Diabetes and longitudinal changes in deep learning-derived measures of vertebral bone mineral density using conventional CT: the Multi-Ethnic Study of Atherosclerosis.

Abstract

To investigate the longitudinal association between diabetes and changes in vertebral bone mineral density (BMD) derived from conventional chest CT and to evaluate whether kidney function (estimated glomerular filtration rate (eGFR)) modifies this relationship. This longitudinal study included 1046 participants from the Multi-Ethnic Study of Atherosclerosis Lung Study with vertebral BMD measurements from chest CTs at Exam 5 (2010-2012) and Exam 6 (2016-2018). Diabetes was classified based on the American Diabetes Association criteria, and those with impaired fasting glucose (i.e., prediabetes) were excluded. Volumetric BMD was derived using a validated deep learning model to segment trabecular bone of thoracic vertebrae. Linear mixed-effects models estimated the association between diabetes and BMD changes over time. Following a significant interaction between diabetes status and eGFR, additional stratified analyses examined the impact of kidney function (i.e., diabetic nephropathy), categorized by eGFR (≥ 60 vs. < 60 mL/min/body surface area). Participants with diabetes had a higher baseline vertebral BMD than those without (202 vs. 190 mg/cm³) and experienced a significant increase over a median followpup of 6.2 years (β = 0.62 mg/cm³/year; 95% CI 0.26, 0.98). This increase was more pronounced among individuals with diabetes and reduced kidney function (β = 1.52 mg/cm³/year; 95% CI 0.66, 2.39) compared to the diabetic individuals with preserved kidney function (β = 0.48 mg/cm³/year; 95% CI 0.10, 0.85). Individuals with diabetes exhibited an increase in vertebral BMD over time in comparison to the non-diabetes group which is more pronounced in those with diabetic nephropathy. These findings suggest that conventional BMD measurements may not fully capture the well-known fracture risk in diabetes. Further studies incorporating bone microarchitecture using advanced imaging and fracture outcomes are needed to refine skeletal health assessments in the diabetic population.