Evidence map›Paper›PMID 38783411›Full record

ArticleMaternal & child nutrition2024

Infant growth by INTERGROWTH-21st and Fenton Growth Charts: Predicting 1-year anthropometry in South African preterm infants.

Sanja Nel, Ute Dagmar Feucht, Tanita Botha, Friedeburg Anna Maria Wenhold

Abstract read
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Article in Maternal & child nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Sanja NelDepartment of Human Nutrition, University of Pretoria, Pretoria, South Africa.ORCID 0000-0003-3175-7340
Ute Dagmar FeuchtResearch Centre for Maternal, Fetal, Newborn & Child Health Care Strategies, University of Pretoria, Atteridgeville, South Africa.ORCID 0000-0002-6339-1040
Tanita BothaResearch Centre for Maternal, Fetal, Newborn & Child Health Care Strategies, University of Pretoria, Atteridgeville, South Africa.ORCID 0000-0002-8861-4466
Friedeburg Anna Maria WenholdDepartment of Human Nutrition, University of Pretoria, Pretoria, South Africa.ORCID 0000-0003-1140-5065

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Post-natal growth influences short- and long-term preterm infant outcomes. Different growth charts, such as the Fenton Growth Chart (FGC) and INTERGROWTH-21st Preterm Post-natal Growth Standards (IG-PPGS), describe different growth curves and targets. This study compares FGC- and IG-PPGS-derived weight-for-postmenstrual age z-score (WZ) up to 50 weeks postmenstrual age (PMA50) for predicting 1-year anthropometry in 321 South African preterm infants. The change in WZ from birth to PMA50 (ΔWZ, calculated using FGC and IG-PPGS) was correlated to age-corrected 1-year anthropometric z-scores for weight-for-age (WAZ), length-for-age (LAZ), weight-for-length (WLZ) and BMI-for-age (BMIZ), and categorically compared with rates of underweight (WAZ < -2), stunting (LAZ < -2), wasting (WLZ < -2) and overweight (BMIZ > + 2). Multivariable analyses explored the effects of other early-life exposures on malnutrition risk. At PMA50, mean WZ was significantly higher on IG-PPGS (-0.56 ± 1.52) than FGC (-0.90 ± 1.52; p < 0.001), but ΔWZ was similar (IG-PPGS -0.26 ± 1.23, FGC -0.11 ± 1.14; p = 0.153). Statistically significant ΔWZ differences emerged among small-for-gestational age infants (FGC -0.38 ± 1.22 vs. IG-PPGS -0.01 ± 1.30; p < 0.001) and appropriate-for-gestational age infants (FGC + 0.02 ± 1.08, IG-PPGS -0.39 ± 1.18; p < 0.001). Correlation coefficients of ΔWZ with WAZ, LAZ, WLZ and BMIZ were low (r < 0.45), though higher for FGC than IG-PPGS. Compared with IG-PPGS, ΔWZ < -1 on FGC predicted larger percentages of underweight (42% vs. 36%) and wasting (43% vs. 39%) and equal percentages of stunting (33%), while ΔWZ > + 1 predicted larger percentages overweight (57% vs. 38%). Both charts performed similarly in multivariable analysis. Differences between FGC and IG-PPGS are less apparent when considering ΔWZ, highlighting the importance of assessing growth as change over time, irrespective of growth chart.

Indexed as

AnthropometryGrowth ChartsInfant, PrematureBody HeightBody Mass IndexBody WeightChild DevelopmentFemaleGestational AgeGrowth DisordersHumansInfantInfant, NewbornMaleSouth AfricaThinnessbirthweightgrowthgrowth chartsmalnutrition(MeSH terms) Infantprematureweight gain

Identifiers

PMID38783411
PMCPMC11574635

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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.