MétaCan
Menu
Back to cohort
Record W4409138276 · doi:10.1007/s44202-025-00335-6

Analysis of cognitive status of Alzheimer’s disease’s subjects and its association with biomarkers (amyloid beta and tau proteins) using NACC data

2025· article· en· W4409138276 on OpenAlexaff
Vaghawan Prasad Ojha, Richard Hunt Bobo, Seyedadel Moravveji, Lucien Gnegne Meteumba, Shantia Yarahmadian

Bibliographic record

VenueDiscover Psychology · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité Laval
FundersNational Institute on AgingNational Institutes of Health
KeywordsBETA (programming language)Alzheimer's diseaseAmyloid (mycology)DiseaseAmyloid betaAssociation (psychology)PsychologyMedicineTau proteinCognitionOncologyNeuroscienceInternal medicinePathologyPsychotherapistComputer science

Abstract

fetched live from OpenAlex

Despite extensive research on amyloid-beta ( $$A\beta$$ ) and tau protein aggregation, the precise role of biomarkers in Alzheimer’s disease (AD) remains ambiguous. We use data provided by National Alzheimer’s Coordinating Center (NACC) to understand the relation between key biomarkers phosphorylated Tau (Ptau $$_{181}$$ ), Amyloid-beta ( $$A\beta _{1-42}$$ ), Total tau (Ttau), their ratios and the cognitive status of subjects. We use both Cerebrospinal Fluid (CSF) biomarkers, analyzed Positron Emission Tomographi (PET) outcomes and presence of hippocampal atrophy. This study analyzes biomarkers such as phosphorylated Tau (Ptau $$_{181}$$ ), Amyloid-beta $$A\beta _{1-42}$$ ), Total Tau (Ttau), their ratio, as well as clinical data from individuals diagnosed with Alzheimer’s and controls, compiled by the NACC. Total of 1821 rows data collected from 1347 unique subjects with CSF biomarkers were analyzed. These subjects are further categorized in different Phases and Changes of cognitive impairments. We utilized statistical techniques (ANOVA, Seive Plot, Logistic Regression) and visualization methods (Boxplot, Barplot, Violin Plot) to understand the relationships. A significant correlation was identified between severe cognitive impairment and the phosphorylated ( $$Ptau_{181}$$ ) to $$A\beta _{1-42}$$ ratio and total Tau (Ttau) to $$A\beta$$ ratio, which was negatively correlated in subjects with normal cognitive status. In both case the p-value were $$<0.005$$ .Low mean of $$A\beta$$ were found in the subjects with worst cognitive status, whereas higher mean of Ptau and Ttau were observed, which is the known characteristics of AD pathology. Likewise, higher variability in CSF Ptau, CSF Ttau, and CSF $$A\beta _{1-42}$$ biomarkers were found in subjects with worsened cognitive status. Additionally, there was a higher presence of hippocampal-atrophy and tau protein evidence in AD. Biomarkers such as $$A\beta _{1-42}$$ , $$Ptau_{181}$$ , Ttau and their ratio seems to exhibit significant variability and correlation with cognitive decline in AD. Targeting these biomarkers at specific disease stages may improve the efficacy of treatments. We further evaluated this by segregating subjects into multiple phases of the disease and different changes category. But this study assumes independence of these biomarkers and does not consider the confounders such as age, ethnicity, time of onset, diagnosis, underlying clinical pathologies which may be affecting the underlying process.

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)0.000
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 score0.046
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.395
Teacher spread0.339 · 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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueDiscover PsychologySame topicAlzheimer's disease research and treatmentsFrench-language works237,207