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Characterizing low femoral neck BMD in Qatar Biobank participants using machine learning models
Nedhal Al-Husaini1, Rozaimi Razali1, Amal Al-Haidose1
1Department of Biomedical Sciences, College of Health Sciences, QU-Health, Qatar University, Doha, 2713, Qatar.
Background:
Identifying determinants of low bone mineral density (BMD) is crucial for understanding the underlying pathobiology and developing effective prevention and management strategies. Here we applied machine learning (ML) algorithms to predict low femoral neck BMD using standard demographic and laboratory parameters.
Methods:
Data from 4829 healthy individuals enrolled in the Qatar Biobank were studied. The cohort was split 60% and 40% for training and validation, respectively. Logistic regression algorithms were implemented to predict femoral neck BMD, and the area under the curve (AUC) was used to evaluate model performance. Features associated with low femoral neck BMD were subjected the statistical analysis to establish associated risk.
Results:
The final predictive model had an AUC of 86.4% (accuracy 79%, 95%CI: 77.98-80.65%) for the training set and 85.9% (accuracy 78%, 95% CI: 75.92-80.61%) for the validation set. Sex, body mass index, age, creatinine, alkaline phosphatase, total cholesterol, and magnesium were identified as informative features for predicting femoral neck BMD. Age (odds ratio (OR) 0.945, 95%CI: 0.945-0.963, p < 0.001), alkaline phosphatase (OR 0.990, 95%CI: 0.986-0.995, p < 0.001), total cholesterol (OR 0.845, 95%CI: 0.767-0.931, p < 0.001), and magnesium (OR 0.136, 95%CI: 0.034-0.571, p < 0.001) were inversely associated with BMD, while BMI and creatinine were positively associated with BMD (OR 1.116, 95%CI: 1.140-1.192, p < 0.001 and OR 1.031, 95%CI: 1.022-1.039, p < 0.001, respectively).
Conclusion:
Several biological determinants were found to have a significant global effect on BMD with a reasonable effect size. By combining standard demographic and laboratory variables, our model provides proof-of-concept for predicting low BMD. This approach suggests that, with further validation, an ML-driven model could complement or potentially reduce the need for imaging when assessing individuals at risk for low BMD, which is an important component of fracture risk prediction.
Clinical Trial Number:
Not applicable.
