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Cognitive transformation as a marker of resilience. Thompson, R. Conceptualizing mindfulness and acceptance as components of psychological resilience to trauma. Troy, A. Resilience in the face of stress: Emotion regulation as a protective factor. Southwick, B. Litz, D. Friedman Eds. Cambridge: Cambridge University Press. Seeing the silver lining: cognitive reappraisal ability moderates the relationship between stress and depressive symptoms. Emotion, 10 6 , — A person-by-situation approach to emotion regulation: Cognitive reappraisal can either help or hurt, depending on the context.
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Psychoneuroendocrinology, 85, — Exploringemotion regulation as a mediator of the relationship between resilience and distress in cancer. Psycho-oncology, 28 7 , — Walsh, W. How are we measuring resilience following childhood maltreatment? Is the research adequate and consistent? What is the impact on research, practice, and policy? Emotional resilience mediates the relationship between mindfulness and emotion. Psychological Reports, 3 , — Specifically, chronic stress, via extended exposure to cortisol, is associated with reduced NR3C1 expression, leading to glucocorticoid resistance and impaired negative feedback inhibition of the HPA axis [ 32 ].
Reduced levels of glucocorticoid receptors, in turn, bind less cortisol. This effectively decreases the number of ligand-bound receptor complexes available to translocate to the nucleus and regulate the expression of genes, including anti-inflammatory genes. Thus, reduced levels of NR3C1 expression can lead to impaired immune function [ 33 ]. Further, evidence exists for elevated levels of pro-inflammatory cytokines in children and adults exposed to ELA [ 34 , 35 ]. Dysregulation of the immune system in the context of ELA, as well as the resulting increase of pro-inflammatory cytokines, can increase risk for a host of diseases, from autoimmune to atherosclerosis and cancer [ 36 ].
Here, we delineate a program of research to study ELA-related programming of biological systems with emphasis on processes related to glucocorticoid signaling and inflammation. Experimental studies usually employ either a between- or within-person designs [ 37 ], and measure physiological or gene expression changes over relatively short time frames. Our novel program of research uses a within-person, between-groups experimental design, and aims to test whether ELA leads to dysregulation of physiological, gene expression and pro-inflammatory cytokines in response to a canonical laboratory stressor, compared with a resting control condition, and compared with individuals without exposure to ELA.
The within-person control condition enables us to disentangle the effects of acute stress from noisy gene expression measurements in the same individuals. The between-person acute stress condition enables tests of potential programming of biological systems between ELA and control individuals, which theory suggests in more likely to manifest during times of duress [ 26 , 27 ]. Measuring stress-induced physiological, immune, and gene expression changes enable tests across a wide array of biological and cellular pathways that may play a downstream role in disease susceptibility. This conceptual framework integrates environmental, psychological, and biological elements of what Cohen et al.
Finally, to substantiate group differences between ELA and control individuals, we compared individuals who reported at least three major traumatic events up to age 18 years [ 39 ], while the control group were selected only if they reported none. In a randomized within-subjects, between-groups experimental design, we induced acute stress in the lab Trier Social Stress Test, TSST and included a no-stress control condition separated by one week. During these sessions, we obtained repeated measurements of physiological reactivity, plasma levels of pro-inflammatory cytokines, and PBMC gene expression over a 4-hour window post-test. Primary analyses aimed to replicate previously known associations between physiological measures i. Based on prior literature, we hypothesized that individuals exposed to ELA will evince dysregulated physiological, NR3C1 gene expression, and pro-inflammatory changes to an acute laboratory stressor, a response pattern that may reveal dynamic biological signatures of early-life programming with implications for life-long health.
Participants were healthy male college students at the Pennsylvania State University, recruited by word of mouth and advertisements on campus bulletin boards. We focused on men in this exploratory study due to known sex differences in the stress response [ 41 ] and the small sample size for stratified analyses. To obtain the sample who were exposed to ELA, a trained clinical interviewer conducted a phone interview to screen over eligible men using the Stressful Life Events Screening Questionnaire SLESQ [ 40 ], a item self-report measure that assesses lifetime exposure to traumatic events. We asked respondents 11 specific and two general categories of events, such as death of a parent or sibling, life-threatening accident, and sexual and physical abuse.
Based on evidence that three or more traumatic events confers higher risk for disease [ 39 ], and considering the severity of the traumatic events, participants who responded to at least 3 incidents up to age 18 years independently reviewed and reached consensus by MZ and IS were invited to participate in the ELA group. In addition, the SLESQ was used to screen participants without a history of traumatic exposures to serve as the control group. Selection criteria stipulated that subjects were between 18—25 years, without current medical illness or endocrine illness for example, asthma, diabetes, thyroid disease or pituitary gland disorders confirmed by self-report and physical examination , were currently non-smokers and were not using medication on a regular basis, including psychiatric medication.
The final sample included 12 men, 6 of whom experienced early adversity i. Demographics of the sample are presented in Table 2. Participants received a modest monetary incentive for participation. Participants made two visits to the CRC during weekdays, one week apart, on the same day. Testing was scheduled to begin at am and end by pm. We used a randomized counter-balanced order for the two sessions i. Lab personnel were also blind to group status. Participants were given specific instructions to refrain from excessive physical activity on the day of the testing, consuming alcohol for 12 hours before their arrival, and eating and drinking besides water for 2 hours prior to the testing session.
After arrival and consent, trained nurses completed a physical examination and inserted an IV catheter into the antecubital vein 30 minutes after arrival 30 minutes prior to testing. The TSST session was scheduled to begin at pm to minimize the effects of circadian changes in cortisol, and was carried out as described previously [ 42 ]. Briefly, the TSST consists of a free speech and a mental arithmetic task of 10 minutes duration performed in front of a panel of two committee members mixed gender with a camera and microphone situated between the interviewers. Participants were told that they would play the role of an interviewee for a job and have 5 minutes to make an argument for their candidacy. After 5 minutes, the second task emphasizing cognitive load commenced.
In this task, participants were asked to count backwards from 1, in multiples of If a mistake was made, they were instructed to start again from the beginning. In the no-stress control condition, participants were instructed to sit in a room, read magazines, and to refrain from any stressful activities e. After the second blood draw, approximately 60 minutes after the TSST session and 90 minutes after the first baseline measure in the no-stress control condition, participants were administered a set of questionnaires.
These questionnaires were administered in both sessions and the average score was calculated before analyses see below for details. Considering the long time-frame of the study and the repeated collection of multiple blood samples, a standardized low-calorie meal was provided after the third blood draw approximately at pm. Fig 1 outlines the study design. Salivary cortisol was repeatedly assessed from 7 saliva samples at the following time-points: 30 minutes after arrival 30 minutes prior to testing , 1 minute prior to testing, immediately after testing 15 minutes after last sample in the control condition , and 15, 30, 60 and 90 minutes post-test. Systolic and diastolic blood-pressure were measured at the same time points as salivary cortisol.
Salivette swabs Sarstedt, Germany were used to collect saliva. Salivary cortisol was assessed, in duplicate, through an enzyme immunoassay protocol Salimetrics with known controls. Intra-assay CV was 9. Gene expression changes were measured repeatedly from the four blood samples at each session at the following time-points: 30 minutes after arrival 30 minutes prior to testing , and at 30 75 minutes after the first sample in the no-stress condition , 90 and minutes post-test Fig 1. Given known changes in immune cell redistribution and composition in response to acute stress [ 20 ], complete blood count with differential was measured within 24 hours by Quest Diagnostics using additional 4 ml EDTA collection tubes.
Whole blood samples were collected in 10 mL EDTA blood tubes via an IV catheter into the antecubital vein, and immediately centrifuged for 10 minutes at g prior to collection of plasma. PBMCs were immediately isolated through density-gradient centrifugation using Ficoll. The duration from blood sampling to stabilization of RNA never exceeded 55 minutes. PCR reactions were set-up using the complementary QIAgility robotic pipettor Qiagen to ensure maximum pipetting accuracy. Samples were assayed in duplicate. All repeated, within-subject samples were run on the same plate.
The same threshold was used for reactions assessing housekeeping and NR3C1 genes. Thus, each sample on a given plate has two Ct values e. It follows that the fold change for each baseline sample is always equal to one i. Inflammatory assays were performed on plasma isolated from whole blood. Samples were run in duplicate. Intra-assay variability was 8. The lower limits of detection for inflammatory markers were 0. Samples with concentrations below the curve fit range were assigned a value of 0 for analyses considering those analytes. This occurred for 13 samples Samples for all other analytes were within detection ranges. We administered several questionnaires to assess levels of adverse exposures in the past 12 months and mental health symptoms.
Specifically, participants completed the following questionnaires at both sessions; the Life-Event Stress Scale LESS [ 44 ], which consists of 42 common events associated with some degree of disruption of an individual's life and provide a standardized measure of the impact of a wide range of common stressors in the past year; and the Life Events Questionnaire LEQ [ 45 ], an item inventory-type questionnaire for the measurement of life changes during the past year. The LEQ consists of items that are designed primarily for use with students. We further assessed levels of anxiety and depressive symptoms using the Beck anxiety inventory [ 46 ], Beck depression inventory [ 47 ], and State-Trait Anxiety Inventory [ 48 ], as well as perceived stress levels using the item Perceived Stress Scale [ 49 ].
As noted above, given gene expression changes may depend on specific cell populations [ 20 ], we measured complete blood cell counts during both experimental sessions, as well as PBMC counts, in duplicate, using a Countess automated cell counter Invitrogen. Other potential covariates included; age, body mass index, and socioeconomic status i. Statistical analyses of cortisol data used log transformed cortisol values at 7 time-points and area under the curve with respect to increase AUCi [ 50 ]. Blood pressure values were reduced to 4 measures, from 30 minutes prior to testing to 15 minutes after samples 1—4 to evaluate the fast sympathetic response.
Moreover, systolic and diastolic blood pressures were combined to derive a measure of the mean arterial pressure MAP to describe the average response in blood pressure i. For the four pro-inflammatory cytokines, considering high correlations [ 52 ] Pearson correlations ranged from. In each instance, the first component was extracted for use in subsequent analyses. The four repeated items mapped to components with eigenvalues of 2. The four repeated items in the TSST session mapped onto components with eigenvalues 2. The four repeated items in the no-stress session mapped onto components with eigenvalues 1. The components mapped using data from both sessions were used to investigate within-person differences across sessions, while the components mapped within each session independently were used to investigate between-person differences i.
ELA status within each session. Scores in all repeated questionnaires for both sessions were averaged to increase reliability Pearson correlations ranged from. None of the demographics measures differed significantly between the ELA and control groups Table 2 , and thus were not included as covariates in the analysis. Repeated measures general linear models GLMs , ordinary least squares multiple regression analyses, Pearson product-moment correlations, and t-tests were carried out as appropriate. In addition to these analyses, univariate tests were applied to summary cortisol measures AUCi, [ 50 ] to ascertain reliability of findings, as well as blood pressure MAP , gene expression, and pro-inflammatory cytokine measures.
Huynh-Feldt corrections were applied if sphericity significant differences in variance between groups was significant, and only adjusted results are reported. Adjustment for multiple comparisons was performed using Bonferroni correction in accordance with FDA guidelines for multiplicity in clinical trials [ 53 ]. As the primary endpoints, analyses for MAP and cortisol were controlled for collectively, and analyses within each secondary endpoint i. For the purposes of multiplicity, analyses within subgroups defined by ELA status were each considered additional tests to be accounted for. Results following adjustment and domains within which analyses were adjusted are summarized in S2 Table.
The ELA and control group did not differ in demographics measures i. While group differences existed in exposure to traumatic events up to age 18 i. Overall, these findings confirm some [ 54 ], but not all studies [ 7 ], indicating increased physiological reactivity to acute stress in young adults exposed to early adversity, compared with non-exposed individuals. Change scores were calculated by standardizing summary AUCi using data from both sessions and subtracting participant values from the no-stress session from those in the TSST session.
Error bars represent standard error of the mean. Change scores are expressed for the full sample grey , ELA group red , and control group green. In the whole sample, there was a significant within-subjects effect of TSST vs. Bonferroni correction for multiple comparisons was performed across analyses of primary endpoints and within analyses of each secondary endpoint in accordance with FDA guidelines for multiplicity in clinical trials [ 53 ].
All significant findings within primary endpoints passed correction for multiple testing at a false discovery rate of 0. By contrast, results of analyses with inflammatory principal components, as well as those with raw inflammatory cytokines, did not pass correction for multiple testing. A summary of results and Bonferroni-corrected p-values is provided in S2 Table. Overall, results were robust to the removal of any individual participant. Differences in sample characteristics and self-report measures remained consistent upon removal of any given participant, as did physiological, inflammatory, and gene expression responses to the TSST relative to the no-stress session.
This pilot study delineates a program of research to study ELA-related programming of biological systems using a within-person, between-groups experimental design. To our knowledge, this is the first investigation of stress-induced gene expression and pro-inflammatory cytokines changes within-individuals, comparing stratified groups of ELA-exposed and control individuals. By comparing a validated laboratory-based stressor to a no-stress condition within the same individuals, we are able to disentangle the effects of acute stress from noisy measurements in the same individuals.
Preliminary results provide evidence in humans of a dysregulated pattern of NR3C1 gene expression activation as a consequence of ELA. Importantly, these changes manifest more acutely in the presence of stress-induced cortisol release as compared to a no-stress resting condition. As predicted by previous research, the ELA group evince higher cortisol response and lower NR3C1 gene expression in response to the TSST compared with controls, with no difference between groups in the no-stress condition.
For pro-inflammatory cytokines, only IL-6 increased significantly in response to the laboratory-induced stressor. Overall, this pilot study provides a framework to investigate the biological embedding of ELA via a dynamic and dysregulated pattern spanning multiple levels of analysis genomic and physiological , and affords the opportunity to parse out whether such dysregulation is context dependent i. This conceptual framework integrates multiple definitions of stress and disease at the environmental e.
These preliminary findings concur with the receptor-mediated model of glucocorticoid signaling resistance [ 55 ]. First, ELA was associated with increased physiological response to the TSST compared with controls, confirming some [ 54 ], but not all studies [ 7 ], indicating increased physiological reactivity in young adults exposed to early adversity. Second, chronic exposure to cortisol, as a consequence of ELA, can lead to a compensatory response whereby glucocorticoid sensitivity decreases e.
Here we replicated prior evidence of reduced NR3C1 expression levels in ELA-exposed individuals, but only in response to acute laboratory stress. Third, in vitro studies have established a connection between glucocorticoid exposure and diminished capacity of immune cells to inhibit pro-inflammatory cytokines in individuals exposed to psychological stress. Here, only pro-inflammatory cytokine IL-6 increased significantly in response to acute stress, replicating previous studies [ 56 ]. Methodological strengths of this program of research include a laboratory-based within-subjects experimental design, which allows stronger causal inferences.
We collected repeated measurements over a relatively long time scale to document changes in gene expression and pro-inflammatory cytokines. Our within-subjects, between-groups design, combined with four repeated measurements in each session, reduced biological variability and increased power to detect true associations. Although this preliminary study lacked sufficient power to investigate three-way interactions i. Time x Session x Status or perform multivariate analyses across outcomes, the strength of the design affords the opportunity to test for such associations at comparably smaller sample sizes than studies without as many repeated measures. Furthermore, inclusion of a no-stress control session allows researchers the chance to identify differences that are context i.
We acknowledge limitations. First, this was a pilot feasibility study with a small sample size. Although comparable to similar prior investigations [ 26 , 31 , 57 ], the results from this study still need to be interpreted with caution. Notwithstanding, the strength of the within-subjects experimental design combined with the leave-one-out sensitivity analysis and adjustment for multiple comparisons alleviate concerns about spurious findings of the results reported herein. Second, we focused on men in this exploratory study due to known sex differences in the stress response [ 41 ] and the small sample size for stratified analyses. Future studies with larger sample size including both males and females are needed to replicate these findings.
Third, we focused on a single hypothesis-driven gene. There are multiple biological pathways that are activated in response to stress that may play a downstream role in disease susceptibility, such as the conserved transcriptional response to adversity pathway [ 30 ]. Prior research has investigated multiple genes using microarrays [ 23 , 26 — 28 , 31 ]. Fourth, this study did not consider specific types of ELA, or timing of exposure. Here, we focused specifically on severity of multiple i. Future research can explore specific types of ELA in different populations and settings.
Fifth, while this controlled laboratory experimental design allows stronger causal inferences, there are other alternative explanatory variables that can explain individual differences in response to stress e. Further, our study included non-Hispanic white males and thus future research need to test whether the association generalizes to other populations. Finally, although we included a no-stress condition to control for the higher degree of noise associated with gene expression measurements, the control session did include the stress of venipuncture.
However, this is unavoidable technical limitation for collecting sufficient immune cells of high quality for gene expression research. In conclusion, ELA may program physiological systems in a maladaptive manner more likely to manifest during times of duress, predisposing individuals to the negative health consequences of everyday stressors. Although increased activation of the glucocorticoid-immune signaling in response to acute stress is considered adaptive in the short-term, persistent activation can increase risk for mental and physical health problems. This program of research and preliminary analyses provides a framework to investigate new targets for therapeutic interventions mitigating the negative effects of early adversity, such as pharmacological agents acting on the glucocorticoid receptor [ 58 ].
Further, while previous risk factors and biomarkers of stress contributed to our understanding of biological embedding processes, these are nevertheless static characteristics that have not explained health outcomes very well. For example, considering high failure rates for depression treatments, and in order to tailor individual interventions, identifying objective changes in stress-induced gene expression may help to predict short-term intervention efficacy in clinical and non-clinical settings.
An example for such an effort could be to utilize models of dynamic cellular markers as individual-level factors to account for variation in intervention response and clinical outcomes [ 17 — 19 ]. Thus, future research in this area can have a range of impacts for basic science, intervention studies and clinical practice that will influence treatments to match the specific cellular processes operating within an individual.
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