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揭开代谢医学的未来:组学技术推动代谢紊乱精准治疗的个性化解决方案。

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders.

作者信息

Singh Samradhi, Sarma Devojit Kumar, Verma Vinod, Nagpal Ravinder, Kumar Manoj

机构信息

ICMR- National Institute for Research in Environmental Health, Bhopal Bypass Road, Bhouri, Bhopal, 462030, Madhya Pradesh, India.

Stem Cell Research Centre, Department of Hematology, Sanjay Gandhi Post-Graduate Institute of Medical Sciences, Lucknow, 226014, Uttar Pradesh, India.

出版信息

Biochem Biophys Res Commun. 2023 Nov 19;682:1-20. doi: 10.1016/j.bbrc.2023.09.064. Epub 2023 Sep 29.

Abstract

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

摘要

代谢紊乱在全球范围内日益普遍,导致高发病率和死亡率。多种代谢疾病可通过个性化医疗来解决。个性化医疗的目标是使医生有能力为患有代谢问题的患者预测最佳治疗方案。通过分析患者的代谢组学、蛋白质组学、基因概况和临床数据,医生可以识别相关的诊断和预测生物标志物,并为急性和慢性代谢疾病制定治疗计划和疗法。为实现这一目标,临床数据和多组学的实时建模对于查明潜在的生物学机制、风险因素以及可能有助于促进复杂疾病早期诊断和预防的有用数据至关重要。整合人工智能和机器学习等前沿技术对于整合各种形式的数据、检查多个变量、建立临床指标数据库以辅助决策以及制定道德规范以解决相关问题至关重要。这篇综述文章旨在探讨利用组学方法进行个性化医疗治疗代谢紊乱的潜力。它着重介绍了基因组学、表观基因组学、蛋白质组学、代谢组学和营养基因组学的最新进展,强调了它们在变革个性化医疗方面的作用。

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