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An interpretable metabolomic-driven machine learning model for early prediction of knee structural OA progression
Afshin Jamshidi1, Guangju Zhai2, Ming Liu2
1Osteoarthritis Research Unit, University of Montreal Hospital Research Centre (CRCHUM), Montreal, QC, Canada.
Rheumatology (Oxford, England)
|December 23, 2025
Summary
A new deep learning model using serum metabolites accurately predicts knee osteoarthritis progression. This approach enables personalized risk assessment for early intervention in this common musculoskeletal disease.
Area of Science:
- Biochemistry
- Genomics
- Medical Diagnostics
Background:
- Osteoarthritis (OA) is the most common chronic musculoskeletal condition.
- Early identification of individuals at risk for knee structural progression is crucial for timely interventions.
- Serum metabolomics offers a potential biomarker source for OA progression.
Purpose of the Study:
- To develop and validate a machine/deep learning (ML/DL) prognostic model for knee OA structural progression.
- To utilize serum metabolomic data for predicting OA progression.
- To enable personalized risk assessment and early intervention strategies.
Main Methods:
- Serum metabolomic data from two independent cohorts (n=180 and n=137) were analyzed.
- Metabolites, their ratios, and demographic data (age, sex, BMI) were evaluated using ML/DL models.
- Feature selection involved variable clustering and elastic net regularization, with performance assessed by AUC, accuracy, sensitivity, and specificity.
Main Results:
- A model incorporating six specific metabolites (SM (OH) C22:2, proline, citrulline, LysoPC a C18:0, glutamate, C12-DC), sex, and age demonstrated high predictive performance.
- The model achieved excellent test performance (AUC 0.98, accuracy 0.89) and validation performance (AUC 0.89, accuracy 0.85).
- An Artificial Neural Network with a domain-adversarial component showed superior performance.
Conclusions:
- A validated metabolomics deep learning framework can predict knee OA structural progression.
- This novel approach facilitates personalized risk stratification for knee OA.
- The findings support early intervention strategies for individuals at high risk of OA progression.
