Biomarkers for Alzheimer's disease are upregulated in patients with diabetic retinopathy
Bibliographic record
Abstract
Background Diabetes has been linked to increased prevalence of dementia, but the link between diabetic retinopathy (DR) and Alzheimer's disease (AD) remains unclear. Objective This study aimed to evaluate potential associations between DR and AD-related protein biomarkers in plasma and ocular fluid. Methods A prospective, cross-sectional study collected human blood, vitreous, aqueous, and tear samples and measured amyloid-β (Aβ 40 , Aβ 42 ), total-tau (t-tau), phosphorylated-tau (ptau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) by digital immunoassays. Results The study included 79 eyes (79 patients) [41 females (59.4%); mean (SD) age 57.1 (12.2) years] of which DR was present in 44 (55.7%). All six biomarkers were significantly higher in plasma in participants with DR compared to those without DR [Aβ 40 p = 0.002, Aβ 42 p = 0.002, t-tau = 0.013, ptau181 p = 0.005, GFAP p = 0.010, and NfL p < 0.001]. Within vitreous, DR participants had significantly elevated t-tau (p = 0.002), ptau181 (p = 0.049), and NfL (p = 0.006); and within aqueous, higher NfL (p = < 0.001). Neuropsychological testing scores were lower in participants with DR than those without but did not reach statistical significance (Montreal-Cognitive-Assessment: p = 0.070; Mini-Mental-State-Exam: p = 0.057). Conclusions This study showed significant increases of AD associated protein biomarkers in plasma, vitreous, and aqueous in patients with DR. These results support a potential biological link between DR and AD pathology and suggest that DR, which tends to occur in younger individuals, may be a predictive factor for AD.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".