Frontiers in immunology · 2025
Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosis.
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Wei Siyu, Yue Zehong, Sun Chen, Zou Yuping, Chen Haiyan, Tao Junxian, Xu Jing, Xu Yuan, Wang Ning, Guo Yan, Ren Qinduo, Wang Chang, Lu Songlin, Ma Ye, Dong Yu, Zhang Chen, Sun Hongmei, Tang Guoping, Kong Fanwu, Lv Wenhua, Shang Zhenwei, Zhang Mingming, Jiang Yongshuai, Lyu Hongchao
Аннотация
INTRODUCTION: Psoriasis is a chronic immune-mediated inflammatory skin disease with a significant global burden. Current risk assessment lacks integration of proteomic data with genetic and clinical factors. This study aimed to develop a plasma proteomics-based risk score (ProtRS) to improve psoriasis prediction.
METHODS: Using data from 53,065 UK Biobank (UKB) participants (1,122 psoriasis cases; 51,943 controls), we integrated 2,923 plasma proteins, polygenic risk score (PRS), and seven clinical risk factors. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm with 10-fold cross-validation identified stable proteins for ProtRS construction. Population Attributable Fractions (PAFs) for risk factors were calculated.
RESULTS: LASSO regression identified 26 highly stable proteins forming ProtRS-26. ProtRS-26 significantly outperformed PRS and clinical risk factors alone. Combining ProtRS-26 with PRS and clinical factors further improved prediction. Key proteins were enriched in pro-inflammatory pathways and skin-derived. PAF analysis identified hypertension and obesity as major modifiable risk factors.
DISCUSSION: Plasma proteomics significantly enhances psoriasis risk prediction compared to genetic and clinical factors alone. ProtRS-26 provides a robust tool for early screening and personalized prevention.
METHODS: Using data from 53,065 UK Biobank (UKB) participants (1,122 psoriasis cases; 51,943 controls), we integrated 2,923 plasma proteins, polygenic risk score (PRS), and seven clinical risk factors. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm with 10-fold cross-validation identified stable proteins for ProtRS construction. Population Attributable Fractions (PAFs) for risk factors were calculated.
RESULTS: LASSO regression identified 26 highly stable proteins forming ProtRS-26. ProtRS-26 significantly outperformed PRS and clinical risk factors alone. Combining ProtRS-26 with PRS and clinical factors further improved prediction. Key proteins were enriched in pro-inflammatory pathways and skin-derived. PAF analysis identified hypertension and obesity as major modifiable risk factors.
DISCUSSION: Plasma proteomics significantly enhances psoriasis risk prediction compared to genetic and clinical factors alone. ProtRS-26 provides a robust tool for early screening and personalized prevention.