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Record W4403816958 · doi:10.1093/eurheartj/ehae666.498

Red-cell omega-3 fatty acids associate with increased hippocampal volume and superior longitudinal cognition in atrial fibrillation

2024· article· en· W4403816958 on OpenAlexaff
Peter Lee, T Ziswiler, S Aeschbacher, Martin F. Reiner, Meret Allemann, Carolina Balbi, Lukas Pirpamer, Marco Duering, Roberto Paladini, Leo H. Bonati, M Kuehne, Nicolas Rodondi, David Conen, Stefan Osswald, Jürg H. Beer

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsPopulation Health Research Institute
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMedicineAtrial fibrillationCardiologyHippocampal formationInternal medicineCognitionPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Atrial fibrillation (AF) is associated with cognitive impairment and dementia. Omega-3 Fatty Acids (n-3 FAs) have anti-thrombotic, anti-inflammatory and anti-oxidative properties; however, their neuroprotective effects remain controversial. Here, we report the association of n-3 FAs with both structural (hippocampal volume) and functional (cognition) readouts in a large cohort of AF pts. Purpose a) To determine the association of n-3 FAs and left and right hippocampal volume at baseline in AF pts. b) To assess the predictive value of HV and n-3 FAs for the risk of cognitive impairment after a 7-year follow-up period. Methods This prospective ongoing multicenter Swiss Atrial Fibrillation study (Swiss-AF) included 2,359 pts with documented AF and baseline red-cell n-3 FAs levels, measured by gas chromatography and mass spectrometry. Hippocampal volume (HV) and segmentation based intracranial volume (ICV) were analyzed by FreeSurfer's (v 7.4.0) subregion segmentation module and Sequence Adaptive Multimodal SEGmentation from brain MRI in 1,736 pts. Detailed neurocognitive assessments in pts at baseline and follow up periods were performed. We used mixed-effect linear regression models to determine the association of n-3 FAs and HV. For conversion to cognitive impairment (defined as MoCA score < 26), we performed cox-proportional hazards regression model. Two adjustment models were applied as described in the figures. Results Patients with AF (mean age, 73 years old [±8.5 SD]; 157 women [27%]) with 9569 cognitive evaluations (mean follow-up, 3.6 years [±2.3 SD]) were assessed. At baseline, we observed statistically significant associations between total n-3 FAs levels (EPA + DHA + ALA + DPA) and individual n-3 FAs (EPA, DHA, DPA) with increased HV after adjustment for ICV and other covariates (Fig1A, 1B). After all adjustments, EPA remained associated with both bilateral HV and right HV. Furthermore, we observed a significant association between HV and neurocognitive assessments, as measured both in global (MoCA, CoCo) and specific tests (TMT-A, TMT-B, DSST, SF) (Fig1C). Over time, quartile 4 (Q4, highest n-3) compared to quartile 1 (Q1, lowest), had a 16% lower risk of cognitive impairment (HR, 0.84 [CI 0.75-0.93]; P=0.001) for total n-3 FAs and even 30% lower specifically for EPA (HR, 0.7 [CI 0.63-0.78]; P<0.001). The risk of cognitive impairment was 47% lower (HR, 0.53 [CI 0.45-0.63]; P<0.001) when the highest HV Q4 was compared to lowest HV Q1 (Fig2A). In combination, the highest quartile of bilateral HV and EPA demonstrated an increased probability of being cognitive impairment-free and they had a 72% lower (HR, 0.28 [0.18-0.43]; P<0.001) risk of cognitive impairment (Fig2B). Conclusions 1. Red cell n-3 FAs levels are associated with higher HV in AF pts. 2. Higher baseline levels of n-3 FAs, particularly of EPA, are associated with both an increased HV and a decreased risk of cognitive impairment after 7 years follow up.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.304
Teacher spread0.269 · 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 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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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