The PDS assessment showed all cancer and control mothers reported having lived through or witnessed at least one traumatic event in the past and most reported multiple traumas. All subjects were asked to select the event that “bothers you the most” and answer all subsequent questions about PTSD symptoms with respect to that “worst” event. All cancer mothers reported their child’s cancer as the worst trauma experienced. Of 17 cancer mothers, 8 met all DSM-IV criterion (A-F) for PTSD and 2 met subthreshold criteria (developed by Hawk et al., 2000
). Remaining cancer mothers (n
= 7) and controls (n
= 9) did not meet categorical criteria for PTSD. One of the 8 cancer mothers meeting all criteria had few symptoms, low symptom severity, and mild functional impairment on the PDS. All other subjects showed consistency across these dimensions (i.e., clearly no/low or moderate/severe PTSS). This subject was dropped from the analyses due to the questionable diagnostic status. The final laboratory sample (n
= 25) consisted of 3 groups: (1) PTSS
: Cancer mothers with moderate to severe posttraumatic stress symptoms (n
= 9); (2) No PTSS
: Cancer mothers with no/low symptoms (n
= 7); and (3) Control
: Mothers of healthy children (n
= 9). The rate of PTSD among Phase II laboratory challenge cancer mothers (7/16 or 44%) was similar to the larger Phase I telephone sample (27/66 or 41%). BDI depression scores were clinically significant (above 13) in 55.6% of PTSS mothers (5 of 9), but not in any No PTSS or control mothers.
As expected, groups were significantly different on the number of PTSD symptoms and symptom severity on the PDS. See . PTSS mothers had more symptoms [F(2,24) = 31.91, p < .001] and greater symptom severity [F(2,24) = 19.44, p < .001] than the other two groups, who did not differ. The number of years since the “trauma” (for cancer mothers, this was taken as the time since their child’s cancer diagnosis) did not differ across groups and ranged from 2 to 12 years. The number of prior traumas reported by each group was not significantly different and ranged from 1 to 9.
Table 1 Mean (and SD) number of prior traumas, PTSD symptoms and symptom severity as a function of group as assessed by PDS (Foa, 1995)
Subjects ranged from 29 to 55 years of age (M = 39.9 ± 7.4). Cancer mothers were significantly older than controls [F(2,25) = 9.22, p = .001]. The greatest age difference was between No PTSS (M = 47 ± 6.0) and Control (M = 34.44 ± 5.22), with PTSS mothers in between (M = 40.00 ± 6.2). Age was retained as a covariate for all analyses.
Subjects identified themselves as Caucasian (64%), Asian (8%), Latina (8%), African American (8%) or “other” (12%). Groups did not differ on the basis of race/ethnicity, economic status (low-middle-high income), or health behaviors. Subjects reported for the past 24 h a range of 0-60 min of aerobic exercise, consumption of 0-2 alcoholic drinks and 0-5 cups of coffee, 6-12 h of sleep, 0-1 cigarettes and no drug use.
3.2. Subjective units of distress
As seen in , the MAT and Imagery Stressors were effective in increasing SUD anxiety compared to neutral phases (i.e., Baseline and Recovery phases). A repeated measures ANOVA confirmed the effect of phase [F(1,21) = 36.24, p < .001] and a group × phase interaction [F(2,21) = 4.82, p = .02]. The overall group effect was significant [F(2,21) = 9.16, p < .001] and contrasts indicated the PTSS group reported more distress than either No PTSS (p = .008) or control groups (p < .001), who did not differ (p = .33). Parameter estimates further showed these group differences were significant at MAT and Imagery stressor phases and Recovery 1; groups did not differ significantly at baseline (p = .10) or final recovery (p = .15). The interaction suggests that although all mothers responded with anxiety to the stressor challenges, PTSS mothers reacted more strongly.
Mean (and SE) SUD rating across phases. Note: A procedural error resulted in no recorded SUD rating for one control subject.
3.3. Immune outcomes
Greater alcohol consumption [F(1,19) = 3.05, p = .10] and better sleep quality [F(1,19) = 29.64, p < .001] were associated with lower percentage total lymphocytes. These potential health modifiers were therefore retained in all analyses. Age was significantly associated with NK cells but not with any other immune outcome. Nonetheless, because of group age differences, age was included in all analyses. Coffee consumption and minutes of aerobic exercise were associated with NK cell levels and thus were included in all NK analyses. See below for F and p values and the direction of covariate impact for each immune outcome.
3.3.1. Resting values
shows resting lymphocyte distributions (numerative and percentage of total) as a function of group. Repeated measures ANCOVA of total number of lymphocytes over phases indicated no group [F(1,22) = .32, p = .58] or age [F(1,22) < 1, p = .94] effects overall or at any phase. Group and phase patterns were similar for numerative and percentage CD4+, CD8+, NK and total lymphocytes. Thus, we report only percentage data here.
Resting mean (and SD) lymphocyte distributions (numerative and percentage of total) as a function of group
3.3.2. CD4+:CD8+ ratio
After controlling for age, sleep quality, and alcohol consumption, repeated measures analyses indicated trends for Order 4 effect of phase [F(1,19) = 3.71, p = .07] and group × phase interaction [F(2,19) = 2.69, p = .09]. Also, there was a significant group effect [F(2,19) = 5.88, p = .01]. See . Contrasts indicated the group effect was due to significant differences between PTSS versus controls (p = .04) and PTSS versus No PTSS (p = .006), but not between No PTSS and controls (p = .35). Parameter estimates for group differences at each phase showed PTSS CD4+:CD8+ ratios were marginally lower than controls at baseline, Recovery 1 and Recovery Final [p = .06, .08, .10, respectively] and significantly lower than controls at the MAT and Imagery Stressor phases [p = .03, .02, respectively]. There were no significant differences between No PTSS and controls, overall or at any time point. The statistical trends for phase and group X phase interaction are likely due to a response pattern most consistent with SUD anxiety changes across phases among controls (see ), little CD4+CD8+ ratio change for the No PTSS group, and moderate reactivity across phases among PTSS mothers.
Mean (SE) CD4+:CD8+ Ratio across phases. Covariates: age, alcohol, and sleep.
3.3.3. NK cells
After controlling for age, alcohol and coffee consumption, sleep quality, and minutes of aerobic exercise, the repeated measures ANCOVA of NK cells revealed no significant effect of phase [F(1,18) = .64, p = .44], a trend for a group × phase interaction [F(2,19) = 3.24, p = .06] and no overall group effect [F(2,19) = 1.41, p = .27]. See . Contrasts confirmed no significant group differences overall, but parameter estimates showed after the MAT Stressor controls were significantly higher than PTSS (p = .04) but not the No PTSS group (p = .16). Groups were not different at baseline or at any other phase. Visual inspection of suggests the statistical trend for a group × phase interaction reflects the reduced MAT Stressor reactivity among PTSS mothers relative to controls. Analysis of MAT Stressor change scores confirmed significantly greater reactivity among controls (M = 5.05, SE = 1.0) compared to PTSS mothers (M = 1.93, SE = 0.8) (p = .03) but not the No PTSS group (M = 2.31, SE = 1.2) (p = .14).
Mean (SE) NK cells across phases. Covariates: age, alcohol, coffee, aerobics, and sleep.
3.3.4. Impact of covariates
Age and sleep quality were not significantly associated with CD4+ or CD8+ levels, but alcohol consumption (0, 1 or 2 drinks) was associated with significantly higher CD4+ [F(1,19) = 5.02, p = .04] and lower CD8+ levels [F(1,19) = 5.91, p = .03]. Neither alcohol nor coffee consumption was significantly associated with NK levels [F(1,17) = 2.07, p = .17; F(1,17) = 2.89, p = .11, respectively]. In contrast, older age [F(1,17) = 5.68, p = .03], better sleep quality [F(1,17) = 4.47, p = .05], and less aerobic activity [F(1,17) = 8.49, p = .01] were significantly associated with higher NK levels in this sample.
3.4. Correlates of immune indicators
3.4.1. PTSD symptoms
To examine whether group differences in baseline and change score immune indicators were related to PTSS, correlations were conducted. As shown in , the greater the number and severity of symptoms, the higher the CD4+ and the lower the CD8+ levels at rest. Symptoms were unrelated to NK levels at rest, but were strongly associated with less NK change following the MAT stressor. Symptoms were unrelated to CD4+ and CD8+ MAT stressor changes and to all immune changes in response to the Imagery stressor.
Bivariate correlations between PTSD symptoms and immune outcomes at rest and following stressor phases
3.4.2. SUD anxiety
To determine the influence of baseline anxiety on immune outcomes, all ANCOVA analyses were repeated with resting SUD scores as a covariate. Immune group and phase effects were unchanged. Multivariate linear regression analyses with backward elimination were conducted to assess the relative contribution of SUD changes in response to the MAT stressor as a predictor of MAT immune changes. The best model for NK cells [F(2,24) = 7.67, p = .003, R2 = .41] included aerobic activity (β = -.05, p = .09) and number of PTSS symptoms (β = -.33, p = .002). Neither MAT SUD change nor PTSS group status was predictive of NK change in any model. For CD4+, the best model [F (3,23) = 3.68, p = .03, R2 = .36] included age (β = -.10, p = .13), PTSS status (β = 1.21, p = .06) and MAT SUD change (β = -.59, p = -.02). Number of PTSS symptoms was not predictive of CD4+ change in any model. For CD8+, the best model [F(4,23) = 4.99, p = .006, R2 = .51] included alcohol (β = 1.61, p = .11), PTSS status (β = 2.10, p = .02) MAT SUD change (β = .91, p < .001) and number of PTSS symptoms (β = .28, p = .08). Thus, subjective distress and PTSS status appear to be independent contributors to CD4+ and CD8+ immune changes in response to a stressor whereas only PTSS status predicts NK changes.
3.4.3. Symptoms of depression
BDI scores did not correlate with baseline immune levels or change scores for any immune indicator.
This first study of lymphocyte distribution under psychological challenge among mothers of child cancer survivors indicates PTSD symptoms are associated with tonic elevation of CD4+:CD8+ ratios and blunted NK reactivity to generic challenge. Confirming prediction of group differences at rest, the PTSS group had higher percentage CD4+ and lower CD8+ levels than non-symptomatic women. Contrary to expectations, only cancer mothers with PTSS showed evidence for blunted reactivity. Also unlike predictions, reactivity to challenge was greatest to the non-social MAT stressor compared to the personalized challenge for all groups.
PTSS effects were evident in several ways. PTSS mothers had a significantly smaller change in NK cells following the MAT challenge than controls. Resting CD4+ and CD8+ levels and NK MAT change scores were all significantly correlated with number of PTSS symptoms, providing further evidence of tonic and phasic immune differences associated with PTSS. Multivariate regression results showing the separate predictive value of changes in subjective distress and PTSS status in estimating CD4+ and CD8+, but not NK MAT changes, indicates PTSS immune differences cannot be attributed to subjective distress alone.
For all groups, the direction of lymphocyte distribution in response to stressor challenge (percentage increases in NK and CD8+, decreases in CD4+) was consistent with previous studies (Cacioppo et al., 1998
; Mills et al., 1995
; Pike et al., 1997
; Strauman et al., 1993
; Valdimarsdottir et al., 2002
). Likewise, PTSS-related immune effects found here provide replication evidence for some previous studies. Elevated resting CD4+ levels in PTSS mothers replicate findings of male combat veterans with chronic partial PTSD (Boscarino and Chang, 1999
). Low resting levels of CD8+ are in keeping with some PTSS findings (Ironson et al., 1997
), but not others (Boscarino and Chang, 1999
; Laudenslager et al., 1998
; Wilson et al., 1999
). The absence of PTSS baseline differences in NK cells is in contrast to data from a natural disaster sample (Ironson et al., 1997
Effects for cancer mothers as a chronic stress group are equivocal. Only PTSS mothers showed significantly reduced NK reactivity consistent with previous laboratory challenge studies among individuals high in life stress, daily hassles or tendency to worry (Benschop et al., 1994
; Pike et al., 1997
; Segerstrom et al., 1999
). Although No PTSS mothers appeared to have blunted CD4+:CD8+ ratio reactivity (all phases) and smaller NK MAT Stressor change scores relative to controls, these differences were not statistically significant. Small sample size may be one explanation for the failure to detect significant differences. Alternatively, the absence of No PTSS differences may reflect exceptional resilience among mothers who do not develop long-term PTSS despite exposure to acute and chronic stress associated with their child’s cancer. Past laboratory challenge studies did not assess PTSS, thus it is also possible these symptoms mediated reactivity effects of previous research. Further research will be necessary to clarify whether PTSS confers immune changes separate from those associated with a history of chronic stress.
Group effects found here were not limited to individualized stressors, but emerged under generic challenge. The absence of strong reactivity to the Imagery Stressor in any group was unexpected based on previous data indicating interpersonal stressors have greater impact on lymphocyte distribution than non-social stressors (Herbert and Cohen, 1993
). The significant rise in SUD anxiety during the Imagery Stressor as well as the MAT Stressor indicates subjects were as distressed by the Imagery as by the MAT Stressor. Thus, blunted lymphocyte distribution does not appear to be due to failure of the Imagery Stressor to induce distress. Although groups returned to baseline levels after the MAT Stressor, the inability to mount a significant change in lymphocyte distribution after the Imagery Stressor suggests reactivity may be temporarily blocked or reduced by an earlier stressor. Alternatively, circadian rhythm of immune measures may have influenced immune response strengths later in the laboratory session as compared to earlier. However, if that were the case, one would expect a rising pattern in CD4+, CD8+ and NK cell counts during the period coinciding with the lab session (approximately 6-8 p.m.), not stasis or a decline (Kronfol et al., 1997
). Replication with generic and individualized stressors counterbalanced for order and separated by days would clarify the relative strength of group effects under each stressor condition.
Additional limitations of this study should be addressed in future research. First, this work must be replicated with a larger sample size to increase confidence in patterns found here. Also, whereas health behaviors such as sleep quality, exercise, use of alcohol, and coffee were statistically controlled here, a larger sample would allow for statistical modeling that could determine a mediating or moderating role for these factors, as has been done for health behaviors and depression (Miller et al., 1999
). Second, blunted reductions in NK cell percentages following laboratory stress recently reported in females in the luteal phase of menses (Pehlivanoglu et al., 2001
) suggests menses stage may influence outcomes. Finally, recent evidence for dampened NK cytotoxicity in the face of increased cell number (Pike et al., 1997
; Strauman et al., 1993
) emphasizes the importance of multi-method designs to clarify the mechanisms of immune change.
The present findings are preliminary and should be viewed as a guide for future research. Nonetheless, these data indicate posttraumatic stress symptoms are associated with changes in tonic lymphocyte distribution and immune responsivity to psychological challenge. In addition, these changes may be distinct from patterns associated with subjective distress more generally. Further research will be needed to assess the extent to which differential lymphocyte distribution effects found in the laboratory have direct implications for health in the natural environment.