Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis

Abstract

Objectives: The differential diagnosis of seronegative rheumatoid arthritis (negRA) and psoriasis arthritis (PsA) is often difficult due to the similarity of symptoms and the unavailability of reliable clinical markers. Since chronic inflammation induces major changes in the serum metabolome and lipidome, we tested whether differences in serum metabolites and lipids could aid in improving the differential diagnosis of these diseases.

Methods: Sera from negRA and PsA patients with established diagnosis were collected to build a biomarker-discovery cohort and a blinded validation cohort. Samples were analysed by proton nuclear magnetic resonance. Metabolite concentrations were calculated from the spectra and used to select the variables to build a multivariate diagnostic model.

Results: Univariate analysis demonstrated differences in serological concentrations of amino acids: alanine, threonine, leucine, phenylalanine and valine; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1, L5/L1 and L6/L1, but yielded area under the curve (AUC) values lower than 70%, indicating poor specificity and sensitivity. A multivariate diagnostic model that included age, gender, the concentrations of alanine, succinate and creatine phosphate and the lipid ratios L2/L1, L5/L1 and L6/L1 improved the sensitivity and specificity of the diagnosis with an AUC of 84.5%. Using this biomarker model, 71% of patients from a blinded validation cohort were correctly classified.

Conclusions: PsA and negRA have distinct serum metabolomic and lipidomic signatures that can be used as biomarkers to discriminate between them. After validation in larger multiethnic cohorts this diagnostic model may become a valuable tool for a definite diagnosis of negRA or PsA patients.

Keywords: differential diagnosis; lipidomics; metabolomics; psoriatic arthritis; seronegative arthritis.

Figure 1

PsA and negRA patients have distinct spectral profiles that do not correlate with clinical and demographic covariates. Representative water-suppressed and baseline-corrected (A) ¹H single-pulse and (B) CPMG NMR spectra of blood serum from patients with PsA and negRA assigned with the regions and metabolites and lipid groups included in the untargeted and targeted analysis: (1) formate, (2) histidine, (3) phenylalanine, (4) tyrosine, (5) α-glucose, (6) proline, (7) lactate, (8) creatinine, (9) creatine, (10) creatine phosphate, (11) threonine, (12) choline, (13) sarcosine; (14) citrate, (15) glutamine, (16) succinate, (17) acetoacetate, (18) glutamate, (19) acetate, (20) alanine, (21) β-hydroxybutyrate, (22) valine, (23) isoleucine and (24) leucine. (L1) Lipid methyls, (L2) lipid aliphatic chain, (L3) lipid β-methylenes, (L4) lipid allylic methylenes, (L5) lipid α-methylenes, (L6) lipid polyunsaturated allylic methylenes and (L7) lipid alkenes. Fumarate (10 mM in 99.9% D₂O) was used as an internal standard. (C) Correlograms showing the Pearson correlation coefficients between the clinical or demographic variables and the ¹H spectral regions, and hierarchical clustering with Euclidean distance metric for the full discovery cohort, and the split PsA and negRA groups. negRA, seronegative rheumatoid arthritis; NMR, nuclear magnetic resonance; PsA, psoriasis arthritis.

Figure 2

Metabolomic profiles obtained from the ¹H and CPMG NMR spectra of serum samples from negRA and PsA patients in the disovery cohort after supervised PLS-DA analysis and random forest analysis. (A) Pairwise scores plots between the five principal components with the corresponding variances shown in the diagonal. (B) Significant features identified by random forest. The features are ranked by the mean decrease in classification accuracy when they are permuted. (C) Cumulative error rates by random forest classification. The overall error rate is shown as the red line; the blue and green lines represent the error rates for each disease. negRA, seronegative rheumatoid arthritis; NMR, nuclear magnetic resonance; PLS-DA, partial least squares discriminant analysis; PsA, psoriasis arthritis.

Figure 3

The concentrations of several metabolites and lipid groups allow the distinction between negRA and PsA patients. (A) Dot plots of the metabolites and lipid ratios included in the targeted analysis and that present significant differences between the two patient groups in the discovery cohort. Lines indicate the mean and 95% CI. (B) Summary bar graph for quantitative enrichment analysis showing the changes between negRA and PsA metabolomes in the discovery cohort. (C) Correlograms showing the Pearson correlation coefficients between the clinical or demographic variables and the metabolites, and hierarchical clustering with Euclidean distance metric for the full discovery cohort, and the split PsA and negRA groups. (D) ROC curve for the modelled probability $p_{PsA}$ based on the cross-validation in the discovery cohort. (E) Summary bar graph for quantitative enrichment analysis showing the changes between negRA and PsA metabolomes in the blinded validation cohort. (F) ROC curve for the modelled probability $p_{PsA}$ based on the blinded validation cohort. (G) ROC curve for the modelled probability $p_{PsA}$ based on the reassessed validation cohort. negRA, seronegative rheumatoid arthritis; PsA, psoriasis arthritis; ROC, receiver operating characteristic.