Plasma lipid metabolites as biomarkers of early white matter degeneration in Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) is characterized by progressive white matter (WM) degeneration. Circulating lipid metabolites may serve as early indicators of WM microstructural changes. In this study, we investigated the associations between plasma lipid metabolites and WM integrity across the cognitive continuum. METHODS: We included 173 participants from the Alzheimer's Disease Neuroimaging Initiative (51 cognitively normal [CN], 88 mild cognitive impairment [MCI], 34 AD) database. Plasma metabolites were quantified using targeted lipidomics, and diffusion tensor imaging (DTI) metrics were derived from 52 predefined WM regions. RESULTS: Regression analyses revealed widespread metabolite-DTI associations in MCI, particularly within the corpus callosum. The callosal body and splenium showed significant inverse associations with phosphatidylcholines (PCs) and multiple lysophosphatidylcholines (lysoPCs) species. In AD group, inverse relationships between PCs and the internal capsule were observed. DISCUSSION: Circulating lipid metabolites are linked to WM microstructure in both prodromal and clinical AD, supporting their potential as sensitive biomarkers of early vulnerability and disease progression. Highlights: Circulating lipid metabolites link to white matter integrity in early Alzheimer's disease (AD)Phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), and propionylcarnitine associate with tract-specific diffusion magnetic resonance imaging (dMRI) metricsNo metabolite-white matter associations detected in established ADPlasma metabolites may serve as biomarkers of early white matter degeneration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".