Evidence map›Paper›PMID 42476949›Full record

ArticleDiabetes, obesity & metabolism2026

Postprandial Glycemic Control With Automated Insulin Delivery With Control-IQ Technology: The Impact of Meal Composition.

Marco Marigliano, Claudia Piona, Elisa Morotti, Maddalena Macedoni, Parto Kharazizadeh, Valentina Mancioppi, Francesca Tomasselli, Mara Tommasi, Claudio Maffeis

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In one paragraph

Article in Diabetes, obesity & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Marco MariglianoSection of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy.ORCID 0000-0002-3232-8807
Claudia PionaSection of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy.ORCID 0000-0001-5168-8317
Elisa MorottiSection of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy.
Maddalena MacedoniSection of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy.
Parto KharazizadehSection of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy.
Valentina MancioppiSection of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy.
Francesca TomasselliSection of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy.
Mara TommasiSection of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy.
Claudio MaffeisSection of Pediatric Diabetes and Metabolism, Department of Surgery, Dentistry, Pediatrics, and Gynecology, University of Verona, Verona, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsTo evaluate the efficiency of the AID system with Control-IQ technology and carbohydrate counting (CHC)-based premeal bolus to control postprandial glucose (PPG) after mixed meals with different macronutrient compositions in adolescents with type 1 diabetes (T1D). MATERIAL AND

methodsIn this prospective crossover study, 25 adolescents with T1D using an automated insulin delivery (AID-Tandem Tslim X2 with Control-IQ) consumed three iso-caloric meals (745 kcal) on separate days: a standard meal (56.5% carbohydrate), a high-fat (HF) meal (45.9% fat), and a high-protein (HP) meal (24.4% protein). A CHC-based bolus was administered 15 min before each meal. Continuous glucose monitoring metrics and AID insulin delivery were assessed over 6 h. The primary outcome was 6-h time in range (TIR, 70-180 mg/dL).

resultsSix-hour TIR was significantly lower after HF (65.2%) and HP meals (49.9%) compared with the standard meal (84.3%) (p < 0.01 and p < 0.001, respectively). Postprandial glucose exposure (AUC above 140 and 180 mg/dL) was significantly higher after HF and HP meals at both 3 and 6 h. The AID system delivered significantly more insulin than the pre-programmed basal for HF (+26% at 6 h, p < 0.05) and HP meals (+55% at 6 h, p < 0.001), without increasing hypoglycemia.

conclusionsDespite a substantial autonomous increase in insulin delivery, AID systems using Control-IQ technology and a standard CHC-based pre-meal bolus have not achieved adequate postprandial glycemic control after HF and HP meals. Proactively timed, meal-specific insulin strategies are required to improve postprandial outcomes after nutritionally complex meals.

Indexed as

Diabetes Mellitus, Type 1Glycemic ControlHypoglycemic AgentsInsulinMealsAdolescentBlood GlucoseContinuous Glucose MonitoringCross-Over StudiesDietary CarbohydratesFemaleHumansHypoglycemiaInsulin Infusion SystemsMalePostprandial PeriodBlood GlucoseDietary CarbohydratesHypoglycemic AgentsInsulinadolescentsautomated insulin deliverycontrol‐IQhigh‐fat mealhigh‐protein mealpostprandial glucosetime in rangetype 1 diabetes

Identifiers

PMID42476949
PMCPMC13538753

What Socratic holds

Textmetadata
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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.