Predicting MRI-derived total brain volume from DXA-derived head composition in middle-aged and older adults: WASEDA'S
Toshiharu Tsutsui1,2, Suguru Torii3,4, Kumpei Tanisawa1
1Faculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Background:
Total brain volume (TBV) derived from brain MRI is an important marker of brain structural health in middle-aged and older adults, but MRI is resource-intensive and not always feasible in largescale or repeated assessments. We examined whether dual-energy X-ray absorptiometry (DXA)-derived head composition measures can estimate MRI-derived TBV in middle-aged and older adults.
Methods:
This study included 314 participants (≥ 40 years) who underwent whole-body DXA (head ROI manually defined using a sub-region tool) and 3T brain MRI within 1 year. MRI-derived TBV was defined as the sum of gray and white matter volumes. We developed multivariable linear regression models using either DXA-derived head lean-and-fat mass or head fat mass as the primary predictor. Nested models were fitted: Model 1 (predictor only), Model 2 (+ age and sex), and Model 3 (+ BMI). Apparent model performance was summarized using R² and RMSE, and internal validation was performed using 1,000 bootstrap resamples to obtain optimism-corrected performance estimates. Calibration was evaluated using calibration-in-the-large (CITL) and calibration slope. Agreement between observed and predicted TBV was assessed using Bland-Altman analysis. Sensitivity analyses additionally adjusted for the MRI-DXA measurement interval and evaluated sex-stratified performance.
Results:
Model 3 was treated as the prespecified primary model because it was the fully adjusted model including clinically relevant covariates. In Model 3, both head lean-and-fat mass and head fat mass were positively associated with TBV, whereas age was negatively associated and male sex was associated with larger TBV. Across the nested models, optimism-corrected bootstrap validation showed broadly similar performance, with numerically slightly higher R² values and lower RMSE values for Model 3. Calibration was favorable in both predictor-based primary models (CITL approximately 0; calibration slope approximately 1.00). Bland-Altman analyses showed small mean bias with evidence of proportional bias across the TBV range. Bootstrap validation indicated stable performance. Sensitivity analyses yielded similar results after accounting for measurement interval and across sex strata.
Conclusions:
DXA-derived head composition measures can provide a practical approximation of MRI-derived TBV in middle-aged and older adults, with good calibration and stable internal validation performance.
