Are bedtime nutritional strategies effective in preventing nocturnal hypoglycaemia in patients with type 1 diabetes?
Bibliographic record
Abstract
Hypoglycaemia remains the major limiting factor for adequate diabetes control for patients with type 1 diabetes (T1D), especially during the night-time. Although nutritional strategies for nocturnal hypoglycaemia (NH) prevention are regularly suggested in clinical practice, there is no evidence-based recommendation for the usefulness and optimal composition of a bedtime snack. The aim of this narrative review was to analyse the current state of knowledge on nutritional strategies to prevent NH in individuals with T1D. A literature search was conducted, using PubMed and Medline (1946 to 2013); 16 studies were retrieved. Overall, the level of evidence was low. Results indicated that a calibrated bedtime snack based on bedtime blood glucose (BG) level could be effective to reduce NH occurrence for patients treated with human or animal insulin (short-acting combined with lente, ultralente and/or intermediate-acting insulin), but there is no evidence for patients treated with insulin analogues as part of multiple daily injections or insulin pump regimen. Some evidence suggests that including uncooked cornstarch or alanine in the bedtime snack composition could provide some benefits for the prevention of NH. Individualized recommendations of a bedtime snack intake for patients or situations at high risk for NH (long standing diabetes, hypoglycaemia unawareness, prior physical activity, alcohol consumption, bedtime BG close to hypoglycaemia threshold) appear as a prudent recommendation. On the basis of the available evidence, a bedtime snack cannot be recommended systematically but it might be useful if prescribed in an individualized fashion; further research is needed to evaluate these strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".