Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults
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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 2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC). 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 Singapore Ministry of Health’s National Medical Research Council, NMRC/OFLCG/MOH-001327-03 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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Previous studies have demonstrated the feasibility of employing ML techniques to utilize various data types for identifying and predicting the risk of DKD in T2DM patients.
“Previous studies have demonstrated the feasibility of employing ML techniques to utilize various date types for identifying and predicting the risk of DKD in T2DM patients (26, 27).”
From Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis (2025) · cites this paper as [26]
The Introduction describes using imaging, metabolite, and genetic data with ML to predict DKD risk and identify predictors; the Discussion reports improved prediction of incident DKD. However, the study describes its participants as adults with diabetes, without specifying that they all had T2DM, so the claim’s population qualifier is not established by this source. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIn the Asian cohort by Sabanayagam et al., combining serum metabolomic features with clinical covariates improved AUC from 0.797 to 0.843 for DN prediction.
“in the Asian cohort by Sabanayagam et al. [101], combining serum metabolomic features with clinical covariates improved AUC from 0.797 to 0.843 for DN prediction.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [101]
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- Authors
- Sabanayagam, C., He, F., Nusinovici, S., Li, J., Lim, C., Tan, G., & Cheng, C. Y.
- Affiliations
- Singapore National Eye Center (SG)
- National University of Singapore (SG)
- Singapore General Hospital (SG)
- Corresponding author
- Sabanayagam, Charumathi
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2023-09-14
- Volume
- 12
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC10531395
- OpenAlex
- W4386725636
- Cited by
- 51
- References
- 40
- Citations by year
- 2026: 20 · 2025: 18 · 2024: 11 · 2023: 2
- FWCI
- 10.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Singapore Ministry of Health’s National Medical Research Council (NMRC/OFLCG/MOH-001327-03, NMRC/HCSAINV/MOH-001019-00)
- Topics
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- diabetic nephropathy, risk prediction, machine learning, elastic net, estimated glomerular filtration rate, diabetic retinopathy, blood metabolites, ketone bodies, HbA1c, hypertension, antidiabetic drugs, Asian adults, feature selection, novel risk factors, diabetes complications, retinal imaging
- MeSH terms
- Machine Learning, Adult, Diabetes Mellitus, Diabetic Nephropathies, Glycated Hemoglobin, Humans, Research Design, Cohort Studies
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