SYSTEMATIC REVIEW article

Front. Public Health, 19 August 2021

Sec. Children and Health

Volume 9 - 2021 | https://doi.org/10.3389/fpubh.2021.649825

Adverse Childhood Events and Health Biomarkers: A Systematic Review

  • 1. EPIUnit - Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal

  • 2. Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal

  • 3. Faculty of Medicine Purpan, LEASP UMR 1027, Inserm-Université Toulouse III Paul Sabatier, Toulouse, France

  • 4. Department of Epidemiology and Health Systems, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland

  • 5. Unit of Population Epidemiology, Division of Primary Care Medicine, Department of Community Medicine, Primary Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland

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Abstract

Background: This systematic review aimed to summarize evidence reporting epigenetic and/or neuro-immuno-endocrine embedding of adverse childhood events (ACEs) in children, with a particular focus on the short-term biological effect of those experiences.

Methods: A search was conducted in PsycINFO®, PubMed®, Isi Web of Knowledge and Scopus, until July 2019, to identify papers reporting the short-term biological effects of exposure to ACEs.

Results: The search identified 58 studies, that were included in the review. Regarding exposure, the type of ACE more frequently reported was sexual abuse (n = 26), followed by life stressors (n = 20) and physical abuse (n = 19). The majority (n = 17) of studies showed a positive association between ACEs and biomarkers of the immune system. Regarding DNA methylation 18 studies showed more methylation in participants exposed to ACEs. Two studies presented the effect of ACEs on telomere length and showed that exposure was associated with shorter telomere length.

Conclusion: Overall the associations observed across studies followed the hypothesis that ACEs are associated with biological risk already at early ages. This is supporting evidence that ACEs appear to get “under the skin” and induce physiological changes and these alterations might be strongly associated with later development of disease.

Introduction

Adverse childhood experiences (ACEs) are stressful and traumatic events that occur in childhood and adolescence, until the age of 18 years and encompass various aspects of family dysfunction such as experiences of sexual abuse, physical or emotional abuse, and physical neglect (1). These experiences cause suffering to children (2) and undermine their sense of safety, stability, and bonding (3), and consequently impact their normal growth and development (4).

ACEs have been compellingly associated with a life-long increased risk for psychopathology and stress-related chronic health problems (510). Evidence shows that exposure to ACEs is strongly associated with a higher likelihood of developing ischemic heart disease, cancer, stroke, chronic bronchitis, emphysema or diabetes later in life and even with pre-mature death (1, 2, 11, 12). However, the potential mechanisms involved in the biological embodiment of social adversity in early ages that would be translated into an increased risk of disease later in life are still not fully understood (1315).

Two main biological pathways are proposed to explain how the ACEs “get under the skin” and be associated with later negative health outcomes. Indirectly, it can be explained by the adoption of unhealthy behaviors (e.g., poor diet, sedentary behavior, smoking), that are socially patterned and thus more likely to be acquired by individuals from contexts of greater social adversity, and also associated with increased risk of disease later in life; or via a direct physiological disruption of regulatory pathways responsive to stress caused by adverse experiences. These alterations might be precursors of disease onset later in life, may start to operate early in life and be tracked over the life course. Exposure to adverse experiences may result in a variety of physiological changes during childhood (2, 16), including epigenetic mechanisms (13, 15), alteration of neural function and structure (1315), increased activation of neurobiological systems, such as the hypothalamic-pituitary-adrenal (HPA) axis or the sympathetic nervous system (16, 17). Therefore, increased activation of these systems leads to a cascade of physiological processes (1618), which in adults, was linked with the development of central fat, dysregulated carbohydrate metabolism and the accumulation of blood lipids in the arterial lining, all of which accelerate chronic disease development (19).

Evidence allows us to hypothesize that exposure to adversity during the first years of life might already be biologically embedded well before adult life, independently of the effects of behaviors in this association. Exposure to stressful circumstances between conception into adolescence causes a cascade of physiological responses that may modify an individual's biology in the long term in a way that makes them vulnerable to develop disease later in life (7, 9, 18, 20).

As a biomarker or a biological marker is a measurable indicator of some biological state or condition and is often measured and evaluated to examine normal biological processes, pathogenic processes, or pharmacologic responses to therapeutic intervention, in this work we aimed to identify biomarkers that are part of biological/physiological systems and therefore can suffer alterations as a result of exposure to adversity. We know that ACEs impact a child's life, and those “scars” can be identified and are perceptible, such as internalizing (e.g., anxiety, depression) and externalizing (e.g., aggression) problems and learning difficulties (21). This review aims to investigate the “hidden” effects of such exposures on children's biology that can be measured and quantified and may have a major impact already in childhood but can also have the potential to be programming children's health and translating into negative health outcomes later in life.

Thus, identify the physiological systems that may be immediately affected by the exposure to adversity already at early ages would allow understanding the pathways by which ACEs may impact later development of disease, to estimate the impact of ACEs would have later in life, and consequently define interventions to protect children in a trajectory of increased risk of poor health or to mitigate the effects already in place to avoid the development of disease in the adult life. Therefore, this review aims to systematically summarize evidence reporting epigenetic and/or neuro-immuno-endocrine embedding of adverse experiences in childhood. Specifically, it aims to describe which ACEs have been associated within a short time span until quantification of biological markers, to identify which physiological systems have been more investigated to explain the association between ACEs and later development of disease, and finally, to describe the impact and consequences of ACEs on the normal functioning of physiological systems. In addition, it is intended to discuss potential methodological issues that might explain inconsistencies among studies, which should be addressed and enhanced in future research.

Methods

Search Strategy

PsycINFO®, PubMed®, Isi Web of Knowledge and Scopus were searched until July 2019, to identify published papers reporting biological effects of exposure to ACEs before the age of 18 years. The keywords were chosen based on the literature and previously published theoretical reviews (22) and systematic reviews (23, 24), according to the usually used markers to measure biological alterations, adapted to each database and included the following terms: child maltreatment, child trauma, child adversity, early life stress, child abuse, child neglect, emotional stress, violence, bullying, and C-reactive Protein, CRP, Tumor Necrosis Factor, TNF-α, cytokine, interleukin, IL-6, inflammatory, inflammation, fibrinogen, white blood cell, methylation, DNA, DNA methylation, nervous system, amygdala, amygdala volume, hippocampus, hippocampal volume, prefrontal cortex volume, endocrine system, HPA axis, cortisol.

Selection of Studies

The list of references retrieved was screened independently by two reviewers (SSo and VR), following pre-defined criteria, to determine the eligibility of each article (Figure 1). Inclusion criteria are as following: case-control and cohort studies; original research; studies evaluating adverse childhood experiences; studies reporting biomarker measures in adulthood (≤18 years old); studies reporting an association between ACEs and biomarkers. The criteria for exclusion of studies were the following: (1) research not involving humans (e.g., in vitro or animal research); (2) non-eligible publication types (reviews, editorials, comments, guidelines, conference abstracts); (3) studies in disease setting samples; (4) studies reporting biomarker measures in adulthood (>18 years-old); (5) studies not reporting an association between ACEs and biomarkers; (6) other (studies evaluating allostatic load, adverse experiences during pregnancy, post-traumatic stress disorder, laboratory procedures to induce stress).

Figure 1

Figure 1

PRISMA flow diagram of the literature search.

ACEs were defined considering Felitti exposure categories (1), namely psychological, physical and sexual abuse, and household dysfunction. Also, we included in the review any adverse experiences involving close relationships (caregivers, family and peers). Then, adverse experiences were categorized into: sexual abuse (includes any type of sexual abuse reported), life stressors (that includes a more thorough and comprehensive summary of adversities related with relationships such as the death of a family member, trouble with a teacher, exposure to community violence), physical abuse (includes abuse perpetrated by parents, caregivers or other relatives and by teachers) and physical neglect (includes physical neglect by parents or other caregivers). Biomarkers were defined according to the definition from the International Program on Chemical Safety, led by the World Health Organization (WHO) and in coordination with the United Nations and the International Labor Organization, as “any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence of outcome or disease” (25). Biological markers were then divided by the biological mechanism with which they fitted better (Table 1).

Table 1

Biological markerDescription
Immune system
CRPAcute-phase protein of hepatic origin whose circulating concentrations rise in response to inflammation.
IL-6Important mediator of fever and of the acute phase response
TNF-αCytokine involved in systemic inflammation and one of the cytokines that make up the acute phase reaction.
IL-1bCytokine and important mediator of the inflammatory response, involved in a variety of cellular activities, including cell proliferation, differentiation, and apoptosis.
IL-10Cytokine with multiple, pleiotropic, effects in immunoregulation and inflammation.
IL-12p70Interleukin naturally produced by dendritic cells, macrophages and neutrophils, that stimulates the production of interferon-gamma and TNF-α from T cells and natural killer cells.
IL-8Induces chemotaxis in target cells, primarily neutrophils but also other granulocytes, causing them to migrate toward the site of infection, also stimulates phagocytosis once they have arrived.
CortisolPrevents the release of substances in the body that cause inflammation).
Structural and functional brain changes
BDNFActs on certain neurons of the central nervous system and the peripheral nervous system, helping to support survival of existing neurons, and encouraging growth and differentiation of new neurons and synapses.
Hippocampal volumeChronic stress resulting in elevated levels of cortisol, is seen to be a cause of neuronal atrophy in the hippocampus; this atrophy results in a smaller hippocampal volume.
Amygdala volume and amygdala functional connectivityAmygdala is a key region of the brain and plays a crucial role in processing fear, mediates the ability to associate emotional significance to a formerly neutral stimulus, triggers a host of adaptive responses to threatening stimuli, for example, by regulating the magnitude and duration of serotonergic responses.
Gray matterContains most of the brain's neuronal cell bodies; includes regions of the brain involved in muscle control, and sensory perception such as seeing and hearing, memory, emotions, speech, decision making, and self-control.
Neurologic abnormalitiesStructural, biochemical or electrical abnormalities in the brain, spinal cord or other nerves.
Pituitary gland volumeMediates the stress response, via the hypothalamic–pituitary–adrenal axis and can be adversely affected by an over- or under-production of associated hormones.
Voxel-based morphometryTechnic that allows the detection of focal microstructural differences in brain anatomy in vivo between groups of individuals without requiring any a priori decision concerning which structure to evaluate.
Genetic and epigenetic
MethylationThe addition of a methyl group on a substrate, or the substitution of an atom (or group) by a methyl group. DNA methylation, including how it occurs and where it occurs, is an important component in numerous cellular processes, including embryonic development, genomic imprinting, X-chromosome inactivation, and preservation of chromosome stability. Given the many processes in which methylation plays a part, errors in methylation to a variety of harmful consequences, including several human diseases.
Telomere lengthTelomeres, the specific DNA–protein structures found at both ends of each chromosome, protect genome from nucleolytic degradation, unnecessary recombination, repair, and inter-chromosomal fusion. Telomeres therefore play a vital role in preserving the information in genome. As a normal cellular process, a small portion of telomeric DNA is lost with each cell division, and telomere length reaches a critical limit, the cell undergoes senescence and/or apoptosis. Thus, telomere length may serve as a biological clock to determine the lifespan of a cell and an organism.
CopeptinCopeptin measurement has been useful in various clinical indications, including the diagnosis of diabetes insipidus and the monitoring of sepsis and cardiovascular diseases, particularly, closely linked to the pathophysiological pathways of heart failure and acute coronary syndrome.
LeptinThe roles of leptin include regulation of energy homeostasis, neuroendocrine function, metabolism and regulation of immune function. Circulating leptin levels serve as an indicator for energy reserves and directs the central nervous system to adjust food intake and energy expenditure accordingly. Leptin exerts immediate effects by acting on the brain to regulate appetite.
Dehydroepiandrosterone (DHEA)DHEA is reported to reduce proliferation of human aortic smooth muscle cells, and to improve cellular immune function, after inhibiting apoptosis. Furthermore, DHEA may have beneficial effect in patients with atherosclerosis, immunodeficiency disease or inflammatory disease.

Description of biological markers divided by the biological mechanism.

The decisions taken independently by the authors in each step were compared, and discrepancies were solved by consensus or after discussion with a third researcher (SF). PRISMA flow diagram of the literature search is depicted in Figure 1.

Data Extraction

Two investigators (SSo and VR) independently extracted data from 58 studies regarding the year of publication, country, and region where the study was conducted, sample characteristics (sample, sample size, participant's age, female proportion, type of ACEs, the instrument used to measure adverse experiences, age at event exposure and biological marker assessed).

Data Synthesis and Analysis

Two summary tables of results were created, compiling the extracted information (Tables 2, 3). Studies were divided according to the different development phases of growth using the age at which ACEs occurred, as following: toddlerhood (0–2 years); childhood (3–12 years and further classification into play from 3 to 5 years and middle childhood from 6 to 12 years); and adolescence (13–18 years and divided in mid-adolescence from 13 to 15 years and late adolescence from 15 to 18 years). Due to heterogeneity of ACEs measures, analytic methods and in the biomarkers, a qualitative description of the association and the strength of the reported association were assigned based on the magnitude of the reported effect measures (85), defined according to the author's results description, as strong or weak, and statistical significance of the provided results. Results were then summarized in a table presenting positive and inverse associations (associations were classified as positive when authors reported that participants exposed to adverse experiences presented higher levels of biological markers, and as inverse when a decrease in the biological markers when adversity was reported), and the strength of association (Table 4).

Table 2

ReferencesSample sizeParticipants' age (years) (range/mean)Female proportion (%)Type of ACEInstrument to assess ACEAge at the ACE (years)Age at the measure of biomarkerTime between exposure and biomarker measureaBiomarkerQualityb
Toddlerhood: 0–3 years
Bhopal et al. (26)T: 43612.4n.m.Life stressors*n.m.12 months10–1Cortisol22
Dahmen et al. (27)T: 51ACE+: 10.6ACE+:50.0MaltreatmentGerman self-report questionnaire0–310.67.6–10.6Hippocampal volume19
ACE+: 25ACE-: 10.4ACE-: 44.0
Childhood: 3–12 years
Bucker et al. (28)T: 62ACE+:9.44ACE+:38.9Sexual abuse, maltreatment, and/or neglectn.m.3–123–120–9IL-12p70, IL-6, IL-8, IL-10, IL1β, TNF-α and BDNF19
ACE+:36ACE-: 8.96ACE-: 42.3
Chen et al. (29)T: 516ACE+: 10ACE+:40.2Life stressors*Exposure to violenceLifetime0–90–9DNA methylation (ADCYAP1R1)21
ACE+: 271ACE-: 11ACE-: 50.6Scale questionnaire
Cicchetti and Handley (30)T: 534 ACE+: 285T: 9.41
ACE+: 9.45
ACE-: 9.97
48.5Abuse and neglectMaltreatment classification systemLifetime9.49.4DNA methylation (NR3C1)21
Cicchetti et al. (31)T: 4898–12 (M = 9.72)ACE+:42.7Abuse and neglectMaltreatment Classification System0–90–90–9CRP21
ACE+: 267ACE-: 53.7
Fujisawa et al. (32)T: 85M = 12.935.3Physical, emotional, and sexual abuse, physical and emotional neglectn.m.Early in life12.9-DNA methylation19
ACE+: 44
Shalev et al. (33)T: 236T1: 549.2Life stressors*, bullying and physical maltreatmentn.m.5–105–100–5Telomere length21
T2: 10
Slopen et al. (34)T: 5,802IL-6: 10 and 1549.8Life stressors* and sexual abusen.m.0–810–150–15IL-6; CRP22
CRP: 10
Play: 3–5 years
Bruce et al. (35)T: 1773–6ACE+:46.0Physical and sexual abuse, physical neglect, and emotional maltreatmentMaltreatment classification systemLifetime3–63–6Cortisol15
ACE+: 117ACE-:47.0
Parade et al. (36)T: 23151.2 months52.4Physical and sexual abuse, physical neglect, and emotional maltreatmentSystem for coding subtype and severity of maltreatment in child protective records3–53–50–2DNA methylation20
ACE+: 123
Parent et al. (37)T: 260 ACE+: 1343–5
ACE+: 8.1
ACE-: 8.4
53.8Physical and sexual abuse, physical neglect and emotional maltreatmentThe diagnostic infant and preschool assessmentPast 6 months3–50.5DNA methylation21
Tyrka et al. (38)T: 1843–551.1Physical and sexual abuse, physical neglect, and emotional maltreatmentDiagnostic infant and preschool assessmentPast 6 months3–50.5DNA methylation (NR3C1)20
Tyrka et al. (39)T: 1743–551.7Physical and sexual abuse, physical neglect, and emotional maltreatmentDiagnostic infant and preschool assessmentPast 6 months3–50.5DNA methylation (FKBP5 and NR3C1)19
Middle childhood (6–12 years)
Baldwin et al. (40)T: 1,73218.451.3Several types of victimizationn.m.5, 7, 10, 12186–13CRP21
Bevans et al. (41)T: 687.6–13.8 (M = 10.7)56.0Life stressors*The life events checklist and UCLA PTSD index for DSM-IV child- and parent-report versionsLifetime10.710.7Cortisol14
Buchweitz et al. (42)3310–14 (M = 11.45)42.4Life stressors* and sexual abuseJuvenile victimization questionnaire (reduced version)Lifetime11.411.4Cortisol17
Bush et al. (43)T: 1789–11 (M = 10.92)47.0Life stressors*n.m.Lifetime10.910.9DNA methylation21
Cicchetti et al. (44)T: 548M = 9.4047.8Abuse and neglectMaltreatment classification systemLifetime9.49.4DNA methylation21
ACE+: 298
Cicchetti et al. (45)T: 384M = 9.2539.5Abuse and neglectMaltreatment classification systemLifetime9.259.25Cortisol22
Coelho et al. (46)T: 136ACE+: 9.44ACE+:47.8Physical, emotional and sexual abuse, physical and emotional neglectChildhood trauma questionnaireLifetime9.49.4Copeptin20
ACE+: 65ACE-: 8.99ACE-: 52.2
Danese et al. (47)T: 17212n.m.Physical maltreatmentChildhood trauma questionnaire5–12120–7Leptin and CRP19
ACE+: 81
Doom et al. (48)T: 341M = 8.449.6Physical, emotional and sexual abuse, physical and emotional neglectMaltreatment classification systemLifetime8.48.4Cortisol18
ACE+: 187
Doom et al. (49)T: 2477.9–10.9 (M = 9.42)47.8Abuse and neglectMaltreatment classification systemLifetime9.427.9–10.9Cortisol and DHEA18
ACE+: 137
Drury et al. (50)T: 80 ACE+: 465-15 (M = 10.2)
ACE+: M = 0.4
ACE-: M = 9.9
T: 49.0 ACE+:57.0 ACE-:38.0Life stressors*Part of preschool age psychiatric assessmentLifetime10.210.2Telomere length18
Huang et al. (51)T: 32ACE+= 16.0ACE+:53.8Physical and sexual abuse, and/or witnessed domestic violenceChildhood adversity interview<10 (persistent for ≥6 months)15.890–10Voxel-based morphometry21
ACE+=19ACE-: 15.9ACE-:73.7
Naumova et al. (52)T: 287–1032.1Foster caren.m.Lifetime8.148.14DNA methylation20
ACE+: M = 8.14
ACE-: M = 8.35
Non et al. (53)T: 13612.5ACE+:48.0Foster caren.m.Lifetime1212DNA methylation21
ACE+: 82ACE-:51.0
Park et al. (54)T: 794.0-8.0 (M = 6.1)50.6Life stressors*Life events scale for young childrenPast 12 months6.061Amygdala functional connectivity20
Romens et al. (55)T: 5611–14 (M = 12.1)46.4Physical maltreatmentChild protective services recordsLifetime12.112.1DNA methylation (NR3C1)20
ACE+: 18
Simsek et al. (56)T: 76ACE+: M = 13.4ACE+: 28.0Sexual abusen.m.11.713.41.7Cortisol21
ACE+: 38ACE-: M = 13.5ACE-: 28.0
Stroud et al. (57)T: 11312.3100Life stressors*Youth Life stress interviewLifetime12.312.3Cortisol21
Trickett et al. (58)T: 1736–16 (M = 11)100Sexual abusen.m.7.86–166–16Cortisol21
ACE+: 84
Vaillancourt et al. (59)T: 154147 months51.9BullyingAdapted from (60)Past 3 months12.20.25Cortisol19
Whittle et al. (61)T: 11712.748.7Physical and sexual abuse, physical neglect, and emotional maltreatmentChildhood trauma questionnaire<1212.712.7Hippocampal and amygdala volumes21
Yang et al. (62)T: 1925–14 (M = 10.2)58.0Physical, sexual, emotional abuse and witnessed domestic violencen.m.Past 6 months10.20.5DNA methylation21
ACE+: 96
Adolescence: 13–18 years
Cicchetti et al. (63)T: 60ACE+: 9–15 (M = 11.31)ACE+:60.0AbuseMaltreatment and abuse chronology of exposure (pediatric version)Lifetime9–159–15DNA methylation21
ACE+: 35ACE-: 10–14 (M = 11.76)ACE-:56.0
Cisler (64)T: 56 ACE+: 2611–17
ACE+: 15.2
ACE-: 14.7
100Physical, emotional, and sexual abuse, physical and emotional neglectNational survey of adolescents and childhood trauma questionnaireLifetime11–1711–17Amygdala functional connectivity16
Copeland et al. (65)T: 1,3099–1652.5BullyingBullying part of CAPA9–169–160–7CRP21
Humphreys et al. (66)T: 1789.1–14.0 (M = 11.4)57.0Life stressors*, physical and sexual abuseTraumatic events screening inventory for childrenLifetime9.1–14.09.1–14.0Hippocampal volume17
Ito et al. (67)T: 10413.049.0Physical, emotional, and sexual abuseMedical records and the department of social services recordsLifetime1313Neurological abnormalities15
Kaess et al. (68)T: 6912.6230.0Physical, emotional, and sexual abuse, physical and emotional neglectChildhood trauma questionnaireLifetime14–1614–16Pituitary gland volume20
Malhi et al. (69)T: 20112–17100Emotional abuse and/or neglectChildhood trauma questionnaireLifetime12–1712–17Hippocampal volume20
Östberg et al. (70)T: 19814–1659.2BullyingPressure and activation stress scaleLifetime14–1614–16Cortisol19
Pagliaccio et al. (71)T: 1209–14 (M = 11.2)48.3Life stressors*Preschool-age psychiatric assessment and childhood and adolescent psychiatric assessmentLifetime9–149–14Amygdala functional connectivity22
Ruttle et al. (72)T: 33014.5–19.2n.m.Life stressors*Adolescent perceived events scale and the life events survey9–1814.5–19.21.2–10.2Cortisol20
Saxbe et al. (73)T: 21M = 16.943.0Life stressors*Survey of children's exposure to community violence, domestic conflict index and conflict tactics scale–parent/child11.79–13.9316.922.99–5.13Amygdala and hippocampal volume21
Simsek et al. (74)T: 86 ACE+: 448–17
ACE+: 13.1
ACE-: 13.8
ACE+:72.7 ACE-:71.4Sexual abusen.m.22.72 months before examination8–171.9Cortisol, BDNF18
Mid adolescence: 13–15 years
Efstathopoulos et al. (75)T: 1,14913–1454.4Bullying and Life stressors*n.m.Lifetime13–1413–14DNA rmethylation (NR3C1)20
Late adolescence: 15–18 years
Edmiston et al. (76)T: 4212–17 (M = 15.3)50.0Physical, emotional and sexual abuse, physical and emotional neglectChildhood trauma questionnaireLifetime15.3315.33Gray Matter18
Esposito et al. (77)T: 83ACE+: 12.7–18.7 (M = 15.7)ACE+:50.0 ACE-: 54.5Life stressors*The life events checklist (child/adolescent version)Past year151DNA methylation19
ACE+:50ACE-: 13.0–17.2 (M = 15.4)
0–18 years
Marzi et al. (78)T: 1,46818n.m.Domestic violence, bullying, physical maltreatment, sexual abuse, emotional abuse and neglect, and physical neglectJuvenile victimization questionnaire and childhood trauma questionnaire5, 7, 10, and 12 and 12–18180–6DNA methylation (NR3C1)21
Radtke et al. (79)T: 46M = 1560.9Life stressors*, physical, emotional and sexual abuse, physical and emotional neglectKERF-I<1811–180–18DNA methylation (NR3C1)19
Serbulent et al. (80)T: 27ACE+: 3–16 (M = 15)74.0Sexual abusen.m.72 h before the examination0–1872 hIL6, IL10, cortisol22
ACE+: 17ACE-: 6–16 (M = 10.4)
Tyborowska et al. (81)T: 37M = 14.6 and M = 17.122.0Life stressors*Life events questionnaire and Coddington's life events scale for children<5 and 14–170–170–17Gray matter volume20
Van Der Knaap et al. (82)T: 46814–18 (M = 16.1)50.4Life stressors*n.m.0–1516.11.1–16.1DNA methylation20
Van Der Knaap et al. (83)T: 939M = 16.2n.m.Life stressors*Childhood trauma questionnaire (adaptation)0–1516.21.2–16.2DNA methylation (SLC6A4)22
White et al. (84)T: 5373–16
ACE+: M = 9.86
ACE-: M = 10.08
50.6 ACE+:46.1 ACE-:54.5Physical and sexual abuse, physical neglect and emotional maltreatmentMaltreatment classification systemLifetime3–163–16Cortisol22

Descriptive characteristics of all included studies (n = 58).

a

Time between exposure to ACEs and measure of biomarker.

b

Quality of reporting of the included studies was assessed using the Strengthening Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. All studies scoring higher than the median in the STROBE checklist for cohort, case-control, and cross-sectional studies (combined) and thus revealing a satisfactory to good quality were included.

*

Life stressors (e.g., death of a family member, trouble with a teacher).

Table 3

ReferencesCountryStudy designSampleYear of the surveyPrevalence of ACEs (%)
Toddlerhood: 0–3 years
Bhopal et al. (26)IndiaLongitudinalSPRING-ELS2015n.m.
Dahmen et al. (27)GermanyCase-controlCommunity2006–2007Amongst cases: 51.0
Childhood: 3–12 years
Bucker et al. (28)BrazilCase-controlMulti-cohortn.m.Amongst cases: Neglect: 91.75 Physical abuse: 52.8 Sexual abuse: 19.4
Chen et al. (29)Puerto RicoCase-controlNeighborhood clusters2009–20101.20
Cicchetti and Handley (30)USACase-controlResearch summer camp programn.m.Amongst cases: Emotional maltreatment: 62.5 Neglect: 75.4 Physical abuse: 28.4 Sexual abuse: 8.8
Cicchetti et al. (31)USACase-controlResearch summer camp programn.m.Amongst cases: 54.6
Fujisawa et al. (32)JapanCase-controlCommunityn.m.Amongst cases: 52.4
Shalev et al. (33)United KingdomLongitudinalEnvironmental-risk study1995
2000
Overall: 45.8 Bullying: 24.1 Domestic IPV: 16.9 Physical maltreatment: 26.7
Slopen et al. (34)USALongitudinalAvon longitudinal study of parents and childrenn.m.n.m.
Play: 3–5 years
Bruce et al. (35)USACase-controlCommunityn.m.Amongst cases: 68.8
Parade et al. (36)USACase-controlCommunityn.m.53.0
Parent et al. (37)USALongitudinalCommunityn.m.51.5
Tyrka et al. (38)USACross-sectionalCommunityn.m.Amongst cases: Emotional maltreatment: 66.2 Lack of supervision: 27.0 Neglect: 12.2 Physical abuse: 12.2 Sexual abuse: 21.6
Tyrka et al. (39)USACross-sectionalCommunityn.m.Amongst cases: Emotional maltreatment: 68.1 Lack of supervision: 30.4 Neglect: 11.6 Physical abuse: 11.6 Sexual abuse: 18.8
Middle childhood (6–12 years)
Baldwin et al. (40)United KingdomLongitudinalEnvironmental risk1994–1996 to 2012–201426.5
Longitudinal twin study
Bevans et al. (41)USACross-sectionalCommunityn.m.n.m.
Buchweitz et al. (42)BrazilCross-sectionalCommunityn.m.Lifetime: 82.5 Last year: 72.5
Bush et al. (43)USALongitudinalPeers and Wellness Study2003–2005; 2010n.m.
Cicchetti et al. (44)USACase-controlResearch summer camp programn.m.Amongst cases: Emotional abuse: 59.4 Neglect: 71.2 Physical abuse: 27.2 Sexual abuse: 8.7
Cicchetti et al. (45)USACase-controlResearch summer camp programn.m.Amongst cases: Emotional maltreatment: 74.3 Neglect: 79.4 Physical abuse: 37.1 Sexual abuse: 16.6
Coelho et al. (46)BrazilCross-sectionalHigh Risk Cohort Study for Psychiatric Disordern.m.Amongst cases: 47.8
Danese et al. (47)USACase-controlEnvironmental-Risk Longitudinal Twin Studyn.m.n.m.
Doom et al. (48)USACase-controlMulti-cohortn.m.Amongst cases: Emotional maltreatment: 49.7 Neglect: 66.3 Physical abuse: 29.9 Sexual abuse: 6.4
Doom et al. (49)USACase-controlSummer camp programn.m.Amongst cases: Emotional abuse: 49.7 Neglect: 66.3 Physical abuse: 29.9 Sexual abuse: 6.4
Drury et al. (50)USACase-controlCommunityn.m.57.0
Huang et al. (51)USACase-controlPart of a larger studyn.m.14.7
Naumova et al. (52)RussiaCase-controlCommunityn.m.Amongst cases: 50.0
Non et al. (53)RomaniaCase-controlBucharest early intervention projectn.m.Amongst cases: 50.0
Park et al. (54)USACross-sectionalPart of two larger studiesn.m.n.m.
Romens et al. (55)USACase-controlCommunityn.m.32.0
Simsek et al. (56)TurkeyCase-controlDepartment of Child Psychiatry at Dicle University HospitalMay–November 2012n.m.
Stroud et al. (57)USACase-controlPart of a larger studyn.m.n.m.
Trickett et al. (58)USACase-controlCommunityn.m.n.m.
Vaillancourt et al. (59)CanadaCross-sectionalCommunityn.m.Physical bullying: 20.8 Social bullying: 43.5 Verbal bullying: 58.4
Whittle et al. (61)AustraliaLongitudinalOrygen adolescent development studyn.m.n.m.
Yang et al. (62)USACase-controlCommunity2011Amongst cases: Emotional abuse: 65.0 Neglect: 83.0 Physical abuse: 65.0 Sexual abuse: 24.0 Witness domestic violence: 70.0
Adolescence: 13–18 years
Cicchetti et al. (63)TanzaniaCase-controlCommunityn.m.n.m.
Cisler (64)USACase-controlCommunityn.m.Amongst cases: 46.4
Copeland et al. (65)USALongitudinalGreat smoky mountains studyn.m.n.m.
Humphreys et al. (66)USACross-sectionalPart of a larger studyn.m.98.0 (at least 1 event > 6 years)
Ito et al. (67)USACross-sectionalMedical recordsn.m.66.9
Kaess et al. (68)AustraliaCross-sectionalOrygen adolescent development studyn.m.19.0 (CTQ > 35)
Malhi et al. (69)AustraliaCross-sectionalCommunityn.m.37.8
Östberg et al. (70)SwedenCross-sectionalSchool stress and support study201013.5
Pagliaccio et al. (71)USACross-sectionalPreschool depression studyn.m.n.m.
Ruttle et al. (72)USALongitudinalWisconsin study of families and workn.m.n.m.
Saxbe et al. (73)USALongitudinalUrban samplen.m.n.m.
Longitudinal study of youth
Simsek et al. (74)TurkeyCase-controlDepartment of Child Psychiatry at Dicle University HospitalDecember 2011 and April 2012n.m.
Mid adolescence: 13–15 years
Efstathopoulos et al. (75)SwedenCross-sectionalKUPOL project2013–2014n.m.
2014–2015
Late adolescence: 15–18 years
Edmiston et al. (76)USACross-sectionalCommunityn.m.85.7
Esposito et al. (77)USACase-controlCommunityn.m.n.m.
0–18 years
Marzi et al. (78)United KingdomLongitudinalEnvironmental risk longitudinal study1999–2000; 2001–2002; 2006–2007; 2012–201328.1
Radtke et al. (79)GermanyCross-sectionalCommunityn.m.n.m.
Serbulent et al. (80)TurkeyCase-controlDepartment of child protective serviceMay 2016–July 2016Amongst cases: 63.0
Tyborowska et al. (81)NetherlandsLongitudinalNijmegen longitudinal study on child and infant developmentn.m.n.m.
Van Der Knaap et al. (82)NetherlandsLongitudinalTracking adolescents' individual lives survey2001–2002
2003–2004
2005–2007
2008–2010
Physical abuse: 38.7 Sexual abuse: 7.1 Other trauma: 24.8
van der Knaap et al. (83)NetherlandsLongitudinalTracking adolescents' individual lives survey2001–2002
2003–2004
2005–2007
2008–2010
Physical abuse: 35.5 Sexual abuse: 7.0 Other trauma: 22.6
White et al. (84)GermanyCase-controlCommunityn.m.n.m.

Descriptive characteristics of all included studies (n = 58).

Table 4

ReferencesBiomarkerType of ACEsDirection of associationStrength of association
Genetic and epigenetic
Bush et al. (43)DNA methylationLife stressors*PositiveWeak to moderate
Cicchetti et al. (63)AbusePositiveStrong
Cicchetti et al. (44)Abuse and neglectPositiveStrong
Fujisawa et al. (32)Physical, emotional and sexual abuse, physical, and emotional neglectPositiveStrong
Naumova et al. (52)Foster carePositiveStrong
Non et al. (53)Foster careInverseStrong
Parade et al. (36)Physical and sexual abuse, physical neglect, and emotional maltreatmentInverseStrong
Parent et al. (37)Physical and sexual abuse, physical neglect, and emotional maltreatmentPositiveStrong
Tyrka et al. (38)Physical and sexual abuse, physical neglect, and emotional maltreatmentPositiveStrong
Van Der Knaap et al. (82)NR3C1 CpG1Life stressors*PositiveStrong
NR3C1 CpG2PositiveStrong
NR3C1 CpG3InverseStrong
Yang et al. (62)DNA methylationPhysical, sexual, emotional abuse, and witnessed domestic violencePositiveStrong
Esposito et al. (77)Life stressors*PositiveWeak
Van Der Knaap et al. (83)SLC6A4Life stressors*PositiveStrong
Chen et al. (29)ADCYAP1R1Life stressors*PositiveWeak
Cicchetti and Handley (30)NR3C1Abuse and neglectPositiveStrong
Marzi et al. (78)NR3C1Domestic violence, bullying, physical maltreatment, sexual abuse, emotional abuse and neglect, and physical neglectPositiveWeak
Radtke et al. (79)NR3C1Life stressors*, physical, emotional and sexual abuse, physical and emotional neglectPositiveStrong
Romens et al. (55)NR3C1Physical maltreatmentPositiveStrong
Tyrka et al. (39)FKBP5Physical and sexual abuse, physical neglect and emotional maltreatmentAdversity compositeInverseStrong
Lifetime contextual stressPositiveStrong
Past-month contextual stress and the number of traumatic life eventsNo association-
Efstathopoulos et al. (75)NR3C1Bullying and life stressors*PositiveStrong
Shalev et al. (33)Telomere lengthLife stressors*, bullying and physical maltreatmentInverseStrong
Drury et al. (50)Life stressors*InverseStrong
Immune system
Bevans et al. (41)CortisolLife stressors* (within the past 12 months, recent and frequent trauma and afternoon cortisol)PositiveStrong
Life stressors* (within the past 12 months, recent and frequent trauma and morning cortisol)No association-
Bhopal et al. (26)CortisolLife stressors*PositiveStrong
Bruce et al. (35)Physical and sexual abuse, physical neglect, and emotional maltreatmentPositiveStrong
Physical neglect (severity)InverseStrong
Buchweitz et al. (42)Life stressors*PositiveStrong
Cicchetti et al. (45)Abuse and neglectPositiveStrong
Doom et al. (48)Physical, emotional and sexual abuse, physical, and emotional neglectPositiveStrong
Doom et al. (49)Abuse and neglectPositiveStrong
Östberg et al. (70)Bullying (girls)InverseWeak
Bullying (boys)InverseStrong
Ruttle et al. (72)Life stressors*No association-
Simsek et al. (56)Sexual abusePositiveStrong
Simsek et al. (74)Sexual abusePositiveStrong
Stroud et al. (57)Life stressors*InverseStrong
Trickett et al. (58)Sexual abuseInverseStrong
Vaillancourt et al. (59)BullyingNo association-
White et al. (84)Physical and sexual abuse, physical neglect, and emotional maltreatmentInverseStrong
Serbulent et al. (80)Sexual abuseNo association-
IL-6PositiveStrong
IL-10No association-
Baldwin et al. (40)CRPSeveral types of victimization (girls)PositiveStrong
Cicchetti et al. (31)Abuse and neglect (only for those with at least one A allele)PositiveWeak
Copeland et al. (65)BullyingNo association-
Bullying (victims)PositiveStrong
Bullying (bullies)InverseStrong
Bullying (bully-victims)No association-
Danese et al. (47)Physical maltreatmentNo association-
Bucker et al. (28)IL-12p70Sexual abuse, maltreatment, and/or neglectNo association-
IL-6No association-
IL-8No association-
IL-10No association-
IL1βNo association-
TNF-αPositiveStrong
BDNFPositiveStrong
Slopen et al. (34)IL-6Life stressors* and sexual abusePositiveStrong
CRPPositiveStrong
Structural and functional brain changes
Cisler (64)Amygdala functional connectivityPhysical, emotional and sexual abuse, physical and emotional neglectInverseStrong
Pagliaccio et al. (71)Life stressors*PositiveStrong
Park et al. (54)Life stressors*InverseStrong
Dahmen et al. (27)Hippocampal volumeMaltreatmentInverseStrong
Edmiston et al. (76)Gray matterPhysical, emotional and sexual abuse, physical, and emotional neglectInverseStrong
Tyborowska et al. (81)Life stressors*InverseStrong
Humphreys et al. (66)Hippocampal volumeLife stressors*, physical, and sexual abuseInverseModerate
Kaess et al. (68)Pituitary gland volumePhysical, emotional and sexual abuse, physical, and emotional neglectPositiveWeak
Whittle et al. (61)Hippocampal volumePhysical and sexual abuse, physical neglect, and emotional maltreatmentPositiveStrong
Amygdala volumeInverseStrong
Malhi et al. (69)Hippocampal volumeEmotional abuse and/or neglectInverseStrong
Saxbe et al. (73)Hippocampal volumeLife stressors*InverseStrong
Amygdala volumeInverseWeak
Simsek et al. (74)BDNFSexual abuseInverseStrong
Ito et al. (67)Neurological abnormalitiesPhysical, emotional, and sexual abuseNo association-
Huang et al. (51)Voxel-based morphometryPhysical abuse, sexual abuse, and/or witnessed domestic violenceInverseStrong
Other
Coelho et al. (46)CopeptinPhysical, emotional, sexual abuse, physical, and emotional neglectPositiveStrong
Doom et al. (49)DHEAAbuse and neglect (boys)InverseStrong
Danese et al. (47)LeptinPhysical maltreatmentPositiveStrong

Direction and strength of association between exposure to ACEs and biomarker by biological mechanism (positive associations indicate that biomarker increases with ACEs exposure and/or frequency; inverse associations indicate that biomarker decreases with ACEs exposure and/or frequency).

*

Life stressors (e.g., death of a family member, trouble with a teacher).

The Methodological Quality of Studies

The quality of reporting of the included studies was assessed using the Strengthening Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies (86). All studies scoring higher than the median in the STROBE checklist for cohort, case-control, and cross-sectional studies (combined) and thus revealing a satisfactory to good quality were included (Table 2).

Results

The characteristics of the 58 included publications are described in Tables 2, 3.

Twelve studies were conducted in Europe (5 countries), 36 in the Americas (4 countries), six in Asia (4 countries), three in Australia and one in Africa (Tanzania). Most studies were conducted in the United States of America (USA) (31 studies), and the sample size varied from 21 to 5,802 participants. Studies were divided according to the time at which ACEs occurred. The distribution of papers is as follows: two papers during toddlerhood, 34 studies during childhood (seven from 3 to 12 years, five from play - 3 to 5 years- and 21 from middle childhood − 6–12 years), 15 studies during adolescence (12 studies in adolescence−13–18 years, one in mid-adolescence−13–15 years and two in late adolescence−15–18 years) and seven studies that present ACEs measured from an overall period—comprising experiences occurred before 18 years (Tables 2, 3). In childhood, most publications (15 studies) are in the “immune system” and “genetic and epigenetic” categories, while “structural and functional brain changes” has three publications. During adolescence there are six publications with biomarkers from the “immune system,” nine studies from the “structural and functional brain changes,” and seven studies from the “genetic and epigenetic” category.

Publication of studies increased over time, with most of the studies being published after 2012. The first study using DNA methylation as a biomarker of exposure to adversity was published in 2012, and after that, the number of papers studying the association with genetic and epigenetic biomarkers has been consistently increasing (Figure 2).

Figure 2

Figure 2

Frequency (number of studies*) by biological mechanism, published per year. *Some papers evaluated more than one biomarker, within or not the same biological mechanism. Year 2019 includes papers published online until July 2019.

We observed that ACEs were mostly assessed by standardized instruments, although some authors used non-validated questions (17 studies). The most frequently used instrument was the Childhood Trauma Questionnaire (nine studies), followed by the Maltreatment Classification System (eight studies). High heterogeneity was found among studies both in the exposure measurement and in the outcome summary measures. Regarding exposure, the most frequent adverse event measured in these studies was sexual abuse (26 studies, 16 studies in childhood and 10 studies in adolescence), followed by the life stressors category, that includes the death of a family member, trouble with a teacher, exposure to community violence, among others (20 studies, 10 in childhood, and 10 in adolescence), by physical abuse (18 studies, 11 studies in childhood and seven in adolescence) and physical neglect (15 studies, nine studies in childhood and six in adolescence). The minimum time from exposure to ACEs and measurement of biomarkers was 72 h and a maximum of 18 years. In toddlerhood the average time between exposure to ACEs and measurement of biomarkers was 2 years, in childhood was 7.2 years and during adolescence was 5.5 years (Table 2).

We categorized papers according to the outcome measured, i.e., referring to the biological marker used to assess the effect of ACEs on biological mechanisms. Biological markers were then divided by the biological mechanism with which they fitted better: “immune system” (including CRP, IL-6, TNF-α, IL-1b, IL-10, IL-12p70, IL-8, and cortisol), “structural and functional brain changes” (BDNF, hippocampal volume, amygdala volume, amygdala functional connectivity, gray matter, neurologic abnormalities, pituitary gland volume, voxel-based morphometry), “genetic and epigenetic” (including methylation and telomere length) and others [including copeptin, leptin, and dehydroepiandrosterone (DHEA)].

In almost all studies, exposure to ACEs was associated with biomarker alterations already during childhood, while six found no evidence of effect modification (Table 4).

Mainly due to the nature and type of biomarkers, associations observed can be expressed through an increase or a decrease in respective biomarkers. An increase is mainly reflected if higher biomarker levels are observed after exposure to ACEs than it would be expected if no exposure to ACEs occurred. A decrease will be defined if the observed biomarker levels are lower than after exposure to ACEs than they would be if no exposure to ACEs were in place. Thirty-nine studies presented a positive association, meaning that participants exposed to adverse experiences presented higher levels of biological markers, and 29 studies showed inverse associations, corresponding to a decrease in the biological markers when adversity was reported. We observed that most authors study the association of ACEs with biomarkers of the immune system followed by genetic and epigenetic biomarkers and then structural and functional brain changes.

Biomarkers

Immune System

Of the studies that addressed the biological consequences of ACEs on the immune system, 16 focused on cortisol, five on CRP, three on IL-6, two on IL-10, one on TNF-α, IL-1b, IL-12p70 and IL-8. Of these, the majority (17 studies) showed a positive association between ACEs exposure and biomarkers of the immune system, meaning that those exposed to adverse experiences presented higher levels of biomarkers of the immune system. Other studies showed an inverse association (five studies), with exposure to ACEs being associated with lower levels of biomarkers, or no association (five studies). The majority of studies presented strong associations, while four publications reported weak associations between exposure to ACEs and biomarkers. Regarding the type of ACEs more associated with biomarkers of the immune system, we saw that the categories sexual abuse, life stressors and physical abuse, neglect, maltreatment were the more prevalent (Figure 3). Changes in cortisol levels can be observed as early as between 3 and 6 years. Also, analyzing the distribution of publications by age, 11 studies on the immune system were conducted between the ages of 6 and 12 years. Four studies were conducted between 13 and 18 years and three between 3 and 12 years.

Figure 3

Figure 3

Frequency (number of studies*) categorized by presence of association (positive, negative) or absence of association (no association) found between exposure to ACEs and biomarker, by biological mechanism and type of ACE. *Some papers evaluated more than one biomarker, within or not the same biological mechanism.

Structural and Functional Brain Changes

The authors measured the impact of ACEs in the structural and functional brain changes, using several types of outcomes. Hippocampal volume was measured in five studies, and amygdala functional connectivity in three, BDNF, amygdala volume and gray matter in two, neurologic abnormalities, pituitary gland volume and voxel-based morphometry in one study each. Of these, three studies showed a moderate or weak association between exposure to ACEs and the outcomes measured, while all the others presented a strong association. Most studies showed an inverse association of ACEs, namely when reporting the association between sexual abuse, life stressors and physical abuse, neglect, maltreatment with structural and functional brain changes (Figure 3). Amygdala functional connectivity was the biomarker of the group “Structural and functional brain changes,” that presented changes measured earlier (mean = 6.1 years). Examining the distribution of publications by age, the majority of studies in this category (seven studies) were conducted between the ages of 13 and 18 years, and three studies were conducted between 6 and 12 years.

Genetic and Epigenetic

DNA methylation was assessed in 20 studies, with 18 showing more methylation in participants exposed to ACEs, four showing less methylation and one reporting no association. Methylation is observed in a multiplicity of genes or focused on specific genes, such as NR3C1, SLC6A4, and FKBP5. The effect of ACEs on telomere length was presented in two studies and showed that exposure was associated with shorter telomere length. The majority of associations observed was regarding the association with sexual abuse, life stressors and physical abuse (Table 4). DNA methylation is altered as early as between 3 and 5 years, and changes in telomere length can be observed at 10.2 years. Also, analyzing the distribution of publications by age, eight studies of the “genetic and epigenetic” category were conducted between 3 and 12 years and seven studies were conducted between the ages of 6 and 12 years.

Others

One study evaluated copeptin, and other DHEA, and both showed a positive association with ACEs exposure and were conducted in middle childhood, i.e., between 6 and 12 years old. A study on leptin showed no association with ACEs.

Exposures

Types of Abuse

Among the 11 studies evaluating abuse (28, 30, 38, 39, 44, 45, 48, 49, 62, 82, 83), only three present the associations with biomarkers stratified by types of abuse (45, 82, 83). In one study (45), it was observed that average morning cortisol in the group of sexually and physically abused participants (M = 0.99, SD = 0.83) was higher than in the non-maltreated (M = 0.32, SD = 0.67), emotionally maltreated (M = 0.35, SD = 0.58), neglected (M = 0.28, SD = 0.65), and physically abused (M = 0.14, SD = 0.62) participants. Regarding methylation results, results of linear regression models (82) showed that exposure to stressful life events between birth and 15 years and exposure to traumatic youth experiences significantly predicted higher methylation rates in amplicon 1. In amplicon 2, an only single exposure to sexual abuse predicted higher methylation rates (B = 0.44, P = 0.001). For amplicon 3, repeated exposure to other traumatic youth experiences was associated with lower methylation rates (B = −0.26, P = 0.01). The other study (83) reports that exposure to perinatal adversity or traumatic young experiences was not related to methylation, while exposure to stressful life events in the first 15 years of life significantly predicted higher methylation levels. In the model including both stressful life events during childhood and adolescence, exposure to stressful life events in adolescence was related to higher methylation levels.

Bullying Involvement

Six studies were identified evaluating bullying as an experience of adversity. One studied the impact of bullying on CRP, and found that CRP levels were higher in victims of bullying and lower in aggressors (65); two studies reported that DNA methylation was higher among victims of bullying (75, 78), and other that telomere length was shorter (33). Two other studies evaluated cortisol and one described lower levels of cortisol among victims of bullying (70) while another study found no association (59).

Discussion

This review shows that exposure to ACEs might impact the immune system, structural and functional brain changes and genetic and epigenetic changes, and these changes can be observed as early as childhood. However, a high heterogeneity is observed between included studies in ACEs measures, analytic methods and heterogeneity in the biomarkers.

Assessment of ACEs Among Children

In these studies, ACEs were assessed through different methods of inquiry and instruments. The development and testing of measures of retrospective adult recall of ACEs have been a fruitful area of research for the past few decades with several measures being developed and field-tested. Thus, most studies used retrospective measures to identify exposure to ACEs. The major issue raised is that several critical aspects of the measurement systems are inconsistent across studies, making it difficult to synthesize knowledge generated to date (87). In this review, by focusing on studies that assess exposure and outcome measured in the first 18 years of life, we see that biological alterations caused by exposure to traumatic events can be observed in the first years of life. The majority of included publications studies the effect of adversity in toddlerhood and childhood, i.e., before the age of 12 years (36 studies) while 15 studies evaluated adverse experiences between 13 and 18 years of age.

The heterogeneity on measurement instruments used gives rise to another assessment inconsistency, in particular, the fact that not all types of victimization are alike. The majority of studies presents results by adversity composite or number of adversities, by chronicity or timing of abuse, and few have analyzed by type of trauma. Some involve physical injury (sexual or physical abuse), whereas others involve psychological insult (emotional abuse or neglect). Also, some papers refer only to one type of adversity while others report several exposures to ACEs. In this review, we observed that sexual abuse was, among the categories of ACEs studied, the type of adversity that most studies presented in association with different biomarkers. This might be explained by the fact that the biological embedding of social experiences occurs sooner when the experience is very traumatic or repeated over time (88).

Potential Biological Mechanisms to the Embodiment of ACEs

The impact of ACEs in the immune system, structural and functional brain, as well as the genetic and epigenetic changes, was explored in the reviewed studies including samples of children. Overall, the associations observed followed the hypothesis that ACEs are associated with biological risk, which can be expressed through increases or decreases in respective biomarker levels above or under the expected levels if no exposure to ACEs was in place, depending on the nature and type of biomarker.

Immune and non-immune cells produce cytokines, messenger proteins such as TNF-α, IL-1β, IL-8, IL-6, IL-10, and IL-12p70, whose role is to regulate immune responses and interplay between pro and anti-inflammatory mediators (89, 90). CRP is an acute-phase protein synthesized by the liver in response to systemic effects of inflammation (91) and may intervene in the biological chain that embeds exposure to ACEs. Cortisol is the product of the HPA axis and has been widely used as a stress biomarker. All of these biomarkers play a role in the regulation of the immune responses and interplay between pro and anti-inflammatory mediators (89, 90) indicating an interrelated activation of the entire inflammatory cascade (92). More recently, evidence has reviewed the effect of early exposure to adversity on the chronic inflammatory state (23, 93) and concluded that early adversity is likely to increase inflammation (18, 23, 24, 93) and risk for poor health outcomes in adulthood (8, 93), independent of clinical comorbidities (23, 24). Our results show that these biomarkers seem to present alterations in the first 18 years of life, and thus the effect of exposure to childhood adversity in the immune system, in particular in the inflammatory biomarkers, where alterations were reported as early as between 3 and 6 years.

Several papers included in this review assessed methylation in a multiplicity of genes or focused on specific genes, such as NR3C1, SLC6A4, and FKBP5. These three genes seem to play an active role in the biological embodiment of exposure to ACEs and we hypothesized that the effect of adversities would be observed on alterations already at early ages. On one hand, NR3C1 is a gene known to encode glucocorticoid receptor, involved in inflammatory responses (94), and the higher level of methylation has been associated with childhood violence (95); and the SLC6A4 gene that encodes an integral membrane protein and seems to play a role in depression-susceptibility in people experiencing emotional trauma (96). FKBP5 encodes to a protein member of the immunophilin protein family, which play a role in immunoregulation and basic cellular processes. Genetic studies have identified a role of this gene in post-traumatic stress disorder, depression and anxiety (97) and have been found to interact with childhood trauma to predict the severity of adult post-traumatic stress disorder (98).

Although multiple types of epigenetic modifications have already been identified (99), all involve chemical modifications that regulate chromatin structure and/or DNA accessibility. Methylation, corresponding to the covalent modification of DNA whereby methyl groups are coupled to cytosine residues at CpG sites, is perhaps the best studied of these epigenetic mechanisms, due in part to its tractability (100). In this review, we identified several studies evaluating DNA methylation after exposure to ACEs. As dynamic molecular markers that have been shown to change with age (101) and experience (102), epigenetic signatures are attractive candidates for elucidating the underlying mechanisms of complex diseases (103).

Emerging evidence shows that environmental signals give rise to epigenetic changes, affecting phenotypic trajectories by altering the expression of genes (104). Thus, changes in epigenetic regulation of gene expression seem to be responsible for an increased immune activation via modifications of the HPA axis. Neuroplasticity-related methylation patterns (13, 105) may be a possible mechanism through which the association between early adverse experiences and long-term alterations in human stress response and immune systems are mediated.

Also, although not very conclusive, some structural and functional brain changes after exposure to adverse experiences have been identified by the studies explored in the review. Six studies concluded that hippocampal and amygdala volume and gray matter decreased after participants experienced adverse experiences. However, more evidence is needed to have a comprehensive view of the effect of ACEs in these systems.

Impact of ACEs on the Physiological Systems

Most of the included studies showed a significant impact of ACEs on the different physiological systems. Nevertheless, some studies showed increases in biomarker levels, while others presented decreases in those levels, depending on the nature and type of biomarker. Regarding telomere length, amygdala and hippocampal volume, the direction of the observed associations was consistent with our hypotheses. Telomere length decline is a normal consequence of cellular division, aging, differentiation, and senescence. Accelerated telomere shortening in adults has been associated with a history of childhood maltreatment and early adversity (106, 107). DNA methylation also can occur via hypermethylation, i.e., increased methylation, that was found in the promoter region of SLC6A4 in adult men after early and recent life stress (108), or hypomethylation, i.e., decreasing methylation, observed at intron 7 of FKBP5 in adults exposed to childhood trauma (109). Thus, the direction of methylation may depend on the gene, promoter and/or region studied. However, we did not expect to find different directions of association for biomarkers such as cortisol. But, there is some evidence of the attenuation hypothesis (110), suggesting that exposure to early and severe stress leads to an initial heightened stress response, that may be suppressed over time. This suppression may be suggestive of an adaptive response. Cortisol levels increase immediately after exposure to ACEs, and attenuate after a certain time, but continue to reflect the effects of severe trauma. Evidence from primates showed that early life stressors, when not tremendously severe, were associated with the subsequent development of biological and social resilience suggesting that ACEs represent a challenge that, when overcome, bring about functional adaptations (111). Regarding amygdala functional connectivity, some inconsistencies might be explained by within-subject variability and fluctuations in large-scale network patterns, including connectivity between a limbic and default mode network, results that seem to suggest that bi-nodal functional connectivity, may generally reflect larger-scale network patterns.

Additionally, our review shows that age at exposure is very different across publications, varying from <6 months to under 18 years old. The wide range of ages included is due to the inclusion of all experiences occurred before adult life, and thus during the major period of growth and development of a human being. Although there is great variability across studies, it has been defended that given the vast array of developmental processes occurring between conception and adolescence, every developmental window is in fact characterized by a different susceptibility depending on various environmental factors (112).

With this review, we cannot assess if the experiences reported are single episodes or if they are related to several experiences throughout childhood and adolescence resulting in cumulative exposures during these maturation periods. The exception is one study that specifically states that adversity must last for at least 6 months (51). There is evidence showing that cumulative exposures seem to have stronger associations with later health outcomes (1). This means that we could be looking at an interplay between biological functions and the environment across the life course which we cannot disentangle from the mechanism of accumulation. For example, an individual most at risk of developing cancer or ischemic heart disease after childhood exposure to violence or adversity is also more likely to have accumulated further negative experiences over time and to adopt risky health behaviors as a stress-reducing escape. However, by restricting the search to studies with participants 18-year-old or younger, the time for accumulation of risk-taking behaviors is sufficiently limited to avoid an impact on the studied association. Moreover, when compared to adult life, neurodevelopment during childhood and adolescence is more plastic and susceptible to programming influences from stressful environmental and social contexts (113). Also, even though there is evidence that different biomarkers show alterations upon exposure to ACEs, we cannot disregard that these alterations do not necessarily mean an increased risk of disease onset. The development and progression of disease may occur due to the interplay of a group of correlated molecules or a network, rather than from the malfunction of the individual gene, protein, or biomarker.

Biological Consequences of Bullying Involvement

It is not consensual to include bullying as an adverse experience in childhood. However, the awareness of this problem has widely increased, and it was shown to compromise the child's health. Literature settles on the conviction that social and psychological effects of bullying involvement may be independent of other childhood experiences (114), but the biological mechanisms of the embodiment of these experiences are still not fully elucidated. Although some authors agree that one potential mechanism is related to the chronic systemic low-grade inflammation (115), once inflammation is activated similarly by a diverse range of health risky behaviors (poor diet, sedentary life) and environmental challenges (low socioeconomic status, psychosocial stress) (116), others support the hypothesis of embodiment throughout HPA axis activation or autonomic nervous system (ANS) activation. Bullying has also some specificity as the type of involvement, as the victim or as the aggressor or both simultaneously might have a different biological impact. Evidence has shown that although being bullied predicted higher increases in CRP levels, bullying others predicted lower increases in CRP compared with those uninvolved in bullying, even when controlling for potential confounders (65). This review identified six studies evaluating bullying as an experience of adversity. Thus, further investigation is needed to explore the impact of children's type of involvement in bullying on different biological markers.

Nowadays, another important and prevalent form of bullying is by using technologies and social media, named cyberbullying. Due to the potential of widespread accessibility of victims and an infinite audience by using communication technologies (117), cyberbullying is another important source of stress and consequently to biological alterations that can later lead to disease. This is another important issue that deserves attention in future studies.

Strengths and Limitations

We believe that this review is comprehensive and robust enough to show the studied association. Even though there is always the possibility of residual confounding when exploring the association between childhood exposure and biological markers, we believe that studying these biomarkers already during childhood is an important step to eliminate the effect of health-risk behaviors that may confound this association. We must acknowledge that different biological, psychological and social aspects may contribute to the changes in the biomarkers studied, which are difficult to control for. However, our results are in line with previously reported associations (23, 104, 118, 119), and allow us to retrieve important conclusions on the effect of early exposure to ACEs and alterations in human stress response and biological systems, already during childhood. The reported biomarkers were also chosen based on previously published literature, and others emerged from the search, showing that several systems may be affected by adverse experiences in childhood. Even though we cannot exclude the hypothesis that more biomarkers might be affected by these experiences, we believe that our comprehensive search allowed us to catch most studies. Nevertheless, excluding allostatic load (AL) from our search might be considered a limitation. AL is posited to represent a sub-clinical measure of physiological wear and tear resulting from chronic exposure to life course stressors providing a measure of cumulative physiological dysregulation across multiple biological systems. We did not include the AL in our search because, despite its apparent utility, AL is affected by many methodological and conceptual choices that have hampered its potential clinical utility. Among those, we may highlight the difficulty to agree on a core set of biomarkers that define the construct, and the different AL scoring algorithms, limiting our ability to compare results across studies. Moreover, the cumulative nature of the allostatic load, identifying which biological system would suffer the most the impact of exposure to adversity would be more difficult. Also, to our knowledge, only one publication assessed the effect of maltreatment on allostatic load in children (120). In this study (120), participants were aged 8–10 years, included maltreated or non-maltreated low-income children that attended a summer research day camp. The authors observed that maltreatment did not independently predict differences in allostatic load levels.

Additionally, due to the diversity of ACEs measures, analytic methods and heterogeneity in the biomarkers we were not able to calculate a summary measure of association between ACEs and biological markers, and thus we were unable to conduct a meta-analysis. Instead, a qualitative description of the strength of association was assigned based on the magnitude of effect measures.

None of the exclusion criteria chosen to conduct this review is related to any aspect of human differences such as socioeconomic status, race, ethnicity, language, nationality, sex, gender identity, sexual orientation, religion, geography, ability, age, or culture. Thus, this review holds diversity as a core value and all papers were included based on the criteria defined and no other.

Understanding the biological mechanisms between ACEs and negative health outcomes is important as it offers avenues for treatments that could target these intermediary pathways to prevent or reduce the risk and burden of diseases such as cancer and cardiovascular disease. Nevertheless, we must be aware that some of the associations may be mediated by depression (121) or even life course socioeconomic and health behavioral factors (18), as these factors have been suggested as impacting inflammatory processes.

Conclusion

Despite the considerable inconsistency in ACEs assessment, most articles reviewed found an association between exposure to ACEs and biological markers, where the increase or decrease in the biomarker is associated with a heightened risk to subsequent health. Experiences of violence in childhood appear to “get under the skin” and induce physiological changes, such as increases in immune, structural, and functional brain changes, and genetic and epigenetic markers, from childhood. Thus, supporting evidence of a more immediate biological impact of these exposures and alterations might be strongly associated with the later development of disease. These results allow us to argue that the population's burden of disease could be reduced if all violence toward children was successfully prevented (122) and when it does occur, appropriately treated to mitigate the consequences (123). Exposure to adverse childhood experiences should be prevented as a question of human rights, and children should be protected against all types of abuse by law enforcement and providing nurturing childhood environments. Moreover, as adverse experiences seem to impact children's biology and children may be growing in a trajectory of worse health throughout life, beginning at early ages, when exposure to adversity cannot be prevented, clinicians may have an important role in helping identify any biological alterations related with adversity victimization and intervene to mitigate their impact on health.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Statements

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

SSo and SF designed the study and wrote the protocol. SSo managed the literature searches, extraction of data and analyses, and wrote the first draft of the manuscript. VR collaborated in the extraction of data. SF helped solve differences in the data extraction. SF, MK-I, and SSt reviewed and discussed the manuscript. All authors contributed to and have approved the final manuscript.

Funding

This work was supported by the European Regional Development Fund (ERDF) through the Operational Program Competitiveness and Internationalization and national funding from the Foundation for Science and Technology (FCT), Portuguese Ministry of Science, Technology and Higher Education under the projects BioAdversity: How childhood social adversity shapes health: The biology of social adversity (POCI-01- 0145-FEDER-016838; Reference PTDC/DTP-EPI/1687/2014), HIneC: When do health inequalities start? Understanding the impact of childhood social adversity on health trajectories from birth to early adolescence (POCI-01-0145-FEDER-029567; Reference PTDC/SAU-PUB/29567/2017). It is also supported by the Unidade de Investigação em Epidemiologia - Instituto de Saúde Pública da Universidade do Porto (EPIUnit) (POCI-01-0145-FEDER-006862; Reference UID/DTP/04750/2013), PhD Grants SFRH/BD/108742/2015 (to SSo) and SFRH/BD/103726/2014 (to VR) co-funded by FCT and the Human Capital Operational Programme (POCH/FSE Program); FCT Investigator contract CEECIND/01516/2017 (to SF). We also thank the support of the LIFEPATH project funded by the European Commission (Horizon 2020 Grant No. 633666).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Summary

Keywords

biomarkers, biology of social adversity, ACES, review—systematic, adverse childhood events

Citation

Soares S, Rocha V, Kelly-Irving M, Stringhini S and Fraga S (2021) Adverse Childhood Events and Health Biomarkers: A Systematic Review. Front. Public Health 9:649825. doi: 10.3389/fpubh.2021.649825

Received

05 January 2021

Accepted

28 July 2021

Published

19 August 2021

Volume

9 - 2021

Edited by

Morenike Oluwatoyin Folayan, Obafemi Awolowo University, Nigeria

Reviewed by

Susan Elizabeth Esposito, Life University, United States; Nishant Goyal, Central Institute of Psychiatry, India

Updates

Copyright

*Correspondence: Sílvia Fraga

This article was submitted to Children and Health, a section of the journal Frontiers in Public Health

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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