Blood protein kinase activity regulating genes are associated with brain glucose metabolism
Bibliographic record
Abstract
Abstract Background The diagnosis of Alzheimer’s disease (AD) has been greatly improved due to the fundamental role of positron emission tomography (PET) imaging. Also, predicting PET brain imaging alterations using blood‐based biomarkers is of high interest. This way, integrating PET and omics data can provide new insights into AD pathophysiology. Here, we aimed to develop a framework that combines blood transcriptomics with PET data. We hypothesized that integrating omics and PET data will help advance our understanding of AD neurobiology and may reveal relevant new peripheral biomarkers. Method [18F]Fluorodeoxyglucose ([18F]FDG)‐PET imaging and transcriptomics data were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Microarray gene expression profiling from blood samples of 99 Cognitively Unimpaired (CU) and 218 Cognitively Impaired (CI) individuals were submitted to differential expression (DE) analysis. Differentially expressed genes (DEGs) were submitted to Gene Ontology (GO) analysis. The GO terms were clustered by semantic similarity using the GOSemSim method for biological processes ontology. All computations analyses were performed in the R statistical environment. Gene clusters obtained in the previous step were selected to undergo integration with [18F]FDG‐PET images using voxel‐wise generalized linear regression (GLR) models adjusted for age, gender, years of education, and APOEε4 (RMINC package). Result The GO semantic similarity resulted in 16 GO clusters. The voxel‐wise correlation between [18F]FDG‐PET and GO clusters resulted in t‐statistical maps. Afterwards, only statistically significant correlated voxels (uncorrected t‐value > 2.0) were retained (Fig. 1A). A cluster related to the regulation of protein serine/threonine kinase activity showed a strong correlation with the [18F]FDG‐PET signal in the brain. At the voxel level, this cluster has a positive correlation in the precuneus’ gray matter (95.7% left, 81.1% right) as well in the medial frontal gyrus (79.6% left, 44.66% right) and cingulate region (76.9% left, 56.4% right) (Fig. 1B). Conclusion We identified several peripheral biological processes associated with [18F]FDG‐PET metabolism in the brain of CU and CI individuals. Furthermore, the prominent enrichment of protein serine/threonine kinase activity‐related genes highlights its potential as novel AD biomarkers and as a proxy of [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 | 0.000 | 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) | 0.002 | 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".