Influence of alpha‐synuclein on glucose metabolism in Alzheimer's disease continuum: Analyses of α‐synuclein seed amplification assay and FDG‐PET
Bibliographic record
Abstract
INTRODUCTION: We investigated the association between alpha-synuclein (α-syn) pathology and brain glucose metabolism across the cognitive spectrum of Alzheimer's disease (AD) co-pathologies. METHODS: Fluorodeoxyglucose positron emission tomography (FDG-PET) data from 829 Alzheimer's Disease Neuroimaging Initiative participants (648 cognitively impaired [CI], 181 unimpaired [CU]) were compared between α-syn seed amplification assay (SAA) positive and negative groups. Interactions with cerebrospinal fluid (CSF) AD biomarkers were examined. RESULTS: SAA+ was associated with widespread hypometabolism among CI individuals, particularly in posterior cortical regions, independent of CSF amyloid and tau levels in the occipital lobes. Regional hypometabolism mediated the effect of α-syn SAA on disease severity in CI individuals, independent of CSF amyloid and tau levels. There were no influences of SAA on FDG-PET in CU individuals. DISCUSSION: This study supports a model in which α-syn aggregation influences metabolic dysfunction, which then influences clinical disease severity, independent of AD. SAA+ could help optimize participant selection and outcome measures for clinical trials in AD. HIGHLIGHTS: α-synuclein seed amplification positivity (SAA+) was associated with hypometabolism in cognitively impaired individuals. Hypometabolism mediated the influence of α-synuclein on disease severity. Occipital hypometabolism in SAA+ was independent of cerebrospinal fluid levels of Alzheimer's disease pathology. These findings can optimize future clinical trials targeting α-synuclein pathology.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".