risk is much debated. The gut microbiome includes trillions of micro‐organisms that produce various metabolites, including short‐chain fatty acids and bile acids. Variation in microbiome composition could influence metabolism of the host and, therefore, potentially energy balance. This hypothesis is supported by studies in which the microbiomes of human twins discordant for obesity were transplanted into germ‐free mice. Those mice that obtained microbiota from the obese twin gained more fat mass than those that received the microbiota from the lean twin [117]. This finding is additionally supported by studies in mouse models in which transplantation of gut microbiota from an obese mouse donor to a germ‐free recipient results in a significant increase in body fat in the recipient [118]. Several investigators have seen higher BMI and rates of obesity among children or adolescents born by cesarean versus vaginal delivery [119]. One possible explanation for this difference is a different microbiome acquired by route of delivery, although alternatively, the effect may be explained by higher maternal BMI and child weight at birth. Evidence from animal husbandry as well as some human studies have demonstrated an association between antibiotic exposure during the first year of life and subsequent weight gain, risk of overweight, and central adiposity, which could suggest an association between the gut microbiome in early life and later obesity risk [120]. However, in large human studies, effect sizes for early‐life antibiotic exposure and later BMI are quite small [121] suggesting that such a mechanism is unlikely to be a major contributor to obesity rates in the population.
Estimating population attributable risks
The combined predictive value of these developmental risk factors for childhood obesity is substantial. Although each risk factor may be modestly associated with childhood obesity, collectively they may result in enormous differences for populations. An analysis of the Project Viva cohort found that mid‐childhood obesity prevalence at age seven was approximately 30% among children with four early life risk factors (maternal smoking and excessive gestational weight gain during pregnancy; short duration of sleep at 6 months and breastfeeding duration <12 months), compared with only 6% among those who had none of the four [122]. Results were similar in the GUSTO cohort from Singapore [123]. Another group has estimated that 47.2% (95% CI: 30.9%, 63.5%) of type 2 diabetes in youth could be attributed to intrauterine exposure to maternal diabetes and obesity [124]. In a meta‐analysis of data from 162,129 mothers and their children from 37 pregnancy and birth cohort studies from Europe, North America, and Australia, the proportions of childhood overweight/obesity prevalence attributable to maternal overweight, maternal obesity, and excessive gestational weight gain ranged from 10.2 to 21.6% [35].
Risks are likely even higher among certain subgroups of the population. In the United States, rates of obesity are especially high among children from lower‐income families as well as children who are Black, Hispanic, and other race/ethnicity compared with white or Asian‐American [125]. Racial disparities in childhood overweight appear to be largely explained by potentially modifiable early life risk and protective factors [126,127]. Interestingly, among low‐income and nutritionally at‐risk children aged 2–4 years enrolled in the Federally‐funded US Women, Infants, and Children (WIC) Program, the overall crude prevalence of obesity actually decreased from 15.9% in 2010 to 13.9% in 2016 [128]. Promising evidence suggests that changes in the WIC food package that provided healthier food purchases during pregnancy and the early postpartum years within the WIC program may be responsible for these declines [129].
Implications for policy and practice
In summary, strong evidence has identified a number of early life experiences that predict later obesity risk. However, we should be cautious about directly translating these observational associations into recommendations for public health or clinical practice, especially for exposures related to weight, weight gain, or growth. Beyond the general concern that it is difficult to fully eliminate confounding from observational studies, exposures such as “maternal obesity” or “fetal growth” do not directly map to a target trial. For some of the exposures discussed above, including maternal diet quality or smoking, one might imagine the intervention trial that would mimic with our observational analysis – for example, advice to stop smoking or not to consume sugary beverages during pregnancy. However, there are many likely paths that lead to, for example, maternal obesity entering pregnancy. Even if maternal obesity is a strong, consistent predictor of offspring obesity independent of likely confounding factors, we don’t know when and how we might intervene to interrupt this link. Should we act on the woman’s diet quality, her overall caloric intake, her physical activity, or all of these, and which would be most effective? Should the intervention occur 1 month, 1 year, or 10 years prior to conception? Because of these many complexities in a causal interpretation of weight‐related exposures, we should think of many of these factors as risk markers rather than causal risk factors.
Despite these considerations, we can use this knowledge to identify children at higher risk for subsequent obesity. We, therefore, encourage providers caring for children to take a complete health history going back to the prenatal period. Those caring for women should encourage dietary habits that will support long‐term health for the woman and may also benefit her offspring. Providers should work with women to help them enter pregnancy at optimal weight and with any chronic health conditions identified and in good control.
The evidence summarized above suggests that interventions promoting healthy diet and behaviors can redirect trajectories, especially if they begin early. Furthermore, because it is challenging for individuals to maintain healthful behaviors in the face of social and cultural pressures, we support policies that help limit exposures to likely obesogens. Examples include supporting smoke‐free public spaces, restricting the availability of sugary beverages in child care and school settings, and reducing exposures to toxic chemicals. It should also be remembered that the children of today will be the parents of tomorrow, and so improvements in obesity risk factors might benefit more than one generation.
Acknowledgments
Dr. Matthew W. Gillman authored a version of this chapter in the prior edition of this book, from which we took inspiration.
References
1 1. Skinner AC, Ravanbakht SN, Skelton JA, Perrin EM, Armstrong SC. Prevalence of obesity and severe obesity in US children, 1999–2016. Pediatrics 2018; 141(3):e20173459. doi:10.1542/peds.2017‐3459
2 2. Di Cesare M, Sorić M, Bovet P, et al. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med 2019; 17(1):212. doi:10.1186/s12916‐019‐1449‐8
3 3. WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards. Weight‐for‐Age, Weight‐for‐Length, Weight‐for‐Height and Body Mass Index‐for‐Age: Methods and Development. Geneva: World Health Organization, 2006, Available from https://www.who.int/childgrowth/publications/technical_report_pub/en (accessed July 7, 2020).
4 4. de Onis M, Onyango A, Borghi E, et al. Worldwide implementation of the WHO Child Growth Standards. Public Health Nutr 2012; 15(9):1603–10. doi:10.1017/S136898001200105X
5 5. Kuczmarski R, Ogden CL, Guo S. 2000 CDC growth charts for the United States: methods and development. Vital Health Stat 2002; 11(246):1–190.
6 6. Aris IM, Rifas‐Shiman SL, Li L‐J, et al. Association of weight for length vs body mass index during the first 2 years of life with cardiometabolic risk in early adolescence. JAMA Netw Open 2018; 1(5):e182460. doi:10.1001/jamanetworkopen.2018.2460
7 7. Rifas‐Shiman SL, Gillman MW, Oken E, Kleinman K, Taveras EM. Similarity of the CDC and WHO weight‐for‐length growth charts in predicting risk of obesity at age 5 years. Obesity 2012; 20(6):1261–5. doi:10.1038/oby.2011.350
8 8. Boeke CE, Oken E, Kleinman KP, Rifas‐Shiman SL, Taveras EM, Gillman MW. Correlations among adiposity measures in school‐aged children. BMC Pediatr 2013; 13:99. doi:10.1186/1471‐2431‐13‐99
9 9.