ArticleFrontiers in immunology2024
Serum and urine lipidomic profiles identify biomarkers diagnostic for seropositive and seronegative rheumatoid arthritis.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Remnant cholesterol inflammation index (RCII) as a potential biomarker for ACPA-negative rheumatoid arthritis: evidence from dual-cohort analysis and serum lipidomics.Clinical rheumatology · 2026Article
- Discovery of biomarkers for primary Sj ögren's syndrome based on multi-omics data, construction of diagnostic models, and clinical correlation analysis.Arthritis research & therapy · 2026Article
- Comorbidity risk characteristics of rheumatoid arthritis in the context of depression-associated lipid metabolism.Frontiers in immunology · 2026Article
- Identifying AIM2 Circulating Methylation Levels as a Novel Diagnostic Biomarker for Rheumatoid Arthritis Using Targeted DNA Methylation Sequencing.Endocrine, metabolic & immune disorders drug targets · 2026Article
- Progress in multi-omics studies of osteoarthritis.Biomarker research · 2025Review
- Phospholipids and Sphingolipids in Osteoarthritis.Biomolecules · 2025Review
- Lysophosphatidylcholine as a Novel Diagnostic Biomarker in Kawasaki Disease: Based on Immunometabolism-Related Signature.Journal of inflammation research · 2025Article
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5 authors.
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Abstract
Objective: Seronegative rheumatoid arthritis (RA) is defined as RA without circulating autoantibodies such as rheumatoid factor and anti-citrullinated protein antibodies; thus, early diagnosis of seronegative RA can be challenging. Here, we aimed to identify diagnostic biomarkers for seronegative RA by performing lipidomic analyses of sera and urine samples from patients with RA. Methods: We performed untargeted lipidomic analysis of sera and urine samples from 111 RA patients, 45 osteoarthritis (OA) patients, and 25 healthy controls (HC). These samples were divided into a discovery cohort (n = 97) and a validation cohort (n = 84). Serum samples from 20 patients with systemic lupus erythematosus (SLE) were also used for validation. Results: The serum lipidome profile of RA was distinguishable from that of OA and HC. We identified a panel of ten serum lipids and three urine lipids in the discovery cohort that showed the most significant differences. These were deemed potential lipid biomarker candidates for RA. The serum lipid panel was tested using a validation cohort; the results revealed an accuracy of 79%, a sensitivity of 71%, and a specificity of 86%. Both seropositive and seronegative RA patients were differentiated from patients with OA, SLE, and HC. Three urinary lipids showing differential expression between RA from HC were identified with an accuracy of 84%, but they failed to differentiate RA from OA. There were five lipid pathways that differed between seronegative and seropositive RA. Conclusion: Here, we identified a panel of ten serum lipids as potential biomarkers that can differentiate RA from OA and SLE, regardless of seropositivity. In addition, three urinary lipids had diagnostic utility for differentiating RA from HC.
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