Abstract
ObjectivesIncorporation of genetic factors in psychosocial/perioperative models for predicting chronic postsurgical pain (CPSP) is key for personalization of analgesia. However, single variant associations with CPSP have small effect sizes, making polygenic risk assessment important. Unfortunately, pediatric CPSP studies are not sufficiently powered for unbiased genome wide association (GWAS). We previously leveraged systems biology to identify candidate genes associated with CPSP. The goal of this study was to use systems biology prioritized gene enrichment to generate polygenic risk scores (PRS) for improved prediction of CPSP in a prospectively enrolled clinical cohort.MethodsIn a prospectively recruited cohort of 171 adolescents (14.5 ± 1.8 years, 75.4% female) undergoing spine fusion, we collected data about anesthesia/surgical factors, childhood anxiety sensitivity (CASI), acute pain/opioid use, pain outcomes 6–12 months post-surgery and blood (for DNA extraction/genotyping). We previously prioritized candidate genes using computational approaches based on similarity for functional annotations with a literature-derived “training set.” In this study, we tested ranked deciles of 1336 prioritized genes for increased representation of variants associated with CPSP, compared to 10,000 randomly selected control sets. Penalized regression (LASSO) was used to select final variants from enriched variant sets for calculation of PRS. PRS incorporated regression models were compared with previously published non-genetic models for predictive accuracy.ResultsIncidence of CPSP in the prospective cohort was 40.4%. 33,104 case and 252,590 control variants were included for association analyses. The smallest gene set enriched for CPSP had 80/1010 variants associated with CPSP (p < 0.05), significantly higher than in 10,000 randomly selected control sets (p = 0.0004). LASSO selected 20 variants for calculating weighted PRS. Model adjusted for covariates including PRS had AUROC of 0.96 (95% CI: 0.92–0.99) for CPSP prediction, compared to 0.70 (95% CI: 0.59–0.82) for non-genetic model (p < 0.001). Odds ratios and positive regression coefficients for the final model were internally validated using bootstrapping: PRS [OR 1.98 (95% CI: 1.21–3.22); β 0.68 (95% CI: 0.19–0.74)] and CASI [OR 1.33 (95% CI: 1.03–1.72); β 0.29 (0.03–0.38)].DiscussionSystems biology guided PRS improved predictive accuracy of CPSP risk in a pediatric cohort. They have potential to serve as biomarkers to guide risk stratification and tailored prevention. Findings highlight systems biology approaches for deriving PRS for phenotypes in cohorts less amenable to large scale GWAS.
Highlights
Chronic post-surgical pain (CPSP) is an underrecognized and undertreated problem with an incidence of 14.5–38% in children after major surgery, that significantly contributes to prolonged opioid use (Kain et al, 1996; Kehlet et al, 2006; Macrae, 2008; Rabbitts et al, 2017; Harbaugh et al, 2018)
We recently described a systems-biology approach to identify genes and genetic pathways involved in CPSP (Chidambaran et al, 2020)
Using the literature derived genes (N = 31) as “training genes,” we previously identified novel candidate genes based on their similarity scores (“guilt by association”) to the curated training genes using ToppFun application of the Transcriptome Ontology Pathway PubMed based prioritization of genes (ToppGene) Suite, a one-stop portal of computational software tools for gene enrichment (Chen et al, 2009)
Summary
Chronic post-surgical pain (CPSP) is an underrecognized and undertreated problem with an incidence of 14.5–38% in children after major surgery, that significantly contributes to prolonged opioid use (Kain et al, 1996; Kehlet et al, 2006; Macrae, 2008; Rabbitts et al, 2017; Harbaugh et al, 2018). CPSP is defined as chronic pain that develops or increases intensity after a surgical procedure and persists beyond healing—at least 3 months after surgery (Werner and Kongsgaard, 2014). It has been recognized as a unique pain state recently in the International Classification of Diseases (ICD-11) (Schug et al, 2019). Targeted, individualized preventive and therapeutic measures are needed to decrease CPSP occurrence. Development of such measures is impeded by the inability to accurately predict individual risk for CPSP
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