ArticleDiabetes care2014
Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes.
Article in Diabetes care, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed, 101 citations in OpenAlex.
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- Longitudinal study reveals plasma glycans associations with prediabetes/type 2 diabetes in KORA study.Cardiovascular diabetology · 2025Article
- ECG features improve multimodal deep learning prediction of incident T2DM in a Middle Eastern cohort.Scientific reports · 2025Article
- Factors Associated With Type 2 Diabetes in Older Japanese With Similar Genetic Risk Scores: The Bunkyo Health Study.Journal of the Endocrine Society · 2025Article
- Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks.Nature communications · 2024Article
- Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese.Journal of advanced research · 2024Article
- Hemoglobin A1c Genetics and Disparities in Risk of Diabetic Retinopathy in Individuals of Genetically Inferred African American/African British and European Ancestries.Diabetes care · 2024Article
- AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.Nature communications · 2024Article
- Predictive model for early detection of type 2 diabetes using patients' clinical symptoms, demographic features, and knowledge of diabetes.Health science reports · 2024Article
- Advancements in precision medicine: multi-omics approach for tailored metformin treatment in type 2 diabetes.Frontiers in pharmacology · 2024Review
- An epidemiological introduction to human metabolomic investigations.Trends in endocrinology and metabolism: TEM · 2023Review
- Metformin Monotherapy Alters the Human Plasma Lipidome Independent of Clinical Markers of Glycemic Control and Cardiovascular Disease Risk in a Type 2 Diabetes Clinical Cohort.The Journal of pharmacology and experimental therapeutics · 2023Observational
- Molecular Biomarkers for Cardiometabolic Disease: Risk Assessment in Young Individuals.Circulation research · 2023Review
- An update on diagnosis and therapeutics for type-2 diabetes mellitus.Bioinformation · 2023Article
- Metabolic and Genetic Markers Improve Prediction of Incident Type 2 Diabetes: A Nested Case-Control Study in Chinese.The Journal of clinical endocrinology and metabolism · 2022Article
- Genetic Susceptibility to Insulin Resistance and Its Association with Estimated Longevity in the Hungarian General and Roma Populations.Biomedicines · 2022Article
- Circulating metabolite profile in young adulthood identifies long-term diabetes susceptibility: the Coronary Artery Risk Development in Young Adults (CARDIA) study.Diabetologia · 2022Observational
- Relationship between glucose homeostasis and obesity in early life-a study of Italian children and adolescents.Human molecular genetics · 2022Article
- A novel transcriptional risk score for risk prediction of complex human diseases.Genetic epidemiology · 2021Article
- 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
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
Abstract
objectiveA genetic risk score (GRS) comprised of single nucleotide polymorphisms (SNPs) and metabolite biomarkers have each been shown, separately, to predict incident type 2 diabetes. We tested whether genetic and metabolite markers provide complementary information for type 2 diabetes prediction and, together, improve the accuracy of prediction models containing clinical traits. RESEARCH DESIGN AND
methodsDiabetes risk was modeled with a 62-SNP GRS, nine metabolites, and clinical traits. We fit age- and sex-adjusted logistic regression models to test the association of these sources of information, separately and jointly, with incident type 2 diabetes among 1,622 initially nondiabetic participants from the Framingham Offspring Study. The predictive capacity of each model was assessed by area under the curve (AUC).
resultsTwo hundred and six new diabetes cases were observed during 13.5 years of follow-up. The AUC was greater for the model containing the GRS and metabolite measurements together versus GRS or metabolites alone (0.820 vs. 0.641, P < 0.0001, or 0.820 vs. 0.803, P = 0.01, respectively). Odds ratios for association of GRS or metabolites with type 2 diabetes were not attenuated in the combined model. The AUC was greater for the model containing the GRS, metabolites, and clinical traits versus clinical traits only (0.880 vs. 0.856, P = 0.002).
conclusionsMetabolite and genetic traits provide complementary information to each other for the prediction of future type 2 diabetes. These novel markers of diabetes risk modestly improve the predictive accuracy of incident type 2 diabetes based only on traditional clinical risk factors.
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