Association between dietary antioxidant intakes and female infertility: evidence from the national health and nutrition examination survey 2013−2020
Bibliographic record
Abstract
Background Approximately one in eight women aged 15–49 years seek medical care for infertility. Oxidative stress is a critical factor in female infertility. The Composite Dietary Antioxidant Index (CDAI) is a novel metric for assessing overall dietary antioxidant capacity. This study aimed to investigate the association between CDAI, its individual components, and infertility risk.Methods A cross-sectional analysis was conducted using 2013–2020 National Health and Nutrition Examination Survey (NHANES) data. The CDAI was calculated based on dietary intake of vitamins A, C, E, zinc, selenium, and carotenoids. Weighted logistic regression models were used to assess associations between CDAI (and its components) and infertility. Restricted cubic spline (RCS) analysis was applied to evaluate potential non-linear relationships. Subgroup and sensitivity analyses were performed to test the robustness of the findings.Results A negative association between CDAI and infertility was observed (OR = 0.95; 95%CI [0.91−0.99], P = 0.014). Stratification by CDAI quartiles showed a consistent decreasing trend in infertility risk (Q4 vs. Q1: OR = 0.52; 95%CI [0.33–0.84], P for trend = 0.003). RCS analysis indicated a linear negative relationship between CDAI and infertility (P for non-linear = 0.278). Higher carotenoid intake was inversely associated with infertility risk, whereas intakes of vitamin A and C showed V-shaped, non-linear associations with infertility (P for non-linear < 0.05). These findings remained stable across subgroup and sensitivity analyses.Conclusion CDAI is linearly and inversely associated with the prevalence of female infertility, highlighting the potential importance of antioxidant-rich diets in promoting women’s reproductive health.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".