The First Exploratory Personalized Medicine Approach to Improve Bariatric Surgery Outcomes Utilizing Psychosocial and Genetic Risk Assessments: Encouraging Clinical Research

A Pharmacogenomics-Based In Silico Investigation of Opioid Prescribing in Post-operative Spine Pain Management and Personalized Therapy

Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management after spine surgery presents significant challenges. Therefore, this study undertook a novel pharmacogenomics-based in silico investigation of FDA-approved opioid medications. The DrugBank database was employed to identify all FDA-approved opioids. Subsequently, the PharmGKB database was utilized to filter through all variant annotations associated with the relevant genes. In addition, the dpSNP ( https://www.ncbi.nlm.nih.gov/snp/ ), a publicly accessible repository, was used. Additional analyses were conducted using STRING-MODEL (version 12), Cytoscape (version 3.10.1), miRTargetLink.2, and NetworkAnalyst (version 3). The study identified 125 target genes of FDA-approved opioids, encompassing 7019 variant annotations. Of these, 3088 annotations were significant and pertained to 78 genes. During variant annotation assessments (VAA), 672 variants remained after filtration. Further in-depth filtration based on variant functions yielded 302 final filtered variants across 56 genes. The Monoamine GPCRs pathway emerged as the most significant signaling pathway. Protein-protein interaction (PPI) analysis revealed a fully connected network comprising 55 genes. Gene-miRNA Interaction (GMI) analysis of these 55 candidate genes identified miR-16-5p as a pivotal miRNA in this network. Protein-Drug Interaction (PDI) assessment showed that multiple drugs, including Ibuprofen, Nicotine, Tramadol, Haloperidol, Ketamine, L-Glutamic Acid, Caffeine, Citalopram, and Naloxone, had more than one interaction. Furthermore, Protein-Chemical Interaction (PCI) analysis highlighted that ABCB1, BCL2, CYP1A2, KCNH2, PTGS2, and DRD2 were key targets of the proposed chemicals. Notably, 10 chemicals, including carbamylhydrazine, tetrahydropalmatine, Terazosin, beta-methylcholine, rubimaillin, and quinelorane, demonstrated dual interactions with the aforementioned target genes. This comprehensive review offers multiple strong, evidence-based in silico findings regarding opioid prescribing in spine pain management, introducing 55 potential genes. The insights from this report can be applied in exome analysis as a pharmacogenomics (PGx) panel for pain susceptibility, facilitating individualized opioid prescribing through genotyping of related variants. The article also points out that African Americans represent an important group that displays a high catabolism of opioids and suggest the need for a personalized therapeutic approach based on genetic information.

Summary Document Research on RDS Anti-addiction Modeling: Annotated Bibliography

Annotated bibliography of genetic addiction risk severity (GARS) publications, pro-dopamine regulation in nutraceuticals (KB220 nutraceutical variants), and policy documents. Further research is required to encourage the field to consider "Reward Deficiency Syndrome (RDS) Anti-addiction Modeling" which involves early risk identification by means of genetic assessment similar to GARS, followed by induction of dopamine homeostasis by means of genetically guided pro-dopamine regulation similar to KB220. These results suggest that genetically based treatments may be a missing piece in the treatment of substance use disorder (SUD).

The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective

Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.

Genetic Correlates as a Predictor of Bariatric Surgery Outcomes after 1 Year

This study analyzed genetic risk assessments in patients undergoing bariatric surgery to serve as a predictive factor for weight loss parameters 1 year after the operation. Thirty (30) patients were assessed for Genetic Addiction Risk Severity (GARS), which analyzes neurogenetic polymorphisms involved in addiction and reward deficiency. Genetic and psychosocial data collected before the operation were correlated with weight loss data, including changes in weight, body mass index (BMI), and percent of expected weight loss (%EWL). Results examined correlations between individual gene risk alleles, 1-year body weight data, and psychosocial trait scores. Spearman's correlations revealed that the OPRM1 (rs1799971) gene polymorphism had significant negative correlation with 1-year weight (rs = -0.4477, p < 0.01) and BMI (rs = -0.4477, p < 0.05). In addition, the DRD2 risk allele (rs1800497) was correlated negatively with BMI at 1 year (rs = -0.4927, p < 0.05), indicating that one risk allele copy was associated with lower BMI. However, this allele was positively correlated with both ∆Weight (rs = 0.4077, p < 0.05) and %EWL (rs = 0.5521, p < 0.05) at 1 year post-surgery. Moreover, the overall GARS score was correlated with %EWL (rs = 0.4236, p < 0.05), ∆Weight (rs = 0.3971, p < 0.05) and ∆BMI (rs = 0.3778, p < 0.05). Lastly, Food Cravings Questionnaire (FCQ) scores were negatively correlated with %EWL (rs = -0.4320, p < 0.05) and ∆Weight at 1 year post-surgery (rs = -0.4294, p < 0.05). This suggests that individuals with a higher genetic addiction risk are more responsive to weight loss treatment, especially in the case of the DRD2 polymorphism. These results should translate clinically to improve positivity and attitude related to weight management by those individuals born with the risk alleles (rs1800497; rs1799971).