Evidence map›Paper›PMID 42029052›Full record

ArticleJournal of diabetes science and technology2026

Quantifying the Effect of Fat and Protein on the Postprandial Glucose Excursion in Individuals With Type 1 Diabetes Using an Automated Insulin Delivery System.

Edoardo Faggionato, Michele Schiavon, Laya Ekhlaspour, Ryan S Kingman, Jennifer L Sherr, Gregory P Forlenza, Bruce A Buckingham, Chiara Dalla Man

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In one paragraph

Article in Journal of diabetes science and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Edoardo FaggionatoDepartment of Information Engineering, University of Padova, Padova, Italy.ORCID 0000-0001-7737-6987
Michele SchiavonDepartment of Information Engineering, University of Padova, Padova, Italy.ORCID 0000-0003-0590-2399
Laya EkhlaspourDivision of Endocrinology, Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0002-3263-1419
Ryan S KingmanDivision of Pediatric Endocrinology, Stanford University, Stanford, CA, USA.ORCID 0000-0001-7133-5092
Jennifer L SherrDepartment of Pediatric Endocrinology and Diabetes, Yale University, New Haven, CT, USA.ORCID 0000-0001-9301-3043
Gregory P ForlenzaBarbara Davis Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID 0000-0003-3607-9788
Bruce A BuckinghamDivision of Pediatric Endocrinology, Stanford University, Stanford, CA, USA.ORCID 0000-0003-4581-4887
Chiara Dalla ManDepartment of Information Engineering, University of Padova, Padova, Italy.ORCID 0000-0002-4908-0596

Funding

Modeling and modulating insulin delivery in automated insulin delivery systems to accommodate for meal compositionsK23DK121942 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI EKHLASPOUR, LAYA · 2020 to 2024
$964k
NIDDK NIH HHS K23 DK121942
6 · The paper itself

Abstract

backgroundQuantifying the effect of meal composition (MC) on postprandial glucose excursions would allow optimizing insulin therapy, accounting for fat and protein that can affect gastric retention (GR), glucose rate of appearance (R

methodsA total of 120 individuals with type 1 diabetes (age = 15.5 ± 11.5 years, weight = 51.3 ± 28.0 kg) were monitored under free-living conditions while using CGM and CSII, and MC was carefully recorded. We extracted 353 CGM and CSII traces using predefined criteria and classified them into low or high fat content and low or high protein content. Finally, the MI-OMM was used to estimate GR, R

resultsMI-OMM was able to fit CGM profiles and provided precise and physiologically plausible parameter estimates. Comparison among different classes of meals showed that a high content of fat and protein in the meal significantly slowed both GR (

conclusionsIn this work, the effect of MC on postprandial glucose excursion was quantified in real-life conditions with the help of a model-based methodology. These results are usable for redesigning current insulin therapies, accounting for the presence of fat and protein in meals.

Indexed as

CGMCSIIdecision support systemmathematical modelmeal compositionoutpatient

Identifiers

PMID42029052
PMCPMC13109242

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.