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Record W4406201258 · doi:10.1002/alz.092364

Plasma proteomic profiling of mild cognitive impairment in two cohorts using the NULISAseq CNS Disease panel

2024· article· en· W4406201258 on OpenAlexaff
Guglielmo Di Molfetta, Andréa Lessa Benedet, Bingqing Zhang, Nesrine Rahmouni, Stijn Servaes, Jenna Stevenson, Ilaria Pola, Kaj Blennow, Henrik Zetterberg, Pedro Rosa‐Neto, Nicholas J. Ashton

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsCognitive impairmentProfiling (computer programming)DiseaseMedicineCognitionPsychologyPsychiatryInternal medicineComputer science

Abstract

fetched live from OpenAlex

Abstract Background Recently, the development of ultra‐sensitive immunoassays has allowed for the detection, in blood, of proteins related to the pathophysiology of Alzheimer’s disease (AD), with phosphorylated tau (p‐tau) being the most promising. However, current methods are often limited by their ability to measure one analyte, lacking the potential for discovery and inclusion of additional biomarkers with supplemental value. In this pilot study, we explored proteomic changes using the novel NUcleic acid Linked Immuno‐Sandwich Assay (NULISA™) platform, focusing on patients with mild cognitive impairment (MCI). Method In this study, MCI patients with a confirmed Aβ status were recruited from two independent cohorts. A discovery cohort (mean[SD] age, 73.5 [5.5] years; 25 females [62.5%]) was classified by cerebrospinal fluid Aβ42/40 (Aβ+ =28; Aβ‐ =12). For the second cohort (mean[SD] age, 70.7 [7.31] years; 39 females[42.9%]; CDR 0.5), from the TRIAD study, amyloid positron emission tomography was utilized instead (Aβ+ =47; Aβ‐ =44). We performed the NULISAseq CNS Disease Panel measurements on the plasma samples (n=131) in two separate batch analyses. LIMMA models were used to evaluate differential expression of protein NPQ values between the two MCI (Aβ+ Vs. Aβ‐) patient groups, with a total of 121 proteins included in the analysis. Result In the discovery cohort (n=40), only p‐tau217 survived multiple comparison (Log 2 FC, 1.49; P adj <0.001). Further targets were present with a significant unadjusted p‐value (P <0.05) (Figure 1b). In the larger TRIAD cohort (n=91), p‐tau217 (Log 2 FC, 1.29; P adj <0.001), p‐tau231 (Log 2 FC, 0.76; P adj <0.05), p‐tau181 (Log 2 FC, 0.42; P adj <0.05) and GFAP (Log 2 FC, 0.78; P adj <0.05) remained significant after adjusting for multiple testing. Similarly to the discovery cohort, additional targets were identified as significantly changed with an unadjusted p‐value (P<0.05) (Figure 1A). This included PARK7, which was the only target present in both. Conclusion In this study, consisting of two independent groups of MCI patients characterized by Aβ burden, we utilized the NULISAseq CNS Disease Panel to identify dysregulated proteins of the prodromal stage of AD. This novel multiplex technology continues to demonstrate the importance of p‐tau217, p‐tau231, p‐tau181 and GFAP as indicators of cerebral amyloid deposition while offering additional markers that may increase their utility.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.288
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.043
GPT teacher head0.307
Teacher spread0.264 · 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 teacher head, 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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Citations0
Published2024
Admission routes1
Has abstractyes

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