Association of Lipidomics Signatures in Blood with Clinical Progression in Preclinical and Prodromal Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Lipidomics may provide insight into biochemical processes driving Alzheimer's disease (AD) pathogenesis and ensuing clinical trajectories. OBJECTIVE: To identify a peripheral lipidomics signature associated with AD pathology and investigate its potential to predict clinical progression. METHODS: We used Bayesian elastic net regression to select plasma lipid classes associated with the CSF pTau/Aβ42 ratio as a biomarker of AD pathology in preclinical and prodromal AD cases from the ADNI cohort. Consensus clustering of the selected lipid classes was used to identify lipidomic endophenotypes and study their association with clinical progression. RESULTS: In the APOE4-adjusted model, ether-glycerophospholipids, lyso-glycerophospholipids, free-fatty acids, cholesterol esters, and complex sphingolipids were found to be associated with the CSF pTau/Aβ42 ratio. We found an optimal number of five lipidomic endophenotypes in the prodromal and preclinical cases, respectively. In the prodromal cases, these clusters differed with respect to the risk of clinical progression as measured by clinical dementia rating score conversion. CONCLUSION: Lipid alterations can be captured at the earliest phases of AD. A lipidomic signature in blood may provide a dynamic overview of an individual's metabolic status and may support identifying different risks of clinical progression.
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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 | 0.001 |
| 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".