Genetic Score in ZMIZ1/TGF‑β/STAT Pathway Forecasts Biologic Drug Persistence in Psoriasis

In a newly published study in Advances in Therapy, investigators report that a pathway‑based genetic score may predict how long patients with moderate‑to‑severe psoriasis remain on biologic therapy before discontinuation. 

The research team retrospectively reviewed 875 biologic treatment episodes across 312 patients. They developed a “functional genetic score” derived from seven single nucleotide polymorphisms (SNPs) within genes of the ZMIZ1 / TGF‑β / STAT axis—a signaling network implicated in immunometabolic regulation. Patients were dichotomized by whether their genetic score was above or below the median, and the primary outcome was time to biologic discontinuation. 

Analyses revealed that patients harboring a higher genetic score had notably longer biologic drug survival. After adjusting for demographic, clinical, and inflammatory covariates, the hazard ratio (HR) for discontinuation in the high‑score group was 0.74 (95% CI 0.62–0.89, p = 0.0015), indicating roughly a 26 % reduced risk of early stopping compared to the low‑score group. Interestingly, this association held despite those high‑score patients presenting with higher baseline levels of inflammatory markers (TNF‑α, IL‑1β, IL‑15) and leptin.

Stratified by biologic class, the predictive power of the score was strongest for anti‑IL12/23 therapies (HR = 0.44, 95% CI 0.26–0.75, p = 0.002) and remained significant, though weaker, for anti‑TNF agents (HR = 0.79, 95% CI 0.62–0.99, p = 0.045). In contrast, for anti‑IL17/IL23 biologics, no significant association was observed after multivariable adjustment. Notably, individual circulating biomarkers on their own did not independently predict biologic persistence.

The authors interpret these findings to suggest that inherited genetic variation in immunometabolic pathways may shape both baseline inflammatory state and drug retention in psoriasis. They propose that integrating such a genetic score into clinical decision support tools could help personalize biologic therapy selection and forecast long‑term treatment durability. 

Nonetheless, the authors acknowledge key limitations: the retrospective design, the need for external validation, and the absence of publicly available data, though they state willingness to share with qualified researchers under methodologically justified protocols. Their work signals a step toward leveraging pharmacogenetic signatures to improve biologic therapy management in psoriasis.