Deep Learning-derived Bone Mineral Density and Longitudinal White Matter Microstructure and Cognitive Decline: Multi-Ethnic Study of Atherosclerosis
Abstract
Background White matter (WM) microstructural degeneration and WM hyperintensities (WMH) are imaging markers of brain aging. Low bone mineral density (BMD) and brain aging co-occur, but longitudinal evidence linking skeletal health to WM injury and cognitive decline remains limited. Purpose To determine whether deep learning-derived thoracic vertebral BMD (vBMD) from noncontrast chest CT is associated with longitudinal changes in WM integrity, WMH burden, and cognitive decline. Materials and Methods This secondary analysis of a prospective study conducted from September 2016 to March 2024 included participants without clinically recognized cardiovascular disease from the Multi-Ethnic Study of Atherosclerosis. Participants underwent noncontrast chest CT, multimodal brain MRI (diffusion tensor imaging and fluid-attenuated inversion recovery), and cognitive testing. Thoracic vBMD was quantified at baseline using a validated deep learning algorithm. Linear mixed-effects models related baseline vBMD to longitudinal changes in WMH volume, WM fractional anisotropy, and cognition, adjusting for demographics, apolipoprotein E-ε4, cardiometabolic risk factors, lifestyle, and medications. Results This study included 715 participants (median age [IQR], 69 years [65-75 years]; 397 men and 318 women). Lower baseline vBMD was associated with faster decline in total WM fractional anisotropy (n = 405; β = -0.036 SD/year/0.1 g/cm3 vBMD decrease [95% CI: -0.065, -0.006]; nominal P = .02),…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 715 participants or records.
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