Developing and validating a nomogram prediction model for osteoporosis risk in the UK biobank: a national prospective cohort.

Abstract

Background: Osteoporosis is a prevalent bone disease that increases frailty. Developing a nomogram prediction model to predict osteoporosis risk at multiple time points using bone mineral densities, behavioral habits, and clinical risk factors would be essential to identify individual risk and guide prevention.

Methods: The study population from the UK Biobank was followed from 2014 to December 31st, 2022. The study outcome was identified as the first occurrence of osteoporosis in the UK Biobank during the follow-up period. After rebalancing with the synthetic minority over-sampling technique, a nomogram prediction model was developed using a LASSO Cox regression. Model discrimination between different risk levels was visualised with Kaplan-Meier curves, and model performance was evaluated with integrated c-index, time-dependent AUC, calibration curves and decision curve analysis (DCA).

Results: The model identified several risk factors for osteoporosis, including higher age, underweight, and various clinical risk factors (such as menopause, lower hand grip strength, lower bone mineral density, fracture history within 5 years, and a history of chronic disease including hypercholesterolemia, cardiovascular disease, bone disease, arthritis, and cancer). Kaplan-Meier curves showed that risk levels predicted by the nomogram model were significantly distinct. The c-indexes were 0.844 and 0.823 for training and validation datasets, respectively. Time-dependent AUC, calibration curves and DCA indicated good discrimination, model fit and clinical utility, respectively.

Conclusions: The nomogram model could properly quantify the five-year risk of osteoporosis and identify high-risk individuals. This might effectively reduce the burden of osteoporosis on the population.