ArticleStatistical methods in medical research2021
Glucodensities: A new representation of glucose profiles using distributional data analysis.
Article in Statistical methods in medical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.Statistics in medicine · 2026Article
- Continuous glucose monitoring as a tool in early-stage type 1 diabetes.Diabetologia · 2026Review
- Do Metrics of Temporal Glycemic Variability Reveal Abnormal Glucose Rates of Change in Type 1 Diabetes?Journal of diabetes science and technology · 2026Article
- Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study.BMC medical research methodology · 2026Article
- Distribution-valued data graphical model estimation based on M-LDQ feature embedding.Journal of applied statistics · 2026Article
- Continuous Glucose Monitoring Data Analysis 2.0: Functional Data Pattern Recognition and Artificial Intelligence Applications.Journal of diabetes science and technology · 2025Article
- Glucodensity functional profiles outperform traditional continuous glucose monitoring metrics.Scientific reports · 2025Article
- Evaluation of Asymptomatic Fasting Hypoglycemia in Outpatients Without Diabetes.Journal of the American Board of Family Medicine : JABFM · 2025Article
- FAST VARIABLE SELECTION FOR DISTRIBUTIONAL REGRESSION WITH APPLICATION TO CONTINUOUS GLUCOSE MONITORING DATA.The annals of applied statistics · 2025Article
- Article
- Improved detection of decreased glucose handling capacities via continuous glucose monitoring-derived indices.Communications medicine · 2025Article
- fastfrechet: An R package for fast implementation of Fréchet regression with distributional responses.Journal of open source software · 2025Article
- Impact of Continuous Glucose Monitoring and its Glucometrics in Clinical Practice in Spain and Future Perspectives: A Narrative Review.Advances in therapy · 2024Review
- Bayesian adaptive selection of basis functions for functional data representation.Journal of applied statistics · 2024Article
- Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis.Scientific reports · 2023Article
- Distributional data analysis via quantile functions and its application to modeling digital biomarkers of gait in Alzheimer's Disease.Biostatistics (Oxford, England) · 2023Article
- Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis.Frontiers in clinical diabetes and healthcare · 2023Article
- Scalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimer's Disease.Scientific reports · 2022Article
- Time above range for predicting the development of type 2 diabetes.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biosensor data have the potential to improve disease control and detection. However, the analysis of these data under free-living conditions is not feasible with current statistical techniques. To address this challenge, we introduce a new functional representation of biosensor data, termed the glucodensity, together with a data analysis framework based on distances between them. The new data analysis procedure is illustrated through an application in diabetes with continuous-time glucose monitoring (CGM) data. In this domain, we show marked improvement with respect to state-of-the-art analysis methods. In particular, our findings demonstrate that (i) the glucodensity possesses an extraordinary clinical sensitivity to capture the typical biomarkers used in the standard clinical practice in diabetes; (ii) previous biomarkers cannot accurately predict glucodensity, so that the latter is a richer source of information and; (iii) the glucodensity is a natural generalization of the time in range metric, this being the gold standard in the handling of CGM data. Furthermore, the new method overcomes many of the drawbacks of time in range metrics and provides more in-depth insight into assessing glucose metabolism.
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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.