**4.2.2 Profile of autonomous nervous system activities (HR, HF power of ECG)**

Figure 5 shows the profile of changing in the heat rate (HR) during task/break period in the time series in the session A (Fig. 5(a)) and B (Fig. 5(b)). It should be noted each values were found by averaging every three minutes so that it makes easier to illustrate the difference in the profile with that of biomarkers as depicted later. There was no any significant change in the time series regardless of task, break, or recovering period in both session A and B, while it has a tendency to decrease in the task period. It is known through the past experimental psychological researches that the heart rate frequently decreased rather than increased under a certain type of laboratory stressors by which subjects need to concentrate on or just keep silence in perceiving the situation, such as vigilance task, noise exposure, mental arithmetic task, etc (Williams, 1986). At any rate this results again ensure the nature of the calculation task as a mild stressor as we intended.

Fig. 5. Profile of heart rate in session A and B. Error bar represent standard error of the mean (S.E.M). Gray background represents a calculation task period.

Figure 6 shows the profile of changing in the high frequency power of ECG signal (HF power) during task/break period in the time series in the session A (Fig. 6(a)) and B (Fig. 6(b)). As these figures shows, HF power remarkably decreased during the task and recovered to the basal level in the subsequent break period. HF power changes according to respiratory regulated heart beat-to-beat interval modulation, which is so-called Respiratory Sinus Arrhythmia (RSA) (Andreassi, 2007). RSA is dominantly subject to the parasympathetic nervous system, so HF power is considered as an index of the parasympathetic nervous system activities. When one look at the results of our study, it is quite understandable since HF power decreased during the task and recovered in the rest period. Moreover it should be emphasized that such switch-over of the HF power took place rapidly according to the task/break schedule, and well reproduced over time regardless of the repetition of the task/break as seen the session B in particular. Therefore with regard to the "sensitivity" of HF power against a mild stressor, HF power can be compared with a binary switch as it turn on during the task and off in the break without any cumulative effect over time.

Fig. 6. Profile of HF power of ECG in session A and B. Error bar represent standard error of the mean (S.E.M). Gray background represents a calculation task period.

#### **4.2.3 Profile of IgA**

258 Biomarker

**4.2 Results of the experiment: The difference in the sensitivity among HPA and SAM** 

The task performance did not show any change by repetition of the task during sessions or between the session A and B. In fact the task, which is the addition of two double-digit integers at every 3.0 seconds, is too easy for the subjects, who were university students, to

Regarding with POMS, only the factor V (vigour-activity) out of six mood factors showed significant decrease after the calculation task (*p*<.05, *t*-test). Other factors did not show any significant change in the scores while the factor F (fatigue) showed a trend of decrease (*p*<.1). In other words, the calculation task introduced in our study was not an intensive stressor which gives subjects negative mood such as tension, threat, or hostility just like

Figure 5 shows the profile of changing in the heat rate (HR) during task/break period in the time series in the session A (Fig. 5(a)) and B (Fig. 5(b)). It should be noted each values were found by averaging every three minutes so that it makes easier to illustrate the difference in the profile with that of biomarkers as depicted later. There was no any significant change in the time series regardless of task, break, or recovering period in both session A and B, while it has a tendency to decrease in the task period. It is known through the past experimental psychological researches that the heart rate frequently decreased rather than increased under a certain type of laboratory stressors by which subjects need to concentrate on or just keep silence in perceiving the situation, such as vigilance task, noise exposure, mental arithmetic task, etc (Williams, 1986). At any rate this results again ensure the nature of the

[min]<sup>55</sup>

Fig. 5. Profile of heart rate in session A and B. Error bar represent standard error of the mean

Figure 6 shows the profile of changing in the high frequency power of ECG signal (HF power) during task/break period in the time series in the session A (Fig. 6(a)) and B (Fig. 6(b)). As these figures shows, HF power remarkably decreased during the task and recovered to the basal level in the subsequent break period. HF power changes according to

Heart Rate [bpm]

(b)


[min]

**4.2.2 Profile of autonomous nervous system activities (HR, HF power of ECG)** 

**biomarkers** 

Heart Rate [bpm]

(a)

**4.2.1 Behavior and POMS** 

find any difference in their performance.

TSST but rather a mild stressor as we intended.

calculation task as a mild stressor as we intended.


(S.E.M). Gray background represents a calculation task period.

Figure 7 shows the profile of changing in the concentration of salivary secreted IgA during task/break period in the time series in the session A (Fig. 7(a)) and B (Fig. 7(b)). It increased during the task, decreased during the break, and recovered to the initial (basal) level. This simple fact demonstrates the congruity of IgA as a stress biomarker of a mild stressor.

Fig. 7. Profile of IgA in session A and B. Error bar represent standard error of the mean (S.E.M). Gray background represents a calculation task period.

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

Cortisol Concentration[μg/dL]

stressor.

particular targets.

**4.2.5 Profile of CgA** 

(a)


(S.E.M). Gray background represents a calculation task period.

Salivary Hormones, Immunes and Other Secretory Substances as Possible Stress Biomarker 261

[min](b)Cortisol Concentration[μg/dL]

Fig. 8. Profile of cortisol in session A and B. Error bar represent standard error of the mean

With regard to the "sensitivity" against a mild stressor, it can be assumed by our experimental results that cortisol would possess greater time constant than that of IgA, then greater than HF power of course. Comparing with the profile of IgA in the session B, cortisol still showed a cumulative effect in such a short-period of but repetition of the simple calculation task. In this sense, cortisol should be much sensitive than IgA against a mild

In the past cortisol studies, the stress response of cortisol against "mild" stressors, which are typified by the cognitive tasks without any threat or performance pressure such as passive stroop task and mental arithmetic task, showed inconsistent results, while that against acute and strong stressors such as academic oral defense with psychosocial evaluation always resulted in the increase of cortisol. Then a review article concluded that cortisol can be a useful stress biomarker for an acute and stronger stressor with psychosocial evaluation rather than a mild stressor (Dickerson, 2004). However looking into the result of our experiment in which the precise changing in the level of salivary cortisol were assessed every 3 minutes, a significant stress response can be observed even by a mild stressor if only salivary cortisol was assessed by an appropriate timing and duration corresponding to a

Figure 9 shows the profile of changing in the concentration of salivary secreted CgA during task/break period in the time series in the session A (Fig. 9(a)) and B (Fig. 9(b)). It increased in the task period and decreased in the break. Moreover both salivary flow rate and HF power, which represent parasympathetic nervous activities, have no significant correlation

The transient increase of CgA against laboratory stressors were also observed in the past CgA studies (Miyakawa, 2006; Kanamaru, 2006; Kanamaru, 2005). However some studies showed inconsistent results (e.g., Yamakoshi, 2009). It might attributes to the nature of high "temporal sensitivity" of CgA against a mild stressor illustrated in our study. As Fig. 9(a) shows CgA seems to possess a certain time constant, however it might be smaller than that of IgA even a little greater than that HF power. This means the slight difference in the saliva sampling time

between CgA. So CgA can also be taken as a plausible biomarker for a mild stressor.

and duration might result in a big difference in the level of CgA in some cases.

0.10 0.15 0.20 0.25 0.30 0.35 0.40


On the other hand IgA showed a remarkable difference in the profile over time comparing with that of HF power. In session A, IgA started to increase gradually after the onset of the first calculation task. Following the end of the first task, IgA started to decreased but because of certain latency in recovering, it did not recover to the basal level. These latencies over time in IgA increasing induced by the onset of a stressor and decreasing in the recovering resulted in cumulative profile of IgA as depicted in Fig. 7(a): where the average concentration of IgA during the first task period was significantly higher than that of the second task (*p*<.01, *t*-test), and it took around 30 minutes for the elevated IgA to return to the basal level. Moreover there was no significant correlation between IgA concentration and saliva flow rate. Therefore the cumulative effect observed in session A was not merely as a result of the change in saliva flow, which is thought to be mediated by the change in autonomous nervous system activity.

Comparing with the results in session A and B, there was no such remarkable cumulative effect in session B: there was no any difference in the initial (basal) level, but regarding with the total IgA secreted during all through the sessions, it was significantly higher in session A than that of B (*p*<.01). This difference in the IgA secretion profile in the session A and B is rather intriguing, because the total task/break period, and moreover the task/break ratio (2:1) were the exactly same for both session A and B. It could happen by a nonlinear feature of the IgA secretion depending on the duration of the task: IgA secretion rate might increase depending on the duration of the task. The change in the secretion rate would result in greater elevation of IgA concentration even taking an account of the difference in the duration of task period in session A, i.e. 18 minutes, and session B, 6 minutes. Subsequent recovering period was not enough in session A but appropriate in session B even it was equivalent in terms of task/break ratio. As such IgA secretion profile in the time series might reflect both the duration and repetition (or schedule) of the task/break. The idea just described was introduced into the kinetic mathematical model we proposed later (see section 5 for more detail).

With regard to the "sensitivity" against a mild stressor, IgA is not such sensitive to the onset or the end of a mild stressor as depicted in HF power profile (Fig. 6). However seen from another point, IgA secretion has cumulative feature, in other words it can represent a sort of hysteresis of a given stressor. Therefore IgA is not so sensitive against the temporal change in the situation, but it is still sensitive even for a mild stressor persisting certain time duration. In the field of dynamics, e.g. control engineering, it can be understood as the characteristic in the "time constant" or "relaxation time": in this case HF power possess small time constant against a mild stressor, and by contrast IgA possess relatively greater one. All together IgA can be a useful biomarker for a mild and long-lasting stressor.

#### **4.2.4 Profile of cortisol**

The cumulative secretion profile was more remarkable in the cortisol as depicted in Figure 8. As these figure shows salivary cortisol concentration showed a cumulative increase all through the task/break period in both session A and B. Moreover the elevated cortisol level did not recovered to the basal level despite of 20 minutes of recovering period after the sessions. HF power and saliva flow rate had no significant correlation with cortisol. It is understandable to think of that the secretion of cortisol reflects HPA system activity as already mentioned.

Fig. 8. Profile of cortisol in session A and B. Error bar represent standard error of the mean (S.E.M). Gray background represents a calculation task period.

With regard to the "sensitivity" against a mild stressor, it can be assumed by our experimental results that cortisol would possess greater time constant than that of IgA, then greater than HF power of course. Comparing with the profile of IgA in the session B, cortisol still showed a cumulative effect in such a short-period of but repetition of the simple calculation task. In this sense, cortisol should be much sensitive than IgA against a mild stressor.

In the past cortisol studies, the stress response of cortisol against "mild" stressors, which are typified by the cognitive tasks without any threat or performance pressure such as passive stroop task and mental arithmetic task, showed inconsistent results, while that against acute and strong stressors such as academic oral defense with psychosocial evaluation always resulted in the increase of cortisol. Then a review article concluded that cortisol can be a useful stress biomarker for an acute and stronger stressor with psychosocial evaluation rather than a mild stressor (Dickerson, 2004). However looking into the result of our experiment in which the precise changing in the level of salivary cortisol were assessed every 3 minutes, a significant stress response can be observed even by a mild stressor if only salivary cortisol was assessed by an appropriate timing and duration corresponding to a particular targets.

#### **4.2.5 Profile of CgA**

260 Biomarker

On the other hand IgA showed a remarkable difference in the profile over time comparing with that of HF power. In session A, IgA started to increase gradually after the onset of the first calculation task. Following the end of the first task, IgA started to decreased but because of certain latency in recovering, it did not recover to the basal level. These latencies over time in IgA increasing induced by the onset of a stressor and decreasing in the recovering resulted in cumulative profile of IgA as depicted in Fig. 7(a): where the average concentration of IgA during the first task period was significantly higher than that of the second task (*p*<.01, *t*-test), and it took around 30 minutes for the elevated IgA to return to the basal level. Moreover there was no significant correlation between IgA concentration and saliva flow rate. Therefore the cumulative effect observed in session A was not merely as a result of the change in saliva flow, which is thought to be mediated by the change in

Comparing with the results in session A and B, there was no such remarkable cumulative effect in session B: there was no any difference in the initial (basal) level, but regarding with the total IgA secreted during all through the sessions, it was significantly higher in session A than that of B (*p*<.01). This difference in the IgA secretion profile in the session A and B is rather intriguing, because the total task/break period, and moreover the task/break ratio (2:1) were the exactly same for both session A and B. It could happen by a nonlinear feature of the IgA secretion depending on the duration of the task: IgA secretion rate might increase depending on the duration of the task. The change in the secretion rate would result in greater elevation of IgA concentration even taking an account of the difference in the duration of task period in session A, i.e. 18 minutes, and session B, 6 minutes. Subsequent recovering period was not enough in session A but appropriate in session B even it was equivalent in terms of task/break ratio. As such IgA secretion profile in the time series might reflect both the duration and repetition (or schedule) of the task/break. The idea just described was introduced into the kinetic mathematical model we proposed later (see

With regard to the "sensitivity" against a mild stressor, IgA is not such sensitive to the onset or the end of a mild stressor as depicted in HF power profile (Fig. 6). However seen from another point, IgA secretion has cumulative feature, in other words it can represent a sort of hysteresis of a given stressor. Therefore IgA is not so sensitive against the temporal change in the situation, but it is still sensitive even for a mild stressor persisting certain time duration. In the field of dynamics, e.g. control engineering, it can be understood as the characteristic in the "time constant" or "relaxation time": in this case HF power possess small time constant against a mild stressor, and by contrast IgA possess relatively greater

one. All together IgA can be a useful biomarker for a mild and long-lasting stressor.

The cumulative secretion profile was more remarkable in the cortisol as depicted in Figure 8. As these figure shows salivary cortisol concentration showed a cumulative increase all through the task/break period in both session A and B. Moreover the elevated cortisol level did not recovered to the basal level despite of 20 minutes of recovering period after the sessions. HF power and saliva flow rate had no significant correlation with cortisol. It is understandable to think of that the secretion of cortisol reflects HPA system activity as

autonomous nervous system activity.

section 5 for more detail).

**4.2.4 Profile of cortisol** 

already mentioned.

Figure 9 shows the profile of changing in the concentration of salivary secreted CgA during task/break period in the time series in the session A (Fig. 9(a)) and B (Fig. 9(b)). It increased in the task period and decreased in the break. Moreover both salivary flow rate and HF power, which represent parasympathetic nervous activities, have no significant correlation between CgA. So CgA can also be taken as a plausible biomarker for a mild stressor.

The transient increase of CgA against laboratory stressors were also observed in the past CgA studies (Miyakawa, 2006; Kanamaru, 2006; Kanamaru, 2005). However some studies showed inconsistent results (e.g., Yamakoshi, 2009). It might attributes to the nature of high "temporal sensitivity" of CgA against a mild stressor illustrated in our study. As Fig. 9(a) shows CgA seems to possess a certain time constant, however it might be smaller than that of IgA even a little greater than that HF power. This means the slight difference in the saliva sampling time and duration might result in a big difference in the level of CgA in some cases.

Salivary Hormones, Immunes and Other Secretory Substances as Possible Stress Biomarker 263

[min]<sup>150</sup>

Fig. 10. Profile of DHEA in session A and B. Error bar represent standard error of the mean

**4.3 Discussion of the experiment: "Sensitivity" of the biomarkers as interpreted by** 

To our knowledge our experiment was the first one to simultaneously illustrate the precise changing in the level of four stress biomarkers, which are IgA, cortisol, CgA, and DHEA, against a mild stressor. The results of our experiment plausibly demonstrate the possible candidacy of these substances as a biomarker for a mild laboratory stressor. Moreover the differences in the "sensitivity" among them as interpreted by the time constant were successfully demonstrated. The substances represents SAM system which are CgA and IgA sensitively increase and decrease by the onset and the end of the stressor, whereas those represents HPA system which are DHEA and cortisol showed cumulative effect over time. So our results might represent a part of complex dynamics of two major physiological stress reaction pathways, which are SAM and HPA. Moreover there also seems to be a difference in the time constant among SAM and HPA biomarker as CgA has the smallest, IgA places in the next, DHEA comes after IgA, and cortisol is the one which has the greatest time constant. It should represent further mechanism underlying the complexity of these systems and might be as a result of adaptive response in the sense of long evolutionary history: since cortisol possess a great impact on human physiology such as controlling blood pressure, it must be inefficient in term of energy consumption if it were as "sensitive" as CgA. In other word, it is of no use in responding a mild laboratory stressor but it should work in more

**5. Kinetic model of biomarkers in the response to a mild laboratory stressor:** 

The difference in the sensitivity of stress biomarkers observed in our experiment can be restate as the difference in the time constant as described above. Here a mathematical model of the response of biomarkers is suggested to describe the experimental result as the

Basic assumptions for the model are introduced according to our experimental fact, and are

difference in the time constant: from on/off binary response to cumulative one.

DHEA Concentration[pg/mL]

(b)


[min]

critical situation in our life.

**A preliminary description** 

quite simple as follows:

**5.1 Constitution of the kinetic model** 

DHEA

Concentration[pg/mL]

(a)


(S.E.M). Gray background represents a calculation task period.

**the time constant; SAM comes first and HPA in the later** 

Fig. 9. Profile of CgA in session A and B. Error bar represent standard error of the mean (S.E.M). Gray background represents a calculation task period.

CgA possesses high "temporal sensitivity" as just described. On the other hand there could not find any cumulative effects like as HF power. Then it can be assumed that CgA possesses relatively small time constant. Since CgA is considered to represent sympathetic nervous system activity and thus to be a possible biomarker for SAM system activity as described in the subsection 2.3, this small time constant of CgA illustrated in our experiment is reasonable. So CgA can be a useful biomarker for detecting the change in the situation, i.e. the onset or the end of a given stressors but not suitable for estimating cumulative effect over time.

#### **4.2.6 Profile of DHEA**

The stress-induced secretion of salivary DHEA in session A and B seem to be cumulative as shown Figure 10(a) and 10(b). Moreover elevated DHEA during the both sessions did not return to the basal level during 20 minutes of recovering period, while total secretion of DHEA was larger in session A than B. Parasympathetic nervous activities indexed as HF power and saliva flow rate does not have significant correlation. There were some studies demonstrated that stress-induced DHEA secretion had reached its peak around 20 minutes after the onset of a strong stressor, TSST (Izawa, 2008; Sugaya, 2007). On the other hand looking at the result of our experiment assessing precise chaining of DHEA secretion in the time series, there might have a peak in DHEA in the last half of the second task period or much later in session A. Considering the difference in the nature of TSST and the calculation task, it can be assumed that the level of DHEA might represents the intensity of a given stressor, as the same as cortisol.

Comparing the profile of DHEA and other biomarkers, DHEA might possess relatively greater time constant: which should be ranked between that of IgA and cortisol. Therefore regarding the sensitivity of DHEA as a stress biomarker, it is not so sensitive to the onset or the end of the stressor, however it can still be a useful biomarker for long-lasting and a mild stressor like as cortisol. Since DHEA is a biomarker representing HPA system activity, it is understandable for that the stress response of DHEA was similar with cortisol. However there seems to be still a slight difference of response over time. It should be a matter in discussion in future.

(b)

[min]

Fig. 9. Profile of CgA in session A and B. Error bar represent standard error of the mean

CgA possesses high "temporal sensitivity" as just described. On the other hand there could not find any cumulative effects like as HF power. Then it can be assumed that CgA possesses relatively small time constant. Since CgA is considered to represent sympathetic nervous system activity and thus to be a possible biomarker for SAM system activity as described in the subsection 2.3, this small time constant of CgA illustrated in our experiment is reasonable. So CgA can be a useful biomarker for detecting the change in the situation, i.e. the onset or the end of a given stressors but not suitable for estimating cumulative effect

The stress-induced secretion of salivary DHEA in session A and B seem to be cumulative as shown Figure 10(a) and 10(b). Moreover elevated DHEA during the both sessions did not return to the basal level during 20 minutes of recovering period, while total secretion of DHEA was larger in session A than B. Parasympathetic nervous activities indexed as HF power and saliva flow rate does not have significant correlation. There were some studies demonstrated that stress-induced DHEA secretion had reached its peak around 20 minutes after the onset of a strong stressor, TSST (Izawa, 2008; Sugaya, 2007). On the other hand looking at the result of our experiment assessing precise chaining of DHEA secretion in the time series, there might have a peak in DHEA in the last half of the second task period or much later in session A. Considering the difference in the nature of TSST and the calculation task, it can be assumed that the level of DHEA might represents the intensity of a given

Comparing the profile of DHEA and other biomarkers, DHEA might possess relatively greater time constant: which should be ranked between that of IgA and cortisol. Therefore regarding the sensitivity of DHEA as a stress biomarker, it is not so sensitive to the onset or the end of the stressor, however it can still be a useful biomarker for long-lasting and a mild stressor like as cortisol. Since DHEA is a biomarker representing HPA system activity, it is understandable for that the stress response of DHEA was similar with cortisol. However there seems to be still a slight difference of response over time. It should be a matter in

CgA Concentration[pmol/mL]


[min]

over time.

**4.2.6 Profile of DHEA** 

stressor, as the same as cortisol.

discussion in future.

CgA Concentration[pmol/mL]

(a)


(S.E.M). Gray background represents a calculation task period.

Fig. 10. Profile of DHEA in session A and B. Error bar represent standard error of the mean (S.E.M). Gray background represents a calculation task period.

#### **4.3 Discussion of the experiment: "Sensitivity" of the biomarkers as interpreted by the time constant; SAM comes first and HPA in the later**

To our knowledge our experiment was the first one to simultaneously illustrate the precise changing in the level of four stress biomarkers, which are IgA, cortisol, CgA, and DHEA, against a mild stressor. The results of our experiment plausibly demonstrate the possible candidacy of these substances as a biomarker for a mild laboratory stressor. Moreover the differences in the "sensitivity" among them as interpreted by the time constant were successfully demonstrated. The substances represents SAM system which are CgA and IgA sensitively increase and decrease by the onset and the end of the stressor, whereas those represents HPA system which are DHEA and cortisol showed cumulative effect over time. So our results might represent a part of complex dynamics of two major physiological stress reaction pathways, which are SAM and HPA. Moreover there also seems to be a difference in the time constant among SAM and HPA biomarker as CgA has the smallest, IgA places in the next, DHEA comes after IgA, and cortisol is the one which has the greatest time constant. It should represent further mechanism underlying the complexity of these systems and might be as a result of adaptive response in the sense of long evolutionary history: since cortisol possess a great impact on human physiology such as controlling blood pressure, it must be inefficient in term of energy consumption if it were as "sensitive" as CgA. In other word, it is of no use in responding a mild laboratory stressor but it should work in more critical situation in our life.
