Cerebrospinal fluid irisin and its correlation to Alzheimer's disease biomarkers in a Brazilian dementia cohort
Bibliographic record
Abstract
Abstract Background Physical exercise (PE) is pointed as a potential nonpharmacological preventative and interventional strategy to slow down the decline of cognition, both in clinically healthy individuals and patients with Alzheimer's disease (AD). Irisin, produced by skeletal muscle during PE has shown to play a role in the nervous system, and it is speculated that it may have a neuroprotective role. Methods Using a well‐established Brazilian cohort, we selected samples from 25 cognitively unimpaired and 27 cognitively impaired older adults (aged 55+) ‐ diagnosed with amnestic mild cognitive impairment (aMCI) or Alzheimer's disease. In addition to clinical diagnosis, amyloid status was used to stratify participants, using CSF biomarker cutoffs established for the cohort. Using the Single Molecule Array (SIMOA) platform, NfL, GFAP, IL‐6 were measured in plasma and pTau181 and pTau217 in CSF. Statistical analysis was performed using GraphPad Prism 9 and Rstudio. Results A significant correlation was observed between CSF irisin, pTau181 and pTau217 levels in control individuals, with those with the highest levels of pTau also having the highest irisin. Intriguingly, this effect was not present in the cognitively impaired group. In our cohort, correlation data between CSF irisin and inflammatory plasma biomarkers did not show significance, except for IL‐6 in the aMCI group and NfL in the AD group. Conclusion In conclusion, our results may suggest that high levels of irisin could protect participants with abnormal CSF pTau from cognitive impairment. These data point to irisin as a potential resilience biomarker in amyloid positive older individuals.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".