Objective:To explore the value of lumbar volumetric bone mineral density (vBMD) measured by quantitative computed tomography (QCT) in predicting coronary artery calcification (CAC).Methods:This retrospective, cross-sectional study included a total of 991 patients (504 male and 487 female) who underwent coronary artery CT angiography (CTA) and chest, abdomen, or lumbar spine CT examinations at Taihe County People′s Hospital from January 2023 to June 2024. Lumbar vBMD was measured by QCT. The coronary artery calcification score (CACS) was calculated using an artificial intelligence-assisted diagnostic system. Patients were categorized into a low calcification group (CACS≤100, 592 cases) and a moderate-to-severe calcification group (CACS>100, 399 cases). Independent-sample t-tests, Mann-Whitney U tests, or χ2 tests were employed to analyze the differences in clinical data and lumbar vBMD between the two groups. Binary logistic regression was employed to control confounding factors and analyze the correlation between lumbar vBMD and moderate-to-severe CAC. Multivariate binary logistic regression was used to identify independent predictors of moderate-to-severe CAC and construct a combined prediction model. The receiver operating characteristic curve was used to evaluate the efficacy of lumbar vBMD and the combined model in predicting moderate-to-severe CAC. The comparison of the area under the curve (AUC) was conducted using the DeLong test. Results:The age, incidence of diabetes, hypertension, and osteoporosis were significantly higher in the moderate-to-severe calcification group than in the low calcification group, while lumbar vBMD was significantly lower in the former group (all P<0.05). The body mass index, smoking history, and hyperlipidemia had no statistical differences between the two groups (all P>0.05). Following adjustment for potential confounding variables, the results of binary logistic regression analysis revealed that lumbar vBMD was not significantly associated with the presence of moderate-to-severe CAC in the overall study population or in male ( OR=0.998, 95% CI 0.993-1.003, P=0.379; OR=1.000, 95% CI 0.993-1.006, P=0.918). However, lumbar vBMD was a statistically significant predictor in females ( OR=0.992, 95% CI 0.985-0.999, P=0.032). Multivariate binary logistic regression analysis further demonstrated that age ( OR=1.048, 95% CI 1.019-1.077, P=0.001), hypertension ( OR=2.649, 95% CI 1.719-4.082, P<0.001), and lumbar vBMD ( OR=0.992, 95% CI 0.984-0.999, P=0.022) were independent predictors of moderate-to-severe CAC in women. The AUC of lumbar vBMD for predicting moderate-to-severe CAC in female patients was 0.684 (95% CI 0.641-0.725), and the AUC of the combined model was 0.746 (95% CI 0.705-0.784), with a statistically significant difference ( Z=3.26, P=0.001). Conclusions:Lumbar vBMD measured by QCT is an independent predictor of moderate-to-severe CAC in women and demonstrates moderate predictive value. The predictive performance can be enhanced by integrating age and hypertension into a combined predictive model.

Objective:To explore the value of lumbar volumetric bone mineral density (vBMD) measured by quantitative computed tomography (QCT) in predicting coronary artery calcification (CAC).Methods:This retrospective, cross-sectional study included a total of 991 patients (504 male and 487 female) who underwent coronary artery CT angiography (CTA) and chest, abdomen, or lumbar spine CT examinations at Taihe County People′s Hospital from January 2023 to June 2024. Lumbar vBMD was measured by QCT. The coronary artery calcification score (CACS) was calculated using an artificial intelligence-assisted diagnostic system. Patients were categorized into a low calcification group (CACS≤100, 592 cases) and a moderate-to-severe calcification group (CACS>100, 399 cases). Independent-sample t-tests, Mann-Whitney U tests, or χ2 tests were employed to analyze the differences in clinical data and lumbar vBMD between the two groups. Binary logistic regression was employed to control confounding factors and analyze the correlation between lumbar vBMD and moderate-to-severe CAC. Multivariate binary logistic regression was used to identify independent predictors of moderate-to-severe CAC and construct a combined prediction model. The receiver operating characteristic curve was used to evaluate the efficacy of lumbar vBMD and the combined model in predicting moderate-to-severe CAC. The comparison of the area under the curve (AUC) was conducted using the DeLong test. Results:The age, incidence of diabetes, hypertension, and osteoporosis were significantly higher in the moderate-to-severe calcification group than in the low calcification group, while lumbar vBMD was significantly lower in the former group (all P<0.05). The body mass index, smoking history, and hyperlipidemia had no statistical differences between the two groups (all P>0.05). Following adjustment for potential confounding variables, the results of binary logistic regression analysis revealed that lumbar vBMD was not significantly associated with the presence of moderate-to-severe CAC in the overall study population or in male ( OR=0.998, 95% CI 0.993-1.003, P=0.379; OR=1.000, 95% CI 0.993-1.006, P=0.918). However, lumbar vBMD was a statistically significant predictor in females ( OR=0.992, 95% CI 0.985-0.999, P=0.032). Multivariate binary logistic regression analysis further demonstrated that age ( OR=1.048, 95% CI 1.019-1.077, P=0.001), hypertension ( OR=2.649, 95% CI 1.719-4.082, P<0.001), and lumbar vBMD ( OR=0.992, 95% CI 0.984-0.999, P=0.022) were independent predictors of moderate-to-severe CAC in women. The AUC of lumbar vBMD for predicting moderate-to-severe CAC in female patients was 0.684 (95% CI 0.641-0.725), and the AUC of the combined model was 0.746 (95% CI 0.705-0.784), with a statistically significant difference ( Z=3.26, P=0.001). Conclusions:Lumbar vBMD measured by QCT is an independent predictor of moderate-to-severe CAC in women and demonstrates moderate predictive value. The predictive performance can be enhanced by integrating age and hypertension into a combined predictive model.