Early Introduction of Complementary Foods is Associated with Higher BMI‐for‐Age but Not % Body Fat at 3 Years in Breast Fed Infants
Bibliographic record
Abstract
Nutrition during the first months of life can affect the long term development of obesity. Previous studies were limited by retrospective analysis of infant feeding. We aimed to establish whether timing of complementary foods was associated with BMI and body composition at 3 years of age using a longitudinal design. Healthy breastfeeding infants were recruited from the vitamin D dose response study (NCT00381914) in Montreal, Quebec. Age of first solid food introduction and infant's diet were recorded during the first year. Children were evaluated at 3 years of age for weight and height to calculate BMI‐for‐age Z‐scores using CDC growth charts. Body composition was assessed using dual‐energy x‐ray absorptiometry. Timing of introduction of solids was significantly associated with BMI‐for‐age Z‐score but not % body fat or lean mass at 3 years of age (Table 1). In a multivariate regression model, introduction of solids 蠄4 months, predicted a +0.96 (95% CI: 0.28, 1.64) increase in BMI‐for‐age Z‐score compared to starting solids between 4 ‐ 6 months. This sample represents some selection bias; however, suggests timing of complementary foods may impact growth by 3 y of age. Table 1 Mean ± SD (n) BMI‐for‐age Z‐score and body composition at 3 years by timing of solid food introduction (n=65) At 3 years Timing of Introduction of Solids P ‐value ≤ 4 months (n=8) 4 ‐ 6 months (n=46) ≥ 6 months (n=11) BMI for age Z‐score, CDC growth charts 1.37 ± 0.85 0.48 ± 0.71 0.14 ± 0.89 0.003 n (%) Overweight, ≥ 85 th and <95 th percentile 4 (50%) 10 (22%) 3 (27%) 0.048 n (%) Obese, ≥ 95 th percentile 1 (12.5%) 0 0 % Body Fat 30.5 ± 4.1 29.8 ± 4.2 27.0 ± 5.7 0.138 % Lean Mass 65.6 ± 4.0 66.2 ± 4.2 68.8 ± 5.5 0.176 Grant Funding Source: Canadian Institutes of Health Research
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".