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Record W4415243548 · doi:10.1016/j.metop.2025.100400

FDG-PET brain glucose hypometabolism predicts Alzheimer's disease progression pathways in cognitively normal adults: A longitudinal competing risks modeling

2025· article· en· W4415243548 on OpenAlexaff
Mustafa S. Alhasan, Ayman S. Alhasan, James Milburn, Mohammed Khalil, Abdullah Almaghraby, Omar Alharthi, Seham Hamoud, Muhammed Amir Essibayi, Yasir Hassan Elhassan, Fabrício Stewan Feltrin, Sumit Singh, Ahmed Y. Azzam

Bibliographic record

VenueMetabolism Open · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsDiseaseDementiaDiabetes mellitusAlzheimer's diseaseBiomarkerDegenerative diseaseCarbohydrate metabolism

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.373
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2025
Admission routes1
Has abstractyes

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