Serum Metabolome Identifies Cancer in Rheumatic Disease


A diagnostic model based on the concentrations of four metabolites and one lipid ratio can reliably predict cancer in patients with rheumatic and musculoskeletal diseases (RMDs) or paraneoplasia, providing high sensitivity and specificity.


  • The metabolome profile can differentiate between nonspecific inflammatory symptoms such as those associated with paraneoplastic conditions or RMDs, which can help accelerate cancer diagnosis and treatment.
  • To assess if changes in the serum metabolome profile could indicate cancer in patients with RMD, researchers performed nuclear magnetic resonance analysis of the sera of patients with rheumatoid arthritis (RA) with a history of invasive cancer (n = 56; age, 69.9 years; 76.8% women) or without such history (n = 52; age, 56.1 years; 57.7% women).
  • Blinded validation was conducted in a cohort of patients with RA or spondyloarthritis with or without a history of invasive cancer.
  • Additionally, the model performance was tested in a cohort of patients having RA or spondyloarthritis with active cancer or cancer treatment, pulmonary and lymphoid type cancers, paraneoplastic syndromes, and facultative solid noninvasive precancerous lesions and nonmelanoma skin cancer; in samples prior to the development of malignancy; and in a cohort of patients with systemic lupus erythematosus (SLE).
  • The final model comprised five variables. The goodness of fit of the model was described using the area under the receiver operating characteristic curve (AUC).


  • Based on the concentrations of acetate, creatine, glycine, and formate and the L1/L6 lipid ratio, the diagnostic model yielded an excellent AUC (0.987) and high sensitivity (0.932) and specificity (0.946) for cancer diagnosis in patients with RA.
  • The diagnostic model yielded an AUC of 0.937 in the blinded validation cohort of patients with RA and an AUC of 0.927 in the merged RA and spondyloarthritis cohort.
  • Although the diagnostic model accurately diagnosed cancer in all the patients with paraneoplasia, it could do so accurately in only 50% of patients with noninvasive or in situ precancerous lesions and nonmelanoma skin cancers.
  • The performance of the model was poor in the SLE cohort (AUC, 0.656), and it could not identify patients at risk for later invasive cancer development.


“This limited-invasive assay has considerable potential of high clinical value to facilitate timely diagnosis of cancer in paraneoplastic rheumatic syndromes as well as become a valuable active surveillance tool in RA and SpA [spondyloarthritis] patients with a high risk of developing cancer,” the authors wrote.


The study, led by Karolina Gente, MHBA, Heidelberg University Hospital, Heidelberg, Germany, was published online on April 1, 2024, in Annals of the Rheumatic Diseases.


The limited invasiveness during sampling might account for the model’s inability to identify three early-stage, low-grade tumors and its nonreliability in identifying noninvasive facultative precancerous lesions and nonmelanoma skin cancers. Given its poor performance in the SLE cohort, the model may not be suitable for universal application in more systemic rheumatic diseases.


This study was supported by an unrestricted investigator-initiated grant from the Foundation Commission of the Medical Faculty, University of Heidelberg, Heidelberg, Germany. The authors declared no conflicts of interest.

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