The influence of maternal and infant nutrition on cardiometabolic traits: novel findings and future research directions from four Canadian birth cohort studies
Bibliographic record
Abstract
A mother's nutritional choices while pregnant may have a great influence on her baby's development in the womb and during infancy. There is evidence that what a mother eats during pregnancy interacts with her genes to affect her child's susceptibility to poor health outcomes including childhood obesity, pre-diabetes, allergy and asthma. Furthermore, after what an infant eats can change his or her intestinal bacteria, which can further influence the development of these poor outcomes. In the present paper, we review the importance of birth cohorts, the formation and early findings from a multi-ethnic birth cohort alliance in Canada and summarise our future research directions for this birth cohort alliance. We summarise a method for harmonising collection and analysis of self-reported dietary data across multiple cohorts and provide examples of how this birth cohort alliance has contributed to our understanding of gestational diabetes risk; ethnic and diet-influences differences in the healthy infant microbiome; and the interplay between diet, ethnicity and birth weight. Ongoing work in this birth cohort alliance will focus on the use of metabolomic profiling to measure dietary intake, discovery of unique diet-gene and diet-epigenome interactions, and qualitative interviews with families of children at risk of metabolic syndrome. Our findings to-date and future areas of research will advance the evidence base that informs dietary guidelines in pregnancy, infancy and childhood, and will be relevant to diverse and high-risk populations of Canada and other high-income countries.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".