ArticleeLife2024
Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Lifestyles, metabolome and diabetic kidney disease: a cohort study.QJM : monthly journal of the Association of Physicians · 2026Article
- Quantitative metabolomics and machine learning identify differential metabolomic biomarkers across normoglycaemia, pre-diabetes and diabetes in a Qinghai Plateau population.BMJ public health · 2026Article
- Metabolomics in Traditional Chinese Medicine for Diabetes Mellitus: Mechanistic Insights, Biomarker Discovery, and Clinical Application Prospects.International journal of analytical chemistry · 2026Review
- EAT-Lancet and plant-based diets, plasma metabolomic signatures, and biological aging.npj aging · 2025Article
- Use of technology in prediabetes and precision prevention.Journal of diabetes investigation · 2025Review
- Metabolomics in cardiometabolic diseases: Key biomarkers and therapeutic implications for insulin resistance and diabetes.Journal of internal medicine · 2025Review
- Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus.Nutrients · 2025Article
- Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics.Cardiovascular diabetology · 2025Article
- Metabolomics uncovers the diabetes metabolic network: from pathophysiological mechanisms to clinical applications.Frontiers in endocrinology · 2025Review
- Sarcosine, Trigonelline and Phenylalanine as Urinary Metabolites Related to Visceral Fat in Overweight and Obesity.Metabolites · 2024Article
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Authors and funding
11 authors.
Funding
Abstract
Background: Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. Methods: This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. Results: During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Conclusions: Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions. Funding: Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).
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