P1‐226: BRAIN FUEL CONSUMPTION IN MILD ALZHEIMER'S DISEASE (AD): COMPARISON OF GLUCOSE AND KETONES USING PET IMAGING
Bibliographic record
Abstract
18 F-FDG-PET is widely used in helping diagnose Alzheimer's disease (AD) due to the localized decrease in brain glucose uptake, especially in the temporal and parietal cortex. Ketones are the brain's principal non-glycolytic energy substrates. Our group developed a new brain ketone PET tracer, 11 C-acetoacetate (11 C-AcAc), to address the question: in mild AD, does regional brain ketone uptake reflect the same pattern of deteriorating brain glucose uptake? Cognitively healthy controls (n=30, age 75 y) were compared to mild AD (n=10, age 76 y). The clinical diagnosis of AD was made according to NINCDS-ADRDA criteria. Structural 1.5T MR and PET imaging with both 11 C-AcAc and 18 F-FDG were acquired for all participants. Dynamic PET images were co-registered to MR images and corrected for partial volume effect. Cerebral metabolic rates (CMR; μmol/100 g/min) for glucose (CMR glu) and ketones (CMR ket) were calculated. Group differences were examined using Mann Whitney U-tests with a multiple comparison correction set at 0.05. Global CMR glu was 12% lower in AD compared to the healthy age- matched controls (34.2±5.0 vs. 38.3±4.7 μmol/100g/min, respectively; P = 0.033). Most of this difference was confined to the posterior cingulate, thalamus, angular and supramarginal gyri, and temporal cortex where CMR glu was 13-32% lower in AD (P ≤0.022). In contrast to brain glucose uptake, CMR ket was not different in AD vs. controls, either globally (0.35±0.17 vs. 0.31± 0.24μmol/100g/min, respectively; P = 0.379) or in any individual brain regions (P ≥0.173). In both controls and AD groups, CMR ket was positively correlated to plasma ketones (r = +0.898 and r = +0.865, respectively; P ≤0.001). Relative to cognitively-healthy age-matched controls, regions of the brain with deteriorating glucose uptake in mild AD appear to have no problem taking up ketones. Our findings support the concept of using ketones as a potential complementary strategy to counteract brain energetic deficit and cognitive impairment in mild AD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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".