RESUMO
IMPORTANCE: Advanced liver fibrosis is a precursor to cirrhosis, a leading cause of mortality. People with severe psoriasis are at risk for liver disease, but our understanding of advanced fibrosis in individuals with psoriasis is limited. OBJECTIVES: To describe the prevalence of and evaluate the clinical factors associated with advanced liver fibrosis in people with severe psoriasis. DESIGN, SETTING, AND PARTICIPANTS: The Co-morbidities in Severe Psoriasis study, a prospective observational cohort study in a large center serving London and Southeast England, was conducted from October 18, 2012, to April 2, 2015; 400 adults with severe psoriasis (Psoriasis Area Severity Index score, ≥10) were recruited from outpatient clinics. Statistical analysis was conducted from October 2, 2016, to March 3, 2017. MAIN OUTCOMES AND MEASURES: The primary outcome was a diagnosis of advanced liver fibrosis determined by transient elastography, a noninvasive criterion standard test. Clinical factors evaluated included psoriasis-specific and metabolic indices, alcohol use, and methotrexate exposure. RESULTS: Of 400 patients recruited (108 women and 289 men; mean [SD] age, 49.5 [13] years), 333 had a successful transient elastography scan and were included in final analysis. Forty-seven patients (14.1%; 95% CI, 10.4%-17.9%) had advanced liver fibrosis as diagnosed by transient elastography. The clinical factors that produced the best-fit model for advanced fibrosis were central obesity (waist circumference), insulin resistance, aspartate aminotransferase level, platelet count, psoriasis disease severity, and reduced alcohol use (R2 = 0.54). CONCLUSIONS AND RELEVANCE: Findings from this study suggest that advanced fibrosis is common in severe psoriasis. Abdominal obesity (by waist circumference) and insulin resistance were associated with the presence of advanced fibrosis. Longitudinal work to characterize the hepatic sequelae of central obesity, insulin resistance, and inflammation as well as the influence of systemic drugs (methotrexate and biologics) will inform future personalized therapeutic decision-making. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02174367.
RESUMO
Burgeoning availability of genome-wide association study (GWAS) results and national biobank data has led to growing interest in performing multi-trait genetic analyses. Numerous multi-trait GWAS methods that exploit either summary statistics or individual-level data have been developed, but their relative performance is unclear. Here we develop a simulation framework to model the complex networks underlying multivariate genetic epidemiology, enabling the vast model space of genetic effects on multiple correlated traits to be explored systematically. We perform a comprehensive comparison of the leading multi-trait GWAS methods, finding: (1) method performance is highly sensitive to the specific combination of genetic effects and phenotypic correlations, (2) most of the current multivariate methods have remarkably similar statistical power, and (3) multivariate methods may offer a substantial increase in the discovery of genetic variants over the standard univariate approach. We believe our findings offer the clearest picture to date of the relative performance of multi-trait GWAS methods and act as a guide for method selection. We provide a web application and open-source software program implementing our simulation framework, for: (i) further benchmarking of multivariate GWAS methods, (ii) power calculations for multivariate genetic studies, and (iii) generating data for testing any multivariate method in genetic epidemiology.