Differences due to sex in cortical thickness and CSF biomarkers in normal aging and Alzheimer’s dementia
Bibliographic record
Abstract
Abstract Background In normal aging, a thinner cortex in males has been reported by various studies. Longitudinal trends in cortical thinning in males and females in healthy aging and in Alzheimer’s Dementia (AD) have yet to explored. In addition, CSF biomarkers also play an important role in detecting AD. In this study, we explored the differences in cortical thinning and CSF biomarkers between males and females in both healthy and AD subjects. Method Baseline differences and differences up to two‐ years were studied using the data from two publicly available datasets ‐ Alzheimer's Disease Neuroimaging Initiative (ADNI) [Baseline scans‐ healthy control (HC): 423, AD: 330; follow up scans ‐ HC: 1191, AD: 536] and Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) [Baseline scans only ‐ HC: 319, AD: 72.] Only stable subjects (diagnosis does not change in the follow up scans) healthy controls and AD subjects were used. Generalized linear regression models were used to control the effect for age, intracranial volume, and scanner effects. For analyzing the differences between CSF biomarker levels, only age was controlled. Result In the Healthy control group, males showed a thinner cortex than females at baseline. Both males and females showed significant cortical thinning in different regions over two years. In the AD group, males showed a thinner cortex than females at baseline and over two years; however, the number of regions showing a significantly thinner cortex in males compared to females were less as opposed to the case in the control group. On the other hand, no significant differences were observed in the level of CSF biomarkers between males and females at either baseline and follow up measurements for both control and AD groups. Conclusion In AD, females appear to experience greater cortical thinning than males such that the sex difference was less evident in AD than in controls. In the AD group, the sex difference in cortex continued to decrease through the two‐year study period. For CSF biomarkers, no significant difference was observed for males and females. This result suggests that neuro‐imaging is more sensitive to male and female differences than CSF biomarkers.
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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 | 0.000 |
| 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".