Association of Clinicopathological Features with IgA Nephropathy: A Principal Component Analysis

Abstract Background: IgA nephropathy(IgAN) is the leading form of glomerular disease worldwide. Currently, the pathogenesis of IgAN is unclear and IgAN can only be diagnosed by renal biopsy, which lacks non-invasive methods. This study aims to analyze the association between clinicopathological characteristics and IgAN by principal component analysis. Methods: Based on a combination of z-test and PCA, the fit of this model was evaluated with logistic regression analyses. Results: Data from 847 patients with biopsy-proven IgAN in 1395 cases from May 2008 to April 2013 were analyzed. the average age is 33.156±12.308 years old and males account for 43%. Z-test selected 27 clinical and pathological indicators related to IgAN, and the principal component prediction model was established based on these 27 indicators. Logistic regression model providing 91.93% IgAN renal recall rate and 71.29% overall accuracy, which shows that the PCA model has high reliability. Conclusions: As the model result shows, the higher level spheroid hardening rate, serum creatinine, blood uric acid and lower level eGFR might promote the occurrence of IgAN, which also provides more information for non-invasive diagnosis of IgAN patients.