Effects of Moderate Cycling Exercise on Blood Glucose Regulation Following Successful Clinical Islet Transplantation
Bibliographic record
Abstract
Context: Islet transplantation is effective in preventing hypoglycemia in patients with type 1 diabetes (T1D). However, it is unknown whether transplanted islets regulate plasma glucose concentrations appropriately during and after exercise in human islet transplant recipient (ITxs). Objective: To determine the effect of exercise on plasma glucose, insulin, and glucagon concentrations in ITxs compared with control subjects (CONs) without diabetes. Intervention: Participants completed two conditions in random order: 45 minutes of aerobic exercise (60% VO2peak) and 45 minutes of seated rest. Blood samples were drawn at baseline, immediately after exercise or rest, and every 15 minutes throughout a 60-minute recovery period. Postexercise (24 hours) interstitial glucose was monitored with continuous glucose monitoring (CGM). Results: Twenty-four participants (12 ITxs, 12 CONs) completed the protocol. Plasma glucose decreased more over time with exercise in ITxs compared with CONs [main effects of treatment (P = 0.019), time (P = 0.001), and group (P = 0.012)]. Plasma glucose was lower during exercise vs rest in ITxs but not CONs [treatment by group interaction (P = 0.028)]. Plasma glucose decreased more during exercise than during rest [treatment by time interaction (P = 0.001)]. One ITx and one CON experienced plasma glucose concentrations <3.5 mmol/L at the end of exercise, both of whom returned above that threshold within 15 minutes. Nocturnal CGM glucose <3.5 mmol/L was detected in two CONs but no ITxs. Conclusion: Despite a greater plasma glucose decline during exercise in ITxs, hypoglycemia risk was similar during and after exercise in ITxs compared with CONs.
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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 | 0.001 |
| 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 | 0.000 | 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".