Associations Between Deep Learning-Derived Fat, Muscle, and Bone Measures From Abdominal Computed Tomography Scans and Fall Risk in Persons Aged 20 Years or Older.

OBJECTIVE

To determine whether abdominal computed tomography (CT) measures of body composition are associated with fall risk in adults aged 20 to 89 years.

PATIENTS AND

METHODS

We identified persons who received an abdominal CT scan from 2010 to 2020 using the Rochester Epidemiology Project. We calculated subcutaneous adipose and visceral adipose tissue area, skeletal muscle area and density, and vertebral bone area and density using a validated deep learning algorithm applied to CT abdominal section. Sex-specific tertiles of body composition biomarkers were used for primary analyses. We identified falls using International Classification of Diseases codes and verified via chart review. Associations between body composition tertiles and falls were assessed using Cox proportional hazards models, and models were adjusted for body mass index and the presence of 18 chronic conditions.

RESULTS

We included 3972 persons aged 20 to 89 years. Subcutaneous and visceral fat area, skeletal muscle area, bone area, and bone density were not associated with fall risk (all adjusted P>.05). By contrast, lower muscle density was associated with an increased risk of falls (adjusted hazard ratio, for the lowest tertile vs the middle tertile: 2.31; 95% CI, 1.70-3.14). The association between low muscle density and an increased risk of falls was most sizable in persons aged 45 to 64 years (adjusted hazard ratio, 4.98; 95% CI, 2.80-8.85).

CONCLUSION

Muscle density measures from abdominal CT scans may be useful for understanding physiologic changes in the abdomen that place persons at an increased risk of falls as early as middle age.