Association of age at first birth and risk of non-alcoholic fatty liver disease in women: evidence from the NHANES
Bibliographic record
Abstract
Abstract Background Numerous studies have suggested that age at first birth (AFB) is inversely associated with metabolic diseases, but positively associated with liver cancer in women. Non-alcoholic fatty liver disease (NAFLD) is a canonical example of metabolic dysfunction and inflammation-based liver disease, while the association between AFB and risk of NAFLD remains unclear. We aimed to investigate the association between AFB and the odds of NAFLD in women. Methods Women older than 20 years at the time of the survey were analyzed using National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018 in the US. AFB was obtained with self-administered questionnaires. NAFLD was diagnosed as fatty liver index ≥ 60. Odds ratios (ORs) and 95% confidence intervals (CI) were estimated using logistic regression models. Results Of the 12,188 women included in this study, 5,670 (46.5%) had NAFLD. Compared to individuals with AFB of 30–32 years old (reference group), the fully adjusted ORs and 95% CI in women with AFB < 18 years, 18–20 years, 21–23 years, and 24–26 years were 1.59 (95% CI: 1.18, 2.13), 1.58 (95% CI: 1.20, 2.08), 1.38 (95% CI: 1.04, 1.83), and 1.33 (95% CI: 1.01–1.77), respectively. Yet there was no significant difference between AFB of 27–29 years, 33–35 years, or > 35 years compared with the reference group. Conclusions Women with younger AFB have higher odds of NAFLD in later life. Policymakers should consider focusing on those with earlier AFB for screening and prevention of NAFLD.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".