Association between glucagon-like peptide-1 receptor agonists and risk of suicidality: A comprehensive analysis of the global pharmacovigilance database.

AIM: To evaluate the potential association between suicidality and glucagon-like peptide-1 receptor agonists (GLP-1RAs), as well as other medications used for obesity and diabetes, using comprehensive global data. MATERIALS AND METHODS: This study utilized the World Health Organization's pharmacovigilance database, encompassing adverse drug reaction reports from 1967 to 2023, from 170 countries (total reports, N = 131 255 418). We present the reported odds ratios (RORs) with 95% confidence intervals (CIs) and information component (IC) with IC025 regarding the association between GLP-1RA use and suicidality. RESULTS: Although reports of GLP-1RA-associated suicidality increased gradually from 2005 to 2023 (n = 332), no evidence of an association was observed (ROR 0.15 [95% CI 0.13 to 0.16]; IC -2.77 [IC025 -2.95]). The lack of evidence of an association persisted regardless of whether GLP-1RAs were used for diabetes treatment (ROR 0.13 [95% CI 0.11 to 0.14]; IC -2.95 [IC025 -3.14]) or obesity treatment (ROR 0.44 [95% CI 0.34 to 0.58]; IC -1.16 [IC025 -1.62]). However, an association was found between suicidality and other diabetes medications excluding GLP-1RAs (ROR 1.13 [95% CI 1.10 to 1.15]; IC 0.17 [IC025 0.13]). Similarly, the potential association with suicidality was observed in medications used to treat obesity excluding GLP-1RAs (ROR 1.08 [95% CI 1.01 to 1.14]; IC 0.10 [IC025 0.01]). CONCLUSIONS: The suspected association between GLP-1RA use and suicidality, as raised by the European Medicines Agency, was not found in our global analysis. This indicates that the sporadic reports of GLP-1RA-associated suicidality are likely influenced by factors such as comorbidities present in the GLP-1RA user population.

Effect of Hyperprolactinemia on Bone Metabolism: Focusing on Osteopenia/Osteoporosis.

Prolactin is a hormone secreted from lactotroph cells in the anterior pituitary gland to induce lactation after birth. Hyperprolactinemia unrelated to lactation is a common cause of amenorrhea in women of a childbearing age, and a consequent decrease in the gonadotropin-releasing hormone (GnRH) by a high prolactin level can result in decreased bone mineral density. Osteoporosis is a common skeletal disorder characterized by decreased bone mineral density (BMD) and quality, which results in decreased bone strength. In patients with hyperprolactinemia, changes in BMD can be induced indirectly by the inhibition of the GnRH-gonadal axis due to increased prolactin levels or by the direct action of prolactin on osteoblasts and, possibly, osteoclast cells. This review highlights the recent work on bone remodeling and discusses our knowledge of how prolactin modulates these interactions, with a brief literature review on the relationship between prolactin and bone metabolism and suggestions for new possibilities.

Weight Management Health Note, a Mobile Health Platform for Obesity Management Developed by the Korean Society for the Study of Obesity.

The Weight Management Health Note application, developed by the Korean Society for the Study of Obesity (KSSO), was designed to assist individuals in weight management and enhance overall well-being. The Committee of IT-Convergence Treatment of Metabolic Syndrome of the KSSO designed this application. Committee members reviewed and supervised the application's underlying driving algorithms and scientific rationale. A healthcare-specific application developer subsequently finalized the application. This application encompasses a myriad of features, including a comprehensive food diary, an exercise tracker, and tailor-made lifestyle recommendations aligned with individual needs and aspirations. Moreover, it facilitates connections within a community of like-minded individuals endeavoring to manage their weight, fostering mutual support and motivation. Importantly, the application is rich in evidence-based health content curated by the KSSO, ensuring users access accurate information for effective obesity management. Looking ahead, the KSSO is committed to orchestrating diverse academic research endeavors linked to this application and refining its functionalities through continuous feedback from users. The KSSO aspires for this application to serve as a valuable resource for individuals striving to manage their health and enhance their quality of life.

Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.

This study aimed to develop and validate a machine learning (ML) model tailored to the Korean population with type 2 diabetes mellitus (T2DM) to provide a superior method for predicting the development of cardiovascular disease (CVD), a major chronic complication in these patients. We used data from two cohorts, namely the discovery (one hospital; n = 12,809) and validation (two hospitals; n = 2019) cohorts, recruited between 2008 and 2022. The outcome of interest was the presence or absence of CVD at 3 years. We selected various ML-based models with hyperparameter tuning in the discovery cohort and performed area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort. CVD was observed in 1238 (10.2%) patients in the discovery cohort. The random forest (RF) model exhibited the best overall performance among the models, with an AUROC of 0.830 (95% confidence interval [CI] 0.818-0.842) in the discovery dataset and 0.722 (95% CI 0.660-0.783) in the validation dataset. Creatinine and glycated hemoglobin levels were the most influential factors in the RF model. This study introduces a pioneering ML-based model for predicting CVD in Korean patients with T2DM, outperforming existing prediction tools and providing a groundbreaking approach for early personalized preventive medicine.