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September 3, 2026BMC ImmunologyOpen Access

A multiomics-clinical model to predict treatment response in psoriasis

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Authors

AZAiyan ZhouWTWenfeng Tian

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Overview

Retrospective study demonstrates an integrated multiomics-clinical model accurately predicts treatment response in plaque psoriasis, suggesting potential to guide personalized biologic therapy.

Key Points

  • Develop and validate a multiomics-clinical predictive model for achieving a 75% reduction in Psoriasis Area and Severity Index (PASI 75) in patients with psoriasis.
  • Analyzed retrospective data from 342 patients with moderate-to-severe plaque psoriasis, split into training (n=240) and validation (n=102) sets.
  • Screened demographic, clinical, and multiomics variables using univariate selection and LASSO regression to build a multivariable logistic regression model evaluated by AUC, calibration, and decision curves.
  • The six-variable model achieved an AUC of 0.806 (95% CI: 0.683–0.919) in the validation set with good calibration (Hosmer-Lemeshow P > 0.05).
  • The HLA-Cw6 genotype (OR = 3.165) and serum IL-17 A (OR = 1.015) were identified as the strongest independent predictors of response.

Cite This Study

Zhou et al. (2026) studied this question.

synapsesocial.com/papers/6a993600636c6408cfa7eb4ehttps://doi.org/10.1186/s12865-026-00901-0
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