Relationship between plasma cortisol with the neuropathology of patients across the Alzheimer’s disease continuum
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".