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Background <p>The variability in treatment response in people with rheumatoid arthritis (RA) warrants the prediction of patients at high risk of treatment failure. Identification of biomarkers linked to clinical remission in RA is currently a challenge. Metabolomics may help to identify such biomarkers as it allows for a comprehensive exploration of disease-related variations that extends beyond the genome and proteome. This hypothesis-free exploratory metabolomics study aimed to profile serum metabolic alterations in early RA to understand the metabolic changes associated with disease activity and therapeutic response.</p> Methods <p>The study included 220 early RA participants from the NORD-STAR study, randomized at baseline into four arms, ranging from conventional anti-rheumatic treatment to biological drugs: methotrexate combined with prednisolone (1), certolizumab (2), abatacept (3), or tocilizumab (4). Untargeted metabolomics was performed in serum samples at baseline and 24-week follow-up. Participants achieving clinical disease activity index remission at 24 weeks were defined as responders. Machine learning models for treatment response were constructed using random forest, logistic regression, support vector machine and extreme gradient boosting algorithms based on selected features.</p> Results <p>We identified 278 metabolites, of which 39 were associated with baseline disease activity, including several acylcarnitines and amino acids. We also found 17 baseline metabolites associated with remission at 24 weeks in the overall cohort, including malic acid (β=-0.4), cytidine (β = 0.4), arginine (β = 0.3), and citrulline (β = 0.2), as well as specific metabolites and metabolic pathways associated with remission in the four treatment arms. Fifteen features were identified using machine learning-based multivariable selection. The best predictive model using logistic regression achieved AUC of 0.75 in training and 0.73 in the test set.</p> Conclusions <p>Our study has identified several baseline metabolites and metabolic pathways associated with disease activity and response to different treatments in early RA. By integrating metabolomics and clinical data, we developed predictive models for response to treatment in early RA, though their predictive performance remains limited.</p>

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Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月26日

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  • Tahzeeb Fatima,
  • Yuan Zhang,
  • Georgios K. Vasileiadis,
  • Araz Rawshani,
  • Ronald van Vollenhoven,
  • Jon Lampa,
  • Bjorn Gudbjornsson,
  • Espen A. Haavardsholm,
  • Dan Nordström,
  • Gerdur Gröndal,
  • Kim Hørslev-Petersen,
  • Kristina Lend,
  • Marte S. Heiberg,
  • Merete Lund Hetland,
  • Michael Nurmohamed,
  • Mikkel Østergaard,
  • Till Uhlig,
  • Tuulikki Sokka-Isler,
  • Anna Rudin,
  • Cristina Maglio

摘要

Background

The variability in treatment response in people with rheumatoid arthritis (RA) warrants the prediction of patients at high risk of treatment failure. Identification of biomarkers linked to clinical remission in RA is currently a challenge. Metabolomics may help to identify such biomarkers as it allows for a comprehensive exploration of disease-related variations that extends beyond the genome and proteome. This hypothesis-free exploratory metabolomics study aimed to profile serum metabolic alterations in early RA to understand the metabolic changes associated with disease activity and therapeutic response.

Methods

The study included 220 early RA participants from the NORD-STAR study, randomized at baseline into four arms, ranging from conventional anti-rheumatic treatment to biological drugs: methotrexate combined with prednisolone (1), certolizumab (2), abatacept (3), or tocilizumab (4). Untargeted metabolomics was performed in serum samples at baseline and 24-week follow-up. Participants achieving clinical disease activity index remission at 24 weeks were defined as responders. Machine learning models for treatment response were constructed using random forest, logistic regression, support vector machine and extreme gradient boosting algorithms based on selected features.

Results

We identified 278 metabolites, of which 39 were associated with baseline disease activity, including several acylcarnitines and amino acids. We also found 17 baseline metabolites associated with remission at 24 weeks in the overall cohort, including malic acid (β=-0.4), cytidine (β = 0.4), arginine (β = 0.3), and citrulline (β = 0.2), as well as specific metabolites and metabolic pathways associated with remission in the four treatment arms. Fifteen features were identified using machine learning-based multivariable selection. The best predictive model using logistic regression achieved AUC of 0.75 in training and 0.73 in the test set.

Conclusions

Our study has identified several baseline metabolites and metabolic pathways associated with disease activity and response to different treatments in early RA. By integrating metabolomics and clinical data, we developed predictive models for response to treatment in early RA, though their predictive performance remains limited.