ArticleNPJ digital medicine2024
A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- A digital twin-enhanced decision support system improves time-in-range in type 1 diabetes: a randomized clinical trial.Scientific reports · 2025Trial
- Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.Pediatric nephrology (Berlin, Germany) · 2026Review
- Data, Process, and Data-Driven Representations of Digital Twins in Diabetes: Scoping Review.JMIR diabetes · 2026Review
- Bladder Digital Twins for Neuromodulation: Current Platforms and a Roadmap to Closed-Loop Therapy.International neurourology journal · 2026Article
- Nephrology 2050: an evidence-informed roadmap from precision prevention to kidney regeneration.International urology and nephrology · 2026Review
- Applications of digital twin technology in cardiometabolic disease management: a scoping review.BMC medical informatics and decision making · 2026Article
- Digital Twin Applications in Diabetes Management: Scoping Review.JMIR diabetes · 2026Review
- The Potential of Digital Twins for Pediatric Rare Diseases.CPT: pharmacometrics & systems pharmacology · 2026Review
- Translational Potential: Kidney Tubuloids in Precision Medicine and Regenerative Nephrology.Pharmaceutics · 2026Article
- Medical AI across Data Regimes to Promote Proactive Health.Health data science · 2026Review
- Development and Validation of a Nomogram for Predicting Chronic Kidney Disease in Older Patients with Type 2 Diabetes Mellitus and Cardiovascular Disease.Journal of multidisciplinary healthcare · 2026Article
- Application of Digital Twin Technology to Enhance Chronic Diseases Management: A Systematic Review.International journal of telemedicine and applications · 2026Review
- The role of digital twins in P4 medicine: A paradigm for modern healthcare.NPJ digital medicine · 2025Review
- Cardiometabolic risk reduction with digital twinning in patients with type 2 diabetes.Cardiovascular diabetology. Endocrinology reports · 2025Review
- From Architecture to Outcomes: Mapping the Landscape of Digital Twins for Personalized Diabetes Care-A Scoping Review.Journal of personalized medicine · 2025Review
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- Big Data-Driven Health Portraits for Personalized Management in Noncommunicable Diseases: Scoping Review.Journal of medical Internet research · 2025Article
- The Potential Use of Digital Twin Technology for Advancing CAR-T Cell Therapy.Current issues in molecular biology · 2025Review
- Optimizing metabolic health with digital twins.npj aging · 2025Review
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients' health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans.
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Registered trials
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