In this Q&A, Vinod Chandran, MBBS, MD, DM, PhD, discussed a method to analyze biomarkers that may differentiate patients with psoriasis and psoriatic arthritis.
A method using solid-phase microextraction–liquid chromatography-mass spectrometry (SPME-LC-MS) was able to detect fatty acids and similar lipids that may help differentiate patients with psoriasis and psoriatic arthritis(PsA). This is according to a small study published in Metabolites.1
In this Q&A, corresponding author Vinod Chandran, MBBS, MD, DM, PhD, associate professor at the University of Toronto and director of the psoriatic arthritis program at the Schroeder Arthritis Institute in Toronto, discussed the research and its findings.
The researchers aimed to develop an LC-MS method to be used in combination with SPME to analyze fatty acids and similar molecules. They tested 25 chromatographic methods based on published lipid studies on two LC columns. As a proof of concept, serum samples from 27 patients with psoriasis (51% female, mean age 43 years), 26 patients with PsA (46% female, mean age 47 years), and 25 health controls (40% female, mean age 48 years) were processed using SPME and run on LC-MS. The best method for analyzing fatty acids and fatty acid-like molecules identified 18 compounds that were statistically significant between patients with psoriasis and those with PsA.
Why was the study conducted?
Approximately a quarter of patients with psoriasis have PsA. There is strong interest in discovering predictive biomarkers for patients with PsA among those with psoriasis, to shorten the time to diagnosis and management.
Metabolomics provides a powerful platform for identifying biomarkers for a complex, multifactorial disease such as PsA. Previous studies have reported examples of lipid dysregulation in serum taken from patients with PsA. Additionally, the high prevalence of metabolic syndrome and cardiovascular events in these patients makes lipids of key interest in psoriatic disease.
We sought to develop a metabolomics method using SPME-LC-MS to analyze serum lipids and identify any significant differences between patients with PsA and those with psoriasis.
What were the surprises from the findings?
The method we developed and optimized was capable of detecting lipids. Several classes of lipids such as acylcarnitines, lysophospholipids, and sphingolipids were significantly different between the patients with psoriasis and those with PsA. Specific focus was placed on valerylcarnitine, due to its confirmed identity using tandem mass spectrometry.
How significant are the findings?
This is preliminary data that provides a good direction for future research in lipid biomarker discovery in psoriatic disease. We have developed and optimized a method that can be used on additional samples in a more expansive follow-up study.
What is the current practice and how could the findings possibly change things?
Currently, PsA is diagnosed in patients with evidence of an inflammatory articular disease, who meet certain clinical, laboratory, and imaging criteria. Patients with psoriasis are often screened at appointments with a dermatologist or general practitioner for signs of inflammatory arthritis.
Discovering accurate and reliable biomarkers for PsA could improve the ability of clinicians external to the field of rheumatology to identify patients with PsA. This may lead to prompt referral to a rheumatologist and early diagnosis and management of PsA by a rheumatologist.
What are the takeaway points for clinicians?
Emerging ‘omics’ sciences such as metabolomics have the potential to identify biomarkers that may aid in improving the diagnosis of PsA. Serum lipids such as valerylcarnitine have been found to be dysregulated in PsA. However, further research is needed to develop a biomarker panel to aid in clinical decision-making.
Koussiouris J, Looby N, Kulasingam V, Chandran V. A Solid-Phase Microextraction-Liquid Chromatography-Mass Spectrometry Method for Analyzing Serum Lipids in Psoriatic Disease. Metabolites. 2023;13(8):963. Published 2023 Aug 20. doi:10.3390/metabo13080963