Association Between the Retinal Biomarkers and Cognitive Impairment in Community‐dwelling Older Adults in Taiwan
Bibliographic record
Abstract
Abstract Background The early detection of preclinical dementia is crucial, prompting investigations into retinal biomarkers using optical coherence tomography (OCT). Inconsistent and limited longitudinal studies have been done to clarify the association between the retinal nerve fiber layer (RNFL) and ganglion cell‐inner plexiform layer (GC‐IPL) thickness and cognitive function over time. This study aims to explore the association between retinal biomarkers and cognitive function over time in non‐demented older adults. Method This seven‐year prospective cohort study included 264 non‐demented older adults at baseline (2015‐2017) from the ongoing Taiwan Initiative for Geriatric Epidemiological Research. Cognitive function underwent biennial follow‐ups to 2022, with assessments including global and domain‐specific cognition (memory, attention, executive function, and verbal fluency) measured using the Montreal Cognitive Assessment–Taiwanese version (MoCA‐T) and a battery of neuropsychological tests. Retinal data were collected using OCT at baseline. Generalized linear mixed models were utilized to examine the relationship between the RNFL, GC‐IPL thickness, and cognitive function, adjusting for apolipoprotein E ε4 status, age, sex, education years, and age‐related macular degeneration. Result At baseline, the performance of global cognition (MoCA‐T) decreased as GC‐IPL deviated from the mean (77.1 μm) (quadratic GC‐IPL: β= ‐0.32 x 10 ‐2 ; 95% CI: ‐0.61 x 10 ‐2 to ‐0.03 x 10 ‐2 ). Similar findings were found for memory performance (quadratic GC‐IPL: β= ‐0.19 x 10 ‐2 ; 95% CI: ‐0.31 x 10 ‐2 to ‐0.06 x 10 ‐2 ), and executive function (quadratic GC‐IPL: β= ‐0.07 x 10 ‐2 ; 95% CI: ‐0.14 x 10 ‐2 to ‐0.003 x 10 ‐2 ) over seven years. These results remained significant in females after stratification by sex. Notably, no significant association was observed between RNFL thickness and cognition. Conclusion We found a non‐linear relation between GC‐IPL thickness and poor performance of global cognition, memory, and executive function, particularly among women. These findings highlight the significance of GC‐IPL thickness as an early biomarker of cognitive impairment, informing strategies for timely intervention in aging populations.
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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 | 0.000 |
| 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".