[6,7].
BMI is not a direct assessment of body composition, and thus some caution is appropriate as it is composed of both lean mass and fat mass. Nevertheless, BMI is strongly correlated with other more direct measures of adiposity such as skinfold thickness, dual‐x‐ray absorptiometry (DXA), and bioimpedance, especially at the higher end of the distribution [8]. Obesity, measured using BMI, often persists from childhood or adolescence into adulthood; therefore, children with obesity are more likely to become adults with obesity, further highlighting the importance of early interventions [9]. While multiple other techniques exist that more directly assess fat and lean mass, there is no evidence to suggest that any measure is better than BMI for diagnosing obesity in childhood or predicting adult obesity and morbidity [10]. Thus, evidence‐based guidelines recommend using BMI to clinically screen children and adolescents for obesity to allow for subsequent referral and multidisciplinary treatment [11]. In parallel, most researchers continue to use BMI as the primary measure of obesity in their studies.
Conceptual frameworks
Two paradigms offer useful conceptual frameworks for understanding the developmental origins of obesity. One is termed the “life course approach to chronic disease” [12]. Factors may act in the preconceptional phase through the prenatal period, into infancy, childhood, and beyond, to determine the risk of chronic disease. Orthogonal to the time axis, influencing factors can range from the social/built/natural environment (macro) through behavior, physiology, and genetics (micro). Factors interact with each other over the life course, with different determinants being more or less important at different life stages. Under this paradigm, some factors may be more deterministic than others.
“Programming” refers to insults at critical or sensitive periods of development that have lifelong, sometimes irreversible consequences. A commonly known example is that of the synthetic estrogen diethylstilbestrol (DES), for which intrauterine exposure is associated with risk for clear cell adenocarcinoma of the vagina and cervix in late life, but postnatal exposure is not [13]. Furthermore, exposure to hyperglycemia during fetal life predisposes subsequent increased risks for obesity and type 2 diabetes [14–16]. Other factors may contribute to chronic disease through accumulation of risk. For example, chronic exposure to elevated lipid levels across childhood is associated with pre‐atherosclerotic coronary lesions among adolescents [17]. Similarly, excess consumption of energy relative to energy expenditure is expected to result in greater adiposity at any point in the life course, with greater adiposity following longer‐lasting excesses.
A second, related paradigm is called the developmental origins of health and disease (DOHaD). Based on similar principles, DOHaD focuses primarily on the prenatal period and infancy as determinants of long‐term health [18]. Both of these frameworks call attention to the period of developmental plasticity in determining lifelong trajectories of health. This plastic period is different for different organs and systems but is generally felt to be most important before birth or the first years of life (Fig. 3.1) [18,19]. Factors that occur later in life, e.g. childhood or adult risk factors, then modify trajectories started during early life. For example, among rats prenatally programmed to develop obesity, hyperinsulinemia, and hyperleptinemia, leptin treatment soon after birth normalized caloric intake, locomotor activity, body weight, fat mass, and fasting plasma glucose, insulin, and leptin concentrations in adult life [20].
Figure 3.1 Lifecourse view of noncommunicable disease risk.
Source: From Hanson and Gluckman [19]. Copyright © 2014 the American Physiological Society.
Study designs
Questions about developmental origins of obesity by necessity require longitudinal data on early life exposures and outcomes into childhood or beyond. The majority of data on humans come from prospective and retrospective cohort studies, which are reasonably efficient in that they allow for simultaneous collection of information on many characteristics that can be considered as exposures, outcomes, or covariates in multiple analyses.
One example of a prospective cohort study is the Project Viva study, which one of us (EO) leads [21]. Project Viva recruited women in early pregnancy from eastern Massachusetts, United States, 1999–2002. The cohort includes 2128 singleton births who have been followed via in‐person research visits during pregnancy, after birth, and during infancy, early childhood, mid‐childhood, early adolescence, and mid/late adolescence. Additional data collection has occurred from mailed questionnaires, medical records, insurance claims, analysis of biospecimens, and output from personal monitors, such as spirometry for lung function, accelerometry for physical activity, and actigraphy for sleep. This cohort has served as the substrate for a number of analyses examining questions related to this topic, and we will use examples from several of these papers throughout this chapter.
The major concern related to analyses of observational data from cohort studies is that residual confounding from poorly measured or unmeasured variables may underlie observed associations [22]. Additionally, loss to follow‐up can result in bias. Well‐designed randomized controlled trials are the gold standard study design for addressing causal questions and eliminating confounding effects. Trials have been conducted to address some exposures, for example, treatment of gestational diabetes [23], lifestyle interventions to reduce gestational weight gain [24], and provision of nutrient supplements [25]. However, the utility of evidence from trials is limited because they are relatively expensive and thus rarely performed; only a single or small number of exposures can be examined per study; it is often infeasible or at least highly impractical to begin interventions before prenatal care begins, so typically they start at the end of the first trimester; and potentially harmful exposures (such as smoking) cannot be experimentally assigned.
Researchers have increasingly applied creative analytic approaches to observational data to bring us closer to causal interpretations of these analyses. One approach is to compare outcomes following discordant exposures among siblings [16], who presumably share similar genetic and sociodemographic characteristics; thus, it is more likely that any differences in obesity relate to the difference in exposure [16,26]. Examples include studies of siblings with discordant exposures to intrauterine diabetes or breastfeeding [16,26]. Alternatively, investigators have examined the same question in two or more cohorts with different social patterning of exposure [27]. This approach has been applied in studies of maternal diet and breastfeeding, among others [27,28]. If the exposure‐outcome relationship is similar in both settings, it is not likely explained by the social factors that predict exposure. Further, modern sophisticated statistical approaches have been described that minimize bias from loss to follow‐up and can support more confident causal inferences from observational data [29].
Quasi‐experimental study designs take advantage of “natural experiments” and thus minimize confounding. Mendelian randomization analysis is one type of quasi‐experimental design. This approach uses genetic variants to determine whether an observational association between a risk factor and an outcome is consistent with a causal effect [30]. Mendelian randomization relies on the natural, random assortment of genetic variants during meiosis yielding a random distribution of genetic variants in a population [30]. Examples of genetic variants studied in developmental origins of obesity research include genetic risk scores for gestational diabetes or birth size [31,32]. In the case of Mendelian randomization studies, it is Mother Nature herself who performed the “experiment.” Other quasi‐experimental studies leverage anthropogenic variation in exposure, for example state‐specific policies to limit smoking or second‐hand smoke exposure [33]. In both cases, exposure status should not be linked to other important predictors of outcome, and thus confounding is minimized. Finally, while there are many developmental and physiologic
