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Machine Learning-Driven Prediction of Low BMD in Postmenopausal Women Using Cytokine, RANKL/OPG, and Oxidative Stress
Fawaz Azizieh1, Sedra M AlRamadan2, Mahamed G H Omran3
1College of Integrative Studies, Abdullah Al Salem University, Office A38, Building 31, Khaldiya Campus, Kuwait City 72303, Kuwait.
Biomedicines
|June 26, 2026
Summary
Machine learning models effectively identify low bone mineral density (BMD) by integrating cytokine, bone regulatory, and oxidative stress biomarkers. This approach aids in early osteoporosis risk stratification.
Area of Science:
- Biochemistry
- Immunology
- Biomarkers
Background:
- Postmenopausal osteoporosis is a significant health concern.
- Bone mineral density (BMD) assessment is crucial for risk stratification.
- Cytokine, RANKL/OPG, and oxidative stress markers play roles in bone metabolism.
Purpose of the Study:
- To evaluate cytokine, RANKL/OPG, and oxidative stress biomarker profiles in postmenopausal women with varying BMD.
- To develop machine learning models for early identification of low BMD.
Main Methods:
- Seventy-one postmenopausal women were classified by BMD (normal, osteopenia, osteoporosis).
- Quantified ten cytokines, RANKL, OPG, and five oxidative stress markers.
- Applied machine learning classifiers and SHAP for feature interpretation.
Main Results:
- Low BMD associated with elevated pro-resorptive cytokines (TNF-α, IL-6, IL-12) and reduced anti-resorptive cytokines (IL-4, IL-10, IL-23).
- OPG and antioxidant enzymes (catalase, SOD2, PRX2) were lower in low BMD groups.
- Integrated models achieved high performance (F1 ≈ 0.90) for normal vs. low BMD prediction.
Conclusions:
- Integrating cytokine, bone-regulatory, and oxidative stress markers improves ML-based prediction of low BMD.
- This approach supports enhanced early osteoporosis risk stratification.
- Biomarker panels show potential for early detection and management of osteoporosis.