Nedhal Al-Husaini, Rozaimi Razali, Amal Al-Haidose, Mohammed Al-Hamdani, Atiyeh M Abdallah
{"title":"使用机器学习模型表征卡塔尔生物库参与者的低股骨颈骨密度。","authors":"Nedhal Al-Husaini, Rozaimi Razali, Amal Al-Haidose, Mohammed Al-Hamdani, Atiyeh M Abdallah","doi":"10.1186/s12891-025-08726-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9189,"journal":{"name":"BMC Musculoskeletal Disorders","volume":"26 1","pages":"492"},"PeriodicalIF":2.2000,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12085013/pdf/","citationCount":"0","resultStr":"{\"title\":\"Characterizing low femoral neck BMD in Qatar Biobank participants using machine learning models.\",\"authors\":\"Nedhal Al-Husaini, Rozaimi Razali, Amal Al-Haidose, Mohammed Al-Hamdani, Atiyeh M Abdallah\",\"doi\":\"10.1186/s12891-025-08726-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>\",\"PeriodicalId\":9189,\"journal\":{\"name\":\"BMC Musculoskeletal Disorders\",\"volume\":\"26 1\",\"pages\":\"492\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2025-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12085013/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Musculoskeletal Disorders\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12891-025-08726-5\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Musculoskeletal Disorders","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12891-025-08726-5","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
Characterizing low femoral neck BMD in Qatar Biobank participants using machine learning models.
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.
期刊介绍:
BMC Musculoskeletal Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of musculoskeletal disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
The scope of the Journal covers research into rheumatic diseases where the primary focus relates specifically to a component(s) of the musculoskeletal system.