Cortisol secretory activity in older people in relation to positive and negative well-being
Introduction
It is well established that both laboratory-induced and naturalistic daily stressors activate the hypothalamic–pituitary–adrenal axis (HPA-axis) as evidenced by increased cortisol levels (e.g., Kirschbaum et al., 1993; Ockenfels et al., 1995; Smyth et al., 1998; Hanson et al., 2000; Peeters et al., 2003). However it is clear that individual differences known to contribute to general well-being (such as in self-esteem and affect) can modulate stress-induced elevations in cortisol (Smyth et al., 1998; Pruessner et al., 1999; Polk et al., 2005; Jacobs et al., 2007). The importance of subjective measures of well-being in relation to health outcomes is increasingly being recognised. However, to date relatively little has been published linking measures of well-being and cortisol status. Human well-being is a complex phenomenon and currently there is no single agreed formula for its measurement. In the past, measures of psychological well-being tended to focus upon the presence or absence of negative symptoms such as anxiety or low self-esteem. Recently, more recognition is being given to the importance of positive well-being including components such as autonomy, environmental mastery, personal growth, positive relations, purpose in life and self-acceptance (Ryff, 1989; Ryff and Singer, 1996). Interestingly, psychometric exploration of positive and negative well-being has tended to indicate that they can function somewhat independently and that positive well-being is not simply the absence of negative feelings and experience, or vice versa (Diener and Emmons, 1984; Watson et al., 1988; Huppert and Whittington, 2003).
An important psychometric approach to the measurement of well-being is seen in the work of Huppert and Whittington (2003) where they construct separate positive and negative well-being scales from the 30-item General Health Questionnaire (GHQ-30) (Goldberg, 1972). A particular benefit of using the GHQ-30 for measures of positive and negative well-being in this way derives from its extremely widespread and global use as self-report instrument in medical research. Using this approach it has been shown that although positive and negative well-being are quite strongly and significantly negatively correlated, there is also evidence of some degree of independence. Approximately, 40% of variance is shared, but just over a third of over 6000 participants in a large survey were either above average on both positive and negative well-being or below average on both. The importance of recognising this partial independence of scales is forcefully underlined by a previous report (Whittington and Huppert, 1998) that absence of positive well-being is more predictive of 7-year follow-up mortality than presence of negative well-being.
There are relatively few studies which have examined the importance of positive in addition to negative well-being in relation to cortisol secretion. Seeman and McEwen (1996) reported that HPA axis reactivity was lower in participants with more supportive social relationships. Moreover, among women with breast cancer, mean daytime salivary cortisol levels were significantly lower in those whose perceived quality of social support was higher (Turner-Cobb et al., 2000). More recently, trait optimism in a sample of students was shown to be inversely related to salivary cortisol levels, particularly in a 40-min period immediately after awakening. By contrast, trait pessimism was not related to cortisol levels (Lai et al., 2005). Similarly significant associations between aspects of eudaimonic positive well-being (e.g., personal growth and purpose in life) and lower cortisol secretion have been reported, with the effect being most marked for morning cortisol levels and in those over 75 years of age. In the same study, negative well-being (e.g., anxiety, anger) was not associated with cortisol secretion across the day (Ryff et al., 2006). These results also support the partial ‘independence view’ of positive and negative well-being, at least in relation to associations with cortisol secretion. Consistent with these findings, state happiness, assessed from aggregated momentary experience samples of happiness over the working day, was inversely related to total cortisol secretion over the day (from 08:00 to 22:00 h) an effect independent of psychological distress (Steptoe et al., 2005). A follow-up study from the same group using a similar method of momentary assessment of state happiness found a comparable inverse association between cortisol and happiness (again, independent of psychological distress) but this time the effects were limited to the first 60 min after awakening—the awakening cortisol response (ACR) (Steptoe et al., 2007). The authors claimed that these results supported the notion that positive well-being was directly related to health-related biological processes.
The ACR is a discrete aspect of the cortisol circadian cycle. In healthy adults salivary free cortisol concentrations increase by 50–160% in the first 30 min immediately after awakening (see Clow et al., 2004 for a review). Although the physiological role of the ACR has not been clearly defined, evidence suggests that it is under a distinct regulatory influence, different from the rest of the diurnal cortisol secretory cycle and sensitive to a range of psychosocial variables (Clow et al., 2004). Indeed, this is illustrated by the findings of Steptoe et al. (2007) cited above, where state happiness was found to be associated with reduced cortisol secretion in the first 60 min post-awakening but not over the rest of the diurnal cycle. Other studies (Steptoe and Wardle, 2005; Ryff et al., 2006) have not examined cortisol immediately after awakening (the ACR) but more generally point to morning, rather than evening, cortisol secretion as most sensitive to individual differences in well-being. Examination of the relationship between well-being and cortisol secretion therefore necessitates careful examination of time-of-day effects and warrants separate analysis of both aspects of the diurnal cycle: the ACR and the diurnal decline over the rest of the day.
Because positive and negative well-being, while clearly related to each other, do not always relate to other variables in the same way, we believe that more detailed examination of interactive as well as main effects of the positive and negative dimensions of well-being are warranted. In the light of the role of cortisol in a range of health related outcomes and policy initiatives aimed at the promotion of ‘positive ageing’, we chose to explore these questions in a sample of community dwelling older people. This study population was also warranted as it has been shown that well-being and cortisol secretion were most clearly associated in those over 75 years of age (Ryff et al., 2006). This may partly reflect greater potential variation in cortisol measures as a result of the aging process. There is evidence that the HPA-axis may become more dysfunctional as people age, with generally higher levels of cortisol frequently being observed and changes in normal circadian rhythms (Sapolsky, 1999). However, such changes vary considerably amongst older adults, especially in circadian patterning (see, for example, Ice et al., 2004) which argues the importance of examining for psychosocial correlates of such variation. Thus, in this study we set out to explore possible relationships between diurnal cortisol secretion (the ACR and the diurnal decline) and aspects of well-being in a group of high functioning home dwelling senior citizens.
It was our general guiding hypothesis that positive and negative well-being would not simply represent opposite ends of a bi-polar continuum and this may be evidenced by more complex associations with measures of cortisol secretion across the day. We specifically wished to extend our analytic strategy by examining interactive as well as simple effects of positive and negative well-being. It was also our hypothesis that well-being would be more closely related to cortisol secretion in the morning (the ACR) than cortisol secretion across the rest of the day.
Section snippets
Participants
The study sample consisted of 16 men and 34 women. Age ranged from 59 to 91 years with a mean age of 73.98±6.96 (S.D.) years. There were 26 people who lived alone and 24 people who lived with a partner or family member. None of the participants smoked cigarettes. There were 43 people who were retired and 7 people semi-retired; 30 participants had attended college or university. Mean perceived socioeconomic status (possible range 1–10, see later) was 7±1.76. No participants were taking any
Distribution of positive and negative well-being scores
Figure 1 presents the frequency distributions of the negative (CGHQ) and positive (POS-GHQ) well-being scales.
The distributions in Figure 1a and b closely mirror those given by Huppert and Whittington (2003), based on over 6000 participants. It shows that negative well-being is highly skewed, while positive well-being is more normally distributed with a small standard deviation. Measures of central tendency and dispersion were also near identical to those presented by Huppert and Whittington
Discussion
The main finding of this study was that cortisol levels were lower in the 45 min post-awakening period for a sub-group of overall ‘best’ well-being participants (34%), i.e., those who combined high scores on positive well-being and low scores on negative well-being. By contrast, there was no mirroring finding whereby the ‘worst’ well-being participants, i.e., those combining low positive well-being and high negative well-being (35%) showed higher cortisol levels than other sub-groups. Indeed,
Role of funding sources
This work was funded by the Higher Education Funding Council of England through support of the WestFocus consortium of universities. The funding source was not involved or relevant in any way to determine the project itself nor aspects of journal submission.
Conflict of interest
We declare that there are no conflicts of interest involved for any of the authors of this manuscript.
Acknowledgements
We wish to acknowledge support from WestFocus, a collaborative partnership between several universities whose projects are supported by the Higher Education Funding Council of England (HEFCE). Particular thanks go to Andrew Hall from Vivatec Ltd., UK, in arranging the availability of the actimeters and help with the interpretation of the results.
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