Table 1 presents the characteristics of participants stratified by BMI tertiles (Tertile 1: BMI < 28.28, Tertile 2: BMI 28.28–34.10, Tertile 3: BMI ≥ 34.10) and survival status (Non-death vs. Death). A higher proportion of females was observed in higher BMI categories, and the proportion of females was lower among deceased participants compared to survivors. Older participants were more prevalent among deceased individuals across all BMI tertiles, with mean age decreasing as BMI tertile increased. Notably, the mean age was significantly higher among those who died. Lower educational attainment and widowed/divorced/separated marital status were more common among deceased participants across all BMI tertiles. Higher BMI tertiles were associated with increased weight, WC, BMI, WHtR, BRI, and ABSI. Among deceased participants, WC, WHtR, BRI, Conicity Index, and ABSI were higher compared to survivors within the same BMI tertile, whereas weight and BMI were lower. Deceased participants exhibited lower RBC and PLT counts, while higher BMI tertiles were associated with increased WBC, RBC, and PLT counts. Hemoglobin and serum albumin levels were consistently lower among deceased participants, with serum albumin levels gradually decreasing across BMI tertiles. ALT and AST levels peaked in BMI Tertile 3, with AST levels significantly higher in deceased participants compared to survivors. HDL-C levels declined as BMI tertile increased, whereas LDL-C and HbA1c levels rose with higher BMI tertiles. TC and LDL-C levels were generally lower among deceased participants across all BMI tertiles. Conversely, SBP was consistently higher among deceased individuals in all BMI tertiles. The prevalence of hypertension, CKD, CHD, and cancer was markedly higher among deceased individuals across all BMI tertiles. Stroke and CHF were also more common among deceased participants, particularly in the highest BMI tertile.

Table 2 presents the mortality rates for different variables, including BMI, WHtR, BRI, Conicity Index, and ABSI, along with death rates (per 1,000 person-years) and their corresponding 95% CI. The analysis was adjusted for potential confounders, including gender, age, education level, marital status, income-to-poverty ratio, alcohol consumption, smoking status, blood parameters (such as WBC, RBC, PLT, hemoglobin), serum albumin, ALT, AST, TC, HDL-C, LDL-C, HbA1c, SBP, use of diabetic medications, insulin use, as well as the presence of hypertension, CKD, CHF, CHD, stroke, and cancer. Mortality rates decreased progressively across BMI tertiles. The highest mortality rate was observed in the first tertile (BMI < 28.28) at 3.1 per 1,000 person-years (95% CI: 2.8–3.3), while the lowest was in the third tertile (BMI ≥ 34.10) at 1.7 per 1,000 person-years (95% CI: 1.6–1.9). The differences were statistically significant (P < 0.001). The highest mortality rate for WHtR was in the first tertile (WHtR < 0.61) at 2.5 per 1,000 person-years (95% CI: 2.2–2.7), while the second and third tertiles showed identical mortality rates of 2.2 per 1,000 person-years (95% CI: 2.0–2.4). The differences were statistically significant (P = 0.013). Similar to WHtR, the first tertile (BRI < 5.65) had the highest mortality rate at 2.5 per 1,000 person-years (95% CI: 2.2–2.7), while the second and third tertiles both had mortality rates of 2.2 per 1,000 person-years (95% CI: 2.0–2.4). The differences were statistically significant (P = 0.013). The first tertile (Conicity Index < 133.54) had the lowest mortality rate at 1.6 per 1,000 person-years (95% CI: 1.4–1.8), while the third tertile (≥ 140.50) had the highest rate at 3.2 per 1,000 person-years (95% CI: 2.9–3.5). However, the differences were not statistically significant (P = 0.103). Mortality rates showed a clear upward trend across ABSI tertiles. The lowest mortality rate was in the first tertile (ABSI < 0.82) at 1.2 per 1,000 person-years (95% CI: 1.0–1.4), while the highest was in the third tertile (≥ 0.86) at 4.0 per 1,000 person-years (95% CI: 3.7–4.3). The differences were highly statistically significant (P < 0.001). Supplementary Material Table S1 presents mortality rates stratified by BMI tertiles and further subdivided by tertiles of other anthropometric indices (WHtR, BRI, Conicity Index, and ABSI). In BMI Tertile 3 (≥ 34.10), the first ABSI tertile (< 0.82) had the lowest mortality rate at 1.0 per 1,000 person-years (95% CI: 0.8–1.3), while the third ABSI tertile (≥ 0.86) had the highest at 2.9 per 1,000 person-years (95% CI: 2.5–3.3), showing a significant upward trend in mortality with increasing ABSI tertiles (P = 0.004).

The Kaplan-Meier survival curves illustrate differences in survival probabilities across tertiles for various metrics, including BMI, WHtR, BRI, Conicity Index, and ABSI, as shown in Fig. 1. BMI shows a clear and significant difference (p < 0.0001), with Tertile 1 (lowest BMI) having the poorest survival and Tertile 3 (highest BMI) the best. WHtR and BRI demonstrate no significant differences between tertiles (p = 0.13). The Conicity Index reveals significant differences (p < 0.0001), with Tertile 3 showing the poorest survival. Similarly, ABSI has significant differences (p < 0.0001), where Tertile 3 (highest ABSI) has the lowest survival probability. The number-at-risk tables provide additional insight into participant distribution and follow-up duration for each tertile. Supplementary Material Figure S2 presents Kaplan-Meier survival curves stratified by BMI tertiles and further grouped by tertiles of other anthropometric indices (WHtR, BRI, Conicity Index, and ABSI). Across all BMI tertiles, ABSI tertiles showed significant differences in survival probabilities (P = 0.0001 for BMI Tertile 1, P < 0.0001 for Tertiles 2 and 3). Survival probabilities declined markedly with increasing ABSI tertiles, with the lowest survival observed in Tertile 3.

Kaplan-Meier Survival Analysis of All-Cause Mortality BMI Body mass index, WHtR Waist-to-height ratio, BRI Body roundness index, ABSI A body shape index

Figure 2 illustrates the HR 95% CI and trend tests (P for trend) for BMI, WHtR, BRI, Conicity Index, and ABSI across tertiles under three statistical models (Model 0, Model 1, and Model 2). In the fully adjusted model, participants in the higher tertile (Tertile 2) for BMI (HR = 0.70, 95% CI: 0.59–0.83), WHtR (HR = 0.84, 95% CI: 0.70–0.99), BRI (HR = 0 0.84, 95% CI: 0.70–0.99) were negatively associated with all = cause mortality compared to those in the lowest tertile (Tertile 1). Conversely, in the fully adjusted model, participants in the highest tertile (Tertile 3) for ABSI (HR = 1.55, 95% CI: 1.24–1.93) was positively associated with all-cause mortality compared to those in the lowest tertile (Tertile 1). Additionally, the association between Conicity Index and all-cause mortality was not statistically significant in the fully adjusted model. Supplementary Material Table S2 presents weighted Cox proportional hazards regression analysis of mortality stratified by BMI tertiles and further grouped by tertiles of other anthropometric indices (WHtR, BRI, Conicity Index, ABSI). ABSI is significantly associated with all-cause mortality across all BMI tertiles, particularly in the unadjusted and partially adjusted models, with ABSI Tertile 3 in BMI Tertile 3 showing a significant risk increase even in the fully adjusted model.

Weighted Cox Proportional Hazards Regression Analysis of Anthropometric Indicators and Their Relationship with All-Cause Mortality BMI Body mass index, WHtR Waist-to-height ratio, BRI Body roundness index, ABSI A body shape index Model 0 did not adjust for any covariate Model 1 adjusted for gender, age Model 2 adjusted for gender, age, education status, marital status, income-to-poverty ratio, alcohol consumption, smoking status, WBC, RBC, PLT, hemoglobin, serium albumin, ALT, AST, TC, HDL-C, LDL-C, HbA1c, SBP, diabetic pills using, insulin using, hypertension, CKD, CHF, CHD, stroke and cancer

After adjusting for confounding factors such as gender, age, education level, marital status, income-to-poverty ratio, alcohol consumption, smoking status, WBC, RBC, PLT, hemoglobin, serum albumin, ALT, AST, TC, HDL-C, LDL-C, HbA1c, SBP, use of diabetes medications, insulin use, and histories of hypertension, CKD, CHF, CHD, stroke, and cancer, Fig. 3 shows the dose-response relationships between BMI, WHtR, BRI, Conicity Index, and ABSI with all-cause mortality. BMI, WHtR, and BRI exhibit significant U-shaped curves, with the lowest mortality risks observed at BMI = 35.57, WHtR = 0.68, and BRI = 7.69, respectively. Deviations from these optimal values on either side are associated with increased mortality risk (P overall < 0.001, P non-linear < 0.001). The relationship between the Conicity Index and all-cause mortality is overall significant (P overall = 0.016) but shows no significant non-linear trend (P non-linear = 0.297). Mortality risk remains relatively stable at lower Conicity Index values but begins to increase as the Conicity Index exceeds approximately 136.99, suggesting a threshold effect. The association between ABSI and all-cause mortality is highly significant (P overall < 0.001), with no evidence of a non-linear trend (P non-linear = 0.593). A clear dose-response relationship is observed, with mortality risk progressively increasing as ABSI values rise. The reference point for ABSI is approximately 0.84, where the HR equals 1. Supplementary Material Figure S3 illustrates RCS curves for the association between anthropometric indices (WHtR, BRI, Conicity Index, ABSI) and mortality risk, stratified by BMI tertiles. In the third BMI tertile, ABSI demonstrates a significant nonlinear relationship with mortality risk (P non-linear = 0.047), where mortality risk increased sharply at higher ABSI values.

RCS Analysis of Anthropometric Indicators and Their Relationship with All-Cause Mortality BMI Body mass index, WHtR Waist-to-height ratio, BRI Body roundness index, ABSI A body shape index, HR Hazard ratios, CI Confidence intervals Adjusted for gender, age, education status, marital status, income-to-poverty ratio, alcohol consumption, smoking status, WBC, RBC, PLT, hemoglobin, serium albumin, ALT, AST, TC, HDL-C, LDL-C, HbA1c, SBP, diabetic pills using, insulin using, hypertension, CKD, CHF, CHD, stroke and cancer

Based on the analysis of Table 3; Fig. 4, ABSI showed the best diagnostic performance among single metrics, with an weighted AUC of 0.653 (95% CI: 0.635–0.670), significantly outperforming other single metrics (BMI, WHtR, BRI, and Conicity Index). Its optimal cutoff value was 0.853, with a sensitivity of 0.591 and a specificity of 0.653. BMI and Conicity Index achieved weighted AUCs of 0.578 and 0.590, respectively, slightly lower than ABSI, while WHtR and BRI had the lowest performance with weighted AUCs of 0.526. Combining BMI with other metrics further improved diagnostic performance, with BMI & ABSI achieving the highest weighted AUC of 0.669 (95% CI: 0.653–0.686), significantly better than other combinations. BMI & Conicity Index followed with an weighted AUC of 0.666, while BMI & WHtR and BMI & BRI achieved weighted AUCs of 0.637 and 0.640, respectively.

Weighted ROC Curves for Predicting All-Cause Mortality Using Anthropometric Indicators and their combinations BMI Body mass index, WHtR Waist-to-height ratio, BRI Body roundness index, ABSI A body shape index

Supplementary Material Table S3 presents the results of the sensitivity analysis conducted after excluding individuals who died from accidental causes (n = 315). ABSI demonstrated a significant association with higher mortality risk, particularly in the third tertile (HR = 1.60, 95% CI: 1.26–2.03, P < 0.001). In the BMI-stratified analysis, individuals with high BMI (≥ 34.20) showed a significant increase in mortality risk associated with the third tertile of ABSI (HR = 2.03, 95% CI: 1.33–3.11, P = 0.001).