Mini-Dose Glucagon as a Novel Approach to Prevent Exercise-Induced Hypoglycemia in Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE Patients with type 1 diabetes who do aerobic exercise often experience a drop in blood glucose concentration that can result in hypoglycemia. Current approaches to prevent exercise-induced hypoglycemia include reduction in insulin dose or ingestion of carbohydrates, but these strategies may still result in hypoglycemia or hyperglycemia. We sought to determine whether mini-dose glucagon (MDG) given subcutaneously before exercise could prevent subsequent glucose lowering and to compare the glycemic response to current approaches for mitigating exercise-associated hypoglycemia. RESEARCH DESIGN AND METHODS We conducted a four-session, randomized crossover trial involving 15 adults with type 1 diabetes treated with continuous subcutaneous insulin infusion who exercised fasting in the morning at ∼55% VO2max for 45 min under conditions of no intervention (control), 50% basal insulin reduction, 40-g oral glucose tablets, or 150-μg subcutaneous glucagon (MDG). RESULTS During exercise and early recovery from exercise, plasma glucose increased slightly with MDG compared with a decrease with control and insulin reduction and a greater increase with glucose tablets (P < 0.001). Insulin levels were not different among sessions, whereas glucagon increased with MDG administration (P < 0.001). Hypoglycemia (plasma glucose <70 mg/dL) was experienced by six subjects during control, five subjects during insulin reduction, and none with glucose tablets or MDG; five subjects experienced hyperglycemia (plasma glucose ≥250 mg/dL) with glucose tablets and one with MDG. CONCLUSIONS MDG may be more effective than insulin reduction for preventing exercise-induced hypoglycemia and may result in less postintervention hyperglycemia than ingestion of carbohydrate.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".