Brain γ-Tocopherol Levels Are Associated with Presynaptic Protein Levels in Elderly Human Midfrontal Cortex
Bibliographic record
Abstract
BACKGROUND: Higher vitamin E intake has been widely related to lower risks of cognitive decline and dementia. Animal models suggest that this relationship might be (partially) explained by the protection of vitamin E against presynaptic protein oxidation. OBJECTIVE: In this cross-sectional study, we aimed to examine the associations between brain tocopherols and presynaptic protein levels in elderly humans. METHODS: We examined associations of α- and γ-tocopherol brain levels with presynaptic protein levels in 113 deceased participants (age 88.5±6.0 years, 45 (40%) female) from the prospective Memory and Aging project. Three distinct presynaptic proteins, a SNARE protein composite, a synaptotagmin synaptophysin composite and the protein-protein interaction between synaptosomal-associated protein 25 (SNAP-25), and syntaxin were measured in two cortical brain regions. Linear regression models assessed associations of brain tocopherols with presynaptic protein levels. RESULTS: Higher brain γ-tocopherol levels were associated with higher levels of the SNARE protein composite, complexin-I, complexin-II, the synaptotagmin synaptophysin composite, and septin-5 in the midfrontal cortex (B(SE) = 0.272 to 0.412 (0.084 to 0.091), p < 0.001 to 0.003). When additionally adjusted for global Alzheimer's disease pathology, cerebral infarcts, and Lewy body disease pathology, these associations remained largely similar. No associations were found between α-tocopherol and presynaptic protein levels. CONCLUSION: In this cross-sectional study, we found higher brain γ-tocopherol levels were associated with presynaptic protein levels in the midfrontal cortex. These results are consistent with a proposed role of vitamin E to maintain presynaptic protein levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".