Beyond Bone Mineral Density: Real-World Fracture Risk Profiles and Therapeutic Gaps in Postmenopausal Osteoporosis

Abstract

Background/Objectives: Osteoporosis remains a leading cause of morbidity in postmenopausal women, yet many high-risk individuals remain undiagnosed or untreated. This study aimed to assess the prevalence of osteoporosis and osteopenia, treatment patterns, and skeletal fragility indicators in a large cohort of postmenopausal women undergoing DXA screening. Methods: We analyzed data from 1669 postmenopausal women aged 40-89 years who underwent DXA evaluation. BMD status was categorized as normal, osteopenia, or osteoporosis. Treatment status was classified based on active antiosteoporotic therapy, calcium/vitamin D supplementation, hormonal therapy (historical use), or no treatment. Logistic regression models were used to explore independent predictors of osteoporosis and treatment uptake. Results: A total of 45.0% of women had osteoporosis and 43.5% had osteopenia. Despite this, 58.5% of the population, over half of women with osteoporosis, were not receiving any active pharmacologic treatment. Bisphosphonates were the most prescribed therapy (17.9%), followed by calcium/vitamin D supplements (20.6%). A prior history of fragility fractures and radiological bone lesions were significantly associated with lower BMD (p < 0.05). Historical hormone replacement therapy (HRT) use was not associated with current BMD (p = 0.699), but women with HRT use reported significantly fewer fractures (p < 0.001). In multivariate analysis, later menopause age and low BMD status predicted higher odds of receiving active treatment. Conclusions: Our findings highlight a substantial care gap in osteoporosis management, with treatment primarily initiated reactively in more severe cases. Improved screening and earlier intervention strategies are urgently needed to prevent fractures and reduce the long-term burden of osteoporosis.

Keywords: DXA; bone mineral density; fracture risk; postmenopausal osteoporosis; treatment gap.

Figure 1

Histogram and density curve of participant age distribution. Gray bars indicate ages ≤ 60 years; blue bars indicate ages > 60 years.

Figure 2

Pareto distribution of self-reported fragility fractures by anatomical site. The dotted yellow line indicates the 95% cumulative frequency threshold, highlighting dominant fracture sites.

Figure 3

Predicted probability of osteoporosis diagnosis by skeletal fragility indicators: (a) personal history of fragility fractures (PFH), (b) radiologically observed bone lesions (BL), and (c) family history of osteoporotic fractures (FFH). On the x-axis, 0 indicates the absence and 1 the presence of each respective risk factor.

Figure 4

Distribution of study participants by osteoporosis treatment category. Blue bars indicate the number of patients in each treatment group, while the orange line represents the corresponding percentage of the total cohort.

Figure 5

Predicted probability of not receiving active osteoporosis treatment based on independent predictors from the logistic regression model. (a) Association with age at menopause (years); (b) association with DXA diagnostic category. Shaded areas and error bars indicate 95% confidence intervals.

Figure 6

Predicted probability of osteoporosis based on independent predictors from the logistic regression model: (a) association with age (years), showing increased risk with advancing age; (b) association with body mass index (BMI, kg/m²), revealing an inverse correlation; (c) association with diabetes status, indicating a lower predicted osteoporosis risk. Shaded areas and error bars indicate 95% confidence intervals.