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Record W4390199882 · doi:10.1002/alz.077962

Manipulation of Dietary DHA does not Alter DHA Brain Uptake Kinetics in Mice Despite Changing Brain and Plasma DHA Levelsvs

2023· article· en· W4390199882 on OpenAlexaff
Marlon Vincent V. Duro, Juno S. Van Valkenburgh, Laila Abdullah, Diana E. Ingles, Zhiheng Cai, Bilal E. Kerman, Shaowei Wang, Russell E. Jacobs, Naomi Sta. Maria, Francesca Zanderigo, Étienne Croteau, Stephen C. Cunnane, Stanley I. Rapoport, Kai Chen, Hussein N. Yassine

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDocosahexaenoic acidChemistryInternal medicineEndocrinologyHigh-performance liquid chromatographyMetabolismFatty acidBiochemistryPolyunsaturated fatty acidMedicineChromatography

Abstract

fetched live from OpenAlex

Abstract Background Increased brain docosahexaenoic acid (DHA) uptake reflects a compensatory mechanism for maintaining brain DHA homeostasis and can be captured through PET‐MRI scan uptake studies using radiolabeled DHA. It has been previously shown that APOE4 status and alcohol intake alter brain DHA uptake in mice. Whether a change in DHA intake affects the brain DHA incorporation coefficient (K*) however is not known. Method Four‐month‐old C57BL/6J mice were fed either a diet rich in DHA (15 kcal% Fat, 1.7 g/kg ALA, and 7g/kg DHA) or deficient in DHA (15 kcal% Fat and 0.9 mg/kg ALA) for 4 months. At 8 months, anesthetized mice were subjected to dynamic PET‐MRI for 30 min following i.v. injection of 22‐[18F]fluorodocosahexaenoic acid ([18F]FDHA), prepared in‐house through radiosynthesis. Using an image‐derived cardiac input function, brain uptake was assessed by estimating [18F]FDHA K* values using both the Patlak model approach and the irreversible two‐tissue compartmental model (Irr2TCM) with and without correction of the brain radioactivity for radioactive spillover from the skull. Mice were then sacrificed, and the plasma and brains were analyzed using liquid chromatography‐mass spectrometry (LC‐MS) to evaluate differences in the unlabeled concentrations of fatty acids into phospholipids, triglycerides, and other lipids. Result There was no significant difference in the [18F]FDHA K* estimates (with either Patlak or Irr2TCM) between mice fed a DHA‐rich or a DHA‐deficient diet. LC‐MS based lipidomic analysis showed that the DHA‐rich diet significantly increased DHA concentration in triglycerides in plasma and in phosphatidylethanolamine in the brain. Conclusion These results suggest that under physiological conditions, increased dietary intake of DHA in wild‐type mice increases DHA in brain phosphatidylethanolamine and in plasma triglycerides without changing the DHA K*. The net effect likely is increased brain DHA uptake, equal to the product of unesterified DHA plasma concentration and K*. Imaging brain DHA with dynamic [18F]FDHA PET‐MRI is a potentially useful tool not only to investigate the APOE4 phenotype, which promotes DHA dysregulation, but also to assess dietary and pharmacological interventions that affect brain DHA homeostasis as well as to guide future treatments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.066
GPT teacher head0.326
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

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