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Clinical severity prediction in children with osteogenesis imperfecta caused by COL1A1/2 defects
Lin Yang1, Bo Liu2,3, Xinran Dong2
1Department of Pediatric Endocrinology and Inherited Metabolic Diseases, Children's Hospital of Fudan University, 399 Wan Yuan Road, Shanghai, 201102, China.
Insights
A new model predicts Osteogenesis Imperfecta (OI) clinical severity using COL1A1/2 gene variant features. This tool aids in prognosis and management of this genetic bone disorder.
Area of Science:
- Genetics
- Molecular Biology
- Medical Diagnostics
Background:
- Osteogenesis Imperfecta (OI) is a genetic disorder affecting collagen production, with 90% of cases linked to COL1A1/COL1A2 gene variants.
- The Sillence classification categorizes OI into four types, each with distinct clinical presentations from mild to lethal.
Purpose of the Study:
- To develop a clinical severity prediction model for Osteogenesis Imperfecta (OI).
- To leverage variant features within COL1A1/2 genes for accurate OI prognosis and management.
Main Methods:
- A random forest model was trained using 790 records from the Human Gene Mutation Database.
- The model utilized variant-type features and optimized features for COL1A1 and COL1A2 gene defects.
Main Results:
- The prediction model achieved an AUC of 0.902 for mild/moderate OI using optimized COL1A1 features and 0.731 for COL1A2 defects.
- Clinical validation on 17 patients showed prediction accuracies of 76.5% for COL1A1 and 88.2% for COL1A2 defects.
Conclusions:
- An Osteogenesis Imperfecta severity prediction model was successfully established using COL1A1/2 gene variant features.
- The model demonstrates a prediction accuracy of 76-88%, offering a valuable tool for clinical practice.
Abstract:
Osteogenesis imperfecta (OI) is a genetic disease with an estimated prevalence of 1 in 13,500 and 1 in 9700. The classification into subtypes of OI is important for prognosis and management. In this study, we established a clinical severity prediction model depending on multiple features of variants in COL1A1/2 genes.
Introduction:
Ninety percent of OI cases are caused by pathogenic variants in the COL1A1/COL1A2 gene. The Sillence classification describes four OI types with variable clinical features ranging from mild symptoms to lethal and progressively deforming symptoms.
Methods:
We established a prediction model of the clinical severity of OI based on the random forest model with a training set obtained from the Human Gene Mutation Database, including 790 records of the COL1A1/COL1A2 genes. The features used in the prediction model were respectively based on variant-type features only, and the optimized features.
Results:
With the training set, the prediction results showed that the area under the receiver operating characteristic curve (AUC) for predicting lethal to severe OI or mild/moderate OI was 0.767 and 0.902, respectively, when using variant-type features only and optimized features for COL1A1 defects, 0.545 and 0.731, respectively, for COL1A2 defects. For the 17 patients from our hospital, prediction accuracy for the patient with the COL1A1 and COL1A2 defects was 76.5% (95% CI: 50.1-93.2%) and 88.2% (95% CI: 63.6-98.5%), respectively.
Conclusion:
We established an OI severity prediction model depending on multiple features of the specific variants in COL1A1/2 genes, with a prediction accuracy of 76-88%. This prediction algorithm is a promising alternative that could prove to be valuable in clinical practice.
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