Timing, duration, and differential susceptibility to early life adversities and cardiovascular disease risk across the lifespan: Implications for future research

Abstract

Early life adversities (ELA), include experiences such as child maltreatment, household dysfunction, bullying, exposure to crime, discrimination, bias, and victimization, and are recognized as social determinants of cardiovascular disease (CVD). Strong evidence shows exposure to ELA directly impacts cardiometabolic risk in adulthood and emerging evidence suggests there may be continuity in ELA’s prediction of cardiometabolic risk over the life course.

Extant research has primarily relied on a cumulative risk framework to evaluate the relationship between ELA and CVD. In this framework, risk is considered a function of the number of risk factors or adversities that an individual was exposed to across developmental periods. The cumulative risk exposure approach treats developmental periods and types of risk as equivalent and interchangeable. Moreover, cumulative risk models do not lend themselves to investigating the chronicity of adverse exposures or consider individual variation in susceptibility, differential contexts, or adaptive resilience processes, which may modify the impact of ELA on CVD risk.

To date, however, alternative models have received comparatively little consideration. Overall, this paper will highlight existing gaps and offer recommendations to address these gaps that would extend our knowledge of the relationship between ELA and CVD development. We focus specifically on the roles of: 1) susceptibility and resilience, 2) timing and developmental context; and 3) variation in risk exposure. We propose to expand current conceptual models to incorporate these factors to better guide research that examines ELA and CVD risk across the life course.

Overview

Early life adversities (ELA), which include experiences such as child maltreatment, household dysfunction (Felitti et al., 1998), bullying, exposure to crime, discrimination, bias, and victimization (Slopen et al., 2014; Suglia et al., 2015) are recognized as social determinants of cardiovascular disease (CVD). Strong evidence shows exposure to ELA leads to elevated cardiometabolic risk in adulthood; for example, ELA is prospectively associated with blood pressure trajectories in young adults, (Su et al., 2015) and higher blood pressure (Alastalo et al., 2013; Janicki-Deverts et al., 2012; Riley et al., 2010) and obesity in adulthood (Midei and Matthews, 2011). In addition, ELA is associated with a range of outcomes in adulthood, including elevated Hemoglobin A1c (Widom et al., 2012), incident metabolic syndrome (Midei et al., 2013), Type 2 diabetes (Rich-Edwards et al., 2010), and inflammation (Danese et al., 2007). Emerging evidence suggests similar relationships between ELA and cardiometabolic risk factors in childhood and adolescence. A recent study reported that verbally abusive behavior among mothers predicted higher systolic blood pressure, independent of body mass index, in children ages 5–6 (Smarius et al., 2018). The accumulation of ELA is also associated with obesity in early childhood (Suglia et al., 2012) with evidence suggesting adult obesity may be shaped by patterns of weight gain in early childhood (Rundle et al., 2020; Rundle et al., 2019; Stettler et al., 2003). Associations between ELA and inflammation have also been observed in adolescents in parallel to adult study findings (Miller and Chen, 2010). In sum, these research findings suggest there may be continuity in ELA’s impact on cardiometabolic risk over the life course. However, additional studies of children and adolescents are needed to precisely delineate the unique trajectories of individual cardiometabolic risk factors and the emergence of cardiometabolic disease over time. Addressing key gaps in our understanding of the role of timing, duration, and differential susceptibility to the effects of ELA, can inform important prevention and intervention efforts.

Experiences of ELA tend to cluster, particularly among racial/ethnic minorities and children in low-income households. As a result, extant research has primarily relied on a cumulative risk framework to evaluate the relationship between ELA and CVD. In this framework, the risk is considered a function of the total number of adversities that an individual is exposed to across developmental periods. The cumulative risk exposure approach treats developmental periods and numbers and types of adversities as equivalent and interchangeable, yet, the evidence indicates that adversity exposures may be more formative during specific developmental periods, and certain types of adversity may influence health differentially. Moreover, the research on the health consequences of ELA has primarily relied on a ‘risk factor-disease model’ (i.e., purely examining predictors of disease), without considering individual variation in susceptibility, differential contexts, or most importantly adaptive resilience processes, which may modify the impact of ELA on CVD risk.

An alternative to the cumulative risk model, a model that considers the timing, duration, type and context of exposure may be better suited to informing interventions (Figure 1). To date, however, alternative models have received comparatively little consideration. This conceptual review will highlight gaps in existing research on the relation between ELA and CVD and offer recommendations to address these gaps, specifically examining: 1) variability in susceptibility and resilience, 2) timing and developmental context; and 3) variability in risk exposure.

Fig. 1.

Expanded conceptual model of the relation between childhood adversities and cardiometabolic health.

VARIATION IN SUSCEPTIBILITY AND RESILIENCE

Individual Differences in Biological Reactivity to Early Adversity

Studies examining the relationship between ELA and health outcomes demonstrate considerable heterogeneity in outcomes among those who experienced ELA. For example, although evidence for the link between ELA and increased inflammation exists, this pathway is not evident for all individuals. Numerous studies assessing childhood stress and inflammation have failed to find associations between these two variables, or have found this link only among select subsets of youths, such as those with high adiposity, individual cognitive appraisal styles, socioeconomic backgrounds, or among particular racial or ethnic groups (Blevins et al., 2017; Chiang et al., 2017; Ehrlich et al., 2016; Giletta et al., 2018; Heard-Garris et al., 2020; Hostinar et al., 2017; Low et al., 2013). Differential response to ELA may be due to differences in biological sensitivity to a range of environmental influences or differential social context.

Biological Sensitivity to Context (BSC) theory (Boyce et al., 2005) proposes that children differ in their susceptibility to environmental influence “for better and for worse”, depending on their psychobiologic reactivity to the environment. Aligned with this theory, studies show that more reactive children (as indexed by heightened autonomic or adrenocortical responses to laboratory challenges, for example) display an increased sensitivity to both positive and negative environmental conditions (Bush and Boyce, 2016; Ellis et al., 2011). Research indicates that ELA exposures can result in either hyper-reactivity (high BSC) or hypo-reactivity (low BSC) of the autonomic nervous system (ANS) and hypothalamic-pituitary-adrenal (HPA) axis (Del Giudice et al., 2011). Several studies have noted that hyper- stress reactivity is associated with conditions that promote the development of CVD (Chida and Steptoe, 2010; Ginty et al., 2017). Hypo- stress reactivity has emerged as a marker of chronic dysregulation of the neuroendocrine stress response which is associated with increased risk for CVD (Ginty et al., 2017; Wiggert et al., 2016) including increases in blood pressure, coronary artery calcification and higher carotid intima media thickness (Turner et al., 2020). Studies to evaluate interventions designed to reduce CVD or CVD risk should consider heterogeneity in stress-reactivity and how it may impact the effects of the intervention. This is particularly important given that relatively strong intervention effects may occur among more reactive children and adults, representing a subset of those exposed to ELA.

Resilience

Risk models typically focus on measuring inherent risk characteristics and estimating the odds of disease given a specific risk factor or combination of risk factors. For example, a commonly cited statistic is that among individuals with no ELA, the lifetime prevalence of depression is 20%. In contrast, among individuals with five or more ELA, the lifetime prevalence of depression increases to 60% (Chapman et al., 2004). This is a large difference and an important finding as it points to the role of resiliency among the 40% of ELA-exposed individuals who did not become depressed. Given the “disease risk” orientation of research, however, the vast majority of studies linking ELA and health have focused on the population that develops the disease, with limited research on the population that remains healthy even in the face of ELA exposures (i.e., those who are resilient). Resilience is domain specific, and this orientation to disease risk extends to health outcomes as well. For example, in a recent study of 8609 young women (Loxton et al., 2021), the prevalence of current smoking increased in a graded fashion across categories of ELA exposures, ranging from 10.5%, to 32.1% among those who reported 0 and 4+ ELA, respectively. Similarly, the prevalence of severe obesity (BMI≥35) was 4.0% among those not reporting any adversities to 13.2% among those reporting 4+ ELA. While these patterns show an increasing level of risk associated with greater ELA exposure, from a resiliency perspective, more emphasis should be placed on better understanding the characteristics and circumstances of the majority of individuals who are exposed to a high number of ELA but do not smoke (67.9%) or become severely obese (86.8%). Further examination of the characteristics and circumstances of individuals who have ELA exposures but do not experience negative outcomes could identify targets for intervention.

One common definition of resilience is the capacity to realize better than expected outcomes, given an assessment of risk (Masten and Barnes, 2018; Wortman, 2004). This explanation highlights the development of resilience, namely that resilience does not occur in the absence of risk. However, it neglects consideration or quantification of positive outcomes that exceed the ‘absence of disease’. This limitation both contributes to and is driven by limitations in our conventional measures. For example, many chronic diseases have clinically-defined healthy, pre-clinical, and clinical disease states (e.g., healthy weight, overweight, obese), without a clear understanding of what outcomes are especially beneficial for long-term wellbeing in the context of risk exposure. In some cases, it may be possible to identify a resilient biological state – that is, a biological adaptation to adversity that protects against later disease. Likewise, several early life positive psychosocial and behavioral assets can promote the development of favorable cardiovascular health in adulthood (Slopen et al., 2017). Identifying these positive factors and mechanisms would open direct links to intervention that would offer some advantages over current approaches that aim to achieve biological states in adversity-exposed individuals that mirror those of non-adversity exposed individuals. It may be that what works well in low-risk conditions is, in fact, not adaptive in adverse conditions and that a more nuanced consideration would better direct intervention dollars once the risk has occurred (vs. in prevention models).

TIMING, TYPE AND DEVELOPMENTAL CONTEXT

Timing/Duration

There is increasing interest in identifying factors that underlie individual variation in responses to ELA, such as the developmental period of exposure. The term “sensitive period” refers to specific time points in development when individuals are most likely to be influenced by (or have the maximal response to) social or environmental exposures (Ben-Shlomo and Kuh, 2002; Hertzman and Boyce, 2010). Studies have investigated sensitive periods at multiple points in development, including the prenatal (Bale et al., 2010; Brown et al., 2000), childhood (Zeanah et al., 2011), and adolescent periods (Andersen and Teicher, 2008; Blakemore and Mills, 2014; Gunnar et al., 2019). However, it is challenging to rigorously test for sensitive periods in observational cohort designs given that negative (or positive) social exposures naturally cluster and also can influence the likelihood of subsequent exposures (Evans et al., 2013; McLaughlin, 2016). Studies that have examined the timing of ELA concerning outcomes in youth that are associated with later cardiovascular risk, such as adiposity (Jun et al., 2011; Ziol-Guest et al., 2009) suggest that timing of exposure to ELA matters – however, across existing research, studies are not consistent about which period of exposure presents the greatest risk.

ELA is hypothesized to have a negative impact on physical health because it occurs during a sensitive developmental window when a child first develops close relationship bonds (i.e., attachment relationships). Individual differences in attachment insecurity have a significant impact on both mental and physical health across the lifespan (Anderson et al., 2012; Anderson and Whitaker, 2011; Goossens et al., 2012). Adolescence may be an especially important period for investigating these individual differences because of the heightened sensitivity of adolescents to social-environmental cues (Blakemore & Mills, 2014). Evidence from psychology and neuroscience underscores adolescence as a sensitive period for the development and consolidation of emotion regulation skills as well as the maturation of neural regions involved in affect regulation (Steinberg, 2005).

Developmentally oriented models have largely focused on sensitive periods when risk exposures are hypothesized to be particularly salient for cardiovascular health (Barker, 1992; Bleil et al., 2015; Jasik and Lustig, 2008). For example, the Fetal Origins Hypothesis (Barker, 1992)—or Developmental Origins of Health and Disease Hypothesis—focuses on a range of detrimental exposures, including ELA, that occur during the period of gestation, ultimately shaping trajectories of cardiovascular health into adulthood (Barker et al., 1990; Hales et al., 1991; Valdez et al., 1994). Mechanisms of such effects, although not fully elucidated, may include epigenetic pathways through which early environments alter gene expression (without changing DNA sequences) (Dunn et al., 2019; Hao et al., 2018; Klengel et al., 2013).

Relative to prenatal exposures, ELA during other developmental periods such as puberty are less well studied. Yet, like the gestational period, puberty is a discrete developmental period during which time a dynamic set of biological events occur, shaping sexual maturation and the potential for human reproduction. It is plausible that the critical organizational processes that occur during puberty, if disrupted, could impact reproductive development and associated hormonal and metabolic factors relevant to adulthood cardiometabolic diseases (Bleil et al., 2015). Interestingly, related evidence shows ELA exposures influence the timing of puberty itself. That is, ELA exposures predict earlier pubertal timing (Belsky et al., 1991; Ellis, 2004; Moffitt et al., 1992), which, in turn, is a risk factor for post-pubertal weight gain, worsening CVD risk factor profiles, incident cardio-metabolic disease, and early mortality (Cooper et al., 1999; Feng et al., 2008; Frontini et al., 2003; Jacobsen et al., 2009; Lakshman et al., 2009). Recent work has shown that exposure to ELA during pubertal development is associated with higher adiposity in adulthood compared to experiencing ELA at other developmental periods (Riem and Karreman, 2019). In addition, adolescence is a life stage characterized in part by the uptake of health behaviors and social relationships, which can influence mental and physical health over the life course (Sawyer et al., 2012). Moreover, health risk behaviors, including tobacco use, alcohol, and substance abuse, tend to be initiated earlier among those who experience ELA and may be a pathway linking ELA to later health outcomes (Doom et al., 2017; Duke, 2018). Finally, it is notable that the prenatal and pubertal periods are not only periods of increased sensitivity biologically but are also associated with distinct changes in family and social functioning, possibly further compounding effects of prior ELA exposures on health.

Type of adversities

Individual ELA have been associated with cardiovascular health outcomes. Experiences of child maltreatment for example have been shown to impact several mental health and physical health outcomes including CVD. A systematic review noted that 22 out of 24 studies reviewed noted child maltreatment to be associated with cardiovascular disease (Basu et al., 2017). Specific types of maltreatment, specifically sexual and physical abuse, have also been associated with CVD (Fuller-Thomson et al., 2012; Rich-Edwards et al., 2012; Thurston et al., 2017). Other individual ELA, however have not been as extensively studied for their individual association with CVD. As noted, ELA tend to cluster, thus making it difficult to examine individual forms of adversity for their impact on health outcomes. Partly due to this clustering, the predominant research relies on a cumulative risk framework where adversities are aggregated without consideration of whether different adversities may impart a differential impact on health outcomes. Recent work has proposed a dimensional approach to childhood adversity proposing two dimensions, threat and deprivation (McLaughlin and Sheridan, 2016). Work examining whether different dimensions of child adversity differentially impact cardiometabolic health outcomes is lacking, but recent work has demonstrated this differential impact in relation to biological aging markers (Colich et al., 2020; Sumner et al., 2019; Sun et al., 2020). An important consideration when examining different types of adversity is the interrelatedness to timing, duration and developmental context which may serve to modify the impact of specific types of adversities on cardiometabolic health.

The Moderating Role of Developmental Context

Given substantial heterogeneity in outcomes among individuals exposed to similar profiles of adversity, there is growing focus on moderators of the effects of ELA on later health and developmental outcomes, both positive and negative (Bethell et al., 2019; Traub and Boynton-Jarrett, 2017). Protective factors are conceptualized as health-promoting resources, relationships, or contexts that help children cope with or recover from ELA (i.e., not mere absence of a negative exposure or experience). Potentially protective processes and factors occur across levels of a child’s social ecology (Cicchetti and Toth, 2016). These include individual (e.g., genetic factors, self-regulation) (Bakker et al., 2011; Niitsu et al., 2019) familial (e.g., stable and nurturing relationships) (Farrell et al., 2017), community (e.g., availability of supportive programs and social supports in times of need) (Luthar et al., 2015) and social and policy factors (e.g., policies that provide access to high-quality accessible childcare) (Gartland et al., 2019; Watamura et al., 2011).

Caregiving relationships play a central role in human development, parent and family systems are a key protective factor. High-quality parent-child relationships may prevent or mitigate lasting changes resulting from ELA, as shown in animal studies (Gunnar et al., 2015; Sanchez, 2006; Winslow et al., 2003), observational studies of children (Asok et al., 2013; Bernard et al., 2019; Kertes et al., 2009; Suglia et al., 2009), parenting intervention studies (Chen et al., 2018; Miller et al., 2014), and retrospective studies of adults (Carroll et al., 2013; Chen et al., 2011). For example, numerous studies assessing childhood stress and dysregulation of the stress-response system have shown that maternal warmth buffers the impact of early life stress on physical health outcomes (Farrell et al., 2017; Howell et al., 2017). Studies suggest that warm and/or responsive caregivers can buffer children exposed to ELA from poor outcomes relevant to cardiometabolic health measured in childhood (e.g., body mass index (BMI) (Bernard et al., 2019), telomere shortening (Asok et al., 2013)) and in adulthood (e.g., pro-inflammatory signaling (Chen et al., 2011), metabolic syndrome (Miller et al., 2011), and other measures of multisystem biological risk (Carroll et al., 2013)). Accordingly, adversities that compromise the capacity of parents to provide nurturing care may exacerbate the impact of ELA, thus presenting a greater risk for negative outcomes among children. Further research on the impact of ELA on specific developmental periods may inform whether interventions may work better if timed to occur in particular periods. In addition, an examination of the relative impact of particular exposures on health outcomes may identify “key” exposures that if targeted with interventions might be sufficient or generalize to protecting against other correlated exposures.

VARIATION IN RISK OF EXPOSURE TO ELA

Individuals belonging to marginalized groups are at greater risk of experiencing ELA. Common experiences of bias and discrimination among these individuals result in individual- and societal-level experiences of victimization (e.g., bullying, harassment) and stigmatization, possibly contributing to disparities in health (Hatzenbuehler et al., 2013). Moreover, individuals’ identification with multiple marginalized groups (e.g., transgender identity and African American) termed ‘intersectionality’ may combine to make one uniquely vulnerable to the experience and health impacts of ELA (Crenshaw, 1990).

Racial and ethnic health disparities seen in cardiometabolic health conditions, such as diabetes, hypertension, and obesity, have roots in structural inequities (Havranek et al., 2015). Disparities can be stratified by race and ethnicity, in addition to SES and other demographic factors. Specifically, differences in SES, neighborhood resources, and exposure to adversity, including race/ethnicity-based adversities, such as discrimination may explain racial/ethnic disparities in cardiometabolic disease prevalence and outcomes (Liu et al., 2018; Suglia et al., 2020; Vasquez et al., 2019).

The risk of exposure to ELA may vary based on demographic characteristics, including race/ethnicity, sex/gender, sexual orientation, family SES, urbanicity, and disability status. ELA disproportionately impact underserved groups (Austin et al., 2016a; Llabre et al., 2017). For example, Non-Hispanic Blacks and Hispanics are exposed to a high number of ELA compared to Non-Hispanic whites (Merrick et al., 2018). Racial/ethnic minority children and those from financially disadvantaged households experience higher levels of the most common adversities, including high rates of economic hardship, parental separation, and incarcerated parents (N. Heard-Garris et al., 2018; Merrick et al., 2018). Racial/ethnic minorities may experience higher levels of adversity as well as additional adversities that Non-Hispanic white groups do not experience and may experience racism during “sensitive periods”, such as early childhood and adolescence (N. J. Heard-Garris et al., 2018; Priest et al., 2014).

Some research suggests the prevalence of ELA may be higher among sexual and gender minorities (SGM) than their non-SGM peers (Andersen and Blosnich, 2013; Andersen et al., 2015). Sexual and gender minorities are more likely to experience physical and verbal bullying in childhood as compared to their heterosexual peers (Andersen and Blosnich, 2013; Austin et al., 2016a). Transgender individuals may experience more emotional abuse and both physical and emotional neglect as compared to similar cisgender individuals (Schnarrs et al., 2019). SGM are more likely to have psychological distress and engage in risky health behaviors, like smoking and alcohol use, potentially widening health disparities (Hart et al., 2018; Schnarrs et al., 2019). For SGM, bullying and ELA exposure may help to explain disparities that disadvantage this population (Andersen and Blosnich, 2013).

Individuals with disabilities are more likely to report ELA than their counterparts without disabilities, including sexual abuse (Austin et al., 2016b; Schussler-Fiorenza Rose et al., 2014). Berg et al. found children with autism are more likely to experience ELA (Berg et al., 2016), which, in turn, is associated with unmet healthcare needs (Berg et al., 2018). However, more studies that examine the role of ELA in the development of cardiometabolic health disparities based on ability status and developmental conditions are needed.

Increased risk of exposure to ELA among specific subgroups, as described above, has relevance for the timing, duration, and developmental context within which ELA are experienced. Living in underserved neighborhoods or low SES households may expose children to adversities across multiple developmental periods, which may include periods of increased vulnerability for experiences of adversity. Families experiencing social adversities are the most vulnerable, often lacking interpersonal support, economic resources, or access to governmental programmatic support to navigate these adversities. The additional burden of experiencing multiple adversities, and for longer durations, may impact children’s biological susceptibility to adversities as well as their families’ resilience.

The increased likelihood of exposure, differential duration, and exposure to multiple adversities may translate into a higher risk of adverse cardiometabolic outcomes. For example, Black individuals with ELA histories of child abuse or household difficulties are 3 times more likely to drink alcohol heavily as compared to non-Hispanic whites with similar backgrounds (Lee and Chen, 2017). Hispanic/Latinx populations also have an increased prevalence of ELA as compared to whites, second only to non-Hispanic Blacks. In a nationally representative study published in 2010, Hispanics with ELA histories of child abuse were 11 times more likely to drink alcohol heavily than non-Hispanic whites with similar histories (Lee and Chen, 2017). In contrast, a South Dakota-based study demonstrated American Indians had more ELA as compared to non- American Indians; however, there was no evidence for differential associations between negative health outcomes and health risk behaviors (i.e., mental health conditions, alcohol abuse, or smoking) among American Indians as compared to non-American Indians (Warne et al., 2017). These findings suggest that racial or ethnic minority status does not always signify worse outcomes. Some studies have suggested that women in comparison to men are more vulnerable to the impact of adversity as it relates to cardiometabolic outcomes, either because women are exposed to more adversity or because they are more vulnerable to its effects (Pedersen et al., 2016). For example, among young children, sex differences have been noted in the relation between adversity and obesity, with girls being at increased risk of obesity in relation to adversity in early childhood (Liu et al., 2019).

Despite the existing evidence linking ELA to CVD, few studies have examined how the duration, timing and context of exposure help to explain the increased burden of cardiometabolic outcomes among underserved populations including racial/ethnic minorities, immigrants, individuals with disabilities, and SGM. Furthermore, few studies have examined how structural factors that drive these inequities increase exposure to ELA and limit resources that would help vulnerable populations in limiting the impact of these adversities.

Conclusions and Future Research Directions

There are several gaps in the evidence linking ELA and cardiometabolic risk that limit our ability to explain patterns of disease risk and resilience over time. Most notably, studies often use a blunt instrument to define multiple co-occurring ELA exposures across broad swaths of development (sometimes from birth to age 18) (Felitti et al., 1998). There are, however, sensitive periods in development that can affect, in the short- and long-term, underlying risk for cardiometabolic disease. In these periods, the impact of adversity exposures is likely to be amplified (Geelhoed and Jaddoe, 2010; Johnson W, 2015). Similarly, there is expected developmental variability in the association between ELA and biological processes such as DNA methylation (Dunn et al., 2019; Klengel et al., 2013). Therefore, future studies should aim to characterize this developmental variability by 1) comparing the impact of similar exposures at different points in development; 2) examining level of severity of similar exposures at different points in development. For example, number of times the exposure occurred, along with duration of the particular ELA exposure under investigation.

Alongside attention to developmental processes, additional research is needed to describe individual and contextual risk and resilience factors that modulate the relationship between ELA and cardiometabolic risk. Individual factors such as sex/gender, racial or ethnic identity (Slopen et al., 2010), SGM, ability status, coping styles (Bergh et al., 2015), and genetic and behavioral risks (Gooding et al., 2014) have received relatively little study. Contextual and structural factors as wells as social conditions, such as poverty are connected with abuse, neglect and many of other adversities children face. Because of their contribution as critical ELA factors, they need sustained attention in research studies especially when examining the ELA and cardiometabolic health association. Studies should explicitly measure the quality of caregiver/child relationships, and family systems given their critical role in buffering or potentiating the effects of ELA on health and developmental outcomes (Shonkoff and Garner, 2012). Finally, future research should also include systematic assessments of characteristics beyond the individual and family-level. These studies would incorporate the role of social policies, place-based characteristics, and other contextual factors to provide a more holistic understanding of the influence of ELA on health (Maguire-Jack et al., 2021; Slopen and Williams, 2021).

Additionally, significant gaps exist in the literature that examines ELA as a driver of disparities in cardiometabolic outcomes. Current research indicates multiple minority groups may be exposed to adversity differentially in early life. Thus, these groups experience not only higher levels of adversity as compared to non-minority groups but also additional adversities increasing the likelihood they are experienced during sensitive developmental periods. Thus, it is plausible that both the timing and accumulation of ELA among minority children matter for later life CVD risk. However, this literature is still immature, and many minority groups have been understudied. Additionally, ELA as a contributor to health disparities are rarely studied considering multiple minority statuses simultaneously (e.g., intersectionality). When this and other potential factors that give rise to differential ELA exposure are more thoroughly studied, we may better explain disparities in cardiometabolic health outcomes and, therefore, better understand how to intervene effectively.

The next wave of research should inform the development of interventions that address both ELA and population variation in ELA that drives health inequality. For example, in addition to traditional “lifestyle” behavioral intervention targets, psychosocial factors such as emotional counseling, stress reduction, and social support may be potential intervention targets for those at risk of poor cardiometabolic health outcomes due to ELA exposure. Specifically, such interventions may help to mitigate how ELA become biologically embedded and internalized (e.g., depression) amongst at-risk individuals (Cheong et al., 2017). Evidence exists that nurturing home environments, characterized by warmth, safety, support, and empathetic parenting relationships, are associated with positive cardiovascular health outcomes (Appleton et al., 2013; Slopen et al., 2017). Building on these studies, interventions that foster positive family functioning/environments, affect interventions, or the use of stress coping skills which may not have been designed to address physical health outcomes should be evaluated for their potential impact on cardiometabolic health outcomes. For example the Strong African-American Families program designed to enhance supportive parenting has been shown to ameliorate the impact of ELA on prediabetes in young adulthood (Brody et al., 2017).

Moreover, as ELA exposure disproportionately affects marginalized groups, prevention and intervention efforts must also be tailored to meet the needs of a range of populations made vulnerable. Research that addresses the type, timing, duration and context of ELA exposures within marginalized groups would aid in the development of specialized interventions for these groups uniquely. Doing so may not only improve and promote good cardiovascular health but may also work to reduce cardiovascular health disparities as well.