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.

Autoantibodies in psoriatic disease.

Psoriasis is an inflammatory skin disease affecting over 8 million people in the US and Canada. Approximately, a quarter of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA). Psoriatic disease encompassing both psoriasis and PsA is regarded as an immune-mediated inflammatory disease, exhibiting both autoimmune and autoinflammatory features. A review of the current literature on the presence and clinical significance of autoantibodies found in psoriatic disease are presented. The frequency of several autoantibodies in psoriasis and PsA patients as well as their clinical significance regarding disease diagnosis, disease activity and treatment response are reviewed. Additionally, the basic principles of antibody assays are presented, and the methods used for each study are analyzed. Despite historically described as a rheumatoid factor negative (seronegative) disease, an array of autoantibodies has been identified in patients with psoriatic disease. This points to an autoimmune component potentially playing a role in psoriatic disease; however, additional evidence is needed to determine the clinical utility of these autoantibodies.

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.

Autoantibodies in Psoriatic Disease.

BACKGROUND: Psoriasis (Ps) is an inflammatory skin disease affecting over 8 million people in the USA and Canada. Approximately a quarter of patients with Ps have an inflammatory arthritis termed psoriatic arthritis (PsA). Psoriatic disease encompassing both Ps and PsA is regarded as an immune-mediated inflammatory disease, exhibiting both autoimmune and autoinflammatory features. Innate immune cell activation promotes inflammation and the cellular infiltrate in inflamed tissue is predominantly lymphocytic. CONTENT: A narrative review of the current literature on the presence and clinical significance of autoantibodies found in psoriatic disease are presented. The frequency of several autoantibodies in Ps and PsA patients as well as their association with disease diagnosis, disease activity, and treatment response are reviewed. SUMMARY: Despite historically described as a rheumatoid factor negative (seronegative) disease, an array of autoantibodies has been identified in patients with psoriatic disease. Many of the autoantibodies reviewed are elevated in Ps and PsA patients and are associated with disease activity, treatment response, and cardiovascular disease risk. The identification of autoantibodies in Ps and PsA patients points to an autoimmune component potentially playing a role in psoriatic disease; however, additional evidence is needed to determine the clinical utility of these autoantibodies and their contribution to disease pathogenesis.

Metabolomics Studies in Psoriatic Disease: A Review.

Metabolomics investigates a broad range of small molecules, allowing researchers to understand disease-related changes downstream of the genome and proteome in response to external environmental stimuli. It is an emerging technology that holds promise in identifying biomarkers and informing the practice of precision medicine. In this review, we summarize the studies that have examined endogenous metabolites in patients with psoriasis and/or psoriatic arthritis using nuclear magnetic resonance (NMR) or mass spectrometry (MS) and were published through 26 January 2021. A standardized protocol was used for extracting data from full-text articles identified by searching OVID Medline ALL, OVID Embase, OVID Cochrane Central Register of Controlled Trials and BIOSIS Citation Index in Web of Science. Thirty-two studies were identified, investigating various sample matrices and employing a wide variety of methods for each step of the metabolomics workflow. The vast majority of studies identified metabolites, mostly amino acids and lipids that may be associated with psoriasis diagnosis and activity. Further exploration is needed to identify and validate metabolomic biomarkers that can accurately and reliably predict which psoriasis patients will develop psoriatic arthritis, differentiate psoriatic arthritis patients from patients with other inflammatory arthritides and measure psoriatic arthritis activity.