Psychological trauma exposure and posttraumatic stress disorder (PTSD) have been associated with advanced epigenetic age. However, whether epigenetic aging measured at the time of trauma predicts the subsequent development of PTSD outcomes is unknown. Moreover, the neural substrates underlying posttraumatic outcomes associated with epigenetic aging are unclear.

We examined a multi-ancestry cohort of women and men (n = 289) who presented to the emergency department (ED) after trauma. Blood DNA was collected at ED presentation, and EPIC DNA methylation arrays were used to assess four widely used metrics of epigenetic aging (HorvathAge, HannumAge, PhenoAge, and GrimAge). PTSD symptoms were evaluated longitudinally at the time of ED presentation and over the ensuing 6 months. Structural and functional neuroimaging was performed 2 weeks after trauma.

After covariate adjustment and correction for multiple comparisons, advanced ED GrimAge predicted increased risk for 6-month probable PTSD diagnosis. Secondary analyses suggested that the prediction of PTSD by GrimAge was driven by worse trajectories for intrusive memories and nightmares. Advanced ED GrimAge was also associated with reduced volume of the whole amygdala and specific amygdala subregions, including the cortico-amygdaloid transition and the cortical and accessory basal nuclei.

Our findings shed new light on the relation between biological aging and trauma-related phenotypes, suggesting that GrimAge measured at the time of trauma predicts PTSD trajectories and is associated with relevant brain alterations. Furthering these findings has the potential to enhance early prevention and treatment of posttraumatic psychiatric sequelae.

This study will evaluate radiation medicine professionals’ perceptions of clinical and professional risks and benefits, and the evolving roles and responsibilities with artificial intelligence (AI).

Radiation oncologists (ROs), medical physicists (MPs), treatment planners (TP-RTTs) and treatment delivery radiation therapists (TD-RTTs) at a cancer centre in preliminary stages of implementing an AI-enabled treatment planning system were invited to participate in uniprofessional focus groups. Semi-structured scripts addressed the perceptions of AI, including thoughts regarding changing roles and competencies. Sessions were audiorecorded, transcribed and coded thematically through consensus-building.

A total of 24 participants (four ROs, five MPs, seven TP-RTTs and eight TD-RTTs) were engaged in four focus groups of 58 minutes average duration (range 54–61 minutes). Emergent themes addressed AI’s impact on quality of care, changing professional tasks and changing competency requirements. Time-consuming repetitive tasks such as delineating targets, generating treatment plans and quality assurance were thought conducive to offloading to AI. Outcomes data and adaptive planning would be incorporated into clinical decision-making. Changing workload would necessitate changing skills, prioritising plan evaluation over generation and increasing interprofessional communication. All groups discussed AI reducing the need for TP-RTTs, though displacement was thought more likely than replacement.

It is important to consider how professionals perceive AI to be proactive in informing change, as gains in quality and efficiency will require new workflows, skills and education.