## 1 Introduction  
Psoriasis is a chronic, immune-mediated, systemic inflammatory disease that affects over 2.5% of the global population, including 1.25 million Canadians [1, 2]. It commonly manifests as chronic plaque psoriasis, where red, itchy scalp plaques develop on the scalp, elbows, and other parts of the body [2]. Approximately 25% of psoriasis patients also carry the additional burden of inflammatory arthritis and have psoriatic arthritis (PsA) [3].  
Patients with PsA have cutaneous and musculoskeletal inflammation [2; 4]. The progressive joint damage, stiffness, pain, and disability associated with PsA is responsible for numerous other comorbidities, especially those associated with high risk for cardiovascular diseases [5; 6], including diabetes [7; 8], obesity [9; 10], andlipidemia [11; 12]. This leads to a reduced overall quality of life and early mortality for these patients. While their precise etiologies are unknown, psoriasis and psoriatic arthritis have complex pathogenesis involving environmental, immunologic, and genetic factors [13].  
Measuring and monitoring disease activity is critical to the management of PsA. Assessing PsA activity involves evaluating multiple domains including skin and musculoskeletal domains [14]. Skin lesions are usually assessed using the Psoriasis Area and Severity Index (PASI) [15]. Despite skin plaques being a prominent feature of psoriatic disease, current methods for evaluating skin disease activity, including PASI, body surface area affected, and physician global assessments, are largely subjective, imprecise and have high inter-rater variability [15; 16]. As such, there is a tremendous clinical need for identifying valid, objective, and reliable biomarkers for routine clinical assessment of PsA disease activity.  
Recently, there has been growing interest in performing mass spectrometry-based metabolomics studies to discover such biomarkers and further elucidate disease pathogenesis [17]. Metabolomic analysis involves studying small molecule metabolites (<1500 Da) present in biological cells, tissues, or fluids and includes compounds with diverse chemical and physical properties like sugars, nucleotides, amino acids, and lipids to name a few [17; 18]. Positioned at the intersection between the genome, transcriptome and proteome with environment, metabolomic analysis involves studying the interaction of real-life end products of multiple biological processes with environmental stimuli [17; 18]. It is a powerful technique for biomarker discovery in a complex disease such as PsA that results from the interaction between multiple biological and environmental factors.  
The purpose of this study was to perform comprehensive metabolomic profiling of serum samples from PsA patients with varying levels of psoriasis activity, as defined by the PASI, to identify metabolomic biomarkers associated with skin disease activity. To achieve this goal, we first used SPME to extract metabolites from 150 serum samples from PsA patients. This was followed by metabolomic data acquisition using a fatty-acid-focused high-performance liquid chromatography-mass spectrometry (HPLC-MS) methodology [19]. We subsequently identified tentative biomarkers for PsA skin disease activity via data analyses using multiple machine learning algorithms.