Influences of the perinatal diet on maternal and child health: insights from the GUSTO study
Bibliographic record
Abstract
Maternal and child health are intrinsically linked. With accumulating evidence over the past two decades supporting the developmental origins of health and diseases hypothesis, it is now widely recognised that nutrition in the first 1000 d sets the foundation for long-term health. Maternal diet before, during and after pregnancy can influence the developmental pathways of the fetus and lead to health consequences later in life. While maternal and infant mortality rates have declined significantly in the past two decades, the growing burden of obesity and chronic non-communicable diseases in women of reproductive age and children is on a rapid rise worldwide, in developed and developing countries. A key contributory factor is malnutrition, which is a consequence of consuming poor quality diets. Suboptimal macronutrient balance and micronutrient inadequacies can lead to undesirable maternal body composition and metabolism, in turn influencing the health of the mother and leading to longer-term metabolic and cognitive health consequences in the infant. The GUSTO (Growing Up in Singapore Towards healthy Outcomes) study, a mother-offspring multi-ethnic cohort study in Singapore, has contributed to this body of evidence over the past 10 years. This review will illustrate how nutritional epidemiological research through a birth cohort has illuminated the importance and urgency of maternal and child nutrition and health in a modern, industrialised setting. It underscores the importance of a number of critical nutrients during pregnancy, in combination with healthy dietary patterns and appropriate meal timing, for optimal maternal and child health.
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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 | 0.003 |
| 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 | 0.000 | 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".