Affect and physical health
Schenk, Maria
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Schenk, M. (2017). Affect and physical health: Studies on the link between affect and physiological processes. Rijksuniversiteit Groningen.
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Studies on the link between affect and physiological processes
All right reserved. No part of this thesis may be reproduced, stored in a retrieval system, or transmitted in any form, or by any means, without the written permission from the author or, when appropriate, from the publisher of the publication.
This thesis was realized in collaboration with the Espria Academy. Espria is a health care group in the Netherlands consisting of multiple companies targeted mainly at the elderly population. Publication of this thesis was supported by the research institute SHARE (School of HeAlth RE-search) of the University Medical Center Groningen / University of Groningen
Cover design & lay-out: Esther Dekker (Esthers pen) Printed by: Ridderprint, Ridderkerk
Studies on the link between affect and physiological processes
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 12 juli 2017 om 16.15 uur
door
Hendrika Maria Schenk
geboren op 30 november 1982 te Zwolle
Prof. dr. P. de Jonge Prof. dr. J.P.J. Slaets
Beoordelingscommissie
Prof. dr. R. Sanderman Prof. dr. A.W.M. Evers Prof. dr. J.K.L. Denollet
Chapter 1 General introduction
Part I Techniques in intensive day-to-day studies in biopsychological research
Chapter 2 Identification of inflammatory markers suitable for non-invasive, re-peated measurement studies in biobehavioral research: a feasibility study
Chapter 3 The relationship between 63 days of 24-h urinary free cortisol and hair cortisol levels in 10 healthy individuals
Chapter 4 Measuring BDNF in saliva using commercial ELISA: results from a small pilot study
Chapter 5 Let’s get Physiqual – an intuitive and generic method to combine sen-sor technology with ecological momentary assessments
Part II Affect and health
Chapter 6 Differential association between Affect and Somatic Symptoms at the Between- and Within-individual Level
Chapter 7 Associations between Positive Affect, Negative Affect and Allostatic Load: a Lifelines Cohort Study
Chapter 8 Dynamical associations between day-to-day fluctuations in inflam-matory markers and affect in healthy individuals
Chapter 9 General Discussion Summary Nederlandse samenvatting Dankwoord SHARE 9 21 33 51 67 93 107 123 137 153 159 167 169
Emotions and affect
Emotions, and the ability to identify and describe them, is a hallmark of what makes us human1. Emotions can be defined as positive, when something makes us happy, grateful or feel amazed, for example graduating, falling in love or becoming a parent. Similarly positive emotions are evident in the small things in life, such as a beautiful sunset, having a great moment with friends or drinking a glass of good wine. Emotions can also be defined as negative: “It’s just another manic Monday” from The Bangles expresses what every-body experiences every once in a while. Daily obstacles, stress and feelings of depres-sion can influence everyday life. In addition many people experience periods in which stress levels or feelings of depression increase or become overwhelming. Furthermore, an experience can be so unpleasant, and release such intense emotions, that it becomes a trauma2.
The experience of feeling, or nonreflective emotions is called affect3. Although the terms ‘emotion’ and ‘affect’ are often used to point out the same thing, they are not com-pletely synonymous. Whereas emotions are usually directed at an object or person and can be feigned; affect is a non-conscious, but consciously accessible, neurophysiological state3. In psychology, affect is often measured using the Positive Affect Negative Affect Scale (PANAS)4, a scale consisting of ten descriptors for positive affect (PA) for example ‘inspired’, ‘excited’ or ‘active’, and ten terms to describe negative affect (NA) for exam-ple ‘guilty,’ ‘scared,’ or ‘nervous.’
Affect is considered to have a physiological effect5. For example, when nervous, a physiological response like sweating can occur. The increase of heart rate, blood pressure and skin temperature are also indicators of intense affective states. The anatomical link responsible for the physiological effect upon affect, is formed by the hypothalamic-pitui-tary-adrenal (HPA-) axis, the sympathetic nerve system (SNS) and the parasympathetic nerve system (PNS). Through this link, affect induces and provokes physical signs, a fortio-ri, affect influences processes on molecular level.
Affect and health
Mental wear and tear influences physical health6. A positive association between ne-gative affect (NA) and somatic symptoms has been found in healthy individuals7–9. In addition, mental disorders can function as precipitating and perpetuating factors in the development or presence of somatic symptoms10–12. Negative life events, stress, and NA are all associated with poor health outcomes, including cardiovascular disease, diabetes, depression, and mortality13,14. This association may partly be due to harmful health be-haviors, which are associated with NA, and mediate the association between psychologi-cal distress and poor health outcomes15,16. Nonetheless, the association between NA and disease may also be due to dysregulation of glucocorticoid and downstream systems17,18.
studies that have been undertaken show that patients who report higher levels of positive affect (PA) report lower severity of somatic symptoms19. Moreover, PA is associated with a reduced risk of cardiovascular disease and mortality20–23. The influence of PA on health outcomes might be explained by a positive influence on health behaviors. Individuals with higher levels of PA report more beneficial health behaviors, for example less smoking, more exercise and less alcohol intake24–26. However, positive attributes, such as optimism, self-esteem and social status, and PA are inversely associated with metabolic syndrome and cardiometabolic risk, even after adjusting for health behaviors27–29. In addition, se-veral studies show beneficial associations between PA and biological profiles30. Increased levels of PA have been shown to correlate negatively with blood pressure31, and directly decrease levels of cortisol32. The effects of NA and PA appear to be opposite, whereas NA deteriorates health, PA seems to counteract this effect, and might even have a positive influence on health.
Affect and the neuroendocrine and immune system
The interaction and bidirectional role of affect and the neuroendocrine and immune sys-tem is receiving increasing attention in biopsychological research6,17,18,33. In contrast to prolonged psychological stress and depression, the current paradigm argues that short-term stress is useful and enables the body to respond properly in a harmful situation and promotes survival. Prolonged stress and depression are associated with malfunction of the HPA-axis and low-grade inflammation, which lead to increased risk for (psycho-) pathology34,35. However, to date, the knowledge about the bidirectional association of affect and the neuroendocrine and immune system is mainly focused on depression, nega-tive affect and pathological processes such as sickness behavior, somatic disease in men-tal disorders, or menmen-tal disorders in somatic disease. In addition, most studies available in biopsychological research have a cross-sectional design, which only disclose associations between individuals. Unraveling the bidirectional role of affect and the physiology in healthy individuals could enable us to gain a greater understanding about the link bet-ween affect and etiology of disease.
Between-individual versus within-individual
As mentioned, most studies available in biopsychological research have a cross-sectional design. Cross-sectional, between-subjects associations found in these studies have all too often been misconceived as indicating a causal effect, but this is not necessarily true. In fact, the association within-subject can be different in size or even sign from the associa-tion between subjects36. Increasing attention has been received by studies which focus on the individual, so called idiographic studies37. Instead of focusing on for example whether people who have more negative affect do report more somatic symptoms, idiographic studies explore whether within a person more negative affect leads to more or less soma-tic symptoms. By using time series analyses in data collected in a longitudinal idiographic observational study, one can learn about possible causalities, and biopsychological
pro-cesses within individuals.
In the field of biopsychology, there are already some longitudinal studies performed in relation to the association between affect and biomarkers. However, interpretation of the results of these studies is limited for several reasons: 1) studies are mostly perfor-med in a laboratorial setting thus lacking ecological validity38, 2) they cover only a short period of time in which the effect of one intense stressor is studied39,40, or 3) have long time windows between assessments, hampering the study of causality of such dynamic processes. Therefore, an intensive day-to-day study in a daily environment is necessary to increase ecological validity of research in biopsychological studies.
Limitations of current assessment methods
The relationship between psychological factors and inflammatory and metabolic biomar-kers would be studied preferably at a high frequency, over a longer period of time, in a non-clinical, natural everyday environment. To date, however, physiological measures and biomarkers are generally measured using inconvenient devices or methods. For example in an everyday environment, collecting venous blood, to measure levels of biomarkers, in a repeated measurement would be far too invasive and unpractical. This challenges the field of biobehavioral research to find new approaches, using advanced technologies in intensive day-to-day measurements to collect samples and data. Fortunately, several bio-markers are also excreted and expressed in for example urine, hair and saliva, which can be obtained in a non-invasive way, diminishing the intensity of the research protocol. In addition, the emergence of wearables and smartwatches is making sensors a ubiquitous technology to measure daily rhythms in physiological measures, such as movement and heart rate. Therefore, a closer look should be taken into the possibilities of non-invasive data-collection and the application in biopsychological research.
Outline of this thesis
The primary question is whether and in what way affective states, which are experienced in daily life, influence biomarkers related to health and physiological processes. Therefo-re, this thesis has two main aims: 1) to explore the possibilities for measuring biomarkers in specimens which can be obtained non-invasively and combining existing technologies, such as Fitbit devices, or smartwatches with self-reported data in intensive day-to-day measurement, and 2) to examine the between-subject and within-subject associations be-tween both PA and NA, and subjective and objective physiological measures, obtained from the general population and healthy individuals.
Part 1: New methods in biobehavioral research
In part one of this thesis several approaches of non-invasive sampling of objective measu-res in different biomaterials are described. It is essential to realize when measuring the
patterns of expression of biomarkers in biomaterials that it reflects distinct patterns of expression. Biomarkers in saliva cover a small time window, whereas levels of biomarkers in urine reflect an accumulation of expression of biomarkers of the previous few hours. Scalp hair grows slowly, and measuring biomarkers in scalp hair shows the expression over the last few weeks to months.
The feasibility of non-invasive sampling in a daily environment is explored in
chap-ter 2, using an intensive day-to-day study design in 10 healthy individuals, which
collec-ted 24-hour urine during a 63-day study period. Furthermore the presence and inter- and intra-individual differences were explored of immunological biomarkers in urinary samples, since intra-individual variability is a requirement for time series analysis.
Scalp hair is assumed to provide a historical time line of systemic secretion of cortisol and therefore of interest in biopsychological research and clinical practice41. In chapter 3 the correlation between two well-established measures of cortisol secretion, namely uri-nary cortisol and hair cortisol, are compared to see how well they correlated.
Brain-derived neurotrophic factor (BDNF) is a confirmed marker of brain plasticity and of interest for biobehavioral research and the etiology of psychiatric disorders42. The possibility to measure brain-derived neurotrophic factor (BDNF) in saliva is explored in
chapter 4.
Integrating data from commercially available sensors and service providers into one unified format for use in Ecological Momentary Assessments (EMA) or Experience Sam-pling Methods (ESM), and Quantified Self (QS) can provide new insights into the inter-action of mental and physiological processes in daily life. The development of a new platform for researchers is described in chapter 5, namely ‘Physiqual.’
Part 2: Association between affect and physiology
In part two of this thesis, the between-subject and within-subject associations between PA, NA and subjective and objective physiological measures are presented. Data was obtained from Lifelines43, a study across the general population, and from healthy indivi-duals in an idiographic study design.
In chapter 6 a study is described, using data from the project ‘HowNutsAreTheDutch’ (or hoegekis.nl in Dutch). ‘HowNutsAreTheDutch’ is an ongoing study on the mental state of the Dutch and Flemish population. It is an online platform which contains a diary stu-dy, an intensive day-to-day survey in which 43 items about affect, daily activity, sleep, and physical discomfort were included44. Using diary data, the between-individual and within-individual associations between PA, NA and somatic symptoms is studied.
The association between PA and NA and objective biomarkers is explored in a large cohort, and presented in chapter 7. Data of the Lifelines study was used. In this study over 165,000 individuals from the north of the Netherlands are included. Levels of objective biomarkers were measured in blood samples obtained from the participants aged 18 years and older. The correlation between PA, NA and a selection of biomarkers was cal-culated and adjusted for sex, age, exercise, smoking, and alcohol use.
In chapter 8, the within-individual association between PA, NA and eight inflammatory markers is assessed in data obtained from a longitudinal idiographic observational study. Ten healthy volunteers completed the entire study period of 63 consecutive days. During this period, participants were asked to fill out an electronic diary in the morning and in the evening, before going to bed. The questionnaires contained items about affect, stres-sful events, sleep (duration, quality), amount of exercise, caffeine, alcohol and nicotine use. Additionally, participants were asked to collect urine over a period of 24 hours in two portions (morning and day). Excretion of eight inflammatory markers was obtained by measuring concentrations with a multiplex assay, in urine samples.
In this thesis the association between PA and NA and the expression of biomarkers in a cross-sectional and longitudinal study design, and the general question whether and in what way affective states which are experienced in daily life influence health and physiological processes are explored.
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Techniques in intensive day-to-day studies
in biopsychological research
2
Identification of inflammatory markers
suitable for non-invasive, repeated
measurement studies in biobehavioral
research: a feasibility study
Abstract
IntroductionStudying the role of the immune system as a part of the interaction between mental and physical health is challenging. Especially when studying individuals with an intensive, lon-gitudinal study design in their daily life, non-invasive sampling techniques are a necessity. Urine can be collected in a non-invasive way, but this may be demanding for participants and little is known about fluctuation over time of inflammatory markers in urine samples. The aim of this study was to investigate the feasibility of non-invasive sampling, and to explore inter- and intra-individual differences in inflammatory markers in urine.
Materials & Methods
Ten healthy individuals collected 24-hour urine for 63 consecutive days. Multiplex ana-lyses were used to quantify levels of C-reactive protein (CRP), Fractalkine, Interleukin-1 receptor-antagonist (IL-1RA), interferon-α (IFNα), interferon-γ (IFNγ), Interferon gam-ma-induced protein 10 (IP-10), Macrophage inflammatory protein-1β (MIP-1β), and Vascular Endothelial Growth Factor (VEGF) in 24-hour-urine. Cross-correlations between the night and 24-hour portions were calculated, to examine whether 24-hour urine could be replaced by solely the night portion to increase feasibility. Inter- and intra-individual differences were examined in urinary levels of and fluctuations in inflammatory markers. Results
This study showed that levels of inflammatory markers are detectable in urine. Cross-cor-relation results showed the corCross-cor-relation between levels of inflammatory markers in the night portion and the 24-hour urine varied widely between individuals. In addition, ana-lyses of time series revealed striking inter- and intra-individual variation in levels and fluctuations of inflammatory markers.
Conclusion
We show that the assessment of urinary inflammatory markers is feasible in an intensive day-to-day study in healthy individuals. However, 24-hour urine cannot be replaced by a night portion to alleviate the protocol burden. Levels of inflammatory markers show substantial variation between and within persons.
Introduction
The interaction between mental and physical health and the bidirectional role of the im-mune system therein is receiving increasing attention1–3. Psychological stress is associated with low-grade inflammation and an increased risk for pathology2,4,5, while low-grade inflammation might play a role in the etiology and persistence of depression in turn6.
Several studies already have been conducted on the role of low-grade inflammation in relation to mental health. However, the interpretation of the results of these studies is limited for two reasons. First, studies are mostly performed in a laboratorial setting thus lacking ecological validity7. Second, they cover only a short period of time in which the effect of one intense stressor is studied8,9. Preferably, the relationship between psycho-logical factors and inflammation would be studied in an idiographic study, which focuses on the dynamics of events over time within individuals. Accordingly, an idiographic study design allows analyses at the within-person level and provides information about individ-ual variation and the relationships between fluctuations in variables over time10,11.
A non-clinical, natural daily environment is favorable for studying psychobiological associations, representing the conditions of the real world, ameliorating the extrapola-tion of the outcome to a natural context7,12,13. To date, however, inflammatory markers are generally measured in venous blood14, but collecting venous blood in an everyday environment would be far too invasive and unpractical. Fortunately, some inflammatory markers are also excreted and expressed in urine and saliva15, which can be obtained in a non-invasive way. The number of markers that is measured in urine and saliva is increas-ing16–19, therefore samples of these materials could be useful for ecological assessments in day-to-day, idiographic studies.
Before implementing non-invasive ways to measure immunological biomarkers in id-iographic studies, several questions have to be addressed. The first question is which inflammatory markers are detectable and stable in urine of healthy individuals. Second, collection of 24h urine may be challenging for participants. As an alternative, the first morning void could be used for biomarkers. It is unknown whether a first morning void provides sufficient insight into a person’s levels of inflammation throughout the day, and could thus replace the more burdensome 24-hour urine collection procedure. Third, idio-graphic studies are based on variability within individuals, thus it is important to assess to which degree levels of detectable inflammatory markers fluctuate over time.
To assess if the collection of urine and measurement of inflammatory markers in urine is feasible, we set up an intensive day-to-day two month pilot study. We used the data generated in this study to answer the following three research questions: 1) is the collec-tion of urine and measuring inflammatory markers feasible in a study with a of repeated measurement design? 2) Is 24-hour urine a necessity, or do the night portion and 24-hour urine correlate highly, and could therefore the night portion replace the collection of 24-hour urine? 3) Is there inter- and intra-individual variability of inflammatory markers, which makes them suitable for time series analyses?
Materials & Methods
A pilot study was performed to test which inflammatory markers could be reliably mea-sured in 24-hour urine of healthy individuals. It is important to realize that in a day-to-day longitudinal study, participants are engaging in their daily routines, and samples cannot be processed and stored at -80ºC immediately. Therefore it was studied whether the markers were stable at room temperature for 24-48 hours. Since repeated mea-surement studies are meant to provide information about (bidirectional) relationships between variables over time, it was assessed whether inflammatory markers showed intra-individual variation20. Three individuals collected 24-hour urine for 5 consecutive days, in which we assessed presence, stability at room temperature for 48 hours, and fluctuation of 39 inflammatory markers, using Multiplex assays. Based on this pilot study, eight inflammatory markers were selected for an intensive day-to-day study, namely C-reactive protein (CRP), Fractalkine, Interferon (IFN)-α, Interferon (IFN)-γ, Interleukin-1 receptor antagonist (IL-1ra), Interferon gamma-induced protein 10 (IP10), Macrophage Inflammatory Proteins (MIP)-1β and Vascular endothelial growth factor (VEGF).
Subjects
Ten healthy participants (7 females) collected 24-hour urine for 63 consecutive days. Participants were asked to daily report health complaints or use of medication by use of a web based electronic diary. When infectious symptoms or use of anti-inflammatory drugs were reported, values of that particular day were excluded for further analyses. Participants were paid 5 euros for each day in which they completed the sampling proto-col. The study protocol was approved by the Medical Ethical Committee of the University Medical Center Groningen.
Urine
Urine was collected in two portions. The first portion consisted of the ‘night portion.’ Voiding during the night was also appointed to the ‘night portion’. The second portion consisted of the remaining voids of the day until bedtime, called the ‘day portion’. Urine containers (BD Biosciences, Franklin Lakes, NJ, USA) were weighed after collection on a scale, accurate up to 1 gram, to determine total output. The ‘day portion’ was stored at room temperature during the accumulation period. Every other morning, the research-er collected the samples and transfresearch-erred them to the laboratory. Then two separate samples of the ‘night portion’ and ‘day portion’ were aliquoted into 2.0 ml cryotubes. Subsequently, samples were stored at -80°C until further analyses. Completeness of the 24-hour urine samples was assessed by use of 24-urinary creatinine output; cases were excluded from further analyses when 24-hour urine samples were incomplete. A sample was considered incomplete if the 24 hr creatinine output was lower than 2 SD’s from the persons own mean21.
Analyses of inflammatory markers
Before analysis, urine samples were centrifuged after thawing for 1650 x g at 4°C for 10 minutes. Concentration of CRP, Fractalkine, IFN-α, IFN-γ, IL-1ra, IP10, MIP-1β and VEGF in the night and day portion was assessed using 2 different magnet bead multiplex assays (Merck Millipore, Billerica, MA, USA) and a Luminex 200 analyzer (Luminex®, Austin, TX, USA), following protocol. Results were analyzed using Milliplex Analyst V5.1 software (VigeneTech Inc, Carlisle, MA, USA). Total concentrations of inflammatory mark-ers were calculated using the following equation: (concentration night portion/ml) * (total output night portion (ml)) + (concentration day portion/ml) * (total output day portion (ml)) = total excretion of inflammatory marker per day. The intra-assay and inter-assay coefficients of variance were respectively: 1.5-15% and 3.5-20%.
Data analyses
Descriptive statistics of eight inflammatory markers were calculated for each individu-al, presented in a boxplot and examined. Missing time series data was imputed using the package ‘Amelia’ 22from a time series (like variables collected for each year in a country; the number of imputed data sets was 50. Auto Regressive Integrated Moving Average (ARIMA) models were fitted to study the association between levels of inflam-matory markers as measured in the night and 24-hour urine portion. Each time series was detrended and demeaned, and ARIMA residuals were stored, using the package ‘astsa’ (http://www.stat.pitt.edu/stoffer/tsa4/). The value of the lag 0 of the cross-correlation function (CCF) between the ARIMA residuals of the night and 24-hour urine portion was calculated for each inflammatory marker, within each individual, to assess whether the night portion could replace the 24-hour urine. Analyses were done using Rstudio (version 0.99.896, Inc., Boston, MA, http://www.rstudio.com).
Results
Feasibility of collection of urine in 10 healthy individuals
Urine samples of 10 different individuals (Table 1) collected in an idiographic study were analyzed for the concentration of the following eight inflammatory markers: CRP, Fractal-kine, IFNα, IFNγ, IL-1ra, IP10, MIP1β and VEGF. As a measure of feasibility, we assessed completeness of the 24-hour urine samples as determined by 24-hour urinary creatinine output. Based on this method the following samples were excluded for further analyses: day 23, 24, 28, 29, 32 for participant 1, day 41 for participant 2, day 63 for partici-pant 5, day 22 for participartici-pant 6, day 1 and 34 for participartici-pant 7, day 56 for participartici-pant 8, and day 40 for participant 10. Due to technical issues regarding the assays, no results for CRP were acquired for individuals 7, 8, 9 and 10.
Table 1: Sample characteristics repeated measurement study
1 2 3 4 5 6 7 8 9 10
Sex m f f f m m f f f f
Age (years) 24 58 29 33 39 19 21 21 48 22
BMI 23.2 26.5 20.0 17.2 20.0 21.6 21.3 20.1 25.3 23.1
Smoking yes no no no yes no no no no no
Night portion vs 24-hour urine
Cross-correlation function (CCF) was used for each inflammatory marker within each indi-vidual to assess whether the night portion could replace the 24-hour urine. Cross-correla-tions between the night portion and the 24-hour urine are shown in table 2. Cross-cor-relations between the night portion and the 24-hour urine ranged from -0.053 to 0.968. IFNγ did not show enough non-zero data points to execute CCF in any of the individuals. The majority of the correlation coefficients reached the upper 95% confidence limit at p ≤ 0.05. IP10 showed significant moderate to strong correlations between the night portion and 24-hour urine for almost all individuals. ID 4, 5, 8 and 10 showed significant correlations for all IM between the night portion and the 24-hour urine.
Inter- and intra-individual differences
Each inflammatory marker showed considerable differences in median levels, and inter-quartile ranges (IQR) between different participants (Figure 1). ID 5 showed overall the lowest median excretion of inflammatory markers. ID 2 and ID 10 showed the highest median excretion of inflammatory markers. The differences between the excretion levels are substantial in several cases, e.g. the interquartile ranges of CRP in ID 5 and 6 do not overlap and median levels show a difference of around 100% in those participants. The same holds true for levels of Fractalkine in ID 8 and ID 9, or IFNγ in ID 9 and ID 10. Or even more prominent, ranges between IL1ra excretion levels in ID1 and ID2, or ID 2 and ID 5 show great differences.
Table 2: Correlation between excretion of inflammatory markers in the night portion and
24-hour urine
ID CRP F r a c t a
l-kine IFNα IFNγ IL-1RA IP10 MIP1β VEGF
1 0.161 0.341* 0.178 NA 0.490* 0.197 NA 0.747* 2 0.559* 0.048 0.229 NA 0.556* 0.738* 0.458* 0.330* 3 0.087 0.034 -0.053 NA 0.138 0.465* 0.156 0.245 4 0.372* 0.423* 0.528* NA 0.321* 0.621* 0.636* 0.528* 5 0.373* 0.410* 0.532* NA 0.330* 0.610* NA 0.530* 6 0.968* 0.028 0.008 NA 0.625* 0.691* 0.342* 0.428* 7 NA 0.409* 0.234 NA 0.360* 0.528* NA 0.094 8 NA 0.508* 0.475* NA 0.524* 0.422* 0.411* 0.448* 9 NA 0.400* 0.532* NA -0.036 0.785* 0.307* 0.510* 10 NA 0.296* 0.594* NA 0.463* 0.577* NA 0.464*
Note: Results of the cross-correlation function (CCF), analyzing the correlation (B) between the night por-tion and 24-hour urine. N=63 for each correlapor-tion. IFNγ did not show enough non-zero data points to execute CCF. Due to technical issues regarding the assays, no results for CRP were acquired for individual 7, 8, 9 and 10. Number of imputed data sets was 50. Each time series was detrended and demeaned before analysis, [RESIDUALS]. (*) indicates values that surpass 95% confidence intervals, indicating a significant correlation (p ≤ 0.05), 95% CI = 0.252 (±2/√n).
Discussion
In this study we showed that collecting urine in a day-to-day environment is feasible in healthy individuals, even for a longer period of time. Our results also revealed that 24-hour urine cannot be replaced by a night portion for analysis of inflammatory markers due to the wide variety and the overall moderate strength of the correlation between levels of inflammatory markers in the night portion and the 24-h urine. Furthermore, explorative analyses showed that median levels measured over an extended period of time differ between individuals, and the range, and thus minimum and maximum of excretion levels of different inflammatory markers between individuals and within-indi-vidual differed as well.
Day-to-day studies require non-invasive techniques to diminish the burden of the study on participants. Urine is appealing for research in the field of behavioral science, since it is a direct filtrate of the blood and can be collected using non-invasive techniques. Moreover, urine contains an accumulation of small metabolites which can cross the glo-merular filtration barrier, thus providing an integrative measure of low grade inflamma-tion. Based on the literature and a pilot study, we selected eight inflammatory markers to be measured in a repeated measurement studies. The biomarkers which we assessed
are in line with recent literature.15,23,24 A drawback of urinary markers is that their levels are influenced by health status, since the kidney produces or excretes many molecules as a response to inflammatory disease or kidney injury25,26. Knowledge about health condi-tions of participants, or the presence of disease, symptoms, injuries or use of medication which affect the body, kidney or urinary tract during the study period, is therefore crucial. The associations between the night and 24-hour portion range from ‘very weak and negative’ to ‘very strong and positive’ (Table 2). Although the majority of the correlations were significant, the correlations between the night portion and 24-hour urine showed a wide range for different markers within each individual, and also a wide range between individuals. Due to this variety, it is unfortunately impossible to replace the 24-hour urine with a night portion in future studies. The sometimes large differences, and thus low cor-relation, between excretion of inflammatory markers in the night portion and 24-hour portion is partly explained by the fact that the 24-hour portion is larger and covers a wider time period than the night portion. However, physiological processes such as sleep duration or quality during the night might influence expression of certain inflammatory markers27.
Several studies already showed that cytokine patterns are highly heterogeneous be-tween individuals15,28–30, however, no study showed this evidently that this is also valid within-individuals. The heterogeneity in levels of inflammatory markers is striking. Hetero-geneity in the levels of inflammatory markers within individuals creates the opportunity to study whether this is due to stress, affect, behavior or life style. In addition, visual inspec-tion raises the quesinspec-tions whether time series of inflammatory markers can be translated to immunological profiles. It may be speculated that such profiles might reflect specific susceptibilities and have relevance for health outcomes.
The strength of this study is its novelty due to the assessment of eight inflammatory markers in a repeated measurement study design. The idiographic study design gives insight into the large differences between individuals and the fluctuation of the inflam-matory markers within individuals. This study has limitations that need to be mentioned as well. The high costs of the analyses techniques forced us to narrow down the number of inflammatory markers in the cohort study. In addition, this intensive study was feasible, but the participants in the current study were paid to be compliant with the protocol.
In conclusion, we showed that the measurement of inflammatory markers in urine has the potential for successful incorporation in idiographic research. The data also confirms the need for a longitudinal approach in biobehavioral research, since cross-sectional data would not have uncovered the wide distribution of inflammation levels within-indi-viduals. In future research, intensive day-to-day studies might provide new insights into the role of low-grade inflammation in individual psychological processes.
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3
The relationship between 63 days
of 24-h urinary free cortisol and hair
cortisol levels in 10 healthy individuals
SL van Ockenburg, HM Schenk, A van der Veen,
EFC van Rossum, IP Kema, JGM Rosmalen
34
Abstract
BackgroundInterest in measuring cortisol in scalp hair is increasing because of its assumed ability to provide a historical timeline of previous systemic levels of cortisol. Yet, it remains uncer-tain how well hair cortisol represents the total systemic secretion of cortisol over time. Methods
Ten healthy individuals collected 24-hour urine samples for 63 consecutive days and provided a hair sample at the end of the study period. 24-hour urinary creatinine levels in every urine samp le were determined to assess completeness of the samples. Cortisol levels in 24-hour urine sam ples and in hair were measured with liquid chromatography tandem mass spectrometry. The correlation between urinary cortisol and hair cortisol were calculated using Kendall’s tau.
Results
We found a nonsignificant moderate correlation between average urinary cortisol secre-tion and average hair cortisol concentrasecre-tion rT=0.422, p=.089.
Conclusions
Hair cortisol concentration correlates small to moderately with 24-hour urinary cortisol concentration over a period of 63 days.
Introduction
Cortisol is a pulsatile secreted steroid hormone that can be measured in different biolo-gical specimens such as blood1, urine2, saliva3, nails4 and scalp hair5. Interest in measuring cortisol in scalp hair is on the rise in both stress research6,7 and clinical practice8 due to its usefulness as a non-invasive long-term measure of cortisol exposure/production with the unique possibility to create a historical timeline to assess previous systemic levels of cortisol. Conditions associated with altered cortisol production have been studied in rela-tion to hair cortisol concentrarela-tion (HCC), for example pregnancy, Cushing’s disease and Addison’s disease8,9. These studies show that HCC followed the clinical course of patients’ conditions, thus providing indirect evidence for the validity of HCC as a measure of his-torical levels of cortisol10.
Because hair cortisol is being increasingly used in clinical studies, researchers started exploring the relationship between HCC and cortisol concentrations in other frequently analyzed biological specimens such as saliva, urine or feces. Animal studies generally show a strong correlation between mean cortisol concentrations in saliva and feces, and HCC (r = .48 - .90)11–13. In human studies, however, only a weak to moderate correlation was found between salivary cortisol concentration and HCC (r =.06 – .57)9,14–20. The dis-crepancy in results between animal studies and human studies might be due to the fact that data in human studies were collected over short time period (i.e. a maximum of 6 days). As cortisol levels in other biological specimens than hair are known to have high intra-individual variation over time21,22 it is only natural that short-term measures of syste-mic cortisol exposure do not correlate well with a long-term measure of systesyste-mic cortisol exposure such as HCC.
In conclusion, to date it is unclear how well HCC corresponds to other important well-known measures of systemic cortisol exposure such as 24-hour free urinary cortisol levels (24-h UFC). Only a study that measures several individuals for an extensive time period can address this question. In the present study, we investigated the correlation between 63 days of 24-h UFC (i.e. an estimate of total cortisol output over the period of two months) and hair cortisol in the corresponding time period. We expect to find higher correlations than in previous studies, as we expect the high day-to-day fluctuations of urinary cortisol levels to “average out” over a longer time span.
Material and methods
Study populationThe study was a longitudinal prospective observational study generating time series data of 10 healthy participants who collected 24-h urine samples for 63 consecutive days and donated a hair sample at the end of the study period. They were paid €5 per day of study participation, thus a total of €315 after completion of the entire study period.
36
Inclusion criteria were being a healthy adult between the ages of 18 and 65 years and being available for 63 consecutive days. Exclusion criteria were any current somatic and/or mental illnesses and medication use other than oral contraceptives or occasional acetaminophen. A total of 11 participants were included in the study. One person dis-continued participation in the study due to a major life event after two days. The study protocol was approved by the Medical Ethics Committee of the University Medical Center Groningen (UMCG) in the Netherlands. All participants were given extensive written and oral information about the study’s purposes and protocol and had the option to consult an independent physician for additional information. Before enrollment participants gave written informed consent.
Urinary cortisol
Participants collected all urine in two separate containers each day for 63 consecutive days. They were instructed to use the “night container” from the moment they went to bed until the first morning void. The “day container” was for all urine produced after the first morning void until the last void before going to bed. Containers were stored at room temperature until they were collected, every Monday, Wednesday, and Friday. Before processing, the urine containers were weighted on a scale with a precision of 1 gram. Urine samples from the “day container” and the “night container” were aliquoted separately in 2 ml cups with screwcap (Sarstedt, Nümbrecht, Germany) and stored at -80 degrees Celsius.
Urinary cortisol concentrations were measured with isotope dilution liquid chroma-tography tandem mass spectrometry (LC-MS/MS). The day urine and night urine were measured in separate wells. To minimize the effects of interrun variability on the data, all urine samples of one participant were analyzed in one lot. Intra- and interrun coefficients of variation for the lower range of cortisol in urine were 2.4% and 7.8% respectively. Intra- and interrun coefficients of variation for the higher range of cortisol in urine were 1.4% and 3.8% respectively. 24-hour urinary free cortisol excretion (24-h UFC) was computed in the following way: (cortisol concentration of night urine X volume of night urine) + (cortisol concentration of day urine X volume of day urine)= 24-h UFC. Then two variables were computed to represent the systemic exposure to cortisol over the course of one month (UFC-1 represents total cortisol secretion of the first month of study and UFC-2 represents total cortisol secretion of the second month of study). UFC-1 and UFC-2 were computed by summing all the 24-h UFC of the respective month for each participant. A graphical display of the two variables UFC-1 and UFC-2 with respect to the time period they represent can be found in figure 1.
Urinary creatinine excretion was used to assess compliance with the sampling protocol. Urinary creatinine was measured with the creatinine plus enzymatic assay on the Roche Modular. The intra-run coefficient of variation was 0.9% and the inter-run coefficient of variation was 2.4%. All samples of one participant were analyzed in one run. Each participant showed considerable day-to-day fluctuations in total creatinine output. As this
variation was either normally distributed around the mean or slightly positively skewed, we assume, in accordance with other studies23–25 that this concerns mostly natural physio-logical variation. We considered a sample incomplete if the 24-hour creatinine output was lower than 2 standard deviations (SD’s) from the person’s own mean. This method allows natural physiological variation in creatinine excretion. It can, however, not detect if a participants that was noncompliant from the start. We used this information to assess and report the quality of the data, but did not exclude samples with low creatinine output in our principal analyses, as we are interested in the total cortisol output over one month. A graphical display of the time series of 24-h creatinine excretion of the 10 healthy in-dividuals in the current study has recently been published elsewhere6.
Hair cortisol
At the end of the 9 weeks, study participants were asked to provide a hair sample that was cut by the researcher directly over the scalp. Hair samples were then attached to a piece of plain paper indicating the scalp side and the roots side. Subsequently they were stored at room temperature in an envelope. All samples were analyzed in one run ap-proximately one year later. Hair cortisol concentrations were measured at the laboratory of clinical chemistry in the University Medical Center Groningen with online-solid phase extraction (SPE) combined with a fully validated isotope dilution liquid chromatography tandem mass spectrometry (LC-MS/MS) method. On the ‘scalp side’ two hair samples of 1 cm in length were cut; each 1 cm sample representing the hair growth of approximately one month. Samples were weighted in polypropylene containers to ensure the minimum sample weight of 25 mg required for the laboratory analysis.
Samples were subsequently washed once with dichloromethane. About 50 steel balls were added to the container, together with 50µL deuterated cortisol dissolved in 1450µL methanol. The hair was pulverized using the Ball Mill (Retsch, MM400). The suspension was centrifuged and the supernatant was evaporated to dryness at 45°C using a nitro-gen flow. The samples were resuspended in 10% methanol. Subsequently, 50µL was injected onto the online SPE-LC-MS/MS system. For SPE and LC we used an integrated Symbiosis system from Spark-Holland combined with a Xevo TQ MS mass spectrometer from Waters. A reversed phase Phenyl-Hexyl column from Phenomenex was used. The applied LC-MS/MS conditions were adopted and modified from Li et al. (Li et al., 2012) For this method the intra-run coefficients were 9.3% at 3.5 pg/mg, 6.2% at 8.8 pg/mg and 4.3% at 30.3 pg/mg. The inter-run coefficients were 6.1% at 3.4 pg/mg, 5.5% at 8.8 pg/mg and 6.0% at 10.6 pg/mg. The lower limit of quantitation for hair cortisol was 0.70 pg/mg hair. For hair, we use the concentration of cortisol in picogram per milligram (pg/mg) of hair. Two variables were created representing HCC in pg/mg in the first month (HCC-1) of study (distal hair segment) and in the second month (HCC-2) of study (proximal hair segment). A graphical display of the two variables HCC-1 and HCC-2 with respect to the time period they represent can be found in figure 1.
38
Statistical analyses
First, we assessed the stability of the repeated measures of urinary cortisol and hair cor-tisol over time by calculating the intra-class correlation (ICC) coefficient for UFC-1 and UFC-2, and HCC-1 and HCC-2 respectively. The ICC was calculated using a two factor mixed effect model assessing absolute agreement26. It is known that cortisol levels in hair decrease in the more distal older hair segments, in particular in fragments beyond 3-6 cm, known as the wash-out effect. We therefore also performed a sign test to evaluate if there was a significant difference between the medians of the two hair samples HCC-1 and HCC-1.
Then, to assess the relationship between urinary cortisol and hair cortisol for the first and the second month of study separately, Kendal’s Tau correlation coefficients were calculated between UFC-1 and HCC-1, and UFC-2 and HCC-2 respectively. It is known, however, that a more reliable estimate of the between-subject correlation of repeated measures data can be obtained by first computing the mean value of a variable for each individual and then calculate the correlation using those means27. Therefore we computed the mean value of urinary cortisol (i.e. UFC-1 + UFC-2/2= mean-UFC) and hair cortisol (i.e. HCC-1 + HCC-2/2= mean-HCC) for each individual and then calcu-lated the Kendall’s tau correlation between mean-UFC and mean-HCC. To be sure that lack of compliance to the study protocol did not influence our results, we also conducted a post-hoc analysis in which we calculated the within-individual median 24-h UFC levels of the first (UFC-1median) and second month (UFC-2median) after excluding incomplete urine samples. The median was chosen because 24-h UFC was positively skewed within indi-viduals. Moreover, for post-hoc analysis, we created a sum-score of 24-UFC of day 19 through 49 (UFC-14) creating a variable that represents a shift back in time of 14 days. This variable was used to account for approximately 5 days grow out period (of hair still residing in the hair follicle within the skin) and the residue of hair left behind on the scalp after cutting the sample28.
Based on the previous results of animal studies11–13, which covered a significantly lon-ger time span than human studies, we expected to have correlations between hair cortisol and urinary cortisol levels to be r > 0.7. With the current sample of 10 individuals, we had a power of 0.80 at an alpha level of 0.05 to detect a significant correlation. All analyses were conducted with SPSS 23.0.
Table 1. Population characteristics
Subject Sex Age UFC-1 UFC-2 HCC-1 HCC-2
1 ♂ 24 1934.6 2642.1 4.97 4.32 2 ♀ 58 1017.5 1056.3 2.89 3.80 3 ♀ 29 2039.2 1981.0 2.58 4.69 4 ♀ 33 2731.4 3273.5 4.32 6.24 5 ♂ 39 3598.9 3963.2 9.31 8.15 6 ♂ 19 3475.4 4061.5 3.87 3.56 7 ♀ 21 3164.6 3636.6 2.90 3.40 8 ♀ 21 3559,.5 2827.8 6.37 6.05 9 ♀ 48 2470.4 2761.5 5.09 4.97 10 ♀ 22 3142.7 3498.1 5.12 5.93
Age is expressed in years. UFC-1 and UFC-2 are the total output of urinary cortisol for the first and sec-ond month respectively expressed in nanomol. HCC-1 and HCC-2 are hair cortisol concentration of the first and the second month expressed in picogram/mg.
Figure 1. Scatterplot of urinary cortisol excretion and hair cortisol concentration per month
Legend first month second month
40
Results
Descriptive data with regard to the study population and cortisol values can be found in table 1.
Completeness of the 24-h urine samples
Urine samples were considered to be incomplete if 24-h creatinine output was smaller than two SD’s of a person’s own mean. Based on this method, day 23, 24, 28, 29, and 32 for participant 1 were regarded incomplete, leaving 58 complete days. Participant 2 had incomplete samples at day 41 and day 58. For participant 5 only day 63 was regarded incomplete, for participant 6 day 22, for participant 7 day 1 and day 34, for participant 8 day 56, and finally participant 10 had an incomplete sample at day 40. For participant 3, participant 4, and participant 9, all urine samples were judged com-plete based on their urinary creatinine output. As a sensitivity analysis we also checked the effects of excluding samples of which the 24-h creatinine output was smaller than one SD of a person’s own mean. The median cortisol levels of the full dataset, and after re-moval of incomplete samples based on the 2 SD and 1 SD selection criteria respectively are displayed in a supplemental table (supplement 1).
Test-retest reliability of the repeated measures
The test-retest reliability of urinary cortisol (i.e. the intra-class correlation between UFC-1 and UFC-2) was high with a ICC of 0.926 (95% CI .678 to .982; F=17,021, p<.001). Likewise, the test-retest reliability of hair cortisol (i.e. the intra-class correlation between HCC-1 and HCC-2) was also high with a ICC of 0.895 (95% CI .605 to .973; F=9,686, p<.001). A sign test showed that there was no significant difference between the HCC-1 and HCC-2 (medians 4.74 vs 5.11 pg/mg, p= 1.00), indicating no washout effect. Correlation between 24-h UFC and HCC
The strength of the between-subject correlation between urinary cortisol and hair cortisol (i.e. UFC-1 and HCC-1) in the first month was moderate (rT=0.467, p=.060). The strength of the between-subject correlation between urinary cortisol and hair cortisol (i.e. UFC-2 and HCC-2) in the second month was low (rT=0.200, p=.421). The correlation between the individuals’ means of urinary cortisol and hair cortisol over the two months time period (i.e. mean-UFC and mean-HCC) was also moderate (rT =0.422, p=.089). As a post-hoc analyses, to check if the relatively low correlations that we found could be attributed to incomplete urine samples, we reran the models after excluding possibly incomplete urine samples based on the creatinine output. We did this by first removing all samples that were having a 24-h urinary creatinine output smaller than two SD’s or smaller than one SD from a person’s own mean. Yet, the results were almost similar to the results of the original analyses, indicating that lack of compliance did not explain the results.
and hair cortisol in the first month (i.e. UFC-1median and HCC-1) was moderate after exclusion of samples with a creatinine output smaller than two SD’s (rT=0.467, p=.060) or smaller than one SD from a person’s own mean (rT=0,467 p=.060). Likewise, the strength of the between-subject correlation between the median of urinary cortisol and hair cortisol in the second month (i.e. UFC-2median and HCC-2) was low after exclusion of samples with a creatinine output smaller than two SD’s (rT=0.244, p=.325) or smaller than one SD from a person’s own mean (rT=0.244, p=.325). Finally, to account for a ap-proximately 5 days grow out period (of hair still residing in the hair follicle within the skin) and the residue of hair left behind on the scalp after cutting the sample we calculated the correlation between UFC-14 and HCC-2. The correlation between urinary cortisol and hair cortisol levels of the second month remained low, even after accounting for the grow out period (rT=0.289, p=.245).
Discussion
This is the first study that has investigated the relationship between long-term systemic levels of cortisol, as indexed by two months of 24-h UFC, with concurrent HCC in humans. We found that both urinary cortisol and hair cortisol have a high test-retest reliability in-dicating high reproducibility. Furthermore, we found that the strength of the between-sub-ject correlation between urinary cortisol and HCC was moderate at best. The results need to be interpreted in the light of the strengths and limitations that pertain to this study.
Regarding the test-retest reliability for UFC, the intra-class correlation (0.926), com-paring two consecutive months of urinary cortisol output, was exceptionably high compa-red to what was found in a much larger epidemiological cohort study. In the PREVEND study, stability on a day-to-day basis ranged from 0.69 to 0.72, compared to 0.60 over a two year period29. The difference in findings between our study and the PREVEND study probably arises because we obviated the issue of high day-to-day variability in UFC30 by creating a measure that spans a much longer time period (i.e. a month), thus averaging out these day to day differences. Similarly to UFC, the test-retest reliability for HCC was also high (0.90) compared to two larger epidemiological studies (0.68 to 0.79)31. The fact that the test-retest reliability was so high in this study should also be viewed in the light of the small sample size, which can lead to instability of the estimate, reflected in wide confidence intervals. Another explanation for the high test-retest relia-bility is that our samples were taken at a closer time interval (i.e. two consecutive months) than in Stalder’s study, which sampled at two months and two year interval31.
The low to moderately strong correlations found in our study are in accordance with the other human studies described below. The other most extensive validation study of HCC in humans found a moderate correlation between six consecutive salivary cortisol diurnal profiles and hair samples collected during the 2nd (r=0.43) and 3rd (r=0.54) trimester of pregnancy respectively9. This is very similar to the correlation we found (r=0.42)
