Socioeconomic mediators of the effect of resilience on cardiometabolic biomarkers in the UK Biobank

Socioeconomic mediators of the effect of resilience on cardiometabolic biomarkers in the UK Biobank | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Socioeconomic mediators of the effect of resilience on cardiometabolic biomarkers in the UK Biobank Chris Patrick Pflanz, John Gallacher, Sarah Bauermeister This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6990489/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Exposure to childhood adversity is linked to negative outcomes later in life, including cardiometabolic diseases. Resilient coping in the face of childhood adversity may buffer the deleterious impact of childhood adversity on cardiometabolic biomarkers. However, further research is needed to shed light on factors mediating the effect of resilience on cardiometabolic health. Objective. To investigate the factors that contribute to resilience in the UK Biobank, a large, heterogenous, population-based cohort study. To investigate socioeconomic mediators of the effect of resilience on metabolic health outcomes. Methods. A resilience metric was derived as residuals from multi-linear regression of subjective wellbeing on child adversity. Structural equation models were fitted to the data with resilience as independent variable, metabolic outcomes as dependent variables and socioeconomic variables as mediators. Results. Resilience was associated with higher household income, lower deprivation, lower glycated haemoglobin and lower LDL-to-HDL cholesterol ratio. The effect of resilience on glycated haemoglobin and BMI were mediated by socioeconomic variables. The effect of resilience on LDL-to-HDL cholesterol ratio was mediated by household income, but not by deprivation. Conclusions. Resilience was found to be associated with higher household income, lower Townsend deprivation index, lower glycated haemoglobin and lower cholesterol levels. The effect of resilience on cardiometabolic biomarkers was mediated by socioeconomic indicators including household income and deprivation. Early adversity childhood adverse experiences resilience Townsend deprivation index body mass index glycosylated haemoglobin cholesterol Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Exposure to childhood adversity is linked to early morbidity and mortality (Grummitt et al., 2021) including cardiometabolic disease (Jakubowski et al., 2018; Li et al., 2019) and associated cardiometabolic biomarkers (Miller and Lacey, 2022). Resilient coping in the face of childhood adversity was associated with lower distress and buffered the impact of childhood adversity (Beutel et al., 2017). However, further research is needed to shed light on factors contributing to resilience and develop a broader evidence base for the design of evidence-based resilience fostering interventions. Therefore, the purpose of the present study was to investigate the factors that contribute to resilience in the UK Biobank, a large, heterogenous, population-based cohort study, using structural equation modelling and mediation analysis. Childhood adversity is a broad umbrella term for adverse childhood experiences that can impair children’s physical and mental health and wellbeing (Patterson et al., 2014; Racine et al., 2020; Reid et al., 2017). The U.S. Department of Health & Human Services distinguishes between several types of childhood adversity including physical abuse, psychological or emotional maltreatment, sexual abuse, sex trafficking, medical neglect, neglect or deprivation of necessities (U.S. Department of Health & Human Services, 2021). Childhood adversity has a negative impact on psychosocial development across the lifespan that may include poor overall mental and physical health (Nelson et al., 2020; Ritchie et al., 2011), depression (Chapman et al., 2004), lifestyle risk issues including smoking and sexual risk behaviour high-risk lifestyles (Felitti et al., 2019; Ford et al., 2011; Hillis et al., 2001), increased criminality, substance abuse, youth violence and suicide (Afifi et al., 2009; Fox et al., 2015; Webb et al., 2017; Ye and Reyes-Salvail, 2014), lower socioeconomic status, income and education (Jaffee et al., 2018), and cardio-metabolic and respiratory disease (Danese et al., 2009; Danese and McEwen, 2012; Hughes et al., 2017). Some types of childhood adversities also increase the risk of obesity in adulthood and, in turn, increase the risk for type 2 diabetes (Thomas et al., 2008). In spite of severe childhood adversity, some children are seemingly unaffected and resilient against the negative impact of these adverse experiences (Gartland et al., 2019). However, the factors that foster resilience in these individuals remain under-researched and operationalizing resilience remains a challenge in current research. For example, previous research using psychometric scales suffered from low reliability of the psychometric instruments (Bagci et al., 2014). One reason why it is difficult to measure resilience psychometrically is that resilience is not a static personality trait that remains stable over the lifespan, but is rather a function of coping capacity and cumulative adversity experienced over the life course (Kuenzi, 2022). Therefore, an empirical definition of resilience was suggested that operationalizes resilience as the residual score obtained by regressing adversity severity against psychosocial functioning (Ioannidis et al., 2020). This operational definition has several advantages: a single metric can be used to compare individuals that experienced various levels of childhood adversity with respect to their level of resilience. Resilience can be measured on a continuum rather as a dichotomized construct with individuals either being resilient to adversity or not. Even though resilience remains a largely neglected construct in epidemiological research, the regression method allows researcher to derive a resilience metric from previous cohort datasets that lack psychometric data from resilience scales. Previous research on resilience, using psychometric scales to measure the construct, found that trait resilience is positively associated with indicators of mental health (Hu et al., 2015). Resilience was found to be strongly associated with socio-economic status (Schwartz et al., 2019). Resilience (psychological hardiness) was also a predictor of cardiometabolic health including higher levels of “good” high density lipoprotein cholesterol and lower body mass index (BMI) (Bartone et al., 2016). Lower levels of resilience were also associated with general and central obesity (Foster and Weinstein, 2019; Stewart-Knox et al., 2012). In light of these previous findings and their limitations, the aim of our study was to investigate the effect of resilience on cardiometabolic health in the UK Biobank and to investigate if the effect of resilience on cardiometabolic health was mediated by socioeconomic variables. We operationalized resilience as previously conceptualized by Ioannidis et al. (2020) as the residual fit using a regression model of subjective wellbeing as psychosocial outcome measure against childhood adversity. Methods Design Details of the design, participants, procedure and ethics of the UK Biobank study are available elsewhere (Sudlow et al., 2015). The UK Biobank is a large, population-based study that involved the recruitment of 502,665 participants and the collection of comprehensive baseline data (Sudlow et al., 2015). Ethical approval was granted to Biobank from the Research Ethics Committee - REC reference 11/NW/0382 (Sudlow et al., 2015). A cross-sectional design using baseline data was used for the present investigation. Materials Childhood adversity. Childhood adversity was assessed using items from the Childhood Trauma Screener (CTS-5) (Glaesmer et al., 2013). Subjective wellbeing. Information on subjective wellbeing (happiness) was derived from a bespoke mental-health questionnaire in the UK Biobank that comprised 6 variables scored on a 6-point Likert scale: “Extremely happy”, “Very happy”, “Moderately happy”, “Moderately unhappy”, “Very unhappy”, “Extremely unhappy”. The items were: "In general how happy are you?", "In general how satisfied are you with the work that you do?", "In general how satisfied are you with your health?", "In general how satisfied are you with your family relationships?", "In general how satisfied are you with your friendships?, “"In general how satisfied are you with your financial situation?”. Markers of metabolic risk. Markers of metabolic risk factors included the body mass index (BMI) and glycated (glycosylated) haemoglobin (HbA1c) and cholesterol from UK Biobank blood biochemistry.Blood samples were collected at recruitment (for all 500,000 participants) and repeat assessment ~5 years later (for 20,000 participants), measuring a range of key biochemistry markers (Elliott and Peakman, 2008). High-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol and glycated haemoglobin were drawn from the blood biochemistry assessment at the first visit. The biomarkers were selected for analysis because they represent established risk factors associated with risk of many metabolic diseases. The cholesterol measures were combined into the LDL-to-HDL cholesterol ratio that indicates the ratio of “bad” to “healthy” cholesterol. Socioeconomic indicators. The Townsend deprivation index (Townsend et al., 1988) and average total household income before tax were used as socioeconomic indicators. Demographic variables. Models were adjusted for demographic variables including age, gender, and years of education variable was derived by combining the age at which subjects completed full-time education and qualifications variable. Missing data in the variable age when full-time education was completed were imputed from the Qualifications variable using the following average ages corresponding to the qualifications: 21 years for College/University degree or other professional qualifications (e.g. nursing, teaching), 17 years for A-levels/AS-levels or NVQ/HND/HNC or equivalent, and 15 years for O-levels/GCSE and CSE or equivalent. Statistical analysis Item-response theory Item-response theory and Mokken analysis wereused to optimize the happiness scale, as previously reported (Pflanz and Bauermeister, preprint ). Following item reductions an estimate of the latent trait of happiness was derived using three items measuring general happiness, friendship satisfaction, and family satisfaction. We will refer to the individual level of expression of the latent trait of happiness as theta in the following, wherein theta can be interpreted as a standardized z-score with a latent trait mean = 0 and standard deviation = 1. Regression analysis Multi-linear regression was carried out with the latent happiness trait (theta) from the item-response theory analysis as outcome variable and childhood adversity, sex, age, and years of education as predictors. The residuals of the regression model for each participant were used as an indicator of resilience. Structural equation modelling Structural equation models were fit to the data with resilience as independent variable, the metabolic indicators BMI, glycated haemoglobin, and LDL-to-HDL cholesterol ratio as dependent variables and the socioeconomic variables TDI or household income as mediators (See supplementary methods for details). Results Sample The total UK Biobank sample was 502,430, aged 37 to 73 years. All participants with known email addresses were invited to complete a mental health questionnaire online, which contained the subjective wellbeing questionnaire and childhood trauma screener. After withdrawals, 55,891 participants had complete data including the subjective wellbeing questionnaire, childhood trauma screener, sex, age, and years of education. This subsample was aged 40 to 70 years (M = 56.45 years; SD = 7.82), and was 56.69% female. The mean years of education was 19.93 (SD = 2.55) ranging from 5 to 35 years. The mean childhood adversity score was 1.52 (SD = 2.10) ranging from 0 to 16. Regression analysis Results of the multiple linear regression indicated that there was an overall significant effect when predicting the latent trait (theta) of subjective wellbeing from the item-response theory using sex, years of education, age and childhood adversity as predictors (F(4, 55,886) = 912.16, p < .0001, R 2 = .06, adjusted R 2 =.06). The individual predictors were examined further and indicated that childhood (t = -52.39, p < .001) adversity as well as the predictors of no interest: sex (t = -12.80, p < .001), years of education (t = -17.19, p < .001), and age (t = 17.95, p < .001) were significant predictors in the model. The residuals from the regression model for each participant were used as continuous indicator of resilience that is adjusted for covariation by sex, age, years of education and indicative of whether an individual participant is more or less resilient as predicted by the regression model. We will refer to the residuals as resilience in the following. Figure 1 depicts a binned scatter plot of happiness over childhood adversity and a histogram of the resilience indicator. Structural equation modelling Direct effect model The direct-effect structural equation model with resilience as predictor and household income, TDI, BMI, glycated haemoglobin, and LDL-to-HDL cholesterol ratio showed that resilience significantly predicted household income (β = .075, p < 0.001), TDI (β -.053, p < 0.001), glycated haemoglobin (β = -.026, p < 0.001), LDL-to-HDL cholesterol ratio (β = -.013, p = 0.006), but not BMI (β = .006, p < 0.221). All covariance paths were significant (all p < .05, see Table 3). Mediation analysis Townsend deprivation index as mediator . The structural equation model (Figure 3) showed significant direct effects on glycated haemoglobin (β = -.021, p < 0.001) and LDL-to-HDL cholesterol ratio (β = -.013, p = 0.005) with higher levels of resilience predicting lower levels of glycated haemoglobin and LDL-to-HDL cholesterol, whereas the effect on BMI (β = .008, p < 0.081) was not significant. The effect of resilience on BMI was completely mediated by TDI (β = -.003, p < 0.001) with 57.2% of the effect of resilience on BMI mediated by TDI (Indirect effect / Total effect Ratio = 0. 572). The effect of resilience on glycated haemoglobin was partially mediated by TDI (β = -.003, p < 0.001) showing complementary mediation with 10.7% of the effect of resilience on glycated haemoglobin mediated by TDI (Indirect effect / Total effect Ratio = 0.107). There was no evidence of mediation for the effect of resilience on LDL-to-HDL cholesterol ratio (β = 0, p = 0.094). Table 4 shows the path coefficients and test statistics for the direct and indirect effects in the model as well as the components. Household income as mediator . The structural equation model (Figure 4) showed significant direct effects of resilience on glycated haemoglobin (β = -.021, p < 0.001) and BMI (β = .012, p < 0.016) with higher levels of resilience predicting lower levels of glycated haemoglobin and higher BMI, whereas the effect on LDL-to-HDL cholesterol (β = -.008, p = 0.085) was not significant. The effect of resilience on BMI was partially mediated by household income (β = -.006, p < 0.001) with 100% of the effect of resilience on BMI mediated by household income showing a competitive partial mediation (Indirect effect / Total effect Ratio = 1). The effect of resilience on glycated haemoglobin was partially mediated by household income (β = -.005, p < 0.001) showing complementary mediation with 17.8% of the effect of resilience on glycated haemoglobin mediated by household income (Indirect effect / Total effect Ratio = 0.178). The effect of resilience on LDL-to-HDL cholesterol ratio was completely mediated by household income (β = -.005, p < 0.001) showing indirect-only mediation with 36.5% of the effect of resilience on LDL-to-HDL cholesterol ratio mediated by household income (Indirect effect / Total effect Ratio = 0.365). Table 4 shows the path coefficients and test statistics for the direct and indirect effects in the model as well as the components. Discussion This study investigated the effect of resilience on cardiometabolic biomarkers in the UK Biobank and its mediation by socioeconomic variables. We found that resilience was associated with higher household income, lower TDI, lower glycated haemoglobin and lower LDL-to-HDL cholesterol ratio. The effect of resilience on glycated haemoglobin was partially mediated by TDI and household income. The effect of resilience on BMI was mediated by TDI and household income. The effect of resilience on LDL-to-HDL cholesterol ratio was fully mediated by household income, but not by TDI. We have used a residuals approach to derive the resilience metric for our analysis (Ioannidis et al., 2020) that enabled us to investigate resilience in the UK Biobank despite the lack of resilience scale in the protocol. Overall our findings confirm previous research that showed associations between resilience and higher socio-economic status (Schwartz et al., 2019), higher levels of “good” high density lipoprotein cholesterol (Bartone et al., 2016), and lower levels of general obesity (Foster and Weinstein, 2019; Stewart-Knox et al., 2012). Our study adds to these findings that the effect of resilience on glycated haemoglobin was mediated by TDI and household income, and the effect of resilience on LDL-to-HDL cholesterol ratio was mediated by household income. Overall, our findings show that socioeconomic indicators are important mediators of the effect of resilience on improving metabolic health. Implications These findings have important implications for social policy. As our results showed, the effects of resilience on cardiometabolic biomarkers from blood biochemistry were mediated by socioeconomic indicators of deprivation. Social policies and interventions to support individuals with adverse childhood experiences could include a financial literacy component alongside mental health interventions. Further research Future research could validate resilience estimates derived as residuals from regression models further using psychometric scales to investigate their convergent validity. This was not feasible in the UK Biobank study because the study protocol did not include a psychometric resilience scale. Strengths Our study has several strengths: First, the study’s sample size was large. Second, IRT was used to optimise the subjective wellbeing outcome variable. Compared to summated test scores, IRT has the advantage of improved precision and reliability through the removal of misfitting items (Henning, 1984). Third, the analyses were adjusted for a range of covariates. Limitations The present study has several limitations that merit discussion: The data analysis is cross-sectional, therefore, our ability to test for causality in the mediation analysis is limited. The UK Biobank, similar to other cohort studies, does not include a psychometric resilience scale, e.g. Connor-Davidson Resilience Scale (Connor and Davidson, 2003), and we therefore had to operationally derive a resilience metric using a regression model of child adversity against happiness, but could not validate this metric against already, established psychometric scales of resilience. However, it should be noted that previous research demonstrated that the residuals method of operationalising resilience had good construct and predictive validity (Cahill et al., 2022). Childhood adversity was assessed retrospectively self-report and might be subject to retrospective memory bias. This, in turn, may have impacted the trustworthiness of the resilience metric. Conclusions In conclusion, resilience can be further investigated in cohort studies that lack psychometric resilience scales using regression models to derive the resilience metric. Our analysis showed that resilience is associated with higher household income, lower TDI, lower glycated haemoglobin and lower LDL-to-HDL cholesterol ratio. The effect of resilience on cardiometabolic biomarkers was mediated by socioeconomic indicators including household income and socioeconomic deprivation. Declarations Acknowledgements All analyses were conducted on the Dementias Platform (DPUK) Data Portal using the UK Biobank application 15697 PI John Gallacher for DPUK project 0169. The Medical Research Council supports DPUK through grant MR/T0333771. DPUK supports Sarah Bauermeister and Patrick Pflanz. Access to the data can be requested through UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). Competing interests SB, CPP and JG declare no competing interests Ethics approval and consent to participate Secondary data analysis only with ethical approval in place from source cohort, UK Biobank Research Ethics Committee - REC reference 11/NW/0382. Consent for publication SB, CPP and JG give full consent for publication Availability of data and materials The dataset(s) supporting the conclusions of this article is(are) available in the Dementias Platform UK (DPUK) Data Portal repository, https://portal.dementiasplatform.uk/. Authors' contributions SB and JG conceptualised the idea. CPP and SB analysed and interpreted the data and wrote the manuscript. CPP and JG edited and proofread the manuscript. All authors read and approved the final manuscript. References Afifi, T.O., Boman, J., Fleisher, W., Sareen, J., 2009. The relationship between child abuse, parental divorce, and lifetime mental disorders and suicidality in a nationally representative adult sample. Child Abuse Negl. 33, 139–147. Bagci, S.C., Rutland, A., Kumashiro, M., Smith, P.K., Blumberg, H., 2014. Are minority status children’s cross-ethnic friendships beneficial in a multiethnic context? Br. J. Dev. Psychol. 32, 107–115. https://doi.org/10.1111/bjdp.12028 Bartone, P.T., Valdes, J.J., Sandvik, A., 2016. Psychological hardiness predicts cardiovascular health. Psychol. Health Med. 21, 743–749. https://doi.org/10.1080/13548506.2015.1120323 Beutel, M.E., Tibubos, A.N., Klein, E.M., Schmutzer, G., Reiner, I., Kocalevent, R.-D., Brähler, E., 2017. Childhood adversities and distress - The role of resilience in a representative sample. PLoS One 12, e0173826. https://doi.org/10.1371/journal.pone.0173826 Cahill, S., Hager, R., Chandola, T., 2022. The validity of the residuals approach to measuring resilience to adverse childhood experiences. Child Adolesc. Psychiatry Ment. Health 16, 18. https://doi.org/10.1186/s13034-022-00449-y Chapman, D.P., Whitfield, C.L., Felitti, V.J., Dube, S.R., Edwards, V.J., Anda, R.F., 2004. Adverse childhood experiences and the risk of depressive disorders in adulthood. J. Affect. Disord. 82, 217–225. Connor, K.M., Davidson, J.R.T., 2003. Development of a new resilience scale: The Connor‐Davidson resilience scale (CD‐RISC). Depress. Anxiety 18, 76–82. Danese, A., McEwen, B.S., 2012. Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiol. Behav. 106, 29–39. Danese, A., Moffitt, T.E., Harrington, H., Milne, B.J., Polanczyk, G., Pariante, C.M., Poulton, R., Caspi, A., 2009. Adverse childhood experiences and adult risk factors for age-related disease: depression, inflammation, and clustering of metabolic risk markers. Arch. Pediatr. Adolesc. Med. 163, 1135–1143. Elliott, P., Peakman, T.C., 2008. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int. J. Epidemiol. 37, 234–244. Felitti, V.J., Anda, R.F., Nordenberg, D., Williamson, D.F., Spitz, A.M., Edwards, V., Koss, M.P., Marks, J.S., 2019. Reprint of: 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. 56, 774–786. Ford, E.S., Anda, R.F., Edwards, V.J., Perry, G.S., Zhao, G., Li, C., Croft, J.B., 2011. Adverse childhood experiences and smoking status in five states. Prev. Med. (Baltim). 53, 188–193. Foster, B.A., Weinstein, K., 2019. Moderating Effects of Components of Resilience on Obesity Across Income Strata in the National Survey of Children’s Health. Acad. Pediatr. 19, 58–66. https://doi.org/https://doi.org/10.1016/j.acap.2018.08.012 Fox, B.H., Perez, N., Cass, E., Baglivio, M.T., Epps, N., 2015. Trauma changes everything: Examining the relationship between adverse childhood experiences and serious, violent and chronic juvenile offenders. Child Abuse Negl. 46, 163–173. Gartland, D., Riggs, E., Muyeen, S., Giallo, R., Afifi, T.O., MacMillan, H., Herrman, H., Bulford, E., Brown, S.J., 2019. What factors are associated with resilient outcomes in children exposed to social adversity? A systematic review. BMJ Open 9, e024870. Glaesmer, H., Schulz, A., Häuser, W., Freyberger, H.J., Brähler, E., Grabe, H.-J., 2013. The childhood trauma screener (CTS): Development and validation of cut-off-scores for classificatory diagnostics. Psychiatr. Prax. 40, 220–226. https://doi.org/10.1055/s-0033-1343116 Grummitt, L.R., Kreski, N.T., Kim, S.G., Platt, J., Keyes, K.M., McLaughlin, K.A., 2021. Association of Childhood Adversity With Morbidity and Mortality in US Adults: A Systematic Review. JAMA Pediatr. 175, 1269–1278. https://doi.org/10.1001/jamapediatrics.2021.2320 Henning, G., 1984. Advantages of latent trait measurement in language testing. Lang. Test. 1, 123–133. https://doi.org/10.1177/026553228400100201 Hillis, S.D., Anda, R.F., Felitti, V.J., Marchbanks, P.A., 2001. Adverse childhood experiences and sexual risk behaviors in women: a retrospective cohort study. Fam. Plann. Perspect. 206–211. Hu, T., Zhang, D., Wang, J., 2015. A meta-analysis of the trait resilience and mental health. Pers. Individ. Dif. 76, 18–27. https://doi.org/https://doi.org/10.1016/j.paid.2014.11.039 Hughes, K., Bellis, M.A., Hardcastle, K.A., Sethi, D., Butchart, A., Mikton, C., Jones, L., Dunne, M.P., 2017. The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. Lancet public Heal. 2, e356–e366. Ioannidis, K., Askelund, A.D., Kievit, R.A., van Harmelen, A.-L., 2020. The complex neurobiology of resilient functioning after childhood maltreatment. BMC Med. 18, 32. https://doi.org/10.1186/s12916-020-1490-7 Jaffee, S.R., Ambler, A., Merrick, M., Goldman-Mellor, S., Odgers, C.L., Fisher, H.L., Danese, A., Arseneault, L., 2018. Childhood maltreatment predicts poor economic and educational outcomes in the transition to adulthood. Am. J. Public Health 108, 1142–1147. Jakubowski, K.P., Cundiff, J.M., Matthews, K.A., 2018. Cumulative childhood adversity and adult cardiometabolic disease: A meta-analysis. Heal. Psychol. Off. J. Div. Heal. Psychol. Am. Psychol. Assoc. 37, 701–715. https://doi.org/10.1037/hea0000637 Kuenzi, M., 2022. The impact of life course adversity on later life cognition. https://doi.org/10.13097/archive-ouverte/unige:164543 Li, L., Pereira, S.M.P., Power, C., 2019. Childhood maltreatment and biomarkers for cardiometabolic disease in mid-adulthood in a prospective British birth cohort: associations and potential explanations. BMJ Open 9, e024079. https://doi.org/10.1136/bmjopen-2018-024079 Miller, N.E., Lacey, R.E., 2022. Childhood adversity and cardiometabolic biomarkers in mid-adulthood in the 1958 British birth cohort. SSM - Popul. Heal. 19, 101260. https://doi.org/https://doi.org/10.1016/j.ssmph.2022.101260 Nelson, C.A., Scott, R.D., Bhutta, Z.A., Harris, N.B., Danese, A., Samara, M., 2020. Adversity in childhood is linked to mental and physical health throughout life. BMJ 371, m3048. https://doi.org/10.1136/bmj.m3048 Patterson, M.L., Moniruzzaman, A., Somers, J.M., 2014. Setting the stage for chronic health problems: cumulative childhood adversity among homeless adults with mental illness in Vancouver, British Columbia. BMC Public Health 14, 350. https://doi.org/10.1186/1471-2458-14-350 Racine, N., Eirich, R., Dimitropoulos, G., Hartwick, C., Madigan, S., 2020. Development of trauma symptoms following adversity in childhood: The moderating role of protective factors. Child Abuse Negl. 101, 104375. https://doi.org/10.1016/j.chiabu.2020.104375 Reid, J.A., Baglivio, M.T., Piquero, A.R., Greenwald, M.A., Epps, N., 2017. Human Trafficking of Minors and Childhood Adversity in Florida. Am. J. Public Health 107, 306–311. https://doi.org/10.2105/AJPH.2016.303564 Ritchie, K., Jaussent, I., Stewart, R., Dupuy, A., Courtet, P., Malafosse, A., Ancelin, M., 2011. Adverse childhood environment and late‐life cognitive functioning. Int. J. Geriatr. Psychiatry 26, 503–510. Schwartz, C.E., Zhang, J., Stucky, B.D., Michael, W., Rapkin, B.D., 2019. Is the link between socioeconomic status and resilience mediated by reserve-building activities: mediation analysis of web-based cross-sectional data from chronic medical illness patient panels. BMJ Open 9, e025602. Stewart-Knox, B., E Duffy, M., Bunting, B., Parr, H., Vas de Almeida, M.D., Gibney, M., 2012. Associations between obesity (BMI and waist circumference) and socio-demographic factors, physical activity, dietary habits, life events, resilience, mood, perceived stress and hopelessness in healthy older Europeans. BMC Public Health 12, 424. https://doi.org/10.1186/1471-2458-12-424 Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., 2015. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779. Thomas, C., Hypponen, E., Power, C., 2008. Obesity and type 2 diabetes risk in midadult life: the role of childhood adversity. Pediatrics 121, e1240–e1249. Townsend, P., Phillimore, P., Beattie, A., 1988. Health and deprivation: inequality and the North. Routledge. U.S. Department of Health & Human Services, 2021. Child maltreatment. Webb, R.T., Antonsen, S., Carr, M.J., Appleby, L., Pedersen, C.B., Mok, P.L.H., 2017. Self-harm and violent criminality among young people who experienced trauma-related hospital admission during childhood: a Danish national cohort study. Lancet Public Heal. 2, e314–e322. Ye, D., Reyes-Salvail, F., 2014. Adverse childhood experiences among Hawai ‘i adults: findings from the 2010 Behavioral Risk Factor Survey. Hawai’i J. Med. Public Heal. 73, 181. Tables Table 1. Regression model of the latent happiness trait (theta) from the item-response theory analysis on childhood adversity. Predictor β SE t p 95% CI Sex -0.090 0.007 -12.800 <0.001 -0.104 -0.076 Years of education -0.066 0.004 -17.190 <0.001 -0.073 -0.058 Age 0.066 0.004 17.950 <0.001 0.058 0.073 Childhood adversity -0.181 0.003 -52.390 <0.001 -0.188 -0.174 Intercept 0.072 0.005 15.210 <0.001 0.063 0.081 Note: β : Standardized coefficient, SE: Standard error, CI: Confidence interval. Table 2. Path analysis showing the direct effects of resilience on body-mass index, glycated haemoglobin, cholesterol, deprivation and household income. Effect β SE z p 95% CI LB 95% CI UB Resilience → Household income 0.075 0.005 16.000 <0.001 0.066 0.084 Sex → Household income 0.105 0.005 22.310 <0.001 0.096 0.114 Age → Household income -0.297 0.004 -67.050 <0.001 -0.306 -0.288 Resilience → TDI -0.053 0.005 -10.730 <0.001 -0.063 -0.043 Sex → TDI -0.011 0.005 -2.290 0.022 -0.021 -0.002 Age → TDI -0.106 0.005 -21.590 <0.001 -0.116 -0.097 Resilience → BMI 0.006 0.005 1.250 0.211 -0.004 0.016 Sex → BMI 0.103 0.005 20.860 <0.001 0.093 0.112 Age → BMI 0.036 0.005 7.270 <0.001 0.026 0.046 Resilience → HbA1c -0.026 0.005 -5.250 <0.001 -0.035 -0.016 Sex → HbA1c 0.038 0.005 7.800 <0.001 0.029 0.048 Age → HbA1c 0.193 0.005 40.580 <0.001 0.184 0.202 Resilience → LDL-to-HDL cholesterol -0.013 0.005 -2.730 0.006 -0.022 -0.004 Sex → LDL-to-HDL cholesterol 0.290 0.004 64.690 <0.001 0.281 0.299 Age → LDL-to-HDL cholesterol -0.024 0.005 -5.030 <0.001 -0.033 -0.015 Note: β: Standardized path coefficient, SE: standard error, CI: confidence Interval, LL: lower limit, UL: upper limit. Table 3. Path analysis showing the direct effects of resilience on body-mass index, glycated haemoglobin, and mediation by Townsend deprivation index. Effect β SE z p 95% CI LB 95% CI UB Direct effects Resilience → BMI 0.008 0.005 1.740 0.081 -0.001 0.017 Resilience → HbA1c -0.021 0.005 -4.570 <0.001 -0.030 -0.012 Resilience → LDL-to-HDL cholesterol -0.013 0.005 -2.830 0.005 -0.022 -0.004 Indirect effects Resilience → TDI → BMI a -0.003 0.000 -8.179 <0.001 -0.004 -0.002 Resilience → TDI → HbA1c b -0.003 0.000 -7.557 <0.001 -0.003 -0.002 Resilience → TDI → LDL-to-HDL cholesterol 0.000 0.000 1.673 0.094 0.000 0.001 Components Resilience → TDI -0.055 0.005 -11.610 <0.001 -0.064 -0.045 TDI → BMI 0.055 0.005 11.570 <0.001 0.046 0.064 TDI → HbA1c 0.047 0.005 9.990 <0.001 0.038 0.056 TDI → LDL-to-HDL cholesterol -0.008 0.005 -1.690 0.090 -0.017 0.001 Note : Results are adjusted for covariates (sex, age). β: Standardized path coefficient, SE: standard error, CI: confidence Interval, LL: lower limit, UL: upper limit. a partial competitive mediation. a : Complete mediation, b : Partial mediation Table 4. Path analysis showing the direct effects of resilience on body-mass index, glycated haemoglobin, and mediation by household income. Effect β SE z p 95% CI LB 95% CI UB Direct effects Resilience → BMI 0.012 0.005 2.400 0.016 0.002 0.022 Resilience → HbA1c -0.021 0.005 -4.380 <0.001 -0.031 -0.012 Resilience → LDL-to-HDL cholesterol -0.008 0.005 -1.720 0.085 -0.018 0.001 Indirect effects Resilience → Income → BMI a -0.006 0.001 -11.083 <0.001 -0.007 -0.005 Resilience → Income → HbA1c b -0.005 0.000 -9.596 <0.001 -0.006 -0.004 Resilience → Income → LDL-to-HDL cholesterol c -0.005 0.000 -9.831 <0.001 -0.006 -0.004 Components Resilience → Income 0.076 0.005 16.130 <0.001 0.067 0.085 Income → BMI -0.079 0.005 -15.300 <0.001 -0.089 -0.069 Income→ HbA1c -0.061 0.005 -11.960 <0.001 -0.071 -0.051 Income → LDL-to-HDL cholesterol -0.062 0.005 -12.430 <0.001 -0.072 -0.052 Note : Results are adjusted for covariates (sex, age). β: Standardized path coefficient, SE: standard error, CI: confidence Interval, LL: lower limit, UL: upper limit. a partial competitive mediation. a: competitive partial mediation, b : complementary partial mediation, c : complete mediation Supplementary Methods The supplementary methods file is not available with this version. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6990489","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478065734,"identity":"0134d2a1-63a5-441c-93d8-68eb8fa1e4c5","order_by":0,"name":"Chris Patrick Pflanz","email":"data:image/png;base64,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","orcid":"","institution":"University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Chris","middleName":"Patrick","lastName":"Pflanz","suffix":""},{"id":478065735,"identity":"fd45f18f-3b6c-480d-8dbe-2c3e516c56bd","order_by":1,"name":"John Gallacher","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Gallacher","suffix":""},{"id":478065736,"identity":"b402bfcf-e7e0-4d6c-80f8-9956f0d9794f","order_by":2,"name":"Sarah Bauermeister","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Bauermeister","suffix":""}],"badges":[],"createdAt":"2025-06-27 10:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6990489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6990489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85837674,"identity":"728d9b30-8316-4a87-9191-ce59195e1526","added_by":"auto","created_at":"2025-07-02 08:30:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29809,"visible":true,"origin":"","legend":"\u003cp\u003eBinned scatter plot of the association between childhood adversity and happiness and the histogram of resilience indicator derived from this relationship.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eRed: Vulnerable individuals, Green: Resilient individuals, Childhood adversity: Sum score from the childhood trauma screener, Latent happiness trait (theta): Latent happiness trait from the item-response theory analysis carried out on the bespoke UK Biobank happiness questionnaire, Residuals: Residuals from the regression of the latent happiness trait (theta) from the item-response theory analysis on childhood adversity adjusted for covariates (sex, age, education).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6990489/v1/54017858dbfad1ba871fac9d.png"},{"id":85836247,"identity":"66269a1d-b267-4765-ac67-c60feaf7c18c","added_by":"auto","created_at":"2025-07-02 08:22:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71756,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model showing the direct effects of resilience on socioeconomic indicators and markers of the metabolic syndrome.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003ePath-coefficients are standardized coefficients. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6990489/v1/b103027d836ce98d570e6e35.png"},{"id":85836245,"identity":"66005241-6bea-4190-8278-ab3f1bc7a4a2","added_by":"auto","created_at":"2025-07-02 08:22:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66087,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model showing the mediation of the effect of resilience on markers of the metabolic syndrome using the Townsend deprivation index as mediator\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003ePath-coefficients are standardized coefficients. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6990489/v1/dee2ae7dd30b24d5080a94f6.png"},{"id":85836248,"identity":"2352a5cb-57f2-42df-a799-fa0f68f6e18c","added_by":"auto","created_at":"2025-07-02 08:22:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71152,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model showing the mediation of the effect of resilience on markers of the metabolic syndrome using household income as mediator\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003ePath-coefficients are standardized coefficients. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6990489/v1/ff4b817440f9072419536879.png"},{"id":86216107,"identity":"61a88871-c316-4088-a4e7-87afd000b2e6","added_by":"auto","created_at":"2025-07-08 06:02:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1167803,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6990489/v1/ac8f0d8c-9bd3-4df9-a08a-0bac83c6097d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socioeconomic mediators of the effect of resilience on cardiometabolic biomarkers in the UK Biobank","fulltext":[{"header":"Introduction","content":"\u003cp\u003eExposure to childhood adversity is linked to early morbidity and mortality (Grummitt et al., 2021) including cardiometabolic disease (Jakubowski et al., 2018; Li et al., 2019) and associated cardiometabolic biomarkers (Miller and Lacey, 2022). Resilient coping in the face of childhood adversity was associated with lower distress and buffered the impact of childhood adversity (Beutel et al., 2017). However, further research is needed to shed light on factors contributing to resilience and develop a broader evidence base for the design of evidence-based resilience fostering interventions. Therefore, the purpose of the present study was to investigate the factors that contribute to resilience in the UK Biobank, a large, heterogenous, population-based cohort study, using structural equation modelling and mediation analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChildhood adversity is a broad umbrella term for adverse childhood experiences that can impair children\u0026rsquo;s physical and mental health and wellbeing (Patterson et al., 2014; Racine et al., 2020; Reid et al., 2017). The U.S. Department of Health \u0026amp; Human Services distinguishes between several types of childhood adversity including physical abuse, psychological or emotional maltreatment, sexual abuse, sex trafficking, medical neglect, neglect or deprivation of necessities (U.S. Department of Health \u0026amp; Human Services, 2021). Childhood adversity has a negative impact on psychosocial development across the lifespan that may include poor overall mental and physical health (Nelson et al., 2020; Ritchie et al., 2011), depression (Chapman et al., 2004), lifestyle risk issues including smoking and sexual risk behaviour high-risk lifestyles (Felitti et al., 2019; Ford et al., 2011; Hillis et al., 2001), increased criminality, substance abuse, youth violence and suicide (Afifi et al., 2009; Fox et al., 2015; Webb et al., 2017; Ye and Reyes-Salvail, 2014), lower socioeconomic status, income and education (Jaffee et al., 2018), and cardio-metabolic and respiratory disease (Danese et al., 2009; Danese and McEwen, 2012; Hughes et al., 2017). Some types of childhood adversities also increase the risk of obesity in adulthood and, in turn, increase the risk for type 2 diabetes (Thomas et al., 2008). In spite of severe childhood adversity, some children are seemingly unaffected and resilient against the negative impact of these adverse experiences (Gartland et al., 2019). However, the factors that foster resilience in these individuals remain under-researched and operationalizing resilience remains a challenge in current research. For example, previous research using psychometric scales suffered from low reliability of the psychometric instruments (Bagci et al., 2014). One reason why it is difficult to measure resilience psychometrically is that resilience is not a static personality trait that remains stable over the lifespan, but is rather a function of coping capacity and cumulative adversity experienced over the life course \u0026nbsp;(Kuenzi, 2022). Therefore, an empirical definition of resilience was suggested that operationalizes resilience as the residual score obtained by regressing adversity severity against psychosocial functioning (Ioannidis et al., 2020). This operational definition has several advantages: a single metric can be used to compare individuals that experienced various levels of childhood adversity with respect to their level of resilience. Resilience can be measured on a continuum rather as a dichotomized construct with individuals either being resilient to adversity or not. Even though resilience remains a largely neglected construct in epidemiological research, the regression method allows researcher to derive a resilience metric from previous cohort datasets that lack psychometric data from resilience scales.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious research on resilience, using psychometric scales to measure the construct, found that trait resilience is positively associated with indicators of mental health (Hu et al., 2015). Resilience was found to be strongly associated with socio-economic status (Schwartz et al., 2019). Resilience (psychological hardiness) was also a predictor of cardiometabolic health including higher levels of \u0026ldquo;good\u0026rdquo; high density lipoprotein cholesterol and lower body mass index (BMI) (Bartone et al., 2016). Lower levels of resilience were also associated with general and central obesity (Foster and Weinstein, 2019; Stewart-Knox et al., 2012).\u003c/p\u003e\n\u003cp\u003eIn light of these previous findings and their limitations, the aim of our study was to investigate the effect of resilience on cardiometabolic health in the UK Biobank and to investigate if the effect of resilience on cardiometabolic health was mediated by socioeconomic variables. We operationalized resilience as previously conceptualized by Ioannidis et al. (2020) as the residual fit using a regression model of subjective wellbeing as psychosocial outcome measure against childhood adversity.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetails of the design, participants, procedure and ethics of the UK Biobank study are available elsewhere (Sudlow et al., 2015). The UK Biobank is a large, population-based study that involved the recruitment of 502,665 participants and the collection of comprehensive baseline data (Sudlow et al., 2015). Ethical approval was granted to Biobank from the Research Ethics Committee - REC reference 11/NW/0382 (Sudlow et al., 2015). A cross-sectional design using baseline data was used for the present investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChildhood adversity.\u0026nbsp;\u003c/strong\u003eChildhood adversity was assessed using items from the Childhood Trauma Screener (CTS-5) (Glaesmer et al., 2013). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjective wellbeing.\u0026nbsp;\u003c/strong\u003eInformation on subjective wellbeing (happiness) was derived from a bespoke mental-health questionnaire in the UK Biobank that comprised 6 variables scored on a 6-point Likert scale: “Extremely happy”, “Very happy”, “Moderately happy”, “Moderately unhappy”, “Very unhappy”, “Extremely unhappy”. The items were: \"In general how happy are you?\", \"In general how satisfied are you with the work that you do?\", \"In general how satisfied are you with your health?\", \"In general how satisfied are you with your family relationships?\", \"In general how satisfied are you with your friendships?, “\"In general how satisfied are you with your financial situation?”.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarkers of metabolic risk.\u0026nbsp;\u003c/strong\u003eMarkers of metabolic risk factors included the body mass index (BMI) and glycated (glycosylated) haemoglobin (HbA1c) and cholesterol from UK Biobank blood biochemistry.Blood samples were collected at recruitment (for all 500,000 participants) and repeat assessment ~5 years later (for 20,000 participants), measuring a range of key biochemistry markers (Elliott and Peakman, 2008). High-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol and glycated haemoglobin were drawn from the blood biochemistry assessment at the first visit. The biomarkers were selected for analysis because they represent established risk factors associated with risk of many metabolic diseases. The cholesterol measures were combined into the LDL-to-HDL cholesterol ratio that indicates the ratio of “bad” to “healthy” cholesterol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocioeconomic indicators.\u0026nbsp;\u003c/strong\u003eThe Townsend deprivation index (Townsend et al., 1988)\u0026nbsp;and average total household income before tax were used as socioeconomic indicators. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic variables.\u0026nbsp;\u003c/strong\u003eModels were adjusted for demographic variables including age, gender, and years of education variable was derived by combining the age at which subjects completed full-time education and qualifications variable. Missing data in the variable age when full-time education was completed were imputed from the Qualifications variable using the following average ages corresponding to the qualifications: 21 years for College/University degree or other professional qualifications (e.g. nursing, teaching), 17 years for A-levels/AS-levels or NVQ/HND/HNC or equivalent, and 15 years for O-levels/GCSE and CSE or equivalent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eItem-response theory\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eItem-response theory and Mokken analysis wereused to optimize the happiness scale, as previously reported (Pflanz and Bauermeister, preprint ). Following item reductions an estimate of the latent trait of happiness was derived using three items measuring general happiness, friendship satisfaction, and family satisfaction. We will refer to the individual level of expression of the latent trait of happiness as theta in the following, wherein theta can be interpreted as a standardized z-score with a latent trait mean = 0 and standard deviation = 1. \u003cstrong\u003eRegression analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMulti-linear regression was carried out with the latent happiness trait (theta) from the item-response theory analysis as outcome variable and childhood adversity, sex, age, and years of education as predictors. The residuals of the regression model for each participant were used as an indicator of resilience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural equation modelling\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStructural equation models were fit to the data with resilience as independent variable, the metabolic indicators BMI, glycated haemoglobin, and LDL-to-HDL cholesterol ratio as dependent variables and the socioeconomic variables TDI or household income as mediators (See supplementary methods for details).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total UK Biobank sample was 502,430, aged 37 to 73 years. All participants with known email addresses were invited to complete a mental health questionnaire online, which contained the subjective wellbeing questionnaire and childhood trauma screener. After withdrawals, 55,891 participants had complete data including the subjective wellbeing questionnaire, childhood trauma screener, sex, age, and years of education. This subsample was aged 40 to 70 years (M = 56.45 years; SD = 7.82), and was 56.69% female. The mean years of education was 19.93 (SD = 2.55) ranging from 5 to 35 years. The mean childhood adversity score was 1.52 (SD = 2.10) ranging from 0 to 16.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults of the multiple linear regression indicated that there was an overall significant effect when predicting the latent trait (theta) of subjective wellbeing from the item-response theory using sex, years of education, age and childhood adversity as predictors (F(4, 55,886) = 912.16, p \u0026lt; .0001, R\u003csup\u003e2\u003c/sup\u003e = .06, adjusted R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e=.06). The individual predictors were examined further and indicated that childhood (t = -52.39, p \u0026lt; .001) adversity as well as the predictors of no interest: sex (t = -12.80, p \u0026lt; .001), years of education (t = -17.19, p \u0026lt; .001), and age (t = 17.95, p \u0026lt; .001) were significant predictors in the model. The residuals from the regression model for each participant were used as continuous indicator of resilience that is adjusted for covariation by sex, age, years of education and indicative of whether an individual participant is more or less resilient as predicted by the regression model. We will refer to the residuals as resilience in the following. Figure 1 depicts a binned scatter plot of happiness over childhood adversity and a histogram of the resilience indicator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural equation modelling\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDirect effect model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe direct-effect structural equation model with resilience as predictor and household income, TDI, BMI, glycated haemoglobin, and LDL-to-HDL cholesterol ratio showed that resilience significantly predicted household income (\u0026beta; = .075, p \u0026lt; 0.001), TDI (\u0026beta; -.053, p \u0026lt; 0.001), glycated haemoglobin (\u0026beta; = -.026, p \u0026lt; 0.001), LDL-to-HDL cholesterol ratio (\u0026beta; = -.013, p = 0.006), but not BMI (\u0026beta; = .006, p \u0026lt; 0.221). All covariance paths were significant (all p \u0026lt; .05, see Table 3). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTownsend deprivation index as mediator\u003c/strong\u003e. The structural equation model (Figure 3) showed significant direct effects on glycated haemoglobin (\u0026beta; = -.021, p \u0026lt; 0.001) and LDL-to-HDL cholesterol ratio (\u0026beta; = -.013, p = 0.005) with higher levels of resilience predicting lower levels of glycated haemoglobin and LDL-to-HDL cholesterol, whereas the effect on BMI (\u0026beta; = .008, p \u0026lt; 0.081) was not significant. The effect of resilience on BMI was completely mediated by TDI (\u0026beta; = -.003, p \u0026lt; 0.001) with 57.2% of the effect of resilience on BMI mediated by TDI (Indirect effect / Total effect Ratio = 0. 572). The effect of resilience on glycated haemoglobin was partially mediated by TDI (\u0026beta; = -.003, p \u0026lt; 0.001) showing complementary mediation with 10.7% of the effect of resilience on glycated haemoglobin mediated by TDI (Indirect effect / Total effect Ratio = 0.107). There was no evidence of mediation for the effect of resilience on LDL-to-HDL cholesterol ratio (\u0026beta; = 0, p = 0.094).\u0026nbsp;Table 4 shows the path coefficients and test statistics for the direct and indirect effects in the model as well as the components.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold income as mediator\u003c/strong\u003e. The structural equation model (Figure 4) showed significant direct effects of resilience on glycated haemoglobin (\u0026beta; = -.021, p \u0026lt; 0.001) and BMI (\u0026beta; = .012, p \u0026lt; 0.016) with higher levels of resilience predicting lower levels of glycated haemoglobin and higher BMI, whereas the effect on LDL-to-HDL cholesterol (\u0026beta; = -.008, p = 0.085) was not significant. The effect of resilience on BMI was partially mediated by household income (\u0026beta; = -.006, p \u0026lt; 0.001) with 100% of the effect of resilience on BMI mediated by household income showing a competitive partial mediation (Indirect effect / Total effect Ratio = 1). The effect of resilience on glycated haemoglobin was partially mediated by household income (\u0026beta; = -.005, p \u0026lt; 0.001) showing complementary mediation with 17.8% of the effect of resilience on glycated haemoglobin mediated by household income (Indirect effect / Total effect Ratio = 0.178). The effect of resilience on LDL-to-HDL cholesterol ratio was completely mediated by household income (\u0026beta; = -.005, p \u0026lt; 0.001) showing indirect-only mediation with 36.5% of the effect of resilience on LDL-to-HDL cholesterol ratio mediated by household income (Indirect effect / Total effect Ratio = 0.365). Table 4 shows the path coefficients and test statistics for the direct and indirect effects in the model as well as the components.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the effect of resilience on cardiometabolic biomarkers in the UK Biobank and its mediation by socioeconomic variables. We found that resilience was associated with higher household income, lower TDI, lower glycated haemoglobin and lower LDL-to-HDL cholesterol ratio. The effect of resilience on glycated haemoglobin was partially mediated by TDI and household income. The effect of resilience on BMI was mediated by TDI and household income. The effect of resilience on LDL-to-HDL cholesterol ratio was fully mediated by household income, but not by TDI.\u003c/p\u003e\n\u003cp\u003eWe have used a residuals approach to derive the resilience metric for our analysis (Ioannidis et al., 2020) that enabled us to investigate resilience in the UK Biobank despite the lack of resilience scale in the protocol. Overall our findings confirm previous research that showed associations between resilience and higher socio-economic status (Schwartz et al., 2019), \u0026nbsp;higher levels of \u0026ldquo;good\u0026rdquo; high density lipoprotein cholesterol (Bartone et al., 2016), and lower levels of \u0026nbsp;general obesity (Foster and Weinstein, 2019; Stewart-Knox et al., 2012). Our study adds to these findings that the effect of resilience on glycated haemoglobin was mediated by TDI and household income, and the effect of resilience on LDL-to-HDL cholesterol ratio was mediated by household income. Overall, our findings show that socioeconomic indicators are important mediators of the effect of resilience on improving metabolic health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings have important implications for social policy. As our results showed, the effects of resilience on cardiometabolic biomarkers from blood biochemistry were mediated by socioeconomic indicators of deprivation. Social policies and interventions to support individuals with adverse childhood experiences could include a financial literacy component alongside mental health interventions. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFurther research\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuture research could validate resilience estimates derived as residuals from regression models further using psychometric scales to investigate their convergent validity. This was not feasible in the UK Biobank study because the study protocol did not include a psychometric resilience scale. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study has several strengths: First, the study\u0026rsquo;s sample size was large. Second, IRT was used to optimise the subjective wellbeing outcome variable. Compared to summated test scores, IRT has the advantage of improved precision and reliability through the removal of misfitting items (Henning, 1984). Third, the analyses were adjusted for a range of covariates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe present study has several limitations that merit discussion: The data analysis is cross-sectional, therefore, our ability to test for causality in the mediation analysis is limited. The UK Biobank, similar to other cohort studies, does not include a psychometric resilience scale, e.g. Connor-Davidson Resilience Scale (Connor and Davidson, 2003), and we therefore had to operationally derive a resilience metric using a regression model of child adversity against happiness, but could not validate this metric against already, established psychometric scales of resilience. However, it should be noted that previous research demonstrated that the residuals method of operationalising resilience had good construct and predictive validity (Cahill et al., 2022). Childhood adversity was assessed retrospectively self-report and might be subject to retrospective memory bias. This, in turn, may have impacted the trustworthiness of the resilience metric.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, resilience can be further investigated in cohort studies that lack psychometric resilience scales using regression models to derive the resilience metric. Our analysis showed that resilience is associated with higher household income, lower TDI, lower glycated haemoglobin and lower LDL-to-HDL cholesterol ratio. The effect of resilience on cardiometabolic biomarkers was mediated by socioeconomic indicators including household income and socioeconomic deprivation.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted on the Dementias Platform (DPUK) Data Portal using the UK Biobank application 15697 PI John Gallacher for DPUK project 0169. The Medical Research Council supports DPUK through grant MR/T0333771. DPUK supports Sarah Bauermeister and Patrick Pflanz. Access to the data can be requested through UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSB, CPP and JG declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSecondary data analysis only with ethical approval in place from source cohort, UK Biobank Research Ethics Committee - REC reference 11/NW/0382.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSB, CPP and JG give full consent for publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset(s) supporting the conclusions of this article is(are) available in the Dementias Platform UK (DPUK) Data Portal repository, https://portal.dementiasplatform.uk/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSB and JG conceptualised the idea. CPP and SB analysed and interpreted the data and wrote the manuscript. CPP and JG edited and proofread the manuscript. All authors read and approved the final manuscript. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAfifi, T.O., Boman, J., Fleisher, W., Sareen, J., 2009. The relationship between child abuse, parental divorce, and lifetime mental disorders and suicidality in a nationally representative adult sample. Child Abuse Negl. 33, 139\u0026ndash;147.\u003c/li\u003e\n \u003cli\u003eBagci, S.C., Rutland, A., Kumashiro, M., Smith, P.K., Blumberg, H., 2014. Are minority status children\u0026rsquo;s cross-ethnic friendships beneficial in a \u0026nbsp;multiethnic context? Br. J. Dev. Psychol. 32, 107\u0026ndash;115. https://doi.org/10.1111/bjdp.12028\u003c/li\u003e\n \u003cli\u003eBartone, P.T., Valdes, J.J., Sandvik, A., 2016. Psychological hardiness predicts cardiovascular health. Psychol. Health Med. 21, 743\u0026ndash;749. https://doi.org/10.1080/13548506.2015.1120323\u003c/li\u003e\n \u003cli\u003eBeutel, M.E., Tibubos, A.N., Klein, E.M., Schmutzer, G., Reiner, I., Kocalevent, R.-D., Br\u0026auml;hler, E., 2017. Childhood adversities and distress - The role of resilience in a representative \u0026nbsp;sample. PLoS One 12, e0173826. https://doi.org/10.1371/journal.pone.0173826\u003c/li\u003e\n \u003cli\u003eCahill, S., Hager, R., Chandola, T., 2022. The validity of the residuals approach to measuring resilience to adverse childhood experiences. Child Adolesc. Psychiatry Ment. Health 16, 18. https://doi.org/10.1186/s13034-022-00449-y\u003c/li\u003e\n \u003cli\u003eChapman, D.P., Whitfield, C.L., Felitti, V.J., Dube, S.R., Edwards, V.J., Anda, R.F., 2004. Adverse childhood experiences and the risk of depressive disorders in adulthood. J. Affect. Disord. 82, 217\u0026ndash;225.\u003c/li\u003e\n \u003cli\u003eConnor, K.M., Davidson, J.R.T., 2003. Development of a new resilience scale: The Connor‐Davidson resilience scale (CD‐RISC). Depress. Anxiety 18, 76\u0026ndash;82.\u003c/li\u003e\n \u003cli\u003eDanese, A., McEwen, B.S., 2012. Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiol. Behav. 106, 29\u0026ndash;39.\u003c/li\u003e\n \u003cli\u003eDanese, A., Moffitt, T.E., Harrington, H., Milne, B.J., Polanczyk, G., Pariante, C.M., Poulton, R., Caspi, A., 2009. Adverse childhood experiences and adult risk factors for age-related disease: depression, inflammation, and clustering of metabolic risk markers. Arch. Pediatr. Adolesc. Med. 163, 1135\u0026ndash;1143.\u003c/li\u003e\n \u003cli\u003eElliott, P., Peakman, T.C., 2008. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int. J. Epidemiol. 37, 234\u0026ndash;244.\u003c/li\u003e\n \u003cli\u003eFelitti, V.J., Anda, R.F., Nordenberg, D., Williamson, D.F., Spitz, A.M., Edwards, V., Koss, M.P., Marks, J.S., 2019. Reprint of: 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. 56, 774\u0026ndash;786.\u003c/li\u003e\n \u003cli\u003eFord, E.S., Anda, R.F., Edwards, V.J., Perry, G.S., Zhao, G., Li, C., Croft, J.B., 2011. Adverse childhood experiences and smoking status in five states. Prev. Med. (Baltim). 53, 188\u0026ndash;193.\u003c/li\u003e\n \u003cli\u003eFoster, B.A., Weinstein, K., 2019. Moderating Effects of Components of Resilience on Obesity Across Income Strata in the National Survey of Children\u0026rsquo;s Health. Acad. Pediatr. 19, 58\u0026ndash;66. https://doi.org/https://doi.org/10.1016/j.acap.2018.08.012\u003c/li\u003e\n \u003cli\u003eFox, B.H., Perez, N., Cass, E., Baglivio, M.T., Epps, N., 2015. Trauma changes everything: Examining the relationship between adverse childhood experiences and serious, violent and chronic juvenile offenders. Child Abuse Negl. 46, 163\u0026ndash;173.\u003c/li\u003e\n \u003cli\u003eGartland, D., Riggs, E., Muyeen, S., Giallo, R., Afifi, T.O., MacMillan, H., Herrman, H., Bulford, E., Brown, S.J., 2019. What factors are associated with resilient outcomes in children exposed to social adversity? A systematic review. BMJ Open 9, e024870.\u003c/li\u003e\n \u003cli\u003eGlaesmer, H., Schulz, A., H\u0026auml;user, W., Freyberger, H.J., Br\u0026auml;hler, E., Grabe, H.-J., 2013. The childhood trauma screener (CTS): Development and validation of cut-off-scores for classificatory diagnostics. Psychiatr. Prax. 40, 220\u0026ndash;226. https://doi.org/10.1055/s-0033-1343116\u003c/li\u003e\n \u003cli\u003eGrummitt, L.R., Kreski, N.T., Kim, S.G., Platt, J., Keyes, K.M., McLaughlin, K.A., 2021. Association of Childhood Adversity With Morbidity and Mortality in US Adults: A Systematic Review. JAMA Pediatr. 175, 1269\u0026ndash;1278. https://doi.org/10.1001/jamapediatrics.2021.2320\u003c/li\u003e\n \u003cli\u003eHenning, G., 1984. Advantages of latent trait measurement in language testing. Lang. Test. 1, 123\u0026ndash;133. https://doi.org/10.1177/026553228400100201\u003c/li\u003e\n \u003cli\u003eHillis, S.D., Anda, R.F., Felitti, V.J., Marchbanks, P.A., 2001. Adverse childhood experiences and sexual risk behaviors in women: a retrospective cohort study. Fam. Plann. Perspect. 206\u0026ndash;211.\u003c/li\u003e\n \u003cli\u003eHu, T., Zhang, D., Wang, J., 2015. A meta-analysis of the trait resilience and mental health. Pers. Individ. Dif. 76, 18\u0026ndash;27. https://doi.org/https://doi.org/10.1016/j.paid.2014.11.039\u003c/li\u003e\n \u003cli\u003eHughes, K., Bellis, M.A., Hardcastle, K.A., Sethi, D., Butchart, A., Mikton, C., Jones, L., Dunne, M.P., 2017. The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. Lancet public Heal. 2, e356\u0026ndash;e366.\u003c/li\u003e\n \u003cli\u003eIoannidis, K., Askelund, A.D., Kievit, R.A., van Harmelen, A.-L., 2020. The complex neurobiology of resilient functioning after childhood maltreatment. BMC Med. 18, 32. https://doi.org/10.1186/s12916-020-1490-7\u003c/li\u003e\n \u003cli\u003eJaffee, S.R., Ambler, A., Merrick, M., Goldman-Mellor, S., Odgers, C.L., Fisher, H.L., Danese, A., Arseneault, L., 2018. Childhood maltreatment predicts poor economic and educational outcomes in the transition to adulthood. Am. J. Public Health 108, 1142\u0026ndash;1147.\u003c/li\u003e\n \u003cli\u003eJakubowski, K.P., Cundiff, J.M., Matthews, K.A., 2018. Cumulative childhood adversity and adult cardiometabolic disease: A \u0026nbsp;meta-analysis. Heal. Psychol. \u0026nbsp;Off. J. Div. Heal. Psychol. \u0026nbsp;Am. Psychol. Assoc. 37, 701\u0026ndash;715. https://doi.org/10.1037/hea0000637\u003c/li\u003e\n \u003cli\u003eKuenzi, M., 2022. The impact of life course adversity on later life cognition. https://doi.org/10.13097/archive-ouverte/unige:164543\u003c/li\u003e\n \u003cli\u003eLi, L., Pereira, S.M.P., Power, C., 2019. Childhood maltreatment and biomarkers for cardiometabolic disease in mid-adulthood in a prospective British birth cohort: associations and potential explanations. BMJ Open 9, e024079. https://doi.org/10.1136/bmjopen-2018-024079\u003c/li\u003e\n \u003cli\u003eMiller, N.E., Lacey, R.E., 2022. Childhood adversity and cardiometabolic biomarkers in mid-adulthood in the 1958 British birth cohort. SSM - Popul. Heal. 19, 101260. https://doi.org/https://doi.org/10.1016/j.ssmph.2022.101260\u003c/li\u003e\n \u003cli\u003eNelson, C.A., Scott, R.D., Bhutta, Z.A., Harris, N.B., Danese, A., Samara, M., 2020. Adversity in childhood is linked to mental and physical health throughout life. BMJ 371, m3048. https://doi.org/10.1136/bmj.m3048\u003c/li\u003e\n \u003cli\u003ePatterson, M.L., Moniruzzaman, A., Somers, J.M., 2014. Setting the stage for chronic health problems: cumulative childhood adversity \u0026nbsp;among homeless adults with mental illness in Vancouver, British Columbia. BMC Public Health 14, 350. https://doi.org/10.1186/1471-2458-14-350\u003c/li\u003e\n \u003cli\u003eRacine, N., Eirich, R., Dimitropoulos, G., Hartwick, C., Madigan, S., 2020. Development of trauma symptoms following adversity in childhood: The moderating \u0026nbsp;role of protective factors. Child Abuse Negl. 101, 104375. https://doi.org/10.1016/j.chiabu.2020.104375\u003c/li\u003e\n \u003cli\u003eReid, J.A., Baglivio, M.T., Piquero, A.R., Greenwald, M.A., Epps, N., 2017. Human Trafficking of Minors and Childhood Adversity in Florida. Am. J. Public Health 107, 306\u0026ndash;311. https://doi.org/10.2105/AJPH.2016.303564\u003c/li\u003e\n \u003cli\u003eRitchie, K., Jaussent, I., Stewart, R., Dupuy, A., Courtet, P., Malafosse, A., Ancelin, M., 2011. Adverse childhood environment and late‐life cognitive functioning. Int. J. Geriatr. Psychiatry 26, 503\u0026ndash;510.\u003c/li\u003e\n \u003cli\u003eSchwartz, C.E., Zhang, J., Stucky, B.D., Michael, W., Rapkin, B.D., 2019. Is the link between socioeconomic status and resilience mediated by reserve-building activities: mediation analysis of web-based cross-sectional data from chronic medical illness patient panels. BMJ Open 9, e025602.\u003c/li\u003e\n \u003cli\u003eStewart-Knox, B., E Duffy, M., Bunting, B., Parr, H., Vas de Almeida, M.D., Gibney, M., 2012. Associations between obesity (BMI and waist circumference) and socio-demographic factors, physical activity, dietary habits, life events, resilience, mood, perceived stress and hopelessness in healthy older Europeans. BMC Public Health 12, 424. https://doi.org/10.1186/1471-2458-12-424\u003c/li\u003e\n \u003cli\u003eSudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., 2015. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779.\u003c/li\u003e\n \u003cli\u003eThomas, C., Hypponen, E., Power, C., 2008. Obesity and type 2 diabetes risk in midadult life: the role of childhood adversity. Pediatrics 121, e1240\u0026ndash;e1249.\u003c/li\u003e\n \u003cli\u003eTownsend, P., Phillimore, P., Beattie, A., 1988. Health and deprivation: inequality and the North. Routledge.\u003c/li\u003e\n \u003cli\u003eU.S. Department of Health \u0026amp; Human Services, 2021. Child maltreatment.\u003c/li\u003e\n \u003cli\u003eWebb, R.T., Antonsen, S., Carr, M.J., Appleby, L., Pedersen, C.B., Mok, P.L.H., 2017. Self-harm and violent criminality among young people who experienced trauma-related hospital admission during childhood: a Danish national cohort study. Lancet Public Heal. 2, e314\u0026ndash;e322.\u003c/li\u003e\n \u003cli\u003eYe, D., Reyes-Salvail, F., 2014. Adverse childhood experiences among Hawai \u0026lsquo;i adults: findings from the 2010 Behavioral Risk Factor Survey. Hawai\u0026rsquo;i J. Med. Public Heal. 73, 181.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Regression model of the latent happiness trait (theta) from the item-response theory analysis on childhood adversity.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-12.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-17.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChildhood adversity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-52.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u0026beta; : Standardized coefficient, SE: Standard error, CI: Confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Path analysis showing the direct effects of resilience on body-mass index, glycated haemoglobin, cholesterol, deprivation and household income.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ez\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI LB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI UB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; Household income \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e16.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSex \u0026rarr; Household income \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e22.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAge \u0026rarr; Household income \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-67.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; TDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-10.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSex \u0026rarr; TDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-2.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAge \u0026rarr; TDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-21.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSex \u0026rarr; BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e20.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAge \u0026rarr; BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e7.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; HbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-5.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSex \u0026rarr; HbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e7.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAge \u0026rarr; HbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e40.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; LDL-to-HDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-2.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSex \u0026rarr; LDL-to-HDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e64.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAge \u0026rarr; LDL-to-HDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-5.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e \u0026beta;: Standardized path coefficient, SE: standard error, CI: confidence Interval, LL: lower limit, UL: upper limit.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Path analysis showing the direct effects of resilience on body-mass index, glycated haemoglobin, and mediation by Townsend deprivation index.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ez\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI LB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI UB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirect effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience \u0026rarr; BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience \u0026rarr; HbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience \u0026rarr; LDL-to-HDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience \u0026rarr; TDI \u0026rarr; BMI \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience \u0026rarr; TDI \u0026rarr; HbA1c \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience \u0026rarr; TDI \u0026rarr; LDL-to-HDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResilience \u0026rarr; TDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-11.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTDI \u0026rarr; BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTDI \u0026rarr; HbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTDI \u0026rarr; LDL-to-HDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Results are adjusted for covariates (sex, age). \u0026beta;: Standardized path coefficient, SE: standard error, CI: confidence Interval, LL: lower limit, UL: upper limit. a partial competitive mediation. \u003csup\u003ea\u003c/sup\u003e: Complete mediation, \u003csup\u003eb\u003c/sup\u003e: Partial mediation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Path analysis showing the direct effects of resilience on body-mass index, glycated haemoglobin, and mediation by household income.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ez\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI LB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI UB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirect effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; HbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-4.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; LDL-to-HDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-1.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; Income \u0026rarr; BMI \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-11.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; Income \u0026rarr; HbA1c \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-9.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; Income \u0026rarr; LDL-to-HDL cholesterol \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-9.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eResilience \u0026rarr; Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e16.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eIncome \u0026rarr; BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-15.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eIncome\u0026rarr; HbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-11.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eIncome \u0026rarr; LDL-to-HDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-12.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Results are adjusted for covariates (sex, age). \u0026beta;: Standardized path coefficient, SE: standard error, CI: confidence Interval, LL: lower limit, UL: upper limit. \u003csup\u003ea\u003c/sup\u003e partial competitive mediation. a: competitive partial mediation, \u003csup\u003eb\u003c/sup\u003e: complementary partial mediation, \u003csup\u003ec\u003c/sup\u003e: complete mediation\u0026nbsp;\u003c/p\u003e"},{"header":"Supplementary Methods","content":"\u003cp\u003eThe supplementary methods file is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Early adversity, childhood adverse experiences, resilience, Townsend deprivation index, body mass index, glycosylated haemoglobin, cholesterol ","lastPublishedDoi":"10.21203/rs.3.rs-6990489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6990489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground. \u003c/strong\u003eExposure to childhood adversity is linked to negative outcomes later in life, including cardiometabolic diseases. Resilient coping in the face of childhood adversity may buffer the deleterious impact of childhood adversity on cardiometabolic biomarkers. However, further research is needed to shed light on factors mediating the effect of resilience on cardiometabolic health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective. \u003c/strong\u003eTo investigate the factors that contribute to resilience in the UK Biobank, a large, heterogenous, population-based cohort study. To investigate socioeconomic mediators of the effect of resilience on metabolic health outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods. \u003c/strong\u003eA resilience metric was derived as residuals from multi-linear regression of subjective wellbeing on child adversity. Structural equation models were fitted to the data with resilience as independent variable, metabolic outcomes as dependent variables and socioeconomic variables as mediators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults. \u003c/strong\u003eResilience was associated with higher household income, lower deprivation, lower glycated haemoglobin and lower LDL-to-HDL cholesterol ratio. The effect of resilience on glycated haemoglobin and BMI were mediated by socioeconomic variables. The effect of resilience on LDL-to-HDL cholesterol ratio was mediated by household income, but not by deprivation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions. \u003c/strong\u003eResilience was found to be associated with higher household income, lower Townsend deprivation index, lower glycated haemoglobin and lower cholesterol levels. The effect of resilience on cardiometabolic biomarkers was mediated by socioeconomic indicators including household income and deprivation.\u003c/p\u003e","manuscriptTitle":"Socioeconomic mediators of the effect of resilience on cardiometabolic biomarkers in the UK Biobank","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 08:22:10","doi":"10.21203/rs.3.rs-6990489/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e69af6cb-8cdd-42b7-9bf6-e407630a5242","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-08T05:54:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 08:22:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6990489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6990489","identity":"rs-6990489","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}