Background: CT-based abdominal body composition measures have shown associations with important health outcomes. Artificial intelligence (AI) advances now allow deployment of tools that measure body composition in large patient populations.

Objective: To assess associations of age, sex, and common systemic diseases on CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample.

Methods: This retrospective study included 140,606 adult patients (mean age 53.1±17.6 years; 67,613 male, 72,992 female) who underwent abdominal CT at a single academic institution between January 1, 2000, and February 28, 2021. CT examinations were not restricted based on patient setting, clinical indication, or IV contrast media utilization. Thirteen fully automated AI body composition tools quantifying liver, spleen, and kidney volume and attenuation, vertebral trabecular attenuation, skeletal muscle area and attenuation, and abdominal fat area and attenuation were applied to each patient's first available abdominal CT examination. EHR review was performed to identify common systemic diseases, including cancer, cardiovascular disease (CVD), diabetes mellitus (DM), and cirrhosis, based on relevant ICD-10 codes; 64,789 (46.1%) patients had a diagnosis of at least one systemic disease. Multiple linear regression models were performed in the 118,141 (84.0%) patients with no systemic disease or a single systemic disease, to assess age, sex, and presence of systemic disease as predictors of body composition measures; effect sizes were characterized using the unstandardized regression coefficient B.

Results: Multiple linear regression models using age, sex, and systemic disease as predictors were overall significant for all 13 body composition measures (all p<.001) with variable goodness of fit (R2=0.03-0.43 across models). In the models, age was predictive of all 13 body composition measures, sex of 12 measures, cancer of nine measures, CVD of 11 measures, DM of 13 measures, and cirrhosis of 12 measures (all p<.05).

Conclusion: Age, sex, and the presence of common systemic diseases were predictors of AI-derived CT-based body composition measures.

Clinical Impact: An understanding of the identified associations with common systemic diseases will be critical for establishing normative reference ranges as CT-based AI body composition tools are developed for clinical use.