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Record W4390194425 · doi:10.1002/alz.079758

Relationship between plasma cortisol with the neuropathology of patients across the Alzheimer’s disease continuum

2023· article· en· W4390194425 on OpenAlexaff
Laura Willers Souza, Andrei Bieger, Wyllians Vendramini Borelli, Marco Antônio De Bastiani, Guilherme Bauer‐Negrini, Jonathan M. DuBois, Eduardo R. Zimmer, Zimmer Neuroimaging Lab

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrecuneusNeuropathologyInternal medicinePsychologyAtrophyAlzheimer's Disease Neuroimaging InitiativeLateralization of brain functionMedicineAlzheimer's diseaseCardiologyNeuroscienceEndocrinologyCognitionDisease

Abstract

fetched live from OpenAlex

Abstract Background The relationship between high cortisol levels and brain atrophy in patients across the Alzheimer’s disease (AD) continuum has been relatively unexplored. Thus, this study sought to investigate the association between plasma cortisol levels and brain cortical thickness in patients across the biological and clinical continuum of AD. Method Participants with baseline plasma cortisol concentrations (n = 544), structural MRI, and CSF Aβ1‐42 and CSF p‐tau181 were selected from the ADNI database. Three general linear models (GLMs) were constructed to assess the association between cortical thickness and cortisol levels (Freesurfer’s v7.1.1): (1) cortisol as an independent variable; (2) cortisol, cognitive status, and interaction; (3) cortisol, neuropathology (measured by Aβ1‐42 and p‐tau181 positivity), and interaction. These analyses were corrected for multiple comparisons through cluster formation (p<0.01) and permutation (Monte Carlo simulation of 10,000 iterations). Result Demographics are depicted in Table 1. The cortical thickness showed a negative correlation with plasma cortisol concentrations in the following clusters (first GLM; Figure 1 and 2): left hemisphere – superior parietal (pcc = ‐0.13; p = 0.0002); superior frontal (pcc = ‐0.13; p = 0.0004); superior parietal (pcc = ‐0.13; p = 0.013); precentral (pcc = ‐0.12; p = 0.02); isthmus cingulate (pcc = ‐0.13; p = 0.02); caudal middle frontal (pcc = ‐0.11; p = 0.031); fusiform (pcc = ‐0.12; p = 0.046); right hemisphere – inferior parietal (pcc = ‐0.13; p = 0.0004); precuneus (pcc = ‐0.12; p = 0.0014); superior frontal (pcc = ‐0.12; p = 0.003); parahippocampal (pcc = ‐0.12; p = 0.003); superior parietal (pcc = ‐0.12; p = 0.041). Interestingly, no interactions between cortisol and AD cognitive status and neuropathology were identified (second and third GLMs – data not shown). Conclusion This cross‐sectional study identified a negative correlation between plasma cortisol concentrations and cortical thickness in brain regions typically affected by AD, which was found to be independent of AD diagnosis and neuropathology. This finding suggests that high peripheral cortisol levels may create a vulnerability that is independent of AD neuropathology in regions commonly affected by the disease, which may accelerate disease progression.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.307
Teacher spread0.242 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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