Dysregulated physiological stress systems and accelerated cellular aging

Neurobiology of Aging xxx (2014) 1e9

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Dysregulated physiological stress systems and accelerated cellular aging Dóra Révész a, *, Josine E. Verhoeven a, Yuri Milaneschi a, Eco J.C.N. de Geus b, Owen M. Wolkowitz c, Brenda W.J.H. Penninx a a b c

Department of Psychiatry, EMGO Institute for Health and Care Institute, VU University Medical Center, Amsterdam, the Netherlands Department of Biological Psychology, EMGO Institute for Health and Care Institute, VU University Medical Center, Amsterdam, the Netherlands Department of Psychiatry, University of California San Francisco School of Medicine, San Francisco, CA, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 May 2013 Received in revised form 20 December 2013 Accepted 23 December 2013

Exposure to chronic stressors is associated with accelerated biological aging as indicated by reduced leukocyte telomere length (LTL). This impact could be because of chronic overactivation of the body’s physiological stress systems. This study examined the associations between LTL and the immune system, hypothalamic-pituitary-adrenal axis and autonomic nervous system. LTL was assessed in 2936 adults from the Netherlands Study of Depression and Anxiety. Inflammation markers (interleukin-6, c-reactive protein, tumor necrosis factor-alpha), hypothalamic-pituitary-adrenal-axis indicators (salivary cortisol awakening curve [area under the curve indicators, with respect to the ground and increase], evening levels, 0.5 mg dexamethasone cortisol suppression ratio), and autonomic nervous system measures (heart rate, respiratory sinus arrhythmia, pre-ejection period) were determined. Linear regression analyses were performed and adjusted for sociodemographic, lifestyle and clinical factors. Shorter LTL was significantly associated with higher c-reactive protein, interleukin-6, area under the curve with respect to increase, and heart rate. A cumulative index score was calculated based on the number of highest tertiles of these 4 stress markers. LTL demonstrated a significant gradient within subjects ranging from having zero (5528 base pairs) to having 4 elevated stress markers (5371 base pairs, p for trend ¼ 0.002), corresponding to a difference of 10 years of accelerated biological aging. Contrary to the expectations, shorter LTL was also associated with longer pre-ejection period, indicating lower sympathetic tone. This large-scale study showed that inflammation, high awakening cortisol response, and increased heart rate are associated with shorter LTL, especially when they are dysregulated cumulatively. Ó 2014 Elsevier Inc. All rights reserved.

Keywords: Cellular aging Telomeres Stress Aging Inflammation Autonomic nervous system Cortisol

1. Introduction Aging is accompanied by the onset of various somatic diseases, but variation in the age of onset of these diseases suggests a high degree of variability in biological aging across humans. Lately, telomere length has emerged as a novel marker of biological aging (Blackburn, 2001; Olovnikov, 1996). Telomeres are tandem repeat DNA sequences that form protective caps at chromosome ends (Blackburn, 2001). During each somatic cell division, the DNA loses telomeric repeats with an estimated shortening rate of 14e20 or more base pairs per year, eventually causing replicative cell senescence (Blackburn, 1991; Cawthon et al., 2003; Hartmann et al., 2010; Wikgren et al., 2012). * Corresponding author at: A.J. Ernststraat 1187, Room M1.06, PO Box 74077, 1070 BB, 1081 HL Amsterdam, the Netherlands. Tel.: þ31 20 788 4596; fax: þ31 20 788 5664. E-mail address: [email protected] (D. Révész). 0197-4580/$ e see front matter Ó 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2013.12.027

Successful maintenance of telomeres is crucial to human health before they reach a critically short length and become dysfunctional at a threshold of 77 base pairs (Ledford, 2007). Normal telomere maintenance requires the cellular enzyme telomerase that adds telomeric DNA, thus preserving telomere length and healthy cell function (Blackburn, 2001). Several recent studies have demonstrated an inverse relationship between leukocyte telomere length (LTL) and the risk of subsequent somatic illnesses (e.g., cardiovascular diseases, dementia) and premature death (Epel et al., 2006, 2008; Martin-Ruiz et al., 2006; Ruff et al., 2012; Weischer et al., 2012). LTL is a heritable trait, with genetics contributing to approximately 70% of the variability (Broer et al., 2013), leaving a large part of the variability because of external factors such as environmental and lifestyle factors (Aubert and Lansdorp, 2008). Shorter LTL has often been observed in individuals exposed to psychological stressors, such as childhood and caregiving stress (Epel et al., 2004; Kiecolt-Glaser et al., 2011), as well as in

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individuals with stress-related diseases such as depressive disorders (Hoen et al., 2011; Verhoeven et al., 2013). As individuals differ in their psychological and biological responses to stress, LTL may represent a biomarker for assessing an individual’s cumulative exposure to stressful conditions (O’Donovan et al., 2012). Psychological stress might influence the length of telomeric DNA and overall health, possibly through the alteration of various physiological stress systems. The body’s major physiological stress systems are the immuno-inflammatory system, the hypothalamus-pituitary-adrenal (HPA)-axis, and the autonomic nervous system (ANS). Chronic stress can lead to systemic dysregulations of these systems, or increased “allostatic load”, resulting in systemic inflammation, hyperactivity of the HPA-axis, sympathetic activation and parasympathetic withdrawal (KiecoltGlaser et al., 2003; McEwen, 2008). Such dysregulations have a negative impact on overall health (Carrero et al., 2008; Licht et al., 2010; van Reedt Dortland et al., 2012), possibly by affecting the telomere maintenance system and contributing to the shortening of telomeres. Indeed, in vitro studies in human T-lymphocytes reported that cortisol exposure may result in down-regulation of telomerase activity, and that senescent immune cells showed more inflammatory activity than cells with longer TL (Choi et al., 2008; Merino et al., 2011). Several in vivo studies have also reported that shorter telomeres are associated with dysregulations in inflammatory markers (Carrero et al., 2008; Fitzpatrick et al., 2007; Kiecolt-Glaser et al., 2011, 2013; Masi et al., 2012; O’Donovan et al., 2011), HPA-axis (Epel et al., 2006; Kroenke et al., 2011; Tomiyama et al., 2012; Wikgren et al., 2012), and autonomic nervous system (ANS) function (Epel et al., 2006; Kroenke et al., 2011). All these studies raise the suggestion that dysregulation of central bodily stress systems is related to shorter LTL, but findings are still inconsistent (Parks et al., 2009; Wikgren et al., 2012; Wolkowitz et al., 2011). Several of these previous studies were limited by a small sample (n < 100; Epel et al., 2006; Kroenke et al., 2011; O’Donovan et al., 2009; Ornish et al., 2008; Tomiyama et al., 2012; Wikgren et al., 2012) or a restricted population (one gender [Carrero et al., 2008; Epel et al., 2006; O’Donovan et al., 2009; Tomiyama et al., 2012]; elderly individuals [Kiecolt-Glaser et al., 2011; O’Donovan et al., 2009, 2011; Wikgren et al., 2012]; children [Kroenke et al., 2011]; population with somatic diseases [Carrero et al., 2008; Ornish et al., 2008]); or the lack of adjustment for lifestyle, education, or somatic health indicators (Carrero et al., 2008; Epel et al., 2006; Fitzpatrick et al., 2007; Kiecolt-Glaser et al., 2011; Kroenke et al., 2011; Tomiyama et al., 2012; Wikgren et al., 2012; Wolkowitz et al., 2011). Above all, some studies have indicated that cumulative (or combinations of) dysregulations of multiple physiological stress systems may have the strongest impact on biological aging (Epel et al., 2006; Kroenke et al., 2011; O’Donovan et al., 2011), but this has not been examined yet for the 3 major physiological stress systems together. As inflammation, HPA and ANS markers are separately related to various somatic diseases, the accumulation of these dysregulations might lead to even worse health outcomes. The present study examined to what extent dysregulations of the 3 major physiological stress systems (HPA-axis, inflammation, and ANS) are associated with LTL in a large-scale cohort study including a sample of adult participants and adjusting for multiple lifestyle and somatic health indicators. In addition, the effect of cumulative stress system dysregulations on LTL was investigated. We hypothesized that subjects with (over)activated stress systems, characterized by a pro-inflammatory state, hyperactivity of the HPA-axis, and high sympathetic activation combined with parasympathetic withdrawal will have shorter LTL.

2. Methods 2.1. Study sample Data were from the Netherlands Study of Depression and Anxiety, a large ongoing longitudinal cohort study among 2981 adults (18e65 years), which is described elsewhere (Penninx et al., 2008). Briefly, respondents were recruited between September 2004 and February 2007 from the community, primary care, and in specialized mental health care. Baseline data collection consisted of a medical examination, blood draw, self-report questionnaires and a detailed interview including the lifetime version of the Composite Interview Diagnostic Instrument (CIDI version 2.1) according to the 4th Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria, administered by specially trained research staff. The research protocol was approved by the ethical committee of participating universities and all respondents provided written informed consent. Of all participants, 1737 persons were included with a current (i.e., within the past 6 months) DSMIV diagnosis of depressive (major depressive disorders or dysthymia) and/or anxiety disorders (social phobia, generalized anxiety disorder, panic disorder with/without agoraphobia, and agoraphobia only), 592 persons with a remitted (i.e., lifetime, but not current) depressive and/or anxiety disorder, and 652 healthy subjects with no lifetime psychiatric disorders. We then excluded 45 subjects because of missing LTL data. These subjects were slightly older (45.5 vs. 41.8 years, p ¼ 0.06), but did not differ in years of education or gender from the included subjects. The 2936 remaining participants (95% from European origin) constitute the basic sample for the present study, and had at least information on one of the physiological stress systems available. For specific physiological stress markers, however, we had to exclude subjects with missing data for that assessment, ranging from 27 to 47 missing data points for inflammatory markers, 116e140 missing data for ANS, and 1031e1183 missing data for HPA-axis measures (see below for further details). For HPA-axis analyses, we additionally excluded 159 subjects who used corticosteroids (n ¼ 134) or were pregnant and/or breastfeeding (n ¼ 25). 2.2. Measurements 2.2.1. LTL Fasting blood was drawn from participants in the morning between 8:30 and 9:30 AM. Peripheral blood mononuclear cells from all samples were isolated from whole blood using density-gradient centrifugation (with Ficoll-Paque PLUS) and stored at 80  C freezers. Subsequently, in early 2012, LTL was determined at the laboratory of Telomere Diagnostics, Inc (TDx, Menlo Park, CA, USA), using quantitative polymerase chain reaction as described earlier (Cawthon, 2002). All qPCRs were carried out on a Roche Lightcycler 480 realtime PCR machine with 384-tube capacity (Roche Diagnostics Corporation, Indianapolis, IN, USA). Telomere sequence copy number in each patient’s sample (T) was compared with a single-copy gene copy number (S), relative to a reference sample. The resulting T to S ratio is proportional to mean LTL (Aviv et al., 2011; Cawthon, 2002). To control for inter-assay variability, 8 control DNA samples were included in each run. In each batch, the T to S ratio of each control DNA was divided by the average T to S for the same DNA from 10 runs to obtain a normalizing factor. This was done for all 8-control samples and the average normalizing factor for these samples was used to correct the participant DNA samples to obtain the final T to S ratio. The T to S ratio for each sample was measured twice. If the duplicate T to S value and the initial value varied by more than 7%, the sample was run a third time and the average of the 2 closest values was reported. The reliability of the

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assay was adequate: the included quality control DNA samples on each PCR run illustrated a small intra-assay coefficient of variation (CV ¼ 5.1%), and the inter-assay CV was also sufficiently low (CV ¼ 4.6%), as well as for the telomere (CV ¼ 2.04%) and the single-gene assays (CV ¼ 1.58%), separately. To compare T to S ratios to telomere restriction fragments reported by other studies using Southern blot analysis, we used the following steps to derive a conversion formula. Published work from the Blackburn lab at University of California, San Francisco (UCSF) used a formula of base pairs ¼ 3274 þ 2413*T/S based on comparison of T to S ratios and telomere restriction fragments analysis of a series of genomic DNA samples from the human fibroblast cell line IMR90 (Lin et al., 2010). Comparison of the T to S ratios of the 8 control DNA samples derived from Blackburn lab and TDx lab generated the following formula: T/ S (UCSF) ¼ (T/S[TDx]  0.0545)/1.16. Therefore the final formula we used to convert T to S ratios to base pairs is: base pairs ¼ 3274 þ 2413  ([T/S  0.0545]/1.16). 2.2.2. Inflammation After fasting blood samples were kept frozen at 80  C, circulating plasma levels of interleukin-6 (IL-6, N ¼ 2908), c-reactive protein (CRP, N ¼ 2909), and tumor necrosis factor-alpha (TNF-a, N ¼ 2889) were assessed in duplicate (Vogelzangs et al., 2012). High-sensitivity plasma levels of CRP were measured in duplicate by an in-house enzyme-linked immunosorbent assay (ELISA) based on purified protein and polyclonal anti-CRP antibodies (Dako, Glostrup, Denmark). Plasma IL-6 levels were measured in duplicate by a high sensitivity ELISA (PeliKine Compact ELISA, Sanquin, Amsterdam, The Netherlands). Plasma TNF-a levels were assayed using a high-sensitivity solid phase ELISA (Quantikine HS Human TNF-a Immunoassay, R&D systems, Minneapolis, MN, USA). Intraand inter-assay coefficients of variation for inflammatory markers were between 5%e10% and 10%e15%, respectively. 2.2.3. HPA-axis function Respondents were instructed to collect saliva samples at home on a regular (preferably working) day (Vreeburg et al., 2009b). This has shown a reliable and minimally intrusive method to assess the active, unbound form of cortisol (Kirschbaum and Hellhammer, 1994). Instructions concerning saliva sampling prohibited eating, smoking, drinking tea or coffee, or brushing teeth within 15 minutes before sampling. Furthermore, no dental work 24 hours before sampling was allowed. In total, 2204 subjects returned at least 1-saliva sample and the median time between blood withdrawal and saliva sampling was 8.0 days (25th to 75th percentile: 4e21). Saliva samples were obtained using Salivettes (Sarstedt, Nümbrecht, Germany) at 7 time points on a regular (work) day: at awakening (T1) and 30 (T2), 45 (T3), and 60 (T4) minutes later, and at 10 PM (T5) and 11 PM (T6). In addition, dexamethasone suppression was measured by cortisol sampling the next morning at awakening (T7) after ingestion of 0.5 mg dexamethasone directly after the saliva sample at 23:00 hours (T6). Respondents were instructed to write down the exact sampling times. The values of morning cortisol samples were assigned missing when collected outside of a margin of 5 minutes before or after the time protocol. Samples were stored in refrigerators and returned by regular mail. After receipt, Salivettes were centrifuged at 2000 g for 10 minutes, aliquoted and stored at 80  C. Cortisol analysis was performed by competitive electrochemiluminescence immunoassay (Roche, Basel, Switzerland) (van Aken et al., 2003). The functional detection limit was 2.0 nmol/liter and the intra- and inter-assay variability coefficients were <10%. Various cortisol measures were used to characterize different HPA-axis aspects. First, to indicate the 1-hour cortisol awakening response we calculated the area under the curve with respect to the increase (AUCi) and ground (AUCg, N ¼ 1753) (Pruessner et al.,

3

2003). The AUCg is an estimate of the total cortisol secretion over the first hour after awakening, whereas the AUCi is a measure of the dynamic of the cortisol awakening response, more indicative of the sensitivity of the system, emphasizing changes over time (Pruessner et al., 2003). Second, evening cortisol levels (N ¼ 1905) are indicative of basal HPA-axis activity, because cortisol levels are generally low at the end of the day. Since the 2 evening values were highly correlated (r ¼ 0.75, p < 0.001), the mean was used to reflect evening cortisol (N ¼ 1905). At last, the cortisol suppression ratio after dexamethasone intake is calculated by dividing the cortisol value at T1 by cortisol value at T7 (N ¼ 1806). This ratio examines the adequacy of the negative feedback of the HPA-axis (Carroll et al., 1981). A higher ratio indicates suppression by dexamethasone, which occurs when the feedback loop functions adequately, a lower ratio indicates non-suppression of the HPA-axis. 2.2.4. ANS During the baseline interview, subjects were wearing the VU University ambulatory monitoring system (Licht et al., 2012). This is a light-weight, unobtrusive device that records the electrocardiogram (ECG) and changes in thorax impedance (dZ) from 6 surface electrodes placed at the chest and on the back of the subjects (Willemsen et al., 1996). The inter-beat interval time series were extracted from the ECG signal to obtain heart rate (HR, N ¼ 2819), an indicator of combined sympathetic and parasympathetic nervous system activity. To separately index the cardiac effects of both ANS branches, preejection period (PEP, N ¼ 2796) and respiratory sinus arrhythmia (RSA, N ¼ 2820) were extracted from the combined dZ and ECG signals (Licht et al., 2010). We refer to PEP as a measure of cardiac sympathetic control, as it can reliably index b-adrenergic inotropic drive to the left ventricle (long PEP reflecting low cardiac sympathetic control) and was defined as the interval from the beginning of the left ventricular electrical activity (ECG Q-wave onset), to the beginning of left ventricular ejection (B point in the dZ/dt signal) (Berntson et al., 1994; Cacioppo et al., 1994). On the other hand, RSA reflects cardiac parasympathetic (vagal) control (high RSA reflecting high cardiac vagal control), and was obtained by subtracting the shortest inter-beat interval during heart rate acceleration in the inspirational phase from the longest inter-beat interval during deceleration in the expirational phase for all breaths as described elsewhere (Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, 1996). Movement registration through vertical accelerometry was used to excise periods where subjects were nonstationary (w15 minutes), because postural changes have an effect on RSA and PEP that is partly unrelated to changes in autonomic activity (Houtveen et al., 2005). Automated scoring of RSA and PEP was checked by visual inspection, and valid data were averaged over 98.0  24 minutes time to create a single PEP, RSA, and HR value. 2.3. Covariates Sociodemographic factors included sex, age, and years of attained education. Lifestyle variables included alcohol consumption (no drinker, mild-moderate drinker 1e14 (women)/1e21 drinks per week (men), heavy drinker >14 (women)/>21 (men) drinks per week (Stuurgroep Multidisciplinaire Richtlijnontwikkeling, 2009)), smoking (never, former, current), and physical activity (International Physical Activity Questionnaire [Craig et al., 2003], expressed in 1000 metabolic equivalent minutes in the past week). Health indicators included body mass index (BMI: underweight <18.5, normal weight 18.5e24.9, overweight 25e29.9, and obesity 30) and the number of self-reported chronic diseases for which medical treatment was received (including cardiovascular diseases, diabetes, lung disease, osteoarthritis, rheumatic disease, cancer, ulcer, intestinal problem,

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liver disease, epilepsy, and thyroid gland disease). Frequent medication use (>50% of the time) was registered from the participant’s medication containers and classified using the World Health Organization anatomic therapeutic chemical classification: cardiac medication including beta-blocking agents (C07) and others such as antihypertensives (C01eC05, C08, C09), anti-inflammatory medication (M01A, M01B, A07EB, A07EC), and antidepressants (N06AA, N06AB, N06AF, N06AG, N06AX). Apart from the standard covariates mentioned previously, for the HPA-axis analyses, sampling factors were additionally included, as suggested in a previous study (Vreeburg et al., 2009b): subjects reported awakening time, working status on the sampling day, and season was categorized into dark months (OctobereFebruary) and months with more daylight (MarcheSeptember). Menstrual cycle phase, menopausal status, the use of oral contraceptives (Vreeburg et al., 2009b), or estrogen replacement therapy (data not shown) were not associated with salivary cortisol within this sample and were therefore not included as covariates. For ANS analyses additional adjustments were made for respiratory rate (for RSA), and for mean arterial pressure (systolic blood pressure þ 2  diastolic blood pressure/3) to account for potential between-subject differences in afterload in PEP analyses (Houtveen et al., 2005). Therefore, blood pressure was recorded in a supine position by 2 repeated measurements using the OMRON M4 IntelliSense (HEM-752A, Omron Healthcare, Bannockburn, IL, USA). 2.4. Statistical analyses Baseline characteristics were compared across LTL (short, middle, and long) tertiles using c2 and analysis of variance statistics to describe the frequencies, means, and standard deviations. The levels of CRP, IL-6, TNF-a, mean evening cortisol, cortisol suppression ratio, and RSA were not normally distributed. Therefore, natural logarithm-transformations were used in analyses and these values were presented back-transformed in tables. Pearson correlations were used to determine correlations between LTL and all stress system components. The associations between LTL and each of the inflammatory, HPA-axis, and ANS markers (used both as a continuous measure and categorized as the high-risk tertiles vs. the combined middle- and low-risk tertiles) were tested in separate linear regression analyses. All analyses were adjusted for sociodemographic, lifestyle, health indicators, and medication. Analyses on HPA-axis and ANS were additionally adjusted for a specific set of covariates as described in section 2.3. In subsequent sensitivity analyses we examined whether current depression and/or anxiety diagnosis (current, remitted, or no), or antidepressant use (yes or no) modified the associations between LTL and these stress systems, by checking whether the interaction terms were significantly associated with LTL, and by adding these factors as covariates. At last, to examine the joint impact of all significant physiological stress system mechanisms, a cumulative score was calculated from all high-risk tertiles of the stress markers that showed significant associations with LTL in the a priori hypothesized directions. Fully corrected analyses of covariance were performed to compare the mean LTL of persons with gradually increasing number of stress system elevations. All analyses were conducted using SPSS version 20.0 (IBM Corp, Armonk, NY, USA). 3. Results Table 1 shows the sample characteristics across LTL tertiles. On average, subjects were 41.8 years (standard deviation; SD ¼ 13.1), 66.4% were women, and had 12.2 years of education (SD ¼ 3.3). The average T to S ratio was 1.11 (SD ¼ 0.30), corresponding to 5468

base pairs (bp; SD ¼ 617). Unadjusted linear regression showed LTL to be 14.5 bp shorter for each increasing year of age (standard error ¼ 0.8; p < 0.001). Subjects in the shortest LTL tertile were older, more often male, higher in BMI, more often smokers and heavy drinkers, had more chronic diseases, used more cardiac and anti-inflammatory medication, and more often had their saliva sampling on nonworking days in the months with more daylight. In subjects with the shortest LTL, higher levels of CRP, IL-6, and TNFa were found, together with lower RSA and longer PEP. No differences were found for the HPA-axis measures between the 3 LTL tertiles. Unadjusted Pearson correlations between LTL and the individual stress system markers are shown in Table 2. Within the 3 stress systems, stress markers were highly inter-correlated. LTL was significantly correlated with all 3 inflammatory markers, evening cortisol levels, PEP, and RSA. All inflammation markers were significantly associated with LTL after adjustment for sociodemographics (Table 3). After full adjustment for additional health and lifestyle factors, high levels of CRP, and IL-6 remained significantly associated with LTL, but TNF-a was only borderline significant. Consistent with this, subjects with the highest tertiles of CRP (>2.18 mg/L) and IL-6 (>1.04 pg/mL) showed shorter LTL, which was not confirmed for TNF-a. Of the HPA-axis measures, no significant linear associations were found with LTL. When high-risk tertiles were defined, however, the highest AUCi tertile (>4.1 nmol/L/hr) was significantly associated with short LTL after sociodemographics adjustment and after full adjustment, suggesting a nonlinear association. AUCg, evening cortisol levels and the cortisol suppression ratios were not associated with LTL. Regarding ANS indicators, contrary to our prior expectation, long PEP remained significantly associated with short LTL after full adjustment, whereas only trends remained present for the associations between RSA and LTL and between HR and LTL. However, shorter LTL was significantly associated to the highest tertiles of HR (>75.4 b/min) and PEP (>129.3 ms) compared with those in the lower tertiles. Previously, it has been described in our sample that depressive and anxiety disorders were associated with increased CRP and IL-6 levels (Vogelzangs et al., 2012, 2013), increased AUCg and AUCi (Vreeburg et al., 2009a), but not to other stress system indicators (Licht et al., 2012). However, we found no significant interactions between stress system markers and current or remitted depression and/or anxiety diagnosis (1707 current vs. 603 remitted vs. 626 healthy controls) or antidepressant use (721 users vs. 2202 nonusers) in the association with LTL (all p-values for interaction terms were >0.10, data not shown), and additional adjustment for psychopathology and antidepressant use resulted in similar associations. Furthermore, we did not find significant sex interactions between LTL and any of the stress system markers (all p-values for LTL-by-sex interaction terms were >0.10, data not shown). Subsequently, for the 4 stress system indicators that showed the expected relation with LTL, and were significant at least at p < 0.10, we examined the cumulative index score. This score was defined as the number of highest tertiles on CRP, IL-6, AUCi, and HR, for subjects with complete data on all significant stress indicators (N ¼ 1663). PEP was not included in the cumulative index score because of the unexpected direction of the association with LTL. Fig. 1 shows the LTL of subject groups with various physiological risk profiles. Compared with having no dysregulations (LTL ¼ 5528 bp), each additional high-risk stress marker was significantly associated with shorter LTL after full adjustment for relevant confounders (1 dysregulation: 5419 bp; p ¼ 0.003; 2 dysregulations: 5407 bp; p ¼ 0.002; 3 or 4 dysregulations: 5371 bp; p ¼ 0.002). As the shortening rate found in other studies was between 14e20 bp per year

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Table 1 Sample characteristics shown per telomere length tertile Telomere length (T/S ratio) tertiles (N ¼ 2936)

Sample characteristics

T/S ratio, mean (range) TL (bp), mean (range) Demographics Age (y), mean (SD) Sex (female), n (%) Years of education, mean (SD) Lifestyle and health factors BMI (kg/m2), n (%) Underweight Normal weight Overweight Obese Smoking status, n (%) Never Former Current Drinking behavior, n (%) Non-drinker Mild-moderate drinker Heavy drinker Physical activity (*1000 MET-min/wk), median (IQR) Number of chronic diseases, mean (SD) Cardiac medication, n (%) Anti-inflammatory medication, n (%) Mean awakening time, mean (SD) Season light versus dark, n (%) Working day on saliva collection, n (%) Physiological stress systems

p-value

Short (N ¼ 978)

Middle (N ¼ 979)

Long (N ¼ 979)

0.83 (0.33e0.95) 4877 (3851e5135)

1.06 (0.95e1.17) 5357 (5135e5604)

1.45 (1.18e2.03) 6168 (5605e7392)

d d

46.5 (11.9) 582 (19.8) 12.1 (3.4)

41.9 (12.8) 672 (22.9) 12.2 (3.3)

37.0 (12.8) 696 (23.7) 12.2 (3.2)

<0.001 <0.001 0.74

9 429 347 193

23 517 287 152

34 540 258 147

(1.2) (18.4) (8.8) (5.0)

<0.001

323 (11.0) 304 (10.4) 352 (12.0)

<0.001

(0.3) (14.6) (11.8) (6.6)

227 (7.7) 355 (12.1) 396 (13.5) 163 663 152 2.8 0.7 101 58 7:26 733 666

(0.8) (17.6) (9.8) (5.2)

276 (9.4) 315 (10.7) 388 (13.2)

(5.6) (22.6) (5.2) (3.3) (1.0) (3.4) (2.0) (1:00) (25.0) (22.7)

175 680 124 2.8 0.6 80 39 7:29 704 705

(6.0) (23.2) (4.2) (3.4) (0.9) (2.7) (1.3) (1:00) (24.0) (24.0)

162 720 97 2.8 0.5 47 28 7:28 686 750

(5.5) (24.5) (3.3) (3.4) (0.8) (1.6) (1.0) (1:00) (23.4) (25.5)

0.004

0.11 <0.001 <0.001 0.003 0.69 0.05 <0.001

N

Inflammation, mean (SD) C-reactive protein (mg/L)a Interleukin-6 (pg/mL)a Tumor necrosis factor-alpha (pg/mL)a HPA-axis function, mean (SD) AUCg (nmol/L/hr) AUCi (nmol/L/hr) Mean evening cortisol (nmol/L)a Cortisol suppression ratioa Autonomic nervous system, mean (SD) Heart rate (bpm) Pre-ejection period (ms) Respiratory sinus arrhythmia (ms)a

2909 2908 2889

1.39 (3.3) 0.82 (2.5) 0.84 (1.8)

1.26 (3.5) 0.79 (2.7) 0.86 (1.9)

1.19 (3.6) 0.67 (2.7) 0.80 (1.9)

1753 1753 1905 1806

19.0 2.4 4.78 2.31

18.8 2.1 4.74 2.39

18.8 2.0 4.53 2.47

2819 2796 2820

(7.0) (6.3) (1.7) (1.7)

71.6 (9.7) 121.9 (18.5) 33.5 (1.7)

(6.6) (6.1) (1.8) (1.6)

71.9 (10.0) 119.9 (17.5) 38.8 (1.7)

0.02 <0.001 0.04

(7.4) (6.4) (1.7) (1.7)

0.80 0.45 0.16 0.09

72.3 (9.1) 118.8 (17.4) 43.5 (1.7)

0.27 0.001 <0.001

Key: AUCg, area under the curve with respect to the ground; AUCi, area under the curve with respect to the increase; BMI, body mass index; M, mean; MET, metabolic equivalent; SD, standard deviation; TL, telomere length in base pairs; T/S, telomere-to-single copy gene ratio. a Natural logarithm-transformed factors presented back-transformed.

(Cawthon et al., 2003; Hartmann et al., 2010; Wikgren et al., 2012), in line with our found shortening rate of 14.5 bp per year, these differences correspond to an accelerated “biological aging” of 5e8 years, 6e9 years, and 8e11 years, respectively.

4. Discussion This study found that subjects with more activated stress systems had shorter leukocyte telomere length than those with less activated

Table 2 Unadjusted Pearson correlations between telomere length and the stress system components of inflammation, HPA-axis and ANS Markers

TL

CRP

IL-6

TNF-a

AUCg

AUCi

Evening

Cortisol suppression

HR

PEP

RSA

TL CRPa IL-6a TNF-aa AUCg AUCi Evening cortisola Cortisol suppression ratioa HR PEP RSAa

1 d d d d d d d d d d

0.072b 1 d d d d d d d d d

0.094b 0.312b 1 d d d d d d d d

0.057b 0.135b 0.125b 1 d d d d d d d

0.007 0.020 0.016 0.019 1 d d d d d d

0.005 0.033 0.004 0.020 0.473b 1 d d d d d

0.058b 0.033 0.028 0.005 0.365b 0.085b 1 d d d d

0.045 0.013 0.042 0.023 0.187b 0.263b 0.234b 1 d d d

0.026 0.204b 0.108b 0.012 0.024 0.042 0.001 0.041 1 d d

0.055b 0.118b 0.067b 0.005 0.008 0.014 0.022 0.040 0.249b 1 d

0.201b 0.151b 0.175b 0.074b 0.091b 0.015 0.102b 0.030 0.342b 0.125b 1

Key: ANS, autonomic nervous system; AUCg, area under the curve with respect to the ground; AUCi, area under the curve with respect to the increase; CRP, c-reactive protein; HPA, hypothalamic-pituitary-adrenal; HR, heart rate; IL-6, interleukine-6; PEP, pre-ejection period; RSA, respiratory sinus arrhythmia; TL, telomere length; TNF-a, tumor necrosis factor-a. a Natural logarithm-transformed factors presented back-transformed. b p < 0.05, 2-tailed.

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Table 3 Associations between telomere length and continuous or categorical measuresa of stress system components Stress system components

Inflammation C-reactive proteinb Highest tertile (>2.18 mg/L) Interleukin-6b Highest tertile (>1.04 pg/mL) Tumor necrosis factor-ab Highest tertile (>0.90 pg/mL) HPA-axis function AUCg Highest tertile (>20.79 nmol/L/hr) AUCi Highest tertile (>4.13 nmol/L/hr) Mean evening cortisolb Highest tertile (>5.97 nmol/L) Cortisol suppression ratiob Lowest tertile (<1.98) Autonomic nervous system Heart rate Highest tertile (>75.4 b/min) Pre-ejection period Highest tertile (>129.3 ms) Respiratory sinus arrhythmiab Lowest tertile (<31.3 ms)

N

2909 2908 2889

1753 1753 1905 1806

2820 2796 2820

Adjusted models for age, sex, education years

Fully adjusted modelsc

Beta

p-value

Beta

p-value

0.054 0.046 0.045 0.051 0.034 0.003

0.003 0.01 0.01 0.004 0.05 0.86

0.041 0.035 0.035 0.043 0.032 0.006

0.04 0.06 0.05 0.02 0.07 0.73

0.028 0.037 -0.020 0.061 0.019 0.026 0.028 0.012

0.22 0.10 0.38 0.01 0.39 0.24 0.21 0.59

0.033 0.037 0.019 0.058 0.001 0.005 0.019 0.005

0.16 0.11 0.41 0.01 0.96 0.84 0.40 0.82

0.027 0.032 0.057 0.055 0.027 0.029

0.14 0.08 0.001 0.002 0.22 0.16

0.028 0.034 0.060 0.055 0.030 0.030

0.14 0.07 0.001 0.003 0.19 0.16

HPA-axis function: alcohol use, smoking, physical activity, number of chronic diseases, BMI, awakening time, working status, season (light/dark). Autonomic nervous system: alcohol use, smoking, physical activity, number of chronic diseases, BMI, cardiac medication, respiratory rate (for respiratory sinus arrythmia), mean arterial pressure (for pre-ejection period). Key: AUCg, area under the curve with respect to the ground; AUCi, area under the curve with respect to the increase; BMI, body mass index; HPA, hypothalamic-pituitaryadrenal. a High-risk tertiles are compared to middle- and low-risk tertiles. b Natural logarithm-transformed. c Additionally adjusted for inflammation: alcohol use, smoking, physical activity, number of chronic diseases, BMI, anti-inflammatory medication.

Fig. 1. Telomere length shown in 4 subject groups (0 ¼ no dysregulations vs. 3-4 dysregulations), based on the cumulative score of the highest tertiles of CRP (>2.18 mg/ L), IL-6 (>1.04 pg/mL), AUCi (>4.13 nmol/L/hr), and HR (>75.4 b/min). Adjusted for: age, sex, education years, alcohol use, smoking, physical activity, number of chronic diseases, BMI, anti-inflammatory medication, awakening time, working status, season (light/dark), cardiac medication. Abbreviations: AUCi, area under the curve indicators; BMI, body mass index; CRP, c-reactive protein; HR, heart rate; IL-6, interleukin-6.

ones in a large cohort of both healthy individuals and patients with depressive and anxiety disorders. Shorter LTL was associated with high inflammation levels, HPA-axis hyperreactivity as illustrated by an increased cortisol awakening response, and high heart rate. These associations were not dependent of psychopathology status, as they were consistent in both the healthy subjects and the subjects with current and remitted anxiety or depressive disorders. To our knowledge, this is the first large cohort study that convincingly shows that shorter LTL is present among subjects with cumulative dysregulations of their inflammatory, HPA-axis and ANS markers. The associations between LTL and the inflammatory markers CRP and IL-6 in our study are largely consistent with some earlier research (Carrero et al., 2008; Fitzpatrick et al., 2007; Kiecolt-Glaser et al., 2011, 2013; Masi et al., 2012), although some studies have not consistently observed the same associations (O’Donovan et al., 2011; Wikgren et al., 2012; Wolkowitz et al., 2011). For TNF-a, we did not find a consistent association with LTL, which was observed before as well (Kiecolt-Glaser et al., 2011). Although this might be because of the slightly higher intra- and inter-assay coefficients of variation of the TNF-a assay, the link with LTL remains to be elucidated. However, our findings for CRP and IL-6 are in line with our hypothesis that persistent inflammation is associated with LTL. Overall, inflammation triggers T-cell proliferation and enhances the leukocyte turnover rate, one known cause of telomere shortening. Inflammatory mediators could also inhibit programed cell death (apoptosis), therefore preventing the selective removal of the most heavily damaged cells from the population, and contributing to the accumulation of senescent cells (Mangan and Wahl, 1991). In turn, the senescent cells accumulated in individuals with short LTL secrete factors to communicate their compromised state to the surrounding tissue, possibly contributing to the higher levels of inflammatory mediators in this population (Merino et al., 2011).

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We hypothesized that LTL would be shorter in subjects with high sympathetic and low parasympathetic activity, with long PEP reflecting low sympathetic activation, and high RSA reflecting high parasympathetic activation. As the combination of low parasympathetic and high sympathetic activation would lead to a higher HR, we further hypothesized that HR would be associated with LTL. The latter association was borne out by the results. This supports earlier observations that higher resting HR is strongly correlated to shorter lifespan, possibly because of higher basal metabolic rate, increased oxidative stress, and accelerated deterioration of the cardiovascular system (Zhang and Zhang, 2009). LTL may also have an impact on cardiovascular function, as functional telomeres are required for viable cardiovascular cells in vitro and for cardiomyocyte renewal (Fuster and Andres, 2006; Kajstura et al., 2006). The extent to which LTL reflects telomeres in heart tissue remains largely unclear, although a high correlation between leukocyte and aortic wall tissue telomere length has been reported (Wilson et al., 2008). Furthermore, low RSA was associated with shorter LTL, as expected, although adjustment for sociodemographics and lifestyle factors rendered the association nonsignificant. However, we unexpectedly found that long PEP was significantly associated with shorter LTL. Two earlier relatively small-sampled studies investigated the link between ANS and TL, but also did not find any associations between TL and basal ANS measures (Epel et al., 2006; Kroenke et al., 2011). Rather they found an association between lower parasympathetic activation during stress testing and low telomerase activity (Epel et al., 2006), and between sympathetic activation during stress testing and short buccal cell telomere length (Kroenke et al., 2011). What could have driven the anomalous finding of long PEP being associated with short LTL? It is unlikely that this finding is because of an error in PEP assessment, since we haveein line with hypotheseseconfirmed that short PEP in our sample is related to health factors such as BMI, antidepressant use (Licht et al., 2012), and with the number of baseline metabolic syndrome components (Licht et al., 2010), as well as the increase of metabolic abnormalities over time (Licht et al., 2013). An intriguing possibility is that chronic stress with concurrent high levels of plasma (nor)epinephrine may have led to a down regulation of the cardiac beta-receptors. This would lead to a prolongation of the PEP, even in the presence of high cardiac sympathetic nervous system activity. Desensitization of betaadrenergic receptors has earlier been associated with anxious mood (Yu et al., 2008) and caregiving status (Mills et al., 2004). Studies that measure both PEP and cardiac beta-receptor status are needed to test this speculative idea. In HPA-axis analyses, we found an association between LTL and AUCi, a measure of HPA-axis sensitivity, but not for AUCg, cortisol evening levels, or cortisol suppression ratio. Most of the earlier studies have shown significant associations between cortisol levels and LTL (Epel et al., 2006; Kroenke et al., 2011; Parks et al., 2009; Tomiyama et al., 2012; Wikgren et al., 2012). However, comparing findings directly is limited since these studies have either measured HPA-axis function in 12-hours nocturnal urine (Epel et al., 2006; Tomiyama et al., 2012), first morning urine (Parks et al., 2009), or pre-post test salivary samples (Wikgren et al., 2012). Tomiyama et al. (2012), have measured salivary cortisol awakening response in a small post-menopausal sample, and have not shown significant associations between AUCi andor AUCg and LTL, but these measures were calculated by subtracting the wakeup cortisol value from the wakeup þ30-minutes value. Our findings are not in line with Wikgren et al. (2012) who linked short LTL with low responsiveness to a dexamethasone challenge, for which the dexamethasone intake was lower, compared with our study (3.5 mg per kg of body weight vs. 500 mg in our study). The found association between the highest tertile of AUCi with shorter LTL in our sample suggests that

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cellular aging is especially linked to hyperactivity of the HPA-axis, and less with the more basal activity of the HPA-axis (as assessed with AUCg or evening levels). In our prior work, we have also shown that especially the cortisol awakening response, but not the evening levels or dexamethasone stress test levels were associated with stress-related psychiatric disorders (Vreeburg et al., 2009a, 2010). Such hyperactivity of the HPA-axis has shown to result in reduced telomerase activity in vitro and higher levels of oxidative stress, with subsequent shortening of LTL (Aschbacher et al., 2013; Choi et al., 2008). Shorter LTL might also lead to higher cortisol levels, possibly through inflammation and oxidative stress, but to our knowledge, this direction is not explored extensively yet. Within our study, associations between shorter LTL and separate stress system dysregulations were independent of psychopathology status or antidepressant medication use. To our knowledge, only few studies have examined these associations within a psychiatric population (Kiecolt-Glaser et al., 2011; Wikgren et al., 2012; Wolkowitz et al., 2011). Two of these studies have shown a differential pattern of associations between LTL and IL-6 (Wolkowitz et al., 2011) and HPA-axis indicators (Wikgren et al., 2012) in depressed patients and healthy controls. The reason for these differences is unclear. Overall, our findings support the notion that shorter LTL is associated with physiological stress markers, independently of psychopathology status. In recent analyses performed within our Netherlands Study of Depression and Anxiety sample, depressed patients were found to have shorter LTL compared with healthy control subjects (Verhoeven et al., 2013). The lack of a significant interaction within the present study suggests that there is not merely a link between stress and shortened telomeres via depression, but that other mechanistic pathways are likely to be involved as well. Lastly, we analyzed whether the combination of high-impact physiological stress system dysregulations added up to the shortening of telomeres. Until now, the 3 main stress systems have been mainly investigated in separate analysis, although 2 studies examined 2 combinations of these systems (Epel et al., 2006; Kroenke et al., 2011). We confirm that the simultaneous dysregulation of various inflammatory, HPA-axis and cardiac ANS factors form a cumulative impact on cellular aging. Although these results need further replication, they imply that multiple stress system dysregulations may interact in creating an “allostatic load”. As the term allostasis refers to the process whereby physiological stability is maintained, allostatic load represents the “wear and tear” in the body during stressful events (McEwen, 2008). It still remains to be elucidated whether these (cumulative) dysregulations actually lead to overall health decline, although 2 recent review papers summarized the studies that indicate the importance of physiological stress systems dysregulations for numerous somatic health outcomes, such as mortality, cognitive decline, physical functioning, and cardiovascular health (Juster et al., 2010; Penninx et al., 2013). Some limitations of this large cohort study should be taken into account. Firstly, this is a cross-sectional study, therefore not capturing the high variability of physiological stress markers, and not fully clarifying the causal paths. Short LTL might be a direct consequence of stress physiology, but stress system dysregulations may also be the result of other underlying processes indexed byeor passing througheLTL shortening. Future studies should investigate these associations longitudinally to explain the causality. Secondly, no information was available on telomerase activity to shed light on the maintenance mechanism of telomeres. Thirdly, the compliance with saliva sampling might have been inaccurate, and although hardly feasible in large cohorts, multiple sampling days would have enhanced the HPA function assays. On the other hand, the strengths of this study were its large sample size, enabling us to adjust for

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various lifestyle and health variables in a large adult age range. Furthermore, although circulating leukocytes might not directly reflect the cellular aging process in all parts of the body, high correlations between LTL and telomere length in other body systems (Daniali et al., 2013) and in specific lymphocyte subsets have been shown (Lin et al., 2010). This is, to our knowledge, the first study to link LTL, measured with a frequently used well-validated method, with the major physiological stress systems, and to shed light on the cumulative effect of multiple stress markers on LTL. In summary, we found that subjects with shorter telomeres are characterized by a pro-inflammatory state, a hyperactive HPA-axis, and high heart rate. In addition, cumulative stress system dysregulations resulted in overall shorter LTL. These data clearly indicate that dysregulation of physiological stress systems and cellular aging processes are intertwined, and may influence each other bidirectionally. This could directly explain why the exposure to chronic stressors and stress-related conditions has shown to impact on a multitude of aging-related conditions ranging from heart disease, diabetes, obesity, cognitive and physical impairment to even cancer (Penninx et al., 2013), and establishes a general underlying biological basis for the important psyche-soma interaction. Disclosure statement All authors declare that they have no conflicts of interest. Acknowledgements The infrastructure for the Netherlands Study of Depression and Anxiety study (www.nesda.nl) is funded through the Geestkracht program of the Netherlands Organization for Health Research and Development (ZonMW, grant number: 10-000e1002) and is supported by participating universities and mental health care organizations (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen, Lentis, GGZ Friesland, GGZ Drenthe, IQ Healthcare, Netherlands Institute for Health Services Research and Netherlands Institute of Mental Health and Addiction (Trimbos). Brenda Penninx, Dóra Révésza, Josine E. Verhoeven, and telomere length assaying were supported through an NWO-VICI grant number: 91811602. Owen M. Wolkowitzc is on the Scientific Advisory Board of Telomere Diagnostics, Inc. (TDx). References Aschbacher, K., O’Donovan, A., Wolkowitz, O., Dhabhar, F., Su, y., Epel, E., 2013. Good stress, bad stress and oxidative stress: insights from anticipatory cortisol reactivity. Psychoneuroendocrinology 38, 1698e1708. Aubert, G., Lansdorp, P.M., 2008. Telomeres and aging. Physiol. Rev. 88, 557e579. Aviv, A., Hunt, S.C., Lin, J., Cao, X., Kimura, M., Blackburn, E., 2011. Impartial comparative analysis of measurement of leukocyte telomere length/DNA content by Southern blots and qPCR. Nucleic Acids Res. 39, e134. Berntson, G.G., Cacioppo, J.T., Binkley, P.F., Uchino, B.N., Quigley, K.S., Fieldstone, A., 1994. Autonomic cardiac control III. Psychological stress and cardiac response in autonomic space as revealed by pharmacological blockades. Psychophysiology 31, 599e608. Blackburn, E.H., 1991. Structure and function of telomeres. Nature 350, 569e573. Blackburn, E.H., 2001. Switching and signaling at the telomere. Cell 106, 661e673. Broer, L., Codd, V., Nyholt, D., Deelen, J., Mangino, M., Willemsen, G., Albrecht, E., Amin, N., Beekman, M., de Geus, E., Henders, A., Nelson, C., Steves, C., Wright, M., de Craen, A., Isaacs, A., Matthews, M., Moayyeri, A., Montgomery, G., Oostra, B., Vink, J., Spector, T., Slagboom, P., Martin, N., Samani, N., van Duijn, C., Boomsma, D., 2013. Meta-analysis of telomere length in 19 713 subjects reveals high heritability, stronger maternal inheritance and a paternal age effect. Eur. J. Hum. Genet. 21, 1163e1168. Cacioppo, J.T., Berntson, G.G., Binkley, P.F., Quigley, K.S., Uchino, B.N., Fieldstone, A., 1994. Autonomic cardiac control II. Noninvasive indices and basal response as revealed by autonomic blockades. Psychophysiology 31, 586e598. Carrero, J.J., Stenvinkel, P., Fellstrom, B., Qureshi, A.R., Lamb, K., Heimburger, O., Barany, P., Radhakrishnan, K., Lindholm, B., Soveri, I., Nordfors, L., Shiels, P.G., 2008. Telomere attrition is associated with inflammation, low fetuin-A levels

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