Neurotrophins and cognitive functions in T1D compared with healthy controls: effects of a high-intensity exercise
Bibliographic record
Abstract
Exercise is known to have beneficial effects on cognitive function. This effect is greatly favored by an exercise-induced increase in neurotrophic factors, such as brain-derived neurotrophic factor (BDNF) and insulin-like growth factor-1 (IGF-1), especially with high-intensity exercises (HIE). As a complication of type 1 diabetes (T1D), a cognitive decline may occur, mostly ascribed to hypoglycaemia and chronic hyperglycaemia. Therefore, the purpose of this study was to examine the effects of acute HIE on cognitive function and neurotrophins in T1D and matched controls. Ten trained T1D (8 males, 2 females) participants and their matched (by age, sex, fitness level) controls were evaluated on 2 occasions after familiarization: a maximal test to exhaustion and an HIE bout (10 intervals of 60 s at 90% of their maximal wattage followed by 60 s at 50 W). Cognitive tests and analyses of serum BDNF, IGF-1, and free insulin were performed before and after HIE and following 30 min of recovery. At baseline, cognitive performance was better in the controls compared with the T1D participants (p < 0.05). After exercise, no significant differences in cognitive performance were detected. BDNF levels were significantly higher and IGF-1 levels were significantly lower in T1D compared with the control group (p < 0.05) at all time points. Exercise increased BDNF and IGF-1 levels in a comparable percentage in both groups (p < 0.05). In conclusion, although resting levels of serum BDNF and IGF-1 were altered by T1D, comparable increasing effects on BDNF and IGF-1 in T1D and healthy participants were found. Therefore, regularly repeating acute HIE could be a promising strategy for brain health in T1D.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".