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. 2024 May 3:15:1410365.
doi: 10.3389/fimmu.2024.1410365. eCollection 2024.

Serum and urine lipidomic profiles identify biomarkers diagnostic for seropositive and seronegative rheumatoid arthritis

Rong Li #  1   2 , Jung Hee Koh #  3   4 , Woo Jung Park  2 , Yongsoo Choi  1   5 , Wan-Uk Kim  3   4
Affiliations

Serum and urine lipidomic profiles identify biomarkers diagnostic for seropositive and seronegative rheumatoid arthritis

Rong Li et al. Front Immunol. .

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.

Keywords: diagnosis; lipids; rheumatoid arthritis; serum; urine.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Clinical design and research flow of the comprehensive lipidome study in rheumatoid arthritis patients.
Figure 2
Figure 2
Comprehensive serum and urine lipidome profiles, and screening of differentially-expressed lipids between patients with rheumatoid arthritis (RA), healthy controls (HC), and patients with hand osteoarthritis (OA). (A) Representative LC-MS chromatograms showing the normalized intensity of lipids identified in serum and (B) urine lipid extracts from the RA, OA, and HC groups. (C) Distribution of lipid species in serum and urine samples. A total of 311 annotated serum and 61 urine lipids was categorized into 17 subclasses. (D) OPLS-DA 2D score plots based on the serum lipidome profiles between the RA and HC groups, and (E) between the RA and OA groups. (F) OPLS-DA 2D score plots based on the urine lipidome profiles between the RA and HC groups, and (G) between the RA and OA groups. The Q2 values indicate predictive ability derived from random permutation tests (n=1000). (H) Heatmap of normalized peak intensity shows significant differential expression of serum and (I) urine lipids in RA samples compared with OA and HC samples.
Figure 3
Figure 3
Diagnostic performance of serum lipid biomarker candidates based on ROC analysis. (A) Top ten serum lipids predicted by a random forest multivariate algorithm to distinguish between the RA and OA groups. Random forest analysis was performed using the 26 differentially-expressed serum lipids, including SM t39:0, LPC 22:0, SM d44:7, and PC O-38.7, listed in Table 1 . (B) ROC analysis to identify differences between RA and HC, and (C) between RA and OA, in the discovery cohort. (D) ROC analysis to identify differences between seropositive RA (SPRA) and OA, (E) between SPRA and HC, (F) between seronegative RA (SNRA) and OA, and (G) between SNRA and HC in the validation cohort. (H) ROC analysis to identify differences between SPRA and SLE, and (I) between SNRA and SLE in the validation cohort.
Figure 4
Figure 4
Diagnostic performance of urine biomarker candidates based on ROC analysis. The three differentially-expressed urine lipids CAR 12:0, CAR 14:2, and SM d42:2 listed in Table 1 were used for ROC analysis of group classifications. (A) ROC analysis to identify differences between RA and HC in the discovery cohort, (B) between seropositive RA (SPRA) and HC, and (C) between seronegative RA (SNRA) and HC in the validation cohort. (D) ROC analysis to identify differences between RA and OA in the validation cohort, (E) between seronegative RA (SNRA) and HC and (F) between SNRA and OA in the validation cohort.
Figure 5
Figure 5
Lipids correlated with RA disease activity. (A) Nine representative lipids reflecting disease activity, and (B) model evaluation of RA groups by ROC analysis of moderate-to high disease activity (MDA/HDA) versus low disease activity or remission (LDA/REM).
Figure 6
Figure 6
Lipid ontology enrichment analysis based on differential expression of serum lipids separating seropositive and seronegative RA. A volcano plot identified 12 and six lipids differentially expressed between seropositive and seronegative RA in serum (A) and urine (B), respectively. Expression of five serum lipid-related pathways, shown as a heatmap (C); data were obtained from lipid ontology enrichment analysis of all 12 serum lipids identified in the seropositive and seronegative RA groups.
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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (NRF-2015R1A3A2032927 to WU-K), by the Korea Institute of Science and Technology (2Z06821), and the Catholic Medical Center Research Foundation (made in the program year of 2023, to JK). The study sponsors played no role in study design, data collection, analysis, or interpretation, or in the writing of the manuscript or the decision to submit the manuscript for publication.
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