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. 2024 Oct;20(4):e13663.
doi: 10.1111/mcn.13663. Epub 2024 May 23.

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

Sanja Nel  1   2   3 , Ute Dagmar Feucht  2   3   4   5 , Tanita Botha  2   3   6 , Friedeburg Anna Maria Wenhold  1   2   3
Affiliations

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

Sanja Nel et al. Matern Child Nutr. 2024 Oct.

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.

Keywords: (MeSH terms) Infant; birthweight; growth; growth charts; malnutrition; premature; weight gain.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Timeline of study data collection, including growth charts used for analysis of anthropometric data at each time point.
Figure 2
Figure 2
Early growth (up to 50 weeks postmenstrual age) of South African preterm infants (N = 303) according to the Fenton Growth Chart and INTERGROWTH‐21st Growth Standards. ΔWZ = the change in weight‐for‐age z‐score from birth to the last recorded visit up to 50 weeks postmenstrual age.
Figure 3
Figure 3
Relationships between 1‐year anthropometry and change in weight‐for‐age z‐score (ΔWZ) according to the Fenton Growth Chart and INTERGROWTH‐21st Growth Standards, in terms of correlations and dichotomous categories.
See this image and copyright information in PMC

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