Previous studies have shown significant associations between OPRK1 and susceptibility to opioid dependence and the relationships between libido dysfunction and insomnia among opium addicts who underwent methadone maintenance treatment. The authors investigated the single locus and haplotype association of rs997917, rs6985606, and rs6473797 with susceptibility to opioid addiction. Samples were selected among 202 healthy individuals and 202 opium addicts undergoing methadone maintenance treatment. Genomic DNA was extracted from the whole blood samples of all subjects through a salting out procedure. All three variants were genotyped in the studied subjects using Amplification Refractory Mutation System-Polymerase Chain Reaction (ARMS-PCR). The whole analysis process was performed using SNPAlyze and SPSS ver.20 software packages. According to the single locus analyses, rs997917 and rs6985606 represented significant associations with opium addiction under recessive (p = 0.0128) and co-dominant (p = 0.0001) inheritance models, respectively. The haplotypes C-T-C (Permutation p = 0.014) and C-T-T (Permutation p = 0.0002) were significantly associated with opioid dependence. Among methadone maintenance treatment individuals, rs997917 was significantly associated with insomnia in both allelic and genotypic levels (p = 0.0001 and p = 0.038, respectively). Furthermore, rs6985606 had the only significant association with the co-incidence of insomnia and libido dysfunction in the methadone maintenance treatment group (p = 0.038). The OPRK1 gene variants showed significant association with susceptibility to opioid dependence among Iranians.

Background: Previous studies have documented pain as an important concern for quality of life (QoL) and one of the most challenging manifestations for cancer patients. Thus, cancer pain management (CPM) plays a key role in treating pain related to cancer. The aim of this systematic review was to investigate CPM, with an emphasis on personalized medicine, and introduce new pharmacogenomics-based procedures for detecting and treating cancer pain patients. Methods: This study systematically reviewed PubMed from 1990 to 2023 using keywords such as cancer, pain, and personalized medicine. A total of 597 publications were found, and after multiple filtering processes, 75 papers were included. In silico analyses were performed using the GeneCards, STRING-MODEL, miRTargetLink2, and PharmGKB databases. Results: The results reveal that recent reports have mainly focused on personalized medicine strategies for CPM, and pharmacogenomics-based data are rapidly being introduced. The literature review of the 75 highly relevant publications, combined with the bioinformatics results, identified a list of 57 evidence-based genes as the primary gene list for further personalized medicine approaches. The most frequently mentioned genes were CYP2D6, COMT, and OPRM1. Moreover, among the 127 variants identified through both the literature review and data mining in the PharmGKB database, 21 variants remain as potential candidates for whole-exome sequencing (WES) analysis. Interestingly, hsa-miR-34a-5p and hsa-miR-146a-5p were suggested as putative circulating biomarkers for cancer pain prognosis and diagnosis. Conclusions: In conclusion, this study highlights personalized medicine as the most promising strategy in CPM, utilizing pharmacogenomics-based approaches to alleviate cancer pain.

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. Graphical

Background: Genetic factors like the allele for Brain-Derived Neurotrophic Factor (BDNF) are associated with the outcome of ischemic stroke most likely through affecting neural differentiation and synaptic plasticity. Studies of the association of BDNF G196A gene polymorphism and long-term ischemic stroke outcome in various populations have not been concordant. Objective: In this research, the association of BDNF G196A gene polymorphism and ischemic stroke occurrence were studied in a northern Iranian population with a glance to its 6-month outcome. Methods: The genetic variant of BDNF G196A was examined in Ischemic Stroke (IS) patients (n = 206) and control group (n = 200). In IS individuals, outcome variables such as stroke severity, functional disability, and cognitive impairment were examined, respectively, by NIHSS, Barthel Index, and MoCA in an average of 202 days after the stroke occurrence. Results: The frequency of risk allele G was 12.1% in IS patients and 5.5% in healthy individuals; and the difference was statistically significant (p < 0.0001). The frequency of risk genotype GG, heterozygote and homozygote were 0% and 1%, 24%.3% and 9%, 75.7% and 90%, respectively, for IS and control groups (p < 0.05). After controlling the phenotype confounding factors, logistic regression analysis showed that there was a borderline significant relationship between genotype BDNF GA + GG and IS occurrence (AOR = 1.997,95% CI: 0.252–1.010, p = 0.051). There was no significant difference between the various genotypic groups regarding the severity of the stroke and functional disability. Yet, G allele carriers had more cognitive impairment after IS (p = 0.002). Conclusion: For the first time in an Iranian population, it was demonstrated that BDNF G196A variant plays a major role in stroke occurrence and consequences. It is suggested that, after IS, G allele carriers should have precedence for medicinal and rehabilitation interventions, in order to reduce their cognitive deficiency.

Genetic association of rs678849 along with neuroimaging and biomarker phenotypes, parallel with the known involvements of the OPRD1 in drug abuse, provided additional support for targeting these receptors as potential therapeutic targets in both neurodegenerative diseases and neuropsychiactric disorders such as Alzheimer's disease. Samples were selected among 202 opium-addicted participants undergoing methadone treatment and 202 healthy controls. Genomic DNA of all subjects was extracted from whole blood samples through a Salting Out procedure. Four variants (rs678849, 2236857, 2236855, and 760589) were genotyped in the studied subjects using ARMS-PCR. The analysis was performed using SNPalyze and SPSS ver.20 software. According to single locus analysis, rs678849 under dominant model (p < 0.001), rs2236857 under recessive model (p = 0.006), and the two variants, rs2236855 and rs760589 under co-dominant model, showed significant contributions between groups (p = 0.001 and p = 0.009, respectively). rs2236855 was associated with the development of libido dysfunction in opium-addicted patients undergoing methadone treatment (p = 0.011). Through haplotype analyses, five haplotypes with frequency of more than 5% displayed significant association with opioid dependence in study participants. In conclusion, the four studied OPRD1 gene variants and their haplotypes can play important roles in susceptibility to opioid dependence.

Glucagon-like peptide-1 (GLP-1) has emerged as a pivotal regulator in the management of glucose homeostasis, glycogen metabolism, and energy balance, positioning it as a critical therapeutic target for addressing obesity, metabolic syndrome, and type 2 diabetes mellitus (T2DM). GLP-1 receptor agonists (GLP-1RAs) have shown promise for improving glycemic control and reducing weight through appetite regulation, delayed gastric emptying, and energy expenditure modulation. This narrative review explores the mechanisms of GLP-1-mediated glycogen metabolism and energy expenditure, particularly in key tissues—pancreas, liver, skeletal muscle, and adipose tissue. In the pancreas, GLP-1 enhances insulin secretion and beta-cell function. In the liver, it promotes glycogen synthesis via insulin-dependent and potential insulin-independent pathways, involving protein kinase B (AKT) and AMP-activated protein kinase (AMPK) signaling. Skeletal muscle benefits from GLP-1 through increased glucose uptake, AMPK activation, and mitochondrial function, facilitating glycogen storage. In adipose tissue, GLP-1 stimulates brown adipose tissue (BAT) thermogenesis and energy expenditure, contributing to weight loss. This increase in energy expenditure, along with enhanced glycogen metabolism, is a plausible mechanism for the weight loss observed with GLP-1RAs. Despite these advances, significant knowledge gaps remain, particularly regarding the direct hepatic effects of GLP-1, the extent to which it modulates glycogen metabolism in vivo, and its impact on thermogenesis in humans. Future research focusing on both the tissue-specific actions of GLP-1 and its systemic role in energy homeostasis and metabolic regulation will be essential for optimizing its therapeutic potential.