ArticlePloS one2017
Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Gut-associated metabolites and diabetes pathology: a systematic review.Frontiers in endocrinology · 2025Pooled it
- Mitigation of Metabolic Diseases Through Personalized Nutrition: A Critical In-Depth Review.Food science & nutrition · 2026Review
- Performance of diabetes risk prediction models: a systematic review and meta-analysis.Endocrine connections · 2025Article
- Association of Betaine, Choline, and TMAO with Type 2 Diabetes in Rural China: A Nested Case-Control Study from the Handan Eye Study (HES).Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Explore the changes of metabolites in feces and serum of acute pancreatitis patients with different etiologies by LC-MS based metabolomics strategy.Frontiers in pharmacology · 2025Article
- Article
- Targeted metabolomics-based understanding of the sleep disturbances in drug-naïve patients with schizophrenia.BMC psychiatry · 2024Article
- Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer.Endocrinology and metabolism (Seoul, Korea) · 2023Review
- Preliminary observational study of metabonomics in patients with early and late-onset type 2 diabetes mellitus based on UPLC-Q-TOF/MS.Scientific reports · 2023Observational
- A Review of the Impact of Pharmacogenetics and Metabolomics on the Efficacy of Metformin in Type 2 Diabetes.International journal of medical sciences · 2023Review
- Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine.International journal of molecular sciences · 2022Review
- Possible Gender Influence in the Mechanisms Underlying the Oxidative Stress, Inflammatory Response, and the Metabolic Alterations in Patients with Obesity and/or Type 2 Diabetes.Antioxidants (Basel, Switzerland) · 2021Review
- How Perturbated Metabolites in Diabetes Mellitus Affect the Pathogenesis of Hypertension?Frontiers in physiology · 2021Review
- Research progress and perspective in metabolism and metabolomics of psoriasis.Chinese medical journal · 2020Article
- Altered Metabolome of Lipids and Amino Acids Species: A Source of Early Signature Biomarkers of T2DM.Journal of clinical medicine · 2020Review
- Enhanced single-cell metabolomics by capillary electrophoresis electrospray ionization-mass spectrometry with field amplified sample injection.Analytica chimica acta · 2020Article
- Translational Metabolomics: Current Challenges and Future Opportunities.Metabolites · 2019Article
- Metabolomics of Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2019Review
- Overview of genomics and post-genomics research on type 2 diabetes mellitus: Future perspectives and a framework for further studies.Journal of biosciences · 2019Review
- Detection of Secondary Metabolites as Biomarkers for the Early Diagnosis and Prevention of Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2019Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers.
methodsGas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D.
resultsEstablished markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738-0.850]) and 0.808 [0.749-0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]). Prediction based on non-blood based measures was 0.638 [0.565-0.711]).
conclusionsEstablished measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.