Exploring omics signature in the cardiovascular response to semaglutide: Mechanistic insights and clinical implications.

IF 4.4 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Rui Vitorino
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Abstract

Background: 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.

Results: 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.

Conclusion: 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.

探索心血管对塞马鲁肽反应的全息特征:机理认识和临床意义。
研究背景塞马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,具有显著的心血管疗效:本综述系统研究了与塞马鲁肽心血管效应相关的蛋白质组和代谢组指标。为了确定相关研究,我们进行了全面的文献检索。本综述利用质谱法和核磁共振 (NMR) 等先进分析技术,研究了塞马鲁肽对胰岛素分泌、体重控制、抗炎活性和脂质代谢影响的分子机制。这些 "omics "方法为了解与心血管健康相关的代谢变化提供了重要依据。然而,挑战依然存在,如表达的个体差异性、全面验证的必要性以及将这些数据与临床参数相结合。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果的预测和监测,并可对 2 型糖尿病患者的心血管健康进行更准确、更有效的管理。这篇综述强调了蛋白质组学、代谢组学和人工智能在推动心血管医学发展和改善患者预后方面的变革潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.50
自引率
3.60%
发文量
192
审稿时长
1 months
期刊介绍: EJCI considers any original contribution from the most sophisticated basic molecular sciences to applied clinical and translational research and evidence-based medicine across a broad range of subspecialties. The EJCI publishes reports of high-quality research that pertain to the genetic, molecular, cellular, or physiological basis of human biology and disease, as well as research that addresses prevalence, diagnosis, course, treatment, and prevention of disease. We are primarily interested in studies directly pertinent to humans, but submission of robust in vitro and animal work is also encouraged. Interdisciplinary work and research using innovative methods and combinations of laboratory, clinical, and epidemiological methodologies and techniques is of great interest to the journal. Several categories of manuscripts (for detailed description see below) are considered: editorials, original articles (also including randomized clinical trials, systematic reviews and meta-analyses), reviews (narrative reviews), opinion articles (including debates, perspectives and commentaries); and letters to the Editor.
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