Omics‐derived biological modules reflect metabolic brain changes in Alzheimer's disease
Bibliographic record
Abstract
Abstract INTRODUCTION Brain glucose hypometabolism, indexed by the fluorodeoxyglucose positron emission tomography ([18F]FDG‐PET) imaging, is a metabolic signature of Alzheimer's disease (AD). However, the underlying biological pathways involved in these metabolic changes remain elusive. METHODS Here, we integrated [18F]FDG‐PET images with blood and hippocampal transcriptomic data from cognitively unimpaired (CU, n = 445) and cognitively impaired (CI, n = 749) individuals using modular dimension reduction techniques and voxel‐wise linear regression analysis. RESULTS Our results showed that multiple transcriptomic modules are associated with brain [18F]FDG‐PET metabolism, with the top hits being a protein serine/threonine kinase activity gene cluster (peak‐t(223) = 4.86, P value < 0.001) and zinc‐finger–related regulatory units (peak‐t(223) = 3.90, P value < 0.001). DISCUSSION By integrating transcriptomics with PET imaging data, we identified that serine/threonine kinase activity–associated genes and zinc‐finger–related regulatory units are highly associated with brain metabolic changes in AD. Highlights We conducted an integrated analysis of system‐based transcriptomics and fluorodeoxyglucose positron emission tomography ([18F]FDG‐PET) at the voxel level in Alzheimer's disease (AD). The biological process of serine/threonine kinase activity was the most associated with [18F]FDG‐PET in the AD brain. Serine/threonine kinase activity alterations are associated with brain vulnerable regions in AD [18F]FDG‐PET. Zinc‐finger transcription factor targets were associated with AD brain [18F]FDG‐PET metabolism.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".