Revolutionary Innovations in Diabetes Research: From Biomarkers to Genomic Medicine

T. Addissouky, Majeed M. A. Ali, Ibrahim El Tantawy El Sayed, Yuliang Wang
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Abstract

Diabetes mellitus is a chronic metabolic disease characterized by hyperglycemia resulting from inadequate insulin signaling. Current management relies on biomarkers such as hemoglobin A1c (HbA1c) to guide therapy, but emerging tools offer opportunities to transform care through more personalized approaches. Molecular biomarkers, including microRNAs, metabolites, and proteins, may enable better prediction of disease course and risk of complications in individuals. Genomic medicine leverages knowledge of genetic architecture to guide tailored prevention and treatment based on an individual’s genomic profile. Stem cell research differentiates functional insulin-secreting cells for transplantation into patients as an alternative to exogenous insulin. Gene silencing techniques such as RNA interference can restore defective insulin production and secretion pathways by inhibiting dysregulated gene expression. Artificial intelligence applications automate glucose monitoring, insulin delivery, diagnostic screening for complications, and digital health coaching. Despite barriers to translation, these technologies have disruptive potential for predictive, preventive, precise, and participatory care paradigms in diabetes management. Continued research on molecular biomarkers, pharmacogenomics, stem cell therapies, gene editing, and artificial intelligence (AI) aims to improve patient outcomes through more personalized approaches tailored to the specific biological vulnerabilities underlying each individual’s diabetes.
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糖尿病研究的革命性创新:从生物标记物到基因组医学
糖尿病是一种慢性代谢性疾病,其特点是胰岛素信号不足导致高血糖。目前的治疗依赖于血红蛋白 A1c(HbA1c)等生物标志物来指导治疗,但新兴工具提供了通过更个性化的方法改变治疗的机会。分子生物标志物,包括微 RNA、代谢物和蛋白质,可以更好地预测疾病的进程和个人并发症的风险。基因组医学利用基因结构知识,根据个人的基因组特征指导有针对性的预防和治疗。干细胞研究可分化出分泌胰岛素的功能性细胞,移植到患者体内替代外源性胰岛素。基因沉默技术(如 RNA 干扰)可以通过抑制失调基因的表达,恢复有缺陷的胰岛素生成和分泌途径。人工智能应用可实现葡萄糖监测、胰岛素输送、并发症诊断筛查和数字健康指导的自动化。尽管在转化方面存在障碍,但这些技术在糖尿病管理的预测性、预防性、精确性和参与性护理模式方面具有颠覆性的潜力。对分子生物标志物、药物基因组学、干细胞疗法、基因编辑和人工智能(AI)的持续研究,旨在通过针对每个人糖尿病背后的特定生物脆弱性而量身定制的更个性化的方法来改善患者的治疗效果。
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审稿时长
26 weeks
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