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Cited by 922

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Metabolomics: A New Tool to Reveal the Nature of Diabetic Kidney Disease

Huang, Guoqing, Li, Mingcai, Li, Yan, Mao, Yushan·2022·Laboratory Medicine

Abstract

Metabolomics is a field of systems biology that draws on the scientific methods of other groups to qualitatively or quantitatively characterize small molecule metabolites in organisms, revealing their interconnections with the state of the organism at an overall relative macroscopic level. Diabetic kidney disease (DKD) is well known as a chronic metabolic disease, and metabolomics provides an excellent platform for its clinical study. A growing number of metabolomic analyses have revealed that individuals with DKD have metabolic disturbances of multiple substances in their bodies. With the continuous development and improvement of metabolomic analysis technology, the application of metabolomics in the clinical research of DKD is also expanding. This review discusses the recent progress of metabolomics in the early diagnosis, disease prognosis, and pathogenesis of DKD at the level of small molecule metabolites in vivo.

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Crossref · 10.1093/labmed/lmac041

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Metadata

Authors
Huang, G., Li, M., Li, Y., & Mao, Y.
Venue
Laboratory Medicine
Published
2022-06-24
Volume
53
Issue
6
Pages
545-551
ISSN
  • 0007-5027 (ISSN-L)
  • 1943-7730
OpenAlex
W4205440119
Cited by
922
References
8
FWCI
63.39
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metabolomics: A New Tool to Reveal the Nature of Diabetic Kidney Disease. https://app.cochat.ai/papers/10.1093/labmed/lmac041
The paper
Huang, G., Li, M., Li, Y., & Mao, Y. (2022). Metabolomics: A New Tool to Reveal the Nature of Diabetic Kidney Disease. Laboratory Medicine. https://doi.org/10.1093/labmed/lmac041

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.