The association between three major physiological stress systems and oxidative DNA and lipid damage

Accepted Manuscript Title: The association between three major physiological stress systems and oxidative DNA and lipid damage Authors: Catherine N. Black, Mariska Bot, D´ora R´ev´esz, Peter G. Scheffer, Brenda Penninx PII: DOI: Reference:

S0306-4530(16)30969-6 http://dx.doi.org/doi:10.1016/j.psyneuen.2017.03.003 PNEC 3568

To appear in: Received date: Revised date: Accepted date:

30-11-2016 17-2-2017 2-3-2017

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx.

The association between three major physiological stress systems and oxidative DNA and lipid damage Catherine N. Black, MD1 Mariska Bot, PhD1 Dóra Révész, PhD2 Peter G. Scheffer, PhD3 Brenda W.J.H. Penninx, PhD1

1

Department of Psychiatry and EMGO+ Institute for Health and Care Research, VU University Medical

Center and GGZ inGeest, Amsterdam, The Netherlands 2

Department of Epidemiology and Biostatistics and EMGO+ Institute for Health and Care Research,

VU University Medical Center, Amsterdam The Netherlands 3

Department of Clinical Chemistry, VU University Medical Center, Amsterdam, The Netherlands

Correspondence to: Brenda W.J.H. Penninx. Department of Psychiatry, VU University Medical Center Postbus 74077, 1070 BB Amsterdam Tel: +31 20 788 5674 E-mail: [email protected]

Highlights    

This large-scale cohort examined associations between physiological stress and oxidative damage. Oxidative DNA damage is associated with inflammatory, HPA-axis and ANS markers. Oxidative lipid damage is associated with inflammatory and ANS markers. This study found a dose-response relationship between physiological stress and oxidative damage.

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx.

Abstract Background Increased activity of the three major physiological stress systems (immune-inflammatory system, hypothalamic-pituitary-adrenal-axis [HPA-axis], and autonomic nervous system [ANS]) is part of the pathophysiology of various somatic and psychiatric diseases. Oxidative damage is a key mechanism in both ageing and disease. Elucidating the relationship between these stress systems and oxidative damage would contribute to the understanding of the role of physiological stress in disease. This study therefore investigates associations between various measures of physiological stress and oxidative DNA (8-hydroxy-2’-deoxyguanosine, 8-OHdG) and lipid (F2-isoprostanes) damage.

Keywords: oxidative damage; 8-hydroxy-2’-deoxyguanosine (8-OHdG) ; F2-isoprostanes; inflammation; hypothalamic-pituitary-adrenal axis; autonomic nervous system.

Methods Plasma 8-OHdG and F2-isoprostanes were measured using LC-MS/MS in 2858 subjects (aged 18-65). Plasma inflammation markers, salivary cortisol and ANS markers (three for each stress system) were determined. Linear regression analyses were adjusted for sociodemographics, sampling factors and medication.

Results 8-OHdG was positively associated with all inflammation markers (β=0.047-0.050, p<0.01), evening cortisol (β=0.073, p<0.001), and unexpectedly with low respiratory sinus arrhythmia (RSA) reflecting low ANS stress (β=0.073, p<0.001). F2-isoprostanes were associated with higher C-reactive protein (β=0.072, p <0.001), high ANS stress reflected in heart rate (β=0.064, p<0.001) and RSA (β=-0.076, p=0.001), but not with cortisol. Analyses investigating the cumulative impact of the stress systems demonstrated that the number of systems with ≥1 marker in the high risk quartile showed a positive linear trend with both 8-OHdG (p=0.030) and F2-isoprostanes (p =0.009).

Conclusion This large-scale study showed that markers of inflammation, the HPA-axis and ANS are associated with oxidative DNA damage. Oxidative lipid damage is associated with inflammation and the ANS. Increased physiological stress across systems is associated with increasing oxidative damage in a dose-response fashion. 2

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx.

1. Introduction The concept of allostasis describes the process of achieving stability through change. The body’s stress systems are activated as an adaptive response to an environmental stimulus (McEwen and Wingfield, 2003). However when these systems remain active, long after the original stimulus has ceased, the effects of their activity can become harmful. This state of chronic physiological stress, or allostatic overload, is characterized by hyperactivity of the body’s three major stress systems, the immune-inflammatory system, the hypothalamic pituitary adrenal axis (HPA-axis) and the autonomic nervous system (ANS). This in turn is associated with poor health outcomes, including increased risk of metabolic syndrome, cardiovascular disease, psychiatric disorders and mortality (Licht et al., 2013; McEwen, 2008; Parrinello et al., 2015). In part, this may be due to increased oxidative damage that accompanies, or is a consequence of, increased activity of these three systems.

Oxidative stress is a central mechanism in disease and in physiological ageing (Valko et al., 2007). It results from an imbalance between the production of reactive oxygen species (ROS) and the body’s antioxidant capacity, causing damage to lipids, proteins and DNA. Oxidative stress has been implicated in physiological ageing since what is now called the “oxidative stress theory of aging” was proposed over half a century ago (Harman, 1956; Salmon et al., 2010). The central tenant of this theory is still applicable: oxidative stress causes wear and tear at the macromolecular level, and this damage accumulates over a lifespan leading to decline of cellular function. In addition, oxidative stress markers predict all-cause mortality (Schöttker et al., 2015). Most major age-related diseases have been associated with increased oxidative stress and/or decreased antioxidant status, including cardiovascular disease (Elahi et al., 2009), cancer (Valko et al., 2006), neurological (Miller et al., 2014) and psychiatric disorders (Black et al., 2015). There are identifiable pathways through which systemic inflammation, hyperactivity of the HPA-axis, sympathetic activation and parasympathetic withdrawal, could induce oxidative damage. Pro-inflammatory cytokines are produced in reaction to oxidative stress, but may in turn promote oxidative damage in their target cells. Oxidative stress also amplifies the inflammatory response by activation of transcription factors. This interplay between oxidative stress and inflammation has been well-described in the pathophysiology of endothelial damage in vascular ageing (El Assar et al., 2013). The HPA-axis, which regulates the production and release of cortisol, may increase oxidative stress through modulation of ROS generation and mitochondrial calcium homeostasis (Kasahara and Inoue,

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. 2015). ROS may increase sympathetic tone through inactivation of nitric oxide (NO), which otherwise would have inhibited the sympathetic nervous system (SNS) (Campese et al., 2004).

Markers of these three stress systems, as well as markers of oxidative stress, are increasingly studied in somatic and psychiatric disorders in an attempt to unravel their pathophysiology, to find biological makers with predictive value for course and treatment response, and ultimately to discover new avenues for therapeutic interventions. To adequately interpret current findings and inform future research on the many disorders in which these systems are involved, a clear understanding of how physiological stress and oxidative mechanisms are interrelated is necessary. Although the notion that these stress systems are connected to oxidative stress is wellestablished (Biswas, 2016; Danson and Paterson, 2006; Spiers et al., 2014), the evidence demonstrating associations between them is limited. The association between markers of inflammation and oxidative stress has been studied, demonstrating that C-reactive protein (CRP) is positively associated with oxidative DNA and lipid damage (Alkazemi et al., 2012; Block, 2002; Fujita et al., 2006; Gross et al., 2005; Noren Hooten et al., 2012). Findings are however far from consistent, with other studies reporting mixed findings on the associations between CRP, interleukin-6 and tumor necrosis factor α (TNF-α) and oxidative damage (Broedbaek et al., 2011; Kanaya et al., 2011; Sakano et al., 2009; Sjogren, 2005). Previous research on the HPA-axis and ANS in relation to oxidative stress is scarce. One previous study demonstrated a positive association between urinary cortisol and urinary markers of systemic DNA and RNA damage from oxidation (Joergensen et al., 2011). A small number of studies demonstrate that measures of increased sympathetic and decreased parasympathetic tone are associated with oxidative lipid damage (Pavithran et al., 2008; Thiyagarajan et al., 2013, 2012).

This study aims to further establish the associations between multiple markers of the three major physiological stress systems (immune-inflammatory system, HPA-axis and ANS) and two markers of oxidative damage in a large adult sample. 8-OHdG (8-hydroxy-2’-deoxyguanosine) and F2isoprostanes are markers of oxidative DNA and lipid damage respectively. 8-OHdG is an oxidized derivate of the guanine base and one of the most abundant DNA lesions caused by ROS (Valavanidis et al., 2009a). As this lesion has mutagenic potential there are multiple repair systems in place. 8OHdG that has been excised from DNA by these systems can be measured in plasma and urine. F2isoprostanes are chemically stable products of oxidation of the omega-6 fatty acid arachidonic acid, measureable in all tissues and bodily fluids, and are currently considered to be the marker of choice for oxidative lipid damage (Milne et al., 2015).

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. We hypothesize that high stress (reflected in higher inflammatory markers, higher cortisol levels (HPA-axis), high sympathetic control (reflected in high heart rate and lower pre-ejection period) and low parasympathetic control (reflected in lower heart rate variability assessed through respiratory sinus arrhythmia) will be associated with higher oxidative damage. In addition, this study aims to establish whether there is evidence of a dose-response relationship between physiological stress and oxidative damage, both within each of the stress systems and across all three systems. In other words, is having multiple markers of a stress system in the high risk range associated with more oxidative damage? To our knowledge this is first and largest study to examine all three physiological stress systems with specific markers of oxidative stress.

2 Materials and methods 2.1 Study sample Data are derived from the baseline measurement of the Netherlands Study of Depression and Anxiety (NESDA), an ongoing longitudinal cohort study conducted among 2981 adults (aged 18 to 65 years) recruited from the general population, primary care and mental health care organizations between September 2004 and February 2007 at three research sites in the Netherlands, Amsterdam (N=1224), Leiden (N=896) and Groningen (N=861). The study includes participants with and without psychiatric diagnoses. At baseline, participants in NESDA underwent a 4-hour assessment conducted by trained research staff according to a predesigned protocol, including blood withdrawal, written questionnaires, an interview and a physical examination. Depressive and anxiety disorders were ascertained using the lifetime version of the Composite International Diagnostic Interview (CIDI, version 2.1). A full, detailed description of the NESDA rationale, methods and recruitment has been described in a previous publication (Penninx et al., 2008). The sample for the current study comprised all participants with data on either 8-OHdG (N=2872) or F2-isoprostanes (N=2597) and data on at least one of the physiological stress systems resulting in a sample of 2884 subjects. Subjects who were pregnant or breastfeeding (N=26) were excluded leaving 2858 participants included in the analyses. Of these subjects 1664 had a current depressive or anxiety disorder (past six months), 591 had a remitted depressive or anxiety disorders, 603 had no history of psychopathology. NESDA was approved by the Medical Ethics Committees of the participating institutes and all participants provided written informed consent.

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. 2.2 Measurements 2.2.1 Plasma F2-isoprostanes and 8-hydroxy-2’-deoxyguanosine EDTA blood was collected in the morning after an overnight fast using a vacutainer blood collection tube and transported to local laboratory sites for processing within one hour of withdrawal. Plasma samples were stored at -80 ºC and transported to the Metabolic Laboratory of the VU University Medical Center where F2-isoprostanes and 8-OHdG were determined by liquid chromatography tandem mass spectrometry (LC-MS/MS) between 2012 and 2014. The measurement of both markers in this sample has previously been described in more detail elsewhere (Black et al., 2016). Intra and inter-assay coefficients of variation were 4.6% and 8.2%, respectively for plasma F2-isoprostanes (the total, i.e. free and esterified, concentration of 8-iso prostaglandin F2α [iPF2α-III]) and 3.1% and 6.3% respectively for plasma levels of 8-hydroxy-2’-deoxyguanosine (8OHdG). 2.2.2 Inflammation After fasting, plasma samples were kept frozen at -80 ºC. Circulating plasma levels of Creactive protein (CRP, N=2846), interleukin-6 (IL-6, N=2847) and tumor necrosis factor-alpha (TNF-α, N=2830) were assessed in duplicate (Vogelzangs et al., 2012). High-sensitivity plasma levels of CRP were measured by an in-house enzyme-linked immunosorbent assay (ELISA) based on purified protein and polyclonal anti-CRP antibodies (Dako, Glostrup, Denmark). Intra- and inter-assay coefficients of variation were 5% and 10%, respectively. Plasma IL-6 levels were measured in duplicate by a high sensitivity ELISA (PeliKine Compact ELISA, Sanquin, Amsterdam, The Netherlands). Intra- and inter-assay coefficients of variation were 8% and 12%, respectively. Plasma TNF-α levels were assayed using a high-sensitivity solid phase ELISA (Quantikine HS Human TNF-α Immunoassay, R&D systems, Minneapolis, MN, USA). Intra- and inter-assay coefficients of variation were 10% and 15%, respectively.

2.2.3 Hypothalamic pituitary adrenal axis Respondents were instructed to collect saliva samples at home on a regular (preferably working) day (Vreeburg et al., 2009). This has been demonstrated to be a reliable and minimally intrusive method to assess the active, unbound form of cortisol (Kirschbaum and Hellhammer, 1994). Instructions for the saliva sampling prohibited eating, smoking, drinking, or brushing teeth within 15 minutes before sampling and dental work 24 hours before sampling. In total, 2204 subjects returned at least 1-saliva sample and the median time between blood withdrawal (in which inflammation and oxidative stress markers were determined) and saliva sampling was 8.0 days (25th to 75th percentile: 4-21). Saliva samples were obtained using Salivettes (Sarstedt, Nümbrecht, Germany) at 6 time points on a regular (work) day: at awakening (T1) and 30 (T2), 45 (T3), and 60 (T4) minutes later, and 6

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. at 10 PM (T5) and 11 PM (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 2000x g for 10 minutes, aliquoted and stored at -80 ºC. Cortisol analysis was performed by competitive electrochemiluminescence immunoassay (Roche, Basel, Switzerland) (van Aken et al., 2003). The detection limit was 2.0 nmol/l and the intra- and inter-assay variability coefficients were <10%. Three cortisol measures were used to reflect different aspects of HPA-axis functioning. As measures of the cortisol awakening response, the area under the curve with respect to the ground (AUCg) and the increase (AUCi, N=1726) were calculated (Pruessner et al., 2003). The AUCg is an estimate of the total cortisol secretion over the first hour after awakening. The AUCi is a measure of the dynamics of the cortisol awakening response, it is reflective of the sensitivity of the system, and emphasizes the changes over time (Pruessner et al., 2003). Evening cortisol levels (N=1875) are considered to reflect basal cortisol secretion; the ability of the HPA-axis to return to lower levels of cortisol toward the end of the day after the morning peak levels in of the cortisol awakening response (Kirschbaum and Hellhammer, 1989). Since the 2 evening values were highly correlated (r=0.75, p < 0.001), the mean of these two values was used. Subjects using corticosteroids were excluded (N=133) from all analyses including HPA-axis makers.

2.2.4 Autonomic nervous system During the baseline interview, subjects wore the VU University ambulatory monitoring system (Licht et al., 2012). This is a light-weight, unobtrusive device that records an electrocardiogram (ECG) and changes in thorax impedance (dZ) through 6 surface electrodes placed on the chest and on the back (Willemsen et al., 1996). From this data three measures were derived; (1) heart rate (HR), reflecting the combined effect of sympathetic and parasympathetic activity; (2) Pre-ejection period (PEP), reflecting sympathetic activity; (3) Respiratory sinus arrhythmia (RSA), reflecting parasympathetic activity. Heart rate was obtained by extracting the inter-beat interval time series from the ECG signal (N=2746. Pre-ejection period (PEP, N=2722) and respiratory sinus arrhythmia (RSA, N=2746) were extracted from the combined dZ and ECG signals (Licht et al., 2010). PEP is a measure of cardiac sympathetic control, as it can reliably index b-adrenergic inotropic drive to the left ventricle. Long PEP reflects low cardiac sympathetic control. PEP 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). RSA is a measure of cardiac parasympathetic (vagal) control. High RSA levels reflect high 7

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. cardiac vagal control. RSA 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 (Malik et al., 1996). Because postural changes have effects on PEP and RSA that is unrelated to autonomic activity, data from periods in which subjects were nonstationary (~15 minutes) was excluded (Houtveen et al., 2005). Movement was registered through vertical accelerometry. Automated scoring of RSA and PEP was checked by visual inspection, and valid data were averaged over 98.0±24 (mean±SD) minutes to create single HR, PEP and RSA values.

2.3 Covariates

2.3.1 Covariates included in all analyses Sociodemographic factors included age, sex and research site. Factors relating to measurements of oxidative damage included adherence to fasting instructions prior to blood withdrawal (yes/no), and season of sample collection of 8-OHdG /F2-isoprostanes (Black et al., 2016). Medication use was assessed during the interview by inspection of the participant’s medication containers and classified using the World Health Organization Anatomic Therapeutic Chemical (ATC) classification (World Health Organization Centre for Drug Statistics Methodology, 2010). Use of supplements with antioxidant properties have demonstrated to be associated with oxidative stress makers in this sample. Use of supplements (yes/no) was defined as frequent use (more than half the days in the last month) of any or more of the following supplements: vitamin A (ATC A11CA01), vitamin E (ATC code A11HA03), vitamin C (ATC A11GA01), or a multivitamin supplement (ATC A11BA). Because antidepressant use has previously been demonstrated to be associated with markers of oxidative stress and those of inflammation, HPA-axis and ANS, it was hypothesized to be a potential confounding factor. Use of antidepressants (N06AA, N06AB, N06AF, N06AG, N06AX) of all types was therefore included as covariate. The circulating markers of oxidative damage measured in plasma a cleared through renal excretion, therefore adjustment for kidney function should be considered. Glomerular filtration rate (GFR [ml/min/1.73 m2]), estimated using the Chronic Kidney Disease Epidemiology Collaboration equation (Levey et al., 2009), was highly correlated with age (Pearson’s rho -0.66, p<0.001) in this sample. Additional adjustment for GFR did not affect the overall results; GFR therefore was not included in the final analyses 8

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx.

2.3.2 Covariates specific to each stress system In addition to these general covariates, specific covariates for each stress system were added. Analyses on inflammation markers were additionally adjusted for use of anti-inflammatory medication (M01A, M01B, A07EB, and A07EC). Analyses on markers of HPA-axis functioning were additionally adjusted for sampling factors that were identified in a previous study in this sample (Vreeburg et al., 2009), including reported awakening time, working status on saliva sampling day, and season at time of saliva sampling categorized into dark (October-February) and light (March-September) months. Analyses on ANS markers were additionally adjusted for use of cardiac medication including beta-blocking agents (C07) and antihypertensives (C01-C05, C08, C09). Analyses on RSA were also adjusted for respiratory rate. Analyses on PEP were adjusted for mean arterial pressure ((systolic blood pressure + 2 x diastolic blood pressure)/3) to account for potential between-subject differences in afterload (Houtveen et al., 2005). 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 Statistical analyses were conducted with SPSS version 22.0. Reported values in the description of the sample characteristics are means and standard deviations, or medians and inter quartiles ranges for those with a skewed distribution. To investigate associations between all measures of oxidative damage, inflammation, the HPA-axis and ANS, the associations between each of the markers were calculated with linear regression analyses (adjusted only for research site). 8-OHdG, F2-isoprostanes, CRP, IL-6, TNF-α, evening cortisol and RSA were natural log transformed to obtain a normal distribution for analysis. The associations between 8-OHdG and F2-isoprostanes and each marker of the three stress systems were examined in individual linear regression analyses with oxidative stress markers as the dependent variable and the stress system markers as main predictors. Model 1 was adjusted for age, sex and research site. Model 2 was additionally adjusted for sampling factors relating to the oxidative stress markers (fasting status [yes/no], season of blood), medication use (antioxidant supplement, antidepressants; covariates paragraph 2.3.1), as well as for the individual stress system specific variables (see covariates paragraph 2.3.2). To examine whether the presence of current depression and/or anxiety disorders modified the association between oxidative stress and any of the stress systems, interaction tests were 9

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. performed for each marker by including an interaction term “current depression and/or anxiety (yes/no)” * “marker of stress system” in the abovementioned regression analyses, and checking for significance.

To examine whether the markers within each stress system had a cumulative impact on oxidative damage levels, count variables were created for each of the three stress system, by using the sum of the number of markers (range 0-3) for which a subject fell within the high-risk quartile. For all inflammation and HPA-axis markers and heart rate, the 4th quartile was defined as the highrisk quartile. For PEP and RSA, the 1st quartile was defined as the high-risk quartile, reflecting high sympathetic tone and low parasympathetic tone, respectively. To examine whether the three stress systems had a cumulative impact on oxidative damage levels a count variable was created across stress systems, by using the sum of the number of stress systems (range 0-3) for which a subject had one of more markers that fell within the high risk quartile. These count variables were only created for subjects who had complete data on all variables included in the count. The mean levels of 8-OHdG and F2-isoprostanes were calculated over each of these sum score categories using analysis of covariance (ANCOVA). ANCOVA has the capability to evaluate whether the means of a dependent variable (oxidative stress markers) are equal across categories of an independent variable (the sum of the stress systems in the high-risk quartiles), while statistically controlling for the effects of covariates. Within the ANCOVAs, we adjusted for the same set of variables as in model 2 of the linear regression analysis A p value <0.05 was considered statistically significant.

2.5 Ethical standards The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

3 Results The characteristics of the sample (N=2858) including all subjects with at least one available marker of oxidative damage and one marker of the stress systems are described in Table 1. Subjects had an average age of 41.9 (13.1) years and 66.3% were female. Average plasma 8-OHdG and F2isoprostanes were 43.3 pmol/l and 122.7 pmol/l, respectively.

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. Markers within each stress system were correlated (all p values <.001), as were 8-OHdG and the F2-isoprostanes oxidative damage (β 0.076 p<.001). There were (weak) associations between inflammatory markers and ANS makers, and between HPA-axis markers and ANS markers, but none between inflammatory and HPA-axis markers (Table 2).

In the adjusted analyses (Table 3), 8-OHdG showed positive associations with markers of all three stress systems. 8-OHdG was associated with the inflammatory markers CRP, IL-6 and TNF-α (β=0.047-0.050, p values <0.01). There was also a positive association between 8-OHdG and evening cortisol (β=0.073, p <0.001), and a borderline significant association with AUCg (β=0.045, p 0.053). 8OHdG was not associated with HR or PEP. 8-OHdG was positively associated with RSA (β=0.076, p 0.001). F2-isoprostanes were positively associated with CRP (β=0.072, p <0.001), but not with IL-6 or TNF-α. There were no significant associations between F2-isoprostanes and any of the markers of the HPA-axis. F2-isoprostanes were positively associated with HR (β=0.064, p <0.001), negatively with RSA (β=-0.076, p <0.001), but were not significantly associated with PEP.

Interaction tests were conducted to identify a possible interaction with the presence of depression or anxiety disorders. Tests were performed for all 9 investigated markers for each of the two oxidative damage measures, making 18 tests in total. Of these only 1 was significant (p=0.022), which is very close to what would be expected by chance (probability of 0.05*18 tests=0.9 [number of tests for which significant results can be expected]), indicating that the presence of psychopathology does not modify the associations between oxidative stress and the studied stress systems. In analyses exploring dose-response relationships within each of the three systems, 8-OHdG levels were found to be higher with an increasing number of inflammation and HPA-axis markers falling in high risk quartiles (figure 1.A&B; p values for linear trend 0.004 and 0.029 respectively). There was also a significant linear trend within the number of ANS markers in the high risk range (figure 1C; p for linear trend 0.038), however in the opposite direction than expected, with higher autonomic stress associated with lower 8-OHdG levels. F2-isoprostane levels were also higher with an increasing number of inflammation makers falling within the high risk quartiles (figure 1D; p for linear trend 0.016). There was no association with HPA-axis markers (figure 1E). F2-isoprostanes levels were higher in subjects with ANS markers in the high risk quartiles than in those without (figure 1F); however the linear trend was not statistically significant (p for linear trend 0.087).

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. In analyses exploring dose-response relationships across all three systems both 8-OHdG and F2-isoprostanes levels were higher with an increasing number of stress systems with a marker within the high risk range, p values for linear trend 0.030 and 0.009 respectively (Figure 2).

4 Discussion This study aimed to establish the associations of oxidative damage with multiple markers of the body’s three major physiological stress systems, the immune-inflammatory system, the hypothalamic-pituitary-adrenal axis and the autonomic nervous system, hypothesizing that high physiological stress would be positively associated with oxidative damage. Oxidative DNA damage, measured by plasma 8-OHdG, was positively associated with all inflammation markers (CRP, IL-6 and TNF-α) and evening cortisol levels. Oxidative lipid damage, measured by plasma F2-isoprostanes was positively associated with CRP, but not with any of the HPA-axis markers. F2-isoprostanes were positively associated with heart rate and negatively associated with parasympathetic tone. Contrary to the hypothesis, 8-OHdG was positively associated with parasympathetic tone. There was evidence of a dose-response relationship for 8-OHdG and F2-isoprostanes, both within and across stress systems: having multiple markers of a stress system in the high risk range was associated with more oxidative damage, and falling within the high risk range in more stress systems was associated with more oxidative damage. Although not all stress markers were associated with oxidative damage, or not in the expected the direction, the pattern of findings appears to point to an association between physiological stress, and oxidative damage. The linear association between the number of stress system markers within the high risk ranges suggests a cumulative effect The cumulative associations with oxidative lipid and DNA damage lend further credence to the hypothesis that allostatic overload leads to cellular damage. These findings are likely to be of clinical relevance as both markers of oxidative damage have been associated with the risk and pathophysiology of disease. 8-OHdG is an important lesion with mutagenic potential and its association with cancer is well-established (Valavanidis et al., 2009b). The multiple mechanisms for excision and clearance suggest it is a potential health threat if left in situ. 8-OHdG has also been associated with atherosclerosis and heart failure (Kroese and Scheffer, 2014). F2-isoprostanes have also been implicated in, among others, cardiovascular disease, and they (8-iso-PGF2 in particular) have well-known vasoconstrictive effects (Basu, 2008). They are one of the few markers of oxidative damage that show promise in clinical practice, and may be implemented as predictors of cardiovascular disease progression (Davies and Roberts, 2011).

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. The effect sizes found in this study are small in size, but similar to those found in this sample for age, nicotine exposure and metabolic syndrome (Black et al., 2016). The associations with the physiological stress systems are therefore as large and relevant as key sociodemographic, lifestyle and health determinants of oxidative damage.

Inflammation and oxidative stress are thought to have a bidirectional association, with each increasing the other. Their close relationship suggests a positive correlation between markers of these systems, but previous literature shows more mixed findings than might be expected. A number of large scale (±200-3000 subjects) studies with general population samples have previously demonstrated positive associations between F2-isoprostanes and CRP (Alkazemi et al., 2012; Block et al., 2002; Fujita et al., 2006; Gross et al., 2005), but there are also studies that do not demonstrate this association (Sakano et al., 2009; Sjogren, 2005; Woodward et al., 2009). 8-OHdG’s association with inflammatory markers has been covered in only a few previous studies; CRP was positively associated with 8-OHdG in a large adult sample, but only in women (Sakano et al., 2009). In 149 hypertensive subjects both Il-6 and TNF-α were positively correlated with 8-OHdG (Roselló-Lletí et al., 2012). In 220 elderly subjects 8-OHdG was not associated with either CRP, IL-6 or TNF-α (Broedbaek et al., 2011). These inconsistencies across the literature are likely due to variations in the age and health status of the populations studied, as well a different methods of determining oxidative damage markers and the use of either plasma or urine samples. CRP has been demonstrated to induce intracellular ROS, and increase DNA-damage (8-OHdG) in vitro, in a dose dependent fashion (Noren Hooten et al., 2012). Treatment of the cells with antioxidant N-Acetyl-L-cysteine prior to introducing CRP prevented this DNA damage, an additional indication that CRP induced ROS are involved. CRP-treated cells also showed increased oxidative lipid damage as measured by malondialdehyde (MDA) (Noren Hooten et al., 2012). In our analyses looking at the associations between the number of inflammation markers in the high risk range we found associations with both F2-isoprostanes and 8-OHdG, overall confirming the notion of a strong connection between inflammation and oxidative stress, despite differences between the 8-OHdG and F2-isoprostane findings.

The positive association between 8-OHdG and evening cortisol found this study is in line with findings of a population study, in which 8-OHdG was positively correlated with urinary cortisol levels in 220 elderly subjects (Joergensen et al., 2011). The effect size in Joergensen et al.’s study was considerably larger than that of this study. This is likely due to the difference in cortisol measurement: 24-hour urinary cortisol and salivary cortisol measures are only weakly correlated and cannot be used interchangeably (Yehuda et al., 2003). In addition, Joergensen et al.’s elderly study 13

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. sample most likely has higher rates of somatic disorders than this study’s sample of adults with a mean age of 41.9 (SD 13.1), and therefore has a wider variation in cortisol levels, which would yield a larger effect. A smaller study (N=96) examined the association between cortisol reactivity and F2isoprostanes, 8-OHdG and 8-OxoG (reflecting oxidative RNA damage) in chronically psychologically stressed and non-stressed subjects (Aschbacher et al., 2013). Anticipatory salivary cortisol, measured prior to an acute stressful event was associated with increased F2-isoprostanes and 8-OxodG (but not with 8-OHdG) in chronically psychologically stressed subjects, but not in non-chronically stressed subjects. These findings support the hypothesis that oxidative damage is induced by activation of the HPA-axis, and that both prolonged chronic exposure to increased HPA-axis activity as well as acute exposure are involved. We found no associations between F2-isoprostanes and any HPA-axis marker. The study described above (Aschbacher et al., 2013) is to our knowledge the only previous study examining this specific relationship. The lack of an association in our study may be due to the difference between the cortisol measures between the studies (cortisol reactivity to a stress stimulus vs. cortisol awakening response/basal levels in this study). A study in performing musicians found no significant correlations between baseline or stress reactive cortisol levels and MDA, reflecting oxidative lipid damage (Pilger et al., 2014). The effects of corticosteroids may provide an indication of the mechanisms through which exposure to increased glucocorticoids increases oxidative damage. Treatment with cortisone inhibited mitochondrial I complex activity, and decreased enzymatic and non-enzymatic antioxidants increased both oxidative protein and lipid damage (Tang et al., 2013; Zafir and Banu, 2009). In animal cell cultures exposure to increased cortisol levels has been demonstrated to alter the expression of genes involved in DNA repair, and increase DNA damage (Flint et al., 2007).

To our knowledge, there are no previous studies examining the associations of F2isoprostanes or 8-OHdG with autonomic nervous system functioning. Previous studies (N=50-178), in hypertensive subjects (Pavithran et al., 2008; Thiyagarajan et al., 2013) and subjects with increased fasting glucose (Thiyagarajan et al., 2012) have demonstrated that oxidative lipid damage (MDA) correlates positively with sympathetic activity, and negatively with parasympathetic activity, with associations with antioxidant capacity in the opposite direction. These findings are in line with both our hypothesis and our findings for F2-isoprostane levels, which were negatively associated with parasympathetic tone and positively with heart rate. The more ANS markers in the high risk range, the higher F2-isoprostanes were. The direction of the association between 8-OHdG and RSA was contrary to our hypothesis. RSA, reflecting high parasympathetic control, was positively associated with 8-OHdG. Driven by this 14

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. finding analyses within the ANS markers demonstrated that the more ANS markers in the high risk range, the lower 8-OHdG. Previously, a negative association between PEP (reflecting high sympathetic activity) and telomere length (a measure of cellular ageing) was demonstrated in this sample, leading to speculation that this might be caused by desensitization of beta-adrenergic receptors as can occur in chronic stress (Révész et al., 2014). The associations of heart rate and PEP with 8-OHdG were not significant. Why the findings for the autonomic nervous systems markers and oxidative damage are inconsistent is unclear, and should be explored further if this finding is replicated. Increased heart rate has been associated with (cardiovascular) morbidity and mortality, to which increased oxidative stress is likely a key contributor (Zhang and Zhang, 2009). Increased sympathetic activity might contribute to oxidative damage through higher blood pressure (Rubattu et al., 2014). ROS have been demonstrated to modulate autonomic nervous function; ROS are thought to increase sympathetic activity through inactivation of NO. In animal studies a superoxide dismutase mimetic reduces peripheral sympathetic activity. This antioxidant scavenges superoxide, increasing NO’s half-life and inhibition of sympathetic tone (Campese et al., 2004).

The necessity for correction for multiple comparisons should be considered in a study such as this, that performs multiple tests on the associations between a range of makers. The results in table 1 demonstrate that, particularly within each stress system, the markers are correlated with one another and therefore the tests cannot all be considered independent tests. Using a strict method for adjusting for multiple comparisons, the Bonferroni correction for 18 tests (3 times 3 stress markers for 2 measures of oxidative stress) on the fully adjusted model (model 2 in Table 3), the modified pvalue would be 0.05/18=0.003. The markers of inflammation in relation to 8-OHdG would achieve borderline significance (p values 0.005-0.008) with this new p value, all others are unaffected. Considering that this is most likely an overcorrection due to the correlations between the stress system markers, the results of this study appear to be robust enough to withstand correction for multiple comparisons. This study has some limitations that should be taken into account when interpreting the results. As this is a cross-sectional study no causal relationships can be inferred from the associations. In this sample subjects with diagnoses (or a history) of major depressive or anxiety disorders are overrepresented, which may have consequences for the applicability of the results in the general population. It is however unlikely that the findings would differ radically, as we did not find any interactions with the presence of a current psychiatric diagnosis, and analyses were adjusted for antidepressant use. There may also be an advantage to this sample. Psychiatric disorders such as depression and anxiety have been associated with increased physiological stress (Wolkowitz et al., 15

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. 2010). Therefore it is likely that this sample comprises a particularly broad range of physiological stress, allowing these associations to be uncovered. Redox regulation is complex and dynamic process that cannot be captured in single measures of individual oxidative stress markers (Margaritelis et al., 2016). The markers covered in this study reflect only two products of oxidative damage, and do not necessarily represent other aspects of redox regulation. In addition, for 8-OHdG there is debate as to what extent circulating levels reflect not only the rate of oxidative damage, but also the rate of DNA repair mechanisms (Il’yasova et al., 2012; Poulsen et al., 2014). The validation of many markers of oxidative stress is still ongoing; a recent animal study suggested that 8-OHdG may not be derived from genomic DNA, but oxidatively damaged 2’-deoxyguanosine from the nucleotide pool (Evans et al., 2016). The limitations of these markers of oxidative stress of course also apply to markers of the three physiological stress systems. This study also has some important strengths, including the use of two well-established specific markers of oxidative damage which have been linked to clinical outcomes, measured with gold-standard techniques, in a particularly large sample with appropriate consideration of covariates and covering multiple stress systems, for each of which multiple markers are available.

This study’s findings that inflammation, HPA-axis and ANS function are associated with oxidative damage in a dose-response fashion, supports the hypothesis that increased allostatic load is associated with cellular damage through oxidative stress. Future studies should focus on uncovering how these systems interact in longitudinal and intervention studies, examining whether reducing physiological stress also prevents oxidative damage, and ultimately improves health outcomes.

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. References

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C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. 44–84. doi:10.1016/j.biocel.2006.07.001 Valko, M., Rhodes, C.J., Moncol, J., Izakovic, M., Mazur, M., 2006. Free radicals, metals and antioxidants in oxidative stress-induced cancer. Chem. Biol. Interact. 160, 1–40. doi:10.1016/j.cbi.2005.12.009 van Aken, M.O., Romijn, J.A., Miltenburg, J.A., Lentjes, E.G.W.M., 2003. Automated measurement of salivary cortisol. Clin. Chem. 49, 1408–9. doi:10.1373/49.8.1408 Vogelzangs, N., Duivis, H.E., Beekman, A.T.F., Kluft, C., Neuteboom, J., Hoogendijk, W., Smit, J.H., de Jonge, P., Penninx, B.W.J.H., 2012. Association of depressive disorders, depression characteristics and antidepressant medication with inflammation. Transl. Psychiatry 2, e79. doi:10.1038/tp.2012.8 Vreeburg, S.A., Kruijtzer, B.P., van Pelt, J., van Dyck, R., DeRijk, R.H., Hoogendijk, W.J.G., Smit, J.H., Zitman, F.G., Penninx, B.W.J.H., 2009. Associations between sociodemographic, sampling and health factors and various salivary cortisol indicators in a large sample without psychopathology. Psychoneuroendocrinology 34, 1109–20. doi:10.1016/j.psyneuen.2009.04.024 Willemsen, G.H.M., DeGeus, E.J.C., Klaver, C.H.A.M., VanDoornen, L.J.P., Carrofl, D., 1996. Ambulatory monitoring of the impedance cardiogram. Psychophysiology 33, 184–193. doi:10.1111/j.1469-8986.1996.tb02122.x Wolkowitz, O.M., Epel, E.S., Reus, V.I., Mellon, S.H., 2010. Depression gets old fast: do stress and depression accelerate cell aging? Depress. Anxiety 27, 327–338. doi:10.1002/da.20686 Woodward, M., Croft, K.D., Mori, T.A., Headlam, H., Wang, X.S., Suarna, C., Raftery, M.J., MacMahon, S.W., Stocker, R., 2009. Association between both lipid and protein oxidation and the risk of fatal or non-fatal coronary heart disease in a human population. Clin. Sci. (Lond). 116, 53–60. doi:10.1042/CS20070404 World Health Organization Centre for Drug Statistics Methodology, 2010. Anatomical Therapeutic Chemical (ATC) classification. [WWW Document]. URL http://www.whocc.no/atcddd. Yehuda, R., Halligan, S.L., Yang, R.K., Guo, L.S., Makotkine, I., Singh, B., Pickholtz, D., 2003. Relationship between 24-hour urinary-free cortisol excretion and salivary cortisol levels sampled from awakening to bedtime in healthy subjects. Life Sci. 73, 349–58. Zafir, A., Banu, N., 2009. Modulation of in vivo oxidative status by exogenous corticosterone and restraint stress in rats. Stress 12, 167–77. doi:10.1080/10253890802234168 Zhang, G.Q., Zhang, W., 2009. Heart rate, lifespan, and mortality risk. Ageing Res. Rev. 8, 52–60. doi:10.1016/j.arr.2008.10.001

22

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx.

8-OHdG

F2-isoprostanes

(A) Inflammation p for linear trend 0.004 plasma 8-OHdG pmol/l

49 47 45 43 41 39 37 35 N=1391 0 markers

N=939 1 marker

N=505 2-3 markers

plasma F2-isoprostanes pmol/l

N of inflammatory markers in high risk quartiles

(D) Inflammation p for linear trend 0.016 130 125 120 115 110 105 100 N=1273 0 markers

N=838 1 marker

N=449 2-3 markers

N of inflammatory markers in high risk quartiles

(B) HPA-axis p for linear trend 0.029 plasma 8-OHdG pmol/l

49 47 45 43 41 39 37 35 N=874 0 markers

N=488 1 marker

N=305 2-3 markers

N of HPA-axis markers in high risk quartiles

23

plasma F2-isoprostanes pmol/l

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx.

(E) HPA-axis p for linear trend 0.677 130 125 120 115 110 105 100 N=792 0 markers

N=437 1 marker

N=311 2-3 markers

N of HPA-axis markers in high risk quartiles

(C) ANS p for linear trend 0.038 plasma 8-OHdG pmol/l

49 47 45 43 41 39 37 35 N=1367 0 markers

N=859 1 marker

N=510 2-3 markers

plasma F2-isoprostanes pmol/l

N of ANS markers in high risk quartiles

(F) ANS p for linear trend 0.087 130 125 120 115 110 105 100 N=1250 0 markers

N=775 1 marker

N=453 2-3 markers

N of ANS markers in high risk quartiles

24

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. Figure 1. Mean levels of plasma 8-OHdG (A,B,C) and F2-isoprostanes (D,E,F) by number of inflammatory markers in high risk quartiles (A and E); number of HPA-axis markers in high risk quartiles (B and E); number of ANS markers in high risk quartiles (C and F). ANCOVA’s were performed with log transformed 8-OHdG. Reported values are back-transformed. Error bars indicate 95% confidence intervals. Analyses were adjusted as in model 2. ANS, autonomic nervous system; HPA-axis, hypothalamic pituitary adrenal axis; N, number.

25

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx.

8-OHdG by N of stress systems in high stress range p for linear trend 0.030

plasma 8-OHdG pmol/l

49 47 45 43 41 39 37 35 N=241 0 systems

N=558 1 system

N=579 2 systems

N=223 3 systems

Number of stress systems with ≥1 markers in high risk quartiles

plasma F2-iosprotanes pmol/l

F2-isoprostanes by N of stress systems in high stress range p for linear trend 0.009 130 125 120 115 110 105 100 N=221 0 systems

N=514 1 system

N=518 2 systems

N=188 3 systems

Number of stress systems with ≥1 markers in high risk quartiles

Figure 2. Mean levels of plasma 8-OHdG and F2-isoprostanes by number of stress systems with ≥1 marker in high quartiles. ANCOVA’s were performed with log transformed F2-isoprostanes. Reported values are back-transformed. Error bars indicate 95% confidence intervals. Analyses were adjusted as in model 2.

26

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. Table 1. Sample and subject characteristics (N=2858 ) Mean (SD), Median (IQR), N (%) Sociodemographics Age (years) Female Research site

N 2858 2858 2858

41.9 (13.1) 1896 (66.3%)

Amsterdam Leiden Groningen

1133 (39.6%) 884 (30.9%) 841 (29.4%)

Oxidative stress markers 8-hydroxy-2‘-deoxyguanosine (8-OHdG) (pmol/L) F2-isoprostanes (pmol/L)

2847 2572

Season of blood sample collection

2858

43.3 (17.2) 122.7 (41.0)

Winter Spring Summer Autumn 2858

675 (23.6%) 708 (24.8%) 656 (23.0%) 819 (28.7%) 2726 (95.4%)

Inflammation C-reactive protein (mg/L)* Interleukin-6 (pg/mL)* Tumor necrosis factor –alpha (pg/mL)*

2846 2847 2830

1.2 (0.5-3.0) 0.8 (0.5-1.3) 1.1 (0.6-1.1)

Hypothalamic pituitary adrenal axis AUCg (nmol/L/hr) AUCi (nmol/L/hr) Evening cortisol (nmol/L)*

1783 1783 1938

18.9 (7.0) 2.1 (7.3) 4.8 (3.4-6.6)

Autonomic nervous system Heart rate (bpm) Pre-ejection period (ms) Respiratory sinus arrhythmia (ms)*

2746 2722 2746

72 (10) 120.2 (17.9) 38.6 (27.1-55.4)

Medication Antioxidant supplements (yes/no), n (%) Anti-inflammatory medication (yes/no), n (%) Cardiac medication (yes/no), n (%) Antidepressants (yes/no), n (%)

2858 2858 2858 2858

339 (11.9%) 125 (4.4%) 481 (16.8%) 713 (24.9%)

Fasting status (% yes) Physiological stress systems

*

Not-normally distributed variables are presented as medians and inter-quartile ranges. AUCg, area under the curve with respect to the ground; AUCi, area under the curve with respect to the increase; bpm, beats per minute; IQR, inter quartile range; SD, standard deviation.

27

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. Table 2. Associations between 8-OHdG, F2-isoprostanes and markers of inflammation, hypothalamic pituitary adrenal axis and autonomic nervous system function.

Oxidative stress

Oxidative stress 8-OHdGa F2isoprostan esa

Inflammation

8OHd Ga

F2isoprostan esa

CRPa

IL-6a

TNFαa

β

β

β

β

β

1

.076***

.029

.062**

.059**

-

1

.045*

.015

.089** *

Inflammat ion CRPa -

-

1

.331**

.130**

*

*

a

IL-6

-

-

-

1

.143** *

Hypothalamic pituitary adrenal axis Evenin AUC g AUCi g cortiso la β

Autonomic nervous system HR

PEP

β

β

β

.016

.072**

.060**

.004

-.029

.008

-.003

.012

.060**

-.041*

.087**

.020

-.032

.042

-.007

.030

.023

.060 *

β

β

*

.030

-.007

.109**

.157**

*

*

*

-.042*

.184**

.015

.014

.207** *

.122**

*

a

TNF-α

-

-

-

-

1

HPA- axis AUCg AUGi Evening cortisola

-

-

-

-

RSAa

1

.007

.075** *

.472** *

.370***

.025

-.006

.086** *

***

-

-

-

-

-

-

1

.087

.054

-.012

-

-

-

-

-

-

-

1

-.008

.018

-.015 .104** *

ANS Heart rate PEP RSA

-

-

-

-

-

-

-

1

.252**

.357**

*

*

-

-

-

-

-

-

-

-

-

1

-

-

-

-

-

-

-

-

-

-

a

.133** *

1

natural log transformation; * p<0.05; ** p<0.01; *** p<0.001 Linear regression analyses with the variables in each rows set as the dependent variable and variables in the columns as independent variables. All regression analyses were adjusted for research site. Reported values are standardized regression coefficients. 8-OHdG, 8-hydroxy-2‘-deoxyguanosine; 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 proteins; HPA-axis, hypothalamic pituitary adrenal axis ; HR, heart rate; IL-6, interleukin 6; PEP, pre-ejection period; RSA, respiratory sinus arrhythmia; TNF-α, tumor necrosis factor alpha.

28

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx.

29

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. Table 3. Adjusted associations between 8-OHdG, F2-isoprostanes and markers of inflammation, hypothalamic pituitary adrenal axis and autonomic nervous system function. Associations with individual markers, and the number of makers per system in the high risk quartile 8-OHdGa

N Inflammation CRPa IL-6a TNF-αa

model 1 sociodemographics β p

model 2 full adjustment β p

N

model 1 sociodemographics β p

model 2 full adjustment β p

2837 2838 2821

.034 .036 .047

.058 .048 .010

.047 .048 .050

.008 .008 .005

2561 2562 2545

.076 .031 .009

<.001 .082 .595

.072 .030 .009

<.001 .095 .596

2835

.050

.006

.062

<.001

2560

.047

.008

.044

.012

1719

.046

.050

.045

.053

1547

-.019

.395

AUGi

1719

.033

.161

.033

.153

1547

-.002

.933

Evening cortisola

1867

.055

.014

.073

<.001

1680

-.004

.865

1712

.049

.036

.057

.013

1540

-.006

.778

.007

.760

2735

-.020

.270

.012

.503

2473

.063

<.001

.064

<.001

2711

.008

.663

.028

.131

2454

-.037

.039

Respiratory sinus arrythmiaa

2735

.117

<.001

.076

.001

2473

-.072

.001

Number of ANS markers within high risk quartiles (0-3)

2736

-.053

.005

.047

.015

2478

.055

.003

Number of inflammation markers within high risk quartiles (0-3)

HPA- axis AUCg

Number of HPAaxis markers within high risk quartiles (0-3)

ANS Heart rate Pre-ejection period

a

F2-isoprostanesa

.017 .000 .005

.027 .076

.035

.433 .996 .827

.143 .001

.065

natural log transformation

Linear regression analyses were conducted with 8-OHdG and F2-isoprostanes as dependent variables. Main predictors were, 1) Individual markers of stress systems 2) The number of makers within each system that fell within the high risk quartile summed (range 0-3) .

30

C.N. Black, M. Bot, D. Révész, P.G. Scheffer, B.W.J.H. Penninx. Model 1 research site, age sex. Model 2 research site, age sex, season of blood collection, fasting status yes/no, use of antioxidant supplements yes/no, us of antidepressant medication yes/no. Inflammation: use of anti-inflammatory medication yes/no. HPA-axis (corticosteroid users excluded): awakening time, working on day of sample collection, season dark/light. ANS: use of cardiac medication yes/no, mean arterial pressure (MAP), respiratory rate (RR). 8-OHdG, 8-hydroxy-2‘-deoxyguanosine; 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 proteins; HPA-axis, hypothalamic pituitary adrenal axis ; IL-6, interleukin 6; N, number; TNF-α, tumor necrosis factor alpha.

31