## 1 Introduction  
Psoriasis is an inflammatory skin disease that affects approximately 10 million Americans and 1 million Canadians [1]. The most common form of psoriasis is chronic plaque psoriasis, which clinically manifests as well-demarcated erythematosusoxyl plaques affecting the skin. Approximately a quarter of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA) [2], which is linked to reduced quality of life and limited functional capacity [3]. PsA presents clinically with peripheral joint involvement, enthesitis, tendonitis, dactylitis, inflammatory spinal disease, and other extra-musculoskeletal features [4]. Psoriatic disease patients often present with comorbidities such as cardiovascular disease, obesity, and hyperlipidemia. These conditions are more prevalent in PsA patients than in patients with psoriasis without PsA (PsC) [5]. Recently, there has been growing interest in exploring predictive biological markers (biomarkers) for psoriatic disease, with a specific focus on PsA [6]. Biomarker discovery may help elucidate unexplored mechanisms behind psoriaticatic disease pathogenesis and may help identify novel disease targets. Biomarker research in PsA is in the beginning stages of exploration and has focused mostly on single modality 'omic' studies [7]. There have been a few markers discovered that have been shown to be associated with PsA diagnosis via untargeted genomic, transcriptomic, and proteomic studies; however, the predictive value of these markers has not been sufficient to achieve the high threshold required for a diagnostic test [7]. Future biomarker research in psoriatic disease is transitioning in the direction of additional technologies such as metabolomics. Metabolomics is an emerging field of 'omics' sciences that systematically investigates the diverse array of small molecules (<1500 Da) present in a biological system, including but not limited to sugars, nucleotides, amino acids, organic and inorganic acids, xenobiotics, and lipids. The metabolome is a rapid indicator of biological status [8]. In contrast to the genome and transcriptome, where genetic changes take longer to take effect, the metabolome is dynamic and constantly changing. Thus, the metabolome provides a snapshot of an organism's physiological status at a specific point in time. Furthermore, the metabolome reflects not only the genome but also the environment and microbiome [9]. Metabolomics provides the opportunity to observe interactions between all these factors, which may play a role in the disease's pathophysiology. Therefore, the metabolome can be regarded as the best reflection of the organism's observable phenotype. Metabolomics in combination with other 'omics' scientific studies can be a powerful strategy for capturing the genetic, immunologic, and environmental factors that lead to PsA pathogenesis and may aid in biomarker discovery. A scoping review of the literature revealed that the vast majority of metabolomic studies published in psoriatic disease have identified amino acids and lipids that may be associated with psoriasis diagnosis and activity [6]. Very few studies have examined the metabolome of PsA patients; however, the few that have have reported keto acid and fatty acid dysregulation in PsA compared to PsC [6]. Furthermore, a previous preliminary untargeted metabolomics study revealed that serum levels of select lipids, indicative of dysregulation of fatty acid metabolism, may be associated with PsA activity [10]. As such, there is a definite need to identify and further validate biomarkers that can accurately and reliably predict disease diagnosis, disease activity, and disease conversion from PsC to PsA [6]. Thus, a new untargeted metabolomics approach geared towards the separation of fatty acids from PsA patients is needed. In order to develop and undergo an untargeted metabolomic study, an appropriate workflow must be selected [6].