ArticleJournal of diabetes science and technology2024
Predicting the Risk of Developing Type 1 Diabetes Using a One-Week Continuous Glucose Monitoring Home Test With Classification Enhanced by Machine Learning: An Exploratory Study.
Article in Journal of diabetes science and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Testing methods used to predict disease progression in children with early-stage type 1 diabetes: A systematic review and meta-analysis.Diabetic medicine : a journal of the British Diabetic Association · 2025Pooled it
- Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Article
- Continuous glucose monitoring as a tool in early-stage type 1 diabetes.Diabetologia · 2026Review
- Review
- Impact of Carbohydrate Intake Fluctuations on Glucose Profiles: Insights from Continuous Glucose Monitoring-Based Patient Clustering.Endocrinology and metabolism (Seoul, Korea) · 2026Observational
- Artificial intelligence-driven therapeutics for disease modification in type 1 diabetes: a digital public health and clinical translation framework.Frontiers in public health · 2026Review
- Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning.Nature biomedical engineering · 2025Article
- Enhancing Type 1 Diabetes Immunological Risk Prediction with Continuous Glucose Monitoring and Genetic Profiling.Diabetes technology & therapeutics · 2025Article
- Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics.The Journal of clinical endocrinology and metabolism · 2024Article
- Predicting Type 2 Diabetes Metabolic Phenotypes Using Continuous Glucose Monitoring and a Machine Learning Framework.medRxiv : the preprint server for health sciences · 2024Article
- Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes.Diabetologia · 2024Article
- Consensus Guidance for Monitoring Individuals With Islet Autoantibody-Positive Pre-Stage 3 Type 1 Diabetes.Diabetes care · 2024Article
- Machine learning-based prediction of long-term new-onset diabetes mellitus risk after pancreaticoduodenectomy using radiomics.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDetection of two or more autoantibodies (Ab) in the blood might describe those individuals at increased risk of developing type 1 diabetes (T1D) during the following years. The aim of this exploratory study is to propose a high versus low T1D risk classifier using machine learning technology based on continuous glucose monitoring (CGM) home data.
methodsForty-two healthy relatives of people with T1D with mean ± SD age of 23.8 ± 10.5 years, HbA1c (glycated hemoglobin) of 5.3% ± 0.3%, and BMI (body mass index) of 23.2 ± 5.2 kg/m
resultsThe percent time of glucose >180 mg/dL (T180), glucose range, and glucose CV (coefficient of variation) were the only significant differences between the glycemic features in the two groups with
conclusionsA machine learning technology, combining a potentially self-administered one-week CGM home test, has the potential to reliably assess the T1D risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.