Higher arterial stiffness and blunted vagal control of the heart in young women with compared to without a clinical diagnosis of PTSD.

PURPOSE: Young women are typically thought to be protected from cardiovascular disease (CVD) before menopause. However, posttraumatic stress disorder (PTSD) increases CVD risk in women by up to threefold. Data in predominantly male cohorts point to physiological mechanisms such as vascular and autonomic derangements as contributing to increased CVD risk. The purpose of the study reported here was to determine whether young women diagnosed with PTSD, compared to those without, present with arterial stiffness and impaired autonomic control of the heart. METHODS: A total of 73 healthy young women, ranging in age from 18 to 40 years, with a history of trauma exposure were included in this study, 32 with and 41 without a clinical PTSD diagnosis. We measured resting pulse wave velocity (PWV), central hemodynamics, augmentation pressure and augmentation index (AI) via pulse wave analysis using applanation tonometry. Heart rate variability was also assessed via peripheral arterial tone. RESULTS: In comparison to controls, women with PTSD showed higher central arterial pressure (mean ± standard deviation: systolic blood pressure 104 ± 8 vs. 97 ± 8 mmHg, p < 0.001; diastolic blood pressure 72 ± 7 vs. 67 ± 7 mmHg, p = 0.003), PWV (6 ± 0.3 vs. 5 ± 0.6 m/s, p < 0.001) and AI (22 ± 13 vs. 15 ± 12%, p = 0.007) but lower standard deviation of normal-to-normal intervals (SDNN; 44 ± 17 vs. 54 ± 18 ms, p = 0.005) and root mean square of successive differences between normal heartbeats (RMSSD; 37 ± 17 vs. 51 ± 22 ms, p = 0.002). CONCLUSION: PTSD in young women is associated with higher brachial and central pressures, increased arterial stiffness and blunted parasympathetic control of the heart. These findings illustrate potential mechanisms underlying high risk for CVD in young women with PTSD, suggesting possible treatment targets for this at-risk group.

Human effector and memory CD8+ T cell responses to smallpox and yellow fever vaccines.

To explore the human T cell response to acute viral infection, we performed a longitudinal analysis of CD8(+) T cells responding to the live yellow fever virus and smallpox vaccines--two highly successful human vaccines. Our results show that both vaccines generated a brisk primary effector CD8(+) T cell response of substantial magnitude that could be readily quantitated with a simple set of four phenotypic markers. Secondly, the vaccine-induced T cell response was highly specific with minimal bystander effects. Thirdly, virus-specific CD8(+) T cells passed through an obligate effector phase, contracted more than 90% and gradually differentiated into long-lived memory cells. Finally, these memory cells were highly functional and underwent a memory differentiation program distinct from that described for human CD8(+) T cells specific for persistent viruses. These results provide a benchmark for CD8(+) T cell responses induced by two of the most effective vaccines ever developed.

Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression.

Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by better and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modelling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models can predict clinical outcomes. We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression (TRD), across multiple coils and protocols. We then compared the predictive power of those models. LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC = 0.70, 95% CI = [0.52-0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC = 0.76, 95% CI = [0.58 - 0.94], but likewise, not before. In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.