Metabolomic and lipidomic signatures in autosomal dominant and late-onset Alzheimer disease brains
Bibliographic record
Abstract
Abstract The identification of multiple genetic risk factors for Alzheimer Disease (AD) provides evidence to support that many pathways contribute to AD onset and progression. However, the metabolomic and lipidomic profiles in carriers of distinct genetic risk factors are not fully understood. The metabolome can provide a direct image of dysregulated pathways in the brain, including information on treatment targets. In this study, we interrogate the metabolomic and lipidomic signatures in the AD brain, including carriers of pathogenic variants in APP, PSEN1 , and PSEN2 (autosomal dominant AD; ADAD), APOE ε4 and TREM2 risk variant carriers, and non-carrier sporadic AD (sAD). We generated metabolomic and lipidomic data from parietal cortical tissue from 366 participants with AD pathology and 26 cognitively unimpaired controls using the Metabolon global metabolomics platform. We identified 133 metabolites associated with disease status (FDR q -value<0.05). In sAD brains these include tryptophan betaine (b=-0.57) and N-acetylputrescine (b=-0.14). Metabolites associated with sAD and ADAD include ergothioneine (b=-0.21 and -0.26 respectively) and serotonin (b=-0.34 and -0.58, respectively). TREM2 and ADAD showed association with α-tocopherol (b=-0.12 and -0.12) and CDP-ethanolamine (b=-0.13 and -0.10). β-citrylglutamate levels are associated with sAD, ADAD, and TREM2 compared to controls (b=-0.15; -0.22; and -0.29, respectively). Additionally, we identified a signature of 16 metabolites that is significantly altered between genetic groups (sAD vs. control p = 1.05×10 -7 , ADAD vs. sAD p = 3.21×10 -5 ) and is associated with Braak tau stage and disease duration. These data are available to the scientific community through a public web browser ( http://ngi.pub/Metabolomics ). Our findings were replicated in an independent cohort of 327 individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".