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Review Article | Open Access

Advanced strategies in organoids for metabolic monitoring and metabolic disease modeling

Wenjun Ma1,2Yongxin Xu2Zhifeng Gu1,2 ( )Dong Li1 ( )
Medical School of Nantong University, Nantong 226001, China
Department of Rheumatology, Research Center of Immunology, Affiliated Hospital of Nantong University, Nantong 226001, China
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Abstract

Metabolic diseases have emerged as a paramount global health threat amidst evolving lifestyles, with mechanistic investigations and therapeutic advancements impeded by the absence of physiologically relevant human experimental models. Organoids, self-organizing three-dimensional cultures of self-renewing cells, recapitulate in vivo tissue architecture and function by spontaneously forming structures analogous to native organs. Notably, this technology has demonstrated transformative utility across diverse biomedical fields, including disease modeling, developmental biology, regenerative medicine, and precision oncology, garnering substantial attention as a next-generation biological platform. In this review, we summarize recent progress in organoid-based metabolic monitoring and the generation of metabolic disease-associated organoids, detailing their construction methodologies and research milestones. We further discuss the current challenges and the future opportunities of the development and application of organoid technology in metabolic research.

Graphical Abstract

Current advances in organoid-based metabolic monitoring and metabolic disease modeling are reviewed. This review highlights recent breakthroughs in the use of organoids for real-time metabolic monitoring, as well as the engineering of novel organoid models to investigate human metabolic diseases.

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Nano Research
Article number: 94907988

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Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

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Cite this article:
Ma W, Xu Y, Gu Z, et al. Advanced strategies in organoids for metabolic monitoring and metabolic disease modeling. Nano Research, 2026, 19(1): 94907988. https://doi.org/10.26599/NR.2025.94907988
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Received: 07 July 2025
Revised: 20 August 2025
Accepted: 23 August 2025
Published: 25 December 2025
© The Author(s) 2026. Published by Tsinghua University Press.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).