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Record W3092052624 · doi:10.1093/braincomms/fcaa166

Serum neurofilament light in atrial fibrillation: clinical, neuroimaging and cognitive correlates

2020· article· en· W3092052624 on OpenAlexaffabout
Alexandros A. Polymeris, Michael Coslovksy, Stefanie Aeschbacher, Tim Sinnecker, Pascal Benkert, Richard Kobza, Jürg H. Beer, Nicolas Rodondi, Urs Fischer, Giorgio Moschovitis, Andreas U. Monsch, Anne Springer, Matthias Schwenkglenks, Jens Wuerfel, Gian Marco De Marchis, Philippe Lyrer, Michael Kühne, Stefan Osswald, David Conen, Jens Kühle, Leo H. Bonati

Bibliographic record

VenueBrain Communications · 2020
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsAtrial fibrillationMedicineInternal medicineCardiologyStroke (engine)Diabetes mellitusMontreal Cognitive AssessmentHeart failureVerbal fluency testCognitionNeuropsychologyDiseaseDementiaEndocrinologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Emerging evidence suggests that atrial fibrillation is associated with cognitive dysfunction independently of stroke, but the underlying mechanisms remain unclear. In this cross-sectional analysis from the Swiss-atrial fibrillation Study (NCT02105844), we investigated the association of serum neurofilament light protein, a neuronal injury biomarker, with (i) the CHA2DS2-VASc score (congestive heart failure, hypertension, age 65–74 or >75 years, diabetes mellitus, stroke or transient ischaemic attack, vascular disease, sex), clinical and neuroimaging parameters and (ii) cognitive measures in atrial fibrillation patients. We measured neurofilament light in serum using an ultrasensitive single-molecule array assay in a sample of 1379 atrial fibrillation patients (mean age, 72 years; female, 27%). Ischaemic infarcts, small vessel disease markers and normalized brain volume were assessed on brain MRI. Cognitive testing included the Montreal cognitive assessment, trail-making test, semantic verbal fluency and digit symbol substitution test, which were summarized using principal component analysis. Results were analysed using univariable and multivariable linear regression. Neurofilament light was associated with the CHA2DS2-VASc score, with an average 19.2% [95% confidence interval (17.2%, 21.3%)] higher neurofilament per unit CHA2DS2-VASc increase. This association persisted after adjustment for age and MRI characteristics. In multivariable analyses, clinical parameters associated with neurofilament light were higher age [32.5% (27.2%, 38%) neurofilament increase per 10 years], diabetes mellitus, heart failure and peripheral artery disease [26.8% (16.8%, 37.6%), 15.7% (8.1%, 23.9%) and 19.5% (6.8%, 33.7%) higher neurofilament, respectively]. Mean arterial pressure showed a curvilinear association with neurofilament, with evidence for both an inverse linear and a U-shaped association. MRI characteristics associated with neurofilament were white matter lesion volume and volume of large non-cortical or cortical infarcts [4.3% (1.8%, 6.8%) and 5.5% (2.5%, 8.7%) neurofilament increase per unit increase in log-volume of the respective lesion], as well as normalized brain volume [4.9% (1.7%, 8.1%) higher neurofilament per 100 cm3 smaller brain volume]. Neurofilament light was inversely associated with all cognitive measures in univariable analyses. The effect sizes diminished after adjusting for clinical and MRI variables, but the association with the first principal component was still evident. Our results suggest that in atrial fibrillation patients, neuronal loss measured by serum neurofilament light is associated with age, diabetes mellitus, heart failure, blood pressure and vascular brain lesions, and inversely correlates with normalized brain volume and cognitive function.

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)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.165
GPT teacher head0.408
Teacher spread0.243 · 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".

Quick stats

Citations49
Published2020
Admission routes2
Has abstractyes

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