Insulin Resistance Predicts Medial Temporal Hypermetabolism in Mild Cognitive Impairment Conversion to Alzheimer Disease
Bibliographic record
Abstract
Alzheimer disease (AD) is characterized by progressive hypometabolism on [(18)F]-fluorodeoxyglucose positron emission tomography (FDG-PET) scans. Peripheral insulin resistance (IR) increases AD risk. No studies have examined associations between FDG metabolism and IR in mild cognitive impairment (MCI) and AD, as well as MCI conversion to AD. We studied 26 cognitively normal (CN), 194 MCI (39 MCI-progressors, 148 MCI-stable, 2 years after baseline), and 60 AD subjects with baseline FDG-PET from the Alzheimer's Disease Neuroimaging Initiative. Mean FDG metabolism was derived for AD-vulnerable regions of interest (ROIs), including lateral parietal and posteromedial cortices, medial temporal lobe (MTL), hippocampus, and ventral prefrontal cortices (vPFC), as well as postcentral gyrus and global cerebrum control regions. The homeostasis model assessment of IR (HOMA-IR) was used to measure IR. For AD, higher HOMA-IR predicted lower FDG in all ROIs. For MCI-progressors, higher HOMA-IR predicted higher FDG in the MTL and hippocampus. Control regions showed no associations. Higher HOMA-IR predicted hypermetabolism in MCI-progressors and hypometabolism in AD in medial temporal regions. Future longitudinal studies should examine the pathophysiologic significance of the shift from MTL hyper- to hypometabolism associated with IR.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".