Parental feeding and childhood genetic risk for obesity: Exploring hypothetical interventions with causal inference methods
Parental feeding and childhood genetic risk for obesity: Exploring hypothetical interventions with causal inference methods
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父母喂养和儿童肥胖遗传风险:用因果推理方法探索假设干预措施
DOI:
10.1101/2021.01.07.21249377
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发表时间:
2021
期刊:
影响因子:
--
通讯作者:
Herle M
中科院分区:
文献类型:
--
作者:
Herle M
BackgroundParental-feeding behaviors are common intervention targets for childhood obesity, but often only deliver small changes. Childhood BMI is partly driven by genetic effects, and the extent to which parental-feeding interventions can mediate child genetic liability is not known. Here we aim to examine how potential interventions on parental-feeding behaviors can mitigate some of the association between child genetic liability and BMI in early adolescence, using causal inference methods.MethodsData from the Avon Longitudinal Study of Parents and Children were used to estimate an interventional disparity measure for a child polygenic score for BMI (PGS-BMI) on BMI at 12 years. The approach compares counterfactual outcomes for different hypothetical interventions on parental-feeding styles applied when children are 10–11 years (n= 4248). Results are presented as adjusted total association (Adj-Ta) between genetic liability (PGS-BMI) and BMI at 12 years, versus the interventional disparity measure-direct effect (IDM-DE), which represents the association that would remain, had we intervened on parental-feeding under different scenarios.ResultsFor children in the top quintile of genetic liability, an intervention shifting parental feeding to the levels of children with lowest genetic risk, resulted in a difference of 0.81 kg/m2in BMI at 12 years (Adj-Ta = 3.27, 95% CI: 3.04, 3.49; versus IDM-DE = 2.46, 95% CI: 2.24, 2.67).ConclusionsFindings suggest that parental-feeding interventions have the potential to buffer some of the genetic liability for childhood obesity. Further, we highlight a novel way to analyze potential interventions for health conditions only using secondary data analyses, by combining methodology from statistical genetics and social epidemiology.