Evidence mapPaperPMID 38580749Full record

ArticleScientific reports2024

Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism.

Balázs Erdős, Shauna D O'Donovan, Michiel E Adriaens, Anouk Gijbels, Inez Trouwborst, Kelly M Jardon, Gijs H Goossens, Lydia A Afman, Ellen E Blaak, Natal A W van Riel and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Balázs ErdősMaastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands. balazs@simula.no.
Shauna D O'DonovanDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Michiel E AdriaensMaastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
Anouk GijbelsDivision of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands.
Inez TrouwborstDepartment of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands.
Kelly M JardonDepartment of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands.
Gijs H GoossensDepartment of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands.ORCID http://orcid.org/0000-0002-2092-3019
Lydia A AfmanDivision of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands.
Ellen E BlaakDepartment of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands.ORCID http://orcid.org/0000-0002-2496-3464
Natal A W van RielDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.ORCID http://orcid.org/0000-0001-9375-4730
Ilja C W ArtsMaastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.ORCID http://orcid.org/0000-0001-6462-6692

Funding

DSM Nutritional Products, FrieslandCampina, Danone Nutricia Research, and the Topsector Agri & Food grant number: TiFN 16NH04Dutch Research Council (NWO) grant number: 628.011.027Dutch Research Council (NWO) grant number: 645.001.003
6 · The paper itself

Abstract

Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters' association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.

Indexed as

GlucoseInsulinBlood GlucoseBlood Glucose Self-MonitoringContinuous Glucose MonitoringHumansBlood GlucoseGlucoseInsulin

Identifiers

PMID38580749
PMCPMC11371931

What Socratic holds

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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 Socratic graph.