Lower Brain <sup>18</sup> F-Fluorodeoxyglucose Uptake But Normal <sup>11</sup> C-Acetoacetate Metabolism in Mild Alzheimer's Disease Dementia
Bibliographic record
Abstract
BACKGROUND: The cerebral metabolic rate of glucose (CMRg) is lower in specific brain regions in Alzheimer's disease (AD). The ketones, acetoacetate and β-hydroxybutyrate, are the brain's main alternative energy substrates to glucose. OBJECTIVE: To gain insight into brain fuel metabolism in mild AD dementia by determining whether the regional CMR and the rate constant of acetoacetate (CMRa and Ka, respectively) reflect the same metabolic deficit reported for cerebral glucose uptake (CMRg and Kg). METHODS: Mild AD dementia (Mild AD; n = 10, age 76 y) patients were compared with gender- and age-matched cognitively normal older adults (Controls; n = 29, age 75 y) using a PET/MRI protocol and analyzed with both ROI- and voxel-based methods. RESULTS: ROI-based analysis showed 13% lower global CMRg in the gray matter of mild AD dementia versus Controls (34.2 ± 5.0 versus 38.3 ± 4.7 μmol/100 g/min, respectively; p = 0.015), with CMRg and Kg in the parietal cortex, posterior cingulate, and thalamus being the most affected (p ≤ 0.022). Neither global nor regional CMRa or Ka differed between the two groups (all p ≥ 0.188). Voxel-based analysis showed a similar metabolic pattern to ROI-based analysis with seven clusters of significantly lower CMRg in the mild AD dementia group (uncorrected p ≤ 0.005) but with no difference in CMRa. CONCLUSION: Regional brain energy substrate hypometabolism in mild AD dementia may be specific to impaired glucose uptake and/or utilization. This suggests a potential avenue for compensating brain energy deficit in AD dementia with ketones.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".