Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion

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.

Predictors of Chronic Postsurgical Pain in Elderly Patients Undergoing Hip Arthroplasty: A Multi-Center Retrospective Cohort Study

“…A number of putative risk factors may contribute to CPSP development. 13 Several predictive factors of CPSP have been reported, including genetic background 22,23 and pain history 24,25 and psychological factors. 26,27 In this retrospective study, we identified significant risks factors https://doi.org/10.2147/IJGM.S337170…”

Prognostic models for chronic postsurgical pain-Current developments, trends, and challenges

“…Since that, six more models have been identified by us here (2021 to mid-2023, Table 1). While eight models [32,33,29,[34][35][36][37][38]] used only preoperative factors, three models [39-41] used preoperative and intraoperative factors, seven models [42][43][44][45][46][47][48] used preoperative and postoperative factors, and two models [49,30 & ] used pre-, intra-and postoperative factors. Because of the use of heterogeneous diagnostic factors for CPSP, different time periods and different cutoffs (leading to over-or underestimate CPSP predictors), it is difficult to compare the individual models so far ([9] and Table 1).…”

Current Evidence for Biological Biomarkers and Mechanisms Underlying Acute to Chronic Pain Transition across the Pediatric Age Spectrum

“…Examples of genetic biomarkers are gene expression changes, single-nucleotide polymorphisms (SNPs), patterns of open or closed chromatin, or polygenic risk scores (PRS). For example, PRS from a panel of 20 variants has been proposed for chronic postsurgical pain (CPSP) in children undergoing spine fusion [ 5 ]. Following validation across different cohorts and use of laboratory techniques, genetic biomarkers could be useful for personalizing pain management.…”

“…Chronic pain as a continuum that develops earlier in life is suggested by the evidence that 17% of adults with chronic pain report onset of pain in childhood [ 1 ]. In fact, about 20–40% of acute pediatric pain has the potential to transition into chronic pain in children and adolescents [ 2 , 3 , 4 , 5 , 6 ]. Studying chronic pain transitions in the pediatric age group is of utmost importance because (a) children experiencing chronic pain have a ≈3- to 6-fold higher risk of developing chronic pain and disability in adulthood, such that 40–60% of children with chronic pain continue to experience pain as adults [ 7 ]; (b) chronic pain in children has greater socioeconomic consequences with a high financial burden [ 8 ], stemming from psychosocial and physical disabilities [ 9 , 10 ], impacting quality of life and development of children negatively; and (c) maturational development of the pain connectome is a dynamic process [ 11 ], emphasizing neurodevelopmental differences between children, adolescents, and adults’ nociception [ 12 ].…”