Abstract
Changes in levels of the stress-sensitive hormone cortisol from morning to evening are referred to as diurnal cortisol slopes. Flatter diurnal cortisol slopes have been proposed as a mediator between chronic psychosocial stress and poor mental and physical health outcomes in past theory and research. Surprisingly, neither a systematic nor a meta-analytic review of associations between diurnal cortisol slopes and health has been conducted to date, despite extensive literature on the topic. The current systematic review and meta-analysis examined associations between diurnal cortisol slopes and physical and mental health outcomes. Analyses were based on 179 associations from 80 studies for the time period up to January 31, 2015. Results indicated a significant association between flatter diurnal cortisol slopes and poorer health across all studies (average effect size, r=0.147). Further, flatter diurnal cortisol slopes were associated with poorer health in 10 out of 12 subtypes of emotional and physical health outcomes examined. Among these subtypes, the effect size was largest for immune/inflammation outcomes (r=0.288). Potential moderators of the associations between diurnal cortisol slopes and health outcomes were examined, including type of slope measure and study quality indices. The possible roles of flatter slopes as either a marker or a mechanism for disease etiology are discussed. We argue that flatter diurnal cortisol slopes may both reflect and contribute to stress-related dysregulation of central and peripheral circadian mechanisms, with corresponding downstream effects on multiple aspects of biology, behavior, and health.
Keywords: Circadian rhythms; Diurnal cortisol slopes; Hypothalamic pituitary adrenal (HPA) axis; Mental health; Physical health.
1. Introduction
1.1 Overview
The glucocorticoid hormone cortisol is a primary product of the hypothalamic-pituitary-adrenal (HPA) axis, a key biological stress response system. Cortisol is one of the most frequently employed biomarkers in psychobiological research for several reasons. First, cortisol levels are responsive to social and psychological stress (Dickerson and Kemeny, 2004; Gunnar et al., 2009a). Cortisol levels respond to both acute stress (e.g., acute loneliness or negative social evaluation) and chronic stress (e.g., the stress of poverty or ongoing family conflict) (Adam, 2012). Second, the development and adult functioning of the HPA axis is profoundly influenced by prior developmental experience (Lupien et al., 2009). Third, cortisol has pervasive effects throughout the body and brain, and is thought to play important roles in daily cognitive and behavioral functioning (Lupien et al., 2009). Fourth, cortisol has also been implicated in the etiology of a wide range of mental and physical health outcomes (Chrousos and Gold, 1992). As a result, researchers have suggested that stress-related alterations in cortisol regulation may play a role in mediating associations between stress exposure and later developmental and health outcomes (Lupien et al., 2009; Davis and Sandman, 2010), including both the onset and progression of mental and physical health disorders (Heim et al., 2008).
Past research on cortisol and health has focused on cortisol reactivity to acute stress (Granger et al., 1996; Heim et al., 2008) as well as variations in average basal cortisol levels (Chrousos and Gold, 1992). More recently, researchers have appreciated the importance of circadian variability in cortisol levels, by examining influences on, and consequences of, individual differences in the diurnal (daytime) cortisol rhythm. The current meta-analysis examined associations between one aspect of the diurnal cortisol rhythm – the diurnal cortisol slope (DCS) - and mental and physical health outcomes.
1.2 Diurnal Cortisol Rhythms: Description and Background Research
Cortisol levels typically follow a strong diurnal rhythm: levels are high on waking, surge an average of 50–60% in the 30–40 minutes after waking, drop rapidly in subsequent few hours after the awakening surge and then drop more slowly until reaching a nadir around bedtime (Pruessner et al., 1997; Adam and Kumari, 2009). Variation in cortisol levels as a function of time of day is substantial. In one study, time of day accounted for 72% of the variance in salivary cortisol levels (Adam, 2006). Early research often considered this time-of-day variation to be “nuisance” variation. Over the past 15 years, however, individual differences in the diurnal cortisol rhythm have emerged as a construct of interest (Adam et al., 2008). Researchers have examined the genetic, developmental, and psychosocial determinants of individual differences in the diurnal cortisol rhythm (Adam, 2012), as well as the potential health consequences of variation in the diurnal cortisol rhythm (Sephton et al., 2000).
The diurnal cortisol rhythm has been divided into several key components which provide complementary information. Most often examined are: the average level of cortisol across the day (daily average cortisol or DAC); the size of the post-awakening surge, called the cortisol awakening response (CAR); and the diurnal cortisol slope (DCS), the degree of change in cortisol from morning to evening over the waking day (Adam and Kumari, 2009).
Early research on cortisol rhythms generally focused on DAC (Yehuda et al., 1990; Gunnar et al., 2001; Nicolson, 2004). Since its discovery in the late 90’s (Pruessner et al., 1997), the CAR has also received extensive research attention, with reviews and meta-analyses D Steptoe, 2009; Fries et al., 2009; Clow et al., 2010).
An accumulating body of research focusing on the DCS suggests that it is sensitive to emotional and psychosocial stress (Adam and Gunnar, 2001; Adam et al., 2006; Doane and Adam, 2010) and related to health outcomes (Sephton et al., 2000; Matthews et al., 2006; Kumari et al., 2009; Doane et al., 2013), with both adverse experience and worse health being associated with a flatter DCS across the waking day. It has therefore been proposed that a flattened DCS may be one mechanism by which stress influences negative health outcomes (Sephton and Spiegel, 2003; Adam and Kumari, 2009).
Cortisol has important regulatory effects throughout the body and brain, impacting arousal, energy and metabolic processes, immune and inflammatory system functioning, and mood and sexual behavior (Sapolsky et al., 2000). Cortisol’s diurnal variation may be an important element of its regulatory actions; indeed, cortisol is one pathway by which central circadian rhythms are signaled to multiple peripheral biological systems (Bass and Lazar, 2016; Man et al., 2016). We argue here that disruption of cortisol’s circadian pattern and signaling may affect the functioning of a diverse set of central and peripheral systems, with these effects cascading over time to contribute to a wide variety of negative health outcomes.
For example, prior studies have found associations between flatter cortisol rhythms and depression (Doane et al., 2013), fatigue (Bower et al., 2005b; Kumari et al., 2009), cardiovascular disease (Matthews et al., 2006), and mortality among both breast cancer patients and in community samples (Sephton et al., 2000; Kumari et al., 2011). Findings have, however, been inconsistent, and researchers have not systematically summarized the existing research, or fully explicated the meaning of the DCS or the potential mechanisms by which it may be related to mental and physical health outcomes. Since the early 2000’s (Gunnar and Vazquez, 2001), no systematic reviews on the DCS have been conducted. Moreover, no meta-analyses have been conducted either on the effects of psychosocial experience on DCS or on its associations with health outcomes. The current manuscript addresses the latter question, with an eye to better understanding: a) what is the association between DCS and health (in particular, the average magnitude and direction of the associations as well as its consistency across studies) b) whether the DCS relates to certain types of health outcomes more strongly than to others, c) the meaning of the DCS and the mechanisms by which it may relate to health outcomes, and d) how methodological variations in study design and DCS measurement may contribute to variations in study effect sizes.
1.3 Diurnal Cortisol Slopes: Measurement, Modeling and Moderators
Researchers have referred to the DCS in a wide variety of ways, including diurnal cortisol slopes (Adam and Kumari, 2009), diurnal cortisol declines (Cohen et al., 2006), diurnal cortisol variability (Sannes et al., 2013), diurnal cortisol rhythms (Bower et al., 2005b), and the amplitude of the circadian cortisol rhythm (Goel et al., 2009). Likewise, researchers have quantified the DCS in different ways, which vary in the number and timing of the cortisol samples across the waking day, and in approaches to calculating slope measures from those samples. For the purposes of this review, any measure that provides an indication of the magnitude of the difference between morning and evening cortisol values is considered a measure of the DCS.
Common types of slopes include: 1) wake-to-bed slopes, which examine the absolute change or rate of change in cortisol from immediately upon waking to late evening or bedtime (e.g., Adam et al., 2010; Turner-Cobb et al., 2011); 2) peak-to-bed slopes, which examine the absolute change or rate of change in cortisol from the peak of the CAR to late evening or bedtime, (e.g., Hsiao et al., 2010; Vammen et al., 2014); 3) short daytime slopes, which measure slopes over a shorter portion of the waking day, typically from several hours after waking to evening or bedtime (late decline measures are one example of this; see Hajat et al., 2013); 4) fixed time point slopes (e.g., Bosch et al., 2007; Den Hartog et al., 2003), in which samples are gathered at fixed clock times across the day (e.g., 0800h and 2000h), rather than in relation to time of waking; and 5) amplitude measures, which estimate the peak-to-trough difference of the diurnal cortisol rhythm from intensive repeated measures of cortisol values across the day (e.g., Bao et al., 2004; Fidan et al., 2013a, Fidan et al., 2013b).
For the first three cortisol slope types (i.e., wake-to-bed, peak-to-bed, and short daytime slopes), samples are timed relative to each individual’s sleep-wake schedule, or more specifically, relative to person- and day-specific time of waking. These slopes typically are quantified in one of three ways: a) taking a simple difference between the morning measure and the evening measure; b) taking a simple difference divided by the total time between the two samples; or c) using regression or multilevel growth curve modeling to predict cortisol levels across the day from time of day of measurement for each individual, with the slope obtained from the size of the person-specific beta coefficient for the effect of time of day on cortisol (e.g., Adam et al., 2006; Doane et al., 2013). Fixed-time point slopes are typically calculated using either a simple difference score from morning to evening cortisol levels or with repeated-measures ANOVA examining within-person changes in cortisol from one sampling point to the next. Amplitudes are measured using the cosinor method, which fits a cosine curve to the repeated measures cortisol data and then calculates the characteristics of the curve, including its amplitude.
Although these different types of slope measures allow researchers to measure DCS across a variety of study designs, this heterogeneity has the potential to obscure associations between DCS and health outcomes. One important debate is whether DCS should be calculated from the peak of the CAR value to evening values or from waking values to evening values, excluding the CAR. Researchers have argued for excluding the CAR from DCS measures because the CAR is influenced by different biological mechanisms than the rest of the diurnal cortisol rhythm (Clow et al., 2010; Adam et al., 2015). Although current recommendations suggest that measures should be gathered relative to individual wake times (e.g., Adam and Kumari, 2009), rather than at fixed clock time points, studies have not examined the implications of this choice. The current meta-analysis, through examining type of slope measure as a moderator of the associations between DCS and health outcomes, provides important insights into these and other measurement debates.
Beyond type of slope, other study design factors that may have implications for the size of the association found between DCS and health include the number of samples across the day used to define the slope (Hoyt et al., 2016), the number of days of salivary cortisol data collection (Adam and Kumari, 2009), whether key health behavior confounds are covaried (Adam and Kumari, 2009), and the presence of objective measurement of compliance with sampling times (Kudielka et al., 2003). In addition, given developmental changes in the HPA axis (Gunnar et al., 2009b), the age or developmental stage of participants is another factor that should be considered as a moderator of associations between DCS and health.
1.4 The Current Study
1.4.1 Study Goals
The primary goal of the current study was to provide a meta-analysis of the literature (up until January 31, 2015) assessing the associations between diurnal cortisol slopes and health outcomes. Specifically, we examined the associations between DCS and 12 subtypes of mental and physical health outcomes, namely: 1) anxiety symptoms or disorders; 2) depression symptoms or disorders (excluding bipolar depression); 3) internalizing disorders (symptom scales reflecting a mixture of anxiety and depression symptoms); 4) externalizing symptoms or disorders (a spectrum of behaviors involving anger expression, aggression and delinquency); 5) fatigue symptoms or disorders; 6) immune or inflammatory disorders; 7) obesity (including measures of body mass index or BMI, obesity, and adiposity); 8) cardiovascular disease symptoms and diagnoses; 9) cancer disease status or progression; 10) other mental health outcomes (mental health symptoms or disorders not classified as one of the above disease subtypes); 11) other physical health outcomes (physical health symptoms or disorders not classified as one of the above subtypes); and 12) mortality (death from any cause). While it is challenging to capture the existing literature on DCS and these varied health outcomes in a single meta-analysis, this comprehensive approach allows comparisons of relative effect sizes across different types of health outcomes. Through shedding light on the types of health symptoms and disorders most strongly associated with flatter cortisol slopes, this analysis may provide insights into the key biological pathways linking flattened cortisol slopes to multiple indices of poor health.
A secondary goal was to test whether the size of associations between the DCD and health outcomes would be moderated by the following factors: age of participants, type of slope measure, number of cortisol samples measured per day, number of days of data collection, and a study quality index based on the number of relevant confounds measured and accounted for in design and/or analysis.
1.4.2 Study Hypotheses
We hypothesized that a flatter DCS would be associated with worse health outcomes, across a range of health outcomes. We did not have strong hypotheses regarding which specific types of health outcomes would be most strongly associated with flatter cortisol slopes, although we expected immune and inflammatory outcomes to show robust associations, given the key role played by glucocorticoids in regulating inflammation (Silverman and Sternberg, 2012). We expected that wake-bed cortisol slopes would show stronger associations with health than other types of slope measures, that studies with more samples per day and more days of measurement would show stronger associations, and that studies utilizing objective monitoring of sampling compliance would reveal stronger associations than studies not utilizing objective monitoring. We expected to see DCS-health associations across multiple age groups, with effects potentially being larger in older age groups due to longer histories of stress exposure or more advanced disease processes.
Discussion
1. Overview of Primary Results
Results of this systematic review and meta-analysis provide evidence to support prior assertions that flatter diurnal cortisol rhythms across the day are associated with poorer mental and physical health outcomes (Adam and Kumari, 2009). Notably, effects were both significant and in the predicted direction (flatter slopes associated with worse health outcomes) for 10 out of the 12 physical and mental health outcomes assessed. The significant outcomes included depression, internalizing disorders, externalizing disorders, fatigue, immune/inflammatory outcomes, BMI/obesity, cancer, other mental health outcomes, other physical health outcomes, and mortality.
The average effect size across all health outcomes was .147, and ranged from .09 to .29 for the significant individual health outcomes, with the largest effect size found for immune/inflammatory outcomes. These effect sizes were larger than the average effect sizes previously reported in a meta-analysis of psychosocial variables predicting the cortisol awakening response (CAR) (Chida and Steptoe, 2009), but still relatively small on average. Effects may have been small in size for a number of reasons. There was significant variability in effect directions and sizes, overall and within most of the separate health outcomes. Indeed, one purpose of the meta-analysis was to summarize these varying effect directions and sizes systematically with the goal of revealing the combined effect direction and size across studies. Potential contributions to varying and small effect sizes include: wide variation in type of study population (such as age of participant, or community vs. patient populations); variation and imprecision in DCS measurement and modeling strategies; and imprecision in measurement of health outcomes. Most studies related diurnal cortisol slopes assessed during one time period (generally with the cortisol assessment involving multiple samples over multiple days, summarized into a single DCS measure) to health outcomes measured at one point in time (usually concurrently). Both DCS measures and health measures at any point in time provide “snapshots” of complex and dynamic biological and disease processes. Both cortisol and disease symptoms or diagnoses are subject to state variation that may contribute to measurement error, particularly if not properly accounted for in study design or statistical methods. Given these methodological issues, it is possible that the effect sizes reported here underestimated the true size of DCS-health associations. On the other hand, the tendency for null results to be under-reported in the published literature may have contributed to an upward bias to our average effect size. We note, however, that our publication bias analysis suggested that publication bias had only a small effect on our results.
2. Implications of Primary Results
What are the implications of our finding that flatter diurnal cortisol slopes were related to such a wide variety of negative health outcomes? The broad array of health outcomes with which flatter DCS were associated argues against very specific disease processes, and towards some form of more general, shared mechanism common to multiple disease states. The generality of the DCS-health associations, along with the fact that the majority of the studies in the meta-analysis were cross-sectional in nature, led us to consider four possible explanations for the observed associations between flatter DCS and negative health outcomes: i) direct causal explanation, ii)reverse-causal explanation, iii) shared-causation explanation, and iv) cascading effects explanation. Below we describe each of these possibilities and, based on our results, suggest the likelihood that each of these mechanisms may be at work. We also propose a new concept, stress-related circadian dysregulation or SCiD. We recognize these interpretations and extrapolations are somewhat speculative; our goal in presenting them is to stimulate further research designed to more fully understand the mechanisms underlying the development of DCS-health associations.
2.1. Direct Causal Explanation (HPA axis dysregulation as primary)
In this frequently proposed scenario, flattened diurnal cortisol rhythms precede and contribute to dysregulations in multiple downstream biological and behavioral systems, including inflammation, metabolism, energy, appetite, etc. These dysregulations in turn contribute to the development of specific disease outcomes. Supporting this explanation, considerable evidence indicates glucocorticoids regulate multiple other aspects of biology relevant to mental and physical health.
In understanding associations between DCS and mental health outcomes, it is important to note that receptors for glucocorticoids are present in almost every cell and organ in the body. Glucocorticoids also cross the blood-brain barrier to reach glucocorticoid and mineralocorticoid receptors (GRs and MRs, respectively) in the brain, including in limbic regions centrally involved in emotional and behavioral functioning (de Kloet et al., 2005; McEwen, 2007). Glucocorticoids impact neural systems associated with arousal, reward, fear/threat, and loss (McEwen, 2007), all key components of the Research Domain Criteria (RDoC) dimensional approach to studying psychopathology (Cuthbert and Insel, 2013). These findings provide a basis for the association between altered diurnal cortisol functioning and symptoms of multiple mental health disorders. Higher basal cortisol, particularly in the evening hours, has been implicated in depression (Dahl et al., 1991) whereas lower basal cortisol has been connected to externalizing disorders (Adam et al., 2007). Flatter diurnal cortisol rhythms contain elements of both hypo- and hyper-cortisolism (including low morning and/or high evening cortisol levels), which may explain why they were associated with both internalizing and externalizing disorders in the current analysis. The fact that the association between DCS and anxiety disorders showed a trend in the opposite direction from the other health outcomes (steeper DCS with greater anxiety) requires further investigation. Generally, anxiety has been differentiated from many of the other health disorders discussed here in that it has been associated with hyper-arousal, particularly in the form of elevated physiological arousal (Clark and Watson, 1991), rather than hypo-arousal. Elevated morning cortisol, resulting in a steeper DCS, could contribute to the heightened arousal of anxiety. Further research should investigate this possibility, and should also distinguish between subtypes of anxiety disorders, which are heterogeneous in symptomatology, and may therefore relate in differing ways to cortisol.
In understanding associations between DCS and physical health disorders, it is notable that cortisol plays a role in regulating appetite, metabolism, fat deposition, and visceral adiposity in particular (Epel et al., 2001; Rosmond, 2005), providing a possible mechanism for associations between flatter cortisol slope and the BMI, obesity, and adiposity group of outcomes examined here. Perhaps somewhat surprising in our results was a lack of significant overall association between diurnal cortisol slopes and cardiovascular outcomes. However, only four studies were available in the cardiovascular outcome category. Interestingly, diurnal cortisol slopes significantly prospectively predicted increased CVD-related mortality in one of the mortality studies, with a notable hazard ratio of 1.87. More studies of the relationship between DCS and CVD are needed.
The strongest effect size was found for associations between cortisol and immune and inflammatory outcomes. This finding was perhaps not surprising, given that glucocorticoids are key regulators and modifiers of immune and inflammatory system biology (McEwen, 1998; Webster et al., 2002; Silverman and Sternberg, 2012). Among its immunoregulatory effects, glucocorticoids impact natural killer cells which play a key role in tumor suppression and cancer disease progression and mortality (Sephton and Spiegel, 2003). This relationship provides one plausible mechanism for the meta-analytic association between DCS and cancer progression.
Findings of associations between flatter DCS and fatigue also are not surprising, given cortisol’s important role in energy regulation and prior studies showing waking with lower cortisol levels are associated with increased fatigue (Adam et al., 2006). The presence of lower morning cortisol levels in many instances of flatter DCS help explain why flatter DCS are associated with disorders previously characterized as due to hypocortisolemia (fatigue and immune and inflammatory disorders) (Chrousos and Gold, 1992).
Another potential mechanism by which flatter DCS may have negative effects on peripheral biological systems and related health outcomes, is through the impact of glucocorticoids on peripheral clock gene expression. Diurnal rhythmicity in cortisol is thought to play an important mediating role in synchronizing peripheral biological clocks with the central circadian clock mechanism of the suprachiasmic nucleus (SCN) (Nader et al., 2010; Cermakian et al., 2014; Bass and Lazar, 2016; Man et al., 2016). A loss of HPA-axis signaling is associated with disruptions of peripheral circadian biology (Scheiermann et al., 2013).
Overall, there are plausible biological pathways supporting the possibility that dysregulation of the DCS may play an etiological role in the development of various forms of disease by way of effects on other aspects of central and peripheral biology. Our meta-analytic finding that prospective studies (those using baseline DCS measures to predict future health outcomes) were significant, with similar effect sizes to cross-sectional studies, also lends some support for the direct causal explanation.
2.2. Reverse-causality: HPA dysregulation as secondary
In the reverse-causality explanation, the biological changes, symptoms, and psychological stress associated with the onset or persistence of a specific disorder may lead to an alteration (flattening) of the DCS. Given that the bulk of the research examined was cross-sectional in nature, it is possible that a flattened DCS is a symptom, or a consequence, of a prior disease state. Many diseases result in physical pain and psychological stress. Consequently, a flattened DCS could be caused by pathophysiological or stress-related changes resulting from the experience of the disease itself. In one example, the past experience of depression was shown to have an ongoing or “scar effect” (Doane et al., 2013) on DCS. In this study, individuals having experienced past depression showed a flatter DCS. In the same sample, having a flatter DCS did not predict the later onset of depressive symptoms (Adam et al., 2010). Together, these findings suggest a reverse-causal explanation deserves more attention in future research.
2.3. Shared Causality: Primary Role for Inflammation, Clock Gene Biology, or Sleep?
In a shared causality explanation, a third factor, such as alterations in immune/inflammatory biology or dysregulations of clock gene expression and/or sleep, may cause both a flattening of the DCS and the pathophysiological changes that lead to the development of multiple disorders.
2.3.1. Primary Role of Inflammation?Given that we found the largest effect sizes for the associations between DCS and immune and inflammatory outcomes in the current study, we give particular attention to a possible primary role of inflammation. Pro-inflammatory cytokines are stress-sensitive (Steptoe et al., 2007) and they impact the activity of many other central and peripheral biological systems, including the HPA axis and clock gene activity (Cavadini et al., 2007; Cermakian et al., 2013; Cermakian et al., 2014; Verburg-van Kemenade et al., 2017). Pro-inflammatory cytokines such as IL-1β and TNF-α alter the activity of clock genes (Ohdo et al., 2001; Cavadini et al., 2007) and clock genes, both central and peripheral, contribute to regulation of the DCS. Inflammation also plays a key role in the development of cardiovascular disease (Juonala et al., 2006). Inflammation has been strongly implicated in fatigue and in behavioral withdrawal; elevated inflammation is therefore hypothesized to be one pathway in the development of major depressive disorder (Dantzer et al., 2008; Miller and Raison, 2016). Importantly, it is not just elevated inflammation, but loss of appropriate circadian variability in immune/inflammatory factors that may play a role in negative health outcomes (Arjona et al., 2012; Scheiermann et al., 2013; McEwen and Karatsoreos, 2015; Man et al., 2016).
2.3.2. Primary Role of Clock Gene Expression?Another potential shared causal explanation is that a primary dysregulation of central circadian biology (originating from alterations in clock gene expression in the SCN) causes downstream effects on multiple systems including the HPA axis and inflammation, with consequences for a wide range of disease processes. Estimates suggest that up to 10% of the human genome is under circadian control (Scheiermann et al., 2013). The fact that the measurement approach emerging from circadian biology studies (amplitude measures obtained from cosinor analyses) showed the strongest effect sizes suggest that greater attention to the literature and methods of circadian biology or chronobiology (Otsuka et al., 2016) is warranted in understanding DCS-health associations.
2.3.3 Primary Role of Sleep?Shortened sleep, and/or social jetlag has been found to affect both the DCS as well as metabolic, inflammatory and behavioral outcomes (Spiegel et al., 1999; Sadeh et al., 2003; Doane et al., 2010; Niu et al., 2011; Zeiders et al., 2011; Rutters et al., 2014; Turek, 2016). Notably, a number of the studies reviewed in the current meta-analysis controlled for sleep hours or sleep-wake timing, and still found DCS-health associations (e.g., Doane et al., 2011; Jarcho et al., 2013; Sephton et al., 2013). However, additional sleep characteristics (e.g., night awakenings, sleep latency, sleep efficiency, and sleep architecture) deserve attention as potential contributors to dysregulations in diurnal cortisol rhythms and other health-relevant biological systems.
2.4 Cascading Effects Explanation
In what we are calling the cascading effects explanation, transactional and cascading changes across multiple stress-sensitive biological systems mutually reinforce each other. In this explanation, there could be multiple initial sources of dysregulation. Regardless of the initial source or system of dysregulation, interacting and cascading changes ultimately lead to multi-systemic biological dysregulation, of which a flatter DCS is both an indicator and a precipitating and reinforcing factor. Given the transactional nature of the regulation of interrelated biological systems, and our findings of associations between flatter DCS and multiple health outcomes, it seems plausible that reciprocal and cascading interactions among clock gene mechanisms, sleep, cortisol, inflammation, fatigue, appetite, behavior, and social and psychological experiences jointly contribute to the observed associations between flatter DCS and multiple types of negative health outcomes.
2.5 Stress-related Circadian Dysregulation (SCiD)
Importantly, given evidence that dysregulations in circadian processes may be initiated and maintained by psychosocial stress (Van Reeth et al., 2000; Hall et al., 2004; Sadeh et al., 2004), we propose that flatter DCS and alterations in other aspects of circadian biology are signs of what we are terming “stress-related circadian dysregulation (SCiD).” We suggest that SCiD, examined across multiple biological systems, should be a key focus of future research on stress and health. Future research is need both tracing the psychosocial origins of circadian dysregulations, and the pathways by which subtle initial stress-induced circadian dysregulations over time cascade into major mental and physical health disorders. Multiple co-regulatory systems are likely involved in the development of SCiD suggesting that interventions at any one of a number of levels (e.g. psychological, behavioral, or biological) could help to correct SCiD. For example, altered health behaviors, reduced stress, improved mood, better control of inflammation, and better sleep could all play a role in improved circadian regulation. Interventions simultaneously directed at multiple of these levels may be most effective. Importantly, the SCiD theory implies that in any intervention, it is the righting of rhythms that is important, more so than the righting of levels. Any therapies, including pharmacologic ones, should have the restoration of expected circadian rhythms as a key goal.
3 Moderator Effects
In addition to examining the effect sizes of DCS on various health outcomes and considering the role of DCS in the development of health disorders, another key goal of this study was to examine potential methodological moderators of DCS-health effect sizes. Although results were relatively robust to moderation by study characteristics, the moderation analyses provide important considerations for the design and analysis of future DCS research.
3.1 Age
Despite significant variability in effect sizes by age category, all of the age categories except one were significant. Moreover, all age category effect sizes were in the expected direction of a flatter DCS being associated with worse health outcomes. The one age category that was not significant was infants/toddlers, which included only two studies. Yet, even the infants/toddlers age category showed a trend in the expected direction. Effect sizes were larger for adults than for children of all ages, but were not larger for older adults, such that our hypothesis of increasing effect sizes among older age groups was not consistently supported.
3.2 Slope Type
In examining type of cortisol slope measure, the five studies using formal circadian measures to capture degree of circadian variation (amplitude measures derived from cosinor analysis) showed the strongest average effect size. The circadian nature of the phenomenon being studied suggests that greater use of this statistical approach is warranted for those salivary studies that have relatively intensive repeated measurement of cortisol levels across the day or multiple days. Wake-to-bedtime cortisol slopes and fixed time point slopes also showed reasonable effect sizes. Peak-to-bedtime slopes showed slightly smaller (but still significant effect sizes). Short daytime slopes were the only type of slope measure that did not reveal significant associations with health outcomes. The weaker effect sizes for peak-to-bedtime slopes and short daytime slopes could be due to the fact that these slope measurements are influenced by levels of the CAR, which is a separate index of cortisol regulation (Clow et al., 2010).
3.3 Numbers of Samples Per Day
Interestingly, studies using only two samples or greater than seven samples per day did not, on average, reveal significant associations with health outcomes. Studies using between three and six samples revealed significant and comparable effect sizes to each other. These results suggest 3 to 6 samples may be the optimal number of samples to gather per day. This conclusion is reinforced by a recent study showing that using 5 or 6 samples per day to estimate the DCS closely approximates slopes derived with a more intensive protocol (Hoyt et al., 2016).
3.4 Number of Days
It is particularly noteworthy that more days of data did not appear to increase the size of association between diurnal cortisol slopes and health outcomes (Adam et al., 2006). This is interesting because the DCS is subject to considerable state-related variation. Indeed, it has been noted that DCS measures are not highly stable across days, months or years, making it more surprising that single (or even multiple) day DCS measures were associated with negative health outcomes (Ross et al., 2014). The fact that associations were found with health outcomes, despite the established high level of state-related DCS variation, suggests that the “signal” associated with dysregulated slopes must be strong. Repeated measurement of DCSs over time could be combined with modeling approaches attempting to isolate and examine the predictive power of state vs. trait components of DCS variance.
3.5 Study Quality and Objective Compliance
Our index of study quality, which included factors such as inclusion of health behavior covariates and use of objective compliance indicators, did not significantly predict the size of association between DCS and health outcomes. However, when separated out as its own indicator, studies using objective compliance monitors revealed significantly and notably stronger average effect sizes for associations between DCS and health outcomes than those not employing such monitors. This meta-analytic finding was particularly noteworthy because very few studies (only 16.3% of studies and 9.5% of the findings analyzed) used objective/electronic compliance monitors, despite such methods being strongly recommended by experts in the field (Kudielka et al., 2003; Stalder et al., 2016). Our meta-analytic findings further support the importance of these recommendations.
3.6 Cross-sectional vs. Prospective
Of relevance to the interpretation of our findings, prospective studies showed comparable effect sizes to cross-sectional studies, and strong effects were found for the few prospective studies of mortality using hazard ratios as the effect size index. The presence of significant results for prospective studies lends support to the possibility that disruptions of the DCS may play an etiological role in the development or progression of at least some of the health outcomes assessed here. This finding is most in line with either the direct effects or cascading effects explanations outlined above.
4 Suggestions for Future Research
Based on our review of the literature as well as results of moderator analyses, we have several suggestions for the design and reporting of future DCS research. First, because this is the first meta-analysis of the associations between diurnal cortisol slopes and health outcomes, we took a “forest” approach of examining associations across multiple health outcomes rather than choosing only one health outcome to examine in detail. Building from the current analysis, future meta-analyses can focus on individual health outcomes/domains, updating findings for particular health outcomes as the latest studies in that domain become available, and exploring specific mechanisms for particular health domains in more detail. Second, additional meta-analyses are also needed to identify what types of stress-related or psychosocial variables are most predictive of a flatter DCS, and over what time frames. Third, researchers need to be cognizant of which type of DCS they are measuring, from the design phase through the analysis and reporting phases of their research. This includes researchers being aware of, and justifying their choices regarding whether CAR variance is included or excluded from the DCS measure. Fourth, it may be helpful to disaggregate contributions of low morning cortisol and high evening cortisol levels to a flatter DCS for understanding mechanisms associated with different disease outcomes. Are associations with particular health outcomes due to lower morning levels, higher evening levels, or a combination of both? Fifth, studies should use more than two cortisol samples per day to estimate slopes. This meta-analysis research suggests 3 to 6 samples per day are sufficient for demonstrating links between the DCS and health outcomes. Other recent work suggests including 5–6 samples per day is a good balance between accuracy of DCS measurement and participant demand (Hoyt et al., 2016). When chronometric approaches are employed, more intensive sampling will be required. Sixth, this study highlights the need for improved reporting of details of study results. Examples of improved reporting includes provision of raw correlations between constructs of interest, standardized coefficients in the case of regression and HLM models, and means accompanied by SD or SE measures in the case of group comparisons. Seventh, there is a dearth of studies using prospective approaches (less than 10% of the findings reviewed), limiting interpretation of the causal direction of effects. Future research should employ longitudinal designs, with repeated measures of both DCS and health outcomes to assess causal and potentially cascading effects. Eighth, future studies should measure potential downstream biological pathways by which a flatter DCS relates to health outcomes – including not just fixed but diurnal/circadian measures of factors such as arousal, activity, cognition, health behaviors, sleep, metabolism and inflammation. Finally, it will be important to determine whether naturalistic or experimental reductions in stress, or chronobiologically-informed behavioral (e.g., sleep, light therapy) and pharmacological interventions, can cause reversal of DCS flattening, with resulting improvement in health outcomes.
5 Limitations
There are a number of limitations to the current study. First, we searched for combined terms, such as ‘diurnal cortisol’ rather than employing a more flexible approach of using separate word combinations such as ‘cortisol’ and ‘diurnal’, which may have resulted in some missed studies. In addition, other potential search terms such as ‘decline’, ‘variability’ and ‘drop’ became apparent later in our analysis; again, omission of these search terms may have resulted in some missed studies.
Second, we had to rely on back-calculating some effect sizes from p-values and sample size information, in some cases using non-exact p-values (e.g., p < .05). However, we decided utilizing these estimated effect sizes was superior to excluding a large number of studies. Upon exclusion of studies for which we used p-values and sample sizes to derive effect sizes, a similar (to slightly larger) overall effect size was found. We caution that average effect sizes should not be regarded as exact. Rather, they should be seen as indicators of general patterns of results and of the relative strengths of associations across various health outcomes.
Third, we utilized covariate-adjusted models in some cases (where relevant covariate-adjusted effect sizes were available), but not in others. We believe covariate adjusted models provided the best test of the unique association between cortisol and the health outcome, net of confounders. Utilizing covariates or not did not make a difference in effect sizes, so we are less concerned about lack of comparability across studies on this variable. However, it should be noted that we did not examine inclusion of demographic factors such as socioeconomic status or race/ethnicity as potential effect size moderators; whether or not these variables matter for associations between DCS and health should be addressed in future research.
6 Conclusions
This systematic review and meta-analysis provides evidence that reduced diurnal variation in cortisol (i.e., loss of circadian amplitude or a flattening of the DCS) is related to a wide range of negative health outcomes. Flatter diurnal cortisol slopes were associated with worse health for overall health, and for 10 of 12 subtypes of mental and physical health outcomes examined. Cardiovascular disease and anxiety outcomes were not significantly associated with a flatter DCS in meta-analytic analyses, whereas immune and inflammatory system dysregulation, fatigue, cancer, obesity/BMI/adiposity, externalizing symptoms, internalizing symptoms, depression symptoms and disorders, other mental health, other physical health, and mortality outcomes were all associated with a flatter DCS. Whether changes in the DCS are a mechanism by which disease states emerge, an effect of emerging disease symptoms, or a marker of other forms of circadian dysregulation remain to be determined in future research, and may vary for differing disease states. Regardless, our meta-analysis makes it clear that diurnal cortisol slopes are meaningfully related to health, and that a flatter DCS is a strong candidate mechanism for explaining associations between psychosocial stress and a wide range of mental and physical health outcomes.
Acknowledgments
The authors’ time on this project was supported, in part, by Faculty and Graduate Fellowships from the Institute for Policy Research at Northwestern University. The funder played no role in the conduct of this research, the analyses, interpretations or conclusions.