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Record W4312087602 · doi:10.1002/alz.065811

Validation and utility of plasma neurofilament light as a biomarker for vascular cognitive impairment

2022· article· en· W4312087602 on OpenAlexaboutno aff
Tiffany F. Kautz, Julia J Mathews, Danielle Parent, Tiffany L. Sudduth, Qianqian Liu, Crystal Wiedner, Chen‐Pin Wang, Djass Mbangdadji, Abhay P. Sagare, Alexa Beiser, George Pottanat, Hugo J. Aparicio, Jeffery F. Thompson, Lee‐Way Jin, Mitzi M. Gonzales, Saptaparni Ghosh, Charles DeCarli, Danny J.J. Wang, Donna M. Wilcock, Gary A. Rosenberg, Hanzhang Lu, Joel H. Kramer, Lenore J. Launer, Myriam Fornage, Thomas H. Mosley, Pauline Maillard, Vilmundur Guðnason, Herpreet Singh, Karl G. Helmer, Kristin Schwab, Steven M. Greenberg, Russell P. Tracy, Pia Kivisäkk, Sudha Seshadri, Claudia L. Satizábal

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsReproducibilityMedicineRepeatabilityBiomarkerInternal medicineHyperintensityConfoundingMontreal Cognitive AssessmentDementiaPathologyOncologyDiseaseMagnetic resonance imagingBiologyRadiologyStatistics

Abstract

fetched live from OpenAlex

Abstract Background Neurofilament light chain (NfL) is a broad marker of neuroaxonal injury that is elevated in the CSF in those with vascular cognitive impairment and dementia (VCID). Blood and CSF levels highly correlate, making it an attractive peripheral marker for VCID pathology. As part of the MarkVCID consortium, we sought to evaluate the instrumental validity of plasma NfL using the Quanterix Simoa platform and assess its validity for risk stratification in clinical trials of VCID. Method Plasma NfL was measured using HD‐X and HD‐1 Simoa instruments. For instrumental validation, we used samples from the MarkVCID consortium to evaluate intra‐ and inter‐plate reliability, test‐retest repeatability, and inter‐site reproducibility. We used linear regression models to assess the association of NfL with general cognitive function (GCF) as the primary outcome. In secondary analyses we assessed associations with white matter hyperintensities (WMH), an established small vessel disease marker. Models were adjusted for potential confounders. The clinical validation included established cohorts from the CHARGE consortium (i.e., CARDIA, ARIC, FHS, AGES; n=4,772), the UKY ADRC (n=350), and the UCD ADRC (n=196). Additional analyses are underway in MarkVCID sites. Result We found low coefficients of variation (average CV<12%), high inter‐site reproducibility (overall ICC = 0.93) and high repeatability in blood samples drawn within 30 days (ICC=0.968). There was high consistency across Quanterix instruments (HD‐X and HD‐1; R2≥0.98) and kits (N4PA and single molecule NfL; ICC≥0.81). We observed consistent significant associations between higher NfL concentrations and worse GCF in CHARGE cohorts (meta‐analysis β=‐0.11; [95% CI ‐0.06; ‐0.17]), the UKY ADRC (β=‐0.16; [95% CI ‐0.27; ‐0.05]) and the UCD ADRC (UCD: β=‐0.28; [95% CI ‐0.48; ‐0.08). Secondary analyses revealed significant associations between elevated NfL concentrations and higher WMH burden in CHARGE cohorts and the UCD ADRC (P<0.01). Conclusion Our results suggest plasma NfL can be reliably measured using the Quanterix platform, making this marker ideal for multi‐site clinical trials. We observed consistent associations for plasma NfL concentrations with cognition and WMH across independent samples, providing evidence that plasma NfL can be a useful biomarker for stratification in VCID trials.

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)0.014
metaresearch head score (Gemma)0.025
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 score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.317
Teacher spread0.283 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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