Figures
Abstract
Background
Socioeconomic disadvantage and other social stressors in childhood have been linked with cardiometabolic diseases in adulthood; however the mechanisms underlying these observed associations and the timing of their emergence are unclear. The aim of this review was to evaluate research that examined relationships between socioeconomic disadvantage and other social stressors in relation to less-studied cardiometabolic risk factors among youth, including carbohydrate metabolism-related factors, lipids, and central adiposity.
Methods
We searched PubMed and ISI Web of Science to identify relevant publications between 2001 and 2013.Studies were selected based on 4 criteria: (1) the study examined an association between at least one social or economic stressor and one relevant outcome prior to age 21; (2) the sample originated from a high-income country; (3) the sample was not selected based on a health condition; and (4) a central aim was to evaluate the effect of the social or economic stressor on at least one relevant outcome. Abstracts were screened and relevant publications were obtained and evaluated for inclusion criteria. We abstracted data from selected articles, summarized them by exposures and outcomes, and assigned an evidence grade.
Results
Our search identified 37 publications from 31 studies. Socioeconomic disadvantage was consistently associated with greater central adiposity. Research to date does not provide clear evidence of an association between childhood stressors and lipids or carbohydrate metabolism-related factors.
Conclusions
This review demonstrates a paucity of research on the relationship of socioeconomic disadvantage and other social stressors to lipid and carbohydrate metabolism-related factors in youth. Accordingly, it is not possible to form strong conclusions, particularly with regard to stressors other than socioeconomic disadvantage. Findings are used to inform priorities for future research. An improved understanding of these pathways is critical for identifying novel prevention targets and intervention opportunities to protect the long-term health of children and adolescents.
Citation: Slopen N, Goodman E, Koenen KC, Kubzansky LD (2013) Socioeconomic and Other Social Stressors and Biomarkers of Cardiometabolic Risk in Youth: A Systematic Review of Less Studied Risk Factors. PLoS ONE 8(5): e64418. https://doi.org/10.1371/journal.pone.0064418
Editor: Monika Janda, Queensland University of Technology, Australia
Received: January 31, 2013; Accepted: April 13, 2013; Published: May 17, 2013
Copyright: © 2013 Slopen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported in part by NIH grant DL46200, and funding from the Robert Wood Johnson Foundation to support the research related to the Early Childhood Innovation Project at the Center on the Developing Child. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Cardiometabolic diseases are a leading cause of morbidity and mortality in the United States and the prevention of future cardiometabolic diseases is among the most significant public health challenges faced by contemporary society [1]. Several recent national policy statements point to childhood as a critical period for preventing cardiometabolic risk over the life course [2], . For example, a statement from the American Heart Association highlights emerging evidence that cardiometabolic physiological dysregulation begins in childhood [4], [5] and that risk factor control in children is crucial [2]. A recent statement by the American Academy of Pediatrics (AAP) further identified the importance of the early social environment in setting up risk (or resilience) trajectories, and encouraged pediatric providers to assess family or community-level risk factors that may put children at risk for experiencing toxic social stress [3]. While there is increasing urgency to identify and address determinants of early biological risk factors for adult chronic diseases, our understanding of whether and how social adversity influences cardiometabolic risk factors that emerge early in life remains somewhat limited.
Research in developmental biology has made a compelling case that early exposure to social disadvantage and toxic stress has lifelong consequences for health by virtue of biologically embedding (i.e., altered biological functioning as a result of exposure) [6], [7]. Potentially toxic social stress in childhood may arise from a range of sources such as exposure to violence at home and in the neighborhood, dysfunctional schools, personal maltreatment, household chaos, or absent or punitive parents, among others. And, children who are socioeconomically disadvantaged may be especially vulnerable to biological embedding of disease by virtue of disproportionate exposure to a multitude of stressful influences [6]. Research suggests that such exposures may play a role in early risk of chronic disease later in life, including Type 2 diabetes and cardiovascular disease (CVD) [8]–[11].
The recent AAP policy statement also highlighted the importance of identifying early physiological mechanisms through which early psychologically and physically stressful experiences, such as poverty or maltreatment, increase later risk for disease [3]. Such work is needed to inform development and assessment of interventions that can promote healthy trajectories and disrupt health-damaging trajectories before disease processes are initiated [12]. While numerous relevant studies have been conducted, it is as yet unclear as to whether consistent findings are emerging and how this work may best inform intervention. Moreover, there has been substantial research on socioeconomic disadvantage and other social stressors in relation to overweight and obesity among youth [13]–[15]; however there are other relevant cardiometabolic factors to consider. As a crucial initial step toward translating advances in developmental science into more effective interventions for reducing risk of cardiometabolic disease in adulthood [3], the aim of this review is to assess what is known about the relationship between childhood stressors and important but less-studied cardiometabolic risk factors among youth: carbohydrate metabolism-related factors, lipids, and central adiposity.
Socioeconomic disadvantage and other social stressors, defined as external conditions or events that threaten a child's wellbeing, may occur at the individual, household, or community-level. Stressful experiences may affect cardiometabolic risk through behavioral factors (e.g., unhealthy diet or inactivity), or direct physiological changes resulting from disruption of regulatory pathways. Research shows that adverse experiences are associated with of a variety of physiological changes in children [16], including increased activation of neurobiologic systems responsive to stress, such as the hypothalamic pituitary adrenal (HPA) axis, the sympathetic nervous system, and others [7], [16]–[18]. Increased activation of these systems leads to a cascade of physiological processes [7], [16] which in adults, has been linked with the development of central fat, dysregulated carbohydrate metabolism, and accumulation of blood lipids in the arterial lining, all of which accelerate chronic disease development [19]. However, to date there has been limited examination of whether childhood adversity leads to visible early dysregulation in cardiometabolic processes in youth, beyond a substantial focus on obesity. Consequently, we have a limited understanding of whether increased activation of stress-responsive systems does in fact lead to dysregulated carbohydrate metabolism, accumulation of blood lipids, or central adiposity among children.
Recent review articles report consistent evidence that early socioeconomic disadvantage and other social stressors are associated with childhood overweight and obesity, with reviews focused on family socioeconomic status (SES) [13], neighborhood characteristics [15], and psychosocial stressors [14] as exposures. This research on obesity provides a starting point for understanding the relationship between early experiences and adult chronic disease risk. However, there are other measurable aspects of cardiometabolic regulation (such as glucose metabolism, lipid profiles, or the distribution of fat) in childhood which have been shown to be strong predictors of cardiometabolic parameters into adulthood [20]–[24]. While these processes are highly inter-related (e.g., obesity is associated with elevated levels of glucose, unhealthy lipids, and central adiposity [25], [26]) they also reflect distinct cardiometabolic processes with multifactorial influences and therefore warrant separate investigation. Research on the association between socioeconomic disadvantage and other social stressors and carbohydrate metabolism-related markers, lipids, and central adiposity has not been evaluated systematically; therefore, it is yet unknown if social adversity has a detectable influence on these parameters in childhood or adolescence
To respond to recent interest and calls for greater focus on childhood origins of cardiometabolic diseases [27], we evaluated research that examines socioeconomic disadvantage and other social stressors in relation carbohydrate metabolism-related factors, lipids, and central adiposity in children or adolescents in a systematic literature review. Because of the tight clustering of the carbohydrate metabolism-related biomarkers, we expected a consistent pattern of associations of SES and other social stressors with these outcomes. In contrast, we did not expect consistent associations across lipid biomarkers since they reflect a more diverse set of physiological processes, albeit all related to CVD risk. Systematic reviews are invaluable to researchers, health care providers, and policy makers because they provide an integrated unbiased summary of existing information, and establish whether findings are consistent and can generalize across populations, settings, and differences in study design [28]. A systematic evaluation of the current findings and the quality of existing studies will aid in identifying the most fruitful directions for future research and making informed recommendations. We further consider our findings in relation to reviews of social adversity and other more studied cardiometabolic risk factors in youth. A meta-analysis of the relevant literature was not possible at this time due to the heterogeneity of studies with regard to exposure and outcome measures, study design, covariates, and samples.
Methods
Inclusion Criteria
We applied four inclusion criteria, informed by previous systematic reviews. First, we required that the sample originated from a high-income country, according to the World Bank (Gross National Income per capita>US$12,480 in 2011); this criteria was established because the relationship between stressors and cardiometabolic outcomes may differ in poorer countries [15]. Second, we required that the sample was not selected based on a health condition, because our primary interest was to examine these associations in healthy children and adolescents. Third, studies were required to examine an association between at least one social or economic stressor and one relevant outcome (described below) prior to age 21. We defined childhood broadly in order to include as many studies as possible. Finally, we required that evaluating the effect of the social or economic stressor on the outcomes was central to the analysis (i.e., not simply included as a covariate to adjust for confounding).
Cardiometabolic Outcome Measures
We considered three categories of cardiometabolic indicators: (1) carbohydrate metabolism-related biomarkers, including acute and integrated markers of diabetes risk (e.g., glucose, HbA1c, insulin resistance); (2) common lipid outcomes known to be associated with cardiometabolic risk, including total cholesterol, low density lipoprotein (LDL), high density lipoprotein (HDL), cholesterol, triglycerides, apolipoproteins A and B; and (3) central adiposity (e.g., waist circumference (WC), waist-hip ratio (WHR), waist standard deviation). We did not include metabolic syndrome, because it lacks consensus on definition and are not uniformly accepted as valid within pediatric and adolescent populations [29].
Socioeconomic and Other Social Stressors
In the absence of an explicit operational definition of social and economic stressors for child development research, we relied on a definition of “social context” articulated by Boyce and colleagues [30], which defines social context as “a set of interpersonal conditions, relevant to a particular behavior or disorder and external to, but shaped and interpreted by, the individual child” (p. 146). In line with this broad definition, our review sought to include studies that have considered measures of contextual and material hardships, relative disadvantage, family SES, stressful experiences, and relationship stressors (i.e., with parents or peers).
Search Strategy and Data Extraction
We conducted systematic searches of PubMed and ISI Web of Science (including Science Citation Index Expanded, Social Sciences Citation Index, and Arts and Humanities Citation Index) to identify relevant studies published in English between January 2001 and January 2013. Our Pubmed search was guided by Medical Subject Heading terms and keywords, including but not limited to: body fat distribution, waist-hip ratio, blood glucose, lipids/blood, insulin resistance, socioeconomic factors, social environment and interpersonal relations (see Appendix S1); this search returned 1304 abstracts. A similar strategy was developed for ISI Web of Science, and this search returned 773 abstracts. After we removed duplicate abstracts (n = 351), we screened each abstract according to the four criteria outline above. Of note, we carefully examined studies that focused on composite outcomes (e.g., allostatic load, metabolic syndrome, insulin resistance syndrome) in order to establish whether the study reported associations for component factors as well; if so, the study was eligible for inclusion. After applying our criteria, the PubMed and ISI Web of Science searches yielded 36 relevant studies, and one additional study was identified within a reference section of an identified article (see Figure S1). We reviewed these 37 studies and extracted information related to design, sample, measures, statistical methods, stratification and control variables, and findings. The reported findings are based on models adjusted for standard covariates, including age, sex, and race/ethnicity, when provided, and when effect modification was considered, we include that information.
Evidence Grade
We assessed the strength of the evidence by rating four components of each study's methodology, including study design, sample size, covariates, and exposure measures. For study design, we awarded one point for longitudinal or prospective designs (i.e., repeated measures on the same individual, or following an individual over time with a time lag between the exposure and outcome). For sample size, we awarded one point to studies that had an n greater than 500 [31]. For covariates, we awarded one point to studies that provided results adjusted for at least basic demographics including age and sex. Finally, if a study examined more than one exposure measure (e.g., SES measured using both parental income and education), we awarded one point, as this provided more information about the consistency and generalizability of associations. Studies that received at least three points were identified as “high quality” for the purpose of this review.
Results
Tables 1 and 2 summarize the 37 publications (originating from 31 samples), organized by outcomes. Most studies (32 of 37) examined at least one SES exposure. Table 1 presents studies with SES-related exposures, and Table 2 presents studies with non-SES exposures (“other social stressors”), which may or may not be influenced by SES.
Socioeconomic Status-Related Exposures and Cardiometabolic Risk Markers
Carbohydrate Metabolism-related Outcomes.
Table 1 provides a summary of the eight cross-sectional and three prospective studies of SES and carbohydrate metabolism-related outcomes, including insulin, glucose, HbA1c, insulin sensitivity, acute insulin response to glucose, and insulin resistance. Six of the eleven studies evaluated more than one relevant outcome, and the most common SES measure used was parental education. All eleven studies reported findings adjusted for basic covariates (i.e., at a minimum, age and sex). Overall, the findings lack consistency. Of the eleven studies, three were null [32]–[34], one study found an association in the expected direction (only one exposure and one relevant outcome considered) [35], two studies had conditional findings (whereby the direction of associations varied by country [36] and race [37]) and six studies had mixed findings (three resulted from different associations of the same measure of SES with two different outcome measures, and the other three resulted from discrepant findings with different measures of SES and the same outcomes). When we consider the 3 prospective studies on their own [35], [38], [39], the findings are more consistent (i.e., none of these studies had null findings) and provide some evidence for an association between socioeconomic disadvantage and elevated risk. Four of the eleven studies that examined carbohydrate metabolism-related factors were classified as higher-quality based on our evidence rating [35], [36], [38], [40], and each provided some positive evidence for an association.
Lipid Outcomes.
Fourteen studies examined SES in relation to lipid outcomes (i.e., total cholesterol, LDL and HDL cholesterol, triglycerides, apolipoproteins A and B); twelve of these studies were cross-sectional and two studies were prospective [41], [42]. Most studies considered a single SES exposure, however the majority evaluated more than one lipid outcome. All fourteen studies reported results adjusted for basic covariates (i.e., at a minimum, age and sex). These studies do not indicate a consistent association between SES and lipid outcomes among youth. Of the fourteen studies, seven were null for all associations that were examined [32], [34], [40], [41], [43]–[45], and two showed associations in the direction opposite to the expected direction [42], [46]. Considering the other five studies, four had had mixed results due to differences in the association of SES with multiple lipid outcomes [37], [47]–[49], one had mixed results due to discrepant findings resulting from different measures of SES [50], and in one of these studies, the mixed results also varied by race [37]. Of note, there were not observable patterns across the studies that produced mixed results. Five of the fourteen studies that examined SES in relation to lipid outcomes were rated as higher quality [34], [41], [42], [44], [50], and four of these studies had null results.
Central Adiposity.
Our search identified twelve cross-sectional and five prospective studies which examined SES with central adiposity (measured by WC, WHR, waist standard deviation, and trunk fat (kg)). The majority of these studies incorporated only one measure of SES, and parental education was the most common measure. Sixteen of the seventeen studies reported associations from models adjusted for basic covariates (i.e., at a minimum, age and sex). Although the studies are not entirely consistent, they suggest an inverse relationship between SES and central adiposity in youth. Seven of the seventeen studies found that lower SES was associated with greater central adiposity [51]–[57]. Four studies showed conditional associations by sex (with no consistent pattern) [32], [58]–[60], one study found results conditional by race [37], and four studies had mixed findings [47], [58], [59], [61]; one mixed finding was due to inconsistent associations of SES with WC versus WHR, and three mixed findings were due to inconsistent associations across different SES measures with the same outcome. Only three studies were null for all associations that were examined [43], [62], [63]; notably, all of the null studies used non-US samples. Of the five prospective studies, three studies showed that lower SES was associated with greater central adiposity, one study showed no association between SES and central adiposity, and the other study had mixed results whereby lower SES was associated with greater increases in WC (but not WHR) over time. Eight studies were classified as higher-quality; among these studies, six studies showed at least some evidence of an association [37], [47], [53], [54], [56], [59] (three of these studies had mixed results due differences across measures or were conditional on sex or race [37], [47], [59]), and two studies were null [62], [63].
Other Social Stressors and Cardiometabolic Risk Markers
Our search identified six studies that examined a possible relationship between parenting practices, stressful life events or relational support and our cardiometabolic risk markers (see Table 2). Half of these studies were prospective, as opposed to the preponderance of cross-sectional studies on SES noted above, and the sample sizes were smaller (range: N = 104–451, median N = 210). Roughly half of these studies adjusted for SES while examining the associations of other social stressors to cardiometabolic risks. Five of the six studies reported associations adjusted for basic covariates (i.e., at a minimum, age and sex). With only one or two studies assessing similar exposures and outcomes, it is not possible to assess patterns of associations between these other types of social stressors and our cardiometabolic risks. Two studies considered associations of relevant social stressors (stressful life events and interpersonal stress) in relation to carbohydrate metabolism-related factors: one documented null associations between stressful events and interpersonal stress with insulin and glucose among female adolescents [64], and the other found that mother's low tolerance towards the child predicted higher insulin among girls but not boys [65]. Two studies considered parental rearing practices and maternal responsiveness in relation to multiple lipid outcomes, with mixed results based on outcome [65], [66] and sex [65]. Of the four studies that considered central adiposity, these considered parental responsiveness, rearing practices, lack of supportive relationships, and stressful life events as the stressors, with the latter 3 showing associations with outcomes in the expected direction. Across all six studies, the findings were similarly inconsistent for prospective and cross-sectional studies. Only one study was rated as higher-quality [66]; this prospective study found mixed support for an association between child rearing practices and maternal responsiveness and lipid outcomes, and a null association with central adiposity.
Discussion
Given increasing understanding that a child's early experiences has profound effects on risk for chronic diseases later in life [6], [67] and the escalating societal burden of cardiovascular [68] and metabolic [69] diseases in the United States, it is important to identify how and when disease processes are initiated to develop effective prevention and early intervention strategies. In this systematic review, we identified 37 published articles of socioeconomic disadvantage and other social stressors in relation to three potential mechanisms that may connect early socioeconomic disadvantage and other social stressors to adult cardiometabolic disease: carbohydrate metabolism-related factors, lipids, and waist circumference. The clearest evidence emerged for the relation between socioeconomic disadvantage and central adiposity, which is consistent with findings from recent reviews of childhood experiences and overweight and obesity [13]–[15]. While this finding is important given that central fat may be particularly harmful for long-term health [70], it is not unexpected and unfortunately does not shed new light on other pathways by which social stressors may contribute to development of cardiometabolic diseases. In fact, what this review most clearly demonstrates is that surprisingly, research on the relationship between stressors and carbohydrate and lipid metabolism-related risks is too sparse to be able to form strong conclusions, particularly with regard to non-SES social exposures. Further, the few published studies we found rarely assessed identical exposures and outcomes or used a similar study design. A review of only the prospective or higher-quality studies also showed inconsistent associations between socioeconomic or other social stressors and carbohydrate and lipid metabolism-related factors, without any discernible patterns that could explain the discrepant associations.
While we found relatively few studies on social stressors and carbohydrate and lipid-related risks, considerable research has examined stressful social environments in relation to cognitive, behavioral, and other physical health outcomes in children [71], particularly overweight and obesity [14], [15], [72]–[75]. Although obesity is an important risk factor to consider, indicators from other physiological parameters that may respond to stress are worthy of investigation because they may provide additional insight on the mechanisms that underlie cardiometabolic disorders [76]. Increasing research has documented that the distribution of body fat contributes to diabetes and cardiovascular risk among adults independent of general assessments of adiposity [70], [77]. Other research using NHANES participants aged 12–19 found that in linear regression models adjusted for age, survey period, and race-ethnicity, body fat percentage only explained 2–20% of the variance in lipid concentrations [78]. Such findings suggest that it is important to examine the relationship between stressful experiences and cardiometabolic risk factors beyond basic consideration of adiposity in youth. Research on how socioeconomic disadvantage and other social stressors affect a variety of cardiometabolic risk markers early in life will improve our understanding of how stress experiences become biologically embedded and lead to metabolic alterations and weight change, and may elucidate new pathways and opportunities for earlier interventions to prevent cardiometabolic disorders.
Comparison of the findings from the present review to previous reviews considering similar exposures (social disadvantage and other social stressors) in relation to overweight and obesity [14], [15], [72], [73] and inflammatory biomarkers [75] suggests that studies on the outcomes we consider are fewer and also less consistent. For example, in a review of 45 studies from developed countries (1990 to 2005), Shrewsbury and colleagues [72] found inverse associations between SES and adiposity in 42% of studies, mixed or conditional associations in 31%, and null associations in 27%. These associations were most consistent when parental education was used as the indicator of SES (i.e., 75% of studies that examined education as the exposure found an inverse association). In our review, education was not more consistently associated with outcomes relative to other SES indicators. However, because only a few studies examined parental educational attainment in relation to each specific outcome, additional research is needed to determine if education is in fact a particularly strong predictor of these cardiometabolic factors as well. Carter and colleagues [15] examined 27 studies (1999 to 2009) of the relationship between neighborhood characteristics and child adiposity. Across studies, area-level socioeconomic disadvantage was positively associated with adiposity, and there was some evidence that greater social capital was inversely associated with adiposity [15]. In our review, only 3 studies considered area-level environmental features [45], [56], [57] (and 2 of the 3 examined central adiposity [56], [57]); therefore we do not have enough studies to determine whether area-level measures are consistent predictors of other cardiometabolic outcomes.
There has been increasing interest in whether childhood adversity influences risk of low level chronic inflammation [79]–[81], with more studies focusing on CVD-relevant inflammatory and other immune-related biomarkers in youth relative to those focusing on lipids or carbohydrate metabolism-related factors. Inflammatory processes have been identified as another plausible mechanism by which socioeconomic disadvantage and other social stressors increase later risk for cardiometabolic diseases [7]. A recent systematic review of 20 published studies of social adversity and inflammation in youth suggests a trend towards positive associations [75]. At present, it is unclear whether heightened inflammatory markers in response to childhood adversity appear earlier in development compared to elevations in carbohydrate metabolism-related factors or lipids (which may become evident later, perhaps as a downstream consequence of adiposity). Additional studies are needed in order to establish whether the different strength of findings across domains of outcomes (i.e., adiposity, inflammation, carbohydrate metabolism-related markers, lipids) are a function of more limited research available on carbohydrate metabolism-related markers and lipids or because in fact these alterations are less evident early in life.
Our review suggests a number of priorities for future research. First, our review reveals a striking paucity of longitudinal studies to examine the effects of socioeconomic disadvantage and other social stressors on carbohydrate metabolism-related factors, lipids, and central adiposity. From cross-sectional studies, it is not possible to assess when cardiometabolic risk factors begin to emerge in response to social disadvantage or other stressors. Therefore, the next generation of life course research aiming to identify social and biological mechanisms by which socioeconomic disadvantage and other social stressors are embedded to influence adult health, will require investment in longitudinal cohorts with extensive data collection on social conditions and experiences and health outcomes at multiple time points. Although longitudinal studies are more time-intensive and expensive than cross-sectional studies, they address concerns about the temporal ordering between exposures and outcomes, and provide insight into whether there are particular periods of development when these cardiometabolic biomarkers are especially sensitive to, or resilient against, certain social exposures. Longitudinal studies will further allow investigators to identify if effects of social stress depend on developmental stage (i.e., sensitive periods) and at what point in the life course they are detectable.
Second, our review shows there are many types of social stressors (e.g., child maltreatment, parent psychopathology, parental intimate partner violence) that have not been examined in relation to the markers considered in this review, but that have shown to be relevant to other physiological outcomes (such as BMI [82] or inflammation [79], [80]) in youth. Thus, there is a need for future studies to assess a wider variety of types and severity (ranging from minor to severe, acute and chronic) of social stressors and compare effects within the same sample, to identify which are most toxic in relation to cardiometabolic factors. For example, a review by Berge and colleagues [73] reported substantial evidence that parenting style is associated with child BMI [73]. However, our review only identified one study that (prospectively) examined parenting style in relation to insulin and lipids [65]. Therefore, additional research that considers parenting style and other types of social stressors, in relation to a broader more diverse set of cardiometabolic risk markers would be fruitful.
Third, several researchers have begun to examine childhood stressors in relation to cumulative biological risk scores (e.g., allostatic load) [47], [83]–[85] and cardiovascular risk phenotypes (e.g., metabolic syndrome [86]) in youth. These approaches may be valuable for identifying meaningful dysregulation when the effect of an exposure on one specific biomarker is small or inconsistent, but there is a distinguishable effect when you consider a number of related physiological indicators. Additional research is needed to assess whether composite approaches (incorporating individual or multiple systems) within pediatric populations are meaningful for long-term health outcomes, and if composite approaches provide any advantages for understanding the effects of early adversity for later risk of cardiometabolic disorders.
It is important to acknowledge several limitations to the present review. First, several studies used the same sample to examine more than one type of outcome, or the same outcome at a later time point; this could make the literature appear to be more consistent than it actually is. Related, several studies that considered more than one outcome did not calculate a family-wise error, which may compromise the validity of the statistical associations we report. However, in light of the sparse research in this area, we included all unique findings that exist. Finally, this review is limited to studies published in English, and we cannot account for publication bias towards studies with significant results.
In conclusion, scientific understanding of the biological pathways that connect early life experiences to cardiometabolic risk in adulthood remains limited. With improved understanding of the relationship between social adversity and less-studied cardiometabolic risk factors such as glucose, insulin, and lipids among youth, we may begin to identify key intervention opportunities to protect the health of children and adolescents, and the adults they will become.
Acknowledgments
The authors thank Ms. Carol Mita for her assistance in conducting the literature search and Dr. Jack Shonkoff for his comments on an earlier version of the draft.
Author Contributions
Conceived and designed the experiments: NS EG KK LK. Analyzed the data: NS LK KK EG. Wrote the paper: NS LK EG KK.
References
- 1. Kavey REW, Simons-Morton DG, de Jesus JM (2011) Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents: Summary Report. Pediatrics 128: S213–S256.
- 2. Lloyd-Jones DM, Hong YL, Labarthe D, Mozaffarian D, Appel LJ, et al. (2010) Defining and Setting National Goals for Cardiovascular Health Promotion and Disease Reduction The American Heart Association's Strategic Impact Goal Through 2020 and Beyond. Circulation 121: 586–613.
- 3. Garner AS, Shonkoff JP, Siegel BS, Dobbins MI, Earls MF, et al. (2012) Early Childhood Adversity, Toxic Stress, and the Role of the Pediatrician: Translating Developmental Science Into Lifelong Health. Pediatrics 129: E224–E231.
- 4. Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, et al. (2009) Heart Disease and Stroke Statistics-2009 Update A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 119: E21–E181.
- 5. Berenson GS, Srnivasan SR (2005) Cardiovascular risk factors in youth with implications for aging: The Bogalusa Heart Study. Neurobiology of Aging 26: 303–307.
- 6. Shonkoff JP, Boyce WT, McEwen BS (2009) Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. JAMA 301: 2252–2259.
- 7. Miller GE, Chen E, Parker KJ (2011) Psychological stress in childhood and susceptibility to the chronic diseases of aging: Moving toward a model of behavioral and biological mechanisms. Psychological Bulletin 137: 959–997.
- 8. Rich-Edwards JW, Spiegelman D, Hibert ENL, Jun HJ, Todd TJ, et al. (2010) Abuse in Childhood and Adolescence As a Predictor of Type 2 Diabetes in Adult Women. American Journal of Preventive Medicine 39: 529–536.
- 9. Wegman HL, Stetler C (2009) A Meta-Analytic Review of the Effects of Childhood Abuse on Medical Outcomes in Adulthood. Psychosomatic Medicine 71: 805–812.
- 10. Dong M, Giles WH, Felitti VJ, Dube SR, Williams JE, et al. (2004) Insights into causal pathways for ischemic heart disease: adverse childhood experiences study. Circulation 110: 1761–1766.
- 11. Galobardes B, Smith GD, Lynch JW (2006) Systematic Review of the Influence of Childhood Socioeconomic Circumstances on Risk for Cardiovascular Disease in Adulthood. Annals of Epidemiology 16: 91–104.
- 12. Braveman P, Egerter S, Williams DR (2011) The Social Determinants of Health: Coming of Age. Annual Review of Public Health 32: 381–398.
- 13. Shrewsbury V, Wardle J (2008) Socioeconomic status and adiposity in childhood: a systematic review of cross-sectional studies 1990-2005. Obesity (Silver Spring) 16: 275–284.
- 14. Gundersen C, Mahatmya D, Garasky S, Lohman B (2011) Linking psychosocial stressors and childhood obesity. Obesity Reviews 12: e54–e63.
- 15. Carter MA, Dubois L (2010) Neighbourhoods and child adiposity: a critical appraisal of the literature. Health Place 16: 616–628.
- 16. Danese A, McEwen BS (2012) Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiol Behav 106: 29–39.
- 17. McCrory E, De Brito SA, Viding E (2010) Research Review: The neurobiology and genetics of maltreatment and adversity. Journal of Child Psychology and Psychiatry 51: 1079–1095.
- 18. Fernald LCH, Gunnar MR (2009) Poverty-alleviation program participation and salivary cortisol in very low-income children. Social Science & Medicine 68: 2180–2189.
- 19.
McEwen BS, Gianaros PJ (2010) Central role of the brain in stress and adaptation: Links to socioeconomic status, health, and disease. Biology of Disadvantage: Socioeconomic Status and Health. Oxford: Blackwell Publishing. pp. 190–222.
- 20. Juonala M, Jarvisalo MJ, Maki-Torkko N, Kahonen M, Viikari JSA, et al. (2005) Risk factors identified in childhood and decreased carotid artery elasticity in adulthood - The cardiovascular risk in Young Finns Study. Circulation 112: 1486–1493.
- 21.
Schmidt MD, Dwyer T, Magnussen CG, Venn AJ (2010) Predictive associations between alternative measures of childhood adiposity and adult cardio-metabolic health. Int J Obes.
- 22. Srinivasan SR, Myers L, Berenson GS (2002) Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood - The Bogalusa Heart Study. Diabetes 51: 204–209.
- 23. Garnett SP, Baur LA, Srinivasan S, Lee JW, Cowell CT (2007) Body mass index and waist circumference in midchildhood and adverse cardiovascular disease risk clustering in adolescence. American Journal of Clinical Nutrition 86: 549–555.
- 24. Berenson GS, Srinivasan SR, Bao WH, Newman WP, Tracy RE, et al. (1998) Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. New England Journal of Medicine 338: 1650–1656.
- 25. Bao WH, Srinivasan SR, Berenson GS (1996) Persistent elevation of plasma insulin levels is associated with increased cardiovascular risk in children and young adults - The Bogalusa Heart Study. Circulation 93: 54–59.
- 26. Shah AS, Dolan LM, Gao ZQ, Kimball TR, Urbina EM (2011) Clustering of Risk Factors: A Simple Method of Detecting Cardiovascular Disease in Youth. Pediatrics 127: E312–E318.
- 27. McGill HC, McMahan CA, Herderick EE, Malcom GT, Tracy RE, et al. (2000) Origin of atherosclerosis in childhood and adolescence. American Journal of Clinical Nutrition 72: 1307S–1315S.
- 28. Mulrow CD (1994) Rationale For Systematic Reviews. BMJ: British Medical Journal 309: 597–599.
- 29. Steinberger J, Daniels SR, Eckel RH, Hayman L, Lustig RH, et al. (2009) Progress and Challenges in Metabolic Syndrome in Children and Adolescents: A Scientific Statement From the American Heart Association Atherosclerosis, Hypertension, and Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young; Council on Cardiovascular Nursing; and Council on Nutrition, Physical Activity, and Metabolism. Circulation 119: 628–647.
- 30. Boyce WT, Frank E, Jensen PS, Kessler RC, Nelson CA, et al. (1998) Social context in developmental psychopathology: Recommendations for future research from the MacArthur Network on Psychopathology and Development. Development and Psychopathology 10: 143–164.
- 31. Midei AJ, Matthews KA (2011) Interpersonal violence in childhood as a risk factor for obesity: a systematic review of the literature and proposed pathways. Obes Rev 12: e159–172.
- 32. Ali MK, Bullard KM, Beckles GL, Stevens MR, Barker L, et al. (2011) Household Income and Cardiovascular Disease Risks in US Children and Young Adults Analyses from NHANES 1999–2008. Diabetes Care 34: 1998–2004.
- 33. Eldeirawi K, Lipton RB (2003) Predictors of hemoglobin A1c in a national sample of nondiabetic children - The Third National Health and Nutrition Examination Survey, 1988–1994. American Journal of Epidemiology 157: 624–632.
- 34. Wennlof AH, Yngve A, Nilsson TK, Sjostrom M (2005) Serum lipids, glucose and insulin levels in healthy schoolchildren aged 9 and 15 years from Central Sweden: reference values in relation to biological, social and lifestyle factors. Scand J Clin Lab Invest 65: 65–76.
- 35.
Goodman E, Must A, Daniels SR, Dolan LM (2010) Hostility and adiposity mediate disparities in insulin resistance among adolescents and young adults. J Pediatr 157: 572–577, 577 e571.
- 36. Lawlor DA, Harro M, Wedderkopp N, Andersen LB, Sardinha LB, et al. (2005) Association of socioeconomic position with insulin resistance among children from Denmark, Estonia, and Portugal: cross sectional study. British Medical Journal 331: 183–186.
- 37. Thomas C, Nightingale CM, Donin AS, Rudnicka AR, Owen CG, et al. (2012) Socio-economic position and type 2 diabetes risk factors: patterns in UK children of South Asian, black African-Caribbean and white European origin. PLoS One 7: e32619.
- 38. Goodman E, Daniels SR, Dolan LM (2007) Socioeconomic disparities in insulin resistance: Results from the Princeton School District Study. Psychosomatic Medicine 69: 61–67.
- 39. Gower BA, Fernandez JR, Beasley TM, Shriver MD, Goran MI (2003) Using genetic admixture to explain racial differences in insulin-related phenotypes. Diabetes 52: 1047–1051.
- 40.
van den Berg G, van Eijsden M, Vrijkotte TGM, Gemke R (2012) Socioeconomic inequalities in lipid and glucose metabolism in early childhood in a population-based cohort: the ABCD-Study. BMC Public Health 12.
- 41. Kvaavik E, Glymour M, Klepp KI, Tell GS, Batty GD (2012) Parental education as a predictor of offspring behavioural and physiological cardiovascular disease risk factors. Eur J Public Health 22: 544–550.
- 42. Van Lenthe FJ, Boreham CA, Twisk JW, Strain JJ, Savage JM, et al. (2001) Socio-economic position and coronary heart disease risk factors in youth. Findings from the Young Hearts Project in Northern Ireland. Eur J Public Health 11: 43–50.
- 43. Buchan DS, Ollis S, Thomas NE, Simpson A, Young JD, et al. (2012) Prevalence of traditional and novel markers of cardiovascular disease risk in Scottish adolescents: socioeconomic effects. Applied Physiology Nutrition and Metabolism-Physiologie Appliquee Nutrition Et Metabolisme 37: 829–839.
- 44. Kant AK, Graubard BI (2012) Race-ethnic, family income, and education differentials in nutritional and lipid biomarkers in US children and adolescents: NHANES 2003–2006. Am J Clin Nutr 96: 601–612.
- 45. McCrindle BW, Manlhiot C, Millar K, Gibson D, Stearne K, et al. (2010) Population Trends Toward Increasing Cardiovascular Risk Factors in Canadian Adolescents. Journal of Pediatrics 157: 837–843.
- 46. Alberty R, Albertyova D, Ahlers I (2009) Distribution and correlations of non-high-density lipoprotein cholesterol in Roma and Caucasian children: the Slovak Lipid Community Study. Coll Antropol 33: 1015–1022.
- 47. Goodman E, McEwen BS, Huang B, Dolan LM, Adler NE (2005) Social inequalities in biomarkers of cardiovascular risk in adolescence. Psychosomatic Medicine 67: 9–15.
- 48. Howe LD, Galobardes B, Sattar N, Hingorani AD, Deanfield J, et al. (2010) Are there socioeconomic inequalities in cardiovascular risk factors in childhood, and are they mediated by adiposity? Findings from a prospective cohort study. International Journal of Obesity 34: 1149–1159.
- 49. Murasko JE (2008) Male-female differences in the association between socioeconomic status and atherosclerotic risk in adolescents. Social Science & Medicine 67: 1889–1897.
- 50. Khanolkar AR, Byberg L, Koupil I (2012) Parental influences on cardiovascular risk factors in Swedish children aged 5–14 years. Eur J Public Health 22: 840–847.
- 51. Bjelland M, Lien N, Bergh IH, Grydeland M, Anderssen SA, et al. (2010) Overweight and waist circumference among Norwegian 11-year-olds and associations with reported parental overweight and waist circumference: The HEIA study. Scand J Public Health 38: 19–27.
- 52. Brug J, van Stralen MM, Te Velde SJ, Chinapaw MJ, De Bourdeaudhuij I, et al. (2012) Differences in weight status and energy-balance related behaviors among schoolchildren across Europe: the ENERGY-project. PLoS One 7: e34742.
- 53. Kendzor DE, Caughy MO, Owen MT (2012) Family income trajectory during childhood is associated with adiposity in adolescence: a latent class growth analysis. BMC Public Health 12: 611.
- 54.
Ortega FB, Ruiz JR, Labayen I, Hurtig-Wennlof A, Harro J, et al.. (2012) Role of socio-cultural factors on changes in fitness and adiposity in youth: A 6-year follow-up study. Nutr Metab Cardiovasc Dis.
- 55. Okosun IS, Boltri JM, Eriksen MP, Hepburn VA (2006) Trends in abdominal obesity in young people: United States 1988–2002. Ethnicity & Disease 16: 338–344.
- 56. Wardle J, Brodersen NH, Cole TJ, Jarvis MJ, Boniface DR (2006) Development of adiposity in adolescence: five year longitudinal study of an ethnically and socioeconomically diverse sample of young people in Britain. BMJ 332: 1130–1135.
- 57. Yin Z, Davis CL, Moore JB, Treiber FA (2005) Physical activity buffers the effects of chronic stress on adiposity in youth. Ann Behav Med 29: 29–36.
- 58. Brown DE, Gotshalk LA, Katzmarzyk PT, Allen L (2011) Measures of adiposity in two cohorts of Hawaiian school children. Ann Hum Biol 38: 492–499.
- 59. Jimenez-Pavon D, Ortega FB, Ruiz JR, Chillon P, Castillo R, et al. (2010) Influence of socioeconomic factors on fitness and fatness in Spanish adolescents: the AVENA study. Int J Pediatr Obes 5: 467–473.
- 60. Koziel S, Jankowska EA (2002) Birthweight and stature, body mass index and fat distribution of 14-year-old Polish adolescents. Journal of Paediatrics and Child Health 38: 55–58.
- 61. Moore DB, Howell PB, Treiber FA (2002) Adiposity changes in youth with a family history of cardiovascular disease: impact of ethnicity, gender and socioeconomic status. J Assoc Acad Minor Phys 13: 76–83.
- 62. Ness AR, Leary S, Reilly J, Wells J, Tobias J, et al. (2006) The social patterning of fat and lean mass in a contemporary cohort of children. Int J Pediatr Obes 1: 59–61.
- 63. Wake M, Hardy P, Canterford L, Sawyer M, Carlin JB (2007) Overweight, obesity and girth of Australian preschoolers: prevalence and socio-economic correlates. Int J Obes (Lond) 31: 1044–1051.
- 64. Marin TJ, Martin TM, Blackwell E, Stetler C, Miller GE (2007) Differentiating the impact of episodic and chronic stressors on hypothalamic-pituitary-adrenocortical axis regulation in young women. Health Psychology 26: 447–455.
- 65. Ravaja N, Katainen S, Keltikangas-Jarvinen L (2001) Perceived difficult temperament, hostile maternal child-rearing attitudes and insulin resistance syndrome precursors among children: a 3-year follow-up study. Psychother Psychosom 70: 66–77.
- 66. Buchmann AF, Kopf D, Westphal S, Lederbogen F, Banaschewski T, et al. (2010) Impact of early parental child-rearing behavior on young adults' cardiometabolic risk profile: a prospective study. Psychosom Med 72: 156–162.
- 67. McEwen BS (2003) Early life influences on life-long patterns of behavior and health. Mental Retardation and Developmental Disabilities Research Reviews 9: 149–154.
- 68. Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, et al. (2011) Forecasting the Future of Cardiovascular Disease in the United States. Circulation 123: 933–944.
- 69. Boyle J, Thompson T, Gregg E, Barker L, Williamson D (2010) Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Population Health Metrics 8: 29.
- 70. Snijder M, van Dam R, Visser M, Seidell J (2006) What aspects of body fat are particularly hazardous and how do we measure them? International Journal of Epidemiology 35: 83–92.
- 71. Repetti RL, Taylor SE, Seeman TE (2002) Risky families: Family social environments and the mental and physical health of offspring. Psychological Bulletin 128: 330–366.
- 72. Shrewsbury V, Wardle J (2008) Socioeconomic status and adiposity in childhood: A systematic review of cross-sectional studies 1990-2005. Obesity 16: 275–284.
- 73. Berge JM (2009) A review of familial correlates of child and adolescent obesity: What has the 21st century taught us so far? International Journal of Adolescent Medicine and Health 21: 457–483.
- 74. Batty GD, Leon DA (2002) Socio-economic position and coronary heart disease risk factors in children and young people - Evidence from UK epidemiological studies. European Journal of Public Health 12: 263–272.
- 75. Slopen N, Kubzansky LD, Koenen KC (2011) Childhood adversity and inflammatory and immune biomarkers associated with cardiovascular risk in youth: a systematic review. Brain, Behavior, and Immunity 26: 239–250.
- 76. De Vriendt T, Moreno LA, De Henauw S (2009) Chronic stress and obesity in adolescents: Scientific evidence and methodological issues for epidemiological research. Nutrition, Metabolism and Cardiovascular Diseases 19: 511–519.
- 77. Eckel RH, Alberti K, Grundy SM, Zimmet PZ (2010) The metabolic syndrome. Lancet 375: 181–183.
- 78. Lamb MM, Ogden CL, Carroll MD, Lacher DA, Flegal KM (2011) Association of body fat percentage with lipid concentrations in children and adolescents: United States, 1999–2004. The American Journal of Clinical Nutrition 94: 877–883.
- 79. Danese A, Caspi A, Williams B, Ambler A, Sugden K, et al. (2011) Biological embedding of stress through inflammation processes in childhood. Mol Psychiatry 16: 244–246.
- 80.
Slopen N, Kubzansky LD, McLaughlin KA, Koenen KC (2012) Childhood adversity and inflammatory processes in youth: A prospective study. Psychoneuroendocrinology.
- 81. Fuligni AJ, Telzer EH, Bower J, Cole SW, Kiang L, et al. (2009) A Preliminary Study of Daily Interpersonal Stress and C-Reactive Protein Levels Among Adolescents From Latin American and European Backgrounds. Psychosomatic Medicine 71: 329–333.
- 82.
Jun H-J, Corliss HL, Boynton-Jarrett R, Spiegelman D, Austin SB, et al.. (2011) Growing up in a domestic violence environment: relationship with developmental trajectories of body mass index during adolescence into young adulthood. Journal of Epidemiology and Community Health.
- 83. Evans GW (2003) A multimethodological analysis of cumulative risk and allostatic load among rural children. Dev Psychol 39: 924–933.
- 84. Evans GW, Kim P, Ting AH, Tesher HB, Shannis D (2007) Cumulative risk, maternal responsiveness, and allostatic load among young adolescents. Developmental Psychology 43: 341–351.
- 85. Fuller-Rowell TE, Evans GW, Ong AD (2012) Poverty and Health. Psychological Science 23: 734–739.
- 86. Loucks EB, Magnusson KT, Cook S, Rehkopf DH, Ford ES, et al. (2007) Socioeconomic position and the metabolic syndrome in early, middle, and late life: Evidence from NHANES 1999–2002. Annals of Epidemiology 17: 782–790.