Childhood maltreatment and health in the UK Biobank: triangulation of outcome-wide and polygenic risk score analyses

Childhood maltreatment, which includes emotional abuse and neglect, physical abuse and neglect, and sexual abuse, is common globally, with prevalence ranging from 15 to 25% [1, 2]. Childhood maltreatment is associated with substantial morbidity and mortality [3, 4] across domains of physical [5‐9] and psychosocial health [10‐18] and with worse socioeconomic status [19]. Despite extensive literature documenting a connection between childhood maltreatment and poorer health and well-being, our understanding of the impact of maltreatment in later life is constrained by key substantive and methodological limitations of extant research.

Regarding substantive issues, the relationship of childhood maltreatment with several domains of physical health remains poorly understood. For example, despite the association of childhood maltreatment with ocular [20‐23] and dental [24‐28] health in childhood and increasing recognition of the link between these health outcomes and stress- and inflammation-related disorders [29, 30], the long-term sequelae of childhood maltreatment in these domains are not well documented [31‐33]. Furthermore, findings remain inconclusive for some key physical health outcomes, including blood pressure [34‐39] and chronic pain [40, 41]. In addition, varying approaches to selection and grouping of outcomes limit our understanding of the broad impact of maltreatment, the comparability of findings, and the interpretability of results. Prior work has typically examined just one or a few selected outcomes, which impedes comparison of findings across outcomes and precludes identification of unexpected sequelae of maltreatment. In addition, some prior work has grouped outcomes into broad or inconsistent categories [42, 43], which may conflate outcomes with diverse etiologies and reduce the ability to disentangle potential bio-behavioral pathways linking childhood maltreatment to health.

Regarding methodological issues, evidence suggests that results linking maltreatment to adverse outcomes may be inflated by recall bias in the reporting of maltreatment [44, 45] as well as confounding by childhood poverty and related neighborhood factors [19, 46, 47]. Discussions of recall bias in the literature have focused largely on the advantages of using prospective measures of maltreatment and reports from multiple informants, though these alternatives to retrospective self-reports may underestimate certain types of maltreatment and fail to capture key mechanisms linking maltreatment and mental health [44, 48‐50]. Less examined are techniques such as E-values, which have been used to assess robustness of observational results to unmeasured confounding in the epidemiologic literature [51‐53] but have not been widely used in studies of maltreatment. A relatively new approach is the use of genetic data as a surrogate for exposure, given that genetic markers are affected by types of biases (e.g., weak instrument, population stratification, pleiotropy) that are different from the sources of bias that commonly affect observational studies and produce non-causal results (e.g., residual confounding, reverse causation, information bias) [54‐56]. To date, the use of genetic data as a proxy for childhood maltreatment has been limited [56‐60].

In the present study, we address the substantive limitations by estimating associations between self-reported childhood maltreatment and over 400 adulthood social, economic, and health indicators, including both leading causes of mortality and diverse markers of quality of life. This outcome-wide analysis (OWA) approach aims to: reduce potential investigator bias in outcome selection, facilitate comparison of effect sizes, transparently report null results, use consistent confounding control, correct for multiple testing, and examine robustness of results to unmeasured confounding [61, 62]. We address other methodological issues by repeating the OWA using a polygenic risk score (PRS) for childhood maltreatment, which was derived using both prospective and retrospective data on childhood maltreatment. This PRS is less susceptible to recall bias given the high degree of shared genetic variance between the PRS from prospective and the PRS from retrospective reports [56]. A PRS can be used as a proxy for an exposure based on independent genetic variants and is less susceptible to reverse causation or residual confounding from childhood experiences [56, 63, 64]. Finally, we triangulate across these two OWAs and examine the concordance of results across measures of childhood maltreatment. Given that each OWA is influenced by different types of confounding and measurement bias, the comparison and integration of both sets of results presents an opportunity for more reliable causal inference regarding the effects of maltreatment in later life [55]. Our triangulation approach using genetic data, which to our knowledge has not been applied to the study of maltreatment, allows us to increase our confidence in a causal association when results are concordant.

We conducted two large-scale OWAs that examined the relationship between childhood maltreatment and hundreds of outcomes capturing health and well-being in adulthood. First, we triangulated observational evidence with that of a genetic OWA and identified robust associations of maltreatment with increased risk of mental illness, insomnia, health risk behaviors, asthma, pain, high BMI, and low socioeconomic status, among other outcomes. Second, we linked self-reported maltreatment to a range of previously underexamined outcomes, including a higher risk of hearing difficulties, blurred vision, dental problems, and digestive diseases. Third, many of the novel associations identified in our observational OWA were unlikely to be explained by expected levels of unmeasured confounding, as quantified in sensitivity analyses using E-values [51‐53]. Altogether, our results highlight the far-reaching negative effects of childhood maltreatment in later life, which include both leading causes of mortality as well as extensive influences on quality of life.

In the present study, our concordant results largely aligned with existing literature. We triangulated results linking maltreatment to poorer outcomes across the domains of mental illness [10, 12], sleep disorders [90, 91], chronic pain [40], chronic lung diseases [8], risk behaviors [92‐95], and socioeconomic status [19]. This outcome-wide study does not allow for the investigation of mechanisms underlying these associations. Prior evidence suggests that psychological (e.g., post-traumatic stress disorder), behavioral (e.g., physical activity), and biological (e.g., immune dysregulation) pathways are likely at play [96‐100].

We also identified results unique to each OWA. While our observational OWA identified relationships between maltreatment and many outcomes within the domains of ocular and oral health, digestive diseases, cardiovascular diseases, and related risk factors such as diet and physical activity, the genetic OWA for the most part did not identify such associations. On the other hand, the genetic OWA uniquely identified significant relationships of maltreatment PRS with diastolic blood pressure, glaucoma, and various white blood cell measures. Discordant results may reflect biases resulting in spurious associations or limitations preventing the detection of a true association in only one of the two OWAs. For example, the signals identified only in the observational OWA may be inflated due to residual confounding by childhood socioeconomic status that was not fully captured by maternal smoking status, given the association of childhood socioeconomic status with diet [101, 102], physical activity [103], cardiovascular disease [104, 105], and digestive diseases [106] in adulthood. However, compared to the maternal smoking at birth coefficient, our E-values for many of the results unique to the observational OWA were large, indicating plausible robustness to residual confounding by childhood socioeconomic status. Specifically, prior literature has demonstrated a strong association between maternal smoking during pregnancy and socioeconomic status (e.g., up to sixfold increase in smoking among the lowest vs. highest educated groups) [83, 84], making it less likely for an unmeasured socioeconomic confounder to correlate as strongly with both maltreatment [107] and the outcome in question as did maternal smoking status, beyond concurrent adjustment for maternal smoking status. True effect sizes between maltreatment and outcomes related to digestive and cardiovascular diseases, among others, may be smaller than what was reported in the observational OWA due to residual confounding but likely still non-zero. Furthermore, our PRS at genome-wide significance was likely underpowered [56], and secondary results using PRS based on a p-value threshold of 0.5 exhibited a higher number of significant associations in these domains. While the higher proportion of significant results from the larger PRS should be interpreted with additional caution given the vulnerability of a PRS with more SNPs to noise and violations of instrumental variable assumptions [56, 86‐89], results across observational and genetic OWAs may not be as discordant as they appear. As postulated in prior studies that also found inconsistent results linking maltreatment to cardiovascular outcomes [34‐36], discordant results for blood pressure may be explained by the limitations of our blood pressure measures, the differing relationship of maltreatment with point-in-time measurements versus trajectories of blood pressure, and our inability to account for antihypertensive medication use within the outcome-wide framework. Future observational studies should triangulate across different sources of data, as we explored with blood pressure (self-reported diagnosis vs. laboratory point-in-time measures of blood pressure); future genetic studies may look to identify a higher-powered PRS to further maltreatment investigations.

Our study has several strengths. First, we prioritized an agnostic, data-driven approach to outcome selection, leveraging prior applications of data reduction techniques to distill large-scale phenotype data [66]. In doing so, we examined a comprehensive set of outcomes that contribute meaningfully to both the variation and correlation structures in the human phenome, allowing for comparison of the impact of maltreatment across a large range of relevant outcomes while limiting investigator influence on the outcome selection process. Several outcomes in our comprehensive list have received scant prior attention. Second, we used E-values to examine the robustness of our observational results to unmeasured confounding [83]. Comparison of the E-value and the maternal smoking coefficient, though not immune to bias or violation of assumptions [51], provided a useful metric for determining potential robustness of results. Third, we triangulated results from observational and genetic OWAs with different biases, demonstrating how this outcome-wide triangulation design may be applied to strengthen both novel discovery and causal inference. The results from the observational OWA may be inflated due to confounding by environmental factors [19, 46, 47] or mood-dependent recall bias [44, 45], whereby individuals with a higher burden of mental illness are more likely to report adverse experiences during childhood. The latter phenomenon may have particularly inflated results from the mental health domain. In contrast, the PRS for maltreatment relies on genetic variants that are assigned independently at conception and thus should be less susceptible to environmental confounding; furthermore, the high correlation (r_g = 0.72) between GWAS for prospectively and retrospectively assessed childhood maltreatment indicates that the PRS we used based on a meta-GWAS of retrospectively and prospectively assessed childhood maltreatment was unlikely to be affected by recall bias [54, 56]. Conversely, the genetic OWA is limited by the low variance explained by the PRS [56], resulting in weak instrument bias towards the null [55, 108, 109], and may also be subject to residual population stratification [108] and horizontal pleiotropy (a direct effect of genetics on the outcome that does not act through maltreatment) [109, 110]. However, the orthogonal features of these approaches allowed us to triangulate concordant results for more compelling evidence of maltreatment’s profound impact on health.

In our study, we used the PRS for childhood maltreatment as a tool in the context of outcome-wide analysis to strengthen evidence for the causal adverse effects of childhood maltreatment on a wide range of health domains. The PRS used in this study was developed by Warrier et al., who found that childhood maltreatment is moderately heritable and emerges through the complex interplay of genetic and environmental factors not yet fully understood [56]. Additionally, the heritability of childhood maltreatment is likely at least partially explained by intergenerational transmission, whereby both genes and environments are passed down from parents to children. As noted by Warrier et al., the heritability of childhood maltreatment does “not imply that environmental factors are absent, that the child is to blame, or that the heritability is fixed” ([56] p. 383). We would add that the heritability of childhood maltreatment does not say anything about the effectiveness of environmental interventions to prevent childhood maltreatment or its downstream adverse effects on health. Meta-analysis suggests specific components of interventions (e.g., parenting skills) that are effective in both preventing and reducing childhood maltreatment and ameliorating its adverse effects [111, 112]. For a detailed discussion of the broader implications of research on genetic influences on childhood maltreatment, see the Appendix from Warrier et al. [56].

At least seven study limitations should also be considered. First, in the observational OWA, we relied on retrospective self-reports of maltreatment based on a brief screener and were not able to examine the concordance of these reports with prospective observations of maltreatment. Researchers have documented poor agreement between prospective and retrospective measures of childhood maltreatment and between self-reports and reports from other informants [48, 49]. Of key concern is the role of memory biases related to mood and psychopathology at the time of maltreatment reporting, as mentioned above [44, 45]. It is possible that some of the associations identified in our observational OWA would not replicate in an analysis that used prospective observations of maltreatment [50]. This limitation is less of a concern with our genetic instrument, given the strong genetic correlation between retrospective and prospective reports of maltreatment (r_g = 0.72) [56]. Second, the UK Biobank has limited information on paternal factors from early childhood and thus we could not adjust for paternal factors such as age and lifestyle that may confound the association between maltreatment and health outcomes [113‐117]. Third, the UK Biobank sample is healthier and wealthier than the UK population, which may impact the external validity of our results [71]. Fourth, we cannot rule out confounding of the PRS-health relationships by other phenotypes that share genetic architecture with maltreatment in a way that may bias results, depending on the pathways involved. This limitation weakens the argument of orthogonal bias between the two OWAs and should be robustly investigated in extensions of this work. Fifth, the GWAS used to inform our PRS excluded individuals of non-European ancestry [56], and due to the considerably lower accuracy of PRS in non-European ancestry individuals when drawing on Eurocentric GWAS [69, 70], we were unable to include such groups in our genetic OWA. Our group [118‐120] and others [121‐126] are working to expand genetic studies in non-European ancestry populations, which have been sorely underrepresented in such work. Given the higher burden of adversity (including maltreatment and many of the outcomes investigated here) in such underrepresented populations [127‐133], expansion of this current work is critical. Sixth, certain outcomes were omitted, as variables that were systematically missing were excluded from the prior factor analysis; these included reproductive and maternal health outcomes, previously linked to maltreatment, that were only asked of female participants [66, 134, 135]. Finally, despite the large sample, specific analyses had limited power due to low outcome prevalence, and we were not able to examine the effects of different types of maltreatment.

Despite these limitations, multiple clinical and methodological implications emerge from our findings. We demonstrated the utility of an outcome-wide triangulation design with sensitivity analyses to examine the wide-ranging effects of childhood maltreatment on health and well-being in later life and to address the pervasive challenges of confounding and outcome selection in the field of maltreatment research. Future studies may extend our research using other causal approaches such as the numerous methods of Mendelian Randomization or genomic SEM, which would allow for a more thorough examination of potential biases related to horizontal pleiotropy and shared genetic architecture of complex traits [54, 136‐138]. Studies with comprehensive longitudinal data may extend these analyses with longitudinal or time-to-event analyses and may investigate relationships between outcomes and the likely presence of mediators and moderators in the pathways linking maltreatment and health [139]. From a clinical perspective, screening for childhood maltreatment may be an effective tool for identifying those at increased risk of adverse outcomes, given the diverse potential consequences of childhood maltreatment on human health and well-being identified. Though research on routine screening for adverse childhood experiences remains limited [140], the expected individual- and population-level benefits of mitigating the health consequences of childhood maltreatment are considerable [141]. Additionally, the prevalence of maltreatment and its relationship with myriad domains of health and functioning–including those previously understudied–reinforces the importance of trauma-informed and integrated healthcare across specialties [142, 143]. In conclusion, by utilizing big data, genetics, and the outcome-wide framework, we underscore the urgent need to intervene upon the sweeping effects of childhood maltreatment on long-term health and well-being.

Using an outcome-wide triangulation design with sensitivity analyses, we investigated the relationship of childhood maltreatment with over 400 health outcomes in later life. Our study design proved useful for both novel discovery and causal inference, drawing on orthogonal features of leading epidemiologic approaches to document the far-reaching negative effects of maltreatment on both leading causes of mortality and diverse markers of quality of life.