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Cited by 252Metabolomics and Mass Spectrometry StudiesDiabetes, Cardiovascular Risks, and LipoproteinsDiabetes Management and Research

Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies

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Jin, Qiao, Ma, Ronald Ching Wan·2021·Cells
DOI 10.3390/cells10112832PMC PMC8616415
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Abstract

The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.

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  • By incorporating genetic information, metabolic markers, and lifestyle data, AI models create a more accurate prediction of a patient's risk for developing diabetes-related complications.

    “A study emphasized that by incorporating genetic information, metabolic markers, and lifestyle data, these AI models create a more accurate prediction of a patient’s risk for developing diabetes-related complications (53).”

    From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [53]

    Section 8 discusses integrating genetic, metabolomic, and clinical data to investigate diabetes and its complications, so the claim has a basis in the review. But it does not report that AI models combining those data with lifestyle information improve prediction of an individual patient’s complications. Section 7 cautions that most metabolites examined did not improve prediction beyond established risk factors. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Omics data, including transcriptomics, proteomics, and metabolomics, have injected mechanistic insights into predictive modeling for diabetic complications.

    “Omics data, including transcriptomics, proteomics, and metabolomics, have injected mechanistic insights into predictive modeling for diabetic complications [158–160].”

    From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [159]

    Section 8 (page unavailable) says integrating multi-omics data may reveal biological pathways relevant to diabetic complications, and Section 10 says metabolomics has identified predictive biomarkers. The paper does not show that transcriptomics, proteomics, and metabolomics together have injected mechanistic insights into predictive models; Section 7 notes that metabolomic biomarkers often add limited predictive value to established models. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Authors
Jin, Q., & Ma, R. C. W.
Affiliations
  • Chinese University of Hong Kong (HK)
  • Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
ORCID
Venue
Cells
Publisher
MDPI AG
Published
2021-10-21
Volume
10
Issue
11
Pages
2832
ISSN
2073-4409 (ISSN-L)
DOI
10.3390/cells10112832
PMC
PMC8616415
OpenAlex
W3207456244
Cited by
252
References
175
Citations by year
2026: 31 · 2025: 65 · 2024: 85 · 2023: 49 · 2022: 22
FWCI
4.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Cells (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
the Research Grants Council Research Impact Fund (R4012-18)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Keywords
metabolomics, type 2 diabetes, diabetes complications, metabolomic biomarkers, diabetic nephropathy, diabetic cardiovascular disease, branched-chain amino acids, phenylalanine metabolites, energy metabolism, lipid metabolism, multi-omics integration, systems biology, therapeutic target
MeSH terms
Gastrointestinal Microbiome, Diabetes Mellitus, Type 2, Humans, Biomarkers, Diabetes Complications, Systems Biology, Metabolomics, Metabolome

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