(Prote)omics for Superior Management of Kidney and Cardiovascular Disease-A Thought-Provoking Impulse From Nephrology.

IF 3.4 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Proteomics Pub Date : 2024-11-24 DOI:10.1002/pmic.202400143
Joachim Beige, Michael Masanneck
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Abstract

Chronic kidney disease (CKD) and cardiovascular disease (CVD) are complex conditions often managed by nephrologists. This viewpoint paper advocates for a multi-omics approach, integrating clinical symptom patterns, non-invasive biomarkers, imaging and invasive diagnostics to enhance diagnosis and treatment. Early detection of molecular changes, particularly in collagen turnover, is crucial for preventing disease progression. For instance, urinary proteomics can detect early molecular changes in diabetic kidney disease (DKD), heart failure (HF) and coronary artery disease (CAD), enabling proactive interventions and reducing the need for invasive procedures like renal biopsies. For example, urinary proteomic patterns can differentiate between glomerular and extraglomerular pathologies, aiding in the diagnosis of specific kidney diseases. Additionally, urinary peptides can predict CKD progression and HF development, offering a non-invasive alternative to traditional biomarkers like eGFR and NT-proBNP. The integration of multi-omics data with artificial intelligence (AI) holds promise for personalised treatment strategies, optimizing patient outcomes. This approach can also reduce healthcare costs by minimizing unnecessary invasive procedures and hospitalizations. In conclusion, the adoption of multi-omics and non-invasive biomarkers in nephrology and cardiology can revolutionize disease management, enabling early detection, personalised treatment and improved patient outcomes.

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(Prote)omics for Superior Management of Kidney and Cardiovascular Disease--来自肾脏病学的发人深省的推动力。
慢性肾脏病(CKD)和心血管疾病(CVD)是复杂的疾病,通常由肾脏病专家负责管理。这篇观点论文主张采用多组学方法,将临床症状模式、非侵入性生物标志物、成像和侵入性诊断结合起来,以提高诊断和治疗效果。早期发现分子变化,尤其是胶原蛋白的变化,对于预防疾病进展至关重要。例如,尿液蛋白质组学可以检测出糖尿病肾病(DKD)、心力衰竭(HF)和冠状动脉疾病(CAD)的早期分子变化,从而进行积极干预,减少对肾活检等侵入性手术的需求。例如,尿液蛋白质组模式可以区分肾小球和肾小球外病变,有助于诊断特定的肾脏疾病。此外,尿肽还能预测慢性肾脏病的进展和高血压的发展,为 eGFR 和 NT-proBNP 等传统生物标志物提供了一种非侵入性的替代方法。多组学数据与人工智能(AI)的整合为个性化治疗策略、优化患者预后带来了希望。这种方法还能减少不必要的侵入性程序和住院治疗,从而降低医疗成本。总之,在肾脏病学和心脏病学中采用多组学和无创生物标记物可以彻底改变疾病管理,实现早期检测、个性化治疗和改善患者预后。
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
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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