Purpose: Fragility fractures, the most serious complication of osteoporosis, affect life quality and increase medical expenses and economic burden. Strategies to identify populations with very low bone mineral density (T-scores <-3), indicating very high fracture risk according to the American Association of Clinical Endocrinologists/American College of Endocrinology (AACE/ACE), are necessary to achieve acceptable fracture risk levels. In this study, the characteristics of persons with T-scores <− 3 were analyzed in the Chinese population to identify risk factors and develop a nomogram for very low bone mineral density (T-scores <-3) identification.
Materials and Methods: We conducted a cross-sectional study using the datasets of the Health Improvement Program of Bone (HOPE), with 602 men aged ≥ 50 years and 482 postmenopausal women. Bone mineral density (BMD) was measured using dual energy X-ray absorptiometry (DXA). Data on clinical risk factors, including age, sex, weight, height, previous fracture, parental hip fracture history, smoking, alcohol intake > 3 units/day, glucocorticoid use, rheumatoid arthritis, and secondary osteoporosis were collected. A multivariate logistic regression to evaluate the relationship between the clinical risk factors and very low BMD (T-scores <-3) was conducted. Parameter estimates of the final model were then used to construct a nomogram.
Results: Sixty-three of 1084 participants (5.8%) had BMD T-score <− 3. In multivariable regression analysis, age (odds ratio [OR] = 1.068, 95% confidence interval [CI]: 1.037– 1.099) and weight (OR = 0.863, 95% CI: 0.830– 0.897) were significant factors that were associated with very low BMD (T-scores <-3). These variables were the factors considered in developing the nomogram. The area under the receiver operating characteristic (ROC) curve for the model was 0.861. The cut-off value of the ROC curve was 0.080.
Conclusion: The nomogram can effectively assist clinicians to identify persons with very low BMD (T-scores <-3) and very high fracture risk in the Chinese population.
Keywords: osteoporosis, fracture, risk factors, prediction tool