Impact of macronutrient content of meals on postprandial glucose control in the context of closed‐loop insulin delivery: <scp>A</scp> randomized cross‐over study
Bibliographic record
Abstract
The aim of this randomized four-way cross-over study was to examine the effect of added protein and/or fat in standard meals with a fixed carbohydrate content on postprandial glucose control with closed-loop insulin delivery in adults with type 1 diabetes. Participants (n = 15) consumed breakfast meals with a fixed carbohydrate content (75 ± 1 g) and added protein and/or fat (35 ± 2 g): (1) carbohydrate-only (standard), (2) high protein (HP), (3) high fat (HF) and (4) high fat + protein (HFHP). The closed-loop insulin delivery algorithm generated insulin bolus and infusion rates. The addition of fat, protein or both did not impact 5-hour post-meal sensor glucose area under the curve (AUC) (main outcome), mean sensor glucose or glycaemic peak as compared with a standard meal (P > 0.05). However, time to glycaemic peak was delayed by 40 minutes (P = 0.03) and 5-hour post-meal basal insulin requirements were 39% higher (P = 0.04) with an HFHP meal compared with a standard meal. In conclusion, in the context of closed-loop insulin delivery, protein and/or fat meal content affects the timing of postprandial glycaemic peak, insulin requirements and late glycaemic excursion, without impacting overall 5-hour AUC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".