👤 Ayesha Mateen

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), originally developed for type 2 diabetes mellitus (T2DM) and obesity, show promising potential as a novel treatment for depression, particularly in patients with comorbid metabolic disorders. This narrative review examines the bidirectional relationship between obesity and depression, driven by shared mechanisms such as chronic low-grade inflammation, hypothalamic-pituitary-adrenal axis dysregulation, and impaired neuroplasticity. GLP-1 RAs, including liraglutide and exenatide, demonstrate neuroprotective effects by enhancing brain-derived neurotrophic factor expression and synaptic plasticity, alongside anti-inflammatory properties that reduce proinflammatory cytokines (e.g., tumor necrosis factor-alpha and interleukin-6). They also modulate serotonin turnover in mood-regulating brain regions, mirroring selective serotonin reuptake inhibitors. Preclinical studies in animal models reveal improved behavioral outcomes, while human observational studies and limited clinical trials, such as the LEAD-3 trial, report enhanced mood and quality of life in T2DM and obesity patients. However, challenges, including high treatment costs ($800-$1000/month), injectable administration, and needle-related anxiety, limit patient adherence, and clinical adoption. The lack of large-scale randomized controlled trials targeting depression as a primary outcome further hinders definitive conclusions. This review highlights GLP-1 RAs' potential to address both metabolic and depressive symptoms, offering a holistic approach to managing these interconnected conditions. Future research should focus on long-term efficacy, optimal dosing, and overcoming adherence barriers to establish GLP-1 RAs as a viable psychiatric treatment. Show less

Prostate cancer (PCa) remains a significant global health challenge, with approximately 1.6 million new cases and 366,000 deaths annually. Despite high survival rates for localized prostate cancer, recurrence poses a substantial risk due to inherent biological factors and residual disease. Early detection and intervention are essential for enhancing patient outcomes and reducing mortality. However, traditional diagnostics such as PSA tests, digital rectal examinations, and biopsies often lack specificity resulting in overdiagnosis. There is a pressing need for novel biomarkers to enhance precision medicine approaches for PCa. This study employs a machine learning approach to identify DNA methylation and RNA expression biomarkers predictive of PCa recurrence using datasets from The Cancer Genome Atlas (TCGA). We analyzed 49,133 genes, identifying 684 differentially methylated genes (DMGs) and 691 differentially expressed genes (DEGs) between recurrence and non-recurrence groups. Ten genes (TNNI2, SPIN2, COL5A3, RNF169, CCND1, FGFR1, SLC17A2, FAMM71F2, RREB1, AOX1) were found to have significant correlations between methylation and expression, forming the basis for our predictive model. A support vector machine (SVM) model was developed using these ten genes, achieving an area under the curve (AUC) of 0.773, demonstrating robust predictive capability. Multivariate regression analysis confirmed the SVM score as an independent predictor of recurrence (HR = 0.45; 95% CI 0.28-0.69, P < 0.001). The analysis of recurrence-free survival suggested that patients with low-risk scores experienced significantly better outcomes compared to those with high-risk scores. Functional enrichment analyses of DMGs revealed significant involvement in biological processes such as transcription regulation, signal transduction, and immune response, highlighting the potential mechanistic pathways of these biomarkers. Validation using real-time PCR confirmed differential expression and methylation patterns of the identified genes in prostate cancer (PC3) and non-cancerous cell lines (PNT2). In conclusion, our study hihglights the DNA methylation biomarkers linked to PCa recurrence and introduces a promising SVM model for early prediction, potentially improving treatment outcomes. Further research is needed to explore the biological roles of these genes in PCa aiming to refine therapeutic approaches. Show less