ArticleDiabetes/metabolism research and reviews2017
Glucose time series complexity as a predictor of type 2 diabetes.
Article in Diabetes/metabolism research and reviews, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Frequency of Microvascular Pathology and Hippocampal Atrophy on Magnetic Resonance Imaging in a Community Study of Alzheimer's Disease with Blood-Based Biomarkers.Annals of neurology · 2025Article
- Glucostats: an efficient Python library for glucose time series feature extraction and visual analysis.BMC bioinformatics · 2025Article
- Ultralow-Dimensionality Reduction for Identifying Critical Transitions by Spatial-Temporal PCA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices.Communications medicine · 2025Article
- Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.Scientific reports · 2024Article
- Impact of the Complexity of Glucose Time Series on All-Cause Mortality in Patients With Type 2 Diabetes.The Journal of clinical endocrinology and metabolism · 2023Article
- Article
- Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics.Entropy (Basel, Switzerland) · 2018Article
- Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures.Entropy (Basel, Switzerland) · 2018Article
- Glucose time series complexity as a predictor of type 2 diabetes.Diabetes/metabolism research and reviews · 2017Article
- Decreased complexity of glucose dynamics preceding the onset of diabetes in mice and rats.PloS one · 2017Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundComplexity analysis of glucose profile may provide valuable information about the gluco-regulatory system. We hypothesized that a complexity metric (detrended fluctuation analysis, DFA) may have a prognostic value for the development of type 2 diabetes in patients at risk.
methodsA total of 206 patients with any of the following risk factors (1) essential hypertension, (2) obesity or (3) a first-degree relative with a diagnosis of diabetes were included in a survival analysis study for a diagnosis of new onset type 2 diabetes. At inclusion, a glucometry by means of a Continuous Glucose Monitoring System was performed, and DFA was calculated for a 24-h glucose time series. Patients were then followed up every 6 months, controlling for the development of diabetes.
resultsIn a median follow-up of 18 months, there were 18 new cases of diabetes (58.5 cases/1000 patient-years). DFA was a significant predictor for the development of diabetes, with ten events in the highest quartile versus one in the lowest (log-rank test chi2 = 9, df = 1, p = 0.003), even after adjusting for other relevant clinical and biochemical variables. In a Cox model, the risk of diabetes development increased 2.8 times for every 0.1 DFA units. In a multivariate analysis, only fasting glucose, HbA
conclusionsDetrended fluctuation analysis significantly performed as a harbinger of type 2 diabetes development in a high-risk population. Complexity analysis may help in targeting patients who could be candidates for intensified treatment. Copyright © 2016 The Authors. Diabetes/Metabolism Research and Reviews Published by John Wiley & Sons Ltd.
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