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Record W2808219686 · doi:10.3233/jad-180202

Ketogenic Medium Chain Triglycerides Increase Brain Energy Metabolism in Alzheimer’s Disease

2018· article· en· W2808219686 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Alzheimer s Disease · 2018
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversité LavalInstitut Universitaire en Santé Mentale de QuébecUniversité de Sherbrooke
Fundersnot available
KeywordsKetogenic dietMedium-chain triglycerideEnergy metabolismDiseaseMetabolismAlzheimer's diseaseInternal medicineEndocrinologyMedicineNeuroscienceChemistryTriglycerideBiologyCholesterolEpilepsy

Abstract

fetched live from OpenAlex

BACKGROUND: In Alzheimer's disease (AD), it is unknown whether the brain can utilize additional ketones as fuel when they are derived from a medium chain triglyceride (MCT) supplement. OBJECTIVE: To assess whether brain ketone uptake in AD increases in response to MCT as it would in young healthy adults. METHODS: Mild-moderate AD patients sequentially consumed 30 g/d of two different MCT supplements, both for one month: a mixture of caprylic (55%) and capric acids (35%) (n = 11), followed by a wash-out and then tricaprylin (95%; n = 6). Brain ketone (11C-acetoacetate) and glucose (FDG) uptake were quantified by PET before and after each MCT intervention. RESULTS: Brain ketone consumption doubled on both types of MCT supplement. The slope of the relationship between plasma ketones and brain ketone uptake was the same as in healthy young adults. Both types of MCT increased total brain energy metabolism by increasing ketone supply without affecting brain glucose utilization. CONCLUSION: Ketones from MCT compensate for the brain glucose deficit in AD in direct proportion to the level of plasma ketones achieved.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.296
Teacher spread0.268 · 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