Childhood adversity and cardiovascular disease in early adulthood: a Danish cohort study

Abstract

Aims

To examine the effect of childhood adversity on the development of cardiovascular disease (CVD) between ages 16 and 38, specifically focusing on ischaemic heart disease and cerebrovascular disease.

Methods and results

Register data on all children born in Denmark between 1 January 1980 and 31 December 2001, who were alive and resident in Denmark without a diagnosis of CVD or congenital heart disease until age 16 were used, totalling 1 263 013 individuals. Cox proportional hazards and Aalen additive hazards models were used to estimate adjusted hazard ratios (HRs) and adjusted hazard differences of CVD from ages 16 to 38 in five trajectory groups of adversity experienced between ages 0 and 15. In total, 4118 individuals developed CVD between their 16th birthday and 31 December 2018. Compared with those who experienced low levels of adversity, those who experienced severe somatic illness and death in the family (men: adjusted HR: 1.6, 95% confidence interval: 1.4–1.8, women: 1.4, 1.2–1.6) and those who experienced very high rates of adversity across childhood and adolescence (men: 1.6, 1.3–2.0, women: 1.6, 1.3–2.0) had a higher risk of developing CVD, corresponding to 10–18 extra cases of CVD per 100 000 person-years in these groups.

Conclusions

Individuals who have been exposed to childhood adversity are at higher risk of developing CVD in young adulthood compared to individuals with low adversity exposure. These findings suggest that interventions targeting the social origins of adversity and providing support for affected families may have long-term cardio-protective effects.

Structured graphical abstract

CVD, Cardiovascular disease.

Cardiovascular disease, Ischaemic heart disease, Cerebrovascular disease, Childhood adversity, Adverse childhood experiences, Young adulthood

See the editorial comment for this article ‘Moving beyond lifestyle: the case for childhood adversity, social determinants of health, and psychosocial factors in cardiovascular risk prediction’, by A. H. Kovacs et al., https://doi.org/10.1093/eurheartj/ehac697.

Permissions information

The authors do hereby declare that all illustrations and figures in the manuscript are original and not require reprint permission.

Introduction

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide,¹ and the incidence among young adults seems to rise.^2,3 Although CVD presenting in young adulthood may have a strong genetic influence,⁴ the rising incidence suggests substantial contribution from environmental and behavioural risk factors as well.^2–6 However, the causes of CVD among young adults are understudied owing to the low absolute number of individuals with manifest CVD in this age group.²

Previous studies have shown that the experience of stressful circumstances during childhood such as material deprivation, family loss, and straining family dynamics (also known as ‘childhood adversities’) is associated with a higher risk of CVD among middle-aged and older people,^7–13 but very few studies have investigated the association between childhood adversities and manifest CVD in early adulthood.^7,8 The few studies that have been conducted among young adults have shown an association between childhood adversities and CVD,^14–16 but have been limited by self-reported recall of adverse events. Selection bias is a particular concern in this line of research, since disadvantaged individuals are less likely to participate in surveys.¹⁷

Childhood is a sensitive period where the nervous, endocrine, and immune systems are under rapid development,^18,19 and frequent or chronic exposure to adversity in childhood may influence the development of the physiological stress response.^18–20 This activation may eventually disrupt the immunological balance leading to a more inflammatory phenotype²¹ with a higher risk of atherosclerosis and hypertension.^7,18,19,21 Childhood adversity has also been related to health-threatening behaviours associated with CVD, such as smoking, overeating, and excessive alcohol consumption as means of coping; behaviours that are usually initiated during adolescence.^7,19,20 Due to differences in aetiology between different CVDs, such as ischaemic heart disease (IHD) and cerebrovascular disease (CD), the effects of childhood adversity on these diagnoses may differ, which warrants separate investigation. In addition, CD includes diagnoses which often have a structural aetiology when presenting in young adulthood. For example, subarachnoid haemorrhages are often caused by underlying aneurysms that may be present at birth.²² Although childhood adversity has been associated with behavioural factors that may affect the pathogenesis and growth of aneurysms, such as smoking, alcohol, and drug abuse,^20,22 the role of childhood adversity in conditions with a congenital aetiology is uncertain and should be acknowledged.

We have previously shown a substantially higher mortality rate, including deaths due to diseases of the circulatory system, between ages 16 and 35 among individuals exposed to different trajectories of adversity in childhood and adolescence.²³ We have also shown that the risk of being hospitalized with a diagnosis related to diseases of the circulatory system between ages 16 and 24 was 40% higher among individuals exposed to high vs. low adversity.²⁴ These results suggest that childhood adversity may be an important driver of manifest CVD even in early adult life. However, assessing the entire ICD-10 (International Classification of Diseases 10th revision) chapter of diseases of the circulatory system (I00–I99) is very crude and the effects of childhood adversity on specific subgroups of CVD diagnosed in young adulthood remain to be formally studied.

We build on this previous work by examining the effect of childhood adversity on the development of CVD between ages 16 and 38, where we specifically focus on diagnoses of IHD and CD. We use a total population sample of more than one million individuals with objective, repeated, and prospectively registered information on childhood adversities and CVD diagnoses.

Methods

Study population

In this population-based cohort study, we used data from the DANish LIFE course (DANLIFE) cohort, which includes continuously recorded information from numerous nationwide registers.²⁵ The unique identification number given to all Danish residents permitted exact individual level linkage between registers and between children, parents, and siblings.²⁶ DANLIFE contains information on multiple childhood adversities, morbidity, and mortality on all children born in Denmark between 1 January 1980 and 31 December 2015, totalling 2 223 927 individuals. To cover exposure to adversities across entire childhoods (age 0–15), we restricted the study sample to children born between 1 January 1980 and 31 December 2001 who were alive and resident in Denmark until their 16th birthday without a diagnosis of CVD (ICD-8 codes: 410–414, 430–438/ICD-10 codes: I20.0, I20.1, I21–I25, I60–I69) or congenital heart disease (390–404, 420–429, 746–747/I00–I02, I05–I09, I30–I43, Q20–Q28) in the Danish National Patient Register²⁷ (n = 1 264 320). Among these, 99.9% had complete information on all covariates included in the main analyses, leaving a final study population of 1 263 013 individuals (Figure 1).

Figure 1

Flowchart of the study population.

The DANLIFE study has been approved by the Danish Data Protection Agency through the joint notification of The Faculty of Health and Medical Sciences at The University of Copenhagen (record no 514-0641/21-3000). The Danish Data Protection Agency ensures compliance with national and EU legislation. Registry linkage studies do not require ethical approval by the Danish National Committee on Health Research Ethics according to Danish Law.

Childhood adversities

DANLIFE includes information on 12 different childhood adversities divided into three dimensions: material deprivation, loss or threat of loss in the family, and family dynamics. Material deprivation covers family poverty and parental long-term unemployment. Loss or threat of loss covers parental and sibling somatic illness and death. Family dynamics cover foster care placements, parental and sibling psychiatric illness, parental alcohol and drug abuse, and maternal separation. Supplementary material online, Table S1 provides an overview of the specific definitions of the 12 childhood adversities and the registers providing the information.

We assessed exposure to childhood adversities as allocation to the five most common trajectory groups of adversity across the three dimensions utilizing the prospective and continuously recorded information in the Danish nationwide registers. The five trajectory groups were identified in a previous study among children aged 0–15 years in DANLIFE using a group-based multi-trajectory model.²⁴ All 12 childhood adversities were allowed to occur once for each parent or sibling per year of life of the child and were summed by age and dimension. Each adversity was counted in the years in which it occurred and the same type of adversity could occur multiple times during childhood. Thus, the trajectory groups cover accumulation, timing, and chronicity of the adversity exposures. The trajectory group ‘low adversity’ is characterized by a generally low exposure to childhood adversities. ‘Early life material deprivation’ and ‘persistent material deprivation’ are characterized by material deprivation in early childhood and throughout childhood and adolescence, respectively. The ‘loss or threat of loss’ group is characterized by relatively high rates of parental and sibling severe somatic illness and death. Finally, the ‘high adversity’ group covers high exposure to childhood adversities in all three dimensions, particularly in the family dynamics dimension where individuals on average experienced almost one adversity every year during adolescence. The five trajectory groups of childhood adversity across the three dimensions are illustrated in Supplementary material online, Figure S1.

Cardiovascular disease

CVD was defined as a composite outcome including primary and secondary diagnoses with either IHD or CD registered in the Danish National Patient Register²⁷ between age 16 and the end of follow-up (31 December 2018). This information was supplemented by death due to either IHD or CD registered in the Danish Register of Causes of Death,²⁸ which was available until 1 year before the end of follow-up (31 December 2017). Only the first diagnosis of either IHD or CD was considered. We identified IHD after age 15 using ICD-10 codes I20.0, I20.1, I21–I25 (excluding unspecified angina). CD after age 15 was identified with ICD10 codes I60–I69. In a sensitivity analysis, we excluded ICD-10 codes I60, I62, I65, and I66 from the measure of CD, because these diagnoses are often caused by structural anomalies that may have been present at birth.²²

Covariates

Potential confounders included year of birth, maternal age at birth (<20 years, 20–30 years, > 30 years), parental country of origin (Western/non-Western), and parental cardiometabolic illness (yes/no). We classified parental origin as non-Western if both parents had a nationality from a country outside of Europe, North America, Australia, and New Zealand. Parental cardiometabolic illness included IHD (ICD-8: 410–414/ICD10: I20.0, I20.1, I21–I25), CD (430–438/I60–I69), congestive heart failure (427.09–427.11, 427.19, 428.99, 782.49/I50, I11.0, I13.0, I13.2), peripheral vascular disease (440–445/I70–I74, I77), type 1 diabetes (249/E10), and type 2 diabetes (250/E11). Parental cardiometabolic illness included both primary and secondary diagnoses at any time between 1977, when the Danish National Patient Register was established, and 31 December 2018, as well as causes of death between 1 January 1980 and 31 December 2017.

Family adversity is likely to affect the health of the child already in utero, and indicators of early development such as being born small for gestational age are more likely to be mediators than confounders of the association. Also, other socio-demographic indicators such as parental education are highly correlated with e.g. material deprivation, making it difficult to disentangle causes from effects. Therefore, we decided to only adjusted for the effects of being born small for gestational age (intrauterine growth below the 10th percentile of age- and sex-specific reference curves)²⁹ and parental education at the time of birth (low <10 years, middle 10–12 years, and high >12 years) in supplementary analyses among those with full information on these variables (n = 1 220 872 and n = 1 258 418, respectively).

Statistical analysis

We described the age-specific incidence of CVD from age 16 onward using a Poisson regression model with a spline with 6 degrees of freedom. We estimated the cumulative risk of CVD per 100 000 individuals according to the five childhood adversity trajectories groups with a spline with 3 degrees of freedom. Splines were used for data protection reasons. The degrees of freedom for the splines were chosen to be the lowest while still representing the patterns we saw for the yearly estimates. We also estimated adjusted hazard ratios (HRs) of CVD using Cox proportional hazards models and adjusted hazard differences per 100 000 person-years using Aalen additive hazards models. We used the low adversity group as reference and age as the underlying time scale. Individuals were followed from age 16 until the first diagnosis of CVD, emigration, death, or 31 December 2018. All analyses were stratified by sex. In supplementary analyses, we further adjusted for being born small for gestational age and parental education. In a sensitivity analysis, we excluded diagnoses which are often caused by structural anomalies that may have been present at birth from the CD-specific analysis (i.e. ICD-10 codes I60, I62, I65, and I66). Finally, because parental cardiometabolic illness was counted as an adversity in the loss or threat of loss dimension, we performed a sensitivity analysis in which we restricted the population to those without parental cardiometabolic illness (n = 937 775). All analyses were conducted using R version 4.0.3.

Results

After their 16th birthday, the study population was followed for 10.8 years on average, corresponding to a total follow-up time of 13 645 041 person-years. During this time, 4118 individuals developed CVD. Among these, 966 individuals developed IHD and 3152 individuals developed CD, respectively. For IHD, the most common diagnoses were acute myocardial infarction (462 individuals, 48%) and chronic IHD (236 individuals, 24%). For CD, the most common diagnoses were cerebral infarction (776 individuals, 25%) and nontraumatic subarachnoid haemorrhage (601 individuals, 19%). The number of individuals with each specific diagnosis included in the measures of IHD and CD are available in Supplementary material online, Tables S2 and S3, respectively. A total of 5178 individuals died from another cause than CVD and 96 812 individuals emigrated before the end of follow-up.

Table 1 presents the background characteristics of the study population across the five childhood adversity trajectory groups. The proportion of children born to teenage mothers was larger in all adversity groups compared with the low adversity group and was particularly high in the persistent deprivation and the high adversity groups. The children who experienced persistent material deprivation were more often born to parents with a non-Western origin than the children in any of the other groups were. Having a parent with cardiometabolic illness was markedly more common in the persistent material deprivation (34%), loss or threat of loss (36%), and high adversity (39%) groups compared with the low adversity group (21%). We also observed large differences in the proportion of children born to parents with low education, ranging from 8% in the low adversity group to 54% in the high adversity group. Similarly, the proportion of children born small for gestational age was largest in the high adversity group (22%) and smallest in the low adversity group (11%).

Table 1

Background characteristics of the study population at the time of birth across the five childhood adversity trajectory groups

	All		Low adversity		Early deprivation		Persistent deprivation		Loss or threat of loss		High adversity
	n	%	n	%	n	%	n	%	n	%	n	%
Total	1 263 013	100	684 195	54.2	253 978	20.1	168 934	13.4	115 483	9.1	40 423	3.2
Sex
ȃMale	647 732	51.3	350 983	51.3	129 840	51.1	86 256	51.1	58 789	50.9	21 864	54.1
ȃFemale	615 281	48.7	333 212	48.7	124 138	48.9	82 678	48.9	56 694	49.1	18 559	45.9
Maternal age
ȃ<20 years	34 631	2.8	5960	0.9	8962	3.5	11 490	6.8	3955	3.4	4264	10.5
ȃ20–30 years	849 276	67.2	443 985	64.9	185 223	72.9	119 629	70.8	73 077	63.3	27 362	67.7
ȃ>30 years	379 106	30.0	234 250	34.2	59 793	23.6	37 815	22.4	38 451	33.3	8797	21.8
Parental origin
ȃWesterna	1 227 989	97.2	676 979	98.9	245 079	96.5	154 779	91.6	111 517	96.6	39 635	98.1
ȃNon-Western	35 024	2.8	7216	1.1	8899	3.5	14 155	8.4	3966	3.4	788	1.9
Parental cardiometabolic illness
ȃNo	937 775	74.2	539 929	78.9	188 450	74.2	111 274	65.9	73 616	63.7	24 506	60.6
ȃYes	325 238	25.8	144 266	21.1	65 528	25.8	57 660	34.1	41 867	36.3	15 917	39.4
Parental education
ȃLow	209 144	16.6	54 529	8.0	54 167	21.3	54 556	32.3	24 193	20.9	21 699	53.7
ȃMiddle	622 592	49.3	329 075	48.1	139 212	54.8	82 080	48.6	57 823	50.1	14 402	35.6
ȃHigh	426 682	33.7	299 029	43.7	59 639	23.5	31 071	18.4	33 032	28.6	3911	9.7
ȃMissing	4595	0.4	1562	0.2	960	0.4	1227	0.7	435	0.4	411	1.0
Small for gestational age
ȃNo	1 056 713	83.7	584 659	85.5	211 678	83.3	135 833	80.4	94 688	82.0	29 855	73.9
ȃYes	164 159	13.0	77 544	11.3	34 438	13.6	26 129	15.5	17 071	14.8	8977	22.2
ȃMissing	42 141	3.3	21 992	3.2	7862	3.1	6972	4.1	3724	3.2	1591	3.9

Includes Europe, North America, Australia, and New Zealand.

The age-specific incidence rate of CVD per 100 000 person-years increased with age, although it seemed to stabilize for women after age 30 (Figure 2). The incidence rate of IHD was higher among men than among women at all ages, but specifically after age 25 (see Supplementary material online, Figure S2). For CD, the incidence rate was higher among women than among men between ages 25 and 35. Hereafter, the incidence rate of CD among men exceeded the rate among women (see Supplementary material online, Figure S2).

Figure 2

Age-specific incidence rates of cardiovascular disease per 100 000 person-years for men and women, respectively.

Compared with the low adversity group, the cumulative number of individuals with CVD per 100 000 individuals was higher in all other groups for both men and women, with the highest cumulative number observed in the high adversity group (Figure 3). Based on the cumulative risk plots (Figure 3), we decided that HRs would be useful relative association measures in this context. The relative effects were similar between men and women and were most pronounced in the loss or threat of loss (men: adjusted HR: 1.6, 95% confidence interval [CI]: 1.4–1.8; women: 1.4, 1.2–1.6) and high adversity (men: 1.6, 1.3–2.0; women: 1.6, 1.3–2.0) groups when compared with the low adversity group (Figure 4). This corresponded to 15.6 (95% CI: 10.0–21.2) and 9.7 (4.3–15.0) extra cases of CVD per 100 000 person-years among men and women in the loss or threat of loss group, respectively, and 18.0 (9.1–26.9) and 18.1 (8.6–27.6) extra cases of CVD per 100 000 person-years among men and women in the high adversity group, respectively (Figure 4). The risk of developing CVD was only modestly higher in the early life and persistent material deprivation groups compared with the low adversity group among both men and women. As expected, further adjustment for parental education attenuated the effect across all adversity groups (see Supplementary material online, Table S4). The largest attenuation was seen in the high adversity group where the adjusted HRs attenuated from 1.6 (1.3–2.0) to 1.3 (1.1–1.6) among men and from 1.6 (1.3–2.0) to 1.4 (1.2–1.8) among women. Adjustment for being born small for gestational age did not change the estimates substantially (data not shown).

Figure 3

Cumulative risk of cardiovascular disease per 100 000 individuals in the five childhood adversity trajectory groups for men and women, respectively.

Figure 4

Hazard ratios and hazard differences per 100 000 person-years for cardiovascular disease, ischaemic heart disease, and cerebrovascular disease, respectively. All estimates are adjusted for year of birth, maternal age at birth, parental origin, and parental cardiometabolic illness.

The cumulative number of individuals with IHD and CD per 100 000 individuals in the five adversity groups showed a similar pattern as the cumulative number for CVD (see Supplementary material online, Figures S3 and S4, respectively). The highest cumulative risk was observed in the high adversity group for both men and women. The HRs for IHD were generally higher across all adversity groups when compared with the HRs for CD, but translated into fewer extra cases per 100 000 person-years due to the low absolute number of IHD (Figure 4). Excluding CD diagnoses that may have been caused by congenital anomalies provided similar HRs as the full CD analysis, although the hazard differences were smaller due to fewer cases (see Supplementary material online, Figure S5).

Overall, the results for the subpopulation without parental cardiometabolic illness (n = 937 775) were similar to the results for the full study population (see Supplementary material online, Figure S6 and Table S5). However, as expected, the number of extra cases of CVD per 100 000 person-years in the high adversity group compared with the low adversity group was lower than for the full study population.

Discussion

In a total population sample followed prospectively from birth, we observed a higher risk of developing CVD in early adulthood among individuals exposed to childhood adversity compared with those exposed to low levels of adversity. The risk was most pronounced among individuals who experienced severe somatic illness and death in the family, and among individuals who experienced high and increasing annual rates of adversity throughout childhood and adolescence (Structured Graphical Abstract). The risk was generally comparable between men and women and slightly higher for IHD than for CD, although the corresponding number of extra cases was lower due to the low absolute number of IHD in this age group.

Our results corroborate previous studies showing a higher risk of CVD associated with self-reported adverse childhood experiences in middle-aged and older individuals.^20,30–32 Among the few studies reporting results from early adulthood, Sonu et al. observed a 2.5 times higher risk of CVD among US adults aged 18–34 years, who had been exposed to four or more adverse childhood experiences compared with unexposed individuals in a cross-sectional study.¹⁴ Recall bias can have contributed to the somewhat stronger association between childhood adversity and CVD observed in this previous study compared with ours. Our findings align with those of Pierce et al. reporting 46% higher risk of CVD among individuals in a US cohort with a mean age of 40 years, who reported high exposure to childhood psychosocial adversity compared with unexposed individuals.¹⁵ Similar associations were observed between severe physical and sexual abuse and CVD in a cohort of US nurses aged 25–42 years at baseline.¹⁶ We add to these previous findings by assessing the association in an unselected nationwide population sample with almost complete long-term follow-up.

The observed association between childhood adversity and CVD in early adulthood may be partly explained by adverse health-related behaviours, such as heavy drinking, smoking, and physical inactivity among those exposed to adversity early in life.^7,13,20 In addition, multiple bodily systems undergo rapid development during childhood and the experience of adversity during this period may result in long-lasting alterations in stress responsivity, with enduring elevated inflammatory levels as a consequence.^18,19,21,33 Such alterations in the physiological stress response may predispose individuals experiencing childhood adversity to atherosclerosis and hypertension and mediate the effect of childhood adversity on CVD that may present already in early adulthood. Further investigation into the specific mechanisms linking childhood adversity to IHD and CD in young adulthood remains a challenge for future studies.

The emotional distress and the adoption of unhealthy behaviours following experiences of childhood adversity may differ between males and females,^34–36 but few studies have examined the association between childhood adversities and CVD in men and women separately, and the results have been inconsistent.^8,11 We show sex differences in incidence patterns of CVD from age 30, but we generally find similar associations with childhood adversity in both sexes. These findings are in line with a previous study showing no sex differences in the association between physical maltreatment and CVD in a representative sample of US adults,³⁷ but are in contrast with other studies reporting an inconsistent pattern of sex-related variations.^38–42

It should be noted that exposure to early and persistent material deprivation in isolation from other childhood adversities did not seem to be an important risk factor for CVD in early adulthood in the current study. This seems to contrast the well-established literature showing a clear association between childhood socioeconomic conditions and CVD.^21,43 However, the current study adds new insight into this discussion by modelling adversities in multiple dimensions simultaneously, and showing that the combination of material deprivation and adversities related to loss or threat of loss and straining family dynamics (as combined in the high adversity group) is more strongly related to the risk of CVD than material deprivation alone. Such insights may have important implications for future preventive initiatives targeted at reducing social inequality in cardiovascular health. In line with this, it should be noted that our results were observed within the context of the Danish welfare system with extensive social security and universal healthcare and may not be generalizable to countries with less social security.

Our results indicate a need for preventive measures, but tools for individual screening for childhood adversity to assist clinical decision-making are not yet available.⁴⁴ Instead, upstream structural interventions, such as income supplementation and housing interventions, as well as universal maternity and home visit services seem promising in reducing childhood adversity.^45,46 In addition, families affected by severe illness or death could be identified in hospitals and other clinical settings. Providing timely support for these families has been shown to mitigate the adverse consequences of grief.⁴⁷ In combination, such efforts may have long-term cardio-protective effects.

Strengths and limitations

We used prospective and unselected data with objective and continuously registered information on childhood adversities and CVD. Using trajectories of childhood adversity allowed us to model some of the complex structures of childhood adversity that may affect CVD risk to an extent that is unseen in previous studies. CVD covers a heterogeneous range of diagnoses in terms of aetiology and risk factors. By presenting the results separately for IHD and CD, we were able to assess potential differences in associations with these outcomes across different domains of childhood adversity and between men and women.

An additional strength of this study was that we excluded individuals with a diagnosis of CVD or congenital heart disease before age 16 and performed a sensitivity analysis excluding CD diagnoses that often have a congenital aetiology when presenting in young adulthood. However, some of the remaining diagnoses may also have a congenital aetiology in some individuals, which may have attenuated the associations to some extent.

In addition, there is limited information on adversities when using Danish register data. For example, information on abuse and neglect is not available. However, since exposure to childhood adversities tends to cluster among socially deprived individuals,⁴⁸ it is very likely that those who were exposed to severe but unmeasured adversities, such as abuse and neglect, are included among those highly exposed to adversities in our study. Also, the prevalence of some of the adversities is expected to be underestimated (e.g. alcohol abuse and psychiatric illness), since many cases are never registered, and we may have underestimated the association between childhood adversities and CVD to some extent. However, by combining repeated information on 12 adversities known to be important sources of stress in children, we believe that we have captured a general pattern of stressful adversity exposure.

Cardiometabolic illness in the family could influence both the experience of adversity and the risk of developing CVD. Therefore, we adjusted for parental cardiometabolic illness as a proxy for genetic predisposition to CVD in the main analysis and performed a sensitivity analysis in which we only included those without parental cardiometabolic illness. Nevertheless, residual confounding by genetic predisposition to CVD may have resulted in a slight overestimation of the effect of childhood adversity on CVD.

In conclusion, the incidence of CVD is low in early adult life but increases substantially during this period. This highlights the importance of research into non-genetic early life risk factors, which may be targeted for early cardiovascular prevention. The experience of adversity is common among children, and we show that children who experience long-term and severe stress from somatic illness and death in the family, and children who are exposed to high annual rates of adversity, including deprivation, family loss, and straining family dynamics, particularly have a higher risk of developing CVD in early adulthood. Targeting the social origins of such adversity and ensuring supportive structures for families who are for example challenged by disease in the family may potentially carry long-term cardio-protective effects.

Author contributions

All authors contributed to the idea and designed the study. J.B. did the data linkage and data cleaning for DANLIFE. J.B. and A.R. conducted the analysis. Only J.B., L.K.E., A.R., and N.H.R. had access to raw data due to data protection rules, but all authors had access to summary data. M.L.L. secured the clinical relevance of the study. J.B. wrote the first and final drafts of the manuscript. All authors discussed the results and reviewed the manuscript. All authors have seen and approved the final text.

Supplementary material

Supplementary material is available at European Heart Journal online.

Funding

J.B. was supported by The Danish Heart Association (No. 20-R141-A9430-22171). L.K.E. was supported by a Rubicon grant (No. 45219105) of the Netherlands Organisation for Health Research and Development (ZonMw).

Data availability

The data material contains personally identifiable and sensitive information. According to the Act on Processing of Personal Data, such data cannot be made publicly available. Inquiries about secure access to data under conditions stipulated by the Danish Data Protection Agency should be directed to N.H.R. ([email protected]).

Author notes

Conflict of interest: G.S.A. owns shares in Novo Nordisk A/S. All other authors declare no conflict of interest.