Spine age derived from DXA vertebral fracture assessment images predicts incident fractures and mortality: the Manitoba Bone Mineral Density Registry.

Biological age may better predict health outcomes than chronological age by capturing individual heterogeneity in aging. We investigated whether accelerated spine aging, estimated from DXA vertebral fracture assessment (VFA) using deep learning, predicts fracture and mortality independently of age, vertebral fracture (VF), and BMD. A convolutional neural network model to estimate age from lateral spine radiographs was trained in a Korean cohort (VERTE-X, n = 10 341). Among 27 601 adults aged ≥50 who underwent DXA VFA in Manitoba, Canada (2010-2023), the pre-trained model was fine-tuned to DXA VFA images using 20% randomly sampled subset. Among remaining 80% set, test set included 8810 individuals who completed DXA before 2017 as the outcomes were ascertained through 2018. Predicted spine age difference (PAD = spine age-chronological age) was calculated in the test set. During a mean follow-up of 3.9 yr, 899 incident fractures and 969 deaths occurred. Spine age positively correlated with chronological age (r = 0.89), with a mean difference of 0.0 yr (SD = 3.4). Factors associated with higher PAD include VFs (+1.02 yr), nonvertebral fracture history (+0.22), generalized spine structural artifacts (+1.45), smoking (+1.20), and lower FN BMD (+0.60 per T-score decrement), collectively explaining 66% of PAD variance. Each SD increase in PAD was associated with higher risk of any (adjusted hazard ratio = 1.11), nonvertebral (1.10), major osteoporotic (1.12), and hip fracture (1.25), and mortality (1.12), independent of covariates (all p < .05). In summary, accelerated spine aging detected from DXA VFA predicts fracture and mortality risk independently of age, clinical risk factors, VF, spine structural artifacts, and BMD in individuals at high risk of fracture, supporting its potential to enhance fracture risk assessment.