Serum Cortisol Is Associated With Cerebral Small Vessel Disease-Related Brain Changes and Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship between serum cortisol, cerebral small vessel disease (CSVD) neuroimaging markers, and cognitive performance. METHODS: We recruited patients over 50 years old who attended our hospital for physical examination between November 2020 and July 2021. All participants were subject to brain magnetic resonance imaging (MRI), serum cortisol examination, and the Montreal cognitive function assessment (MoCA). On brain MRI, we scored the presence of each marker of CSVD, including white matter hyperintensity (WMH), lacunes, cerebral microbleeds (CMBs), and enlarged perivascular spaces (EPVS). One point was awarded for the presence of each marker, producing a score between 0 and 4. RESULTS: In total, 158 participants were included in this study with a mean age of 60.5 (56.0-66.3) years; 55.1% were male. In the multivariable analyses, serum cortisol level was an independent predictor of WMH severity, the presence of lacunes/CMBs, moderate-severe EPVS and total CSVD burden after adjusting for confounding factors. Serum cortisol level had positive associations with periventricular/deep Fazekas score, burdens of lacunes/CMBs, moderate-severe EPVS, and total CSVD burden in dose-dependent manner, and was an independent predictor of cognitive impairment. Furthermore, the results of the receiver operating characteristic (ROC) curve analysis revealed an area under curve (AUC) of 0.745 with 64.1% sensitivity and 82.5% specificity, and an AUC of 0.705 with 52.1% sensitivity and 85.5 specificity of cortisol in detecting patients with high CSVD burden and MCI, respectively. CONCLUSIONS: Serum cortisol level is independently associated with each CSVD MRI markers, total CSVD burden and cognitive impairment. These findings provide clues for pathological mechanisms and suggest serum cortisol as a promising biomarker associated with CSVD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".