Inverse associations of dietary and circulating carotenoids with body mass index in adults: A mediation analysis from a large population-based cohort study
Bibliographic record
Abstract
Background The high prevalence of overweight/obesity and low fruit and vegetable consumption in the United States underscores the need to better understand the role of carotenoids in body mass index (BMI) management. While carotenoids have anti-obesity mechanisms, their influence on BMI remains inconclusive. Aim This study aimed to examine the association between total dietary carotenoid intake and the risk of abnormal BMI (≥25 kg/m 2 ), and to validate this relationship using serum carotenoid levels. Methods We analyzed data from 13,449 adults participating in the National Health and Nutrition Examination Survey (NHANES) 2011–2018 cycles, applying weighted multi-variable logistic regression, restricted cubic spline (RCS) modeling, and mediation analysis. Results Individuals with abnormal BMI—representing 73.3% of the cohort—had significantly lower levels of both dietary and serum carotenoids. After full adjustment, those in the highest quintile of dietary carotenoid intake had a lower risk of abnormal BMI compared to those in the lowest quintile (odds ratio: 0.798; 95% confidence interval: 0.678–0.939), a pattern consistent with serum carotenoid levels. RCS analysis revealed a significant non-linear association, with protective effects observed above inflection points of 12.37 μg/kg/day for dietary intake and 0.21 μmol/L for serum concentrations. Mediation analysis further indicated that serum carotenoids accounted for approximately 64.2% of the total effect of dietary intake on BMI. Conclusion These findings suggest that higher carotenoid intake may be associated with a lower risk of abnormal BMI, potentially mediated through circulating carotenoids. Promoting carotenoid-rich diets may offer a promising strategy for obesity prevention, though further research is warranted to refine intake recommendations.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".