, and LCI in forecasting hypertension using a machine learning-based approach. The results not only reaffirmed the relationship between central obesity and hypertension but also revealed that indices incorporating metabolic parameters, particularly LCI and CMI, outperform purely anthropometric measures in predicting the incidence of hypertension.\u003c/p\u003e\u003cp\u003eObesity significantly contributes to hypertension through adipose tissue dysfunction, which leads to vascular inflammation, oxidative stress, and activation of the renin-angiotensin-aldosterone system (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These processes involve altered renal sodium reabsorption, sympathetic nervous system activation, and endocrine changes, all of which affect intrarenal hemodynamics and may compress the kidneys (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Given the complex nature of obesity-related hypertension, effective management requires individualized strategies that incorporate lifestyle modifications and appropriate antihypertensive medications (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHypertensive participants showed elevated levels of CMI, CI, WWI, WHHR, BSA, LCI, FBS, total cholesterol, LDL, and triglycerides.\u003c/p\u003e\u003cp\u003eOne of the key findings in this study was the consistently strong association of LCI with hypertension across all models, even after adjusting for a comprehensive set of confounding factors (HR: 3.64, 95% CI: 2.28\u0026ndash;8.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that LCI, which integrates lipid profiles (total cholesterol, triglycerides, LDL, and HDL), may serve as a more sensitive indicator of metabolic dysfunction contributing to hypertension. Similarly, CMI, which reflects the interplay between dyslipidemia and central adiposity, also maintained its predictive strength, particularly among men AUC (0.74, 95% CI: 0.69\u0026ndash;0.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings align with previous research highlighting the pivotal role of visceral fat and lipid abnormalities in the development of hypertension, insulin resistance, and endothelial dysfunction. Recent studies have highlighted the significant association between lipid profiles and hypertension risk. CMI is significantly associated with hypertension risk, with higher CMI levels observed in hypertensive participants (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). A large cross-sectional study revealed a positive, nonlinear association between CMI and the risk of cardiometabolic multimorbidity in hypertensive patients (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Similarly, a nonlinear positive correlation was observed between CMI and the incidence of metabolic-associated fatty liver disease, with a critical threshold identified (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, LCI has emerged as a potentially superior marker for identifying non-alcoholic fatty liver disease (NAFLD) compared to traditional lipid parameters (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Additionally, elevated lipoprotein (a) [Lp (a)] levels, inflammation, oxidative stress, and chronic kidney disease have been identified as major risk factors for hypertension in non-diabetic patients (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings underscore the importance of regularly monitoring blood pressure and lipid profiles in hypertensive patients to prevent cardiovascular disease and other comorbidities (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, other indices such as CI, WWI, and BSA, although significant in crude models, lost their predictive value after multivariable adjustment. This attenuation suggests that while abdominal adiposity is relevant, it may not independently drive hypertension risk when metabolic factors are considered.\u003c/p\u003e\u003cp\u003eWWI is designed to reflect central obesity by adjusting waist circumference for body weight. Some studies have found a positive association between higher WWI values and an increased risk of hypertension (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). a study analyzing data from the National Health and Nutrition Examination Survey (NHANES) indicated that elevated WWI is linked to a higher prevalence of hypertension (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, the predictive power of WWI diminishes when adjusted for confounding factors such as age, sex, and metabolic parameters (HR: 1.07, 95% CI: 0.66\u0026ndash;1.73, p\u0026thinsp;=\u0026thinsp;0.80). This suggests that while WWI captures aspects of central obesity, it may not fully account for the complex interplay between adiposity and metabolic dysfunction in the development of hypertension.\u003c/p\u003e\u003cp\u003eCI assesses abdominal adiposity by considering waist circumference, weight, and height. However, research indicates that its predictive capability for hypertension is inferior to other indices of abdominal fatness, such as waist circumference, waist-to-hip ratio, and waist-to-stature ratio (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). According to a study by Richelsen et al. (1995), CI was found to be inappropriate for predicting metabolic risk profiles associated with abdominal fatness in non-obese men and performed considerably worse than other abdominal adiposity indices (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). which are consistent with the present study. This limitation arises because CI focuses solely on body shape and fat distribution, without accounting for metabolic health an essential factor in the development of hypertension (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBSA is commonly used to standardize physiological measurements and drug dosages (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Although some studies have investigated the association between BSA and hypertension, the evidence suggests that BSA alone is not a strong predictor of the condition (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). For instance, while BSA has shown a moderate correlation with systolic blood pressure in adolescents (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), it appears to be less effective than waist circumference or waist-to-height ratio in predicting hypertension among overweight and obese adults (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Further research indicates that BSA does not significantly correlate with diastolic blood pressure in the supine position and demonstrates only a modest association when individuals are standing. This limited predictive capability may stem from BSA\u0026rsquo;s inability to distinguish between lean and fat mass or to reflect central fat distribution factors more strongly associated with hypertension risk (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings were further supported by ROC analysis, which demonstrated that both the LCI and CMI exhibited the highest discriminative power, particularly among men, as reflected by the largest AUC values) AUC\u0026thinsp;=\u0026thinsp;0.73), (AUC\u0026thinsp;=\u0026thinsp;0.74) respectively. Notably, in females, LCI (AUC\u0026thinsp;=\u0026thinsp;0.70, 95% CI: 0.65\u0026ndash;0.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and WHHR (AUC\u0026thinsp;=\u0026thinsp;0.70, 95% CI: 0.64\u0026ndash;0.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed comparable AUCs, suggesting that incorporating height into WHHR may provide a more gender-sensitive adjustment for body composition. Collectively, these results underscore the limitations of traditional metrics such as BMI and highlight the clinical value of adopting more refined indices that integrate both anthropometric and biochemical dimensions of cardiometabolic risk.\u003c/p\u003e\u003cp\u003eFrom both pathophysiological and clinical perspectives, the superiority of the LCI and CMI likely stems from their capacity to capture atherogenic dyslipidemia an established hallmark of metabolic syndrome and a known contributor to increased arterial stiffness and vascular resistance. This underlying mechanism may explain their stronger association with hypertension compared to indices that assess only body size or shape. Given the widespread availability of lipid profile data, the calculation of LCI and CMI can be readily implemented in both primary care and public health screening programs (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe analysis indicates that the 40\u0026ndash;50-year age group for LCI is indeed associated with an increased risk of hypertension, particularly in the higher quartiles. For CMI, individuals aged 60 years and above (\u0026ge;\u0026thinsp;60) are also associated with an increased risk, especially in the highest quartile (Q4). These findings reinforce the notion that age and anthropometric indices play a critical role in hypertension risk. Wang et al. (2018) found that the CMI, lipid accumulation product (LAP), and body adiposity index (BAI) were independently associated with an increased risk of hypertension, particularly in the higher quartiles (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Similarly, Aftab et al. (2023) observed a higher prevalence of hypertension in individuals aged 60 and above, and a higher prevalence of pre-hypertension in the 45\u0026ndash;59 age group (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These findings emphasize the need for lifestyle modifications and increased public awareness to prevent hypertension, particularly as people age.\u003c/p\u003e\u003cp\u003ePredictive modeling relies on feature selection algorithms, particularly in healthcare, as they enhance model performance and help identify the most relevant variables (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Combining multiple approaches can further improve their effectiveness (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). While statistical models offer greater interpretability, machine learning excels in predictive accuracy (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). By employing both methodologies, this study found that the LCI and the CMI are the most effective anthropometric markers of adiposity for predicting hypertension. Notably, above certain thresholds, LCI outperformed other indices in terms of predictive accuracy.\u003c/p\u003e\u003cp\u003eThis study systematically evaluates and compares anthropometric measures of adiposity, particularly the LCI and the CMI, for predicting hypertension, using a standardized diagnosis and a prospective community-based design to minimize bias. The consistency of findings across various methodologies reinforces the reliability of LCI. However, limitations include potential biases due to infrequent follow-up visits, reliance on a single baseline evaluation, and limited generalizability, as the study was conducted at a single institution focused on the Iranian population. Also, a further limitation is the 38% loss to follow-up over the 10-year period. However, a sensitivity analysis revealed that participants lost to follow-up had similar baseline levels of key metabolic parameters (blood pressure, lipids, and fasting glucose) and sex distribution compared to those who completed the study. While there were minor differences in some anthropometric measures, the overall similarity in critical cardiovascular risk profiles provides reassurance that the observed associations between the novel indices and hypertension are unlikely to be severely biased by the attrition.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the superior prognostic performance of lipid-integrated indices, particularly LCI and CMI, in identifying the risk of hypertension. Their consistent performance across models and age groups underscores their clinical utility. LCI, in particular, demonstrated a strong and independent association with hypertension. These findings support the incorporation of metabolic indices into routine screening to improve the accuracy of hypertension prediction and prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The study was conducted by the guidelines of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Shahid Sadoughi University of Medical Sciences (Ethics Code: IR.SSU.REC.1403.187). Eligible individuals were enrolled after providing written informed consent. Data collection procedures ensured complete anonymity, and no personally identifiable information was included in the individual records.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eSupplementary Table\u0026nbsp;2\u003c/h2\u003e\u003cp\u003eRisk of hypertension according to age-specific quartiles of anthropometric indices of obesity.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was conducted without any external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePP and FG wrote the paper. MS, PMV, HM, SMN and MS made substantial contributions to study design, intellectual direction, and revision of the drafting of the manuscript. MS and SMN made contributions to data collection. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all study participants and the members of the survey teams at theYazd Cardiovascular Research Center and Afshar Hospital Clinical Research Development Center, Yazd, Iran\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMugisha E, University Ix KI. Management and Therapeutic Intervention for Hypertension: A Comprehensive Review. 2024;9:12\u0026thinsp;\u0026ndash;\u0026thinsp;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International Society of Hypertension global hypertension practice guidelines. 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Medicina. 2020;56(9):455.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, Anthropometric indexes, Public health, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7670443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7670443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo evaluate and compare the power of novel indices in forecasting the 10-year risk of hypertension, and also to identify the most reliable predictor of hypertension using machine learning and conventional statistical techniques.\u003c/p\u003e\u003ch2\u003eMethodology:\u003c/h2\u003e\u003cp\u003eData were obtained from 2,000 adults aged 20 to 74 years who were enrolled in the Yazd Healthy Heart Project and followed for 10 years. Participants underwent comprehensive assessments of anthropometric, biochemical, and lifestyle variables. The discriminative ability of each index was evaluated using Receiver Operating Characteristic (ROC) analysis, Cox regression models, and three machine learning algorithms: Random Forest, XGBoost, and LightGBM.\u003c/p\u003e\u003ch2\u003eOutcomes:\u003c/h2\u003e\u003cp\u003eThe LCI and CMI demonstrated the strongest independent associations with hypertension (LCI: HR\u0026thinsp;=\u0026thinsp;3.64, AUC\u0026thinsp;=\u0026thinsp;0.72; CMI: HR\u0026thinsp;=\u0026thinsp;2.93, AUC\u0026thinsp;=\u0026thinsp;0.72). Among the machine learning models, XGBoost yielded the best predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.76, sensitivity\u0026thinsp;=\u0026thinsp;76.0%, F1-score\u0026thinsp;=\u0026thinsp;54.8%) and exhibited the smallest discrepancy between training and test results. Stratified analyses revealed that LCI was most predictive in middle-aged individuals, whereas CMI demonstrated greater predictive value in older adults. In contrast, the CI, WWI, and BSA lost statistical significance after multivariable adjustment.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe LCI and CMI outperformed traditional anthropometric measures in predicting hypertension across both conventional statistical analyses and machine learning models. Incorporating these indices into clinical screening protocols could enhance early detection and support more targeted prevention strategies.\u003c/p\u003e","manuscriptTitle":"Cardiometabolic Index (CMI), Lipoprotein Combine Index (LCI), Conicity Index (CI), Weight-adjusted Waist Index (WWI), Waist-to-Hip-to-Height Ratio (WHHR), Body Surface Area (BSA) and the 10-year risk of hypertension: A machine learning approach in the Yazd Healthy Heart Project","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:49:57","doi":"10.21203/rs.3.rs-7670443/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9af9588d-71f2-44df-bac1-1dbb58d1fe2f","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T05:24:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 07:49:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7670443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7670443","identity":"rs-7670443","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}