Vertebral heart scoring (VHS) is a semiquantitative method to assess the presence and severity of cardiomegaly by using thoracic radiographs. VHS in rhesus macaques (Macaca mulatta) has not been validated or used routinely in the clinical or research setting. We hypothesized that rhesus macaques with cardiac disease diagnosed by using echocardiography would have higher VHS than animals without cardiac disease. A total of 150 rhesus macaques were enrolled in this study. All animals underwent echocardiography and thoracic radiography (right lateral [RL], dorsoventral [DV], and ventrodorsal [VD] views). According to echocardiography, 121 rhesus macaques had no cardiac disease and were used to establish reference intervals for VHS. The remaining 29 macaques had hypertrophic cardiomyopathy (n = 20) or other cardiac disease (n = 9). Results showed that VHS of RL and VD views were significantly higher in macaques with any of the identified cardiac diseases and in the cardiac disease group that excluded hypertrophic cardiomyopathy. VHS of animals with HCM was not significantly different than that of control animals. In the RL view, VHS was moderately accurate for predicting the presence of cardiac disease, with an AUC of 0.71 and an optimal cut-off value of 10.25 (sensitivity: 62%, specificity: 77%). In the VD view, VHS was a mildly accurate test for cardiac disease, with an AUC of 0.654 and an optimal cut-off value of 10.65 (sensitivity, 66%; specificity, 63%). Study results indicated that VHS could be a useful screening tool for clinically identifying rhesus macaques with cardiac disease. However, VHS is unlikely to replace echocardiographic examination for determining the presence, type, and severity of cardiac disease in this species.

Cardiac biomarkers are an important tool for diagnosing cardiac diseases in both human and veterinary patients. Serum concentrations of N-terminal probrain natriuretic peptide (NT-proBNP) and cardiac troponin I (cTnI) have been used to indicate the presence of various cardiac diseases including hypertrophic cardiomyopathy (HCM) in various species including humans. However, these cardiac biomarkers have not been established as a diagnostic tool for detecting cardiac disease in rhesus macaques. In the rhesus macaque colony at the California National Primate Research Center, naturally occurring HCM and various other cardiac diseases have been identified. In this study, commercially available assays were used to measure serum cTnI and NT-proBNP concentrations to evaluate their utility as a diagnostic screening tool for cardiac diseases in rhesus macaques. This study revealed that the serum cTnI concentration was significantly higher in animals with echocardiographically apparent cardiac disease as compared with the animals that had no cardiac structural and functional changes (the control group). However, no significant differences were detected between animals with HCM and non-HCM cardiac disease. Because the area under the receiver operating characteristic curve was 0.81 when the serum cTnI was compared between the control and cardiac disease groups, serum cTnI was considered a moderately accurate test to predict the presence of cardiac disease. The optimal cut-off value of serum cTnI concentration for diagnosis of cardiac disease was 0.0085 ng/mL, with a sensitivity of 0.68 and specificity of 0.94. Significant but weak correlations were noted between the serum cTnI concentration and several echocardiographic parameters. Conversely, no significant differences in NT-proBNP concentrations were detected between animals with and without cardiac diseases. In conclusion, measurement of serum cTnI can be used to aid in diagnosing cardiac diseases in rhesus macaques. However, cTnI measurement does not replace echocardiographic evaluation to diagnose cardiac diseases in rhesus macaques due to the poor sensitivity of the assay and the weak correlation to with more established echocardiographic markers for cardiac disease.

A substantial minority of women who experience interpersonal violence will develop posttraumatic stress disorder (PTSD). One critical challenge for preventing PTSD is predicting whose acute posttraumatic stress symptoms will worsen to a clinically significant degree. This 6-month longitudinal study adopted multilevel modeling and exploratory machine learning (ML) methods to predict PTSD onset in 58 young women, ages 18 to 30, who experienced an incident of physical and/or sexual assault in the three months prior to baseline assessment. Women completed baseline assessments of theory-driven cognitive and neurobiological predictors and interview-based measures of PTSD diagnostic status and symptom severity at 1-, 3-, and 6-month follow-ups. Higher levels of self-blame, generalized anxiety disorder severity, childhood trauma exposure, and impairment across multiple domains were associated with a pattern of high and stable posttraumatic stress symptom severity over time. Predictive performance for PTSD onset was similarly strong for a gradient boosting machine learning model including all predictors and a logistic regression model including only baseline posttraumatic stress symptom severity. The present findings provide directions for future work on PTSD prediction among interpersonal violence survivors that could enhance early risk detection and potentially inform targeted prevention programs.

Background

Alterations in major stress response systems are present during the immediate aftermath of trauma and may play a role in determining risk for developing posttraumatic stress disorder (PTSD). However, the dynamics and determinants of stress responses during this acute recovery phase, and their relevance for longitudinal clinical course and prognosis, have yet to be fully examined. The objectives of the present study were to characterize stress response and habituation patterns to repeated social stressors in women who recently experienced interpersonal trauma and to determine the extent to which these stress responses were associated with PTSD during prospective follow-up.

Method

This longitudinal study examined salivary cortisol and alpha-amylase and heart rate (HR) responses to repeated stressors in 98 young women (ages 18-30). Participants included women who had experienced an incident of interpersonal trauma (i.e., physical and/or sexual assault) in the three months prior to their baseline assessment (n = 58) and a comparison group of healthy, non-traumatized women (n = 40). Women completed the Trier Social Stress Test (TSST), clinical interviews to evaluate posttraumatic stress symptom severity at the baseline assessment and again at 1-, 3-, and 6-month follow-ups.

Results

Multilevel models revealed a pattern of robust initial cortisol TSST responses and habituation across successive TSSTs; alpha-amylase and HR responses showed no evidence of habituation across TSSTs. Among interpersonal trauma survivors, current PTSD status was associated with more pronounced cortisol responses to the first TSST. Survivors exhibited similarly blunted cortisol responses across follow-up TSSTs regardless of PTSD status, suggesting habituation of cortisol responses among survivors who developed PTSD. PTSD re-experiencing symptoms were uniquely associated with blunting of cortisol TSST responses.

Conclusion

Findings suggest that PTSD as a diagnostic entity is meaningfully associated with cortisol responses to repeated social stressors. Social-evaluative threat is a salient form of danger for interpersonal trauma survivors. Identifying the determinants of cortisol (non)habituation to repeated social-evaluative threat among interpersonal trauma survivors could inform the development of early interventions for PTSD.

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.