Gender disparities in the association between macular thickness and cognitive function among elderly individuals in China
Bibliographic record
Abstract
BACKGROUND: Numerous studies have indicated that optical coherence tomography (OCT) can serve as a valuable tool for assessing cognitive ability by measuring changes in ocular macular thickness. Recently, gender differences in the association between retinal thickness and cognitive function have been suggested. However, it remains unclear whether such gender differences exist in the elderly Chinese. METHODS: This study utilized cross-sectional data from the Rugao Aging Cohort in 2019, comprising 734 healthy elderly individuals. Macular thickness was measured using OCT, while cognitive function was assessed using the Mini-Mental State Examination (MMSE), the Hierarchical Dementia Scale (HDS), and the Montreal Cognitive Assessment (MoCA). Comparative t-tests were employed to evaluate differences in macular thickness between elderly males and females, and multiplelinear regression analyses were conducted to examine the association between ocular macular thickness and cognitive function in males and females separately. RESULTS: Our findings revealed significant differences in all macular layers’ thickness between males and females, that males exhibited a thicker inner ring (P ≤ 0.026) and a thinner outer ring (P ≤ 0.024) compared to females. Cognitive function as assessed by MMSE, MoCA, and HDS exhibited significant associations with the central thickness and the average thickness of each region of all macular layers within the inner macular ring among the elderly participants (β ≥ 0.202, P ≤ 0.045). However, upon gender stratification, we only observed significant associations between cognitive function and the average thickness of the inner macular ring (including the inner temporal, inner nasal, inner superior, and inner inferior regions) of all macular layers (β ≥ 0.333, P ≤ 0.055) in females, instead of in males (β ≥ -0.254, P = 1). Additionally, the significant association between MoCA score and central macular thickness was observed exclusively in females (β = 0.529, P = 0.027). CONCLUSIONS: In the Rugao Aging Cohort, significant gender differences of thickness of all macular layers between males and females were identified, with males exhibiting thicker inner rings and thinner outer rings compared to females. Notably, cognitive decline was associated with the thinning of the central part and inner ring of macula exclusively in females. These results suggest that when macular thickness is used as a risk marker for cognitive decline, its gender bias should be carefully considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".