The role of multiparity and maternal age at first pregnancy in the association between early menarche and metabolic syndrome among middle-aged and older women
Bibliographic record
Abstract
OBJECTIVE: To analyze whether maternal age at first pregnancy and parity are mediators of the association between early menarche and metabolic syndrome in a sample of middle-aged and older women. METHODS: Cross-sectional study of 428 women (40 to 80 y), who had experienced a pregnancy in their lifetime, was performed between 2014 and 2016. Age at first pregnancy, parity, and early menarche were self-reported. Metabolic syndrome was assessed using the criteria described by the National Cholesterol Education Program's Adult Treatment Panel III. The association between metabolic syndrome and early menarche was assessed by logistic regression analysis. The mediating role of age at first pregnancy and multiparity in the relationship between early menarche and metabolic syndrome was assessed through mediation analysis, adjusted for covariates. RESULTS: According to adjusted logistic regression models, early menarche was associated with higher odds of prevalent metabolic syndrome (OR: 2.26; 95% CI: 1.15-4.46). Mediation analysis showed a significant direct effect of early menarche on metabolic syndrome (β: 0.808; 95% CI: 0.107-1.508). Of the two mediators tested, age at first pregnancy was significant (β: 0.065; 95% CI: 0.004-0.221), ie, participants with and without early menarche differ, on average, by 0.879 SDs in the log odds of MetS (total effect), of which 0.065 SDs (8%), on average, would be attributable to the effect of early menarche on age at first pregnancy (indirect effect), which, in turn, affects MetS. CONCLUSIONS: Age at first pregnancy may partially contribute to the association between early menarche and metabolic syndrome among middle-aged and older women who had experienced a pregnancy over their lifetime.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".