Fitness Shifts the Balance of BDNF and IL-6 from Inflammation to Repair among People with Progressive Multiple Sclerosis
Bibliographic record
Abstract
Physical sedentarism is linked to elevated levels of circulating cytokines, whereas exercise upregulates growth-promoting proteins such as brain-derived neurotrophic factor (BDNF). The shift towards a ‘repair’ phenotype could protect against neurodegeneration, especially in diseases such as multiple sclerosis (MS). We investigated whether having higher fitness or participating in an acute bout of maximal exercise would shift the balance of BDNF and interleukin-6 (IL-6) in serum samples of people with progressive MS (n = 14), compared to matched controls (n = 8). Participants performed a maximal graded exercise test on a recumbent stepper, and blood samples were collected at rest and after the test. We assessed walking speed, fatigue, and maximal oxygen consumption (V·O2max). People with MS achieved about 50% lower V·O2max (p = 0.003) than controls. At rest, there were no differences in BDNF between MS and controls; however, IL-6 was significantly higher in MS. Higher V·O2max was associated with a shift in BDNF/IL-6 ratio from inflammation to repair (R = 0.7, p = 0.001) when considering both groups together. In the MS group, greater ability to upregulate BDNF was associated with faster walking speed and lower vitality. We present evidence that higher fitness indicates a shift in the balance of blood biomarkers towards a repair phenotype in progressive MS.
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 | 0.000 | 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".