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Record W4406210337 · doi:10.1002/alz.091535

Determining APOE ε4 carriership by NUcleic acid Linked Immuno‐Sandwich Assay (NULISA)

2024· article· en· W4406210337 on OpenAlexaff
Kübra Tan, Andréa Lessa Benedet, Bingqing Zhang, Nesrine Rahmouni, Wagner S. Brum, Ilaria Pola, Guglielmo Di Molfetta, Kaj Blennow, Nicholas J. Ashton, Henrik Zetterberg

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsCohortApolipoprotein EConcordanceGenotypingBiomarkerMedicineGenotypeOncologyDementiaInternal medicineBiologyGeneticsDiseaseGene

Abstract

fetched live from OpenAlex

Abstract Background The APOE ε4 variant is the largest known genetic risk factor for late‐onset sporadic Alzheimer's disease (AD). Recent blood biomarker models include APOE ε4 status with plasma p‐tau217 for higher accuracy for AD pathology. Thus, protein assays that can accurately determine ε4 carriership simultaneously with plasma p‐tau217 would be advantageous for clinical use. This study aims to evaluate the concordance between the NULISA ApoE4 protein assay and conventional genetic testing for APOE measurement. Method We included two independent cohorts; cohort 1 (Translational Biomarker for Aging and Dementia Cohort [TRIAD]) consisted of 341 participants (mean [SD] age, 64.8[16.1] years; 213 females[62.4%]) and cohort 2 consisted of 38 participants (72.1[16.1] years; 21 females[55.2%]). APOE genotyping was determined by the TaqMan® SNP Genotyping Assay. In cohort 1, ApoE4 and p‐tau217 levels (NPQ) were quantified by the NULISAseq CNS disease panel from Alamar Biosciences. In cohort 2, ApoE4% (ApoE4/ApoEtotal) were quantified by a prototype singleplex assay from Alamar Biosciences Result In cohort 1, we included 341 participants with APOE genotyping (ε4 non‐carriers=221; ε4 carriers=120). ApoE4 NPQ values correctly identified 88.3% of carriers and 94.1% of non‐carriers (Figure 1A). Homozygous ε2 and ε4 carriers were entirely identified. Higher ApoE4 NPQ levels were observed in ε4/ε4 carriers (median [SD], 16.4[0.52]) compared to ε3/ε4 carriers (12.3[5.11]; P<0.0001) but an overlap remained. In these participants, the simultaneously measurement of NULISA plasma p‐tau217 was significantly increased in Aβ+ participants compared to Aβ‐ participants (AUC=0.941; P<0.0001). In cohort 2, we included 38 participants with APOE genotyping (ε3/ε3, n=26; ε3/ε4, n=9; ε4/ε4, n=6) and calculated the ApoE4% from a prototype singleplex NULISA method. Here, ε3/ε3 (0.03 [1.43]), ε3/ε4 (9.37 [1.61] and ε4/ε4 (41.9 [18.0]) individuals were classified with 100% accuracy (Figure 1B). Conclusion Our study assessed ApoE protein assays to determine APOE genotype. The ApoE4 assay within the NULISAseq CNS panel demonstrated high accuracy in distinguishing APOE ε4 carriers from non‐carriers, with some discordance. The reason for the discordance subject to further studies. However, ApoE4% quantification using a prototype singleplex assay distinguished ε4 homozygous, ε4 heterozygous, and non‐carriers with 100% accuracy. This pilot study demonstrates the ability to concurrently determine APOE status and p‐tau217 levels for more accurate diagnostic models of AD pathology.

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)0.004
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.036
GPT teacher head0.318
Teacher spread0.282 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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