FDG-PET brain glucose hypometabolism predicts Alzheimer's disease progression pathways in cognitively normal adults: A longitudinal competing risks modeling
Bibliographic record
Abstract
Introduction: Alzheimer's disease progression follows distinct pathways in cognitively normal individuals: direct conversion to dementia versus sequential decline through mild cognitive impairment (MCI). The metabolic determinants of pathway selection remain unclear, limiting personalized intervention strategies. Methods: We analyzed 1136 cognitively normal participants from the Alzheimer's Disease Neuroimaging Initiative with baseline fluorodeoxyglucose positron emission tomography (FDG-PET) and longitudinal outcomes over ten years. Competing risks regression modeled pathway-specific transitions, while multinomial logistic regression predicted pathway membership using brain glucose metabolism. Cross-validation assessed pathway classification accuracy across temporal splits. Results: Four progression pathways were concluded from our analyses, cognitive stability (32.8 %), sequential MCI-only decline (34.9 %), accelerated MCI-to-dementia progression (15.8 %), and rapid direct conversion (16.5 %). Brain glucose hypometabolism determined pathway selection with significant effects: participants with severe hypometabolism (FDG z-score < -0.5) demonstrated 7.4-fold acceleration in direct conversion velocity compared to preserved metabolism (17.12 vs 2.31 per 100 person-years, P-value<0.001). Pathway prediction models achieved excellent discrimination for direct conversion (AUC = 0.994) and acceptable performance for sequential pathways (AUC = 0.680). Metabolic phenotyping demonstrated peculiar vulnerability profiles, cognitive stability maintained metabolic reserve (FDG +0.57 ± 0.58), while rapid converters demonstrated metabolic failure patterns (FDG -0.18 ± 0.88). Conclusions: Based on our modeling findings, we observed that brain glucose metabolism could serve as a pathway determinant rather than simply a decline predictor, which could play a promising role in precision medicine approaches to Alzheimer's disease prevention. FDG-PET biomarkers can stratify individuals for pathway-specific interventions, transforming reactive dementia care into proactive pathway-guided management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".