Exploration of basal diurnal salivary cortisol profiles in middle-aged adults: Associations with sleep quality and metabolic parameters

ARTICLE IN PRESS Psychoneuroendocrinology (2008) 33, 143–151

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journal homepage: www.elsevier.com/locate/psyneuen

Exploration of basal diurnal salivary cortisol profiles in middle-aged adults: Associations with sleep quality and metabolic parameters N. Lasikiewicza,, H. Hendrickxb, D. Talbotb, L. Dyec a

Psychology, Faculty of Health, Leeds Metropolitan University, Leeds LS1 3HE, UK Unilever Corporate Research, Colworth Science Park, Sharnbrook, Bedford MK44 1LQ, UK c Institute of Psychological Sciences, University of Leeds, Leeds LS2 9JT, UK b

Received 14 February 2007; received in revised form 26 October 2007; accepted 27 October 2007

KEYWORDS Human; Saliva; Cortisol-awakening response; Diurnal profile; Stress; Sleep; Obesity

Summary The use of saliva samples is a practical and feasible method to explore basal diurnal cortisol profiles in free-living research. This study explores a number of psychological and physiological characteristics in relation to the observed pattern of salivary cortisol activity over a 12-h period with particular emphasis on sleep. Basal diurnal cortisol profiles were examined in a sample of 147 volunteers (mean age 46.2177.18 years). Profiles were constructed for each volunteer and explored in terms of the area under the curve (AUC) of the cortisol-awakening response with samples obtained immediately upon waking (0, 15, 30 and 45 min post waking) and at 3, 6, 9 and 12 h post waking to assess diurnal decline. Diurnal mean of cortisol was based on the mean of cortisol at time points 3, 6, 9 and 12 h post waking. Psychological measures of perceived stress and sleep were collected with concurrent biological assessment of fasting plasma glucose, insulin, blood lipids and inflammatory markers. Blunted cortisol profiles, characterised by a reduced AUC, were observed in the majority (78%) of a middle-aged sample and were associated with significantly poorer sleep quality and significantly greater waist-hip ratio (WHR). Blunted cortisol profiles were further associated with a tendency to exhibit a less favourable metabolic profile. These findings suggest that reduced cortisol secretion post waking may serve as an additional marker of psychological and biological vulnerability to adverse health outcomes in middle-aged adults. & 2007 Elsevier Ltd. All rights reserved.

1. Introduction Corresponding author. Tel.: +44 113 812 3909;

fax: +44 113 812 3440. E-mail address: [email protected] (N. Lasikiewicz). 0306-4530/$ - see front matter & 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.psyneuen.2007.10.013

Cortisol demonstrates a clear circadian profile of activity (Born et al., 1999) often displayed graphically by plotting

ARTICLE IN PRESS 144 basal salivary cortisol concentration against time post waking. The diurnal profile includes (i) the cortisol-awakening response (CAR), a dramatic change in cortisol activity during the first 30–45 min post waking (Pru ¨ssner et al., 1997), with an average increase of 9 nmol/l (range of 4–15 nmol/l) (Clow et al., 2004) and (ii) diurnal decline in cortisol, post CAR until reaching a nadir in the evening. It has been noted that approximately 10% of individuals fail to exhibit a CAR (Pru ¨ssner et al., 1997). Failure to produce a textbook cortisol profile may be due to a lack of participant adherence particularly in studies, which adopt a free-living or ambulatory sampling protocol (Kudielka et al., 2003). Further, the pattern of weekday and weekend awakening cortisol has been found to be clearly confounded by suspected non-adherence to sampling protocol, with weekend cortisol profiles demonstrating a flatter cortisol awakening profile (Thorn et al., 2006). However, the pattern of cortisol activity may be associated with other psychological or biological variables. Abnormal diurnal cortisol profiles may be predictive of general health (Smyth et al., 1997; Roberts et al., 2004) and have also been associated with certain pathologies including AIDS (Sapse, 1997). Flattened profiles (a blunted CAR coupled with normal or flattened diurnal decline) have been found in breast cancer patients (Abercrombie et al., 2004) and further, the slope of diurnal cortisol variation can be predictive of earlier mortality (Sephton et al., 2000). In contrast, elevated responses to waking have been observed in type II diabetes (Korenblum et al., 2005) and elevated wake-up cortisol but with a lower area under the curve (AUC) has been observed in chronic illness compared with healthy subjects (Kudielka and Kirschbaum, 2003). The CAR has also been shown to be sensitive to factors such as chronic stress or burnout (Pru ¨ssner et al., 2003), and chronic fatigue (Kudielka and Kirschbaum, 2003). Some studies have demonstrated blunted morning cortisol in states of chronic burnout (Melamed et al., 1999; Morgan et al., 2002). In contrast, Grossi et al. (2005) demonstrated elevated morning cortisol. Furthermore, Mommersteeg et al. (2006) found no evidence of cortisol dysregulation in those with clinical burnout. However, exaggerated cortisol responses to waking have been observed in conjunction with high perceived stress (Schulz et al., 1998; Wu ¨st et al., 2000). Cortisol secretion has also been linked to the sleep–wake cycle. Research has highlighted the consistency between the circadian rhythmicity of adrenocorticotrophic hormone (ACTH) and cortisol with patterns of sleep and waking, in that cortisol is low during nocturnal sleep and the second half of nocturnal sleep is characterised by increasing HPA activity (Weitzman et al., 1971). Simultaneous increases in cortisol and ACTH combined with rapid eye movement sleep (REM) initiate spontaneous waking (Spath-Schwalbe et al., 1992; Born et al., 1999) followed immediately by the CAR. Sleep disturbance can influence cortisol activity on the subsequent day. Backhaus et al. (2004) found a negative correlation between the CAR and subjective rating of sleep quality. Lower cortisol immediately after waking correlated with a higher frequency of nightly awakenings and diminished sleep quality. However, some studies have failed to uncover an association (Hucklebridge et al., 2000).

N. Lasikiewicz et al. In addition to psychological parameters, disturbances in the basal diurnal cortisol profile may also be associated with metabolic disturbance. Rosmond et al. (1998) explored the consistency of the diurnal profile and observed two profile types: (i) high morning and low evening and (ii) a flat profile with lower morning values but little difference in evening values. Those who exhibited the latter profile also exhibited metabolic symptomatology, endocrine abnormalities and central obesity. However, Therrien et al. (2007) observed elevated cortisol responses to waking in males with central obesity which were reversed after weight loss. Rosmond et al. (1998) is the only research found that considers specifically the wider health implications of the shape of the basal cortisol diurnal profile. This study presents data from two studies; one study that included cortisol samples collected over 3 consecutive days and a second study where cortisol samples were collected over a single day of sampling. Both studies adopted an identical protocol. The CAR (as indicated by calculating the AUC) and diurnal decline were explored in middle-aged adults. The association of the cortisol profiles with measures of perceived stress, daily hassles and of sleep quality were examined. Fasting plasma glucose, insulin, blood lipids and inflammatory markers were also examined in relation to cortisol profiles.

2. Method 2.1. Participants One hundred and eighty participants were recruited. However, 33 participants failed to provide sufficient cortisol for analysis (27 from study 1 and 6 from study 2). Consequently, 147 healthy adults (68 males and 79 females) aged between 35 and 65 (mean age of 46.2177.18 years) were included in the analysis. The non-completers did not differ with respect to gender and age to the completers. Eighty-three volunteers provided cortisol samples across 3 consecutive days (mean age of 45.7177.21 years; 41 males and 42 females) and 64 volunteers provided cortisol samples on 1 day of sampling (mean age of 45.8677.19 years; 27 males and 37 females). Participants were not currently on any form of over-the-counter or prescribed medication and were non-smokers. All participants provided full informed consent prior to participation.

2.2. Materials Perceived stress was assessed using the short form of the Perceived Stress Scale (PSS) (Cohen, 1994), measuring perception and appraisal of stress and stressful situations on a five point Likert scale (0–4) from ‘‘never’’ to ‘‘very often’’ (a higher score suggesting greater perceived stress). Cronbach a’s ranges from 0.84 to 0.86 (Cohen et al., 1983). Daily stress was assessed using the Daily Hassles Scale (Kanner et al., 1981). One hundred and seventeen items covering aspects of health, family, friends, and the environment, are rated on a scale of 0 (did not occur), 1, 2 or 3 (somewhat, moderately or extremely). A frequency score is obtained by counting the number of hassles checked. Intensity is calculated by taking the mean of the

ARTICLE IN PRESS Basal diurnal cortisol profiles in middle-aged adults severity rating. Test–re-test assessment demonstrates consistency of scores (average r ¼ 0.79 for frequency and average r ¼ 0.48 for intensity (Kanner et al., 1981)). Subjective sleep quality was assessed using the Leeds Sleep Evaluation Questionnaire (LSEQ) (Parrott and Hindmarch, 1978, 1980) which measures eight aspects of sleep, within four broad areas of sleeping behaviour: (i) getting to sleep, (ii) quality of sleep, (iii) awakening from sleep and (iv) behaviour following waking on a 100 mm visual analogue scale (VAS; a higher score indicating poorer sleep). The LSEQ has good retest reliability (0.63–0.78, Tarrasch et al., 2003) and stability across a range of clinical settings (Zisapel and Laudon, 2003).

2.3. Procedure For the two studies data were drawn from the same population and followed an identical procedure. All participants attended a pre-study briefing, in which the study, equipment, and procedure were explained. Participants were provided with diary sheets and instructions to inform and monitor saliva sample collection. A fasted blood sample for biochemical determination of biomarkers (blood glucose, insulin (to infer insulin resistance using Homeostasis Model Assessment, HOMA), total cholesterol, triglycerides, highdensity lipoprotein (HDL), low-density lipoprotein (LDL), c-reactive protein (CRP) and interleukin-6 (IL-6) was obtained. Participants were asked to refrain from the consumption of alcohol on the nights prior to and during the study. Saliva samples were obtained using salivettes (Sarstedt Ltd, Leicester, England) by the participants at waking, 15, 30, 45 min post waking and at 3, 6, 9 and 12 h post waking (Edwards et al., 2001a, b; Hucklebridge et al., 2002, 2005). The participants were asked to refrain from consuming food or drink other than water (if necessary) at each sample collection time. Additionally, participants were asked to refrain from brushing teeth until after the 45-min sample, to avoid vascular leakage and micro-abrasion (Vining and McGinley, 1987). Participants completed the LSEQ after the 45-min sample. Before retiring, participants completed the Daily Hassles Scale. Samples, once collected, were stored in the participants’ freezer until returned to the research unit.

2.4. Cortisol and biomarker assay Salivette samples were thawed and spun at 3500 rpm. Cortisol was determined by a non-commercial time-resolved fluorescence (DELFIA) immunoassay developed for research purposes at Unilever Corporate Research, Colworth. The amount of cortisol present in the saliva samples was determined using a competitive inhibition immunoassay utilising a Biogenesis polyclonal anti-cortisol antibody and fluorescently labelled cortisol. The in-house assay was adapted to AutoDELFIA (Perkin Elmer) for high throughput testing and was validated against the widely used Salimetrics Kit (Salimetrics LLC). Biochemical analysis of blood glucose was conducted using Hexokinase method on an Olympus AU640 analyser (reagents and analyser from Olympus Diagnostics, UK). Insulin was determined using a Perkin Elmer time-resolved

145 fluoroimmunoassay kit on AutoDelfia. The degree of insulin resistance was calculated using HOMA (Matthews et al., 1985). For determination of total cholesterol, the assay employed the Cholesterol Oxidase method on an Olympus AU640 analyser (reagents and analyser from Olympus Diagnostics, UK). Triglyceride concentration was determined using an enzymatic method on an Olympus AU640 analyser (reagents and analyser from Olympus Diagnostics, UK). HDL level was determined using the immunoinhibition method on an Olympus AU640 analyser (reagents and analyser from Olympus Diagnostics, UK) for HDL cholesterol assessment and LDL concentration was determined from total cholesterol, HDL cholesterol and triglyceride using the Friedewald equation (LDL ¼ TC(HDL+TG/2.2)). IL-6 was determined using the quantitative sandwich enzyme immunoassay technique (R&D Systems high sensitivity kit). Finally, CRP was determined using Immunoturbidimetry on an Olympus AU640 analyser (reagents and analyser from Olympus Diagnostics, UK).

2.5. Data screening and missing data Cortisol data were positively skewed and normalised using a logarithmic transformation. In those volunteers who provided 3 consecutive days of sampling, data were included if each volunteer provided at least 2 sampling days of cortisol data for each time point in order to permit the profile to be aggregated. If this criterion was not met, the volunteer was excluded from analysis. Interpolation of data points was permitted during diurnal decline (between time points 3 and 12 h post waking) but not between time points (0, 15, 30 and 45 min post waking). Thirty-three participants failed to provide sufficient cortisol for analysis and interpolation was performed for nine subjects.

2.6. Statistical analysis All data were analysed using SPSS 14. Where 3 consecutive days of sampling were provided (study 1), Pearson’s correlations were computed to analyse the consistency of the AUC, diurnal mean and slope calculated according to the methods described by Edwards et al. (2001a) and Smyth et al. (1997). Diurnal mean was calculated as the mean of time points, 3, 6, 9 and 12 h post waking. Slope was calculated as the regression of the line of decrease in cortisol concentration against time from 45 min post waking. Both measures collectively represent diurnal decline. Data where participants had provided 3 days of sampling (study 1) were aggregated prior to inclusion and combined with data from single day of sampling (study 2) for further analysis. To explore whether there were distinct profile parameters, a K-means cluster analysis was performed on the individual time points (eight in total) and two profile clusters were extracted. The consistency of these profiles was again explored within each profile cluster, using Pearson’s correlations. The profile clusters were compared with respect to the AUC, slope and diurnal mean using Univariate ANOVA’s with profile cluster as the between subjects factor. Age was included as a covariate. A w2 analysis was conducted to explore the association between

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the number of days sampled and membership of profile clusters. To compare the biological and psychological characteristics of the two profile clusters, a series of ANOVA’s were performed. Perceived stress, daily hassles and biological markers were analysed with profile cluster and gender as between subjects’ factors. A multivariate analysis of variance (MANOVA) was performed on the sleep quality measures with profile cluster and gender as between subjects’ factors. Linear multiple regression analyses using a backward enter model were conducted to determine the best predictors of AUC, slope and diurnal mean from the available biological and psychological measures.

3. Results 3.1. Three day profile consistency Where 3 consecutive days of cortisol data were available (n ¼ 83), profiles were explored for consistency. The correlations between the 3 days of sampling are shown in Table 1. The diurnal profiles demonstrated consistency

Table 1 Pearson’s correlation coefficients between the 3 consecutive sampling days (n ¼ 83). Cortisol index Day 1–Day 2 Day 1–Day 3 Day 2–Day 3 N ¼ 66 N ¼ 63 N ¼ 71 AUC Slope Diurnal mean

r ¼ 0.67 r ¼ 0.20 r ¼ 0.71

r ¼ 0.67 r ¼ 0.41 r ¼ 0.49

r ¼ 0.63 r ¼ 0.21 r ¼ 0.60

 po0.01.

across the 3 days of sampling for AUC and diurnal mean. The correlations for slope were weaker.

3.2. Profile clusters Two profile clusters were formed based on data for the whole sample (n ¼ 147). The composite profile clusters are shown in Figure 1. The number of participants providing 3 consecutive days sampling and one single day of sampling did not differ significantly between the profile clusters (w ¼ 1.102, df ¼ 1, NS). Following the cluster analysis, for those where 3 consecutive days of data was available (n ¼ 83), correlational analysis of consistency within each profile cluster was performed. Similar consistency for Cluster 1 and Cluster 2 in AUC and diurnal mean was observed. However, as before, consistency of slope was weaker (Table 2). Cluster 1 was characterised by a blunted cortisol response to waking. This cluster comprised 78% (n ¼ 114) of the sample with 22% (n ¼ 33) of the sample being assigned to Cluster 2 which exhibited a classically reported CAR. A main effect of profile cluster on the calculated AUC was observed (F(1,142) ¼ 118.016; po0.01). A greater AUC was observed in Profile Cluster 2 compared with Profile Cluster 1 (4.4570.05 and 3.8470.03 LOG nM/l, respectively). Further, a significant main effect of gender was also observed (F(1,142) ¼ 4.616; po0.05). A greater AUC was observed in males compared with females (4.2170.04 and 4.0170.04 LOG nM/l, respectively). The profile cluster  gender interaction was not significant (F(1,142) ¼ 0.530; NS). Age was not found to be a significant covariate (F(1,142) ¼ 1.587; NS). A main effect of profile cluster on slope was also observed (F(1,142) ¼ 82.490; po0.01). A steeper decline in cortisol was observed in Profile Cluster 2 compared with Profile Cluster 1 (6.0370.26 and 3.347 0.14, respectively). There was no main effect of gender (F(1,142) ¼ 0.740; NS) nor profile cluster  gender interac-

40

Cortisol Concentration (nM/l)

35 30 25 Cluster One N=114

Cluster Two N=33

20 15 10 5 0

0

100

200

300 400 500 Time Post Waking (mins)

600

700

Figure 1 Mean (7SEM) 12 h cortisol profiles for Clusters 1 and 2.

800

ARTICLE IN PRESS Basal diurnal cortisol profiles in middle-aged adults

Table 2

147

Pearson’s correlation coefficients between the 3 consecutive sampling days within each profile cluster (n ¼ 83).

Sample

AUC Slope Diurnal mean

Cluster 1

Cluster 2

Day 1–Day 2 N ¼ 55

Day 1–Day 3 N ¼ 51

Day 2–Day 3 N ¼ 58

Day 1–Day 2 N ¼ 11

Day 1–Day 3 N ¼ 12

Day 2–Day 3 N ¼ 13

r ¼ 0.49 r ¼ 0.14 r ¼ 0.68

r ¼ 0.20 r ¼ 0.34 r ¼ 0.30

r ¼ 0.41 r ¼ 0.14 r ¼ 0.48

r ¼ 0.34 r ¼ 0.36 r ¼ 0.72

r ¼ 0.81 r ¼ 0.14 r ¼ 0.63

r ¼ 0.27 r ¼ 0.12 r ¼ 0.67

 po0.05.  po0.01.

tion (F(1,142) ¼ 0.623; NS). Thirdly, a main effect of profile cluster on the diurnal mean was observed (F(1,142) ¼ 4.904; po0.05). Higher mean cortisol was observed in Profile Cluster 2 compared with Profile Cluster 1 (10.1970.48 and 8.9770.26 LOG nM/l, respectively). No main effect of gender (F(1,142) ¼ 1.317; NS) nor profile cluster  gender interaction (F(1,142) ¼ 0.029; NS) was observed.

3.3. Associations of biological and psychological characteristics with profile clusters Biological and psychological characteristics were compared between the profile clusters (Table 3). No effect of gender was observed on any of the measures tested with the exception of CRP. No differences in age were observed between the profile groups. Cluster 1 demonstrated a trend for a higher insulin (F(1,135) ¼ 3.329; p ¼ 0.07) and calculated degree of insulin resistance (HOMA) (F(1,135) ¼ 3.490; p ¼ 0.06). Further, a significant cluster n gender interaction for CRP was observed (F(1,135) ¼ 4.314; po0.05). Post hoc analyses revealed that females in Cluster 1 exhibited greater CRP than females in Cluster 2 (3.49 mg/l70.58 and 1.35 mg/ l70.24, respectively) (p ¼ 0.045). Females in Cluster 1 also had greater CRP than males in Cluster 1 (3.49 mg/l70.58 and 1.91 mg/l70.31, respectively) (p ¼ 0.023). No differences were observed for systolic and diastolic blood pressure, blood lipids and IL-6. Profile Cluster 1 was associated with a significantly greater waist-hip ratio (WHR) (F(1,142) ¼ 3.974; po0.05). There was a trend for greater body mass index (BMI) (F(1,142) ¼ 3.583; p ¼ 0.06). A significant multivariate main effect of profile cluster on the measures of sleep quality was observed (multivariate F(3,119) ¼ 3.258; po0.01). Cluster 1 was associated with significantly poorer sleep quality (a higher VAS score denotes poorer sleep quality), specifically, greater restlessness (F(1,123) ¼ 5.416; po0.05), more difficulty sleeping (F(1,123) ¼ 5.568; po0.05), more periods of wakefulness (F(1,123) ¼ 5.103; po0.05), more time taken to get to sleep (F(1,123) ¼ 6.317; po0.05), more difficult awakening (F(1,123) ¼ 4.393; po0.05) and more time taken to awaken (F(1,123) ¼ 6.274; po0.05). No differences were observed in relation to subjective alertness upon waking and alertness 1 h post waking. Perceived stress (F(1,143) ¼ 2.066;

Table 3 Biological and psychological characteristics of each profile cluster (n ¼ 147) (means7SD). Variable

Cluster 1 N ¼ 114

Cluster 2 N ¼ 33

Age (years) Waist-hip ratio BMI (kg/m2) Blood pressure (systolic) (mmHg) Blood pressure (diastolic) (mmHg) Plasma glucose (mM/l) Plasma insulin (mU/l) Total cholesterol (mM/l) HDL (mM/l) LDL (mM/l) Triglycerides (mM/l) IL-6 (pg/ml) CRP (mg/l) Insulin resistance (HOMA) Perceived stress Daily hassles—intensity Daily hassles—frequency Sleep quality—ease of sleep Sleep quality—speed of sleep onset Sleep quality—restfulness Sleep quality—wakefulness Sleep quality—ease of waking Sleep quality—time taken to awaken Sleep quality—alertness on waking Sleep quality—alertness 1 h after waking

45.3370.67 47.0871.26 0.8770.07 0.8470.01 27.9870.55 25.7471.04 117.8371.47 114.5472.76 76.8070.92 73.5571.74 5.0070.05 6.8170.41 4.9670.09 1.3670.03 3.2070.08 1.3070.08 1.7570.13 2.7170.33 1.5870.12 16.5170.65 1.1870.04 15.0771.75 28.2272.15 28.6472.18

4.8570.10 5.2270.77 4.9070.16 1.4070.05 3.2070.14 1.0870.14 1.3270.24 1.9570.60 1.1270.22 14.5271.23 1.0970.09 16.4773.58 16.9574.26 16.6474.32

39.2772.12 28.3174.20 35.6172.15 24.8474.23 30.6372.04 40.1174.04 30.0172.01 41.1973.98 46.0572.26 48. 2374.47 32.1372.24 31.6074.44

Note: Measures of sleep are VAS scores with a high score indicating poor sleep quality.  po0.05.

p ¼ 0.153) and perceived intensity of daily hassles (F(1,78) ¼ 2.036; p ¼ 0.116) did not differ between profiles.

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Table 4

N. Lasikiewicz et al.

Significant predictors of AUC, slope and diurnal mean following backward linear multiple regression (n ¼ 147).

Outcome variable

Predictors remaining in the final model

B

Standard error B

b

t

p-Value

AUC

Systolic blood pressure Blood glucose Ease of sleep

0.013 0.152 0.011

0.003 0.076 0.002

0.383 0.202 0.471

3.732 1.990 4.627

0.000 0.052 0.000

Slope

Body mass index Ease of sleep Speed of sleep onset

0.081 0.235 0.207

0.043 0.066 0.065

0.219 2.375 2.132

1.878 3.539 3.175

0.066 0.001 0.002

Diurnal mean

Gender

1.0440

0.576

0.222

1.813

0.075

Systolic blood pressure Diastolic blood pressure Wakefulness Time to wake

0.122 0.156 0.037 0.070

0.050 0.060 0.017 0.018

0.567 0.656 0.297 0.519

2.253 2.609 2.192 3.802

0.028 0.012 0.033 0.000

3.4. Predicting AUC, slope and diurnal mean on the basis of biological and psychological characteristics Three backward linear multiple regressions were performed. All psychological and biological predictors previously explored were entered into the analysis with AUC, slope and diurnal mean as outcome variables. The final models displayed in Table 4. Beta values are included to reflect the degree of correlation with each index of cortisol. For AUC the final model included systolic blood pressure, blood glucose and subjective ease of sleep which collectively accounted for 44% of the variance. Ease of sleep was identified as the biggest predictor. The final model for slope included BMI, subjective ease of sleep and subjective speed of sleep onset, which collectively accounted for 22.9% of the variance in the model. Ease of sleep was identified as the biggest predictor of slope. The final model for diurnal mean included gender, systolic and diastolic blood pressure, subjective wakefulness and subjective time taken to wake accounting for 29.6% of the variance with diastolic blood pressure identified as the biggest predictor.

4. Discussion This study aimed to explore the psychological and biological characteristics of diurnal cortisol profiles in a sample of middle-aged adults. In those participants providing 3 consecutive days of cortisol sampling, the consistency of the profiles was explored. Consistency was demonstrated for AUC and diurnal mean, however the consistency of the slope was weaker. A cluster analysis was performed on the combined data set. Two profile clusters emerged. The first showed a blunted CAR, as indicated by a smaller AUC with a flattened diurnal decline (cluster 1) and the second a typical CAR with steep diurnal decline (cluster 2). Exploration of the profile clusters revealed significant associations with a number of psychological and biological characteristics. Cluster 1 was associated with significantly greater WHR. In the same profile, trends were observed for higher insulin

and insulin resistance. Further, Cluster 1 was associated with significantly poorer sleep quality. Ease of sleep was the biggest predictor of AUC and of slope. No gender differences were observed, with the exception of CRP and further no associations between cortisol and age were uncovered. Similar consistency for Profile Cluster 1 and Profile Cluster 2 in terms of the AUC and diurnal mean was observed. However, consistency of slope was, again, weaker. Previous research varies with respect to the number of days sampled for cortisol determination. The number of days sampled has included; single day collection (Steptoe et al., 2004), 2 day collection (Edwards et al., 2001b; Federenko et al., 2004) up to collection over a of 6 day period (Schlotz et al., 2004). Data from study one in the current report were collected across 3 consecutive days. Consistency across this period was demonstrated for AUC and diurnal mean. There was, however, an observed lack of consistency for slope. It is possible that cortisol change during the period immediately post waking (CAR) is less variable than cortisol change across the 3–12 h post waking period. At this time in the profile, extraneous variables such as exposure to stressors, environmental changes or other related behaviours may cause a greater fluctuation in the rate of cortisol decline. The observation that AUC and diurnal mean were consistent across the sampling period could be interpreted in a number of different ways. For example, non-adherence to the protocol is a common problem in research where samples are collected under free-living conditions (Kudielka et al., 2003). The consistency across the 3 days of sampling observed here suggests that non-adherence is unlikely. A blunted profile, as indicated by a smaller AUC, was exhibited by the majority of participants which also argues against the likelihood of non-adherence. However, it must be acknowledged that adherence was not formally measured in the current study. The observation that 78% of the current sample exhibited this type of awakening profile means that the proposed relationship between age and basal cortisol should be reviewed. Previous research has demonstrated that profiles become more flattened as a result of age with blunted cortisol profiles observed in elderly populations (Wolf et al.,

ARTICLE IN PRESS Basal diurnal cortisol profiles in middle-aged adults 2002). As the sample in the current study is middle aged, these data could indicate that the age of onset of blunted profiles may be earlier than previously thought. Given that the two clusters did not differ with respect to age, the blunting observed may be the result of differences in the biological and psychological variables measured. A failure to exhibit a cortisol response to waking may be indicative of impaired cortisol regulation. Individuals who exhibit ‘pathological’ basal cortisol secretion are most likely to also exhibit central obesity and the metabolic syndrome (Bjorntorp and Rosmond, 2000). Rosmond et al. (1998) distinguished two profiles in relation to metabolic disturbance: (i) high morning and low evening and (ii) a flat profile with lower morning values but little difference in evening values. Those who exhibited the latter profile also exhibited metabolic symptomatology, endocrine abnormalities and central obesity. In the current study, lower morning cortisol was associated with a significantly greater WHR, a trend for a greater BMI with a tendency for elevated metabolic disturbance, specifically, greater insulin levels and degree of insulin resistance (HOMA). WHR has often been associated with abnormal cortisol activity (Marin et al., 1992; Ljung et al., 1996, 2000) due to the increased number of glucocorticoid receptors present in visceral fat (Feldman, 1978). Our results offer partial support to the theory that metabolic syndrome is a neuroendocrine disorder (Bjorntorp and Rosmond, 2001). Thus the morning cortisol profile may serve as an additional marker of metabolic vulnerability. It is suggested that future studies measure multiple markers of metabolic syndrome to assess the impact of this dysregulation on multiple physiological systems in a quantifiable model of allostatic load. The finding that blunted cortisol profiles with a smaller AUC were also associated with significantly poorer subjective sleep quality is supported by previous research. Sleep quality has been previously shown to influence the CAR, often resulting in a blunted awakening response (Backhaus et al., 2004; Williams et al., 2005). We found that subjective reporting of sleep disturbance was significantly greater in those who also exhibited a reduced AUC post waking. Specifically, Cluster 1 was associated with greater reports of difficulty in falling asleep, time taken to fall asleep, less restful sleep, and more periods of wakefulness. Cortisol has been associated with the pattern of sleeping and sleep related behaviour, most likely mediated by an interaction between the SCN and HPA axis (Van Cauter and Turek, 1995). A clear relationship between sleep quality and the cortisol profile was observed in the current study. However, to elucidate the nature of this relationship, time of waking and duration of sleep should be considered in future studies. There is evidence to suggest that individuals active in the morning hours demonstrate a greater cortisol response to waking than those active in the evening (Bailey and Heitkemper, 1991). Further, Edwards et al. (2001a) observed that early awakeners also exhibit more elevated cortisol levels throughout the remainder of the diurnal cycle despite showing a steeper decline when compared to late awakeners. Blunted cortisol profiles have been previously associated with burnout (Melamed et al., 1999; Morgan et al., 2002). Yet studies have also observed elevated cortisol response to waking with increased perceived stress (Schulz et al., 1998; Wu ¨st et al., 2000). In the current study, neither perceived

149 stress nor daily hassles were significant predictors of the cortisol profile and, although greater in Cluster 1, these differences were not significant. However, it must be acknowledged that participants were not selected on the basis of their perceived stress score and as a result, the sample were not suffering extreme stress levels compared with those of previous studies. Sleeping behaviour is linked to obesity and body mass (Knutson, 2005) and both have potential links with psychological stress. Recent emerging literature, therefore, considers the potential impact of sleep on metabolic and neuroendocrine function. Young healthy males exposed to a week of sleep debt (less than 6 h sleep) demonstrated reduced glucose clearance following a breakfast meal (Spiegel et al., 1999). A review by Van Cauter et al. (2007) reported consistent risk of obesity and type 2 diabetes in those experiencing chronic partial sleep losses in both adults and children. The same risk is observed with poor sleep quality (Jennings et al., 2007). It has been proposed that sleep loss acts specifically on three pathways (Knutson et al., 2007). First, sleep loss acts directly on glucose metabolism, second, changes in energy balance due to sleep loss lead to increased hunger and intake and third, less energy expenditure leads to increased risk of obesity. Alternatively, although sleep and obesity are related, it is plausible that poor sleep is a consequence of obesity for example, obstructive sleep apnoea (Vgontzas et al., 2000). In conclusion, these findings from a sample of middleaged adults suggest that characteristics of the basal cortisol diurnal profile are associated with a number of psychological and metabolic variables. The pattern of morning cortisol may serve as a marker of vulnerability to the metabolic syndrome and associated adverse health outcomes. Exposure to life style factors such as poor sleep quality demonstrated in the current study and stress observed in previous research could exacerbate and advance cortisol dysregulation. Our data suggest that this may occur at an earlier age than previously thought, potentially leading to increased metabolic risk which requires further longitudinal research.

Role of the funding sources Funding for this study was provided by the Medical Research Council (MRC) and Unilever Corporate Research, Colworth Science Park, Bedford. The MRC had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. Unilever was actively involved in the study design and in the writing of the report.

Conflict of interest All authors state they have no conflict of interest.

Acknowledgements This research was supported with funding from the Medical Research Council (MRC, UK) and Unilever Corporate Research, Colworth Science Park, UK.

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