Early childhood multiple micronutrient supplementation is associated with lower obesity prevalence in later childhood, compared with iron and vitamin A supplementation only
Bibliographic record
Abstract
We examined whether multiple micronutrient (MM) supplementation in early childhood has any lasting effect on child size in later childhood. In a randomized, double‐blind controlled trial in Mexico, infants (n=650) from a prenatal supplementation trial (MM or iron only) received either MM or iron and vitamin A (Fe‐A) supplements for 6d/wk from 3–24mo of age. Child height (ht) and weight (wt) were measured in a 2008 follow‐up visit (n=607; mean age 8.5 ± 0.9y). Using intent to treat analysis [4 group comparisons (by mother and child group) and by child group only] we found no significant differences (p>.1) in child wt, ht, ht‐for‐age or wt‐for‐age Z‐scores (HAZ or WAZ), or prevalence of stunting (HAZ<−2), underweight (WAZ<−2), thinness (BMI‐forage Z‐score (BAZ)< −2) or overweight (BAZ +1 to +1.9). However, the child MM group had significantly lower BMI, BAZ and obesity prevalence (BAZ>+2) than the child Fe‐A group (mean BMI 17.3 ± 2.7 vs. 17.8 ± 2.9 kg/m 2 , mean BAZ 0.5 ± 1.1 vs. 0.8 ± 1.2, obesity 10.2 vs. 17.3%; all p<.05). Analysis by 4 groups showed a similar, though not statistically significant trend (p=.07); children who received MM (in utero and early childhood) had the lowest obesity prevalence (9.2%) and those who received Fe only in utero and Fe‐A in childhood had the highest prevalence (18.3%). Results are consistent with our findings at 2y of age and suggest long term benefits of MM supplementation in early life. Further analysis is required to adjust for contextual factors that may have contributed to this difference after supplementation was completed. Funding: MI; Thrasher Research Fund; UNICEF; CONACYT; INSP Mexico; UC Berkeley; Emory University
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".