Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
Doctoral School of Molecular Cellular and Immune Biology, University of Debrecen, 4032 Debrecen, Hungary.
Int J Mol Sci. 2024 Apr 27;25(9):4781. doi: 10.3390/ijms25094781.
The escalating prevalence of metabolic disorders, notably type 2 diabetes (T2D) and obesity, presents a critical global health challenge, necessitating deeper insights into their molecular underpinnings. Our study integrates proteomics and metabolomics analyses to delineate the complex molecular landscapes associated with T2D and obesity. Leveraging data from 130 subjects, including individuals with T2D and obesity as well as healthy controls, we elucidate distinct molecular signatures and identify novel biomarkers indicative of disease progression. Our comprehensive characterization of cardiometabolic proteins and serum metabolites unveils intricate networks of biomolecular interactions and highlights differential protein expression patterns between T2D and obesity cohorts. Pathway enrichment analyses reveal unique mechanisms underlying disease development and progression, while correlation analyses elucidate the interplay between proteomics, metabolomics, and clinical parameters. Furthermore, network analyses underscore the interconnectedness of cardiometabolic proteins and provide insights into their roles in disease pathogenesis. Our findings may help to refine diagnostic strategies and inform the development of personalized interventions, heralding a new era in precision medicine and healthcare innovation. Through the integration of multi-omics approaches and advanced analytics, our study offers a crucial framework for deciphering the intricate molecular underpinnings of metabolic disorders and paving the way for transformative therapeutic strategies.
代谢紊乱（尤其是 2 型糖尿病[T2D]和肥胖症）的发病率不断上升，这是一个重大的全球健康挑战，需要深入了解其分子基础。我们的研究整合了蛋白质组学和代谢组学分析，以描绘与 T2D 和肥胖相关的复杂分子图谱。利用来自 130 名受试者的数据，包括 T2D 和肥胖症患者以及健康对照者，我们阐明了不同的分子特征，并确定了指示疾病进展的新型生物标志物。我们对心脏代谢蛋白和血清代谢物的全面描述揭示了生物分子相互作用的复杂网络，并强调了 T2D 和肥胖症队列之间的差异蛋白表达模式。途径富集分析揭示了疾病发展和进展的独特机制，而相关性分析则阐明了蛋白质组学、代谢组学和临床参数之间的相互作用。此外，网络分析强调了心脏代谢蛋白的相互关联性，并深入了解了它们在疾病发病机制中的作用。我们的研究结果可能有助于改进诊断策略，并为个性化干预措施的制定提供信息，开创精准医学和医疗保健创新的新时代。通过整合多组学方法和先进的分析技术，我们的研究为揭示代谢紊乱的复杂分子基础提供了一个重要框架，并为变革性的治疗策略铺平了道路。
