ArticleFrontiers in medicine2026
Predictive value of coronary artery calcification for osteoporosis in COPD patients: a retrospective AI-based study.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Patients with chronic obstructive pulmonary disease (COPD) are at high risk for osteoporosis (OP), yet OP screening remains suboptimal in practice. The "bone-vascular axis" hypothesis suggests shared mechanisms between vascular calcification and bone loss. Coronary artery calcification (CAC), a reliable imaging marker of atherosclerosis, may serve as a surrogate indicator of OP risk in this population. Advances in artificial intelligence (AI) now enable automated CAC quantification from routine chest CT scans, offering a potentially efficient, low-cost strategy for opportunistic OP risk assessment in COPD patients. Objective: This study aimed to evaluate the predictive value of artificial intelligence (AI)-based CAC scoring, derived from routine chest computed tomography (CT) scans, for identifying osteoporosis in patients with COPD. Methods: The retrospective study included COPD patients who underwent dual-energy X-ray absorptiometry (DXA) and non-gated chest CT between 2020 and 2025. Osteoporosis was defined as a T-score ≤ -2.5. An AI tool automatically quantified Agatston scores for all four coronary arteries. Statistical analyses involved intergroup comparisons and adjusted logistic regression, with ROC curves assessing predictive performance. Results: Amongst the 262 patients, 117 were diagnosed with osteoporosis. The osteoporosis group was characterized by older age, lower BMI, and greater coronary artery calcification scores in all arteries. Following adjustment for confounding factors including age, sex, and BMI, left anterior descending coronary artery calcification (LAD-CAC; OR = 1.01, 95% CI: 1.00-1.02, Conclusion: Coronary artery calcium scores, particularly LAD-CAC and LCX-CAC, automatically obtained via AI software from routine chest CT scans, are independently associated with osteoporosis in COPD patients and demonstrate moderate predictive capability. This finding suggests a predictive utility for early, convenient, and cost-free screening of OP risk by leveraging routine imaging examinations already performed in COPD patients.
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