The association between macronutrients intake and myopia risk: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Dietary factors have been suggested as potential risk factors for myopia, but research findings on this relationship are inconclusive. The potential predisposing or protective role of macronutrient (carbohydrate, protein, fat) intake in the development of myopia was systematically reviewed, followed by data synthesis by meta-analysis. METHODS: A systematic search was conducted in PubMed, Web of Science, Scopus, and Google Scholar up to the end of June 2023 to identify all relevant studies. All observational studies that assessed the relationship between macronutrient intake with myopia, axial length (AL) of eyes and spherical equivalent refractive error (SE) on individuals younger than 18 years old were included. RESULTS: After removing duplicates and screening studies, four studies were included in the systematic review and meta-analysis. Pooled odds ratios regarding the association between myopia development and nutritional intake were 1.01 (95% CI: 0.94, 1.08), 0.97 (95% CI: 0.86, 1.08), and 0.99 (95% CI: 0.83, 1.18) for carbohydrates, proteins, and fats, respectively, indicating no significant associations. Intake of carbohydrates, proteins, and fats was not significantly associated with either SE or AL. CONCLUSIONS: Intake of carbohydrates, fats, or proteins did not influence the risk of myopia. The relationship between the intake of other macronutrients and myopia is suggested to be scrutinized in future studies. REGISTRATION: The systematic review protocol was registered on PROSPERO (registration number: CRD42024541369).
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".