This retrospective observational study aims to evaluate the long-term survival of biologic therapies in adult patients with moderate-to-severe cutaneous psoriasis, with or without psoriatic arthritis, over a period of up to 10 years. The study investigates the influence of clinical, metabolic, and genetic factors, including SNPs and metabolic syndrome components, on treatment durability. Data were obtained from a single-centre cohort treated in routine clinical practice. This analysis seeks to identify predictors of therapeutic response and to explore pharmacogenetic profiles that may inform personalized treatment strategies.

This retrospective observational cohort study investigates the influence of clinical, anthropometric, lifestyle, cardiometabolic, immunological, and genetic factors on the long-term effectiveness and durability of biologic therapies in patients with moderate-to-severe plaque psoriasis, with or without psoriatic arthritis.

The study includes adult patients diagnosed with plaque psoriasis who initiated treatment with a biologic agent between 2011 and 2021 at a tertiary academic dermatology center. All subjects were systematically assessed through standardized procedures, and follow-up data were collected over a period of up to 10 years. The primary endpoint is biologic drug survival (time to discontinuation), while secondary endpoints include treatment response (PGA), presence of psoriatic arthritis, nail psoriasis, and family history of psoriasis.

Clinical and biomarker data collected at baseline included:

Anthropometric variables: body mass index (BMI), waist circumference.

Lifestyle indicators: Mediterranean diet adherence (MEDAS), physical activity frequency, smoking and alcohol habits, and perceived stress scale.

Cardiovascular and metabolic status: history and treatment of hypertension, type 2 diabetes mellitus, dyslipidemia, and metabolic syndrome, following ATP III/NCEP criteria.

Cardiometabolic biomarkers: leptin, adiponectin, insulin, lipoprotein(a), and HOMA-IR.

Inflammatory profile: a multiplex panel of cytokines and chemokines (including IL-1β, IL-6, IL-8, IL-17, IL-23, TNF-α, IFN-γ, MCP-1, IP-10).

Microparticles: circulating endothelial and platelet-derived microparticles quantified by flow cytometry.

Genotyping was performed using a custom array targeting 450 SNPs in 65 candidate genes previously associated with psoriasis susceptibility, systemic inflammation, and cardiometabolic risk (e.g., IL12B, IL23R, TNFAIP3, TRAF3IP2, HLA-C, CDKAL1, TCF7L2). Quality control included filtering by call rate, Hardy-Weinberg equilibrium, and minor allele frequency (MAF > 5%).

Data integration and quality assurance:

Clinical, laboratory, and genotyping data were integrated using unique patient identifiers.

A complete data dictionary was compiled, with defined variable sources, coding rules (e.g., WHO-ATC for drugs), and standard ranges.

Logical and range-based data checks were conducted. Variables with implausible values (e.g., negative survival time) were excluded or corrected.

A pre-specified imputation plan was applied to address missingness: median or mode imputation for clinical variables; multiple imputation for biomarkers where appropriate.

Variables were harmonized across data sources to ensure consistent definitions and temporal alignment.

All analyses adhered to a predefined statistical analysis plan.

Sample size and power: With over 800 patients and a median follow-up of 5+ years, the study has sufficient statistical power (>80%) to detect hazard ratios of ~1.5 for binary predictors with moderate prevalence (≥20%).

Statistical analysis:

Cox proportional hazards regression was used to assess the association between predictors and biologic drug survival.

Models were adjusted for potential confounders such as age, gender, and comorbidities.

Univariate models were conducted for each clinical and lifestyle variable, excluding SNPs, with false discovery rate (FDR) adjustment.

Stratified analyses were conducted by drug class (e.g., anti-TNF, anti-IL17, anti-IL12/23) and individual drug.

Pharmacogenetic analyses were conducted separately using additive models for each SNP, with interaction testing for cardiometabolic traits.

Results were summarized as hazard ratios (HR) with 95% confidence intervals and adjusted p-values.

All procedures followed STROBE guidelines for observational research. The study protocol was reviewed and approved by the Institutional Ethics Committee, and all patients provided written informed consent for biobanking and retrospective analysis of anonymized data.

This study aims to identify actionable clinical and genetic predictors of biologic therapy durability in real-world psoriasis, contributing to personalized treatment strategies and understanding of cardio-dermatologic interactions.

Inclusion Criteria:

• Adults (≥18 years) diagnosed with moderate-to-severe cutaneous psoriasis.
• Initiation of treatment with a biologic agent between 2010 and 2020.
• Minimum follow-up of 12 months or until drug discontinuation.
• Availability of clinical and treatment data at baseline.
• Availability of at least one blood sample for biomarker/genetic analysis.
• Informed consent obtained for biobanking and genetic analyses.

Exclusion Criteria:

• Diagnosis of other chronic inflammatory skin diseases (e.g., atopic dermatitis, lupus).
• Concomitant participation in interventional clinical trials at baseline.
• Patients with incomplete treatment history or missing survival data.
• Systemic immunosuppressive therapy (e.g., cyclosporine, methotrexate) without biologics during the study period.

Adult patients (≥18 years) with moderate-to-severe cutaneous psoriasis, with or without psoriatic arthritis, who initiated treatment with biologic therapies at a tertiary dermatology center between 2010 and 2020. All participants had routine clinical follow-up and available biological samples for biomarker and genetic analyses.

Individual participant data (IPD) will not be shared at this time. However, data sharing may be considered in the future once additional analyses are completed or if integration with other datasets is agreed upon as part of collaborative research efforts.