Insights from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) Cohort Study
Bibliographic record
Abstract
BACKGROUND: The dramatic emergence of noncommunicable diseases (NCD) in Asia, albeit with ethnic variation, has coincided with the rapid socioeconomic and nutritional transition taking place in the region, with the prevalence of diabetes rising 5-fold in Singapore in less than 4 decades. The Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort study recruited 1,247 expectant mothers of Chinese, Malay, or Indian ethnicity in their first trimester, with detailed longitudinal tracking--through the antenatal period, birth, and the child's first 4 years of life--to examine the potential roles of fetal, developmental, and epigenetic factors in early pathways to metabolic and neurodevelopmental outcomes. KEY MESSAGES: A number of findings with a translational and clinical focus have already emerged. In the mothers, we found that changes and differences in food consumption varied across ethnic groups, with persistence of traditional beliefs, during pregnancy and the postpartum period. During pregnancy, higher maternal glucose levels, even in the absence of gestational diabetes mellitus, had graded relations with infant adiposity. Relations between maternal emotional health and birth outcomes and neurodevelopment have been identified. Genotype (25%) and in particular gene × environment interactions (75%) shape interindividual variations in the DNA methylome at birth. The complex effects of fixed genetic variations and different in utero environments can influence the epigenetic status at birth and the later-life phenotype. CONCLUSIONS: The richness of the clinical data in 3 ethnicities, the extent of the biospecimen collection, and the extensive infancy and preschool follow-up have allowed us to study the biological pathways that link fetal development to health outcomes. In the coming years, more sophisticated analyses of epigenotype-phenotype relationships will become possible as the children grow and develop. Our studies will lead to the development of clinical and population-based interventions to reduce the burden of NCD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".