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"Osteoporotic Fractures"

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"Osteoporotic Fractures"

Original Articles
Application of Machine Learning Algorithms to Predict Osteoporotic Fractures in Women
Su Jeong Kang, Moon Jong Kim, Yang-Im Hur, Ji-Hee Haam, Young-Sang Kim
Korean J Fam Med 2024;45(3):144-148.   Published online January 29, 2024
DOI: https://doi.org/10.4082/kjfm.23.0186
Background
Predicting the risk of osteoporotic fractures is vital for prevention. Traditional methods such as the Fracture Risk Assessment Tool (FRAX) model use clinical factors. This study examined the predictive power of the FRAX score and machine-learning algorithms trained on FRAX parameters.
Methods
We analyzed the data of 2,147 female participants from the Ansan cohort study. The FRAX parameters employed in this study included age, sex (female), height and weight, current smoking status, excessive alcohol consumption (>3 units/d of alcohol), and diagnosis of rheumatoid arthritis. Osteoporotic fracture was defined as one or more fractures of the hip, spine, or wrist during a 10-year observation period. Machine-learning algorithms, such as gradient boosting, random forest, decision tree, and logistic regression, were employed to predict osteoporotic fractures with a 70:30 training-to-test set ratio. We evaluated the area under the receiver operating characteristic curve (AUROC) scores to assess and compare the performance of these algorithms with the FRAX score.
Results
Of the 2,147 participants, 3.5% experienced osteoporotic fractures. Those with fractures were older, shorter in height, and had a higher prevalence of rheumatoid arthritis, as well as higher FRAX scores. The AUROC for the FRAX was 0.617. The machine-learning algorithms showed AUROC values of 0.662, 0.652, 0.648, and 0.637 for gradient boosting, logistic regression, decision tree, and random forest, respectively.
Conclusion
This study highlighted the immense potential of machine-learning algorithms to improve osteoporotic fracture risk prediction in women when complete FRAX parameter information is unavailable.

Citations

Citations to this article as recorded by  
  • Clinical Applicability of Machine Learning in Family Medicine
    Jungun Lee
    Korean Journal of Family Medicine.2024; 45(3): 123.     CrossRef
  • Integrating Machine Learning for Personalized Fracture Risk Assessment: A Multimodal Approach
    Sheikh Mohd Saleem, Shah Sumaya Jan
    Korean Journal of Family Medicine.2024; 45(6): 356.     CrossRef
  • 2,931 View
  • 74 Download
  • 2 Web of Science
  • 2 Crossref
Relationship between Blood Mercury Concentration and Bone Mineral Density in Korean Men in the 2008–2010 Korean National Health and Nutrition Examination Survey
Yang Hee Kim, Jae Yong Shim, Min Seok Seo, Hyung Ji Yim, Mi Ra Cho
Korean J Fam Med 2016;37(5):273-278.   Published online September 21, 2016
DOI: https://doi.org/10.4082/kjfm.2016.37.5.273
Background

The results of previous studies on the association between blood mercury (Hg) and bone mineral density (BMD) are inconsistent. We therefore used a large-scale nationwide representative sample of Korean men to investigate the relationship between these two parameters.

Methods

A nationwide cross-sectional study was conducted using data from the 2008 to 2010 Korean National Health and Nutrition Examination Survey to evaluate the relationship between blood Hg and BMD and the prevalence of osteopenia or osteoporosis in 1,190 men over 50 years of age. BMD was measured by dual-energy X-ray absorptiometry. Osteopenia and osteoporosis were diagnosed for each body site according to World Health Organization T-score criteria.

Results

After adjusting for age, body mass index, caloric energy and calcium intake, vitamin D levels, fish consumption, alcohol consumption, smoking, and exercise, quartiles of blood Hg were positively associated with femur neck T-scores in multiple linear regression analysis (β=0.06, P-value=0.03). Compared with the lowest blood Hg quartile, the odds ratios and 95% confidence intervals for diagnosis of osteopenia or osteoporosis in the second and fourth quartiles were 0.63 (0.41–0.99) and 0.57 (0.36–0.91), respectively, in the femur neck after adjusting for the same co-variables.

Conclusion

High blood Hg levels were associated with reduced odds of decreased femur neck BMD in Korean men. However, subgroup analysis did not show a significant protective effect of blood Hg on osteoporotic fractures. Further research is necessary to clarify the association between blood Hg and BMD.

Citations

Citations to this article as recorded by  
  • Metals accumulation affects bone and muscle in osteoporotic patients: A pilot study
    Beatrice Battistini, Chiara Greggi, Virginia Veronica Visconti, Marco Albanese, Alessandra Messina, Patrizia De Filippis, Beatrice Gasperini, Angela Falvino, Prisco Piscitelli, Leonardo Palombi, Umberto Tarantino
    Environmental Research.2024; 250: 118514.     CrossRef
  • Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning
    Hewei Xiao, Xueyan Liang, Huijuan Li, Xiaoyu Chen, Yan Li
    Ecotoxicology and Environmental Safety.2024; 286: 117238.     CrossRef
  • Association of blood mercury levels with bone mineral density in adolescents aged 12–19
    Ke Xu, Bingqian Gao, Tingfeng Liu, Jiayi Li, Yixin Xiang, Yicheng Fu, Mingyi Zhao
    Environmental Science and Pollution Research.2023; 30(16): 46933.     CrossRef
  • Relationship of blood heavy metals and osteoporosis among the middle-aged and elderly adults: A secondary analysis from NHANES 2013 to 2014 and 2017 to 2018
    Zengfa Huang, Xiang Wang, Hui Wang, Shutong Zhang, Xinyu Du, Hui Wei
    Frontiers in Public Health.2023;[Epub]     CrossRef
  • Associations of blood trace elements with bone mineral density: a population-based study in US adults
    Chunli Wu, Yao Xiao, Yuexia Jiang
    Journal of Orthopaedic Surgery and Research.2023;[Epub]     CrossRef
  • Normal concentration range of blood mercury and bone mineral density: a cross-sectional study of National Health and Nutrition Examination Survey (NHANES) 2005–2010
    Yuchen Tang, Qiong Yi, Shenghong Wang, Yayi Xia, Bin Geng
    Environmental Science and Pollution Research.2022; 29(5): 7743.     CrossRef
  • Associations of multiple metals with bone mineral density: A population-based study in US adults
    Mu-hong Wei, Yuan Cui, Hao-long Zhou, Wen-jing Song, Dong-sheng Di, Ru-yi Zhang, Qin Huang, Jun-an Liu, Qi Wang
    Chemosphere.2021; 282: 131150.     CrossRef
  • Occurrence of mercury in the knee joint tissues
    Magdalena Babuśka-Roczniak, Barbara Brodziak-Dopierała, Joanna Bem, Anna Kruczek, Elżbieta Cipora, Wojciech Roczniak
    Polish Annals of Medicine.2021;[Epub]     CrossRef
  • Exposure to heavy metals and the risk of osteopenia or osteoporosis: a systematic review and meta-analysis
    C. Jalili, M. Kazemi, E. Taheri, H. Mohammadi, B. Boozari, A. Hadi, S. Moradi
    Osteoporosis International.2020; 31(9): 1671.     CrossRef
  • Relationship between heavy metal accumulation and histological alterations in voles from alpine and forest habitats of the West Carpathians
    Zuzana Kompišová Ballová, Filip Korec, Katarína Pinterová
    Environmental Science and Pollution Research.2020; 27(29): 36411.     CrossRef
  • Long-Term Accumulation of Metals in the Skeleton as Related to Osteoporotic Derangements
    Geir Bjørklund, Lyudmila Pivina, Maryam Dadar, Yuliya Semenova, Salvatore Chirumbolo, Jan Aaseth
    Current Medicinal Chemistry.2020; 27(40): 6837.     CrossRef
  • 4,191 View
  • 31 Download
  • 10 Web of Science
  • 11 Crossref
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