Effects of poverty on interacting biological systems underlying child development

Effects of poverty on interacting biological systems underlying child development

Review Effects of poverty on interacting biological systems underlying child development Sarah K G Jensen, Anne E Berens, Charles A Nelson 3rd The d...

597KB Sizes 0 Downloads 56 Views


Effects of poverty on interacting biological systems underlying child development Sarah K G Jensen, Anne E Berens, Charles A Nelson 3rd

The developmental sequelae of childhood poverty are well documented. However, it is not poverty per se, but a multitude of risk factors associated with poverty that have a deleterious effect on children’s development. Key risks factors that are likely to contribute to the adverse developmental effects of poverty include, for instance, food insecurity, infectious disease, and psychological stress related to the child’s rearing environment. In this Review, we highlight synergistic biological pathways through which co-occurring risks related to poverty interact to shape children’s neurocognitive development. We focus on pathways related to neural growth, energy metabolism, inflammation, and neuroendocrine responses to stress as key biological axes through which poverty becomes biologically embedded and might have longterm effects on children’s neurocognitive development. We also discuss how biomarkers targeting these axes can be used to advance research on the biological processes through which poverty affects children’s cognitive outcomes. Although the discussion has global relevance, we focus on low-resource settings where rates of poverty are highest and access to treatment might be limited.

Introduction Poverty is a global challenge with well-documented negative effects on child development and health.1 The World Bank’s definition of poverty (living on less than US$1·90 per day) identifies 11% of the world’s population as poor. Globally, poverty disproportionately affects children, especially those living in sub-Saharan Africa and south Asia. Childhood poverty is also disturbingly prevalent in many high-income countries, estimated at 20% in the USA and the UK.2,3 Although unidimensional monetary definitions of poverty help to track its prevalence, they fail to capture non-financial dimensions of social and biological risks that are likely to contribute to adverse effects of poverty on children’s development. More comprehensive poverty indices—eg, the Global Multi­ dimensional Poverty Index—consider additional factors such as living standards, education, and health (nutrition and child mortality) and identify 30% of the world’s population as poor, almost three times as many as the World Bank’s definition.4 Children born into poor families or families of low socioeconomic status have, on average, poorer neuro­ cognitive outcomes and poorer educational attainment than wealthier peers.5 Such setbacks are, in turn, associated with lower economic productivity, thus contributing to the intergenerational transmission of poverty.6 Although most studies of socioeconomic effects on children’s cognitive outcomes have focused on indicators related to parental income or socioeconomic status, co-occurring risks that accompany poverty are likely to contribute to adverse developmental sequelae associated with poverty. Important poverty-related risks include food insecurity, infectious diseases, environmental contaminants, and psychological stressors such as chaotic living arrangements, stressful events, and community violence.7 In this Review, we provide an overview of the evidence on how different risk factors associated with poverty converge to affect children’s neurocognitive develop­ ment via interacting biological pathways.

Highlighted pathways include processes involved in neural growth, energy metabolism, inflammation, and neuroendocrine stress responses. Increased appreciation of poverty as a complex multi­dimensional risk exposure, and consideration of how interactions among povertyrelated risks shape early child development, might enhance efforts to understand and intervene against adverse effects of poverty on child wellbeing.

Lancet Child Adolesc Health 2017 Published Online July 26, 2017 http://dx.doi.org/10.1016/ S2352-4642(17)30024-X Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA (S K G Jensen PhD, A E Berens MSc, Prof C A Nelson 3rd PhD); and Graduate School of Education, Harvard University, Cambridge, MA, USA (Prof C A Nelson 3rd) Correspondence to: Prof Charles A Nelson 3rd, Laboratories of Cognitive Neuroscience, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA [email protected]

Developmental risks associated with poverty and interactions among risks We conceptualise poverty as a multidimensional construct that represents not only low income but a complex Key messages • Above and beyond scarcity of financial resources, poverty exposes children to a complex constellation of developmental risk factors embedded in contexts of biological risk exposures and psychosocial disadvantage; to fully understand welldocumented associations between poverty and compromised child development observed worldwide, the effects of these complex interrelated risk exposures need to be considered and disentangled • Poverty-associated risk factors affect child development partly through synergistic biological pathways that overlap and interact; key biological pathways include changes in energy metabolism that affect somatic and neural functioning and growth, immune activation resulting in acute or chronic inflammation and dysregulation, and neuroendocrine stress activation resulting in increased allostatic load • Methodological approaches needed to advance understanding of complex povertyrelated effects on child development include statistical models assessing mediation and moderation, randomised controlled trials including multiarm intervention designs, and increased collection of biomarker data • Biomarkers, although historically used mostly in research on physical and mental health outcomes, provide a promising avenue for delineating the processes by which poverty exerts long-term effects on child neurocognitive development; in particular, biomarker data stand to improve the capacity to delineate causal pathways, detect early biological disruption before behavioural deficits emerge, and assess the effectiveness of interventions that seek to ameliorate adverse effects of poverty and related risks on early child development

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X



constellation of co-occurring risk factors. We define developmental risk factors as experiences or exposures that can disrupt a child’s healthy neural development and affect the achievement of early developmental milestones, language development, and cognitive capacities, such as specific executive functions (eg, attention, working memory, problem solving) and intelligence. We note that the term adversity is often used interchangeably with risk factors, and that poverty might be considered a form of adversity. Still, we address poverty as a multidimensional exposure to correlated risk factors that interact in complex ways to shape children’s development. We discuss poverty-related risks that are relevant in all global settings, but acknowledge that some risks are more prevalent in low-income and middle-income countries. We focus primarily on postnatal risks but note that the developmental effect of poverty often begins before birth. Finally, although we focus on biological pathways (including neuroendocrine responses to stress), we recognise that psychosocial pathways also contribute to poverty-related sequelae via similarly complex processes, which are beyond the scope of this Review. Most research on the developmental consequences of poverty has focused on direct associations between individual risk factors and child development (table 1). Yet, significant direct associations might be driven by more complex processes involving interactions among co-occurring risks. This Review is structured around three types of interactions: mediation, cumulative effects, Domain of negatively affected neurocognitive development Prenatal and perinatal risks Biological risks Young maternal age8

Early developmental composite score; executive functioning; IQ and cognition; school readiness or performance

Maternal undernutrition9,10 (including micronutrient and macronutrient deficiency)

Early developmental composite score; executive functioning; IQ and cognition

Maternal overnutrition or overweight11

Motor development; child health*

Maternal diabetes12

Executive functions; IQ and cognition; memory

Maternal infection13

Developmental delay; IQ and cognition; school readiness or performance

Maternal hypertension and pre-eclampsia14

Motor development; language

Early developmental composite score; executive Maternal substance use exposures15 (including alcohol, cigarette smoking, cocaine, marijuana) functioning; IQ and cognition; language Maternal exposure to environmental contaminants15 (including heavy metals, pollutants, and other chemicals)

Early developmental composite score; executive functioning; IQ and cognition; memory

Psychosocial risks Poor maternal mental health and stress16 (including depression, anxiety, chronic financial and social stress, stressful life events, social isolation, natural disaster)

Early developmental composite score; IQ and cognition

Social and infrastructural risks Poor access to health care17 (eg, family planning and prenatal care, safe labour and delivery)

IQ and cognition; birth outcomes*; psychological stress*

Poor public infrastructure18 (poor water sources and sanitation)

IQ and cognition; birth outcomes* (Table 1 continues on next page)


and effect moderation. Mediation occurs when a risk factor affects child development via another factor. Studies of mediation commonly explore whether distal exposures, such as financial hardship, affect child development through processes that are more proximal to the child, such as caregiving experiences. Cumulative effects occur when multiple or repeated risks have an additive effect suggesting that it is the cumulative burden, rather than the individual effects of risks, that adversely affects children’s development. Effect moderation occurs when the effect of a risk exposure is determined by the presence of a third factor. The association between infectious disease and poor cognition might, for instance, depend on maternal education, with high levels of education conferring protection and low levels of education conferring increased vulnerability. Effect moderators can also be intrinsic to the child. Intrinsic factors include genomic variation and biologically driven stress sensitivity that affects how a child responds to risk exposures. Intrinsic factors that increase the children’s vulnerability are commonly referred to as susceptibility factors, whereas factors that promote resilience are referred to as protective factors.

Mediation Poverty-related risks can induce changes across key biological axes that, in turn, can mediate adverse effects of poverty on children’s neurocognitive development (figure 1).

Malnutrition Malnutrition occurs when the body receives insufficient calories or specific nutrients to support metabolic processes underling somatic and neural functioning and growth. In global settings of poverty, malnutrition is often driven by caloric scarcity,9 which, along with infection, is one of the leading causes of stunted child growth. Stunted growth is assessed by anthropomorphic measures of the child’s height-for-age and affects almost 161 million children worldwide.33 The high prevalence of stunting and strong evidence for associations between stunting and poor cognitive outcomes have made stunting one of the most profound and widely discussed indicators of poverty-related developmental setbacks in children in developing countries. However, in many high-income countries, and increasingly in urbanised low-income and middle-income countries, malnutrition can also occur in settings where food is abundant in quantity but deficient in quality, leading to micronutrient deficiencies or adverse metabolic changes, or both, because of unfavourable fat and sugar profiles in lowcost foods. Malnutrition—whether caused by deficiencies of calories or specific micronutrients (eg, iron, zinc, iodine) and whether occurring in settings of child stunting10 or obesity20—has been widely associated with poor neurocognitive developmental outcomes. Caloric energy and specific micronutrients are essential for

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X


many aspects of neural development, including neuronal cell proliferation and differentiation, axonal and dendritic growth, synaptogenesis, myelination, and programmed cell death (which fine-tunes existing neural connections). The availability of nutrients also regulates the synthesis and activity of key neurotransmitters underlying cognitive processing and mood,10 and failure to meet these nutrient requirements is likely to further contribute to adverse neurocognitive outcomes.10 Caloric and specific micronutrient malnutrition is also intricately linked with other physiological systems such as the inflammatory and neuroendocrine pathways involved in the response to stress. Undernutrition, for instance, affects intestinal and skin barrier function, allowing pathogen translocation and increasing the risk of infection and chronic inflammation.34 Both under­ nutrition and overnutrition can affect cytokine signalling implicated in innate, cell-mediated, and antibodymediated immune pathways, and, therefore, can lead to immune dysfunction.34,35 Obesity is associated with lowgrade inflammation in white adipose tissue and chronic immune activation,35 whereas undernutrition can lead to immunosuppression, as indicated by reduced antibody response to vaccination.34 Impaired immune defences or chronic inflammation, or both, in malnourished children are likely to contribute to the increased risk and duration of infections, which could, in turn, contribute to poor cognitive outcomes in poor children. Mal­ nutrition also interacts with the neuroendocrine stress response governed by the hypothalamic–pituitary– adrenal (HPA) axis. Increased concentrations of the stress hormone cortisol have been reported in undernourished children, and some data suggest that HPA-axis involvement might be most pronounced in severely malnourished children.34 A key methodological challenge is to disentangle whether inflammation and neuroendocrine stress activation are related to malnutrition per se or whether they reflect concurrent infections or psychological stress.34

Infection and inflammation Inadequate housing conditions, poor water supplies and sanitation, limited access to health care (including low rates of immunisation and treatment), and other povertyrelated exposures increase the risk of infectious disease among poor children.36 Most research on long-term implications of childhood infectious diseases has focused on physical health outcomes, yet emerging evidence suggests that persistent infection (eg, chronic diarrhoeal disease) might be associated with cognitive and language deficits in infants as young as 12 months.37 Pathways through which common infections can affect cognitive outcomes are not fully understood but probably involve both inflammation and malnutrion.22 Study outcomes from Bangladesh have shown that the adverse developmental effect of enteric infection correlates with an increase in pro-inflammatory cytokine concentrations.38

Domain of negatively affected neurocognitive development (Continued from previous page) Postnatal risks Biological risks Reduced breastfeeding19 (associated with poverty only in some contexts)

Early developmental composite score; language; IQ and cognition

Child malnutrition9,10 (including deficiencies of caloric energy, micronutrients, and macronutrients)

Motor development; IQ and cognition; school readiness and performance

Child overweight20

Motor development; IQ and cognition; school readiness and performance

Child diabetes21

IQ and cognition; executive functioning

Child infectious disease22,13 Neonatal infections

Developmental delay; IQ and cognition; school readiness and performance

Childhood infections

IQ and cognition; language; school readiness and performance

Child exposure to environmental contaminants (including Early cognitive composite score; motor heavy metals,23 pollutants, and other chemicals24) development; executive functioning; IQ and cognition; memory; school readiness and performance Psychosocial risks Child psychological stress25 (including family conflict, poor caregiver mental health, other caregiver or family chronic stressors, stressful life events, homelessness, violence exposure, discrimination)

Executive functioning; IQ and cognition; memory; school readiness and performance

Maternal depression26

Early developmental composite scores; motor development; language; IQ and cognition; school readiness and performance

Violence in the home (including corporal punishment,27 domestic violence and maltreatment28,29)

Executive functioning; language; memory; IQ and cognition

Neighbourhood stressors29 (including community violence and drugs)

IQ and cognition

Social and infrastructural risks Inadequate housing environment15 (including poor housing quality, crowding or chaos in the home, environmental noise, neighbourhood disadvantage)

Executive functioning; IQ and cognition; memory; school achievement and performance

Poor public infrastructure (eg, poor water sources and sanitation)30

Child health*

Poor access to health care31

Child health*

Poor access to and quality of child care, preschool and kindergarten32

Language; IQ and cognition; educational achievement

Exemplary overview of evidence linking poverty-related risk factors with poor neurocognitive outcomes in children. This table is not all-encompassing but provides examples of demonstrated associations. We refer to major reviews whenever possible, and to select studies if no recent review is available. IQ=intelligence quotient. *No direct evidence supports an association between the risk exposure and poor cognitive outcomes, but evidence does exist to link the risk exposure to poor health outcomes, suggesting that poor health in turn can present a risk for poor child development.

Table 1: Examples of direct effects of poverty-related risks and neurocognitive outcomes in children

Peripheral cytokines can enter the brain by crossing the blood–brain barrier or via the vagus nerve. In the brain, cytokines can affect neural development and transmission—for instance, by interacting with metabolic processes involved in the synthesis and activity of neurotransmitters and growth factors.39 Moreover, in some disease settings (eg, perinatal insult, sepsis, and HIV/AIDS), cytokines have been shown to increase blood–brain barrier permeability, making the brain more susceptible to inflammatory and stress mediators and reactive oxygen species.40

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X



Psychological stress causing activation and dysregulation

Psychosocial risks • Stressful living environments (home and neighbourhood) • Stressful life events • Lower caregiver availability (parental stress or poor mental health) • Poorer quality child care

Stress mediators induce neurotoxicity, oxidative stress, altered neural plasticity, disruption of sensitive period timing

Hypothalamic–pituitary– adrenal axis response Induction of neuroendocrine stress response (cortisol mediated)

Activation, dysregulation

Activation, dysregulation


Inflammation • Increase in inflammatory mediators (eg, cytokines) • Mobilisation of innate, adaptive immunity • Chronic inflammation and immunosuppression


Pathogen exposure • Ubiquitous environmental pathogens • Poor sanitation • Poor water supply • Poor housing conditions • Reduced rates of vaccination, health-care access, breastfeeding (in some contexts)

Poor food access or quality • Absolute food scarcity • Poor nutritional quality of available food

Environmental contaminants • Eg, heavy metals, chemicals, pesticides, pollutants

Activation, dysregulation

Infection • Bacterial • Viral • Parasitic

Remodelling Infectious enteropathy

Energy demands, anorexia

Inadequate intake of calories or nutrients, or excess intake of non-needed nutrients (eg, processed sugars)

Immune suppression

Activation, dysregulation

Cognitive stimulation affects neural development via neural plasticity Altered brain structure • Gray matter volume, thickness, surface area • White matter connectivity, density, integrity

Altered blood–brain barrier function and synaptic plasticity, oxidative stress, cytotoxicity

Gut microbial changes Adverse changes to gut microbial composition and function (dysbiosis)

Neurocognitive functioning Negative effects on: • Infant cognitive skills (eg, discrimination of objects, sounds) • Overall cognition (eg, IQ) • Language • Executive functioning (eg, sustained attention, working memory, inhibitory control) • Learning and school performance

Production of bioactive molecules

Malabsorption of nutrients Remodelling

Malnutrition Nutritional deficiencies (energy, macronutrients, micronutrients)

Psychosocial stress in children and parents and scarce financial resources might limit opportunities for learning and stimulation

Inadequate energy or nutrients alters neural development and signalling

Decreased energy and sickness behaviours lead to reduced play, learning, socialisation, and other stimulating activities

Decreased energy might lead to reduced play, learning, socialisation, and other stimulating activities

Bioactive molecule and oxidative stress affect neural development and functioning

Figure 1: Proposed biological pathways that mediate effects of selected poverty-associated risks to neurocognitive outcomes in children Complex interactions among key poverty-related risk factors, focusing on primary biological pathways related to malnutrition, infection and inflammation, and neuroendocrine responses to stress.

Infection is an important contributor to malnutrition and might account for up to 50% of all child underweight and stunting.41 Infection is thought to cause malnutrition in several ways. First, mobilisation of an immune response requires energy and competes with caloric needs of the metabolic processes that support somatic and neural growth.42 Second, inflammation will diverge energy and nutrients away from synthetic pathways that support somatic growth towards processes that sustain an immune response.43 Third, intestinal inflammation caused by enteric infection can induce dysbiosis, an unfavourable shift in the composition of gut 4

microbial flora or host–microbe interactions. Dysbiosis alters digestion and intestinal barrier function, thereby reducing nutrient absorption.41 Environmental enteropathy, a form of dysbiosis linked to infectious disease, is common in low-resource areas in tropical regions.44 Despite strong evidence linking infectionrelated malnutrition to stunted growth, research is needed to clarify the extent to which infection and environmental enteropathy contribute to poor cognitive outcomes in malnourished children. Failure to find relationships between diarrhoeal disease and child cognition after controlling for the child’s nutritional

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X


status suggest that the effect of infection might be driven primarily by malnutrition.45 Inflammation also interacts with the HPA axis, leading to an acute upregulation of cortisol production.39 The increase in cortisol concentrations strengthens some aspects of immune functioning acutely but can lead to long-term dysregulation of the stress response mediated by the HPA axis and has been linked with cortisol resistance,46 which, in turn, might affect children’s neurocognitive development.

Neuroendocrine stress response Poverty often gives rise to myriad psychological stressors such as unmet basic needs for food, warmth, and safety, inadequate housing conditions, family stress, neighbourhood risks, and community violence.47 Although psychosocial stressors vary greatly in nature, they all activate a shared neuroendocrine stress response involving both the HPA axis (leading to an acute increase in cortisol production) and the autonomic (fight or flight) response.7 Although the stress response is adaptive initially, persistent HPA axis and autonomic activity can result in excessive allostatic load, a state of physiological disruption caused by heightened neuroendocrine responses to stress on the body and can lead to long-term dysregulation of the stress-response and other physiological systems.7,47 Stress activation and regulation in children exposed to severe stress have been examined in many studies, but the reported findings have been inconsistent, showing increased, reduced, or no differences in basal cortisol concentrations in relation to childhood stress.48 Severe psychological stress can also affect neural development and functioning. For instance, stress has been found to correlate with volumetric variation in stress-sensitive neural structures involved in memory, attention, and learning, such as the prefrontal cortex.7 Children who grow up in poverty and those who experience stress have similar volumetric reductions in the prefrontal cortex, suggesting that stress mediates effects of poverty on neural development.49 Acute stress activation (as indexed by, for instance, increased cortisol production) has also been shown to correlate with poor executive functioning, memory, intelligence quotient (IQ), and language in children.50,51 Of particular interest are findings from studies of children born into families of low socioeconomic status in the USA,52 which show that altered cortisol signalling mediated associations between poverty-related risks (low income, low maternal educational levels, and racial minority status) and children’s cognitive abilities. Caregiving experiences, which can be a source of stress or of protection depending on their nature and quality, have also been shown to mediate effects of poverty on structural variation in hippocampal volumes in children.53 The HPA axis also interacts with inflammatory and metabolic axes. Stress acutely enhances immune reactivity but chronically suppresses immunity.54 Acute

psycho­ logical stress is associated with an acute inflammatory response, and chronic exposure to psychological stress is associated with long-term immune dysregulation.39 Outcomes of a meta-analysis55 showed that adults with a history of childhood trauma or low socioeconomic status during childhood had increased basal plasma concentrations of proinflammatory cyto­ kines indicative of chronic inflammation.56 Stressinduced immuno­ suppression, as indicated by poor chronic control of latent infections, has also been described.57 Finally, emerging evidence from animal models suggests that psychological stress can alter intestinal permeability, inducing dysbiosis and leading to impaired immune defences and nutrient absorption.58 The HPA axis can affect metabolic processes by diverging energy from synthetic processes towards processes that sustain the stress response. Sustained dysbiosis and metabolic changes might lead to nutritional deficiencies that impede neural development.59

Cumulative effects Many poverty-related exposures are associated with dosedependent effects whereby child development is more affected by severe, frequent, or long-lasting exposures.60 Cumulative effects are generally thought to be driven by prolonged or extreme wear and tear on the body caused by the sustained burden of stress activation and efforts to regain physiological stability (ie, allostasis). Most studies of cumulative effects have been in relation to physical and mental health,61 yet cumulative effects of povertyrelated risks on children’s IQ have been described.50 Cumulative effects can be explored by summing singular risks into a cumulative score, and they can be linear or non-linear. Adverse cognitive and neural outcomes are exponentially more pronounced in the poorest children in a non-linear manner.60 Although cumulative models are important for understanding effects of poverty in some contexts, they conceal unique effects of individual risks and overlook important interactions among risks. Thus, approaches to study cumulative effects should be considered in tandem with models of mediation and moderation.

Effect moderation Much research on poverty has focused on main or average effects, yet children growing up in poverty show highly variable outcomes. Some children living in poverty do as well as peers living in wealthy conditions, whereas other children show severe cognitive delays or impairments. The observation that children respond differently to their environments has generated substantial scientific literature about differential susceptibility. Differential susceptibility has been studied extensively in relation to genomic variation and other intrinsic child factors, such as temperament and neurobiological responsiveness to stress. Children’s overall susceptibility or resilience to poverty-related risks are shaped by intrinsic child factors

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X



(eg, genetic susceptibility factors, sex, age), extrinsic environmental factors (eg, family factors), and characteristics of the risk exposure (eg, timing effects; A

figure 2). Resilience describes children who show little (overt) effects of adverse experiences and reflects the capacity of some children to adapt to externally imposed Cultural factors • Family support systems • Caregiving beliefs and practices • Sex-based beliefs and sex equality

Contextual factors • Housing conditions (including building materials, sanitation, and ventilation) • Access to health care • Neighbourhood stressors

Pr ot ec

Caregiving experiences

tiv ef ac to

Child factors • Genomic variation • Sex • Health and nutrition • Temperament



rs tib




Characteristics of the exposure • Intensity • Duration and chronicity • Timing (age at exposure)





Family factors • Financial resources • Parental psychological resources (including stress and mental health) • Parental social resources (including education)

Child developmental trajectory

B High genetic susceptibility, few protective family factors, but some protective contextual environmental factors

Medium genetic susceptibility, many protective family factors, few protective contextual factors, many protective cultural factors

High genetic susceptibility and few protective factors Low genetic susceptibility but few protective factors


www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X


imbalance. Most research conceptualises susceptibility as an intrinsic characteristic, yet resilience might be driven by a combination of child intrinsic and extrinsic factors.62 These factors can broadly moderate the effect of povertyrelated risks, for better or worse.

Intrinsic child factors Many common genetic polymorphisms contribute to differential susceptibility to early experiences. In particular, polymorphisms in monoamine-regulating genes—including genes encoding the serotonin transporter (SLC6A4), dopamine receptor D4 (DRD4), catechol-O-methyltransferase (COMT), dopamine active transporter (DAT), and monoamine oxidase A (MAO-A)— confer differential susceptibility to poor emotional and cognitive outcomes.63 Moreover, a polymorphism in the apolipo­protein E gene (APOE) moderates the effects of biological adversities such as diarrhoeal disease64 and environmental contaminants15 on neurocognitive outcomes. Genetic factors can also affect intervention effects. In a study of a psychosocial intervention targeting poor families in South Africa,65 intervention effects were moderated by the SLC6A4 polymorphism such that children who carried the short allele benefitted most from the intervention (ie, showed more secure attachment). Neurocognitive effects of various poverty-related risks, including environmental contaminants and maternal depression, have also been shown to be sex dependent.15,26 Apparent sex-dependent effects might be driven by biological factors such as genetic differences linked to sex chromosomes, sex hormones, and differential timing of neurodevelopmental processes in boys and girls,66 as well as by social factors relating to gender, such as differences in the treatment of, and expectations towards, boys and girls. Children’s health status and wellbeing also moderate effects of co-occurring risk exposures through both biological and social pathways. Biologically, the synergistic interactions between malnutrition, inflam­ mation, and neuroendocrine stress moderate the effects of one another by increasing the child’s vulnerability to co-occurring risks.22,30 Additionally, sickness behaviours,

Figure 2: Moderating factors shaping individual differences in how children respond to poverty-related risks (A) Effect moderators can interact to affect child vulnerability and resilience. A child’s resilience is illustrated as a multi-layered circle, in which each layer is a category of protective or susceptibility factors, and the size of the circle represents the child’s overall resilience. The black arrows illustrate that the synergistic interaction between susceptibility factors and protective factors within different contexts of the child’s ecological environment (i,e, at the intrinsic child level, family level, contextual level, and cultural level). Together, protective and susceptibility factors determine the thickness of each layer, which in turn represents the child’s overall susceptibility to environmental threats. Protective factors add towards a thicker layer (less susceptibility) whereas susceptibility factors cause thinning of the layer (increasing the child’s susceptibility). (B) Individual differences in resilience and genetic susceptibility result from multiple factors (represented by varying thickness of different interacting layers).

including decreased physical activity and mood, might affect children’s engagement in stimulating interactions and influence caregivers’ perception of the child’s vulnerability or age. Caregivers might consequently not engage the child in age-appropriate stimulating activities.67 Finally, the child’s age and developmental stage shape the effect of an exposure due to critical periods in delevopment.

Extrinsic environmental factors Poverty is associated with worse neurocognitive out­ comes in children exposed to concurrent risks such as birth complications,68 maternal depression,26 and environmental exposure to lead.23 Such effects might be driven by a paucity of financial, social, and psychological family resources. Poor financial resources might restrict access to medical or psychological treatment, and reduced psychological resources might contribute to increased sensitivity to stress among poor parents.60 Conversely, high maternal IQ and educational level can be protective factors. Children of mothers with high IQ consistently show less severe effects than children of mothers with low IQ of exposure to environmental contaminants on neurocognitive outcomes, including IQ.15 Among families with low socioeconomic status, educated mothers are likely to engage in protective behaviours, such as choosing healthy food, attending recommended health-care visits, and seeking health care when the child in unwell. These behaviors are, in turn associated with improved child nutritional status69 and increased rates of antenatal health-care service use, child immunisation,70 and enrolment in social welfare programmes that support child development.44 Finally, parents’ engagement in stimulating activities with the child moderates effects of, for instance, maternal stress and environmental contaminants on child neuro­ cognitive outcomes, such that increased stimulation is associated with improved outcomes, despite other exposures.15,23

Timing effects The effect of poverty-related risks depends on the timing of the exposure. Many exposures have greatest or most persistent effects during prenatal and early postnatal development—the so-called critical periods of development. During such periods, certain risks can exert severe and long-lasting effects on both neural and cognitive development. Timing effects are, by and large, driven by developmental processes related to neuro­ developmental growth and the achievement of early developmental and cognitive milestones, and thus relate to the child’s maturational stage and age. Prenatal and early postnatal neural development are characterised by important growth spurts that render the brain particularly sensitive to environmental inputs, including nutrients and sensory, cognitive, and socioemotional stimulation (panel 1). The achievement of developmental

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X



Panel 1: The protracted nature of human neural development The human brain develops over a protracted period of time beginning shortly after conception and continuing into adulthood. Neural tube formation starts just 3 weeks after conception when the neural plate begins an inward fold, and specialised proliferative zones form close to the hollow centre of the neural tube. Neurogenesis, the formation of new neurons, starts in these proliferative zones, and from fetal week 8, these newly formed neurons begin to migrate to their destination in cortical or subcortical regions.5 Once at their final location, each neuron forms an axon and dendrites, allowing connections to be established with other neurons locally at first and then with neurons in distal locations. These connections enable transmission of information via electrochemical signals over the synapses. The establishment of shortrange connections leads to the branching of a dendritic tree around the neuronal cell body through a process called arborisation that continues into postnatal development.71 Connections that are being used (stimulated) grow strong as more synapses are formed, whereas connections that are not being used are pruned away in an experience-dependent manner. Dendritic branching and synaptogenesis are the primary drivers of the steep increase in grey matter that occurs during early prenatal and postnatal development. These processes reflect early experience and underpin learning related to the processing of sensory stimulation, language development, and emerging cognitive abilities. Neurons form long-reaching connections with neurons in more distal parts of the brain via long-reaching white matter tracts.72 These tracts develop as the neuronal axon expands through the white matter in a process that starts at about fetal week 25.72 Once established, these white matter tracts become myelinated to support efficient communication, and this myelination gives rise to a steep increase in white matter starting in the third trimester.72 Postnatally, the largest spurt of grey and white matter growth occurs within the first 2 years of life; during this period the brain reaches 80–90% of its adult volume.73 The expansion of both grey and white matter follows a spatiotemporal pattern from inferior to superior (bottom-up) and posterior to anterior (back-to-front) regions.74 This means that regions involved in the processing of sensory information and language grow most rapidly during the first year of life, whereas brain regions involved in cognitive functions related to memory and attention grow more rapidly in the second year of life. Brain regions associated with higher cognitive functions develop later and continue to grow into adolescence.

and cognitive milestones follows similar temporal patterns. During early postnatal development, basic sensory inputs are processed and form a foundation for learning to decode and communicate with gestures and language. Such communicative skills lay the foundation for social interaction and acquisition of more advanced skills, including executive functions. Risk exposures are generally more likely to interfere with skills that are developing than with established skills. Delays in early developmental milestones can impede the development of later skills because of the sequential and progressive nature of skill formation during early development. Timing effects and critical periods have been most widely explored in the context of nutrition; certain nutritional deficits during prenatal and early postnatal development are associated with irreversible neuro­ developmental insult, whereas nutritional deficiencies at late stages of development might be reversible.75 Other exposures for which effects appear to be most pronounced in early development include environmental contaminants,76 maternal depression,26 stress, and caregiving quality.77 8

Methodological challenges for research and intervention Identification of experiential factors that shape children’s development is one initial step towards improving outcomes for children growing up in poverty. Yet the complexity imposed by co-occurring risk and protective factors complicates efforts to isolate independent effects of single risk exposures. Correlations among risk factors give rise to multicollinearity, where two predictors in a model are so highly correlated that their distinct contribution cannot be determined. This phenomenon also complicates efforts to discern timing effects, such as effects of prenatal versus postnatal exposures. Children of mothers with high levels of stress during pregnancy are, for instance, more likely to also experience postnatal stress. Complex environments create ample opportunities for confounding relationships. These relationships occur when a true underlying determinant of an outcome is omitted from a model but correlates with the included predictor and outcome, leading to a spurious appearance that the included predictor affects the outcome directly. For example, maternal IQ might appear to predict a child’s cognitive outcomes, yet this association could have nothing to do with the mother’s IQ as such, but instead be driven by inherited genomic variation. Statistical methods alone cannot perfectly disentangle independent effects of confounded exposures in correlational research, and thus caution is needed when interpreting results; indeed, risk factors that are often considered independent in the epidemiological literature can, in fact, act interdependently along shared causal pathways. Randomised controlled trials are generally considered the gold standard for inferring causal relationships. Targeting specific risks provides an opportunity to examine relationships in a more isolated manner. Nevertheless, even the interpretation of mechanisms underpinning intervention effects is complicated by interactional and confounding relationships. For example, cognitive stimulation interventions often lead to a concurrent decrease in maternal depression, which is likely to contribute to the success of the intervention in improving child cognition.78 The interpretation of evidence from randomised controlled trials therefore depends on the extent to which the study design accounts for other factors that affect child development. Large-scale intervention trials with multiple groups involving targeted manipulations can help to parse out effects of specific exposures (risks or interventions). Animal models also provide an important opportunity to manipulate certain exposures while enforcing similarity in other aspects of the environment. Although animal models have provided key insights into how early stress, caregiving, and nutrition shape cognitive and neural development, other aspects of poverty are difficult to translate to non-human models. For instance, animal models cannot be used to study subjective experiences of

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X


stress or poverty, and various factors that affect human development, such as language exposure and marital discord, might not translate to non-human environments. Moreover, causal relationships in animal models might not be causal in human beings. Future research should take advantage of variability arising from the complex constellations of risk and protective factors that characterise children’s hetero­ geneous environments. Future studies must also be sensitive to widespread individual differences in children’s susceptibility to these environmental risks; this will necessitate inclusion of interaction terms in statistical models and use of methods that are sensitive to individual or subgroup-level differences in child outcomes. Simply averaging effects across all participants can hide the true effect of risks or interventions. In a study of a psychosocial intervention delivered to poor families in South Africa,65 children were divided into subgroups on the basis of

polymorphic variation in SLC6A4; the results showed that intervention effects differed by up to ten times depending on the child’s genotype. Identification of factors that characterise children who respond more or less to risks or interventions will help to clarify differential susceptibility and resilience factors. Such insights can guide the development and assessment of targeted and effective interventions. However, subgroup analyses to explore individual differences and assessment of mediation and moderation in statistical models will require larger sample sizes than those used in studies of main effects. Longitudinal data will be necessary to statistically analyse mediational relationships and characterise prospective relationships.

Biomarkers to assess risks and interventions Attempts to disentangle causal effects linking risk exposures to child outcomes are increasingly facilitated

Association with poverty or low family socioeconomic status (parent income, occupation, or education)

Interpretation of physiological processes involved

Grey and white matter volumes (whole brain or regions of interest)

Reduced whole-brain grey or white matter volume; reduced grey matter volumes in stress-sensitive regions (prefrontal cortex, hippocampal volume)79

Attenuated cerebral growth; attenuated dendritic growth

Cortical thickness (whole brain or regions of interest)

Greater thickness79

Altered neural and dendritic growth; altered experiencerelated synaptic pruning

Cortical surface area size (whole brain or regions of interest)

Smaller cortical surface area79

Attenuated myelination and greater experience-related synaptic pruning

Lower fractional anisotropy in the corpus callosum80

Reduced myelination or altered microstructural organisation of white matter

Haemodynamic activity during rest or cognitive processing

Reduced recruitment of task-relevant neural regions; increased activation of irrelevant regions79

Reduced recruitment of neural networks that support task performance and impaired inhibition of neural processing associated with irrelevant stimuli

Haemodynamic responses to stress

Greater amygdala activation to stressful stimuli such as threatening faces81

Increased neural response to stressors related to a dysregulated stress response

Functional connectivity measured at baseline or during cognitive processes (activational synchronisation of the haemodynamic response in different brain regions)

Reduced functional connectivity at rest in certain networks including the brain’s default mode network;82 reduced neural activation of task-related networks and impaired inhibition of non-relevant networks during task performance;79 accelerated maturation of functional network involved in socioemotional processing;82 and early emergence of mature amygdala-prefrontal connectivity79

Delayed maturation of long-range neural connections that support task performance; increased strength of stress-related networks

Baseline or task-related power

Low activity in the high-frequency oscillations81

Delayed maturation indicated by delay in developmental shift towards more high-frequency power (alpha, beta, gamma) that supports higher cognitive functions

Amplitude and latencies of specific electrophysiological components during cognitive processing (event-related potentials and visual evoked potential)

Smaller and slower neural response to task-relevant cognitive processing coupled with greater response to unattended stimuli83

Attenuated attention to the attended stimuli and poor inhibition of unattended stimuli (increased distraction)

Functional connectivity at rest and during cognitive processing (activational synchronisation of the electrophysiological response in different brain regions)

Research warranted, but decreased synchronisation and reduced connectivity indicative of attenuated network maturation expected

Delayed maturation of long-range neural connections

Lateralisation of task-related functional activation to more specialised regions

Research warranted, but delayed lateralisation expected

Delayed lateralisation might indicate delayed maturation

Neuroimaging Structural MRI

Diffusion tensor imaging Fractional anisotropy, mean diffusivity, myelin water fraction Functional MRI and near-infrared spectroscopy (baseline and task-related)

Electroencephalography and event-related potentials

(Table 2 continues on next page)

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X



Association with poverty or low family socioeconomic status (parent income, occupation, or education)

Interpretation of physiological processes involved

(Continued from previous page:) Frontal left–right asymmetry in the strength of hemispheric activation Greater right relative to left frontal activation in at rest and during cognitive processing or stimulation electroencephalography resting activity in frontal alpha power84

Greater right relative to left frontal activation could be related to altered emotion processing and increased susceptibility to depression

Allostatic load Neuroendocrine dysregulation Basal cortisol

Abnormally elevated or suppressed (conflicting evidence)48 Hypothalamic–pituitary–adrenal axis dysregulation

Cortisol reactivity

Accentuated or attenuated response to pharmacological challenge or laboratory stressor (conflicting evidence)48

Hypothalamic–pituitary–adrenal axis dysregulation

Diurnal cortisol rhythms

Aberrant rhythms (eg, steeper or less steep daytime decline [conflicting evidence])48

Hypothalamic–pituitary–adrenal axis dysregulation

Immune dysregulation Concentrations of pro-inflammatory cytokines and other inflammatory Abnormal increase in concentration or reactivity to threat55,85 markers (eg, C-reactive protein, erythrocyte sedimentation rate) at baseline or in response to psychosocial or pharmacological threat

Chronic inflammation with immunosuppression

Cardiovascular functioning Systolic and diastolic blood pressure

Elevation at rest47

Cardiovascular adaptations to chronic stress

Alterations in parasympathetic and sympathetic resting activity or reactivity, including change from rest to challenge or relative to general population (conflicting evidence)86

Dysregulated autonomic reactivity, including both increased and decreased sympathetic or parasympathetic resting tone or reactivity, linked to excessive childhood stress or chronic stress, or both, and elevated allostatic load

Anthropometry (height-for-age, weight-for-age, upper-arm circumference, body-mass index, head circumference)

Stunted growth or wasting (height-for-age/weight-forage >2 SD below population mean)9

Inadequate (or excessive) caloric or macronutrient intake or absorption, or both

Plasma retinol or ferritin concentration

Abnormally low concentrations9

Vitamin A or iron deficiency

Concentrations of specific micronutrients (eg, vitamins, minerals)

Abnormally low concentrations9

Specific nutritional deficiencies

Serum glucose (fasting or after glucose challenge) and glycated haemoglobin A1c

Increased concentration

Insulin resistance

Serum lipids

Increased total cholesterol, LDL, or triglyceride concentrations; decreased HDL concentration

Aberrant regulation of lipid metabolism and storage

Enzyme immunoassay to measure bacteria (eg, Escherichia coli, campylobacter) in stool

Detection, quantity87


Serum markers of intestinal permeability (eg, α-1-antitrypsin) or inflammation (eg, α-1-acid glycoprotein, calprotectin)

Increased concentration44

Impaired intestinal barrier, increased microbial translocation; dysbiosis or infectious enteropathy

Lactulose to mannitol ratio (urine)

High lactulose relative to mannitol44

Altered gut absorption and permeability, thought to correlate with gut-barrier dysfunction in environmental enteropathy


Oxidative stress marker, increase might result from a birth insult, environmental toxins, pollution, psychological stress, or infection

Autonomic functioning Measures of parasympathetic activity (eg, respiratory sinus arrhythmia) and sympathetic activity (eg, pre-ejection period, electrodermal activity) Other physiological systems Nutritional status

Metabolic health

Enteric health

Cellular oxidative stress Serum markers of isoprostanes

Genomic markers Epigenetic changes (eg, DNA methylation) Targeted or genome-wide assessment of genes relevant to neural Epigenetic modification (eg, differential modification of (eg, GAD1, GRM1), endocrine (eg, GR, CRFR2, POMC), metabolic DNA or histones via attachment or removal of chemical (eg, IGF-2), and immune (eg, TLR4, TGFB2) functioning and regulation groups)10,89

Experience-dependent modification of genes relevant to specific domains of physiological functioning

Cellular ageing Telomere length


Accelerated cellular ageing due to cell stress

Table 2: Biomarkers that might become relevant to neurocognitive outcomes in the context of global poverty

by use of biomarkers that detect physiological and neurodevelopmental change in response to adverse experiences. Biomarkers are measurable physiological 10

signatures of experiences and are widely used in the context of physical and mental health.47 Biomarkers relevant to neurocognitive development should

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X


Panel 2: Key challenges in current biomarker discovery The use of biomarkers depends on several broad challenges. Here we discuss challenges and possible ways of integrating neuroimaging and other biomarkers that assess general physiological axes (nutritional status, metabolic homoeostasis, inflammation, and neuroendocrine stress activation) into studies of developmental effects of poverty and other childhood adversities. Establishment of norms The use of a biomarker depends on the establishment of developmental norms that identify cutoff values for aberrations that are associated with poor outcomes. Such norms might need to consider several factors, including differences in behaviour–biology associations by chronological and developmental age, sex, and culture. Some biomarkers, such as height or stunted growth, have well-established norms in various populations (by age and sex). Yet many biomarkers are still in early phases of development and need further testing to establish developmentally sensitive norms. In neuroimaging, for instance, no appropriate developmental norms exist to guide the interpretation of variation. Structural variation in grey and white matter volumes (measured by MRI) and variation in metabolic and electrophysiological activity (measured via functional MRI, functional near-infrared spectroscopy, or electroencephalography) might reflect normal individual variation, developmental delay, or aberrant neural development. Large-scale, population-based studies are essential to the development of norms. Although some larges studies such as the US National Institutes of Health MRI study of normal brain development91 have been done in high-income countries, large neuroimaging studies have generally not been done in young populations and in low-income countries. These are important goals for future studies. The importance of temporally coupled and longitudinal assessments Many biomarkers, including stress hormones (eg, cortisol) and cytokines (eg, interleukin 1, interleukin 6), vary as a function of diurnal rhythms or external factors such as fasting, hydration, stress, and infections, and thus fluctuate widely within the course of hours or days.92 Sample collection therefore needs to be standardised and synchronised between and within individuals. Many biomarkers, including cortisol and key cytokines, also have short half-lives, meaning that the assessment has to be temporally coupled with the exposure to detect an effect. Repeated assessments that capture prolonged

increasingly be used in the study of poverty and related risks (table 2).85 Most research so far on the effects of poverty has focused on behavioural outcomes, yet behavioural outcomes might be too coarse to detect important variability in certain settings. For example, behavioural assessments might not differentiate between very young children whose behavioural repertoire is limited, and behavioural outcomes can be confounded by

activation or changes with time might provide the most accurate risk profile. Longitudinal studies with repeated assessments are also key to the exploration of critical periods. Ideally, longitudinal studies should include assessments before and after the risk exposure, but any characterisation of change over time could aid our understanding of the involved physiological pathways, timing effects, and critical periods. In neuroimaging, repeated measures are essential to the characterisation of critical periods when the brain might be particularly sensitive to experiences. Challenges of inference from single or indirect measures It is often difficult to discern complex processes of physiological disruption on the basis of a single biomarker. Involvement of the same mediators across multiple pathways (eg, modulation of cortisol by stress and inflammation, modulation of serum ferritin by malnutrition, and infection) and interactions among pathways (eg, metabolic changes during malnutrition, infection, and prolonged stress) need to be considered when developing and using biomarkers.75 A more complete picture of physiological functioning can be gained by inclusion of multiple neuroendocrine and inflammatory markers to assess the hypothalamic–pituitary–adrenal axis and immune systems and their interaction. The reliance on indirect biomarker measures taken from sites physically distant from the presumed site of physiological disruption poses another key challenge. Most human research is limited to biomarkers attainable in serum, faeces, urine, and hair, while many processes of greatest interest occur in, for instance, the CNS. Important epigenetic changes in brain DNA have been identified in animal models, yet most human studies are limited to DNA sampled in peripheral blood or saliva. Similarly, many nutritional and inflammatory biomarkers are sampled in peripheral blood or faeces and might not reflect changes in nutrients or inflammation in the brain. Increased knowledge about the relationships between peripheral biomarkers and processes in the brain are needed. Although neuroimaging offers a direct view of neural morphology and metabolism, challenges remain in interpreting potential factors underlying observed variation within or between individuals. Multimodal approaches combining different methods, longitudinal study designs, and cross-validation in animal studies that allow for histological analyses of neural tissue will help to characterise neurodevelopmental processes underlying developmental outcomes related to poverty and other adverse experiences.

cultural factors such as childrearing practices that affect when children reach certain developmental milestones. Moreover, behavioural outcomes do not provide insight into potential underlying biological mechanisms responsible for behavioural differences and will not identify children with normal cognitive functioning despite physiological disruption. Biomarkers therefore stand to improve the sensitivity with which important

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X



differences in physiological systems are detected before they manifest in behaviour, help to clarify mechanistic pathways, identify children at risk, and aid in assessment of the effectiveness of interventions. Challenges in current biomarker discovery are discussed in panel 2. Neuroimaging data have revealed structural and functional neural correlates of poverty-related risk exposures that might serve as potential biomarkers of adverse developmental consequences of poverty, offering several advantages compared with behavioural assess­ ments. First, neuroimaging could be used to assess developmental maturation in infants for whom the range of testable behavioural skills is limited. Second, neuroimaging measures might be less confounded by cultural factors than behaviour. Third, neuroimaging might improve sensitivity in the detection of adverse developmental trajectories before behavioural changes manifest. Neuroimaging could also help to clarify mechanistic pathways and timing effects, as distinct exposures leave neural signatures that are characteristic of those physiological processes that are most affected. Early experiences can also leave physiological signatures on the genome. Such epigenetic changes are relatively stable molecular alterations that affect gene expression without changing the DNA sequence. Mechanisms of epigenetic modifications include histone modifications, DNA methylation, chromatin-associated complexes, noncoding RNAs, and RNA splicing factors.93 Research findings in both human beings and animals suggest that early psychological stress, malnutrition, and infections Search strategy and selection criteria We searched multiple databases including PubMed, MEDLINE, Embase, and PsycINFO to identify articles published in English. Emphasis was placed on recent peer-reviewed articles and major reviews. Book chapters were also included where appropriate. Searches were done up to March 1, 2017. We searched for the following terms defining risk exposures included in the title or abstract: “poverty”, “low income”, “SES”, “socioeconomic”, “malnutrition”, “nutrition”, “micronutient”, “stunt*” “obesity”, “infection”, “inflammation”, “diarrhea*”, “diarrhoea*”, “cytokine*”, “C-reactive protein*”, “toxin*”, “heavy metal*”, “pollution”, “stress”, and “cortisol”. These risk-specific search terms were combined with the following outcomes terms: “child development*”, “early development*”, “developmental milestone*”, “cognit*”, “language”, “executive funct*”, “intelligence”, and “IQ”. We searched for literature on genetic and epigenetic mediators and moderators of poverty-related risks by adding the following search terms: “genetic”, “epigenetic”, “polymorphism”, and “methylation”. Separate searches were done to identify recent literature about interactions between the physiological axes of interest using combinations of the following search terms: “neurodevelopment”, “neural development”, “caloric defici*”, “*nutrient defici*”, “iron defici*”, “iodine defici*”, “energy metabolism”, “inflammation”, “C-reactive protein*”, “cytokine*”, “stress”, and “cortisol”. We included papers with non-neurocognitive outcomes because certain interactions between pathways, such as interactions between stress and inflammation or stress and metabolism, have been discussed primarily in the context of mental and physical health outcomes despite apparent relevance for neurocognitive effects of childhood poverty. Finally, we searched article reference lists to identify additional relevant sources.


can induce persistent epigenetic changes in genes that regulate metabolic, immune, and stress-related processes and thereby have potential long-term consequences for physical,94 psychological,95 and cognitive health.96 Emerging data are providing evidence that HPA-axismediated changes in gene expression can modulate glucocorticoid receptor functioning, with long-term effects of stress regulation.97 Epigenetic changes have received most attention in relation to psychiatric phenotypes, yet epigenetic changes are most certainly also involved in the biological embedding of poverty via HPA-axis activation, inflammation, and malnutrition. Epigenetic changes might also underlie key interactions among pathways. The link between psychological stress and inflammation might, for instance, involve epigenetic changes in glucocorticoid receptor-regulating genes, causing changes in immune regulatory pathways.55 Allostatic load is a measure of cumulative-stress burden and the resulting dysregulation of physiological systems that are affected by stress. McEwen and colleagues47 have proposed an index of neuroendocrine, immune, cardiovascular, and metabolic biomarkers to identify individuals at risk of poor physical, mental, and neurocognitive outcomes after early life stress. The most widely used biomarker of allostasis is cortisol (including baseline diurnal concentrations and reactivity), which can be measured non-invasively in saliva. Conflicting data have emerged from studies of interpersonal variability in cortisol patterns; childhood stress has been associated with hypercortisolemia, hypocortisolemia, or no difference in cortisol concentration.48 This variability might be explained by differences in sample collection or other effect moderators, including intrinsic child factors (eg, genomic variation), co-occurring risks, protective factors, or characteristics of the stress exposure.48 Finally, biomarkers of cellular oxidative stress and accelerated cellular ageing have also been associated with a wide range of poverty-related risk exposures and stress.88,90

Conclusion The developmental sequelae of childhood poverty are driven by a constellation of interacting risk factors. In this Review, we have highlighted select biological pathways, namely malnutrition, inflammation, and neuroendocrine responses to stress, through which poverty and related risks shape child development. We recognise that other pathways, both biological (eg, sleep) and psychosocial (eg, stimulation, quality of interactions, and attachment), also contribute to the effect of poverty on child development. Full remediation of the adverse developmental effects of poverty will require large-scale policy initiatives such as financial support, increased access to affordable, highquality child care, initiatives to support high-quality child care and early education, and increased access to affordable health care to support healthy development

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X


throughout life and across all levels of society. Immediate implementation of targeted interventions is therefore needed to support child development. Such interventions should be driven by knowledge of how distinct risk factors interact along biological axes and the complex ways by which they affect development. Results from previous intervention studies67,78 suggest that well-designed, high-quality, targeted interventions can effectively attenuate adverse effects of poverty-related risks, although many initiatives did not generate desired effects. Future research should seek to clarify interactions among risk factors that affect intervention effectiveness. Moreover, increased appreciation and exploration of individual differences within populations could increase the understanding of factors that drive susceptibility and resilience to poverty and might identify subgroups of children who would benefit most from intervention. Finally, increased use of biomarkers to characterise physiological imbalances could provide further insights into the complex mechanistic pathways that shape child development, aiding the development and assessment of future interventions. Biomarkers stand to play an important part in future research, both as measurable indicators of biological functions and as outcome measures of interventions. Since many biological assays are still relatively expensive, the most immediate use of biomarkers might be in exemplary studies to delineate biological pathways. Meanwhile, a long-term goal is to identify key biomarkers that are feasible and affordable for use in the assessment of large-scale interventions. Contributors SKGJ conceptualised and drafted the paper. AEB contributed to conceptualisation and offered critical comments. CAN oversaw the conceptualisation, provided scientific guidance, and offered critical comments. All authors approved the final paper are accountable for all aspects of the work.

7 8 9 10

11 12

13 14 15 16



19 20

Declaration of interests We declare no competing interests


Acknowledgments Funding for the preparation of this review was provided by a research grant from the Bill & Melinda Gates Foundation (OPP1111625) to CAN.


References 1 Black MM, Walker SP, Fernald LCH, et al. Early childhood development coming of age: science through the life course. Lancet 2016; 389: 77–90. 2 Department for Work & Pensions. Households below average income: an analysis of the UK income distribution: 1994/95— 2015/16. March 16, 2017. https://www.gov.uk/government/uploads/ system/uploads/attachment_data/file/600091/households-belowaverage-income-1994-1995-2015-2016.pdf. 3 Kayla F, Warren LH, Mohanty A. Monthly and average monthly poverty rates by selected demographic characteristics: 2013. Washington, DC: US Department of Commerce, US Census Bureau, 2017. 4 Alkire S, Conconi A, Roche JM. Multidimensional poverty index 2016: brief methodological note and results. Oxford: Oxford Department of International Development, 2016. 5 Hackman DA, Farah MJ. Socioeconomic status and the developing brain. Trends Cogn Sci 2009; 13: 65–73. 6 Caspi A, Houts RM, Belsky DW, et al. Childhood forecasting of a small segment of the population with large economic burden. Nat Hum Behav 2016; 1: 1–10.


24 25 26 27 28 29

McEwen BSB, Gianaros PPJ. Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Ann N Y Acad Sci 2010; 1186: 190–222. Tearne JE. Older maternal age and child behavioral and cognitive outcomes: a review of the literature. Fertil Steril 2015; 103: 1381–91. Black RE, Victora CG, Walker SP, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 2013; 382: 427–51. Fuglestad A, Rao R, Georgieff MK, Code MM. The role of nutrition in cognitive development. In: Nelson CA, Luciana M, eds.Handbook of developmental cognitive neuroscience, 2nd edn. Cambridge: MIT Press, 2008: 623–42. Yeung EH, Sundaram R, Ghassabian A, Xie Y, Buck Louis G. Parental obesity and early childhood development. Pediatrics 2017; 139: e20161459. Robles MC, Campoy C, Fernandez LG, Lopez-Pedrosa JM, Rueda R, Martin MJ. Maternal diabetes and cognitive performance in the offspring: a systematic review and meta-analysis. PLoS One 2015; 10: e0142583. Mwaniki MK, Atieno M, Lawn JE, Newton CRJC. Long-term neurodevelopmental outcomes after intrauterine and neonatal insults: a systematic review. Lancet 2012; 379: 445–52. Koren G. Systematic review of the effects of maternal hypertension in pregnancy and antihypertensive therapies on child neurocognitive development. Reprod Toxicol 2013; 39: 1–5. Ruiz JDC, Quackenboss JJ, Tulve NS. Contributions of a child’s built, natural, and social environments to their general cognitive ability: a systematic scoping review. PLoS One 2016; 11: e0147741. Van den Bergh BRH, Mulder EJH, Mennes M, Glover V. Antenatal maternal anxiety and stress and the neurobehavioural development of the fetus and child: links and possible mechanisms. A review. Neurosci Biobehav Rev 2005; 29: 237–58. Carson C, Kelly Y, Kurinczuk JJ, Sacker A, Redshaw M, Quigley MA. Effect of pregnancy planning and fertility treatment on cognitive outcomes in children at ages 3 and 5: longitudinal cohort study. BMJ 2011; 343: d4473. Campbell OMR, Benova L, Gon G, Afsana K, Cumming O. Getting the basic rights—the role of water, sanitation and hygiene in maternal and reproductive health: a conceptual framework. Trop Med Int Heal 2015; 20: 252–67. Victora CG, Bahl R, Barros AJD, et al. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect. Lancet 2016; 387: 475–90. Camargos ACR, Mendonça VA, Oliveira KSC, et al. Association between obesity-related biomarkers and cognitive and motor development in infants. Behav Brain Res 2017; 325: 12–16. Tonoli C, Heyman E, Roelands B, et al. Type 1 diabetes-associated cognitive decline: a meta-analysis and update of the current literature. J Diabetes 2014; 6: 499–513. MacIntyre J, McTaggart J, Guerrant RL, Goldfarb DM. Early childhood diarrhoeal diseases and cognition: are we missing the rest of the iceberg? Paediatr Int Child Health 2014; 34: 295–307. Bellinger DC. A Strategy for Comparing the contributions of environmental chemicals and other risk factors to neurodevelopment of children. Env Heal Perspect 2012; 120: 501–07. Guxens M, Sunyer J. A review of epidemiological studies on neuropsychological effects of air pollution. Swiss Med Weekly 2012; 141: w13322. Pechtel P, Pizzagalli DA. Effects of early life stress on cognitive and affective function: an integrated review of human literature. Psychopharmacology (Berl) 2011; 214: 55–70. Stein A, Pearson RM, Goodman SH, et al. Effects of perinatal mental disorders on the fetus and child. Lancet 2014; 384: 1800–19. Ferguson CJ. Spanking, corporal punishment and negative long-term outcomes: a meta-analytic review of longitudinal studies. Clin Psychol Rev 2013; 33: 196–208. Kitzmann KM, Gaylord NK, Holt AR, Kenny ED. Child witnesses to domestic violence: a meta-analytic review. J Consult Clin Psychol 2003; 71: 339–52. Margolin G, Gordis EB. The effects of family and community violence on children. Annu Rev Psychol 2000; 51: 445–79.

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X



30 Ngure FM, Reid BM, Humphrey JH, Mbuya MN, Pelto G, Stoltzfus RJ. Water, sanitation, and hygiene (WASH), environmental enteropathy, nutrition, and early child development: making the links. Ann N Y Acad Sci 2014; 1308: 118–28. 31 Stevens GD, Seid M, Halfon N. Enrolling vulnerable, uninsured but eligible children in public health insurance: association with health status and primary care access. Pediatrics 2006; 117: e751–59. 32 Burger K. How does early childhood care and education affect cognitive development? An international review of the effects of early interventions for children from different social backgrounds. Early Child Res Q 2010; 25: 140–65. 33 Onis M, Branca F. Childhood stunting: a global perspective. Matern Child Nutr 2016; 12: 12–26. 34 Rytter MJH, Kolte L, Briend A, Friis H, Christensen VB. The immune system in children with malnutrition: a systematic review. PLoS One 2014; 9: e105017. 35 de Heredia FP, Gómez-Martínez S, Marcos A. Obesity, inflammation and the immune system. Proc Nutr Soc 2012; 71: 332–38. 36 Kissoon N, Uyeki TM. Sepsis and the blobal burden of cisease in children. JAMA Pediatr 2015; 170: 1–2. 37 Patrick PD, Oriá RB, Madhavan V, et al. Limitations in verbal fluency following heavy burdens of early childhood diarrhea in Brazilian shantytown children. Child Neuropsychol 2005; 11: 233–44. 38 Jiang NM, Tofail F, Moonah SN, et al. Febrile illness and pro-inflammatory cytokines are associated with lower neurodevelopmental scores in Bangladeshi infants living in poverty. BMC Pediatr 2014; 14: 50. 39 Miller AH, Raison CL. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol 2016; 16: 22–34. 40 Ballabh P, Braun A, Nedergaard M. The blood–brain barrier: an overview: structure, regulation, and clinical implications. Neurobiol Dis 2004; 16: 1–13. 41 Deboer MD, Lima AAM, Oría RB, Scharf RJ, Moore SR, Luna MA. Early childhood growth failure and the developmental origins of adult disease: do enteric infections and malnutrition increase risk for the metabolic syndrome? Nutr Rev 2012; 70: 642–53. 42 Mondal D, Minak J, Alam M, et al. Contribution of enteric infection, altered intestinal barrier function, and maternal malnutrition to infant malnutrition in Bangladesh. Clin Infect Dis 2012; 54: 185–92. 43 Hotamisligil GS. Inflammation and metabolic disorders. Nature 2006; 444: 860–67. 44 Watanabe K, Petri WA. Environmental enteropathy: elusive but significant subclinical abnormalities in developing countries. EBioMedicine 2016; 10: 25–32. 45 Walker CLF, Lamberti L, Adair L, et al. Does childhood diarrhea influence cognition beyond the diarrhea-stunting pathway? PLoS One 2012; 7: 1–6. 46 Pace TWW, Miller AH. Cytokines and glucocorticoid receptor signaling: relevance to major depression. Ann N Y Acad Sci 2009; 1179: 86–105. 47 Juster RP, McEwen BS, Lupien SJ. Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev 2010; 35: 2–16. 48 Gunnar MR, Quevedo K. The neurobiology of stress and development. Annu Rev Psychol 2007; 58: 145–73. 49 Lupien SJ, McEwen BS, Gunnar MR, Heim C. Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nat Rev Neurosci 2009; 10: 434–45. 50 Sameroff AJ, Seifer R, Baldwin A, Baldwin C. Stability of intelligence from preschool to adolescence: the influence of social and family risk factors. Child Dev 1993; 64: 80–97. 51 Gunnar MR, Nelson CA. Event-related potentials in year-old infants: relations with emotionality and cortisol. Child Dev 1994; 65: 80–94. 52 Blair C, Raver CC, Granger D, Mills-Koonce R, Hibel L. Allostasis and allostatic load in the context of poverty in early childhood. Dev Psychopathol 2011; 23: 845–57. 53 Luby J, Belden A, Botteron K, et al. The effects of poverty on childhood brain development: the mediating effect of caregiving and stressful life events. JAMA Pediatr 2013; 167: 1135–42. 54 Dhabhar FS, McEwen BS. Stress-induced enhancement of antigen-specific cell-mediated immunity. J Immunol 1996; 156: 2608–15.


55 Baumeister D, Akhtar R, Ciufolini S, Pariante CM, Mondelli V. Childhood trauma and adulthood inflammation: a meta-analysis of peripheral C-reactive protein, interleukin-6 and tumour necrosis factor-α. Mol Psychiatry 2015; 21: 642–49. 56 Slopen N, Kubzansky LD, Mclaughlin KA, Koenen KC. Childhood adversity and inflammatory processes in youth: a prospective study. Psychoneuroendocrinology 2013; 38: 188–200. 57 Slopen N, McLaughlin KA, Dunn EC, Koenen KC. Childhood adversity and cell-mediated immunity in young adulthood: does type and timing matter? Brain Behav Immun 2013; 28: 63–71. 58 Savidge TC. Epigenetic regulation of enteric neurotransmission by gut bacteria. Front Cell Neurosci 2015; 9: 503. 59 Palma G De, Collins SM, Bercik P, Verdu EF. The microbiota-gut-brain axis in gastrointestinal disorders: stressed bugs, stressed brain or both? J Physio 2014; 14: 2989–97. 60 Bradley R, Corwyn R. Socioeconomic status and child development. Annu Rev Psychol 2002; 53: 371–99. 61 Bellis MA, Lowey H, Leckenby N, Hughes K, Harrison D. Adverse childhood experiences: retrospective study to determine their impact on adult health behaviours and health outcomes in a UK population. J Public Health (Oxf) 2013; 36: 81–91. 62 Masten AS. Global perspectives on resilience in children and youth. Child Dev 2014; 85: 6–20. 63 Karg K, Burmeister M, Shedden K, Sen S. The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: evidence of genetic moderation. Arch Gen Psychiatry 2011; 68: 444–54. 64 Petri WA, Miller M, Binder HJ, Levine MM, Dillingham R, Guerrant RL. Enteric infections, diarrhea, and their impact on function and development. J Clin Invest 2008; 118: 1277–90. 65 Morgan B, Kumsta R, Fearon P, et al. Serotonin transporter gene (SLC6A4) polymorphism and susceptibility to a home-visiting maternal-infant attachment intervention delivered by community health workers in South Africa: reanalysis of a randomized controlled trial. PLoS Med 2017; 14: e1002237. 66 Dipietro JAJA, Voegtline KMKM. The gestational foundation of sex differences in development and vulnerability. Neuroscience 2015; 342: 4–20. 67 Prado EL, Dewey KG. Nutrition and brain development in early life. Nutr Rev 2014; 72: 267–84. 68 Torche F, Echevarría G. The effect of birthweight on childhood cognitive development in a middle-income country. Int J Epidemiol 2011; 40: 1008–18. 69 Alderman H, Headey DD. How important is parental education for child nutrition? World Dev 2017; 94: 448–64. 70 de Cantuária Tauil M, Sato APS, Waldman EA. Factors associated with incomplete or delayed vaccination across countries: a systematic review. Vaccine 2016; 34: 2635–43. 71 Tau GZ, Peterson BS. Normal development of brain circuits. Neuropsychopharmacology 2010; 35: 147–68. 72 Dubois J, Dehaene-Lambertz G, Kulikova S, Poupon C, Hüppi PS, Hertz-Pannier L. The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience 2014; 276: 48–71. 73 Knickmeyer RC, Gouttard S, Kang C, et al. A structural MRI study of human brain development from birth to 2 years. J Neurosci 2008; 28: 12176–82. 74 Gilmore JH, Shi F, Woolson SL, et al. Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb Cortex 2012; 22: 2478–85. 75 Suchdev PS, Boivin M, Forsyth BW, Georgieff MK, Guerrant RL, Nelson CA 3rd. Assessment of neurodevelopment, nutrition, and inflammation from fetal life to adolescence in low-resource settings. Pediatrics 2017; 139 (suppl 1): S23–37. 76 Bellinger DC, Matthews-Bellinger JA, Kordas K. A developmental perspective on early-life exposure to neurotoxicants. Environ Int 2016; 94: 103–12. 77 Bakermans-Kranenburg MJ, Van IJzendoorn MH, Juffer F. Earlier is better: a meta-analysis of 70 years of intervention improving cognitive development in institutionalized children. Monogr Soc Res Child Dev 2008; 73: 279–93.

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X


78 Walker SP, Chang SM, Powell CA, Grantham-McGregor SM. Effects of early childhood psychosocial stimulation and nutritional supplementation on cognition and education in growth-stunted Jamaican children: prospective cohort study. Lancet 2005; 366: 1804–07. 79 Pavlakis AE, Noble K, Pavlakis SG, Ali N, Frank Y. Pediatric neurology brain imaging and electrophysiology biomarkers: is there a role in poverty and education outcome research? Pediatr Neurol 2015; 52: 383–88. 80 Seckfort DL, Paul R, Grieve SM, et al. Early life stress on brain structure and function across the lifespan: a preliminary study. Brain Imaging Behav 2008; 2: 49–58. 81 Tomalski P, Moore DG, Ribeiro H, et al. Socioeconomic status and functional brain development—associations in early infancy. Dev Sci 2013; 16: 676–87. 82 Gao W, Alcauter S, Elton A, et al. Functional network development during the first year: relative sequence and socioeconomic correlations. Cereb Cortex 2015; 25: 2919–28. 83 Neville HJ, Stevens C, Pakulak E, et al. Family-based training program improves brain function, cognition, and behavior in lower socioeconomic status preschoolers. Proc Natl Acad Sci 2013; 110: 12138–43. 84 Tomarken AJ, Dichter GS, Garber J, Simien C. Resting frontal brain activity: linkages to maternal depression and socio-economic status among adolescents. Biol Psychol 2004; 67: 77–102. 85 Mansur RB, Cunha GR, Asevedo E, et al. Socioeconomic disadvantage moderates the association between peripheral biomarkers and childhood psychopathology. PLoS One 2016; 11: 1–14. 86 Alkon A, Boyce WT, Tran L, Harley KG, Neuhaus J, Eskenazi B. Prenatal adversities and Latino children’s autonomic nervous system reactivity trajectories from 6 months to 5 years of age. PLoS One 2014; 9: e86283.

87 Amour C, Gratz J, Mduma E, et al. Epidemiology and impact of Campylobacter infection in children in 8 low-resource settings: results from the MAL-ED Study. Clin Infect Dis 2016; 63: 1171–79. 88 Czerska M, Zieliński M, Gromadzińska J. Isoprostanes—a novel major group of oxidative stress markers. Med Pr 2016; 27: 179–90. 89 Jawahar MC, Murgatroyd C, Harrison EL, Baune BT. Epigenetic alterations following early postnatal stress: a review on novel aetiological mechanisms of common psychiatric disorders. Clin Epigenetics 2015; 7: 122. 90 Drury SS, Theall K, Gleason MM, et al. Telomere length and early severe social deprivation: linking early adversity and cellular aging. MolPsychiatry 2012; 17: 719–27. 91 Hair NL, Hanson JL, Wolfe BL, Pollak SD. Association of child poverty, brain development, and academic achievement. JAMA Pediatr 2015; 169: 822–29. 92 Zhou X, Fragala M. Conceptual and methodological issues relevant to cytokine and inflammation marker measurement in clinical research. Curr Opin Metab Care 2010; 13: 541–547. 93 Allis CD, Jenuwein T. The molecular hallmarks of epigenetic control. Nat Rev Genet 2016; 17: 487–500. 94 Cirulli F. Interactions between early life stress and metabolic stress in programming of mental and metabolic health. Curr Opin Behav Sci 2017; 14: 65–71. 95 Provenzi L, Giorda R, Beri S MR. SLC6A4 methylation as an epigenetic marker of life adversity exposures in humans: a systematic review of literature. Neurosci Biobehav Rev 2016; 71: 7–20. 96 Cardenas A, Rifas-Shiman SL, Agha G, et al. Persistent DNA methylation changes associated with prenatal mercury exposure and cognitive performance during childhood. Sci Rep 2017; 7: 288. 97 Arloth J, Bogdan R, Weber P, et al. Genetic differences in the immediate transcriptome response to stress predict risk-related brain function and psychiatric disorders. Neuron 2015; 86: 1189–202.

www.thelancet.com/child-adolescent Published online July 26, 2017 http://dx.doi.org/10.1016/S2352-4642(17)30024-X