Undercorrection of refractive error and cognitive function: the Beijing Eye Study 2011
Bibliographic record
Abstract
Cognitive impairment is a hallmark of age-related dementias such as Alzheimer's disease. It was the aim of our study to search for ocular factors, which are associated with a low cognitive function. To avoid confounding factors by a referral bias, we addressed the question in a population-based investigation. The Beijing Eye Study 2011 is a population-based cross-sectional study in northern China and included 3469 participants. The Medical Ethics Committee of the Beijing Tongren Hospital approved the study protocol, and all participants gave informed written consent. The study has already been described in detail previously (Jonas et al. 2009). Cognitive function was assessed using the Mini-Mental State Examination (MMSE) scale. Cognitive function measurements were available for 3127 (90.1%) study participants. The mean cognitive function score was 26.3 ± 3.7 (median: 27; range: 2–30; 95% CI: 17–30). In multivariate analysis, increasing cognitive function score was significantly associated with younger age (p < 0.001), female gender (p = 0.009), rural region of habitation (p = 0.005), higher body height (p = 0.002), higher level of education (p < 0.001), type of occupation (p = 0.001), lower score of psychic depression (p < 0.001), higher best-corrected visual acuity (p < 0.001), lower amount of undercorrection of refractive error (p = 0.02), wearing of glasses (p < 0.001) and history of cardiovascular disorder (p = 0.002) (Table 1). After adjustment for age, region of habitation, body height, level of education, higher type of occupation, score of psychic depression and best-corrected visual acuity, the cognitive score of our study participants was significantly higher the better corrected was their refractive error. Correspondingly, subjects wearing glasses for correction of their refractive error as compared to subjects without glasses showed a significantly higher cognitive score. Previous studies have provided evidence associating dementia and visual impairments; however, none of the studies showed an association with the degree of myopia or undercorrection of refractive error. In an 8.5-year follow-up study of 625 elderly people with normal cognition at baseline, Rogers and Langa found that poor vision was associated with development of dementia and that individuals with very good or excellent vision at baseline had a 63% reduced risk of dementia over participants with poorer vision (Rogers & Langa 2010). In the recent Singapore Malay Eye Study (Ong et al. 2012), people with visual impairment both before and after refractive correction were significantly more likely to have cognitive dysfunction. In a study on 2140 non-institutionalized Mexican Americans aged 65 and older with a follow-up of up to 7 years, near vision impairment, but not distance vision or hearing impairments, was associated with cognitive decline (Reyes-Ortiz et al. 2005). A magnetic resonance imaging study showed a regional expansion of grey matter volume in area V2 contralateral to the eye operated on cataract, at 6 weeks after cataract surgery (Lou et al. 2013). As our study as cross-sectional investigation did not allow drawing conclusions on longitudinal, causal associations, our findings do not constitute a proof of a causal relationship between undercorrection of refractive error and low cognitive function score. The results however suggest that low vision and the undercorrection of refractive error leading to low habitual vision are associated or risk factors for cognitive dysfunction. It may potentially indicate that not only cerebral training as shown in previous studies, but also adequate vision by providing the best possible correction of refractive error, may be protective measures against the development of cognitive dysfunction. Simple, cheap treatment of refractive errors by providing proper eye glasses to people may thus not only improve their quality of life, but may potentially also provide a cost-effective prophylaxis of dementia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".