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

Are weight loss and metabolic outcomes of bariatric surgery influenced by candidate glucocorticoid receptor gene polymorphisms? A prospective study

BACKGROUND

Bariatric surgery is the most effective treatment for severe obesity. There can be variation in the degree of weight reduction following bariatric surgery. It is unknown whether single nucleotide polymorphisms (SNPs) in the glucocorticoid receptor locus (GRL) affect postoperative weight loss and metabolic outcomes.

MATERIALS/METHODS

We studied the association between selected candidate SNPs and postoperative weight loss and metabolic outcomes in patients with severe obesity undergoing bariatric surgery. The polymorphisms rs41423247 (Bcl1), rs56149945 (N363S) and rs6189/rs6190 (ER22/23EK) were analysed.

RESULTS

The 139 participants included 95 women (68.3%) and had a median (interquartile range) age of 53.0 (46.0-60.0) years and mean (SD) weight of 140.8 (28.8) kg and body mass index of 50.3 (8.6) kg/m2. At baseline, 59 patients had type 2 diabetes (T2D), 60 had hypertension and 35 had obstructive sleep apnoea syndrome treated with continuous positive airway pressure (CPAP). 84 patients (60.4%) underwent gastric bypass and 55 (39.6%) underwent sleeve gastrectomy. There were no significant differences in weight loss, glycated haemoglobin (HbA1c) or lipid profile categorized by genotype status, sex or median age. There was significant weight reduction after bariatric surgery with a postoperative BMI of 34.1 (6.8) kg/m2 at 24 months (p < 0.001).

CONCLUSION

While GRL polymorphisms with a known deleterious effect on adipose tissue mass and function may have a small, additive effect on the prevalence of obesity and related metabolic disorders in the population, we suggest that the relatively weak biological influence of these SNPs is readily overcome by bariatric surgery.

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.

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.