Background: Psoriasis is a common chronic immune-mediated disease with substantial systemic and public health burden, yet early molecular signatures associated with its onset remain insufficiently characterised. Although both circulating proteins and genetic susceptibility may contribute to psoriasis development, their prospective and joint associations with incident psoriasis remain unclear. We aimed to evaluate the individual and combined effects of proteomic and polygenic profiling on psoriasis risk. Methods: We identified psoriasis-related protein signature using plasma proteomic data (2919 proteins) from 39 377 individuals in the UK Biobank. We prioritised eight biomarkers to formulate protein risk scores (ProSs) and computed polygenic risk scores (PRSs) using 62 single-nucleotide polymorphisms (SNPs). Then, we used Cox proportional hazards models to evaluate association of ProS and PRS with psoriasis risk, and performed stratified and sensitivity to explore the robustness of our findings. Results: A total of 479 incident psoriasis cases occurred over a median follow-up of 13.60 years (interquartile range = 12.89-14.31). The hazard ratios (HRs) for psoriasis were 1.40 (95% confidence interval (CI) = 1.08-1.80) and 2.72 (95% CI = 2.17-3.42) in the medium and high ProS groups, respectively, vs. the low ProS group (P-value for trend <0.001). When considering genetic susceptibility, participants with a high PRS and a high ProS had a greater risk of incident psoriasis (HR = 4.25; 95% CI = 2.97-6.07) compared to those with a low PRS and low ProS, as well as a greater 10-year absolute risk (25.99 per 1000 individuals). They also had a relative excess risk of 2.16 (95% CI = 0.98-3.35) due to additive interaction, accounting for 49% (95% CI = 29%-69%) of the psoriasis incidence. In the high genetic risk group, compared with individuals with low ProS, those with high ProS had an excess risk of 18.45 (95% CI = 17.90-18.98) cases per 1000 individuals over 10 years. Conclusions: Integrating the proteomic and polygenic tools could help improve the precision of psoriasis risk classification and facilitate the identification of high-risk populations.
Tian et al. (2026) studied this question.