Pathway to biomarker discovery in psoriatic arthritis

Classifying patients with psoriatic arthritis according to their disease activity status using serum metabolites and machine learning

INTRODUCTION

Psoriatic arthritis (PsA) is a heterogeneous inflammatory arthritis, affecting approximately a quarter of patients with psoriasis. Accurate assessment of disease activity is difficult. There are currently no clinically validated biomarkers to stratify PsA patients based on their disease activity, which is important for improving clinical management.

OBJECTIVES

To identify metabolites capable of classifying patients with PsA according to their disease activity.

METHODS

An in-house solid-phase microextraction (SPME)-liquid chromatography-high resolution mass spectrometry (LC-HRMS) method for lipid analysis was used to analyze serum samples obtained from patients classified as having low (n = 134), moderate (n = 134) or high (n = 104) disease activity, based on psoriatic arthritis disease activity scores (PASDAS). Metabolite data were analyzed using eight machine learning methods to predict disease activity levels. Top performing methods were selected based on area under the curve (AUC) and significance.

RESULTS

The best model for predicting high disease activity from low disease activity achieved AUC 0.818. The best model for predicting high disease activity from moderate disease activity achieved AUC 0.74. The best model for classifying low disease activity from moderate and high disease activity achieved AUC 0.765. Compounds confirmed by MS/MS validation included metabolites from diverse compound classes such as sphingolipids, phosphatidylcholines and carboxylic acids.

CONCLUSION

Several lipids and other metabolites when combined in classifying models predict high disease activity from both low and moderate disease activity. Lipids of key interest included lysophosphatidylcholine and sphingomyelin. Quantitative MS assays based on selected reaction monitoring, are required to quantify the candidate biomarkers identified.

A Solid-Phase Microextraction-Liquid Chromatography-Mass Spectrometry Method for Analyzing Serum Lipids in Psoriatic Disease

Approximately 25% of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA). There is strong interest in identifying and validating biomarkers that can accurately and reliably predict conversion from psoriasis to PsA using novel technologies such as metabolomics. Lipids, in particular, are of key interest in psoriatic disease. We sought to develop a liquid chromatography-mass spectrometry (LC-MS) method to be used in conjunction with solid-phase microextraction (SPME) for analyzing fatty acids and similar molecules. A total of 25 chromatographic methods based on published lipid studies were tested on two LC columns. As a proof of concept, serum samples from psoriatic disease patients (n = 27 psoriasis and n = 26 PsA) were processed using SPME and run on the selected LC-MS method. The method that was best for analyzing fatty acids and fatty acid-like molecules was optimized and applied to serum samples. A total of 18 tentatively annotated features classified as fatty acids and other lipid compounds were statistically significant between psoriasis and PsA groups using both multivariate and univariate approaches. The SPME-LC-MS method developed and optimized was capable of detecting fatty acids and similar lipids that may aid in differentiating psoriasis and PsA patients.

Mendelian Randomization Studies in Psoriasis and Psoriatic Arthritis: A Systematic Review

Psoriasis (PSO) and psoriatic arthritis (PSA) are inflammatory diseases with complex genetic and environmental contributions. Although studies have identified environmental and clinical associations with PSO/PSA, causality is difficult to establish. Mendelian randomization (MR) employs the random assortment of genetic alleles at birth to evaluate the causal impact of exposures. We systematically reviewed 27 MR studies in PSO/PSA examining health behaviors, comorbidities, and biomarkers. Exposures, including smoking, obesity, cardiovascular disease, and Crohn's disease, were causal for PSO and PSA, whereas PSO was causally associated with several comorbidities. These findings provide insights that can guide preventive counseling and precision medicine.

Psoriasis to Psoriatic Arthritis: The Application of Proteomics Technologies

Psoriatic disease (PsD) is a spectrum of diseases that affect both skin [cutaneous psoriasis (PsC)] and musculoskeletal features [psoriatic arthritis (PsA)]. A considerable number of patients with PsC have asymptomatic synovio-entheseal inflammations, and approximately one-third of those eventually progress to PsA with an enigmatic mechanism. Published studies have shown that early interventions to the very early-stage PsA would effectively prevent substantial bone destructions or deformities, suggesting an unmet goal for exploring early PsA biomarkers. The emergence of proteomics technologies brings a complete view of all involved proteins in PsA transitions, offers a unique chance to map all potential peptides, and allows a direct head-to-head comparison of interaction pathways in PsC and PsA. This review summarized the latest development of proteomics technologies, highlighted its application in PsA biomarker discovery, and discussed the possible clinical detectable PsA risk factors in patients with PsC.

Soluble biomarkers for diagnosis, monitoring, and therapeutic response assessment in psoriasis

BACKGROUND

Psoriasis is an inflammatory disease associated with multiple comorbidities. Biomarkers for the assessment of psoriasis, its associated comorbidities, and the therapeutic response are not well characterized. A number of possible biomarkers for the diagnosis and monitoring of psoriasis have been proposed.

PURPOSE

To assess potential biomarkers for diagnosis of psoriasis, its associated comorbidities and response to treatment.

METHODS

We investigated medical databases from 2000 to 2021 and assessed relevant research. In this review, we evaluated the important biomarkers to help predict potential risk of psoriasis and disease activity (Beta-defensin-2, VEGF, Lipocalin-2, and YKL-40) and its possible inflammatory-related comorbidities like cardiovascular diseases (hs-CRP, GlycA, Psoriasin, IL-18, NT-proBNP, and Adipokines). In addition, we described the potential biomarkers for psoriatic arthritis (CXCL10, S100A8 and S100A9, and MicroRNA) and related manifestations such as enthesitis. Finally, we discussed novel markers for monitoring the response to specific treatments (HLA-C 06, PLC, TARC, NLR, and PLR) as well as potentially useful biomarkers for evaluation of therapy-associated adverse events (liver fibrosis-related markers).

CONCLUSION

A wide range of genetic, tissue and serum markers have been investigated in psoriasis; however, most of them are not used in routine clinical practice; and thorough physical examination along with the appropriate application of clinical scoring systems like Psoriasis Area and Severity Index (PASI) score are still of particular importance.