ArticleInternational journal of epidemiology2020
GLU: a software package for analysing continuously measured glucose levels in epidemiology.
Article in International journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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
16 citing papers in PubMed.
- Eight-hour time-restricted eating does not lower daily myofibrillar protein synthesis rates: A randomized control trial.Obesity (Silver Spring, Md.) · 2023Trial
- The Evolving Landscape of Continuous Glucose Monitoring Metrics in Type 1 Diabetes: Narrative Literature Review.JMIR diabetes · 2026Review
- Examining the Influence of Glycemic Management During Sleep on Sleep-Related Bruxism: A Pilot Study.Journal of oral rehabilitation · 2026Article
- Imputation of Missing Continuous Glucose Monitor Data.Journal of diabetes science and technology · 2026Article
- Longitudinal multimorbidity trajectories shape personalized glycaemic patterns.Nature metabolism · 2026Article
- Glucose360: An Open-Source Python Platform with Event-Based Integration for Continuous Glucose Monitoring Data Analysis.Diabetes technology & therapeutics · 2026Article
- From data to insights: a tool for comprehensive Quantification of Continuous Glucose Monitoring (QoCGM).PeerJ · 2025Article
- Participant engagement and involvement in longitudinal cohort studies: qualitative insights from a selection of pregnancy and birth, twin, and family-based population cohort studies.BMC medical research methodology · 2024Article
- AGATA: A Toolbox for Automated Glucose Data Analysis.Journal of diabetes science and technology · 2024Article
- Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants.Cardiovascular diabetology · 2024Article
- A New Analysis Tool for Continuous Glucose Monitor Data.Journal of diabetes science and technology · 2022Article
- Perspective: A Framework for Addressing Dynamic Food Consumption Processes.Advances in nutrition (Bethesda, Md.) · 2022Article
- Postprandial Asymptomatic Glycemic Fluctuations after Gastrectomy for Gastric Cancer Using Continuous Glucose Monitoring Device.Journal of gastric cancer · 2021Article
- Preliminary prospective study of real-time post-gastrectomy glycemic fluctuations during dumping symptoms using continuous glucose monitoring.World journal of gastroenterology · 2021Article
- Developing Digital Tools for Remote Clinical Research: How to Evaluate the Validity and Practicality of Active Assessments in Field Settings.Journal of medical Internet research · 2021Article
- The second generation of The Avon Longitudinal Study of Parents and Children (ALSPAC-G2): a cohort profile.Wellcome open research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Continuous glucose monitors (CGM) record interstitial glucose levels 'continuously', producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night). To analyse these data, researchers tend to derive summary variables such as the area under the curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data. GLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC and proportion of time spent in hypo-, normo- and hyper- glycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user. GLU is implemented in R and is available on GitHub at https://github.com/MRCIEU/GLU. Git tag v0.2 corresponds to the version presented here.
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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.