Ganatra Darshini, Kotlyar Max, Dohey Amanda, Codner Dianne, Li Quan, Abji Fatima, Rasti Mozhgan, Eder Lihi, Gladman Dafna, Rahman Proton, Jurisica Igor, Chandran Vinod
Gladman Krembil Psoriatic Arthritis Program, Schroeder Arthritis Institute, Krembil Research Institute, Toronto, ON, Canada.
Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, Krembil Research Institute, Toronto, ON, Canada.
J Psoriasis Psoriatic Arthritis. 2025 May 19:24755303251344134. doi: 10.1177/24755303251344134.
Psoriatic Arthritis (PsA), an immune mediated inflammatory arthritis, affects a quarter of patients with cutaneous psoriasis, usually after psoriasis onset. Early diagnosis of PsA is challenging. A biomarker-based diagnostic test may facilitate early diagnosis.
We aimed to determine whether specific clinical features or genetic and protein markers, alone or in combination, can distinguish patients with PsA from those with psoriasis without PsA (PsC).
Patients with PsA and PsC were identified from a database of patients with psoriatic disease. Detailed demographic and clinical information were collected at time of assessment. Single-nucleotide polymorphisms (SNPs) of 19 "PsA weighted" genes were genotyped. Serum samples were used to assess 15 protein markers by ELISA. Association between clinical, genetic and protein markers and PsA were determined, and models were developed to discriminate PsA from PsC using machine learning algorithms.
Demographic and clinical information had low predictive value in distinguishing PsA from PsC (AUC - 0.607, < .01). SNP and protein panels also had low value in discriminating PsA from PsC (AUC - 0.691, < .001 and AUC - 0.694, < .001, respectively). Combining protein, SNPs and clinical features provided better discriminatory value (best performing model: Random Forest, AUC - 0.733, < .001).
Combining previously identified clinical, genetic and protein markers have a fair ability to differentiate PsA from PsC. Further studies are required for identifying better diagnostic signatures.
银屑病关节炎（PsA）是一种免疫介导的炎症性关节炎，通常在银屑病发病后影响四分之一的皮肤银屑病患者。PsA的早期诊断具有挑战性。基于生物标志物的诊断测试可能有助于早期诊断。
我们旨在确定特定的临床特征或遗传及蛋白质标志物单独或联合使用时，是否能够将PsA患者与无PsA的银屑病患者（PsC）区分开来。
从银屑病疾病患者数据库中识别出PsA和PsC患者。在评估时收集详细的人口统计学和临床信息。对19个“PsA加权”基因的单核苷酸多态性（SNP）进行基因分型。使用酶联免疫吸附测定（ELISA）法检测血清样本中的15种蛋白质标志物。确定临床、遗传和蛋白质标志物与PsA之间的关联，并使用机器学习算法建立区分PsA和PsC的模型。
人口统计学和临床信息在区分PsA和PsC方面预测价值较低（曲线下面积[AUC] - 0.607，P <.01）。SNP和蛋白质组在区分PsA和PsC方面价值也较低（AUC分别为 - 0.691，P <.001和AUC - 0.694，P <.001）。结合蛋白质、SNP和临床特征具有更好的区分价值（表现最佳的模型：随机森林，AUC - 0.733，P <.001）。
结合先前确定的临床、遗传和蛋白质标志物有一定能力区分PsA和PsC。需要进一步研究以确定更好的诊断特征。
