MétaCan
Menu
Back to cohort
Record W4400640288 · doi:10.1016/j.tjnut.2024.07.011

The Effect of Adding Protein to a Carbohydrate Meal on Postprandial Glucose and Insulin Responses: A Systematic Review and Meta-Analysis of Acute Controlled Feeding Trials

2024· review· en· W4400640288 on OpenAlexaff
Thomas M.S. Wolever, Andreea Zurbau, Katie Koecher, Fei Au‐Yeung

Bibliographic record

VenueJournal of Nutrition · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsSt. Michael's HospitalResearch CanadaArtificial Intelligence in Medicine (Canada)University of Toronto
Fundersnot available
KeywordsPostprandialMeta-analysisMealCarbohydrateInsulinInternal medicineFood scienceEndocrinologyMedicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Protein influences acute postprandial glucose and insulin responses, but the effects of dose, protein type, and health status are unknown. OBJECTIVES: We aimed to determine the acute effect of adding protein to carbohydrate on postprandial responses and identify effect modifiers. METHODS: We searched MEDLINE, EMBASE, and Cochrane databases through 30 July, 2023 for acute, crossover trials comparing acute postprandial responses elicited by carbohydrate-containing test meals with and without added protein in adults without diabetes or with type 2 (T2DM) or type 1 (T1DM) diabetes mellitus. Group data were pooled separately using generic inverse variance with random-effects models and expressed as the ratio of means with 95% confidence interval. Risk of bias and certainty of evidence (Grading of Recommendations Assessment, Development, and Evaluation) were assessed. RESULTS: In 154 trial comparisons of animal, dairy, and plant proteins (without diabetes, n = 22, 67, 32, respectively; T2DM, n = 14, 16, 3, respectively), each gram protein per gram available carbohydrate (g/g) reduced the glucose area under the curve (AUC) less in adults with T2DM than in those without diabetes (-10% compared with -50%, P < 0.05) but increased the insulin AUC similarly (+76% compared with +56%). In subjects without diabetes, each g/g of dairy and plant protein reduced glucose AUC by 52% and 55%, respectively, and increased the insulin AUC by 64% and 45%, respectively (all P < 0.05). Animal proteins significantly reduced the glucose AUC by 31% and increased the insulin AUC by 37% (pooled effects) but without a significant dose-response. In adults with T2DM, animal protein reduced the glucose AUC by 13% and increased the insulin AUC by 105%, with no significant dose-response. Dairy protein reduced the glucose AUC by 18% (no dose-response), but each g/g increased the insulin AUC by 34% (P < 0.05). In adults with T1DM, protein increased the glucose AUC by 40% (P < 0.05, n = 5). Data source (reported AUC compared with calculated AUC) and study methodology quality significantly modified some outcomes and contributed to high between-study heterogeneity. CONCLUSIONS: In people without diabetes, adding dairy or plant protein to a carbohydrate-containing meal elicits physiologically significant reductions in glucose AUC and increases insulin AUC. Animal protein may slightly reduce the glucose AUC and may increase the insulin AUC. In people with T2DM, protein may not have such large and consistent effects. Further research is needed to determine if the effects of protein differ by health status and protein source. This study was registered at PROSPERO as CRD42022322090.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.043
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0250.031
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.362
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NutritionSame topicMuscle metabolism and nutritionFrench-language works237,207