Pathways Linking Birth Weight and Insulin Sensitivity in Early Adolescence: A Double Mediation Analysis
Bibliographic record
Abstract
Purpose: We examined pathways linking birth weight, weight gain from 0 to 2 years, and adiposity during childhood with insulin sensitivity in early adolescence. Methods: Data were from a longitudinal cohort of 630 Quebec white children with a parental history of obesity (Quebec Adipose and Lifestyle Investigation in Youth study). In a subsample of children born at term (n = 395), weight-for-length z score (zWFL) from 0 to 2 years were computed. At 8 to 10 years, the percentage of body fat was assessed using dual energy X-ray absorptiometry. At 10 to 12 years, the Matsuda insulin sensitivity index (ISI) and the homeostasis model assessment for insulin resistance were determined. A linear regression-based approach for mediation analysis was used to estimate the distinct pathways linking zWFL at 0 to 2 years to insulin sensitivity. Results: Every additional unit in zWFL at birth was associated with a 10% (95% CI, 5.26% to 14.85%) increase in the Matsuda ISI in early adolescence, independently of the weight at 0 to 2 years and in childhood. An indirect effect of zWFL at birth on the Matsuda ISI was also observed but in the opposite direction (-4.44; 95% CI, -7.91 to -1.05). This relation was mediated by childhood adiposity but not by weight gain from 0 to 2 years. The indirect effect of weight gain from 0 to 2 years, via childhood adiposity, also led to lower insulin sensitivity (-4.83%, 95% CI, -7.34 to -2.53). The findings were similar for the homeostasis model assessment for insulin resistance or when restricted to children with appropriate-for-gestational-age birth weights. Conclusions: A greater birth weight-for-length resulted in improved insulin sensitivity in early adolescence. However, in the presence of excess childhood adiposity, both a greater birth weight and a faster rate of weight gain from 0 to 2 years resulted in lower insulin sensitivity.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".