Maternal Education in Early Life and Risk of Metabolic Syndrome in Young Adult American Females and Males
Bibliographic record
Abstract
BACKGROUND: Maternal education in a child's early life may directly affect the child's adult cardiometabolic health, but this is difficult to disentangle from biological, social, and behavioral life course processes that are associated with maternal education. These processes may also differ between males and females. METHODS: Using data from the National Longitudinal Study of Adolescent to Adult Health (1995-2009) (N = 4,026 females and 3,192 males), we estimated sex-stratified associations between maternal attainment of less than high school (<HS), high school diploma (HS), or college degree (CD) at the respondent's birth and respondent's risk of metabolic syndrome (MetS); we used marginal structural models (MSM) to account for the influence of major life course risk factors, such as childhood maltreatment, adolescent overweight, adult education, household income, smoking, and physical activity, in mediating associations between maternal education and offspring MetS risk. RESULTS: Each higher level of maternal education was associated with a 36% (Relative Risk = 0.64 [95% Confidence Interval (CI): 0.50-0.82]) reduced risk of MetS among females, but only 19% (RR = 0.81 [95% CI: 0.64-1.01]) reduction among males (P-value interaction < 0.05). Stronger inverse associations were also observed for waist circumference and glycated hemoglobin (HbA1c) among females compared with males (-5 cm vs. -2.4 cm and -1.5% vs. -1.0%, respectively). CONCLUSION: High maternal education in early life was associated with a lower risk of MetS in young adulthood even after accounting for life course risk factors, particularly among females. Results were robust to altered model specifications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".