ArticleeLife2023
Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Prediction models for progression from diabetic kidney disease to end-stage renal disease: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis.Endocrine · 2024Pooled it
- Integrated Multi-Tissue Omics Identifies Acylcarnitine Accumulation as Shared Metabolic Marker of Diabetic Microangiopathy With Cross-Organ Validation.Investigative ophthalmology & visual science · 2026Article
- The diagnostic value of radiomics based on two-dimensional ultrasound in staging diabetic nephropathy.BMC medical imaging · 2026Article
- Article
- Where risk becomes visible: a layered fixed-policy framework for diabetic kidney disease screening in type 2 diabetes.medRxiv : the preprint server for health sciences · 2026Article
- Article
- MYO1C is a urinary extracellular vesicle biomarker and mediator of podocyte injury in diabetic nephropathy.JCI insight · 2026Article
- Literature-informed ensemble machine learning for three-year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model.Diabetes, obesity & metabolism · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Explainable machine learning integrating biochemical and metabolomic biomarkers with conventional clinical factors improves chronic kidney disease prediction and risk stratification.BMC nephrology · 2026Article
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Development and validation of a predictive model for diabetic kidney disease risk in patients with T2DM: a hospital data platform study.Frontiers in endocrinology · 2026Article
- Expression Signatures of Vascular Complication-Associated Proteins in Type 2 Diabetes: A Multiomics Analysis From the FIELD Study.Journal of diabetes research · 2026Article
- Identification and validation of an explainable prediction model of favorable outcome under integrative medicine treatment exposure in DKD adult patients: a retrospective cohort study.Frontiers in digital health · 2026Article
- Genome-Wide Association Studies of Diabetic Kidney Disease in East Asians With Type 2 Diabetes: Achievements and Future Perspectives.Current medicinal chemistry · 2026Review
- From albuminuria to multi-omics signatures: emerging biomarkers and drug targets for early-stage chronic kidney disease.Frontiers in pharmacology · 2026Review
- An integrative analysis of cardiac autonomic neuropathy and nephropathy risk assessed with SUDOSCAN in individuals with type 2 diabetes.Frontiers in endocrinology · 2026Article
- Serum alpha-1-microglobulin as a predictor of multiple complications in type 2 diabetes mellitus patients.World journal of diabetes · 2025Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD). Methods: We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40-80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m Results: ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847-0.856), which was 7.0% relatively higher than by LR 0.795 (0.790-0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies. Conclusions: Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites. Funding: This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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