Study population
Psoriasis patients who were referred to rheumatology for suspected PsA were prospectively recruited in a single medical academic centre in Toronto, Canada. All patients had dermatologist conformed diagnosis of psoriasis and were experiencing musculoskeletal symptoms. None of the patients had a prior diagnosis of PsA. The patients were referred from dermatology and family medicine clinics as well as from a phototherapy centre that serves as a tertiary referral center for dermatologists from the Greater Toronto Area. The control population comprised of osteoarthritis patients with no psoriasis, inflammatory arthritis or other autoimmune diseases. The study was conducted from January 2016 to December 2018.
Case definition
All participating patients and controls underwent a clinical assessment by a rheumatologist. Patients were classified as having PsA if they met the CASPAR classification criteria for PsA. Patients who were classified as not having PsA at baseline were reassessed after 1 year to determine whether they have developed PsA since the baseline visit. The rheumatologist was blinded to the genetic testing results. In addition, clinical information about the patients’ demographics, psoriasis characteristics, duration and severity, family history of PsA, comorbidities, and patient reported outcomes were collected at the baseline visit.
Genetic Testing
A custom multi-SNP genetic assay was designed using the program Assay Design Suite and Typer IV (Agena Biosciences) and genotyped on a MassARRAY system (Agena Biosciences). The custom PsA panel is comprised of 41 variants in or near 19 genes based on genome-wide significance or significant associations in multiple candidate gene studies in psoriasis or PsA studies (including PsA specific variants) (22). Polymorphisms within the following genetic loci were included in the panel: HLA-B, HLA-C, LCE3A, IL-23A, IL-23R, IL-12B, REL, TNIP1, IL-13, PTPN22, TRAF3IP2, TYK2, TNFRSF9, 5q31, KIR2DS2, TNFAIP3, FBXL19, ZNF816A, MICA.
Statistical analysis
The accuracy of genetic testing for differentiating between PsA and non-PsA patients was assessed twice for each of the following outcomes: 1) diagnosis of PsA at baseline; and 2) diagnosis of PsA at 1 year. In addition, we assessed the ability of the genetic testing to distinguish between PsD and non-psoriatic controls.
First, we tested the association between each genetic marker individually and patient diagnosis using Chi Square test and reported the odds ratio (OR) and its 95% Confidence Intervals (CI). Results were reported for significance levels of p < 0.05 and after correcting for multiple testing (p < 0.001 for 41 individual tests).
We then used the entire information from the genetic assay to develop machine-learning classifiers to predict PsA and PsD diagnosis including logistic regression, naïve bayes and random forest. Age and sex were included in each of the models in addition to genetic data. The performance of the resulting classification models at distinguishing between PsA and non-PsA patients and PsD and non-psoriatic controls, was evaluated by calculating their sensitivity, specificity, precision (proportion of subtype that was accurate) and area under the receiver operator curve (AUC). We then selected a number of clinical and demographic variables to assess whether their combination with genetic data could improve the model performance. We selected clinical variables that were reported to be associated with PsA compared to psoriasis alone. The following variables were selected for testing: age, sex, race (Caucasian vs. non-Caucasian), psoriasis duration, a history of uveitis, nail psoriasis, flexural psoriasis, psoriasis area and severity index (PASI), body mass index (BMI), patient pain (visual analogue scale of 0 to10), health assessment questionnaire disability index (HAQ-DI), Functional Assessment of Chronic Illness Therapy – Fatigue (FACIT) and high sensitivity C-Reactive Protein (hsCRP) and family history of PsA.
We used RWeka package in R for the machine learning and statistical analysis.