1056 - Bone Age: An Intuitive Index to Estimate Osteoporosis Progression

Introduction: Ageing is associated with gradual and progressive bone loss, which predisposes to osteoporosis. While conventional dual-energy X-ray absorptiometry (DXA) analysis captures bone ageing patterns within the femur, it has a limited ability to reflect changes in the arrangements of the trabecular microarchitecture. DXA-Region Free Analysis (DXA-RFA) is a validated toolkit with enhanced spatial resolution analysis allowing quantification of local bone deficits. The aim of this study was to first model the bone ageing trajectory in the proximal femur and then deploy this model to estimate the u2018bone ageu2019 as an intuitive metric to reflect the overall trabecular architectural patterns in the femur.Methods: The study dataset comprised 13,338 DXA scans of the proximal femur from Caucasian women (20-97 years), acquired within three different studies: UK Biobank (n=6,918; age range=45-80 years), OPUS (n=1,402; age range=20-40 and 55-79 years), and MRC-Hip (n=5018; age range=75-97 years). To model pixel-by-pixel bone ageing trajectories, pixel bone mineral density (BMD) maps were restored using either Hologic Apex v3.2 or Lunar enCORE v16. The extracted bone maps were then warped into a reference template to eliminate morphological variation and establish an anatomical correspondence between pixel coordinates. At each anatomically normalised pixel, the BMD evolution with ageing was modelled using smooth quantile curves. Fig. 1 shows the median trajectory for bone ageing in the femur. To model osteoporosis progression, the bone age for an individual BMD map is defined as the age a' for which the Euclidian u21132-norm between the given bone map and the median bone map at age a' is minimised (Fig. 2).Results: Subtle spatially complex ageing patterns were observed in the femur (Fig. 1); cortical thinning was observed consistently with ageing around the shaft from the 60thyear onwards. Widespread bone loss was also observed in the trochanteric area.Bone age was highly correlated with neck BMD (r = -0.87; p-value<0.001) (Fig. 3A). Fracture risk increased consistently with bone age. For this analysis, MRC-Hip database with n=178 hip fractures versus n=4,249 fracture-free controls was deployed. Fracture risk was computed as the number of fracture cases divided by the number of subjects at each age band (Fig. 3B). Its power to distinguish between a young healthy population (n = 284; age<40 years) and a more elderly cohort (n=2165; age>80 years) was found to be 5% higher than neck BMD alone (p-value < 0.001; Fig. 3C).Discussion: Bone age is an abstract, but intuitive, concept to reflect the microarchitectural arrangements of trabecular bone in the femur, for which conventional region-based analysis was insensitive. Bone age has the potential to identify subjects at high future risk of hip fracture better than region-based measurement of BMD. Further analysis is required to examine the relationship between bone age and fracture-specific BMD patterns, as well as its sensitivity to treatments.Significance/Clinical Relevance: Bone age may improve the estimate of disease severity and fracture risk to enable better or earlier targeting of interventions to decrease fracture risk. The technique is applicable to widely available measurements from low radiation DXA equipment.