GPH Journal of Medical Sciences

Glycemic Efficacy and Metabolic Outcomes of Dual GLP-1/GIP Receptor Agonist Therapy in Adults with Inadequately Controlled Type 2 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Trial

Background1234567: Dual glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) receptor agonism represents a mechanistically promising approach to improving glycemic control in type 2 diabetes mellitus (T2DM). Prior evidence suggests synergistic incretin effects; however, head-to-head dose-comparison data against placebo are limited in real-world populations. Objective: To evaluate the glycemic efficacy, metabolic safety, and patient-reported outcomes of low-dose versus high-dose dual GLP-1/GIP receptor agonist (DGRA-7) compared with placebo over 24 weeks in adults with inadequately controlled T2DM. Methods: In this multicenter, randomized, double-blind, placebo-controlled trial, 110 adults aged 35-70 with HbA1c 7.5-10.5% were allocated (1:1:1) to subcutaneous DGRA-7 low dose (2 mg/week), high dose (4 mg/week), or matching placebo for 24 weeks. The primary endpoint was change in HbA1c from baseline. Results: Both active treatment groups achieved statistically significant reductions in HbA1c versus placebo (low dose: -1.42%; high dose: -1.87%; placebo: -0.38%; p<0.001 for both). High-dose additionally showed superior reductions in body weight (-3.2 kg), systolic blood pressure (-5.1 mmHg), and HOMA-IR. Conclusions: DGRA-7 at both doses produced clinically meaningful HbA1c reductions with an acceptable safety profile. High-dose therapy conferred additional cardiometabolic benefits, supporting the therapeutic potential of dual incretin receptor agonism in T2DM management.

Sacubitril/Valsartan Versus Enalapril in Heart Failure with Reduced Ejection Fraction: A Randomised, Double-Blind, Placebo-Controlled Trial Evaluating NT-proBNP, Functional Capacity, and Quality of Life at 12 Weeks

Aims: Sacubitril/valsartan (angiotensin receptor-neprilysin inhibitor, ARNI) has demonstrated long-term mortality benefit over enalapril in heart failure with reduced ejection fraction (HFrEF). Whether its superiority extends to short-term neurohormonal, functional, and quality-of-life outcomes compared with both enalapril and placebo in a single randomised trial has not been established. Methods and Results: In this 12-week, randomised, double-blind, placebo-controlled trial, 125 adults with HFrEF (LVEF ≤35%, NYHA class II–IV) were allocated 1:1:1 to sacubitril/valsartan, enalapril, or placebo. NT-proBNP was reduced by 338 pg/mL in the sacubitril/valsartan arm versus 168 pg/mL (enalapril) and 88 pg/mL (placebo; p<0.001 for both comparisons). Six-minute walk test distance improved by 42 metres with sacubitril/valsartan versus 22 metres (enalapril) and 7 metres (placebo). KCCQ quality-of-life scores improved significantly in both active arms, with greater magnitude in the ARNI group. Conclusion: Sacubitril/valsartan produces superior short-term neurohormonal, functional, and quality-of-life benefits compared with enalapril and placebo in HFrEF. These data support the early uptitration of ARNI therapy and reinforce its mechanistic advantage over ACE inhibition in the neurohumoral management of HFrEF.

DOI10.9876/gjms.2026.0004

Efficacy and Durability of High-Frequency Repetitive Transcranial Magnetic Stimulation Targeting the Left Dorsolateral Prefrontal Cortex in Treatment-Resistant Major Depressive Disorder: A Randomized, Sham-Controlled Trial with 6-Month Follow-Up

Importance: Treatment-resistant major depressive disorder (TRD) affects approximately one-third of patients with MDD and is associated with profound functional disability and excess mortality. High-frequency rTMS has regulatory approval for MDD but its specific efficacy in rigorously defined TRD, cognitive safety, and durability beyond the acute treatment phase remain incompletely characterized. Objective: To evaluate the acute antidepressant efficacy, cognitive effects, and 6-month durability of left DLPFC high-frequency rTMS versus sham stimulation and wait-list control in adults meeting operational criteria for TRD. Design, Setting, and Participants: Three-arm, randomized, sham-controlled trial at two academic neuropsychiatric centers. Adults aged 22-65 with a current major depressive episode of at least moderate severity (HDRS-17 score ≥18) and failure of at least two adequate antidepressant trials were enrolled between January 2021 and August 2023. Results: Among 122 randomized participants, active rTMS produced a mean HDRS-17 reduction of -12.4 ± 3.8 points versus -5.1 ± 3.2 (sham) and -1.8 ± 2.9 (wait-list; p<0.001). Response and remission rates were 66.7% and 38.1%. No cognitive deterioration was observed. At 6-month follow-up, 71% of active rTMS responders maintained response. Conclusions: High-frequency left DLPFC rTMS produces robust, durable antidepressant responses in TRD with a favorable cognitive safety profile, reinforcing rTMS as a viable and underutilized therapeutic option.

DOI10.9876/gjms.2026.0003

Glycemic Efficacy and Metabolic Outcomes of Dual GLP-1/GIP Receptor Agonist Therapy in Adults with Inadequately Controlled Type 2 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Trial

Background12345: Dual glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) receptor agonism represents a mechanistically promising approach to improving glycemic control in type 2 diabetes mellitus (T2DM). Prior evidence suggests synergistic incretin effects; however, head-to-head dose-comparison data against placebo are limited in real-world populations. Objective: To evaluate the glycemic efficacy, metabolic safety, and patient-reported outcomes of low-dose versus high-dose dual GLP-1/GIP receptor agonist (DGRA-7) compared with placebo over 24 weeks in adults with inadequately controlled T2DM. Methods: In this multicenter, randomized, double-blind, placebo-controlled trial, 110 adults aged 35-70 with HbA1c 7.5-10.5% were allocated (1:1:1) to subcutaneous DGRA-7 low dose (2 mg/week), high dose (4 mg/week), or matching placebo for 24 weeks. The primary endpoint was change in HbA1c from baseline. Results: Both active treatment groups achieved statistically significant reductions in HbA1c versus placebo (low dose: -1.42%; high dose: -1.87%; placebo: -0.38%; p<0.001 for both). High-dose additionally showed superior reductions in body weight (-3.2 kg), systolic blood pressure (-5.1 mmHg), and HOMA-IR. Conclusions: DGRA-7 at both doses produced clinically meaningful HbA1c reductions with an acceptable safety profile. High-dose therapy conferred additional cardiometabolic benefits, supporting the therapeutic potential of dual incretin receptor agonism in T2DM management.

DOI10.9876/gjms.2026.0002

Impact of Artificial Intelligence on Early Detection of Cardiovascular Diseases: A Systematic Review and Meta-Analysis : Updated

Background: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually. Early detection through artificial intelligence (AI) has shown promise in improving diagnostic accuracy and patient outcomes. Objective: This systematic review and meta-analysis evaluates the efficacy of AI-based diagnostic tools in the early detection of cardiovascular diseases compared to traditional diagnostic methods. Methods: A comprehensive search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases from January 2018 to December 2025. Studies evaluating AI algorithms (machine learning, deep learning, neural networks) for CVD detection using ECG, echocardiography, or cardiac MRI data were included. The PRISMA 2020 guidelines were followed. Risk of bias was assessed using the QUADAS-2 tool. Meta-analysis was performed using random-effects models. Results: Of 3,847 initial records, 67 studies met the inclusion criteria, encompassing 1,284,592 patients across 23 countries. AI-based diagnostic tools demonstrated a pooled sensitivity of 94.2% (95% CI: 92.1–96.3%) and specificity of 91.8% (95% CI: 89.4–94.2%) for CVD detection. Deep learning models, particularly convolutional neural networks (CNNs), outperformed traditional machine learning approaches (AUC: 0.967 vs. 0.891, p < 0.001). AI-assisted diagnosis reduced time-to-diagnosis by an average of 47.3% (95% CI: 38.1–56.5%) and demonstrated a 23.6% improvement in early-stage detection rates compared to conventional methods. Subgroup analysis revealed that AI performance was consistent across age groups, genders, and geographic regions. Conclusions: AI-based diagnostic tools significantly enhance the early detection of cardiovascular diseases, offering superior sensitivity and specificity compared to traditional methods. Integration of AI into clinical workflows has the potential to reduce diagnostic delays and improve patient outcomes. However, standardized validation frameworks and regulatory guidelines are needed before widespread clinical adoption. Updated

DOI10.9876/gjms.2026.0001