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Record W7119880805 · doi:10.1002/alz70856_107634

Accuracy of an Automated Plasma Apolipoprotein E4 Proteotyping Immunoassay for Determination of <i>APOE</i> Genotype

2025· article· en· W7119880805 on OpenAlexaff
R. J. F. Smith, Yara Alkhodair, Moones Yadegari, Mari L. DeMarco

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsSt. Paul's HospitalProvidence Health CarePositive Living Society of British ColumbiaUniversity of British Columbia
Fundersnot available
KeywordsHeterozygote advantageContext (archaeology)GenotypeAlleleImmunoassay

Abstract

fetched live from OpenAlex

BACKGROUND: Anti-amyloid therapies (AAT) for Alzheimer's disease are associated with the risk of amyloid-related imaging abnormalities (ARIA). APOE4 homozygotes (E4/E4) have the highest risk, followed by APOE4 carriers (E4), making APOE genotyping essential for risk assessment. While APOE4 allelic status is routinely determined via DNA (e.g., by RT-PCR), recently developed automated immunoassays offer an alternative approach by measuring the apolipoprotein E4 (apoE4) concentration in plasma (i.e., proteotyping). In this study, we evaluated the diagnostic accuracy of an apoE4 proteotyping assay (Fujirebio Lumipulse) and its suitability for ARIA risk assessment. METHOD: This diagnostic accuracy study included 104 plasma samples from unique individuals with known APOE genotypes determined by RT-PCR: E2/E3 = 8, E3/E3 = 45, E2/E4 = 1, E3/E4 = 44, and E4/E4 = 6. Plasma samples were analyzed using Fujirebio's Lumipulse G1200 system with the Lumipulse G ApoE4 and Pan-ApoE assays. These assays measure apoE4 and total apoE protein concentrations, respectively, and their ratio is used to infer E4 allelic status (non-E4, E4, or E4/E4). The assays were further evaluated for precision, potential interferences, and sample stability across freeze/thaw cycles. RESULT: The plasma proteotyping assay demonstrated 100% accuracy in distinguishing the presence or absence of an E4 allele compared to RT-PCR. It correctly classified all non-E4 individuals (n = 53), all E4/E4 (n = 6), and 41 of 45 E4 heterozygotes (4 heterozygotes were misclassified as E4/E4). The ApoE4 and Pan-ApoE assays had total coefficients of variation of 8.5% and 4.0%, respectively. No significant interference was observed for hemolysate up to ∼5.25 g/L of hemoglobin or for lipemia up to ∼500 mg/dL of intralipid. Additionally, freeze-thaw testing showed no significant impact on assay performance for up to 4 freeze/thaw cycles. CONCLUSION: The evaluated plasma apoE4 phenotyping assay demonstrated perfect accuracy in detecting the presence or absence of an E4 allele. However, it did not reliably distinguish E4 heterozygotes from homozygotes, with several heterozygotes misclassified as E4/E4. In the context of AAT treatment eligibility and ARIA risk assessment, proteotyping is an accurate method for both ruling in and out the presence of an E4 allele but E4/E4 results specifically should be confirmed via genotyping.

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)0.008
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 score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.026
GPT teacher head0.347
Teacher spread0.321 · 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
Published2025
Admission routes1
Has abstractyes

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