Pharmacokinetics of Insulin Aspart and Glucagon in Type 1 Diabetes during Closed-Loop Operation
Bibliographic record
Abstract
BACKGROUND: We assessed the pharmacokinetics of subcutaneous insulin aspart and glucagon during closed-loop operation and their relationship with body composition variables. METHODS: We retrospectively analyzed data collected from closed-loop experiments in 15 type 1 diabetes patients (age 47.1 ± 12.3 years, body mass index 25.9 ± 4.6 kg/m², glycated hemoglobin 7.9% ± 0.7%). Patients received an evening meal accompanied with prandial insulin bolus and stayed in the clinical facility until the next morning. Glucose levels were regulated by dual-hormone closed-loop delivery. Insulin and glucagon were delivered using two subcutaneous infusion pumps installed on the abdominal wall. Plasma insulin and glucagon were measured every 10-30 min. Percentage of body fat, percentage of fat in the abdominal area, and mass of abdominal fat were measured by dual X-ray absorptiometry. RESULTS: A pharmacokinetic model estimated time-to-peak plasma concentrations [t(max) insulin 51 (19) min, t(max) glucagon 19 (4) min, mean (standard deviation)], metabolic clearance rate [MCR insulin 0.019 (0.015-0.026) liter/kg/min, MCR glucagon 0.012 (0.010-0.014) liter/kg/min, median (interquartile range)], and the background plasma concentrations [I(b) insulin 10.2 (6.3-15.2) mU/liter, I(b) glucagon 50 (45-56) pg/ml, median (interquartile range)]. t(max) correlated positively between insulin and glucagon (r = 0.7; p = .007) while MCR correlated negatively (r = -0.7; p = .015). In this small sample size, t(max), MCR, and I(b) of insulin and glucagon did not correlate with percentage of body fat, percentage of fat in the abdominal area, or total mass of abdominal fat. CONCLUSIONS: Insulin and glucagon pharmacokinetics might be related during closed-loop operation. Our data suggest that slower absorption of insulin is associated with slower absorption of glucagon. Body composition does not seem to influence insulin and glucagon pharmacokinetics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".