Diabetology & metabolic syndrome · 2025
Opportunistic AI-derived adiposity measures from coronary artery calcium scans predict new-onset type 2 diabetes in adults without obesity or hyperglycemia: insights from the AI-CVD study within MESA.
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Naghavi Morteza, Azimi Amir, Atlas Kyle, Reeves Anthony P, Zhang Chenyu, Wasserthal Jakob, Mirjalili Seyed Reza, Mozafarybazargany Mohammadhossein, Hashemi Ali, Atlas Thomas, Henschke Claudia I, Yankelevitz David F, Mechanick Jeffrey I, Branch Andrea D, Fried Susan K, Nasir Khurram, Fayad Zahi A, McConnell Michael V, Vliegenthart Rozemarijn, Maron David J, Narula Jagat, Williams Kim A, Shah Prediman K, Budoff Matthew J, Levy Daniel, Benjamin Emelia J, Kloner Robert A, Wong Nathan D
Аннотация
BACKGROUND: The AI-CVD initiative aims to maximize the value of coronary artery calcium (CAC) scans for cardiometabolic risk prediction by extracting opportunistic screening information. We investigated whether artificial intelligence (AI)-derived measures from CAC scans are associated with new-onset Type 2 diabetes mellitus (T2DM) in adults without obesity or hyperglycemia.
METHODS: Baseline CAC scans and up to 23 years of follow-up data were analyzed for participants without obesity (body mass index < 30 kg/m²) and hyperglycemia (fasting plasma glucose < 100 mg/dL) from the Multi-Ethnic Study of Atherosclerosis (MESA). AI-derived measures included liver attenuation index (LAI), subcutaneous fat index (SFI), total visceral fat index (TVFI), epicardial fat index (EFI), skeletal muscle index, and skeletal muscle mean density. Cox regression models compared highest vs. lowest quartiles of each AI-derived metric for T2DM risk. Multivariable models assessed adjusted predictive value using Wald chi-squared statistics. Subgroup analyses stratified participants by demographic and clinical factors.
RESULTS: During a median follow-up of 19.7 years among 2,993 participants (baseline mean age 61.9 ± 10.5 years, 53% women), 257 participants (8.6%) developed T2DM. Key predictors included LAI (HR: 3.13, 95% CI: 2.15-4.55), SFI (HR: 2.85, 95% CI: 1.93-4.21), TVFI (HR: 2.49, 95% CI: 1.72-3.60), and EFI (HR: 1.59, 95% CI: 1.09-2.32). LAI remained the most robust predictor after adjusting for all metrics (Wald χ² = 38.24). Subgroup analyses confirmed LAI's consistent predictive performance.
CONCLUSION: AI-derived adiposity measures from CAC scans-especially liver fat-can identify adults without obesity or hyperglycemia at elevated risk for developing T2DM. These findings underscore the potential of AI-enabled opportunistic screening during CAC imaging to support early T2DM risk stratification in individuals not captured by current clinical guidelines.
METHODS: Baseline CAC scans and up to 23 years of follow-up data were analyzed for participants without obesity (body mass index < 30 kg/m²) and hyperglycemia (fasting plasma glucose < 100 mg/dL) from the Multi-Ethnic Study of Atherosclerosis (MESA). AI-derived measures included liver attenuation index (LAI), subcutaneous fat index (SFI), total visceral fat index (TVFI), epicardial fat index (EFI), skeletal muscle index, and skeletal muscle mean density. Cox regression models compared highest vs. lowest quartiles of each AI-derived metric for T2DM risk. Multivariable models assessed adjusted predictive value using Wald chi-squared statistics. Subgroup analyses stratified participants by demographic and clinical factors.
RESULTS: During a median follow-up of 19.7 years among 2,993 participants (baseline mean age 61.9 ± 10.5 years, 53% women), 257 participants (8.6%) developed T2DM. Key predictors included LAI (HR: 3.13, 95% CI: 2.15-4.55), SFI (HR: 2.85, 95% CI: 1.93-4.21), TVFI (HR: 2.49, 95% CI: 1.72-3.60), and EFI (HR: 1.59, 95% CI: 1.09-2.32). LAI remained the most robust predictor after adjusting for all metrics (Wald χ² = 38.24). Subgroup analyses confirmed LAI's consistent predictive performance.
CONCLUSION: AI-derived adiposity measures from CAC scans-especially liver fat-can identify adults without obesity or hyperglycemia at elevated risk for developing T2DM. These findings underscore the potential of AI-enabled opportunistic screening during CAC imaging to support early T2DM risk stratification in individuals not captured by current clinical guidelines.