Latent classes of anthropometric growth in early childhood using uni- and multivariate approaches in a South African birth cohort

Abstract

Background Defining growth patterns during childhood is key to identifying future health risk and vulnerable periods for potential interventions. The aim of this study was to identify growth profiles in children from birth to five years in a South African birth cohort, the Drakenstein Child Health Study (DCHS) using a Latent Class Mixed Modelling (LCMM) approach. Methods LCMM was used to identify underlying latent profiles of growth for univariate responses of standardized height, standardized weight, standardized body mass index and standardized weight-for-length/height measurements and multivariate response of joint standardized height and standardized weight measurements from birth to five years for a sample of 1143 children from a South African birth cohort, the Drakenstein Child Health Study (DCHS). Allocations across latent growth classes were compared to better understand the differences and similarities across the classes identified given different composite measures of height and weight as input. Results Four classes of growth within standardized height (n 1 =516, n 2 =112, n 3 =187, n 4 =321) and standardized weight (n 1 =263, n 2 =150, n 3 =584, n 4 =142), three latent growth classes within Body Mass Index (BMI) (n 1 =481, n 2 =485, n 3 =149) and Weight for length/height (WFH) (n 1 =321, n 2 =710, n 3 =84) and five latent growth classes within the multivariate response of standardized height and standardized weight (n 1 =318, n 2 =205, n 3 =75, n 4 =296, n 5 =242) were identified, each with distinct trajectories over childhood. A strong association (much greater or lesser than expected proportions (an increase by 25% in some cases), when compared to the proportion of abnormal growth features across the entire cohort) was found between various growth classes and abnormal growth features such as rapid weight gain, stunting, underweight and overweight. Conclusions With the identification of these classes, a better understanding of distinct childhood growth trajectories and their predictors may be gained, informing interventions to promote optimal childhood growth.