TY - JOUR
T1 - Predicting higher child BMI z-score and obesity incidence in Malaysia
T2 - a longitudinal analysis of a dynamic cohort study
AU - Salway, Ruth
AU - Armstrong, Miranda
AU - Mariapun, Jeevitha
AU - Reidpath, Daniel D.
AU - Brady, Sophia
AU - Yasin, Mohamed Shajahan
AU - Su, Tin Tin
AU - Johnson, Laura
N1 - Funding Information:
This work was supported by funding from UK Medical Research Council and the Malaysian Ministry of Higher Education/UK-MY Joint Partnership on Non-Communicable Diseases 2019/MR/T018984/1. Monash University funds the SEACO health and demographic surveillance system. Co-authors of this study are also supported by the National Institute for Health and Care Research Bristol Biomedical Research Centre (MA). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Funding Information:
The authors declare that they have no competing interests. For transparency: LJ has received, for research unrelated to the current paper, institutional funding from UKRI, World Cancer Research Fund, National Institute for Health Research UK, Joint Programs Initiative EU FP7, Alpro foundation, Danone Baby Nutrition, Kellogg Europe, and the Wellcome Trust. MA has received, for research unrelated to the current paper, institutional funding from the Centre for Aging Better, NIHR and Cancer Research UK.
Publisher Copyright:
© The Author(s) 2024.
PY - 2024/5/27
Y1 - 2024/5/27
N2 - Background: To target public health obesity prevention, we need to predict who might become obese i.e. predictors of increasing Body Mass Index (BMI) or obesity incidence. Predictors of incidence may be distinct from more well-studied predictors of prevalence, therefore we explored parent, child and sociodemographic predictors of child/adolescent BMI z-score and obesity incidence over 5 years in Malaysia. Methods: The South East Asia Community Observatory in Segamat, Malaysia, provided longitudinal data on children and their parents (n = 1767). Children were aged 6–14 years at baseline (2013-14) and followed up 5 years later. Linear multilevel models estimated associations with child BMI z-score at follow-up, adjusting for baseline BMI z-score and potential confounders. Predictors included parent cardiometabolic health (overweight/obesity, central obesity, hypertension, hyperglycaemia), and socio-demographics (ethnicity, employment, education). Logistic multilevel models explored predictors of obesity incidence. Results: Higher baseline BMI z-score predicted higher follow-up BMI z-score both in childhood to late adolescence (0.60; 95% CI: 0.55, 0.65) and early to late adolescence (0.76; 95% CI: 0.70, 0.82). There was inconsistent evidence of association between child BMI z-score at follow-up with parent cardiometabolic risk factors independent of baseline child BMI z-score. For example, maternal obesity, but not overweight, predicted a higher BMI z-score in childhood to early adolescence (overweight: 0.16; 95% CI: -0.03, 0.36, obesity: 0.41; 95% CI: 0.20, 0.61), and paternal overweight, but not obesity, predicted a higher BMI z-score in early to late adolescence (overweight: 0.22; 95% CI: 0.01, 0.43, obesity: 0.16; 95% CI: -0.10, 0.41). Parental obesity consistently predicted five-year obesity incidence in early to late adolescence, but not childhood to early adolescence. An adolescent without obesity at baseline with parents with obesity, had 3–4 times greater odds of developing obesity during follow-up (incidence OR = 3.38 (95% CI: 1.14–9.98, mother) and OR = 4.37 (95% CI 1.34–14.27, father) respectively). Conclusions: Having a higher BMI z-score at baseline was a stronger predictor of a higher BMI z-score at follow-up than any parental or sociodemographic factor. Targeting prevention efforts based on parent or sociodemographic factors is unwarranted but early childhood remains a key period for universal obesity prevention.
AB - Background: To target public health obesity prevention, we need to predict who might become obese i.e. predictors of increasing Body Mass Index (BMI) or obesity incidence. Predictors of incidence may be distinct from more well-studied predictors of prevalence, therefore we explored parent, child and sociodemographic predictors of child/adolescent BMI z-score and obesity incidence over 5 years in Malaysia. Methods: The South East Asia Community Observatory in Segamat, Malaysia, provided longitudinal data on children and their parents (n = 1767). Children were aged 6–14 years at baseline (2013-14) and followed up 5 years later. Linear multilevel models estimated associations with child BMI z-score at follow-up, adjusting for baseline BMI z-score and potential confounders. Predictors included parent cardiometabolic health (overweight/obesity, central obesity, hypertension, hyperglycaemia), and socio-demographics (ethnicity, employment, education). Logistic multilevel models explored predictors of obesity incidence. Results: Higher baseline BMI z-score predicted higher follow-up BMI z-score both in childhood to late adolescence (0.60; 95% CI: 0.55, 0.65) and early to late adolescence (0.76; 95% CI: 0.70, 0.82). There was inconsistent evidence of association between child BMI z-score at follow-up with parent cardiometabolic risk factors independent of baseline child BMI z-score. For example, maternal obesity, but not overweight, predicted a higher BMI z-score in childhood to early adolescence (overweight: 0.16; 95% CI: -0.03, 0.36, obesity: 0.41; 95% CI: 0.20, 0.61), and paternal overweight, but not obesity, predicted a higher BMI z-score in early to late adolescence (overweight: 0.22; 95% CI: 0.01, 0.43, obesity: 0.16; 95% CI: -0.10, 0.41). Parental obesity consistently predicted five-year obesity incidence in early to late adolescence, but not childhood to early adolescence. An adolescent without obesity at baseline with parents with obesity, had 3–4 times greater odds of developing obesity during follow-up (incidence OR = 3.38 (95% CI: 1.14–9.98, mother) and OR = 4.37 (95% CI 1.34–14.27, father) respectively). Conclusions: Having a higher BMI z-score at baseline was a stronger predictor of a higher BMI z-score at follow-up than any parental or sociodemographic factor. Targeting prevention efforts based on parent or sociodemographic factors is unwarranted but early childhood remains a key period for universal obesity prevention.
KW - Adolescents
KW - BMI
KW - Cardiometabolic risk factors
KW - Children
KW - Intergenerational obesity
KW - Malaysia
UR - http://www.scopus.com/inward/record.url?scp=85194521461&partnerID=8YFLogxK
U2 - 10.1186/s12889-024-18917-9
DO - 10.1186/s12889-024-18917-9
M3 - Article
C2 - 38802803
AN - SCOPUS:85194521461
SN - 1471-2458
VL - 24
JO - BMC Public Health
JF - BMC Public Health
IS - 1
M1 - 1408
ER -