ArticleJournal of advanced research2024
Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese.
Article in Journal of advanced research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- Plasma Metabolite Associations for Risk and Laboratory Measures of Type 2 Diabetes in a Large-Scale Finnish Prospective Cohort.Diabetes care · 2026Article
- Predicting cardiometabolic multimorbidity trajectory in middle-aged and older Chinese adults: insights from the cohort study on global ageing and adult health.Frontiers in medicine · 2026Article
- An interpretable prediction model for non-invasive identification of the phlegm-dampness constitution based on machine learning.Frontiers in endocrinology · 2026Article
- Early changes in urineMetabolomics : Official journal of the Metabolomic Society · 2025Article
- Type 2 Diabetes Mellitus: A Metabolic Model of Accelerated Aging - Multi-Organ Mechanisms and Intervention Approaches.Aging and disease · 2025Review
- Early Prediction of Fetal Macrosomia Through Maternal Lipid Profiles.International journal of molecular sciences · 2025Article
- Catalpol-a compound fromFrontiers in microbiology · 2025Article
- Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction.Cardiovascular diabetology · 2024Article
- Interplay of cardiovascular health and diabetes: Insights into weight management and risk reduction.Journal of diabetes investigation · 2024Article
- Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.International journal of molecular sciences · 2024Observational
- A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks.Scientific reports · 2024Article
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Authors and funding
11 authors at 3 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
introductionType 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction.
methodsA non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years.
resultsMetabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D.
conclusionsOur findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.
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