Maternal Diet Quality and Infant Growth Trajectories During the First Year of Life (OR35-07-19)
Bibliographic record
Abstract
To examine the association between maternal diet quality and infant weight for length growth trajectory during their first year of life. Participants were singleton infant-mother pairs (N = 77) enrolled in the Pregnancy and Postpartum Observational Dietary Study. Mothers completed socio-demographics and dietary (24-hour recalls) assessments at 3 months postpartum. The Alternate Healthy Eating Index (aHEI) was calculated to measure maternal diet quality. Infant weight for length measures from birth to 12 months were abstracted from pediatric records. World Health Organization guidelines were used to calculate infants’ weight for length percentiles. Group-based trajectory analysis was done to identify subgroups of infants with similar growth profiles and to evaluate the association between maternal aHEI and infant's growth trajectory. Models were adjusted for maternal age, race, education and excessive gestational weight gain (GWG). Mothers’ mean age was 28 years ± 5.2; 27% were Latina, and 55% had some college education or more; 60% had experienced excessive GWG; and their average aHEI was 26.7 ± 7.5. Three infant growth trajectories were identified: a low and stable growth group (43.2%), a rapid growth group (33.5%), and a moderate growth group (23.3%). Maternal aHEI was significantly associated with lower odds of having their infant in the rapid growth group (OR = 0.83; P = 0.012), with each unit increase in aHEI score being associated with 17% lower odds of infant's rapid growth. Trajectory models suggested three patterns of infant growth. Higher maternal diet quality was associated with lower odds of infant rapid growth. Future studies are needed to replicate these findings in larger cohorts and identify mediators of this association to prevent childhood obesity. CCTS (UL1TR001453), NCATS (UL1TR000161), NIMHD (1P60MD006912-02), CDC (U48-DP001933), NIGMS (R25GM113686-02), NHLBI (F30HL128012), and Canadian Institutes of Health Research (DFS-140394).
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".