Socioeconomic position and body composition in childhood in high- and middle-income countries: a systematic review and narrative synthesis

Abstract

Background The relation between socioeconomic position (SEP) and obesity measured by body mass index (BMI), a measure of weight for height, has been extensively reviewed in children, showing consistent associations between disadvantaged SEP and higher BMI in high-income countries (HICs) and lower BMI in middle-income countries (MICs). Fat mass (FM), a more accurate measure of adiposity, and fat-free mass (FFM) are not captured by BMI, but have been shown to track from childhood to adulthood, and be important for cardiovascular health and functional outcomes in later life. It is not clear whether body composition is associated with SEP. We systematically reviewed the association between SEP and body composition in childhood. Methods A systematic review was carried out following PRISMA guidelines. The protocol was pre-registered with PROSPERO (CRD42019119937). Original studies in the English language, which examined the association between SEP and body composition in childhood, were included. An electronic search of three databases was conducted. Two independent reviewers carried out screening, data extraction and quality assessment. Due to heterogeneity in results, a narrative synthesis was conducted. Heterogeneity in findings according to SEP, sex, body composition measure and country income level was investigated. Results 50 papers were included, the majority from HICs. No papers were from low-income countries. Disadvantage in childhood was associated with greater FM and lower FFM in HICs, but with lower FM and lower FFM in MICs. When measures of FFM indexed to height were used there was no evidence of associations with SEP. In HICs, more studies reported associations between disadvantaged SEP and higher FM among girls comparative to boys. Conclusions Inequalities in FM are evident in HICs and, in the opposite direction, in MICs and follow similar trends to inequalities for BMI. Inequalities in height are likely important in understanding inequalities in FFM.

Sex-specific socioeconomic inequalities in trajectories of anthropometry, blood pressure and blood-based biomarkers from birth to 18 years: a prospective cohort study

“…We show that this childhood pattern continues into adolescence and that there was some evidence of inequalities widening across all categories of maternal education among females by 18y while remaining similar over time among males. Our findings are consistent with a recent systematic review, including 50 studies mostly from high-income countries, that reported socioeconomic inequalities in fat mass in childhood that were wider among females compared with males (19). Analyses of the Millennium Cohort Study in the UK also found greater evidence of inequalities in fat mass in females early in childhood, but in contrast to our findings, inequalities widened at a faster rate in males so that by late adolescence, inequalities in females and males were similar (25).…”

Sociodemographic and Socioeconomic Factors Influencing the Body Mass Composition of School-Age Children

“…When evaluations of FFM indexed to height were used, there were no significant associations with socioeconomic position. In high-income countries, more evidence indicated relationships between disadvantaged socioeconomic position and higher BFP among girls compared to boys [ 56 ].…”

Recreational screen time trajectories during early childhood and imaging‐measured body composition at age 7 in the Odense child cohort

“…Information on covariates was parent‐reported or recorded at birth. Previous reports have suggested that children's body composition may be associated with the age and sex, 11 birthweight, 33 ethnicity, 14 PA, 34 sleep, 35 diet 36 and maternal BMI, 37 age 38 , and socioeconomic status (SES) 39 . A directed acyclic graph is presented in Figure S1.…”

“…Previous reports have suggested that children's body composition may be associated with the age and sex, 11 birthweight, 33 ethnicity, 14 PA, 34 sleep, 35 diet 36 and maternal BMI, 37 age 38 , and socioeconomic status (SES). 39 A directed acyclic graph is presented in Figure S1. Age was calculated using the birthdate and the date of DXA examination.…”