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Background: Adverse childhood experiences (ACEs), including childhood maltreatment, have been linked with increased risk of diabetes and obesity during adulthood. A comprehensive assessment on the associations between childhood maltreatment and all major endocrine diseases, as well as the relative importance of different proposed mechanistic pathways on these associations, is currently lacking. Methods: Based on the UK Biobank, we constructed a cohort including 151,659 participants with self-reported data on childhood maltreatment who were 30 years of age or older on/after January 1, 1985. All participants were followed from the index date (i.e., January 1, 1985, or their 30th birthday, whichever came later) until the first diagnosis of any or specific (12 individual diagnoses and 9 subtypes) endocrine diseases, death, or the end of follow-up (December 31, 2019), whichever occurred first. We used Cox models to examine the association of childhood maltreatment, treated as continuous (i.e., the cumulative number of experienced childhood maltreatment), ordinal (i.e., 0, 1 and ≥ 2), or binary (< 2 and ≥ 2) variable, with any and specific endocrine diseases, adjusted for multiple covariates. We further examined the risk of having multiple endocrine diseases using Linear or Logistic Regression models. Then, sequential mediation analyses were performed to assess the contribution of four possible mechanisms (i.e., suboptimal socioeconomic status (SES), psychological adversities, unfavorable lifestyle, and biological alterations) on the observed associations. Results: During an average follow-up of 30.8 years, 20,885 participants received a diagnosis of endocrine diseases. We observed an association between the cumulative number of experienced childhood maltreatment and increased risk of being diagnosed with any endocrine disease (adjusted hazard ratio (HR) = 1.10, 95% confidence interval 1.09–1.12). The HR was 1.26 (1.22–1.30) when comparing individuals ≥ 2 with those with < 2 experienced childhood maltreatment. We further noted the most pronounced associations for type 2 diabetes (1.40 (1.33–1.48)) and hypothalamic–pituitary–adrenal (HPA)-axis-related endocrine diseases (1.38 (1.17–1.62)), and the association was stronger for having multiple endocrine diseases, compared to having one (odds ratio (95% CI) = 1.24 (1.19–1.30), 1.35 (1.27–1.44), and 1.52 (1.52–1.53) for 1, 2, and ≥ 3, respectively). Sequential mediation analyses showed that the association between childhood maltreatment and endocrine diseases was consistently and most distinctly mediated by psychological adversities (15.38 ~ 44.97%), while unfavorable lifestyle (10.86 ~ 25.32%) was additionally noted for type 2 diabetes whereas suboptimal SES (14.42 ~ 39.33%) for HPA-axis-related endocrine diseases. Conclusions: Our study demonstrates that adverse psychological sequel of childhood maltreatment constitutes the main pathway to multiple endocrine diseases, particularly type 2 diabetes and HPA-axis-related endocrine diseases. Therefore, increased access to evidence-based mental health services may also be pivotal in reducing the risk of endocrine diseases among childhood maltreatment-exposed individuals. [ABSTRACT FROM AUTHOR]

Background: Psychological and trauma-related factors are associated with many diseases and mortality. However, a comprehensive assessment of the association between psycho-trauma exposures and aging acceleration is currently lacking., Methods: Using data from 332,359 UK Biobank participants, we calculated biological aging acceleration, indexed by the presence of leukocyte telomere length (LTL) deviation (i.e., the difference between genetically determined and observed LTL > 0). The acceleration of facial aging (i.e., looking older than the chronological age) was assessed using a self-report question. Then, we estimated the associations of each psycho-trauma factor with biological and facial aging acceleration, using logistic regression models adjusted for multiple important covariates. Furthermore, restricted to 99,180 participants with complete psychological and trauma-related data, we identified clusters of individuals with distinct psycho-trauma patterns using the latent class analysis method and assessed their associations with aging acceleration using similar models., Results: We observed most of the studied psycho-trauma factors were associated with biological and facial aging acceleration. Compared to the "Absence of trauma and psychopathology" cluster, the "adverse childhood experiences (ACEs) with psychopathology" cluster showed strong associations with those aging measurements (odds ratio [OR] = 1.13 [1.05 - 1.23] for biological and 1.52 [1.18 - 1.95] for facial aging acceleration), while no such association was observed for the "ACEs without psychopathology" cluster (1.04 [0.99 - 1.09] and 1.02 [0.84 - 1.24]., Conclusions: Our study demonstrated significant associations of psycho-trauma factors with both biological and facial aging acceleration. The differential aging consequences observed among ACEs exposed individuals with and without psychopathology prompt interventions aimed to improve individuals' psychological resilience to prevent aging acceleration., (© 2024. The Author(s).)

Background: Suicidal behaviors, including suicide deaths and attempts, are major public health concerns. However, previous suicide models required a huge amount of input features, resulting in limited applicability in clinical practice., Objective: We aimed to construct applicable models (ie, with limited features) for short- and long-term suicidal behavior prediction. We further validated these models among individuals with different genetic risks of suicide., Methods: Based on the prospective cohort of UK Biobank, we included 223 (0.06%) eligible cases of suicide attempts or deaths, according to hospital inpatient or death register data within 1 year from baseline and randomly selected 4460 (1.18%) controls (1:20) without such records. We similarly identified 833 (0.22%) cases of suicidal behaviors 1 to 6 years from baseline and 16,660 (4.42%) corresponding controls. Based on 143 input features, mainly including sociodemographic, environmental, and psychosocial factors; medical history; and polygenic risk scores (PRS) for suicidality, we applied a bagged balanced light gradient-boosting machine (LightGBM) with stratified 10-fold cross-validation and grid-search to construct the full prediction models for suicide attempts or deaths within 1 year or between 1 and 6 years. The Shapley Additive Explanations (SHAP) approach was used to quantify the importance of input features, and the top 20 features with the highest SHAP values were selected to train the applicable models. The external validity of the established models was assessed among 50,310 individuals who participated in UK Biobank repeated assessments both overall and by the level of PRS for suicidality., Results: Individuals with suicidal behaviors were on average 56 years old, with equal sex distribution. The application of these full models in the external validation data set demonstrated good model performance, with the area under the receiver operating characteristic (AUROC) curves of 0.919 and 0.892 within 1 year and between 1 and 6 years, respectively. Importantly, the applicable models with the top 20 most important features showed comparable external-validated performance (AUROC curves of 0.901 and 0.885) as the full models, based on which we found that individuals in the top quintile of predicted risk accounted for 91.7% (n=11) and 80.7% (n=25) of all suicidality cases within 1 year and during 1 to 6 years, respectively. We further obtained comparable prediction accuracy when applying these models to subpopulations with different genetic susceptibilities to suicidality. For example, for the 1-year risk prediction, the AUROC curves were 0.907 and 0.885 for the high (>2nd tertile of PRS) and low (<1st) genetic susceptibilities groups, respectively., Conclusions: We established applicable machine learning-based models for predicting both the short- and long-term risk of suicidality with high accuracy across populations of varying genetic risk for suicide, highlighting a cost-effective method of identifying individuals with a high risk of suicidality., (©Junren Wang, Jiajun Qiu, Ting Zhu, Yu Zeng, Huazhen Yang, Yanan Shang, Jin Yin, Yajing Sun, Yuanyuan Qu, Unnur A Valdimarsdóttir, Huan Song. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 20.02.2023.)

Serious concerns have been raised about the negative effects of the COVID-19 pandemic on population psychological well-being. However, limited data exist on the long-term effects of the pandemic on incident psychiatric morbidities among individuals with varying exposure to the pandemic. Leveraging prospective data from the community-based UK Biobank cohort, we included 308,400 participants free of diagnosis of anxiety or depression, as well as 213,757 participants free of anxiolytics or antidepressants prescriptions, to explore the trends in incident diagnoses and drug prescriptions for anxiety and depression from 16 March 2020 to 31 August 2021, compared to the pre-pandemic period (i.e., 1 January 2017 to 31 December 2019) and across populations with different exposure statuses (i.e., not tested for COVID-19, tested negative and tested positive). The age- and sex-standardized incidence ratios (SIRs) were calculated by month which indicated an increase in incident diagnoses of anxiety or depression among individuals who were tested for COVID-19 (tested negative: SIR 3.05 [95% confidence interval 2.88-3.22]; tested positive: 2.03 [1.76-2.34]), especially during the first six months of the pandemic (i.e., March-September 2020). Similar increases were also observed for incident prescriptions of anxiolytics or antidepressants (tested negative: 1.56 [1.47-1.67]; tested positive: 1.41 [1.22-1.62]). In contrast, individuals not tested for COVID-19 had consistently lower incidence rates of both diagnoses of anxiety or depression (0.70 [0.67-0.72]) and prescriptions of respective psychotropic medications (0.70 [0.68-0.72]) during the pandemic period. These data suggest a distinct rise in health care needs for anxiety and depression among individuals tested for COVID-19, regardless of the test result, in contrast to a reduction in health care consumption for these disorders among individuals not tested for and, presumably, not directly exposed to the disease., (© 2023. The Author(s).)

Background: Evidence is scarce regarding the potential modifying role of disease susceptibility on the association between a prior cancer diagnosis and cardiovascular disease (CVD)., Methods: We conducted a matched cohort study of UK Biobank including 78,860 individuals with a cancer diagnosis between January 1997 and January 2020, and 394,300 birth year and sex individually matched unexposed individuals. We used Cox model to assess the subsequent relative risk of CVD, which was further stratified by individual genetic predisposition., Results: During nearly 23 years of follow-up, an elevated risk of CVD was constantly observed among cancer patients, compared to their matched unexposed individuals. Such excess risk was most pronounced (hazard ratio [HR] = 5.28, 95% confidence interval [CI] 4.90-5.69) within 3 months after a cancer diagnosis, which then decreased rapidly and stabilised for >6 months (HR = 1.22, 95% CI 1.19-1.24). For all the studied time periods, stratification analyses by both levels of polygenic risk score for CVD and by family history of CVD revealed higher estimates among individuals with lower genetic risk predisposition., Conclusions: Our findings suggest that patients with a recent cancer diagnosis were at an increased risk of multiple types of CVD and the excess CVD risk was higher among individuals with lower genetic susceptibility to CVD, highlighting a general need for enhanced psychological assistance and clinical surveillance of CVD among newly diagnosed cancer patients., (© 2022. The Author(s).)

Objective: To assess the impact of the COVID-19 outbreak on cardiovascular disease (CVD) related mortality and hospitalisation., Design: Community-based prospective cohort study., Setting: The UK Biobank., Participants: 421 372 UK Biobank participants who were registered in England and alive as of 1 January 2020., Primary and Secondary Outcome Measures: The primary outcome of interest was CVD-related death, which was defined as death with CVD as a cause in the death register. We retrieved information on hospitalisations with CVD as the primary diagnosis from the UK Biobank hospital inpatient data. The study period was 1 January 2020 to June 30 2020, and we used the same calendar period of the three preceding years as the reference period. In order to control for seasonal variations and ageing of the study population, standardised mortality/incidence ratios (SMRs/SIRs) with 95% CIs were used to estimate the relative risk of CVD outcomes during the study period, compared with the reference period., Results: We observed a distinct increase in CVD-related deaths in March and April 2020, compared with the corresponding months of the three preceding years. The observed number of CVD-related deaths (n=218) was almost double in April, compared with the expected number (n=120) (SMR=1.82, 95% CI 1.58 to 2.07). In addition, we observed a significant decline in CVD-related hospitalisations from March onwards, with the lowest SIR observed in April (0.45, 95% CI 0.41 to 0.49)., Conclusions: There was a distinct increase in the number of CVD-related deaths in the UK Biobank population at the beginning of the COVID-19 outbreak. The shortage of medical resources for hospital care and stress reactions to the pandemic might have partially contributed to the excess CVD-related mortality, underscoring the need of sufficient healthcare resources and improved instructions to the public about seeking healthcare in a timely way., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)