Evidence mapPaperPMID 21949219Full record

Trial reportDiabetes care2011

Improving the estimation of mealtime insulin dose in adults with type 1 diabetes: the Normal Insulin Demand for Dose Adjustment (NIDDA) study.

Jiansong Bao, Heather R Gilbertson, Robyn Gray, Diane Munns, Gabrielle Howard, Peter Petocz, Stephen Colagiuri, Jennie C Brand-Miller

Open access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Diabetes care, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 24% of its field
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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Observational
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Incorrect Insulin Administration: A Problem That Warrants Attention.Clinical diabetes : a publication of the American Diabetes Association · 2016
    Article
  16. Review
  17. Article
  18. Importance of blood glucose meter and carbohydrate estimation accuracy.Journal of diabetes science and technology · 2012
    Article
  19. Insulin therapy and hypoglycemia.Endocrinology and metabolism clinics of North America · 2012
    Review
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 at 3 institutions in 1 country.

Jiansong BaoBoden Institute of Obesity, Nutrition & Exercise and the School of Molecular Biosciences, University of Sydney, Sydney, Australia.
Heather R Gilbertson
Robyn Gray
Diane Munns
Gabrielle Howard
Peter Petocz
Stephen Colagiuri
Jennie C Brand-Miller
University of Sydney · AUMacquarie University · AURoyal Children's Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveAlthough carbohydrate counting is routine practice in type 1 diabetes, hyperglycemic episodes are common. A food insulin index (FII) has been developed and validated for predicting the normal insulin demand generated by mixed meals in healthy adults. We sought to compare a novel algorithm on the basis of the FII for estimating mealtime insulin dose with carbohydrate counting in adults with type 1 diabetes. RESEARCH DESIGN AND

methodsA total of 28 patients using insulin pump therapy consumed two different breakfast meals of equal energy, glycemic index, fiber, and calculated insulin demand (both FII = 60) but approximately twofold difference in carbohydrate content, in random order on three consecutive mornings. On one occasion, a carbohydrate-counting algorithm was applied to meal A (75 g carbohydrate) for determining bolus insulin dose. On the other two occasions, carbohydrate counting (about half the insulin dose as meal A) and the FII algorithm (same dose as meal A) were applied to meal B (41 g carbohydrate). A real-time continuous glucose monitor was used to assess 3-h postprandial glycemia.

resultsCompared with carbohydrate counting, the FII algorithm significantly decreased glucose incremental area under the curve over 3 h (-52%, P = 0.013) and peak glucose excursion (-41%, P = 0.01) and improved the percentage of time within the normal blood glucose range (4-10 mmol/L) (31%, P = 0.001). There was no significant difference in the occurrence of hypoglycemia.

conclusionsAn insulin algorithm based on physiological insulin demand evoked by foods in healthy subjects may be a useful tool for estimating mealtime insulin dose in patients with type 1 diabetes.

Indexed as

FoodAdultBlood GlucoseDiabetes Mellitus, Type 1FastingFemaleHumansHypoglycemic AgentsInsulinInsulin Infusion SystemsMaleMiddle AgedPostprandial PeriodYoung AdultBlood GlucoseHypoglycemic AgentsInsulin

Identifiers

PMID21949219
PMCPMC3177729
OpenAlexW2093822648

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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.