Early Identification of Low Bone Density Risk Using a Radiofrequency Echographic Multi Spectrometry-Based Prediction...

Area of Science:

• Osteoporosis research
• Medical diagnostics
• Bone health

Background:

• Osteoporosis is a significant public health concern, marked by decreased bone mineral density (BMD) and elevated fracture risk.
• Early detection of low bone density is crucial for effective osteoporosis prevention strategies.

Purpose of the Study:

• To assess a multivariable logistic regression model for identifying low bone density in women.
• The model integrates clinical factors with Radiofrequency Echographic Multi Spectrometry (REMS)-derived parameters.

Main Methods:

• A cohort of 324 women undergoing REMS assessment of the hip and lumbar spine was analyzed.
• Binary logistic regression identified independent predictors of low BMD (T-score < -1 SD).
• Model performance was evaluated using odds ratios, chi-square tests, pseudo-R², and classification accuracy.

Main Results:

• Age, menopausal status, body mass index (BMI), and basal metabolic rate (BMR) were identified as independent factors associated with low BMD.
• Advancing age and menopause increased the likelihood of low bone density.
• Higher BMI and BMR showed an inverse association with low bone density.

Conclusions:

• A logistic regression model combining clinical and REMS data effectively identifies women with low bone density.
• This approach supports enhanced, individualized risk stratification for osteoporosis in clinical practice.