Lutein and Zeaxanthin Intake during Pregnancy and Visual Function in Offspring at 11–12 Years of Age
Bibliographic record
Abstract
(1) Background: Lutein and zeaxanthin (L&Z) are essential dietary nutrients that are a crucial component of the human macula, contributing to visual functioning. They easily cross the placental barrier, so that retinal deposition commences during foetal development. This study aims to assess associations between maternal L&Z intake during pregnancy and offspring visual function at 11-12 years. (2) Methods: Using the Spanish INfancia y Medio Ambiente project (INMA) Sabadell birth cohort, 431 mother-child pairs were analysed. L&Z data were obtained from food frequency questionnaires (FFQ) at week 12 and 32 of pregnancy, alongside other nutritional and sociodemographic covariates. Contrast vision (CS) and visual acuity (VA) were assessed using the automated Freiburg Acuity and Contrast Testing (FRACT) battery. Low CS and VA were defined as being below the 20th cohort centile. Associations were explored using multiple logistic regression. (3) Results: After controlling for potential confounders, L&Z intake during the 1st and 3rd trimester did not reveal any statistically significant association with either CS or VA in offspring at age 11/12 years. (4) Conclusions: No evidence of a long-term association between L&Z intake during pregnancy and visual function in offspring was found. Further larger long-term studies including blood L&Z levels are required to confirm this result.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".