(2024) 22:315
Senaratne et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03505-w

BMC Medicine

RESEARCH ARTICLE

Open Access

The impact of adverse childhood
experiences on multimorbidity: a systematic
review and meta‑analysis
Dhaneesha N. S. Senaratne1*, Bhushan Thakkar1, Blair H. Smith1, Tim G. Hales2, Louise Marryat3 andLesley A. Colvin1

Abstract
Background Adverse childhood experiences (ACEs) have been implicated in the aetiology of a range of healthoutcomes, including multimorbidity. In this systematic review and meta-analysis, we aimed to identify, synthesise,and quantify the current evidence linking ACEs and multimorbidity.
Methods We searched seven databases from inception to 20 July 2023: APA PsycNET, CINAHL Plus, Cochrane CENTRAL, Embase, MEDLINE, Scopus, and Web of Science. We selected studies investigating adverse events occurring during childhood (< 18 years) and an assessment of multimorbidity in adulthood (≥ 18 years). Studies that only assessed
adverse events in adulthood or health outcomes in children were excluded. Risk of bias was assessed using the ROBINS-E tool. Meta-analysis of prevalence and dose–response meta-analysis methods were used for quantitative datasynthesis. This review was pre-registered with PROSPERO (CRD42023389528).
Results From 15,586 records, 25 studies were eligible for inclusion (total participants = 372,162). The prevalenceof exposure to ≥ 1 ACEs was 48.1% (95% CI 33.4 to 63.1%). The prevalence of multimorbidity was 34.5% (95% CI 23.4
to 47.5%). Eight studies provided sufficient data for dose–response meta-analysis (total participants = 197,981). There
was a significant dose-dependent relationship between ACE exposure and multimorbidity (p < 0.001), with every
additional ACE exposure contributing to a 12.9% (95% CI 7.9 to 17.9%) increase in the odds for multimorbidity. However, there was heterogeneity among the included studies (I2 = 76.9%, Cochran Q = 102, p < 0.001).
Conclusions This is the first systematic review and meta-analysis to synthesise the literature on ACEs and multimorbidity, showing a dose-dependent relationship across a large number of participants. It consolidates and enhances
an extensive body of literature that shows an association between ACEs and individual long-term health conditions,risky health behaviours, and other poor health outcomes.
Keywords Adverse childhood experiences, Childhood adversity, Chronic disease, Long-term conditions,
Multimorbidity*Correspondence:
Dhaneesha N. S. Senaratne
dsenaratne002@dundee.ac.uk
Full list of author information is available at the end of the article© Crown 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permitsuse, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third
party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/
publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Senaratne et al. BMC Medicine(2024) 22:315

BackgroundIn recent years, adverse childhood experiences (ACEs)
have been identified as factors of interest in the aetiology of many conditions [1]. ACEs are potentially stressful events or environments that occur before the age of
18. They have typically been considered in terms of abuse(e.g. physical, emotional, sexual), neglect (e.g. physical,
emotional), and household dysfunction (e.g. parental
separation, household member incarceration, household
member mental illness) but could also include otherforms of stress, such as bullying, famine, and war. ACEs
are common: estimates suggest that 47% of the UK population have experienced at least one form, with 12% experiencing four or more [2]. ACEs are associated with pooroutcomes in a range of physical health, mental health,and social parameters in adulthood, with greater ACE
burden being associated with worse outcomes [1–8].
Over a similar timescale, multimorbidity has emerged
as a significant heath challenge. It is commonly defined as
the co-occurrence of two or more long-term conditions(LTCs), with a long-term condition defined as any physical or mental health condition lasting, or expected to last,
longer than 1 year [9]. Multimorbidity is both common
and age-dependent, with a global adult prevalence of 37%
that rises to 51% in adults over 60 [10, 11]. Individuals
living with multimorbidity face additional challenges inmanaging their health, such as multiple appointments,
polypharmacy, and the lack of continuity of care [12–14].
Meanwhile, many healthcare systems struggle to manage
the additional cost and complexity of people with multimorbidity as they have often evolved to address the single
disease model [15, 16]. As global populations continue toage, with an estimated 2.1 billion adults over 60 by 2050,
the pressures facing already strained healthcare systems
will continue to grow [17]. Identifying factors early in the
aetiology of multimorbidity may help to mitigate the consequences of this developing healthcare crisis.
Many mechanisms have been suggested for how ACEs
might influence later life health outcomes, including therisk of developing individual LTCs. Collectively, they contribute to the idea of ‘toxic stress’; cumulative stress during key developmental phases may affect development[18]. ACEs are associated with measures of accelerated
cellular ageing, including changes in DNA methylationand telomere length [19, 20]. ACEs may lead to alterations in stress-signalling pathways, including changes
to the immune, endocrine, and cardiovascular systems[21–23]. ACEs are also associated with both structuraland functional differences in the brain [24–27]. These
diverse biological changes underpin psychological andbehavioural changes, predisposing individuals to poorer
self-esteem and risky health behaviours, which may inturn lead to increased risk of developing individual LTCs

Page 2 of 14[1, 2, 28–32]. A growing body of evidence has therefore
led to an increased focus on developing trauma-informedmodels of healthcare, in which the impact of negative
life experiences is incorporated into the assessment andmanagement of LTCs [33].
Given the contributory role of ACEs in the aetiology
of individual LTCs, it is reasonable to suspect that ACEs
may also be an important factor in the development ofmultimorbidity. Several studies have implicated ACEs in
the aetiology of multimorbidity, across different cohorts
and populations, but to date no meta-analyses have been
performed to aggregate this evidence. In this review, we
aim to summarise the state of the evidence linking ACEs
and multimorbidity, to quantify the strength of any associations through meta-analysis, and to highlight the challenges of research in this area.

Methods
Search strategy and selection criteria

We conducted a systematic review and meta-analysis
that was prospectively registered in the International
Prospective Register of Systematic Reviews (PROSPERO)
on 25 January 2023 (ID: CRD42023389528) and reported
using the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) guidelines.
We developed a search strategy based on previously
published literature reviews and refined it following input
from subject experts, an academic librarian, and patientand public partners (Additional File 1: Table S1). We
searched the following seven databases from inception
to 20 July 2023: APA PsycNET, CINAHL Plus, Cochrane
CENTRAL, Embase, MEDLINE, Scopus, and Web of Science. The search results were imported into Covidence(Veritas Health Innovation, Melbourne, Australia), which
automatically identified and removed duplicate entries.
Two reviewers (DS and BT) independently performed
title and abstract screening and full text review. Discrepancies were resolved by a third reviewer (LC).
Reports were eligible for review if they included adults(≥ 18 years), adverse events occurring during childhood(< 18 years), and an assessment of multimorbidity or
health status based on LTCs. Reports that only assessed
adverse events in adulthood or health outcomes in children were excluded.
The following study designs were eligible for review:
randomised controlled trials, cohort studies, case–control studies, cross-sectional studies, and review articles
with meta-analysis. Editorials, case reports, and conference abstracts were excluded. Systematic reviews without
a meta-analysis and narrative synthesis review articles
were also excluded; however, their reference lists werescreened for relevant citations.

Senaratne et al. BMC Medicine(2024) 22:315

Page 3 of 14

Data analysis

Patient and public involvement

Two reviewers (DS and BT) independently performed
data extraction into Microsoft Excel (Microsoft Corporation, Redmond, USA) using a pre-agreed template.
Discrepancies were resolved by consensus discussion with a third reviewer (LC). Data extracted fromeach report included study details (author, year, study
design, sample cohort, sample size, sample country oforigin), patient characteristics (age, sex), ACE information (definition, childhood cut-off age, ACE assessment tool, number of ACEs, list of ACEs, prevalence),
multimorbidity information (definition, multimorbidity assessment tool, number of LTCs, list of LTCs,
prevalence), and analysis parameters (effect size, model
adjustments). For meta-analysis, we extracted ACE
groups, number of ACE cases, number of multimorbidity cases, number of participants, odds ratios or regression beta coefficients, and 95% confidence intervals(95% CI). Where data were partially reported or missing, we contacted the study authors directly for further
information.
Two reviewers (DS and BT) independently performed
risk of bias assessments of each included study using the
Risk Of Bias In Non-randomized Studies of Exposures(ROBINS-E) tool [34]. The ROBINS-E tool assesses therisk of bias for the study outcome relevant to the systematic review question, which may not be the primary study
outcome. It assesses risk of bias across seven domains;confounding, measurement of the exposure, participant selection, post-exposure interventions, missing
data, measurement of the outcome, and selection of thereported result. The overall risk of bias for each study wasdetermined using the ROBINS-E algorithm. Discrepancies were resolved by consensus discussion.
All statistical analyses were performed in R version
4.2.2 using the RStudio integrated development environment (RStudio Team, Boston, USA). To avoid repetitionof participant data, where multiple studies analysed thesame patient cohort, we selected the study with the bestreporting of raw data for meta-analysis and the largest
sample size. Meta-analysis of prevalence was performed
with the meta package [35], using logit transformations
within a generalised linear mixed model, and reporting
the random-effects model [36]. Inter-study heterogeneity
was assessed and reported using the I2 statistic, CochranQ statistic, and Cochran Q p-value. Dose–response
meta-analysis was performed using the dosresmeta package [37] following the method outlined by Greenland andLongnecker (1992) [38, 39]. Log-linear and non-linear(restricted cubic spline, with knots at 5%, 35%, 65%, and95%) random effects models were generated, and goodness of fit was evaluated using a Wald-type test (denotedby X2) and the Akaike information criterion (AIC) [39].

The Consortium Against Pain Inequality (CAPE) Chronic
Pain Advisory Group (CPAG) consists of individuals withlived experiences of ACEs, chronic pain, and multimorbidity. CPAG was involved in developing the researchquestion. The group has experience in systematic review
co-production (in progress).

Results
The search identified 15,586 records, of which 25 met
inclusion criteria for the systematic review (Fig. 1) [40–64]. The summary characteristics can be found in Additional File 1: Table S2. Most studies examined European(n = 11) or North American (n = 9) populations, with a
few looking at Asian (n = 3) or South American (n = 1)
populations and one study examining a mixed cohort(European and North American populations). The total
participant count (excluding studies performed on thesame cohort) was 372,162. Most studies had a female
predominance (median 53.8%, interquartile range (IQR)
50.9 to 57.4%).
All studies were observational in design, and so risk ofbias assessments were performed using the ROBINS-E
tool (Additional File 1: Table S3) [34]. There were someconsistent risks observed across the studies, especially indomain 1 (risk of bias due to confounding) and domain
3 (risk of bias due to participant selection). In domain 1,
most studies were ‘high risk’ (n = 24) as they controlled
for variables that could have been affected by ACE exposure (e.g. smoking status) [40, 41, 43–64]. In domain 3,
some studies were ‘high risk’ (n = 7) as participant selection was based on participant characteristics that could
have been influenced by ACE exposure (e.g. throughrecruitment at an outpatient clinic) [45, 48, 49, 51, 53, 54,58]. The remaining studies were deemed as having ‘someconcerns’ (n = 18) as participant selection occurred at atime after ACE exposure, introducing a risk of survivorship bias [40–44, 46, 47, 50, 52, 55–57, 59–64].
Key differences in risk of bias were seen in domain 2(risk of bias due to exposure measurement) and domain
5 (risk of bias due to missing data). In domain 2, somestudies were ‘high risk’ as they used a narrow or atypicalmeasure of ACEs (n = 8) [40, 42, 44, 46, 55, 56, 60, 64];
others were graded as having ‘some concerns’ as theyused a broader but still incomplete measure of ACEs
(n = 8) [43, 45, 48–50, 52, 54, 62]; the remainder were ‘lowrisk’ as they used an established or comprehensive list of
ACE questions [41, 47, 51, 53, 57–59, 61, 63]. In domain
5, some studies were ‘high risk’ as they failed to acknowledge or appropriately address missing data (n = 7) [40, 42,
43, 45, 51, 53, 60]; others were graded as having ‘someconcerns’ as they had a significant amount of missing
data (> 10% for exposure, outcome, or confounders) but

Senaratne et al. BMC Medicine(2024) 22:315

Page 4 of 14

Fig. 1 Flow chart of selection of studies into the systematic review and meta-analysis. Flow chart of selection of studies into the systematic review
and meta-analysis. ACE, adverse childhood experience; MM, multimorbidity; DRMA, dose–response meta-analysis

mitigated for this with appropriate strategies (n = 6) [41,50, 56, 57, 62, 64]; the remainder were ‘low risk’ as theyreported low levels of missing data (n = 12) [44, 46–49,
52, 54, 55, 58, 59, 61, 63].
Most studies assessed an exposure that was ‘adverse
childhood experiences’ (n = 10) [41, 42, 50, 51, 53, 57,
58, 61, 63, 64], ‘childhood maltreatment’ (n = 6) [44–46, 48, 49, 59], or ‘childhood adversity’ (n = 3) [47, 54,62]. The other exposures studied were ‘birth phaserelative to World War Two’ [40], ‘childhood abuse’[43], ‘childhood disadvantage’ [56], ‘childhood racial
discrimination’ [55], ‘childhood trauma’ [52], and‘quality of childhood’ (all n = 1) [60]. More than halfof studies (n = 13) did not provide a formal definitionof their exposure of choice [42–45, 49, 52–54, 57, 58,
60, 61, 64]. The upper age limit for childhood ranged
from < 15 to < 18 years with the most common cut-off
being < 18 years (n = 9). The median number of ACEs

Senaratne et al. BMC Medicine(2024) 22:315

Page 5 of 14

measured in each study was 7 (IQR 4–10). In total, 58
different ACEs were reported; 17 ACEs were reported
by at least three studies, whilst 33 ACEs were reported
by only one study. The most frequently reported ACEs
were physical abuse (n = 19) and sexual abuse (n = 16)(Table 1). The exposure details for each study can befound in Additional File 1: Table S4.
Thirteen studies provided sufficient data to allow for ameta-analysis of the prevalence of exposure to ≥ 1 ACE;
the pooled prevalence was 48.1% (95% CI 33.4 to 63.1%,
I2 = 99.9%, Cochran Q = 18,092, p < 0.001) (Fig. 2) [41, 43,
44, 46, 47, 49, 50, 52, 53, 57, 59, 61, 63]. Six studies provided sufficient data to allow for a meta-analysis of theprevalence of exposure to ≥ 4 ACEs; the pooled prevalence was 12.3% (95% CI 3.5 to 35.4%, I2 = 99.9%, CochranQ = 9071, p < 0.001) (Additional File 1: Fig. S1) [46, 50, 51,
53, 59, 63].

Thirteen studies explicitly assessed multimorbidity as
an outcome, and all of these defined the threshold for
multimorbidity as the presence of two or more LTCs [40–42, 44, 46, 47, 50, 55, 57, 60–62, 64]. The remaining studies assessed comorbidities, morbidity, or disease counts[43, 45, 48, 49, 51–54, 56, 58, 59, 63]. The median number of LTCs measured in each study was 14 (IQR 12–21).
In total, 115 different LTCs were reported; 36 LTCs werereported by at least three studies, whilst 63 LTCs werereported by only one study. Two studies did not report
the specific LTCs that they measured [51, 53]. The mostfrequently reported LTCs were hypertension (n = 22) anddiabetes (n = 19) (Table 2). Fourteen studies included atleast one mental health LTC. The outcome details foreach study can be found in Additional File 1: Table S5.
Fifteen studies provided sufficient data to allow for ameta-analysis of the prevalence of multimorbidity; theTable 1 Commonly measured adverse childhood experiences
Adverse childhood experience

Numberofstudies

Studies

Physical abuse

19

Atkinson 2021 [41]; Cromer 2006 [43]; England-Mason 2018 [44]; Godin 2023 [45]; Hanlon 2020 [46];Hosang 2017 [48]; Hosang 2018 [49]; Lin 2021 [50]; Mendizabal 2022 [51]; Noteboom 2021 [52]; Patterson 2014 [53]; Post 2013 [54]; Sheikh 2018 [56]; Sinnott 2015 [57]; Sosnowski 2022 [58]; Stapp 2020[59]; Vasquez 2019 [61]; Yang 2021 [62]; Zak-Hunter 2023 [63]Sexual abuse

16

Atkinson 2021 [41]; Cromer 2006 [43]; England-Mason 2018 [44]; Godin 2023 [45]; Hanlon 2020 [46];Hosang 2017 [48]; Hosang 2018 [49]; Mendizabal 2022 [51]; Noteboom 2021 [52]; Patterson 2014 [53];Post 2013 [54]; Sinnott 2015 [57]; Sosnowski 2022 [58]; Stapp 2020 [59]; Vasquez 2019 [61]; Zak-Hunter
2023 [63]Emotional abuse

12

Atkinson 2021 [41]; Godin 2023 [45]; Hanlon 2020 [46]; Hosang 2017 [48]; Hosang 2018 [49]; Mendizabal 2022 [51]; Noteboom 2021 [52]; Sinnott 2015 [57]; Sosnowski 2022 [58]; Stapp 2020 [59]; Vasquez
2019 [61]; Zak-Hunter 2023 [63]Emotional neglect

12Godin 2023 [45]; Hosang 2017 [48]; Hosang 2018 [49]; Lin 2021 [50]; Mendizabal 2022 [51]; Patterson
2014 [53]; Sinnott 2015 [57]; Sosnowski 2022 [58]; Stapp 2020 [59]; Vasquez 2019 [61]; Yang 2021 [62];Zak-Hunter 2023 [63]Domestic abuse/violence

11

Atkinson 2021 [41]; England-Mason 2018 [44]; Lin 2021 [50]; Mendizabal 2022 [51]; Patterson 2014[53]; Sinnott 2015 [57]; Sosnowski 2022 [58]; Vasquez 2019 [61]; Zak-Hunter 2023 [63]; Yang 2021 [62];Zheng 2022 [64]Physical neglect

10

Godin 2023 [45]; Hosang 2017 [48]; Hosang 2018 [49]; Mendizabal 2022 [51]; Patterson 2014 [53]; Sinnott 2015 [57]; Sosnowski 2022 [58]; Stapp 2020 [59]; Vasquez 2019 [61]; Zak-Hunter 2023 [63]Parental divorce/separation

9

Atkinson 2021 [41]; Chandrasekar 2023 [42]; Henchoz 2019 [47]; Lin 2021 [50]; Mendizabal 2022 [51];Patterson 2014 [53]; Sinnott 2015 [57]; Vasquez 2019 [61]; Zak-Hunter 2023 [63]Parental mental illness

8

Chandrasekar 2023 [42]; Mendizabal 2022 [51]; Patterson 2014 [53]; Post 2013 [54]; Sinnott 2015 [57];Sosnowski 2022 [58]; Vasquez 2019 [61]; Yang 2021 [62]Parental death

7

Atkinson 2021 [41]; Chandrasekar 2023 [42]; Lin 2021 [50]; Sosnowski 2022 [58]; Vasquez 2019 [61];Yang 2021 [62]; Zheng 2022 [64]Household member imprisonment 4

Lin 2021 [50]; Patterson 2014 [53]; Sosnowski 2022 [58]; Zak-Hunter 2023 [63]Parental alcohol/substance misuse

4

Henchoz 2019 [47]; Mendizabal 2022 [51]; Sinnott 2015 [57]; Sosnowski 2022 [58]

Bullying

3

Lin 2021 [50]; Sosnowski 2022 [58]; Zheng 2022 [64]Household economic environment 3

Henchoz 2019 [47]; Sheikh 2018 [56]; Sosnowski 2022 [58]Household member mental illness

3

Atkinson 2021 [41]; Lin 2021 [50]; Zak-Hunter 2023 [63]Parental imprisonment

3

Mendizabal 2022 [51]; Sinnott 2015 [57]; Vasquez 2019 [61]Parental substance misuse

3

Post 2013 [54]; Vasquez 2019 [61]; Zak-Hunter 2023 [63]Psychological abuse

3

Noteboom 2021 [52]; Patterson 2014 [53]; Sheikh 2018 [56]

Most commonly measured adverse childhood experiences (ACEs) in the 25 included studies. Only ACEs included in ≥ 3 studies are listed

Senaratne et al. BMC Medicine(2024) 22:315

Page 6 of 14

Fig. 2 Meta-analysis of prevalence of exposure to ≥ 1 adverse childhood experiences. Meta-analysis of prevalence of exposure to ≥ 1 adverse
childhood experience. ACE, adverse childhood experience; CI, confidence intervalpooled prevalence was 34.5% (95% CI 23.4 to 47.5%,
I2 = 99.9%, Cochran Q = 24,072, p < 0.001) (Fig. 3) [40, 41,
44, 46, 47, 49–52, 55, 57–60, 63].
All studies reported significant positive associations
between measures of ACE and multimorbidity, though
they varied in their means of analysis and reporting of
the relationship. Nine studies reported an association
between the number of ACEs (variably considered as acontinuous or categorical parameter) and multimorbidity[41, 43, 46, 47, 50, 56, 57, 61, 64]. Eight studies reported
an association between the number of ACEs and comorbidity counts in specific patient populations [45, 48, 49,
51, 53, 58, 59, 63]. Six studies reported an association
between individual ACEs or ACE subgroups and multimorbidity [42–44, 47, 55, 62]. Two studies incorporated
a measure of frequency within their ACE measurement
tool and reported an association between this ACE score
and multimorbidity [52, 54]. Two studies reported an
association between proxy measures for ACEs and multimorbidity; one reported ‘birth phase relative to WorldWar Two’, and the other reported a self-report on theoverall quality of childhood [40, 60].
Eight studies, involving a total of 197,981 participants,
provided sufficient data (either in the primary text, or following author correspondence) for quantitative synthesis[41, 46, 47, 49–51, 57, 58]. Log-linear (Fig. 4) and nonlinear (Additional File 1: Fig. S2) random effects models
were compared for goodness of fit: the Wald-type test
for linearity was non-significant (χ2 = 3.7, p = 0.16) andthe AIC was lower for the linear model (− 7.82 vs 15.86)
indicating that the log-linear assumption was valid. There
was a significant dose-dependent relationship between
ACE exposure and multimorbidity (p < 0.001), with every

additional ACE exposure contributing to a 12.9% (95%
CI 7.9 to 17.9%) increase in the odds for multimorbidity(I2 = 76.9%, Cochran Q = 102, p < 0.001).

Discussion
This systematic review and meta-analysis synthesised
the literature on ACEs and multimorbidity and showeda dose-dependent relationship across a large number ofparticipants. Each additional ACE exposure contributed
to a 12.9% (95% CI 7.9 to 17.9%) increase in the odds for
multimorbidity. This adds to previous meta-analyses thathave shown an association between ACEs and individual
LTCs, health behaviours, and other health outcomes [1,28, 31, 65, 66]. However, we also identified substantial
inter-study heterogeneity that is likely to have arisen due
to variation in the definitions, methodology, and analysis of the included studies, and so our results should beinterpreted with these limitations in mind.
Although 25 years have passed since the landmark
Adverse Childhood Experiences Study by Felitti et al.
[3], there is still no consistent approach to determining
what constitutes an ACE. This is reflected in this review,
where fewer than half of the 58 different ACEs (n = 25,
43.1%) were reported by more than one study and no
study reported more than 15 ACEs. Even ACE types thatare commonly included are not always assessed in thesame way [67], and furthermore, the same question canbe interpreted differently in different contexts (e.g. physical punishment for bad behaviour was socially acceptable 50 years ago but is now considered physical abuse in
the UK). Although a few validated questionnaires exist,
they often focus on a narrow range of ACEs; for example,
the childhood trauma questionnaire demonstrates goodSenaratne et al. BMC Medicine(2024) 22:315

Page 7 of 14

Table 2 Commonly measured long-term conditions
Long-term condition

Numberofstudies

StudiesHypertension

22

Arshadipour 2022 [40]; Atkinson 2021 [41]; Chandrasekar 2023 [42]; Cromer 2006 [43]; EnglandMason 2018 [44]; Godin 2023 [45]; Hanlon 2020 [46]; Henchoz 2019 [47]; Hosang 2017 [48];Hosang 2018 [49]; Lin 2021 [50]; Post 2013 [54]; Reyes-Ortiz 2023 [55]; Sheikh 2018 [56]; Sinnott
2015 [57]; Sosnowski 2022 [58]; Stapp 2020 [59]; Tomasdottir 2015 [60]; Vasquez 2019 [61]; Yang
2021 [62]; Zak-Hunter 2023 [63]; Zheng 2022 [64]Diabetes

21

Arshadipour 2022 [40]; Atkinson 2021 [41]; Chandrasekar 2023 [42]; Cromer 2006 [43]; EnglandMason 2018 [44]; Godin 2023 [45]; Hanlon 2020 [46]; Henchoz 2019 [47]; Hosang 2017 [48];Hosang 2018 [49]; Lin 2021 [50]; Post 2013 [54]; Reyes-Ortiz 2023 [55]; Sheikh 2018 [56]; Sinnott
2015 [57]; Sosnowski 2022 [58]; Tomasdottir 2015 [60]; Vasquez 2019 [61]; Yang 2021 [62]; ZakHunter 2023 [63]; Zheng 2022 [64]Cancer17

Arshadipour 2022 [40]; Atkinson 2021 [41]; Chandrasekar 2023 [42]; Cromer 2006 [43]; EnglandMason 2018 [44]; Godin 2023 [45]; Hanlon 2020 [46]; Henchoz 2019 [47]; Lin 2021 [50]; Post 2013[54]; Reyes-Ortiz 2023 [55]; Sinnott 2015 [57]; Sosnowski 2022 [58]; Tomasdottir 2015 [60]; Vasquez
2019 [61]; Yang 2021 [62]; Zheng 2022 [64]Stroke and transient ischaemic attack17

Arshadipour 2022 [40]; Atkinson 2021 [41]; Chandrasekar 2023 [42]; Cromer 2006 [43]; EnglandMason 2018 [44]; Godin 2023 [45]; Hanlon 2020 [46]; Henchoz 2019 [47]; Lin 2021 [50]; Post 2013[54]; Reyes-Ortiz 2023 [55]; Sheikh 2018 [56]; Sinnott 2015 [57]; Sosnowski 2022 [58]; Vasquez 2019[61]; Yang 2021 [62]; Zheng 2022 [64]Asthma15

Cromer 2006 [43]; England-Mason 2018 [44]; Godin 2023 [45]; Hanlon 2020 [46]; Henchoz 2019[47]; Hosang 2017 [48]; Hosang 2018 [49]; Lin 2021 [50]; Post 2013 [54]; Sheikh 2018 [56]; Sinnott
2015 [57]; Tomasdottir 2015 [60]; Yang 2021 [62]; Zak-Hunter 2023 [63]; Zheng 2022 [64]Heart disease

15

Arshadipour 2022 [40]; Atkinson 2021 [41]; Cromer 2006 [43]; England-Mason 2018 [44]; Henchoz
2019 [47]; Hosang 2017 [48]; Hosang 2018 [49]; Lin 2021 [50]; Post 2013 [54]; Sinnott 2015 [57];
Stapp 2020 [59]; Vasquez 2019 [61]; Yang 2021 [62]; Zak-Hunter 2023 [63]; Zheng 2022 [64]Kidney disease

13

Arshadipour 2022 [40]; Atkinson 2021 [41]; Chandrasekar 2023 [42]; Cromer 2006 [43]; Godin 2023
[45]; Hanlon 2020 [46]; Lin 2021 [50]; Post 2013 [54]; Sheikh 2018 [56]; Sosnowski 2022 [58]; Tomasdottir 2015 [60]; Yang 2021 [62]; Zheng 2022 [64]Arthritis

12

Cromer 2006 [43]; England-Mason 2018 [44]; Henchoz 2019 [47]; Hosang 2017 [48]; Hosang 2018[49]; Lin 2021 [50]; Post 2013 [54]; Reyes-Ortiz 2023 [55]; Stapp 2020 [59]; Vasquez 2019 [61]; Yang
2021 [62]; Zheng 2022 [64]Chronic obstructive pulmonary disease 9

Atkinson 2021 [41]; Cromer 2006 [43]; Hanlon 2020 [46]; Hosang 2017 [48]; Reyes-Ortiz 2023 [55];Sheikh 2018 [56]; Sinnott 2015 [57]; Tomasdottir 2015 [60]; Vasquez 2019 [61]Epilepsy and seizures

9

Atkinson 2021 [41]; Chandrasekar 2023 [42]; England-Mason 2018 [44]; Godin 2023 [45]; Hanlon
2020 [46]; Hosang 2017 [48]; Hosang 2018 [49]; Post 2013 [54]; Tomasdottir 2015 [60]Liver disease9

Arshadipour 2022 [40]; Cromer 2006 [43]; Hanlon 2020 [46]; Lin 2021 [50]; Post 2013 [54]; Sosnowski 2022 [58]; Stapp 2020 [59]; Yang 2021 [62]; Zheng 2022 [64]Osteoporosis

9

Atkinson 2021 [41]; Hanlon 2020 [46]; Henchoz 2019 [47]; Hosang 2017 [48]; Hosang 2018 [49];Reyes-Ortiz 2023 [55]; Sheikh 2018 [56]; Sinnott 2015 [57]; Tomasdottir 2015 [60]Respiratory disease8

Arshadipour 2022 [40]; Chandrasekar 2023 [42]; Henchoz 2019 [47]; Lin 2021 [50]; Noteboom 2021
[52]; Sosnowski 2022 [58]; Yang 2021 [62]; Zheng 2022 [64]Depression

7

Arshadipour 2022 [40]; Chandrasekar 2023 [42]; England-Mason 2018 [44]; Hanlon 2020 [46];Henchoz 2019 [47]; Sinnott 2015 [57]; Zak-Hunter 2023 [63]Gastrointestinal disease7

Arshadipour 2022 [40]; Chandrasekar 2023 [42]; Lin 2021 [50]; Noteboom 2021 [52]; Yang 2021 [62];Zak-Hunter 2023 [63]; Zheng 2022 [64]Headache/migraine

7

Atkinson 2021 [41]; England-Mason 2018 [44]; Godin 2023 [45]; Hanlon 2020 [46]; Noteboom 2021
[52]; Post 2013 [54]; Sheikh 2018 [56]Anxiety6

Arshadipour 2022 [40]; Atkinson 2021 [41]; England-Mason 2018 [44]; Hanlon 2020 [46]; Sinnott
2015 [57]; Vasquez 2019 [61]Dyslipidaemia

6

Chandrasekar 2023 [42]; Godin 2023 [45]; Lin 2021 [50]; Tomasdottir 2015 [60]; Yang 2021 [62];Zheng 2022 [64]Multiple sclerosis

6

Atkinson 2021 [41]; Godin 2023 [45]; Hanlon 2020 [46]; Hosang 2017 [48]; Hosang 2018 [49]; Post
2013 [54]Myocardial infarction

6

Atkinson 2021 [41]; Godin 2023 [45]; Sheikh 2018 [56]; Sinnott 2015 [57]; Stapp 2020 [59]; Vasquez
2019 [61]Thyroid disease

6

Cromer 2006 [43]; Godin 2023 [45]; Hanlon 2020 [46]; Noteboom 2021 [52]; Sinnott 2015 [57];Tomasdottir 2015 [60]Angina5

Atkinson 2021 [41]; Sheikh 2018 [56]; Sinnott 2015 [57]; Stapp 2020 [59]; Vasquez 2019 [61]Senaratne et al. BMC Medicine(2024) 22:315

Page 8 of 14

Table 2 (continued)
Long-term condition

Numberofstudies

StudiesCoronary heart disease5

Chandrasekar 2023 [42]; Godin 2023 [45]; Hanlon 2020 [46]; Henchoz 2019 [47]; Reyes-Ortiz
2023 [55]

Mental health/psychiatric disease5

Lin 2021 [50]; Reyes-Ortiz 2023 [55]; Tomasdottir 2015 [60]; Yang 2021 [62]; Zheng 2022 [64]Parkinson’s disease

5

Atkinson 2021 [41]; Chandrasekar 2023 [42]; Hanlon 2020 [46]; Henchoz 2019 [47]; Post 2013 [54]Rheumatoid arthritis

5

Chandrasekar 2023 [42]; Cromer 2006 [43]; Godin 2023 [45]; Sinnott 2015 [57]; Tomasdottir
2015 [60]Dementia and memory-related disease 4

Hanlon 2020 [46]; Lin 2021 [50]; Yang 2021 [62]; Zheng 2022 [64]Gastrointestinal ulcer

4

Cromer 2006 [43]; Godin 2023 [45]; Henchoz 2019 [47]; Stapp 2020 [59]Osteoarthritis

4

Atkinson 2021 [41]; Chandrasekar 2023 [42]; Sinnott 2015 [57]; Tomasdottir 2015 [60]Allergy3

Godin 2023 [45]; Post 2013 [54]; Zak-Hunter 2023 [63]Back pain

3

England-Mason 2018 [44]; Sinnott 2015 [57]; Tomasdottir 2015 [60]Chronic fatigue syndrome3

England-Mason 2018 [44]; Hanlon 2020 [46]; Post 2013 [54]Obesity3

Chandrasekar 2023 [42]; Sosnowski 2022 [58]; Tomasdottir 2015 [60]Other long-term condition

3

Cromer 2006 [43]; Post 2013 [54]; Zak-Hunter 2023 [63]Peripheral vascular disease3

Atkinson 2021 [41]; Hanlon 2020 [46]; Sinnott 2015 [57]

Substance misuse

3

England-Mason 2018 [44]; Hanlon 2020 [46]; Vasquez 2019 [61]Most commonly measured long-term conditions in the 25 included studies. Only LTCs included in ≥ 3 studies are listed

Fig. 3 Meta-analysis of prevalence of multimorbidity. Meta-analysis of prevalence of multimorbidity. CI, confidence interval; LTC, long-term
condition; MM, multimorbidity

reliability and validity but focuses on interpersonal ACEs,
missing out on household factors (e.g. parental separation), and community factors (e.g. bullying) [68]. Many
studies were performed on pre-existing research cohorts
or historic healthcare data, where the study authors had
limited or no influence on the data collected. As a result,very few individual studies reported on the full breadth ofpotential ACEs.

ACE research is often based on ACE counts, where
the types of ACEs experienced are summed into a single
score that is taken as a proxy measure of the burden ofchildhood stress. The original Adverse Childhood Experiences Study by Felitti et al. took this approach [3], as
did 17 of the studies included in this review and our own
quantitative synthesis. At the population level, there arebenefits to this: ACE counts provide quantifiable andSenaratne et al. BMC Medicine(2024) 22:315

Page 9 of 14

Fig. 4 Dose–response meta-analysis of the relationship between adverse childhood experiences and multimorbidity. Dose–response meta-analysisof the relationship between adverse childhood experiences and multimorbidity. Solid black line represents the estimated relationship; dotted blacklines represent the 95% confidence intervals for this estimate. ACE, adverse childhood experience

comparable metrics, they are easy to collect and analyse,
and in many datasets, they are the only means by which
an assessment of childhood stress can be derived. However, there are clear limitations to this method when considering experiences at the individual level, not least theinherent assumptions that different ACEs in the same
person are of equal weight or that the same ACE in different people carries the same burden of childhood stress.
This limitation was strongly reinforced by our patient andpublic involvement group (CPAG). Two studies in thisreview incorporated frequency within their ACE scoring system [52, 54], which adds another dimension to
the assessment, but this is insufficient to understand andquantify the ‘impact’ of an ACE within an epidemiological framework.
The definitions of multimorbidity were consistentacross the relevant studies but the contributory longterm conditions varied. Fewer than half of the 115 different LTCs (n = 52, 45.2%) were reported by more than
one study. Part of the challenge is the classification ofhealthcare conditions. For example, myocardial infarction is commonly caused by coronary heart disease, andboth are a form of heart disease. All three were reported
as LTCs in the included studies, but which level of pathology should be reported? Mental health LTCs were underrepresented within the condition list, with just over halfof the included studies assessing at least one (n = 14,
56.0%). Given the strong links between ACEs and mental
health, and the impact of mental health on quality of life,
this is an area for improvement in future research [31,32]. A recent Delphi consensus study by Ho et al. may
help to address these issues: following input from professionals and members of the public they identified 24
LTCs to ‘always include’ and 35 LTCs to ‘usually include’
in multimorbidity research, including nine mental healthconditions [9].
As outlined in the introduction, there is a strong evidence base supporting the link between ACEs and longterm health outcomes, including specific LTCs. It is notunreasonable to extrapolate this association to ACEs andSenaratne et al. BMC Medicine(2024) 22:315

multimorbidity, though to our knowledge, the pathophysiological processes that link the two have not been
precisely identified. However, similar lines of researchare being independently followed in both fields and theseareas of overlap may suggest possible mechanisms for a
relationship. For example, both ACEs and multimorbidity have been associated with markers of accelerated epigenetic ageing [69, 70], mitochondrial dysfunction [71,72], and inflammation [22, 73]. More work is required tobetter understand how these concepts might be linked.
This review used data from a large participant base,with information from 372,162 people contributing to
the systematic review and information from 197,981
people contributing to the dose–response meta-analysis.
Data from the included studies originated from a rangeof sources, including healthcare settings and dedicatedresearch cohorts. We believe this is of a sufficient scaleand variety to demonstrate the nature and magnitudeof the association between ACEs and multimorbidity inthese populations.
However, there are some limitations. Firstly, althoughdata came from 11 different countries, only two of thosewere from outside Europe and North America, and all
were from either high- or middle-income countries.
Data on ACEs from low-income countries have indicated a higher prevalence of any ACE exposure (consistently > 70%) [74, 75], though how well this predicts healthoutcomes in these populations is unknown.
Secondly, studies in this review utilised retrospective
participant-reported ACE data and so are at risk of recall
and reporting bias. Studies utilising prospective assessments are rare and much of the wider ACE literature isopen to a similar risk of bias. To date, two studies havecompared prospective and retrospective ACE measurements, demonstrating inconsistent results [76, 77]. However, these studies were performed in New Zealand andSouth Africa, two countries not represented by studies
in our review, and had relatively small sample sizes (1037
and 1595 respectively). It is unclear whether these are
generalisable to other population groups.
Thirdly, previous research has indicated a close relationship between ACEs and childhood socio-economic
status (SES) [78] and between SES and multimorbidity[10, 79]. However, the limitations of the included studies meant we were unable to separate the effect of ACEs
from the effect of childhood SES on multimorbidity
in this review. Whilst two studies included childhood
SES as covariates in their models, others used measuresfrom adulthood (such as adulthood SES, income level,
and education level) that are potentially influenced by
ACEs and therefore increase the risk of bias due to confounding (Additional File 1: Table S3). Furthermore, as
for ACEs and multimorbidity, there is no consistently

Page 10 of 14

applied definition of SES and different measures of SES
may produce different apparent effects [80]. The complex relationships between ACEs, childhood SES, andmultimorbidity remain a challenge for research in thisfield.
Fourthly, there was a high degree of heterogeneity
within included studies, especially relating to the definition and measurement of ACEs and multimorbidity.
Whilst this suggests that our results should be interpreted with caution, it is reassuring to see that our metaanalysis of prevalence estimates for exposure to any ACE(48.1%) and multimorbidity (34.5%) are in line with previous estimates in similar populations [2, 11]. Furthermore, we believe that the quantitative synthesis of these
relatively heterogenous studies provides important benefit by demonstrating a strong dose–response relationshipacross a range of contexts.
Our results strengthen the evidence supporting thelasting influence of childhood conditions on adult healthand wellbeing. How this understanding is best incorporated into routine practice is still not clear. Currently,
the lack of consistency in assessing ACEs limits our ability to understand their impact at both the individual andpopulation level and poses challenges for those looking
to incorporate a formalised assessment. Whilst most risk
factors for disease (e.g. blood pressure) are usually only
relevant within healthcare settings, ACEs are relevant tomany other sectors (e.g. social care, education, policing)[81–84], and so consistency of assessment across society
is both more important and more challenging to achieve.
Some have suggested that the evidence for the impactof ACEs is strong enough to warrant screening, which
would allow early identification of potential harms
to children and interventions to prevent them. Thisapproach has been implemented in California, USA [85–87]. However, this is controversial, and others argue thatscreening is premature with the current evidence base
[88–90]. Firstly, not everyone who is exposed to ACEs
develops poor health outcomes, and it is not clear how
to identify those who are at highest risk. Many people
appear to be vulnerable, with more adverse health outcomes following ACE exposure than those who are notexposed, whilst others appear to be more resilient, withgood health in later life despite multiple ACE exposures[91] It may be that supportive environments can mitigate
the long-term effects of ACE exposure and promote resilience [92, 93]. Secondly, there are no accepted interventions for managing the impact of an identified ACE. As
identified above, different ACEs may require input fromdifferent sectors (e.g. healthcare, social care, education,
police), and so collating this evidence may be challenging. At present, ACEs screening does not meet the Wilson-Jungner criteria for a screening programme [94].

Senaratne et al. BMC Medicine(2024) 22:315

Page 11 of 14

Existing healthcare systems are poorly designed to deal
with the complexities of addressing ACEs and multimorbidity. Possibly, ways to improve this might be allocating more time per patient, prioritising continuity ofcare to foster long-term relationships, and greater integration between different healthcare providers (mostnotably primary vs secondary care teams, or physicalvs mental health teams). However, such changes often
demand additional resources (e.g. staff, infrastructure,processes), which are challenging to source when existing
healthcare systems are already stretched [95, 96]. Nevertheless, increasing the spotlight on ACEs and multimorbidity may help to focus attention and ultimately bring
improvements to patient care and experience.

Conclusions
ACEs are associated with a range of poor long-term
health outcomes, including harmful health behaviours
and individual long-term conditions. Multimorbidity is
becoming more common as global populations age, andit increases the complexity and cost of healthcare provision. This is the first systematic review and meta-analysis
to synthesise the literature on ACEs and multimorbidity,
showing a statistically significant dose-dependent relationship across a large number of participants, albeit witha high degree of inter-study heterogeneity. This consolidates and enhances an increasing body of data supporting the role of ACEs in determining long-term healthoutcomes. Whilst these observational studies do not confirm causality, the weight and consistency of evidence issuch that we can be confident in the link. The challenge
for healthcare practitioners, managers, policymakers,
and governments is incorporating this body of evidence
into routine practice to improve the health and wellbeingof our societies.
Abbreviations
ACE	Adverse childhood experience
AIC	Akaike information criterion
CAPE	CONSORTIUM Against pain inequality
CI	Confidence interval
CPAG	Chronic pain advisory group
IQR	Interquartile range
LTC	Long-term condition
PROSPERO	International prospective register of systematic reviews
PRISMA	
Preferred reporting items for systematic reviews
meta-analyses
ROBINS-E	Risk of bias in non-randomised studies of exposures
SES	Socio-economic status(adverse childhood experiences), Table S5: Outcome details (multimorbidity), Figure S1: Meta-analysis of prevalence of exposure to ≥4 adverse
childhood experiences, Figure S2: Dose-response meta-analysis of therelationship between adverse childhood experiences and multimorbidity(using a non-linear/restricted cubic spline model).
Acknowledgements
The authors thank the members of the CAPE CPAG patient and public involvement group for providing insights gained from relevant lived experiences.
Authors’ contributions
DS and LC contributed to review conception and design. DC, BT, BS, TH, LM,
and LC contributed to search strategy design. DS and BT contributed to study
selection and data extraction, with input from LC. DS and BT accessed andverified the underlying data. DS conducted the meta-analyses, with input
from BT, BS, TH, LM, and LC. DS drafted the manuscript, with input from DC,
BT, BS, TH, LM, and LC. DC, BT, BS, TH, LM, and LC read and approved the final
manuscript.
Funding
The authors are members of the Advanced Pain Discovery Platform (APDP)
supported by UK Research & Innovation (UKRI), Versus Arthritis, and Eli Lilly. DS
is a fellow on the Multimorbidity Doctoral Training Programme for Health Professionals, which is supported by the Wellcome Trust [223499/Z/21/Z]. BT, BS,
and LC are supported by an APDP grant as part of the Partnership for Assessment and Investigation of Neuropathic Pain: Studies Tracking Outcomes, Risks
and Mechanisms (PAINSTORM) consortium [MR/W002388/1]. TH and LC aresupported by an APDP grant as part of the Consortium Against Pain Inequality[MR/W002566/1]. The funding bodies had no role in study design, data collection/analysis/interpretation, report writing, or the decision to submit themanuscript for publication.
Availability of data and materials
No additional data was generated for this review. The data used were foundin the referenced papers or provided through correspondence with the study
authors.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Chronic Pain Research Group, Division of Population Health & Genomics,
School of Medicine, University of Dundee, Ninewells Hospital, Dundee DD1
9SY, UK. 2 Institute of Academic Anaesthesia, Division of Systems Medicine,
School of Medicine, University of Dundee, Dundee, UK. 3 School of Health Sciences, University of Dundee, Dundee, UK.

andSupplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03505-w.
Additional File 1: Tables S1-S5 and Figures S1-S2. Table S1: Search strategy,
Table S2: Characteristics of studies included in the systematic review,
Table S3: Risk of bias assessment (ROBINS-E), Table S4: Exposure details

Received: 1 December 2023 Accepted: 14 June 2024

References
1. Hughes K, Bellis MA, Hardcastle KA, Sethi D, Butchart A, Mikton C, et al.
The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. Lancet Public Health. 2017;2:e356–66.
2. 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 Engl. 2014;36:81–91.

Senaratne et al. BMC Medicine

3.

4.
5.

6.

7.
8.

9.
10.

11.

12.
13.

14.
15.
16.
17.
18.
19.

20.
21.
22.
23.

(2024) 22:315

Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V,
et al. Relationship of childhood abuse and household dysfunction to
many of the leading causes of death in adults. The Adverse Childhood
Experiences (ACE) Study. Am J Prev Med. 1998;14:245–58.
Maniglio R. The impact of child sexual abuse on health: a systematic
review of reviews. Clin Psychol Rev. 2009;29:647–57.
Yu J, Patel RA, Haynie DL, Vidal-Ribas P, Govender T, Sundaram R, et al.
Adverse childhood experiences and premature mortality through midadulthood: a five-decade prospective study. Lancet Reg Health - Am.
2022;15:100349.
Wang Y-X, Sun Y, Missmer SA, Rexrode KM, Roberts AL, Chavarro JE,
et al. Association of early life physical and sexual abuse with premature
mortality among female nurses: prospective cohort study. BMJ. 2023;381:
e073613.
Rogers NT, Power C, Pereira SMP. Child maltreatment, early life socioeconomic disadvantage and all-cause mortality in mid-adulthood: findings
from a prospective British birth cohort. BMJ Open. 2021;11: e050914.
Hardcastle K, Bellis MA, Sharp CA, Hughes K. Exploring the health andservice utilisation of general practice patients with a history of adverse
childhood experiences (ACEs): an observational study using electronic
health records. BMJ Open. 2020;10: e036239.
Ho ISS, Azcoaga-Lorenzo A, Akbari A, Davies J, Khunti K, Kadam UT, et al.
Measuring multimorbidity in research: Delphi consensus study. BMJ Med.
2022;1:e000247.
Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research,and medical education: a cross-sectional study. Lancet Lond Engl.
2012;380:37–43.
Chowdhury SR, Das DC, Sunna TC, Beyene J, Hossain A. Global andregional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. eClinicalMedicine.
2023;57:101860.
Noël PH, Chris Frueh B, Larme AC, Pugh JA. Collaborative care needs andpreferences of primary care patients with multimorbidity. Health Expect.
2005;8:54–63.
Chau E, Rosella LC, Mondor L, Wodchis WP. Association between continuity of care and subsequent diagnosis of multimorbidity in Ontario,
Canada from 2001–2015: a retrospective cohort study. PLoS ONE.
2021;16: e0245193.
Nicholson K, Liu W, Fitzpatrick D, Hardacre KA, Roberts S, Salerno J, et al.
Prevalence of multimorbidity and polypharmacy among adults and older
adults: a systematic review. Lancet Healthy Longev. 2024;5:e287–96.
Albreht T, Dyakova M, Schellevis FG, Van den Broucke S. Many diseases,
one model of care? J Comorbidity. 2016;6:12–20.
Soley-Bori M, Ashworth M, Bisquera A, Dodhia H, Lynch R, Wang Y, et al.
Impact of multimorbidity on healthcare costs and utilisation: a systematic
review of the UK literature. Br J Gen Pract. 2020;71:e39-46.
World Health Organization (WHO). Ageing and health. 2022. https://​www.​who.​int/​news-​room/​fact-​sheets/​detail/​ageing-​and-​health.
Accessed 23 Apr 2024.
Franke HA. Toxic stress: effects, prevention and treatment. Children.
2014;1:390–402.
Parade SH, Huffhines L, Daniels TE, Stroud LR, Nugent NR, Tyrka AR. A
systematic review of childhood maltreatment and DNA methylation:
candidate gene and epigenome-wide approaches. Transl Psychiatry.
2021;11:1–33.
Ridout KK, Levandowski M, Ridout SJ, Gantz L, Goonan K, Palermo D, et al.
Early life adversity and telomere length: a meta-analysis. Mol Psychiatry.
2018;23:858–71.
Elwenspoek MMC, Kuehn A, Muller CP, Turner JD. The effects of earlylife adversity on the immune system. Psychoneuroendocrinology.
2017;82:140–54.
Danese A, Baldwin JR. Hidden wounds? Inflammatory links between
childhood trauma and psychopathology. Annu Rev Psychol.
2017;68:517–44.
Brindle RC, Pearson A, Ginty AT. Adverse childhood experiences (ACEs)
relate to blunted cardiovascular and cortisol reactivity to acute laboratorystress: a systematic review and meta-analysis. Neurosci Biobehav Rev.
2022;134: 104530.

Page 12 of 14

24. Teicher MH, Samson JA, Anderson CM, Ohashi K. The effects of childhood
maltreatment on brain structure, function and connectivity. Nat Rev
Neurosci. 2016;17:652–66.
25. McLaughlin KA, Weissman D, Bitrán D. Childhood adversity andneural development: a systematic review. Annu Rev Dev Psychol.
2019;1:277–312.
26. Koyama Y, Fujiwara T, Murayama H, Machida M, Inoue S, Shobugawa
Y. Association between adverse childhood experiences and brain
volumes among Japanese community-dwelling older people: findings
from the NEIGE study. Child Abuse Negl. 2022;124: 105456.
27. Antoniou G, Lambourg E, Steele JD, Colvin LA. The effect of adverse
childhood experiences on chronic pain and major depression in
adulthood: a systematic review and meta-analysis. Br J Anaesth.
2023;130:729–46.
28. Huang H, Yan P, Shan Z, Chen S, Li M, Luo C, et al. Adverse childhood
experiences and risk of type 2 diabetes: a systematic review and metaanalysis. Metabolism. 2015;64:1408–18.
29. Lopes S, Hallak JEC, de Machado Sousa JP, de Osório F L. Adverse
childhood experiences and chronic lung diseases in adulthood: asystematic review and meta-analysis. Eur J Psychotraumatology.
2020;11:1720336.
30. Hu Z, Kaminga AC, Yang J, Liu J, Xu H. Adverse childhood experiences andrisk of cancer during adulthood: a systematic review and meta-analysis.
Child Abuse Negl. 2021;117: 105088.
31. Tan M, Mao P. Type and dose-response effect of adverse childhood experiences in predicting depression: a systematic review and meta-analysis.
Child Abuse Negl. 2023;139: 106091.
32. Zhang L, Zhao N, Zhu M, Tang M, Liu W, Hong W. Adverse childhood
experiences in patients with schizophrenia: related factors and clinicalimplications. Front Psychiatry. 2023;14:1247063.
33. Emsley E, Smith J, Martin D, Lewis NV. Trauma-informed care in the UK:
where are we? A qualitative study of health policies and professional
perspectives. BMC Health Serv Res. 2022;22:1164.
34. ROBINS-E Development Group (Higgins J, Morgan R, Rooney A, Taylor
K, Thayer K, Silva R, Lemeris C, Akl A, Arroyave W, Bateson T, Berkman N,
Demers P, Forastiere F, Glenn B, Hróbjartsson A, Kirrane E, LaKind J, Luben
T, Lunn R, McAleenan A, McGuinness L, Meerpohl J, Mehta S, Nachman
R, Obbagy J, O’Connor A, Radke E, Savović J, Schubauer-Berigan M,
Schwingl P, Schunemann H, Shea B, Steenland K, Stewart T, Straif K, Tilling
K, Verbeek V, Vermeulen R, Viswanathan M, Zahm S, Sterne J). Risk Of Bias
In Non-randomized Studies - of Exposure (ROBINS-E). Launch version, 20
June 2023. https://​www.​risko​fbias.​info/​welco​me/​robins-​e-​tool. Accessed
20 Jul 2023.
35. Balduzzi S, Rücker G, Schwarzer G. How to perform a meta-analysis with
R: a practical tutorial. Evid Based Ment Health. 2019;22:153–60.
36. Schwarzer G, Chemaitelly H, Abu-Raddad LJ, Rücker G. Seriously misleading results using inverse of Freeman-Tukey double arcsine transformation in meta-analysis of single proportions. Res Synth Methods.
2019;10:476–83.
37. Crippa A, Orsini N. Multivariate dose-response meta-analysis: the dosresmeta R Package. J Stat Softw. 2016;72:1–15.
38. Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J
Epidemiol. 1992;135:1301–9.
39. Shim SR, Lee J. Dose-response meta-analysis: application and practiceusing the R software. Epidemiol Health. 2019;41: e2019006.
40. Arshadipour A, Thorand B, Linkohr B, Rospleszcz S, Ladwig K-H, Heier M,
et al. Impact of prenatal and childhood adversity effects around WorldWar II on multimorbidity: results from the KORA-Age study. BMC Geriatr.
2022;22:115.
41. Atkinson L, Joshi D, Raina P, Griffith LE, MacMillan H, Gonzalez A. Social
engagement and allostatic load mediate between adverse childhood
experiences and multimorbidity in mid to late adulthood: the CanadianLongitudinal Study on Aging. Psychol Med. 2021;53(4):1–11.
42. Chandrasekar R, Lacey RE, Chaturvedi N, Hughes AD, Patalay P, Khanolkar
AR. Adverse childhood experiences and the development of multimorbidity across adulthood—a national 70-year cohort study. Age Ageing.
2023;52:afad062.
43. Cromer KR, Sachs-Ericsson N. The association between childhood abuse,
PTSD, and the occurrence of adult health problems: moderation via current life stress. J Trauma Stress. 2006;19:967–71.

Senaratne et al. BMC Medicine(2024) 22:315

44. England-Mason G, Casey R, Ferro M, MacMillan HL, Tonmyr L, Gonzalez A.
Child maltreatment and adult multimorbidity: results from the CanadianCommunity Health Survey. Can J Public Health. 2018;109:561–72.
45. Godin O, Leboyer M, Laroche DG, Aubin V, Belzeaux R, Courtet P, et al.
Childhood maltreatment contributes to the medical morbidity of individuals with bipolar disorders. Psychol Med. 2023;53(15):1–9.
46. Hanlon P, McCallum M, Jani BD, McQueenie R, Lee D, Mair FS. Association
between childhood maltreatment and the prevalence and complexity ofmultimorbidity: a cross-sectional analysis of 157,357 UK Biobank participants. J Comorbidity. 2020;10:2235042X1094434.
47. Henchoz Y, Seematter-Bagnoud L, Nanchen D, Büla C, von Gunten A,
Démonet J-F, et al. Childhood adversity: a gateway to multimorbidity inolder age? Arch Gerontol Geriatr. 2019;80:31–7.
48. Hosang GM, Fisher HL, Uher R, Cohen-Woods S, Maughan B, McGuffin
P, et al. Childhood maltreatment and the medical morbidity in bipolar
disorder: a case–control study. Int J Bipolar Disord. 2017;5:30.
49. Hosang GM, Fisher HL, Hodgson K, Maughan B, Farmer AE. Childhood maltreatment and adult medical morbidity in mood disorders:comparison of unipolar depression with bipolar disorder. Br J Psychiatry.
2018;213:645–53.
50. Lin L, Wang HH, Lu C, Chen W, Guo VY. Adverse childhood experiences
and subsequent chronic diseases among middle-aged or older adults in
China and associations with demographic and socioeconomic characteristics. JAMA Netw Open. 2021;4: e2130143.
51. Mendizabal A, Nathan CL, Khankhanian P, Anto M, Clyburn C, Acaba-Berrocal A, et al. Adverse childhood experiences in patients with neurologic
disease. Neurol Clin Pract. 2022. https://​doi.​org/​10.​1212/​CPJ.​00000​00000​
001134.
52. Noteboom A, Have MT, De Graaf R, Beekman ATF, Penninx BWJH, Lamers
F. The long-lasting impact of childhood trauma on adult chronic physicaldisorders. J Psychiatr Res. 2021;136:87–94.
53. Patterson ML, Moniruzzaman A, Somers JM. Setting the stage for chronic
health problems: cumulative childhood adversity among homelessadults with mental illness in Vancouver. British Columbia BMC Public
Health. 2014;14:350.
54. Post RM, Altshuler LL, Leverich GS, Frye MA, Suppes T, McElroy SL, et al.
Role of childhood adversity in the development of medical co-morbidities associated with bipolar disorder. J Affect Disord. 2013;147:288–94.
55. Reyes-Ortiz CA. Racial discrimination and multimorbidity amongolder adults in Colombia: a national data analysis. Prev Chronic Dis.
2023;20:220360.
56. Sheikh MA. Coloring of the past via respondent’s current psychologicalstate, mediation, and the association between childhood disadvantage
and morbidity in adulthood. J Psychiatr Res. 2018;103:173–81.
57. Sinnott C, Mc Hugh S, Fitzgerald AP, Bradley CP, Kearney PM. Psychosocial
complexity in multimorbidity: the legacy of adverse childhood experiences. Fam Pract. 2015;32:269–75.
58. Sosnowski DW, Feder KA, Astemborski J, Genberg BL, Letourneau
EJ, Musci RJ, et al. Adverse childhood experiences and comorbidity
in a cohort of people who have injected drugs. BMC Public Health.
2022;22:986.
59. Stapp EK, Williams SC, Kalb LG, Holingue CB, Van Eck K, Ballard ED, et al.
Mood disorders, childhood maltreatment, and medical morbidity in US
adults: an observational study. J Psychosom Res. 2020;137: 110207.
60. Tomasdottir MO, Sigurdsson JA, Petursson H, Kirkengen AL, Krokstad S,
McEwen B, et al. Self reported childhood difficulties, adult multimorbidity and allostatic load. A cross-sectional analysis of the Norwegian HUNT
study. PloS One. 2015;10:e0130591.
61. Vásquez E, Quiñones A, Ramirez S, Udo T. Association between adverse
childhood events and multimorbidity in a racial and ethnic diverse sample of middle-aged and older adults. Innov Aging. 2019;3:igz016.
62. Yang L, Hu Y, Silventoinen K, Martikainen P. Childhood adversity and trajectories of multimorbidity in mid-late life: China health and longitudinalretirement study. J Epidemiol Community Health. 2021;75:593–600.
63. Zak-Hunter L, Carr CP, Tate A, Brustad A, Mulhern K, Berge JM. Associations
between adverse childhood experiences and stressful life events andhealth outcomes in pregnant and breastfeeding women from diverse
racial and ethnic groups. J Womens Health. 2023;32:702–14.
64. Zheng X, Cui Y, Xue Y, Shi L, Guo Y, Dong F, et al. Adverse childhood
experiences in depression and the mediating role of multimorbidity

Page 13 of 14

in mid-late life: A nationwide longitudinal study. J Affect Disord.
2022;301:217–24.
65. Liu M, Luong L, Lachaud J, Edalati H, Reeves A, Hwang SW. Adverse
childhood experiences and related outcomes among adults experiencing homelessness: a systematic review and meta-analysis. Lancet
Public Health. 2021;6:e836–47.
66. Petruccelli K, Davis J, Berman T. Adverse childhood experiences andassociated health outcomes: a systematic review and meta-analysis.
Child Abuse Negl. 2019;97: 104127.
67. Bethell CD, Carle A, Hudziak J, Gombojav N, Powers K, Wade R, et al.
Methods to assess adverse childhood experiences of children andfamilies: toward approaches to promote child well-being in policy andpractice. Acad Pediatr. 2017;17(7 Suppl):S51-69.
68. Bernstein DP, Stein JA, Newcomb MD, Walker E, Pogge D, Ahluvalia T,
et al. Development and validation of a brief screening version of theChildhood Trauma Questionnaire. Child Abuse Negl. 2003;27:169–90.
69. Kim K, Yaffe K, Rehkopf DH, Zheng Y, Nannini DR, Perak AM, et al. Association of adverse childhood experiences with accelerated epigenetic
aging in midlife. JAMA Network Open. 2023;6:e2317987.
70. Jain P, Binder A, Chen B, Parada H, Gallo LC, Alcaraz J, et al. The
association of epigenetic age acceleration and multimorbidity atage 90 in the Women’s Health Initiative. J Gerontol A Biol Sci Med Sci.
2023;78:2274–81.
71. Zang JCS, May C, Hellwig B, Moser D, Hengstler JG, Cole S, et al. Proteome analysis of monocytes implicates altered mitochondrial biology
in adults reporting adverse childhood experiences. Transl Psychiatry.
2023;13:31.
72. Mau T, Blackwell TL, Cawthon PM, Molina AJA, Coen PM, Distefano G,
et al. Muscle mitochondrial bioenergetic capacities are associated withmultimorbidity burden in older adults: the Study of Muscle, Mobility andAging (SOMMA). J Gerontol A Biol Sci Med Sci. 2024;79(7):glae101.
73. Friedman E, Shorey C. Inflammation in multimorbidity and disability: an
integrative review. Health Psychol Off J Div Health Psychol Am Psychol
Assoc. 2019;38:791–801.
74. Satinsky EN, Kakuhikire B, Baguma C, Rasmussen JD, Ashaba S, CooperVince CE, et al. Adverse childhood experiences, adult depression, andsuicidal ideation in rural Uganda: a cross-sectional, population-based
study. PLoS Med. 2021;18: e1003642.
75. Amene EW, Annor FB, Gilbert LK, McOwen J, Augusto A, Manuel P, et al.
Prevalence of adverse childhood experiences in sub-Saharan Africa: amulticounty analysis of the Violence Against Children and Youth Surveys(VACS). Child Abuse Negl. 2023;150:106353.
76. Reuben A, Moffitt TE, Caspi A, Belsky DW, Harrington H, Schroeder F, et al.
Lest we forget: comparing retrospective and prospective assessments ofadverse childhood experiences in the prediction of adult health. J Child
Psychol Psychiatry. 2016;57:1103–12.
77. Naicker SN, Norris SA, Mabaso M, Richter LM. An analysis of retrospective and repeat prospective reports of adverse childhood experiencesfrom the South African Birth to Twenty Plus cohort. PLoS ONE. 2017;12:
e0181522.
78. Walsh D, McCartney G, Smith M, Armour G. Relationship between childhood socioeconomic position and adverse childhood experiences (ACEs):a systematic review. J Epidemiol Community Health. 2019;73:1087–93.
79. Ingram E, Ledden S, Beardon S, Gomes M, Hogarth S, McDonald H, et al.
Household and area-level social determinants of multimorbidity: asystematic review. J Epidemiol Community Health. 2021;75:232–41.
80. Darin-Mattsson A, Fors S, Kåreholt I. Different indicators of socioeconomic
status and their relative importance as determinants of health in old age.
Int J Equity Health. 2017;16:173.
81. Bateson K, McManus M, Johnson G. Understanding the use, and misuse,of Adverse Childhood Experiences (ACEs) in trauma-informed policing.
Police J. 2020;93:131–45.
82. Webb NJ, Miller TL, Stockbridge EL. Potential effects of adverse childhood
experiences on school engagement in youth: a dominance analysis. BMC
Public Health. 2022;22:2096.
83. Stewart-Tufescu A, Struck S, Taillieu T, Salmon S, Fortier J, Brownell M, et al.
Adverse childhood experiences and education outcomes among adolescents: linking survey and administrative data. Int J Environ Res Public
Health. 2022;19:11564.

Senaratne et al. BMC Medicine(2024) 22:315

84. Frederick J, Spratt T, Devaney J. Adverse childhood experiences andsocial work: relationship-based practice responses. Br J Soc Work.
2021;51:3018–34.
85. University of California ACEs Aware Family Resilience Network (UCAAN).
acesaware.org. ACEs Aware. https://​www.​acesa​ware.​org/​about/.
Accessed 6 Oct 2023.
86. Watson CR, Young-Wolff KC, Negriff S, Dumke K, DiGangi M. Implementation and evaluation of adverse childhood experiences screening inpediatrics and obstetrics settings. Perm J. 2024;28:180–7.
87. Gordon JB, Felitti VJ. The importance of screening for adverse childhood
experiences (ACE) in all medical encounters. AJPM Focus. 2023;2: 100131.
88. Finkelhor D. Screening for adverse childhood experiences (ACEs): Cautions and suggestions. Child Abuse Negl. 2018;85:174–9.
89. Cibralic S, Alam M, Mendoza Diaz A, Woolfenden S, Katz I, Tzioumi D, et al.
Utility of screening for adverse childhood experiences (ACE) in children
and young people attending clinical and healthcare settings: a systematic review. BMJ Open. 2022;12: e060395.
90. Gentry SV, Paterson BA. Does screening or routine enquiry for adverse
childhood experiences (ACEs) meet criteria for a screening programme?
A rapid evidence summary. J Public Health Oxf Engl. 2022;44:810–22.
91. Morgan CA, Chang Y-H, Choy O, Tsai M-C, Hsieh S. Adverse childhood
experiences are associated with reduced psychological resiliencein youth: a systematic review and meta-analysis. Child Basel Switz.
2021;9:27.
92. Narayan AJ, Lieberman AF, Masten AS. Intergenerational transmission andprevention of adverse childhood experiences (ACEs). Clin Psychol Rev.
2021;85: 101997.
93. VanBronkhorst SB, Abraham E, Dambreville R, Ramos-Olazagasti MA, Wall
M, Saunders DC, et al. Sociocultural risk and resilience in the context ofadverse childhood experiences. JAMA Psychiat. 2024;81:406–13.
94. Wilson JM, Jungner G. Principles and practice of screening for disease.
World Health Organisation; 1968.
95. Huo Y, Couzner L, Windsor T, Laver K, Dissanayaka NN, Cations M. Barriersand enablers for the implementation of trauma-informed care in healthcare settings: a systematic review. Implement Sci Commun. 2023;4:49.
96. Foo KM, Sundram M, Legido-Quigley H. Facilitators and barriers ofmanaging patients with multiple chronic conditions in the community: aqualitative study. BMC Public Health. 2020;20:273.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Page 14 of 14