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Related Experiment Video

Updated: Mar 29, 2026

An Adoptive Transfer Model of Rheumatoid Arthritis in Mice

An Adoptive Transfer Model of Rheumatoid Arthritis in Mice

Published on: June 6, 2025

Discovery of Novel NMR-Based Biomarkers and Interpretable Machine Learning Models for Risk Prediction of Rheumatoid

Hong Lin¹, Rui Wang^2,3, Linyan Lu¹

¹State Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine, Innovation Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China.

|March 27, 2026

Summary

This study identifies novel serum biomarkers, like formic acid and H4PL, for early rheumatoid arthritis (RA) detection. Machine learning models show promise for RA screening and predicting disease activity scores.

Keywords:

NMR-based biomarkers lipoprotein subfractions metabolite rheumatoid arthritis risk prediction

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Last Updated: Mar 29, 2026

An Adoptive Transfer Model of Rheumatoid Arthritis in Mice

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Published on: June 6, 2025

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis

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Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Area of Science:

Biochemistry
Immunology
Data Science

Background:

Early diagnosis of rheumatoid arthritis (RA) is hindered by the inadequacy of current serum biomarkers.
Novel metabolite and lipoprotein biomarkers are needed for improved RA detection and management.

Purpose of the Study:

To discover new serum metabolite and lipoprotein biomarkers for rheumatoid arthritis (RA).
To develop interpretable machine learning models for RA screening and disease activity prediction.

Main Methods:

Utilized 1H-NMR metabolomics on serum from 77 RA patients and 70 controls.
Quantified 38 metabolites and 112 lipoprotein parameters, identifying 7 key biomarkers via LASSO regression.
Developed and validated Random Forest (RF) and DAS-28 prediction models, interpreting RF with SHAP.

Main Results:

Identified significant RA-associated biomarkers including formic acid and High-density lipoprotein 4 phospholipids (H4PL).
The RF model demonstrated strong discriminatory performance in the internal test set.
A preliminary model predicted Disease Activity Score in 28 joints (DAS-28) with R²=0.548.

Conclusions:

This study presents a novel panel of potential RA biomarkers and an interpretable predictive tool.
Findings highlight the potential of specific markers and predictive models, requiring larger validation studies.
The developed DAS-28 prediction model warrants further investigation for clinical utility.