Evidence map›Paper›PMID 25005674›Full record

Trial reportEuropean journal of clinical nutrition2014

Estimating insulin demand for protein-containing foods using the food insulin index.

K J Bell, R Gray, D Munns, P Petocz, G Howard, S Colagiuri, J C Brand-Miller

Abstract readComparative StudyRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in European journal of clinical nutrition, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

17 citing papers in PubMed, 56 citations in OpenAlex.

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  16. Physical activity and type 1 diabetes: time for a rewire?Journal of diabetes science and technology · 2015
    Review
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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

7 authors at 2 institutions in 1 country.

K J BellBoden Institute of Obesity, Nutrition, Exercise and Eating Disorders, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia.
R GraySydney Insulin Pump Clinic, Sydney, NSW, Australia.
D MunnsSydney Insulin Pump Clinic, Sydney, NSW, Australia.
P PetoczDepartment of Statistics, Macquarie University, Sydney, NSW, Australia.
G HowardSydney Insulin Pump Clinic, Sydney, NSW, Australia.
S ColagiuriBoden Institute of Obesity, Nutrition, Exercise and Eating Disorders, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia.
J C Brand-MillerBoden Institute of Obesity, Nutrition, Exercise and Eating Disorders, School of Molecular Bioscience, The University of Sydney, Sydney, NSW, Australia.
University of Sydney · AUMacquarie University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectiveThe Food Insulin Index (FII) is a novel algorithm for ranking foods on the basis of insulin responses in healthy subjects relative to an isoenergetic reference food. Our aim was to compare postprandial glycemic responses in adults with type 1 diabetes who used both carbohydrate counting and the FII algorithm to estimate the insulin dosage for a variety of protein-containing foods. SUBJECTS/

methodsA total of 11 adults on insulin pump therapy consumed six individual foods (steak, battered fish, poached eggs, low-fat yoghurt, baked beans and peanuts) on two occasions in random order, with the insulin dose determined once by the FII algorithm and once with carbohydrate counting. Postprandial glycemia was measured in capillary blood glucose samples at 15-30  min intervals over 3 h. Researchers and participants were blinded to treatment.

resultsCompared with carbohydrate counting, the FII algorithm significantly reduced the mean blood glucose level (5.7±0.2  vs 6.5±0.2 mmol/l, P=0.003) and the mean change in blood glucose level (-0.7±0.2 vs 0.1±0.2 mmol/l, P=0.001). Peak blood glucose was reached earlier using the FII algorithm than using carbohydrate counting (34±5 vs 56±7 min, P=0.007). The risk of hypoglycemia was similar in both treatments (48% vs 33% for FII vs carbohydrate counting, respectively, P=0.155).

conclusionsIn adults with type 1 diabetes, compared with carbohydrate counting, the novel FII algorithm improved postprandial hyperglycemia after consumption of protein-containing foods.

Indexed as

AlgorithmsPostprandial PeriodAdolescentAdultBlood GlucoseDiabetes Mellitus, Type 1Dietary CarbohydratesDietary ProteinsFemaleHumansHyperglycemiaHypoglycemiaInsulinMaleMiddle AgedReference ValuesBlood GlucoseDietary CarbohydratesDietary ProteinsInsulin

Identifiers

PMID25005674
OpenAlexW2059423411

What OpenQuestion 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 OpenQuestion graph.