Evidence map›Paper›PMID 41356348›Full record

ArticleResearch square2025

Infant dietary patterns and early childhood weight outcomes: a secondary analysis from the Starting Early Program Trial.

Lauren Berube, Christina Kim, Andrea Deierlein, Kathlee Woolf, Mary Jo Messito, Rachel Gross

Abstract readPreprint
In one paragraph

Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Lauren BerubeNew York University School of Global Public Health.ORCID 0000-0001-8154-3231
Christina KimNYU Grossman School of Medicine.ORCID 0000-0002-9401-6585
Andrea Deierlein
Kathlee Woolf
Mary Jo MessitoNew York University Grossman School of Medicine.
Rachel GrossNew York University Grossman School of Medicine.

Funding

Addressing Poverty-Related Barriers to Prevent Obesity Beginning in InfancyK23HD081077 · NICHD · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI GROSS, RACHEL SHARON · 2014 to 2018
$653k
NICHD NIH HHS K23 HD081077
6 · The paper itself

Abstract

Background: Limited studies assess how infant dietary patterns impact child weight. The Starting Early Program (StEP) promotes healthy nutrition during pregnancy and infancy and leads to healthier child weight, but whether dietary patterns contribute to weight or mediate StEP weight outcomes has not been studied. Objectives: This secondary analysis identified infant dietary patterns in StEP, determined associations between dietary patterns and child weight outcomes, and examined whether dietary patterns mediated the relationship between StEP and child weight. Methods: Data were from 377 mother-infant dyads enrolled in a randomized trial testing the efficacy of StEP. Infant diet was assessed at 10 months using an interviewer-administered 24-hour recall, and dietary patterns were identified using latent class analysis. Child weights were abstracted from medical records at 12, 24, and 36 months. Associations between infant dietary patterns and weight-for-age z-score (WFAz) and likelihood of being classified as overweight (WFA ≥85 Results: Four classes of infant dietary patterns were identified, characterized by differences in milk feeding and complementary food type and variety: Breastfed-High variety; Formula fed-High variety; Formula fed-Low variety; and Mixed fed-Low variety. Compared to the Breastfed-High variety class, infants in the Formula fed-Low variety class had higher WFAz and were more likely to be classified as overweight at 24 and 36 months. Participation in StEP increased membership in Breastfed-High variety, which mediated the association between StEP and lower WFAz at 24 months. Conclusions: Infant dietary patterns were identified and associated with child obesity risk. StEP promoted an infant dietary pattern that was most consistent with infant feeding guidelines, which mediated intervention effects on child weight.

Indexed as

childhood obesitydietary patternsinfant feedingpreventionweight-for-age

Identifiers

PMID41356348
PMCPMC12676443

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.