Vitorino Rui
Department of Medical Sciences, Institute of Biomedicine iBiMED, University of Aveiro, Aveiro, Portugal.
Cardiovascular R&D Centre-UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto, Portugal.
Eur J Clin Invest. 2025 Feb;55(2):e14334. doi: 10.1111/eci.14334. Epub 2024 Oct 14.
Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, is a widely used drug for the treatment of type 2 diabetes that offers significant cardiovascular benefits.
This review systematically examines the proteomic and metabolomic indicators associated with the cardiovascular effects of semaglutide. A comprehensive literature search was conducted to identify relevant studies. The review utilizes advanced analytical technologies such as mass spectrometry and nuclear magnetic resonance (NMR) to investigate the molecular mechanisms underlying the effects of semaglutide on insulin secretion, weight control, anti-inflammatory activities and lipid metabolism. These "omics" approaches offer critical insights into metabolic changes associated with cardiovascular health. However, challenges remain such as individual variability in expression, the need for comprehensive validation and the integration of these data with clinical parameters. These issues need to be addressed through further research to refine these indicators and increase their clinical utility.
Future integration of proteomic and metabolomic data with artificial intelligence (AI) promises to improve prediction and monitoring of cardiovascular outcomes and may enable more accurate and effective management of cardiovascular health in patients with type 2 diabetes. This review highlights the transformative potential of integrating proteomics, metabolomics and AI to advance cardiovascular medicine and improve patient outcomes.
司美格鲁肽是一种胰高血糖素样肽-1(GLP-1)受体激动剂,是一种广泛用于治疗2型糖尿病的药物,具有显著的心血管益处。
本综述系统地研究了与司美格鲁肽心血管效应相关的蛋白质组学和代谢组学指标。进行了全面的文献检索以识别相关研究。该综述利用质谱和核磁共振(NMR)等先进分析技术来研究司美格鲁肽对胰岛素分泌、体重控制、抗炎活性和脂质代谢影响的分子机制。这些“组学”方法为与心血管健康相关的代谢变化提供了关键见解。然而,仍然存在挑战,如表达的个体差异、全面验证的必要性以及将这些数据与临床参数整合。这些问题需要通过进一步研究来解决,以完善这些指标并提高其临床实用性。
蛋白质组学和代谢组学数据与人工智能(AI)的未来整合有望改善心血管结局的预测和监测,并可能使2型糖尿病患者的心血管健康管理更加准确和有效。本综述强调了整合蛋白质组学、代谢组学和人工智能以推进心血管医学并改善患者结局的变革潜力。