Cross-sectional study on the association between retinal microcirculation changes based on optical coherence tomography angiography and mild cognitive impairment in patients with type 2 diabetes
Bibliographic record
Abstract
Objective To investigate the association between retinal microcirculation changes, assessed using optical coherence tomography angiography (OCTA), and mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM), and to determine whether retinal microcirculation changes can serve as a potential biomarker for MCI in these patients. Methods A total of 100 patients with T2DM who visited Hefei Aier Eye Hospital between April 2023 and December 2024 were selected. Retinal microcirculation indicators, including the perfusion density of the superficial capillary plexus (SCP), the perfusion density of the deep capillary plexus (DCP), the central foveal thickness (CMT) and the area of the foveal avascular zone (FAZ), were evaluated using OCTA. The cognitive function of the patients was assessed using the Montreal Cognitive Assessment. The association between retinal microcirculation indicators and MCI was explored using multivariate logistic regression analysis. Results The perfusion density of SCP and DCP in all patients was lower than the normal value. Patients with CMT outside the normal range accounted for 73%, and those with FAZ outside the normal range accounted for 23%. No significant correlation was found between individual retinal microcirculation indicators and MCI. However, a significant association was observed when the four indicators were combined ( p < 0.001), indicating that retinal microcirculation changes based on OCTA are significantly correlated with MCI in patients with T2DM. Conclusion Retinal microcirculation changes based on OCTA are significantly associated with MCI in patients with T2DM, suggesting that retinal microcirculation indicators may serve as potential biomarkers for MCI in these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".