Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points.

We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367).

Meta-analysis revealed that the interaction between Time 1 (T1) Horvath age residuals and new-onset PTSD over time was significantly associated with Horvath age residuals at T2 (meta β = 0.16, meta p = 0.02, p-adj = 0.03). The interaction between T1 Horvath age residuals and changes in PTSD symptom severity over time was significantly related to Horvath age residuals at T2 (meta β = 0.24, meta p = 0.05). No associations were observed for GrimAge residuals.

Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.

Patients with Fontan failure are high-risk candidates for heart transplantation and other advanced therapies. Understanding the outcomes following initial heart failure consultation can help define appropriate timing of referral for advanced heart failure care.

This is a survey study of heart failure providers seeing any Fontan patient for initial heart failure care. Part 1 of the survey captured data on clinical characteristics at the time of heart failure consultation, and Part 2, completed 30 days later, captured outcomes (death, transplant evaluation outcome, and other interventions). Patients were classified as “too late” (death or declined for transplant due to being too sick) and/or “care escalation” (ventricular assist device implanted, inotrope initiated, and/or listed for transplant), within 30 days. “Late referral” was defined as those referred too late and/or had care escalation.

Between 7/2020 and 7/2022, 77 Fontan patients (52% inpatient) had an initial heart failure consultation. Ten per cent were referred too late (6 were too sick for heart transplantation with one subsequent death, and two others died without heart transplantation evaluation, within 30 days), and 36% had care escalation (21 listed ± 5 ventricular assist device implanted ± 6 inotrope initiated). Overall, 42% were late referrals. Heart failure consultation < 1 year after Fontan surgery was strongly associated with late referral (OR 6.2, 95% CI 1.8–21.5, p=0.004).

Over 40% of Fontan patients seen for an initial heart failure consultation were late referrals, with 10% dying or being declined for transplant within a month of consultation. Earlier referral, particularly for those with heart failure soon after Fontan surgery, should be encouraged.

Excessive and persistent fear of clusters of holes, also known as trypophobia, has been suggested to reflect cortical hyperexcitability and may be associated with mental health risks. No study, however, has yet examined these associations in representative epidemiological samples.

To examine the prevalence of trypophobia in a population-representative youth sample, its association with mental health and functioning, and its interaction with external stress.

A total of 2065 young people were consecutively recruited from a household-based epidemiological youth mental health study in Hong Kong. Trypophobia, symptoms of anxiety, depression and stress, and exposure to personal stressors were assessed. Logistic regression was used to assess the relationships between trypophobia and mental health. Potential additive and interaction effects of trypophobia and high stress exposure on mental health were also tested.

The prevalence of trypophobia was 17.6%. Trypophobia was significantly associated with severe symptoms of anxiety (odds ratio (OR) = 1.83, 95% CI = 1.32–2.53), depression (OR = 1.78, 95% CI = 1.24–2.56) and stress (OR = 1.68, 95% CI = 1.11–2.53), even when accounting for sociodemographic factors, personal and family psychiatric history, resilience and stress exposure. Dose–response relationships were observed, and trypophobia significantly potentiated the effects of stress exposure on symptom outcomes, particularly for depressive symptoms. Those with trypophobia also showed significantly poorer functioning across domains and poorer health-related quality of life.

Screening for trypophobia in young people may facilitate early risk detection and intervention, particularly among those with recent stress exposure. Nevertheless, the generally small effect sizes suggest that other factors have more prominent roles in determining recent mental health outcomes in population-based samples; these should be explored in future work.

Clinical trials provide the “gold standard” evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources – data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.

Three examples of real-world trials that leverage different types of data sources – wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.

Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.

Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.

Despite replicated cross-sectional evidence of aberrant levels of peripheral inflammatory markers in individuals with major depressive disorder (MDD), there is limited literature on associations between inflammatory tone and response to sequential pharmacotherapies.

To assess associations between plasma levels of pro-inflammatory markers and treatment response to escitalopram and adjunctive aripiprazole in adults with MDD.

In a 16-week open-label clinical trial, 211 participants with MDD were treated with escitalopram 10– 20 mg daily for 8 weeks. Responders continued on escitalopram while non-responders received adjunctive aripiprazole 2–10 mg daily for 8 weeks. Plasma levels of pro-inflammatory markers – C-reactive protein, Interleukin (IL)-1β, IL-6, IL-17, Interferon gamma (IFN)-Γ, Tumour Necrosis Factor (TNF)-α, and Chemokine C–C motif ligand-2 (CCL-2) - measured at baseline, and after 2, 8 and 16 weeks were included in logistic regression analyses to assess associations between inflammatory markers and treatment response.

Pre-treatment levels of IFN-Γ and CCL-2 were significantly higher in escitalopram non-responders compared to responders. Pre-treatment IFN-Γ and CCL-2 levels were significantly associated with a lower of odds of response to escitalopram at 8 weeks. Increases in CCL-2 levels from weeks 8 to 16 in escitalopram non-responders were significantly associated with higher odds of non-response to adjunctive aripiprazole at week 16.

Pre-treatment levels of IFN-Γ and CCL-2 were predictive of response to escitalopram. Increasing levels of these pro-inflammatory markers may predict non-response to adjunctive aripiprazole. These findings require validation in independent clinical populations.

None Declared

Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings.

We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021.

Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways.

WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.

Young people are most vulnerable to suicidal behaviours but least likely to seek help. A more elaborate study of the intrinsic and extrinsic correlates of suicidal ideation and behaviours particularly amid ongoing population-level stressors and the identification of less stigmatising markers in representative youth populations is essential.

Participants (n = 2540, aged 15–25) were consecutively recruited from an ongoing large-scale household-based epidemiological youth mental health study in Hong Kong between September 2019 and 2021. Lifetime and 12-month prevalence of suicidal ideation, plan, and attempt were assessed, alongside suicide-related rumination, hopelessness and neuroticism, personal and population-level stressors, family functioning, cognitive ability, lifetime non-suicidal self-harm, 12-month major depressive disorder (MDD), and alcohol use.

The 12-month prevalence of suicidal ideation, ideation-only (no plan or attempt), plan, and attempt was 20.0, 15.4, 4.6, and 1.3%, respectively. Importantly, multivariable logistic regression findings revealed that suicide-related rumination was the only factor associated with all four suicidal outcomes (all p < 0.01). Among those with suicidal ideation (two-stage approach), intrinsic factors, including suicide-related rumination, poorer cognitive ability, and 12-month MDE, were specifically associated with suicide plan, while extrinsic factors, including coronavirus disease 2019 (COVID-19) stressors, poorer family functioning, and personal life stressors, as well as non-suicidal self-harm, were specifically associated with suicide attempt.

Suicide-related rumination, population-level COVID-19 stressors, and poorer family functioning may be important less-stigmatising markers for youth suicidal risks. The respective roles played by not only intrinsic but also extrinsic factors in suicide plan and attempt using a two-stage approach should be considered in future preventative intervention work.

In March 2020, New York City became the epicenter of the coronavirus disease 2019 (COVID-19) pandemic in the United States. Because healthcare facilities were overwhelmed with patients, the Jacob K. Javits Convention Center was transformed into the nation’s largest alternate care site: Javits New York Medical Station (hereafter termed Javits). Protecting healthcare workers (HCWs) during a global shortage of personal protective equipment (PPE) in a nontraditional healthcare setting posed unique challenges. We describe components of the HCW safety program implemented at Javits.

Javits, a large convention center transformed into a field hospital, with clinical staff from the US Public Health Service Commissioned Corps and the US Department of Defense.

Key strategies to ensure HCW safety included ensuring 1-way flow of traffic on and off the patient floor, developing a matrix detailing PPE required for each work activity and location, PPE extended use and reuse protocols, personnel training, and monitoring adherence to PPE donning/doffing protocols when entering or exiting the patient floor. Javits staff who reported COVID-19 symptoms were immediately isolated, monitored, and offered a severe acute respiratory coronavirus virus 2 (SARS-CoV-2) reverse-transcriptase polymerase chain reaction (RT-PCR) test.

A well-designed and implemented HCW safety plan can minimize the risk of SARS-CoV-2 infection for HCWs. The lessons learned from operating the nation’s largest COVID-19 alternate care site can be adapted to other environments during public health emergencies.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.