ArticleJournal of diabetes science and technology2018
Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data.
Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Non-Invasive Continuous Glucose Monitoring in Patients Without Diabetes: Use in Cardiovascular Prevention-A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Discordance Between Glucose Levels Measured in Interstitial FluidFrontiers in endocrinology · 2021Trial
- Integration of Continuous Glucose Monitoring With HbAJMIR diabetes · 2026Article
- Postpartum Glycaemic Phenotype and Continuous Glucose Monitoring at 4-6 Years After Gestational Diabetes Defined by Different Diagnostic Criteria.Journal of diabetes research · 2026Article
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- The sleep for health study: A randomized clinical trial of the impact of insomnia treatment on glycemia in people with prediabetes.Contemporary clinical trials · 2025Article
- Are standardized conditions needed for correct CGM data interpretation in subjects at early stages of glucose intolerance?Diabetology & metabolic syndrome · 2025Article
- Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?Journal of diabetes science and technology · 2024Review
- Metabolic health tracking using Ultrahuman M1 continuous glucose monitoring platform in non- and pre-diabetic Indians: a multi-armed observational study.Scientific reports · 2024Observational
- Difference on Glucose Profile From Continuous Glucose Monitoring in People With Prediabetes vs. Normoglycemic Individuals: A Matched-Pair Analysis.Journal of diabetes science and technology · 2024Article
- 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.Journal of diabetes science and technology · 2024Article
- Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated with deteriorating glucose regulation.Frontiers of medicine · 2023Article
- Continuous Glucose Monitoring in Healthy Adults-Possible Applications in Health Care, Wellness, and Sports.Sensors (Basel, Switzerland) · 2022Review
- Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm.JMIR medical informatics · 2022Article
- Incidence of hyperglycaemic disorders in children and adolescents with obesity.Pediatric endocrinology, diabetes, and metabolism · 2022Article
- Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept.BMJ open diabetes research & care · 2021Article
- Effect of Exercise and Meals on Continuous Glucose Monitor Data in Healthy Individuals Without Diabetes.Journal of diabetes science and technology · 2021Article
- Dysglycemia in adults at risk for or living with non-insulin treated type 2 diabetes: Insights from continuous glucose monitoring.EClinicalMedicine · 2021Article
- Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes.Journal of clinical medicine · 2021Article
- Acute Effect of Height-Adjustable Workstations on Blood Glucose Levels in Women With Impaired Fasting Glucose Levels While Working: A Pilot Study.Translational journal of the American College of Sports Medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach.
methodsThe data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D.
resultsHealthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy.
conclusionsEven with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.