Gender-dependent association of the GNAS1 T393C polymorphism with early aseptic loosening after total hip arthroplasty

Development of an Integrated Platform Using Multidisciplinary Real-World Data to Facilitate Biomarker Discovery for Medical Products

Translational multidisciplinary research is important for the Center for Devices and Radiological Health's efforts for utilizing real‐world data (RWD) to enhance predictive evaluation of medical device performance in patient subpopulations. As part of our efforts for developing new RWD‐based evidentiary approaches, including in silico discovery of device‐related risk predictors and biomarkers, this study aims to characterize the sex/race‐related trends in hip replacement outcomes and identify corresponding candidate single nucleotide polymorphisms (SNPs). Adverse outcomes were assessed by deriving RWD from a retrospective analysis of hip replacement hospital discharge data from the National Inpatient Sample (NIS). Candidate SNPs were explored using pre‐existing data from the Personalized Medicine Research Project (PMRP). High‐Performance Integrated Virtual Environment was used for analyzing and visualizing putative associations between SNPs and adverse outcomes. Ingenuity Pathway Analysis (IPA) was used for exploring plausibility of the sex‐related candidate SNPs and characterizing gene networks associated with the variants of interest. The NIS‐based epidemiologic evidence showed that periprosthetic osteolysis (PO) was most prevalent among white men. The PMRP‐based genetic evidence associated the PO‐related male predominance with rs7121 (odds ratio = 4.89; 95% confidence interval = 1.41−17.05) and other candidate SNPs. SNP‐based IPA analysis of the expected gene expression alterations and corresponding signaling pathways suggested possible role of sex‐related metabolic factors in development of PO, which was substantiated by ad hoc epidemiologic analysis identifying the sex‐related differences in metabolic comorbidities in men vs. women with hip replacement‐related PO. Thus, our in silico study illustrates RWD‐based evidentiary approaches that may facilitate cost/time‐efficient discovery of biomarkers for informing use of medical products.