Karestan Koenen

Background: Post-traumatic stress disorder (PTSD) remains underexplored in Africa, where measurement of potentially traumatic events (PTEs), PTSD symptoms, and their associations may differ from other populations. Objective & Method: We examined the factor structure of PTEs and PTSD symptoms, measured by the Life-Events Checklist-5 and PTSD Checklist-5, respectively, the item-level response patterns in relation to their corresponding factors, the structural and relational aspects of these instruments, and the factors’ associations in clinical samples from Kenya (N = 2,552, μage = 40.49, SD = 11.92) and Uganda (N = 4,497, μage = 38.73, SD = 11.66). Results: In both samples, PTEs were best represented by four factors: sudden death/serious injury, combat/life-threatening experiences, physical/sexual assault, and accidental trauma. PTSD symptoms had a three-factor structure: intrusion/avoidance, alterations in cognition/mood, and alterations in arousal/reactivity. Associations within PTEs and among PTSD-symptoms scores were strong (0.663 < r < 0.959, 95CI = 0.619–1.000), but weaker between them (0.082 < r < 0.250, 95CI = 0.035–0.304). Physical/sexual assault had most of the strongest associations with all PTSD-symptom scores. Conclusions: Our findings support consistent, interpretable structures for PTEs and PTSD symptoms, emphasizing the link between physical and sexual violence with PTSD symptomatology.

Objective: Previous research suggests an association between post-traumatic stress disorder (PTSD) and metabolic syndrome (MetS) but has not disentangled the effects of trauma exposure, PTSD, and psychological resilience after trauma. This study investigated risk of MetS by psychological resilience after trauma exposure. Methods: Data are from 42,425 women in the Nurses’ Health Study II who completed a survey in 2008 evaluating PTSD symptoms, psychological distress, facets of positive psychological well-being (PWB), and a broad range of covariates. Psychological resilience to trauma was characterized as: resilient (no PTSD and high positive PWB), coping without thriving (no PTSD and low positive PWB), and PTSD, with a no-trauma control group. Probable MetS was derived using data on weight, height, diagnosed hypertension, hyperlipidemia, and diabetes, and medication use from biennial surveys between 1989 and 2019. Time to probable MetS was approximated as the return date of the first questionnaire at which 3+ probable MetS criteria were met. Results: Proportional hazards models adjusted for sociodemographic characteristics indicated that, relative to women with no trauma, women categorized as having PTSD (aHR=1.34 [1.05, 1.71]) or as coping without thriving (aHR=1.29 [1.03, 1.62]) experienced a greater hazard of developing MetS, whereas resilient women experienced a similar hazard of developing MetS (aHR=1.06 [0.84, 1.34]). Results remained similar after adjusting for health behaviors and antidepressant use. Conclusions: Future research on trauma and MetS should consider varied trauma responses and assess if interventions that not only reduce psychological distress but also promote PWB could mitigate MetS risk.

Objective: We investigated prospective associations between mental health conditions (depression, anxiety, and posttraumatic stress disorder [PTSD]) and recurrent acute care use among patients with systemic lupus erythematosus (SLE). Methods: Within the All of Us Research Program (version 8), a US-wide cohort of adults who enrolled and consented for linkage to electronic health records, we identified those with SLE by International Classification of Diseases, 10th Revision (ICD-10) or Systematized Nomenclature of Medicine (SNOMED) codes before enrollment. We assessed baseline depression, anxiety, and/or PTSD using ICD-10 or SNOMED codes. Outcomes included emergency department (ED) visits and hospitalizations after enrollment. Incidence rates for acute care events among patients with vs without mental health diagnoses were calculated. Multivariable negative binomial regression models estimated incidence rate ratios (IRRs) with 95% CIs, adjusting for sociodemographic factors and comorbidities. Results: Of 2160 patients with SLE, 973 had depression, anxiety, and/or PTSD (mean age 49.4 [SD 13.5] years; 92.8% female); 1187 did not (mean age 50.4 [SD 14.9] years; 88.1% female). Those with ≥ 1 mental health condition had lower education levels and income and more smoking and comorbidities. Patients were followed up for a mean of 3.02 (SD 1.73) years. One or more mental health diagnosis was strongly associated with higher rates of ED visits (adjusted IRR [aIRR] 1.90, 95% CI 1.60-2.27) and hospitalizations (aIRR 1.33, 95% CI 1.08-1.65). This was true for ED visits and hospitalizations for SLE (aIRR 1.42, 95% CI 1.19-1.69) and for non-SLE conditions (aIRR 1.42, 95% CI 1.17-1.74). Conclusion: In this nationwide SLE cohort, those with vs without mental health diagnoses had increased acute care use for SLE and other causes. Further studies are needed to understand whether effective treatment for mental health diagnoses alters care patterns.

Objective: Polygenic risk scores (PRS) for posttraumatic stress disorder (PTSD) often account for a low amount of variance. Ancestry-related differences in PRS scale and variance limit cross-group comparisons. This methodological challenge further complicates gene-by-environment (GxE) analyses, given that socioenvironmental exposures are inequitably distributed across ethnoracial groups. We constructed an ancestry-calibrated polygenic risk score (AC-PRS) for PTSD in the largest longitudinal study of trauma survivors to date and investigated GxE interactions. Method: Recent trauma survivors (N=1,801) provided a blood specimen for genotyping. Six PTSD trajectories were previously identified from PTSD Checklist for DSM-5 (PCL-5) scores at 2-weeks, 8-weeks, 3-months, and 6-months post-trauma. Greenspace (normalized difference vegetation index [NDVI) and socioeconomic disadvantage (area deprivation index [ADI]) were derived from residential addresses. Logistic regressions examined interactions between newly developed AC-PRS and neighborhood factors on trajectories after adjusting for sociodemographic and trauma-related covariates. Secondary linear models considered GxE interactions on 6-month PCL-5 scores. Results: AC-PRS performed well across ethnoracial groups, explaining significant variability in PTSD trajectories (R ² =.053). ADI moderated the association between AC-PRS and the likelihood of assignment in a high nonremitting trajectory of PTSD symptoms and severity of symptoms at 6-months ( ps < .05). There were no NDVI x AC-PRS interactions in any models. Conclusions: AC-PRS captures genetic risk for PTSD in admixed trauma survivors, demonstrating good discrimination between nonremitting and resilient courses of PTSD. However, neighborhood disadvantage may modify utility of PRS for PTSD, warranting careful consideration when applying these scores across contexts.

Here we developed and deployed the blended genome exome (BGE) method, a DNA library approach that generates low-pass whole-genome (1–4× mean depth) and deep whole-exome (30–40× mean depth) data in a single sequencing run. BGE is cost-effective, empowers most genomic discoveries possible with deep whole-genome sequencing and captures global common single-nucleotide polymorphism diversity. We applied BGE to sequence >53,000 samples from the PUMAS Project (Populations Underrepresented in Mental Illness Associations Studies), including African, African American and Latin American populations. Imputed genotypes showed high concordance with Illumina Global Screening Array calls ( R ² ≥ 95% for minor allele frequency ≥1%; ≥90% for minor allele frequency <1%), with consistent performance across local ancestries in admixed cohorts. For protein-coding copy number variants, deletions and duplications spanning at least three exons had a positive predicted value of ~90% relative to deep whole-genome data. At ~28% of the cost of deep whole-genome sequencing, BGE provides a scalable, reliable platform to expand genomic discovery and equitable access to sequencing in underrepresented populations.

Background: Adverse posttraumatic neuropsychiatric sequelae are common after trauma. Early identification of individuals at risk for these outcomes could enable the deployment of preventive interventions to survivors at greatest risk. Smartphone keystroke biomarkers show promise in identifying individuals with neuropsychiatric symptoms; however, to our knowledge, no research has examined whether they can be used to identify symptoms in the aftermath of trauma. Objective: This study evaluates whether passively collected keystroke data from smartphone use in daily life could identify individuals with high symptom levels, as well as worsening or recovery of symptoms, after trauma exposure. Methods: Data from a diverse cohort of individuals presenting to 27 emergency departments after trauma were analyzed. Inclusion criteria were presenting to the emergency department within 72 hours of trauma, age 18-75, and the ability to speak and read English. Exclusion criteria were solid organ injury, significant hemorrhage, operative intervention, or likely admission for over 72 hours. Participants installed an app that passively collected keystroke data during use of any app on their smartphone, beginning in the emergency department. Participants also completed serial symptom assessments over 8 weeks after trauma exposure. Results: A total of 3445 patients met study criteria, provided informed consent, and completed assessments in the emergency department. Of these, 1072 (mean age 40, SD 13; 616/1072, 57.46%, women; 565/1072, 52.71% non–Hispanic Black) installed the app on their Android smartphone and completed the 8-week assessment and were therefore included in analyses. Keystroke biomarkers related to typing speed, identified using bivariate linear mixed models controlling for false discovery rates, were associated with elevated pain, reexperiencing, and mental fatigue (absolute values of rs=0.22-0.25, Ps=.02). Separate change-of-operation and scrolling keystroke biomarkers were associated with increased reexperiencing symptoms (r=0.18, P=.047) and mental fatigue (rs=0.18-0.19, Ps=.031-.047). Further, changes in specific keystroke biomarkers were associated with worsening or recovery of pain (rs=0.07-0.10, Ps=.02), somatic symptoms (rs=0.02, Ps=.02), mental fatigue (rs=0.02-0.04, Ps=.02), sleep disturbance (absolute rs=0.07-0.09, Ps=.02), reexperiencing (rs=0.02-0.04, Ps=.02), and hyperarousal (rs=0.02-0.04, Ps=.02). Conclusions: In general, slower typing and scrolling speeds were associated with higher symptom levels, with small to medium effect sizes. Keystroke data passively collected via smartphone use may help identify individuals with significant or changing posttraumatic symptoms. Future research should continue to explore these keystroke biomarkers and whether they can be leveraged to connect vulnerable trauma survivors to appropriate services. Overall, these results add to the literature, indicating that passively collected keystroke data may help identify individuals with neuropsychiatric symptoms or changes and are, to our knowledge, the first to test whether keystroke biomarkers are useful in the aftermath of trauma. This represents a critical period during which preventive interventions could be deployed to reduce the long-term burden of trauma-related sequelae.

Residential segregation is associated with differential exposure to air pollution. Hippocampus structure and function are highly susceptible to pollutants and associated with posttraumatic stress disorder (PTSD) development. Therefore, we investigated associations between residential segregation, air pollutants, hippocampal neurobiology, and PTSD in recent trauma survivors. Participants (N = 278; 34% non-Hispanic white, 46% Non-Hispanic Black, 16% Hispanic) completed multimodal neuroimaging two weeks after trauma. Yearly averages of air pollutants (PM_2.5 and NO₂) and racial/economic segregation (Index of Concentration at the Extremes) were derived from each participant's address. Linear models assessed if air pollutants mediated associations between segregation and hippocampal volume, threat reactivity, or parahippocampal cingulum fractional anisotropy (FA) after covarying for age, sex, income, and 2-week PTSD symptoms. Further models evaluated if pollutants or segregation prospectively predicted PTSD symptoms six months post-trauma. We found that non-Hispanic Black participants lived in neighborhoods with significantly greater segregation and air pollution compared to Hispanic and non-Hispanic white participants (ps < 0.001). PM_2.5 concentration was positively correlated with threat reactivity (r(276) = 0.16, p < 0.006), while NO₂ concentration was positively correlated with hippocampus volume (r(276) = 0.17, p < 0.005) and negatively correlated with white matter tract FA (r(276) = -0.18, p < 0.003). There was a significant indirect effect of NO₂ between segregation and FA values (β = 0.08, 95% CI[0.01, 0.15]), and an indirect effect of PM_2.5 between segregation and threat reactivity (β = -0.08, 95% CI[-0.14, -0.01]). There was no direct effect of segregation on hippocampal features. Pollutants and segregation were not associated with PTSD symptoms. In conclusion, residential segregation is associated with greater air pollution exposure, which is in turn associated with variability in hippocampal features among recent trauma survivors. Further research is needed to assess relationships between other environmental factors and trauma and stress-related disorders.

Background: Maternal history of abuse has been associated with their children’s behavior problems, but the role of the timing and chronicity of abuse is less understood. We used a structured life course modeling approach (SLCMA) to examine whether models of accumulation (i.e. cumulative exposure to abuse across periods) or sensitive periods (i.e. developmentally salient timing of abuse) best explained associations with children’s behavior outcomes.Methods: 949 mother-child pairs from the Pregnancy Outcomes, Maternal and Infant Study (PrOMIS), a prospective birth cohort in Peru, were analyzed. Women were recruited during pregnancy and followed up approximately 8 years postpartum. Maternal physical and sexual abuse history during childhood, one year preconception, and during pregnancy was assessed. Maternal report of child behavior problems was obtained using the Child Behavior Check List. Life course hypotheses were tested using SLCMA, and secondary analyses assessed the influence of abuse severity and current stressors.Findings: 65% of mothers reported lifetime experience of abuse, most often physical abuse. Among children, 20% had internalizing and 12% had externalizing behavior problems. The cumulative model of physical abuse and sensitive periods preconception and during pregnancy, most strongly predicted children’s behavior problems (r2 range 0.8% - 1.8%), particularly among girls (externalizing T-score =7.4, 95%CI=1.6, 12.5). Severe maternal physical abuse had the largest association with externalizing behaviors, and current food insecurity was an effect modifier.Interpretation: Maternal physical abuse experienced at critical developmental periods, explained sex-specific problems in child behaviors. Understanding the biological mechanisms behind this transgenerational risk may help design interventions to prevent childhood psychopathology.