ArticleDiabetes, obesity & metabolism2018
A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes.
Article in Diabetes, obesity & metabolism, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.BMC medical informatics and decision making · 2021Pooled it
- Nonlinear Association Between THs, TSH, and HbA1c in Patients With Type 2 Diabetes Mellitus: A Retrospective Study.Journal of diabetes · 2026Article
- Latent class growth mixture modeling of HbA1C trajectories identifies individuals at high risk of developing complications of type 2 diabetes mellitus in the UK Biobank.BMJ open diabetes research & care · 2025Article
- Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.Biomimetics (Basel, Switzerland) · 2023Article
- Precision subclassification of type 2 diabetes: a systematic review.Communications medicine · 2023Article
- Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes.Clinical epidemiology · 2023Article
- Great diversity in the utilization and reporting of latent growth modeling approaches in type 2 diabetes: A literature review.Heliyon · 2022Review
- Research on imbalance machine learning methods for MRBMC medical imaging · 2022Article
- Article
- Machine learning and deep learning predictive models for type 2 diabetes: a systematic review.Diabetology & metabolic syndrome · 2021Review
- Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal.Journal of medical Internet research · 2021Article
- The cost-effectiveness of dapagliflozin compared to DPP-4 inhibitors in the treatment of type 2 diabetes mellitus in the Netherlands.Diabetic medicine : a journal of the British Diabetic Association · 2021Article
- Article
- Association of Body Fat Percentage with Time in Range Generated by Continuous Glucose Monitoring during Continuous Subcutaneous Insulin Infusion Therapy in Type 2 Diabetes.Journal of diabetes research · 2021Observational
- Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes.Current diabetes reports · 2020Review
- Preferences of people with Type 2 diabetes for diabetes care: a discrete choice experiment.Diabetic medicine : a journal of the British Diabetic Association · 2020Article
- Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAPInternational journal of environmental research and public health · 2020Article
- Study on the correlation and predictive value of serum pregnancy-associated plasma protein A, triglyceride and serum 25-hydroxyvitamin D levels with gestational diabetes mellitus.World journal of clinical cases · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient-centred care.
methodsWe conducted a retrospective study in two cohorts, using routinely collected individual patient data from primary care practices obtained from two large Dutch diabetes patient registries. Participants included adult patients newly diagnosed with type 2 diabetes between January 2006 and December 2014 (development cohort, n = 10 528; validation cohort, n = 3777). Latent growth mixture modelling identified distinct glycaemic 5-year trajectories. Machine learning models were built to predict the trajectories using easily obtainable patient characteristics in daily clinical practice.
resultsThree different glycaemic trajectories were identified: (1) stable, adequate glycaemic control (76.5% of patients); (2) improved glycaemic control (21.3% of patients); and (3) deteriorated glycaemic control (2.2% of patients). Similar trajectories could be discerned in the validation cohort. Body mass index and glycated haemoglobin and triglyceride levels were the most important predictors of trajectory membership. The predictive model, trained on the development cohort, had a receiver-operating characteristic area under the curve of 0.96 in the validation cohort, indicating excellent accuracy.
conclusionsThe developed model can effectively explain heterogeneity in future glycaemic response of patients with type 2 diabetes. It can therefore be used in clinical practice as a quick and easy tool to provide tailored diabetes care.
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