Multi-omics approaches for biomarker discovery and precision diagnosis of prediabetes DOI Creative Commons
Junying Song, Chuanfu Wang, Tong Zhao

et al.

Frontiers in Endocrinology, Journal Year: 2025, Volume and Issue: 16

Published: March 14, 2025

Recent advancements in multi-omics technologies have provided unprecedented opportunities to identify biomarkers associated with prediabetes, offering novel insights into its diagnosis and management. This review synthesizes the latest findings on prediabetes from multiple omics domains, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, radiomics. We explore how these elucidate molecular cellular mechanisms underlying analyze potential predictive value disease progression. Integrating data helps address limitations of traditional diagnostic methods, enabling early detection, personalized interventions, improved patient outcomes. However, challenges such as integration, standardization, clinical validation translation remain be resolved. Future research leveraging artificial intelligence machine learning is expected further enhance power technologies, contributing precision tailored management prediabetes.

Language: Английский

Multi-omics approaches for biomarker discovery and precision diagnosis of prediabetes DOI Creative Commons
Junying Song, Chuanfu Wang, Tong Zhao

et al.

Frontiers in Endocrinology, Journal Year: 2025, Volume and Issue: 16

Published: March 14, 2025

Recent advancements in multi-omics technologies have provided unprecedented opportunities to identify biomarkers associated with prediabetes, offering novel insights into its diagnosis and management. This review synthesizes the latest findings on prediabetes from multiple omics domains, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, radiomics. We explore how these elucidate molecular cellular mechanisms underlying analyze potential predictive value disease progression. Integrating data helps address limitations of traditional diagnostic methods, enabling early detection, personalized interventions, improved patient outcomes. However, challenges such as integration, standardization, clinical validation translation remain be resolved. Future research leveraging artificial intelligence machine learning is expected further enhance power technologies, contributing precision tailored management prediabetes.

Language: Английский

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