A systematic review of the associations between dietary intake and diabetic retinopathy
Bibliographic record
Abstract
Purpose The evidence linking diet with diabetic retinopathy ( DR ) is growing, but DR ‐specific dietary guidelines are still lacking. We conducted a comprehensive systematic assessment of the association between dietary intake and DR , and determined key areas for further research. Methods PubMed, Embase, Medline, and the Cochrane Central register of controlled trials were systematically searched for papers published between Jan 1967 and Jan 2017 according to standardized criteria. Interventional and observational studies, investigating nutrient intake, food and beverage consumption, and dietary patterns, were included. Data extraction was performed through a standardized extraction form, and study quality was evaluated using a modified Newcastle‐Ottawa scale for observational studies, and the Cochrane collaboration tool for interventional studies. Results Of 4265 titles initially identified, 31 studies (3 interventional, 9 cohort, 4 case‐control, 15 cross‐sectional) were retained. The evidence suggests the intake of dietary fibre, oily fish, and a Mediterranean diet to be protective of DR . Conversely, higher caloric intake was associated with higher DR risk. No significant associations of DR with carbohydrate, vitamin D and sodium intake were found. The association between DR and antioxidants, fatty acids, proteins, alcohol, and other popular beverages such as tea and coffee remains equivocal. Conclusions Diet is a crucial aspect of DR management, with dietary components including dietary fibre, oily fish, and a Mediterranean diet being protective of DR , and a high caloric intake associated with greater DR risk. However, further cohort studies to untangle the effects of other key dietary components on DR , such as antioxidants, fatty acids, proteins, alcohol and popular beverages, are needed in order to better inform clinical guidelines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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".