Retrospective Study: Automated Multiorgan CT Markers Predict Diabetes and Cardiometabolic Comorbidities – Radiology

Study Design and Population: This retrospective study analyzed data from 32,166 Korean adults (mean age, 45 years) who underwent health screenings, including fluorodeoxyglucose PET/CT scans, between 2012 and 2015. The study aimed to evaluate the predictive ability of automated CT-derived markers, such as visceral and subcutaneous fat, muscle area, bone density, liver fat, and aortic calcification, for diabetes and associated cardiometabolic conditions.

Main Findings: Visceral fat index showed the highest predictive performance for both prevalent and incident diabetes, with an AUC of 0.70 for men and 0.82 for women in cross-sectional analyses. Combining visceral fat, muscle area, liver fat, and aortic calcification improved prediction, yielding a C-index of 0.69 for men and 0.83 for women. Additionally, the study found that these CT markers were effective in identifying metabolic syndrome, fatty liver, coronary artery calcium scores >100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95.

Implications for Practice: Automated CT-derived markers can effectively predict diabetes and multiple cardiometabolic comorbidities, surpassing traditional anthropometric measures. These findings suggest that integrating such automated assessments into routine clinical practice could enhance risk stratification and preventive care, particularly through opportunistic screening during routine CT scans.

Reference: Chang Y, Yoon SH, Kwon R, et al. (2024). Automated Comprehensive CT Assessment of the Risk of Diabetes and Associated Cardiometabolic Conditions. Radiology, 312(2), e233410. DOI: https://doi.org/10.1148/radiol.233410.