Prospective associations of appetitive traits at 3 and 12 months of age with body mass index and weight gain in the first 2 years of life
Bibliographic record
Abstract
BACKGROUND: Appetitive traits in childhood such as food responsiveness and enjoyment of food have been associated with body mass index (BMI) in later childhood. However, data on appetitive traits during infancy in relation to BMI in later childhood are sparse. We aimed to relate appetitive traits in infancy to subsequent BMI and weight gain up to 24 months of age. METHODS: Data of 210 infants from the Singapore GUSTO mother-offspring cohort was obtained. The Baby Eating Behavior Questionnaire (BEBQ) and the Child Eating Behavior Questionnaire (CEBQ) were administered to mothers when their offspring were aged 3 and 12 months respectively. Height and weight of offspring were measured at ages 3, 6, 9,12,15,18 and 24 months. The association of appetitive traits with both BMI z-score and weight gain were evaluated using multivariate linear regression. RESULTS: Food responsiveness at 3 months was associated with higher BMI from 6 months up to 15 months of age (p < 0.01) and with greater weight gain between 3 and 6 months of age (p = 0.012). Slowness in eating and satiety responsiveness at 3 months was significantly associated with lower BMI at 6 months (p < 0.01) and with less weight gain between 3 to 6 months of age (p = 0.034). None of the appetitive traits at 12 months were significantly associated with BMI or weight gain over any time period. CONCLUSION: Early assessment of appetitive traits at 3 months of age but not at 12 months of age was associated with BMI and weight gain over the first two years of life. TRIAL REGISTRATION: Clinical Trials identifier NCT01174875.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".