Hostname: page-component-7b9c58cd5d-bslzr Total loading time: 0.001 Render date: 2025-03-15T14:32:18.693Z Has data issue: false hasContentIssue false

Childhood adversity moderates the influence of proximal episodic stress on the cortisol awakening response and depressive symptoms in adolescents

Published online by Cambridge University Press:  22 November 2017

Lisa R. Starr*
Affiliation:
University of Rochester
Kimberly Dienes
Affiliation:
University of Manchester
Catherine B. Stroud
Affiliation:
Williams College
Zoey A. Shaw
Affiliation:
University of Rochester
Y. Irina Li
Affiliation:
University of Rochester
Fanny Mlawer
Affiliation:
University of Delaware
Meghan Huang
Affiliation:
University of Rochester
*
Address correspondence and reprint requests to: Lisa R. Starr, Department of Clinical and Social Sciences in Psychology, University of Rochester, 491 Meliora Hall, Box 270266, Rochester, NY 14627; E-mail: lisa.starr@rochester.edu.
Rights & Permissions [Opens in a new window]

Abstract

Childhood adversity (CA) is known to predict sensitization to proximal stressors. Researchers have suggested that disruptions in hypothalamus–pituitary–adrenal axis functioning may be a biological mechanism. If so, CA may predict altered associations between proximal life stress and markers of cortisol secretion. We examined whether CA moderates associations between recent episodic stress and (a) the cortisol awakening response (CAR), and (b) depressive symptoms, in 241 adolescents aged 14–17 years (cortisol n = 196). Salivary cortisol was sampled at 0, 30, and 60 min postawakening for 2 days. The CAR was calculated as the area under the curve with respect to increase and waking cortisol. CA and episodic stress were assessed using contextual-threat-method-coded objective interviews. CA significantly interacted with episodic stress to predict both the CAR and depression. Among those with low CA, episodic stress predicted increased CAR but did not predict depression. For adolescents with high CA, episodic stress predicted lower CAR and higher depression. These interactions were found only for independent (uncontrollable, fateful) events, and not for dependent (self-generated) stress. Increased allostatic load resulting from CA exposure may interfere with adolescents' ability to optimally regulate their CAR in relation to recent stress, contributing to increased depression risk.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2017 

Researchers seeking to understand the impact of early adversity on trajectories of psychopathology have increasingly utilized multiple levels of analyses to capture risk and resilience processes in both biological and behavioral strata (Cicchetti & Blender, Reference Cicchetti and Blender2004). In particular, depression is a multifaceted phenomenon that affects not only behavioral and affective systems but also cognition, interpersonal processes, and biological systems including neurobiological and neuroendocrinological processes. Understanding the complex framework within which each of these pathways connects and contributes to the development of depression is a challenge that calls for research designs that utilize multiple assessment methods at different levels of analyses. Moreover, experiences that occur in childhood may initiate developmental cascades that contribute to long-term outcomes. The link between early childhood adversity and alterations in neurobiological and neuroendocrinological processes has been well established in the literature (Cicchetti & Rogosch, Reference Cicchetti and Rogosch2001; Heim, Plotsky, & Nemeroff, Reference Heim, Plotsky and Nemeroff2004; Tyrka, Burgers, Philip, Price, & Carpenter, Reference Tyrka, Burgers, Philip, Price and Carpenter2013). However, less attention has been paid to how these alterations intersect with proximal experiences, especially recent stressful events. The current study examines how early adverse experiences modify the relationship between recent episodic stress and both cortisol regulation and depression.

Childhood Adversity (CA) and Stress Sensitization

Research suggests that CA predicts increased depressive reactivity to life stress in a process termed stress sensitization. A number of studies have shown that a history of CA lowers the threshold of stressor severity required to trigger a depressive episode (Espejo et al., Reference Espejo, Hammen, Connolly, Brennan, Najman and Bor2007; Hammen, Henry, & Daley, Reference Hammen, Henry and Daley2000; Harkness, Bruce, & Lumley, Reference Harkness, Bruce and Lumley2006; La Rocque, Harkness, & Bagby, Reference La Rocque, Harkness and Bagby2014; Rudolph & Flynn, Reference Rudolph and Flynn2007) and predicts stronger associations between proximal stressors and depression and other negative outcomes (Kim et al., Reference Kim, Martins, Shmulewitz, Santaella, Wall, Keyes and Hasin2014; McLaughlin, Conron, Koenen, & Gilman, Reference McLaughlin, Conron, Koenen and Gilman2010; Shapero et al., Reference Shapero, Black, Liu, Klugman, Bender, Abramson and Alloy2014; Starr, Hammen, Conway, Raposa, & Brennan, Reference Starr, Hammen, Conway, Raposa and Brennan2014). Although there are multiple plausible mechanisms linking CA to stress sensitization (e.g., Szyf, McGowan, & Meaney, Reference Szyf, McGowan and Meaney2008), one likely pathway is via disruption of hypothalamus–pituitary–adrenal (HPA) axis development (Heim, Newport, Mletzko, Miller, & Nemeroff, Reference Heim, Newport, Mletzko, Miller and Nemeroff2008; McEwen, Reference McEwen1998).

Overview of HPA Axis and Stress Regulation

The HPA axis is a major part of the biological stress response that prepares the body to optimally respond to threat. Cortisol, the hormonal end product of the HPA axis, is often used to index HPA axis functioning. Cortisol affects multiple bodily systems, including immune functioning, energy metabolism, and neurobiological circuits (Heim & Nemeroff, Reference Heim and Nemeroff2001; Raison & Miller, Reference Raison and Miller2003); consequently, abnormalities in cortisol regulation have been linked to a wide variety of clinical and physical health problems, including depression (Chida & Steptoe, Reference Chida and Steptoe2009).

The cortisol awakening response (CAR)

Although numerous indicators of HPA axis functioning have been examined in the literature (for a review, see Granger et al., Reference Granger, Fortunato, Beltzer, Virag, Bright and Out2012), one particularly relevant to depression (and the focus of the present study) is the CAR. In addition to being released in response to environmental threat (De Kloet, Reference De Kloet2004), cortisol secretion follows a typical daily pattern, with peak concentration levels in the morning followed by declining levels throughout the day, reaching nadir at bedtime (e.g., Adam & Kumari, Reference Adam and Kumari2009; Pruessner et al., Reference Pruessner, Wolf, Hellhammer, Buske-Kirschbaum, von Auer, Jobst and Kirschbaum1997). The CAR is an elevation of approximately 50%–156% in cortisol secretion that occurs approximately 30–45 min postawakening (Clow, Thorn, Evans, & Hucklebridge, Reference Clow, Thorn, Evans and Hucklebridge2004). It is thought to be distinct from daily, or diurnal, cortisol secretion (Wilhelm, Born, Kudielka, Schlotz, & Wust, Reference Wilhelm, Born, Kudielka, Schlotz and Wust2007). Although its exact function is uncertain, it has been suggested that the CAR represents the marshaling of resources to deal with the stress of the day (Chida & Steptoe, Reference Chida and Steptoe2009; Fries, Dettenborn, & Kirschbaum, Reference Fries, Dettenborn and Kirschbaum2009; Powell & Schlotz, Reference Powell and Schlotz2012). In line with this, the “boost” hypothesis posits that the CAR serves a short-term adaptive function by mobilizing the body's resources (via influencing metabolic processes) to help meet perceived daily demands (Adam, Reference Adam2006; Fries, Hesse, Hellhammer, & Hellhammer, Reference Fries, Hesse, Hellhammer and Hellhammer2005).

Alterations in the size of the CAR are thought to reflect dysregulation in the functioning of the HPA axis and have been implicated in negative clinical and health outcomes, including depression (Adam et al., Reference Adam, Doane, Zinbarg, Mineka, Craske and Griffith2010; Chida & Steptoe, Reference Chida and Steptoe2009). Two recent prospective analyses of the same adolescent sample indicated that a greater than average CAR predicted onsets of major depression (Adam et al., Reference Adam, Doane, Zinbarg, Mineka, Craske and Griffith2010; Vrshek-Schallhorn et al., Reference Vrshek-Schallhorn, Doane, Mineka, Zinbarg, Craske and Adam2013); other studies suggest that elevated waking cortisol in at-risk individuals prospectively predicts depression (Goodyer, Bacon, Ban, Croudace, & Herbert, Reference Goodyer, Bacon, Ban, Croudace and Herbert2009; Goodyer, Herbert, Tamplin, & Altham, Reference Goodyer, Herbert, Tamplin and Altham2000; Halligan, Herbert, Goodyer, & Murray, Reference Halligan, Herbert, Goodyer and Murray2007; Harris et al., Reference Harris, Borsanyi, Messari, Stanford, Cleary, Shiers and Herbert2000). In addition, loneliness and internalizing symptoms have also been associated with a greater CAR (Doane & Adam, Reference Doane and Adam2010; Saridjan et al., Reference Saridjan, Velders, Jaddoe, Hofman, Verhulst and Tiemeier2014; Saxbe, Reference Saxbe2008). However, existing literature also shows that a smaller than average CAR can reflect burnout and other health problems (Chida & Steptoe, Reference Chida and Steptoe2009) and has also been associated with various negative outcomes, including trait loneliness, internalizing symptoms, posttraumatic stress disorder (PTSD), rumination, and fatigue (Gartland, O'Connor, Lawton, & Bristow, Reference Gartland, O'Connor, Lawton and Bristow2014; Keeshin, Strawn, Out, Granger, & Putnam, Reference Keeshin, Strawn, Out, Granger and Putnam2014; Kuehner, Holzhauer, & Huffziger, Reference Kuehner, Holzhauer and Huffziger2007; McGinnis, Lopez-Duran, Martinez-Torteya, Abelson, & Muzik, Reference McGinnis, Lopez-Duran, Martinez-Torteya, Abelson and Muzik2016; Sladek & Doane, Reference Sladek and Doane2015). Thus, it appears that dysregulation in the CAR is associated with risk for depression and related problems, although the exact nature of this relationship may be complex and methodologically dependent (see Stalder et al., Reference Stalder, Kirschbaum, Kudielka, Adam, Pruessner, Wust and Clow2016), requiring further elucidation.

CA and cortisol regulation

HPA axis activation may be adaptive in the short term by allowing the body to manage the stressor at hand. However, chronic HPA axis activation due to repeated exposure to stressors during vulnerable developmental periods may lead to sustained alterations in HPA axis functioning and related neural structures and corresponding problems with stress regulation. Aligning with this model, a large number of studies have linked negative childhood experiences to cortisol dysregulation (Cicchetti & Rogosch, Reference Cicchetti and Rogosch2012; Heim et al., Reference Heim, Newport, Mletzko, Miller and Nemeroff2008; McCrory, De Brito, & Viding, Reference McCrory, De Brito and Viding2010; Tarullo & Gunnar, Reference Tarullo and Gunnar2006; Trickett, Negriff, Ji, & Peckins, Reference Trickett, Negriff, Ji and Peckins2011).

Central to research examining the impact of CA on the HPA axis is the examination of allostasis, or the body's response to changes in the environment, including response to stressors. Allostasis involves many biological mechanisms that help an organism respond to threat, such as elevations and return to homeostasis in heart rate, breathing, and cortisol secretion. However, repeated stress exposure during critical periods of development may lead to allostatic load or a breakdown in the allostatic system (McEwen & Seeman, Reference McEwen and Seeman1999).

Although cortisol elevations occur in response to environmental stress, over time this pattern may change, such that the HPA axis inadequately responds to the presence of environmental stressors, leading to negative health outcomes including psychopathology (McEwen, Reference McEwen2004). In accordance with this pattern, meta-analytic findings indicate that time since stressor onset is negatively correlated with HPA axis activity (Miller, Chen, & Zhou, Reference Miller, Chen and Zhou2007). This suggests that stress exposure leads to hypercortisolism initially, but over time, in response to prolonged HPA axis activation as a result of chronically stressful conditions, hypocortisolism develops (e.g., Gunnar & Fisher, Reference Gunnar and Fisher2006; Miller et al., Reference Miller, Chen and Zhou2007; Tarullo & Gunnar, Reference Tarullo and Gunnar2006).

Consistent with this model, and looking at the CAR specifically, CA has been associated with both greater than average CAR (Engert, Efanov, Dedovic, Dagher, & Pruessner, Reference Engert, Efanov, Dedovic, Dagher and Pruessner2011; Gonzalez, Jenkins, Steiner, & Fleming, Reference Gonzalez, Jenkins, Steiner and Fleming2009; Lu, Gao, Huang, Li, & Xu, Reference Lu, Gao, Huang, Li and Xu2016; Lu et al., Reference Lu, Gao, Wei, Wu, Liao, Ding and Li2013) and smaller than average CAR (Meinlschmidt & Heim, Reference Meinlschmidt and Heim2005; Quevedo, Johnson, Loman, LaFavor, & Gunnar, Reference Quevedo, Johnson, Loman, LaFavor and Gunnar2012). The disparate findings can likely be partially attributed to methodological and demographic variables (e.g., pubertal development, CAR calculation method, and the type, timing, and severity of early adversity; Chida & Steptoe, Reference Chida and Steptoe2009; Gustafsson, Anckarsäter, Lichtenstein, Nelson, & Gustafsson, Reference Gustafsson, Anckarsäter, Lichtenstein, Nelson and Gustafsson2010; Miller et al., Reference Miller, Chen and Zhou2007; Quevedo et al., Reference Quevedo, Johnson, Loman, LaFavor and Gunnar2012). However, the variation in findings may also reflect legitimate complexities of HPA axis functioning. This may include the existence of untested moderators, such as the presence of recent stressors. Nevertheless, no studies have examined the interactive effect of CA and proximal episodic stress in the prediction of the CAR. The current study addresses this gap.

CA as a moderator of the association between recent episodic stress and the CAR

If the CAR represents an adaptive mechanism for managing stressful contexts, one would expect that among those with optimal HPA axis functioning, the CAR would be positively correlated with recent significant life stressors. Stressful life events predict continued hassles in daily life (Wagner, Compas, & Howell, Reference Wagner, Compas and Howell1988), and an elevated CAR may allow for recruitment of resources necessary to cope with ongoing demands and promote allostasis (McEwen, Reference McEwen1998), potentially protecting against negative outcomes such as depression. Recent life stress is associated with elevations in the CAR (see Chida & Steptoe, Reference Chida and Steptoe2009, for a meta-analysis) and other markers of diurnal cortisol activity (Stroud, Chen, Doane, & Granger, Reference Stroud, Chen, Doane and Granger2016). However, it is possible that exposure to childhood adversities could disrupt this process. A developmental history of repeated activation of the HPA axis may lead to increased allostatic load, which could be reflected in inadequate responding (i.e., decreased CAR) to stressful contexts (as denoted by high recent episodic stress; McEwen, Reference McEwen2004). This may in turn leave adolescents with fewer metabolic resources to manage the aftermath of these recent stressors, making them more vulnerable to depression. It has been hypothesized that youths who develop hypocortisolism in response to chronic stress exposure may be less able to adapt to future stressors (Cicchetti & Rogosch, Reference Cicchetti and Rogosch2012), potentially accounting for stress sensitization effects. Building on these ideas, the current study examines whether CA moderates the association between recent episodic stress and (a) the CAR, and (b) depressive symptoms.

Sensitization to independent versus dependent stressors

The effect of environmental stress on the HPA axis appears to be contingent upon the qualitative nature of the stressors (Miller et al., Reference Miller, Chen and Zhou2007; Stroud et al., Reference Stroud, Chen, Doane and Granger2016). For example, the literature on naturalistic life stress draws a crucial distinction between independent and dependent stressors (Hammen, Reference Hammen2005). Independent stressors are fateful events outside of the individual's control (e.g., death of loved one or sudden loss of parental employment), whereas dependent stressors are events to which the individual has at least partially contributed (e.g., interpersonal conflict or academic failure). Thus, event independence can be considered a marker of the controllability of naturalistic events. The controllability of stress has been identified as an important dimension likely to influence HPA axis response (Dickerson & Kemeny, Reference Dickerson and Kemeny2004; Miller et al., Reference Miller, Chen and Zhou2007). In laboratory studies, uncontrollable stressors provoke a more pronounced HPA axis response (Dickerson & Kemeny, Reference Dickerson and Kemeny2004), perhaps because a lack of control makes acute stress inherently more threatening. However, experiencing more persistent uncontrollable stressors, including naturalistic stressors that are more personally and persistently impactful than laboratory stressors, may instead lead to a blunting of cortisol responses (Miller et al., Reference Miller, Chen and Zhou2007), perhaps aligning with withdrawn and learned helplessness behaviors associated with depression. In contrast, controllable stressors (such as dependent events) may lead to an increase in cortisol production to mobilize metabolic resources for coping.

A history of CA may be particularly relevant in moderating the influence of uncontrollable, independent stressors on the HPA axis. Childhood adversities are themselves inherently more likely to be uncontrollable experiences, as from a developmental standpoint, children typically lack autonomy over many aspects of their environment (Harkness et al., Reference Harkness, Alavi, Monroe, Slavich, Gotlib and Bagby2010). Independent proximal stressors may be reminiscent of these negative childhood experiences.

Consequently, children with a long history of such experiences may be more attuned to the uncontrollable nature of independent stressors, and more likely to disengage both emotionally (through increased depression) and physiologically (by failing to deploy metabolic resources for coping).

Some evidence suggests that early adversity may predict sensitization to independent, but not dependent, events, although evidence is mixed. At least two studies have shown that adolescents with a history of maltreatment require a lower threshold of independent (but not dependent) stress to trigger a depressive episode (Harkness et al., Reference Harkness, Bruce and Lumley2006; La Rocque et al., Reference La Rocque, Harkness and Bagby2014). Another study showed that independent (but not dependent) events interacted with childhood maltreatment to predict alcohol consumption among women (Young-Wolff, Kendler, & Prescott, Reference Young-Wolff, Kendler and Prescott2012). In contrast, Shapero et al. (Reference Shapero, Black, Liu, Klugman, Bender, Abramson and Alloy2014) found that childhood emotional abuse predicted stronger associations between stress and depressive symptom increases only for dependent events, and an additional study found that event independence did not influence stress sensitivity patterns (Oldehinkel, Ormel, Verhulst, & Nederhof, Reference Oldehinkel, Ormel, Verhulst and Nederhof2014). Further, in addition to CA, research suggests that depression history also sensitizes individuals to stressors (with less severe stressors required to trigger recurrences vs. first onsets; e.g., Monroe & Harkness, Reference Monroe and Harkness2005; Post, Reference Post1992), and that form of stress sensitization also appears to be stronger for independent versus dependent stressors (Stroud, Davila, Hammen, & Vrshek-Schallhorn, Reference Stroud, Davila, Hammen and Vrshek-Schallhorn2011). However, few studies have examined the discrepant impact of independent versus dependent stressors on cortisol regulation. In one exception, in a sample of early adolescent girls, Stroud et al. (Reference Stroud, Chen, Doane and Granger2016) found that independent, but not dependent, stressors predicted level of latent trait cortisol, after adjusting for. However, no studies to our knowledge have examined the CAR specifically in association with independent versus dependent stress or assessed cortisol regulation in response to dependent versus independent stressors as moderated by CA. To address this gap in the literature, we examined whether CA moderated the association between independent versus dependent stressors and (a) the CAR and (b) depressive symptoms.

Developmental Considerations

Adolescence is likely a critical period to consider these research questions given increasing biological changes and environmental challenges. Early adolescence is characterized by changes in adrenocortical functioning, including increases in basal cortisol levels and cortisol reactivity to stress (Gunnar, Wewerka, Frenn, Long, & Griggs, Reference Gunnar, Wewerka, Frenn, Long and Griggs2009; Shirtcliff et al., Reference Shirtcliff, Allison, Armstrong, Slattery, Kalin and Essex2012). This period is also accompanied by increased autonomy seeking and conflict with parents, reduced support in school environment, and greater motivation for peer acceptance and romantic experiences (Collins, Reference Collins, Montemayor and Adams1990, Reference Collins2003; Seidman, Allen, Aber, Mitchell, & Feinman, Reference Seidman, Allen, Aber, Mitchell and Feinman1994). Moreover, associations between HPA axis activity and environmental stress differ according to gender, age, and pubertal status (Gunnar et al., Reference Gunnar, Wewerka, Frenn, Long and Griggs2009; Pendry & Adam, Reference Pendry and Adam2007). These factors may heighten adolescents' reactivity to proximal stressors, resulting in a surge in onset of depressive symptoms and disorders in adolescence (Birmaher et al., Reference Birmaher, Ryan, Williamson, Brent, Kaufman, Dahl and Nelson1996; Hankin et al., Reference Hankin, Abramson, Moffitt, Silva, McGee and Angell1998; Kessler, Avenevoli, & Ries Merikangas, Reference Kessler, Avenevoli and Ries Merikangas2001; Lewinsohn, Hops, Roberts, Seeley, & Andrews, Reference Lewinsohn, Hops, Roberts, Seeley and Andrews1993). As such, CA has been associated with lower severity of proximal episodic stress prior to depression onset in adolescence (Harkness et al., Reference Harkness, Bruce and Lumley2006; Shrout et al., Reference Shrout, Link, Dohrenwend, Skodol, Stueve and Mirotznik1989). In contrast, its effect on subsequent stress reactivity in prepubertal youth and adults (Bifulco, Brown, Moran, Ball, & Campbell, Reference Bifulco, Brown, Moran, Ball and Campbell1998; Kendler, Kuhn, & Prescott, Reference Kendler, Kuhn and Prescott2004; McLaughlin et al., Reference McLaughlin, Conron, Koenen and Gilman2010; Slavich, Monroe, & Gotlib, Reference Slavich, Monroe and Gotlib2011) has been variable across studies. In a recent study, La Rocque et al. (Reference La Rocque, Harkness and Bagby2014) directly compared the relation of CA and stress sensitization across developmental periods and found that childhood maltreatment was associated with heightened sensitization to proximal stressors in adolescence, but not adulthood. Moreover, this relation was specific to independent stressors, aligning with our hypotheses. These results suggest that adolescence might be a sensitive period during which youth with a history of adversity are most sensitive to stress, but do not consider neurobiological mechanisms that may explain this association. The current study extends such findings by investigating how HPA axis functioning, and more specifically the CAR, may relate to increased sensitization to stressors in adolescents with a history of CA.

The Present Study

We examined the associations between CA, recent episodic stress, and neuroendocrinological and emotional outcomes in a sample of adolescents recruited from the community. Specifically, our hypotheses were as follows: (a) CA will moderate the association between recent episodic stress and the CAR, (b) this moderation effect will be particularly strong for independent episodic stressors, (c) CA will intensify the association between episodic stress and depression, and (d) this moderation effect will again be particularly robust for independent stressors. CA and recent episodic stressors were both assessed using gold-standard objective stress interviews coded using contextual threat methods (Harkness & Monroe, Reference Harkness and Monroe2016; Monroe, Reference Monroe2008). CA was assessed as a cumulative index of major adverse events occurring over the adolescents' lifetime (excluding the prior year), and episodic stress was assessed as a sum of past-year stressors, both in line with the notion that continued, repeated exposure to stress (as opposed to exposure to a single major event) result in greater allostatic load (Evans, Reference Evans2003; Evans, Kim, Ting, Tesher, & Shannis, Reference Evans, Kim, Ting, Tesher and Shannis2007; Lupien et al., Reference Lupien, Ouellet-Morin, Hupbach, Tu, Buss, Walker and McEwen2006).

Method

Participants

The full sample included 241 adolescents aged 14–17 years (M age = 15.90 years, SD = 1.09; 54% female) who participated with their primary caregiver. Adolescents were excluded from the study if there was evidence of pervasive developmental disorder, a prior diagnosis of bipolar or psychotic disorder, and any major physical or neurological disorder. Exclusion criteria also included English reading or language difficulties and prior participation of another household member in the study. In addition, to participate in the cortisol component of the study, adolescents could not be using any steroid-based medications, be currently pregnant, or have an endocrine disorder. Twelve participants were ineligible to participate in cortisol collection, but were permitted to participate in other study procedures.

Participants were recruited from a midsized metropolitan area in the Northeast United States. To obtain a sufficiently sized sample, we utilized multiple recruitment methods. First, 134 families (50.6%) were recruited using advertisements posted online and in the community and distributed to participating families. Special attention was given to posting flyers in socioeconomically diverse areas of the community. Second, 97 families (40.2%) were recruited using a commercial mailing list. These candidates were randomly drawn from a commercial mailing list of families identified by a survey-marketing firm as having a child in the eligible age range. Commercial mailing lists have been established as a cost-effective recruitment method that yields samples demographically comparable to random digit dialing (Wilson, Starr, Taylor, & Dal Grande, Reference Wilson, Starr, Taylor and Dal Grande1999), and have previously been used to examine internalizing disorder risk in adolescent samples (Foti, Kotov, Klein, & Hajcak, Reference Foti, Kotov, Klein and Hajcak2011). Selected families were sent a letter to provide initial details about the study, followed by phone calls from study staff to give more detailed information about the study. Third, a small number of participants (n = 10, 4.1%) were recruited using ResearchMatch, a national health volunteer registry containing a large population of volunteers who have consented to be contacted by researchers about health studies. There were no differences across recruitment method on gender, age, or racial ethnic group. However, adolescents recruited via advertisements were more likely to receive subsidized lunch at school than those recruited through alternate methods (χ2 = 10.50, p = .005). Study procedures were approved by the University of Rochester Research Subjects Review Board.

The full sample included 130 girls and 111 boysFootnote 1 (age range = 14.00–17.97). Participants identified the following racial/ethnic backgrounds: 73.9% White, 12.2% Black, 4.1% Asian, 7.1% multiracial, 2.1% other or no race reported, and 0.4% Native American. In addition, 9.1% identified as Hispanic or Latino. The median parent-reported annual family income was $80,000–$89,999. In addition, 24.1% of parents reported that their child received free or reduced-price lunch at school (an index of economic hardship). For the majority of families, the participating parent was the biological mother (87.6%); the remaining families participated with a biological father (8.7%) or other guardian (3.7%).

Measures

Depressive symptoms

Adolescents' current symptoms of major depressive disorder (MDD) were assessed using the Schedule for Affective Disorders and Schizophrenia for School-Aged Children—Present and Lifetime version (Kaufman et al., Reference Kaufman, Birmaher, Brent, Rao, Flynn, Moreci and Ryan1997), which is a semistructured diagnostic interview that has demonstrated strong validity and reliability. Consistent with prior work (e.g., Rao, Daley, & Hammen, Reference Rao, Daley and Hammen2000; Starr et al., Reference Starr, Davila, Stroud, Li, Yoneda, Hershenberg and Miller2012), to capture both major depression and subsyndromal symptoms of the disorder, trained interviewers rated the disorder dimensionally on a 5-point scale: 0 = no symptoms, 1 = mild symptoms, 2 = moderate, subthreshold symptoms, 3 = DSM-IV criteria met, 4 = DSM-IV criteria met with high severity. To assess interrater reliability, 20% of the audiotaped interviews were rated by a second coder blinded to initial ratings, with 100% reliability. To capture self-reported depression severity, adolescents also completed the 21-item Beck Depression Inventory—II (BDI; Beck, Steer, & Brown, Reference Beck, Seer and Brown1996), a widely used self-report measure of depressive symptoms with strong psychometric properties. The 21 BDI items are rated from 0 to 3 and assess affective and somatic symptoms of depression. The Cronbach α value was 0.88.

Episodic stress

Episodic stressors were assessed using the UCLA Life Stress Interview (Hammen, Reference Hammen1991), a semistructured interview developed to assess life stress in different domains. Acute or episodic life stressors over the past 12 months were assessed in six domains: close friendships, peer relationships, romantic relationships, family relationships, academic functioning, and behavioral functioning. For each event, interviewers elicited information about the surrounding context, including relevant circumstances, duration, prior experience with similar events, and available resources. An objective negative impact rating for each event was then obtained by a trained team of coders, based on the degree of impact on a typical individual within the context of the event. In cases where both parent and child nominated the same event, information from both respondents was integrated. Negative impact was rated on a scale from 1 = no negative impact to 5 = extremely severe impact. The team also rated independence of each event, which was dichotomized as dependent versus independent. A second team of coders, blinded to the original ratings, rated a subset of events with excellent reliability, interclass correlation (ICC) = 0.87. Severity scores were summed (excluding “nonevents,” rated as 1) to obtain indices of total episodic stress, total independent stress, and total dependent stress.

CA

A modified version of the Youth Life Stress Interview (Rudolph & Flynn, Reference Rudolph and Flynn2007; Rudolph et al., Reference Rudolph, Hammen, Burge, Lindberg, Herzberg and Daley2000) was administered to parents to assess adolescents' level of CA. Trained interviewers asked a series of questions to assess the adolescent's exposure to negative life events and circumstances across their entire lifetime, excluding events within the past year to distinguish from recent stressors. Probes assess potential exposure to particularly stressful or negative events and circumstances (e.g., death of a close family member or friend, separation from parents, parental conflict or separation, chronic physical illness of family members, period of significant financial difficulties, and chaotic family living circumstances). Using the same probes as those used for episodic stressors on the Life Stress Interview, the interviewer then elicited objective information surrounding the event, including context and relevant circumstances that can modify the impact of the event. A team of coders then rated the negative impact on the same scale of 1 = no negative impact to 5 = extremely severe impact, accounting for contextual factors.

Participants reported an average of 4.56 events (range = 0–13). Ratings for all lifetime events were summed to achieve an overall lifetime adversity score, excluding nonevents (those rated as 1). Reliability using independent raters yielded an ICC of 0.97.

Pubertal development

The Pubertal Development Scale (Petersen, Crockett, Richards, & Boxer, Reference Petersen, Crockett, Richards and Boxer1988) was administered for inclusion as a covariate in cortisol analyses. The Pubertal Development Scale is a self-report scale with four questions (responses ranging from 1 = has not yet begun to 4 = seems completed) assessing growth, skin changes, and body hair. Life Stress Interview girls were asked two additional questions about breast development and whether they had begun menstruating (1 = no and 2 = yes). Boys were asked two questions about changes in facial hair and voice. Responses were averaged across all items to yield an overall pubertal development scale score. For the menstruation item, a response of no was coded as 1 and a response of yes was coded as 4.

Procedure

Participating youth and their parents or guardians provided consent/assent, after which they were separately interviewed and completed a battery of questionnaires. Families were paid $160 for participation in all study procedures and entered into raffles to encourage compliance.

Cortisol

At the end of their laboratory visit, participants were given materials to collect salivary cortisol from their home. Families were given detailed verbal and written instructions on how to collect ambulatory saliva samples, and were provided with a website link with additional written instructions and a video demonstrating all procedures. Study staff and all instructional materials heavily emphasized the importance of accurate timing and reporting. Participants were instructed to collect ambulatory salivary cortisol samples four times a day for 2 consecutive days. Sample collection days were timed between Tuesday and Thursday because of well-established findings suggesting substantial differences in morning cortisol on Mondays (Kelly, Young, Sweeting, Fischer, & West, Reference Kelly, Young, Sweeting, Fischer and West2008) and on weekends (Schlotz, Hellhammer, Schulz, & Stone, Reference Schlotz, Hellhammer, Schulz and Stone2004; Friday is often included as a weekend in cortisol research; see Broderick, Arnold, Kudielka, & Kirschbaum, Reference Broderick, Arnold, Kudielka and Kirschbaum2004). Participants collected samples immediately after waking (“before you get out of bed, right after you open your eyes”), 30 min after waking, 60 min after waking, and 12 hr after waking on 2 consecutive weekdays (the final sample of the day was not used in the current analyses). Because toothpaste and certain foods and drinks can degrade or dilute salivary cortisol, adolescents were asked to refrain from brushing teeth, eating, and drinking for 30 min prior to collecting each sample (Kudielka, Hawkley, Adam, & Cacioppo, Reference Kudielka, Hawkley, Adam and Cacioppo2007). However, to accommodate school preparations, some flexibility was required around the timing of the third sample. If participants had to eat, drink, or brush teeth within the first 60 min of waking, they were asked to do so immediately after completing the second sample, and then delay the third sample to 30 min after completing those activities.

Samples were collected using Salivette® Cortisol (Sarstedt Inc.) synthetic swabs designed explicitly for determination of cortisol from saliva. To collect each sample, participants placed a swab from the container in their mouth, and let it collect saliva until it was saturated.

Participants then indicated whether they ate, drank, brushed their teeth, or participated in vigorous activity in the 30 min before each sample. Participants also indicated their waking time and how many hours they slept, and female participants provided information on their menstrual cycle. Completed samples and information forms were mailed to the lab, where samples were stored at –20 °C. Of the original sample of 241, 12 were excluded from cortisol procedures for medical reasons, and 18 declined to participate in cortisol procedures or failed to return samples, leaving 211 participants with samples that were assayed. Samples were shipped to Dresden, Germany, where they were assayed for cortisol using time-resolved immunoassay with fluorescence detection (dissociation-enhanced lanthanide fluorescence immunoassay; Dressendörfer, Kirschbaum, Rohde, Stahl, & Strasburger, Reference Dressendörfer, Kirschbaum, Rohde, Stahl and Strasburger1992). The laboratory conducting the assays has reported intra- and interassay coefficients of variance below 12%.

Electronic MEMS® caps recorded the time and date that each bottle containing Salivettes was opened for a randomly selected 28 of the 211 participants (13.3%) in order to check that accurate time reporting occurred (to encourage compliance, all participants were told there was a chance they would be monitored, as suggested by Adam & Kumari, Reference Adam and Kumari2009). Data were downloaded using MEMS software (PowerView, Version 3.5.2). The timing and sample intervals that the participants reported collecting the samples closely corresponded to the MEMS data. For the critical interval between Samples 1 (awakening) and 2 (30 min postawakening), the MEMS-recorded time intervals deviated from self-reported time intervals by an average of only 2.63 min (average MEMS-recorded interval = 31.94 min), and 96% of MEMS-based intervals were within 7 min of the self-reported interval. Similar accuracy was found for the Sample 2 to Sample 3 interval.

The CAR was calculated using area under the curve (AUC) analyses with respect to ground (AUCg) and increase (AUCi; Pruessner, Kirschbaum, Meinlschmid, & Hellhammer, Reference Pruessner, Kirschbaum, Meinlschmid and Hellhammer2003). AUC is a trapezoidal formula frequently used in endocrinological research because it provides a single variable to comprise information contained in repeated measures over time (Pruessner et al., Reference Pruessner, Kirschbaum, Meinlschmid and Hellhammer2003). AUCg measures overall cortisol secretion by assessing differences of measurements from the ground, or 0, and AUCi focuses on change over time with reference to the first value, or baseline sample (Sample 1 [S1]). AUCi and S1 are the most commonly used outcome variables in CAR research, because AUCi includes the change over time from baseline, and S1 is waking time cortisol and has been shown to be related to clinical and health outcomes apart from the curve of the CAR (Stalder et al., Reference Stalder, Kirschbaum, Kudielka, Adam, Pruessner, Wust and Clow2016). Therefore, these were the focus of the current analyses.

Mean CAR AUCi and S1 were calculated across 2 days of sampling. Two days is not enough to capture within-person variability, and therefore the outcomes were collapsed across the 2 days (Segerstrom, Sephton, & Westgate, Reference Segerstrom, Sephton and Westgate2017). Both variables were Winsorized to 3 SD to correct for outliers (two data points for AUCi, three for S1).

CAR calculations are extremely sensitive to variability in sampling. Therefore, careful measures were taken to exclude values that might not accurately represent the CAR. Eleven out of 211 participants (5.2%) were missing cortisol values and were excluded from CAR AUCi analyses. Of those 11, 5 had waking cortisol values and were included in S1 analyses for a total of 6 participants with missing data (2.8%). Some participants were missing data only on 1 day of sampling. This led to an elimination of 8 days of CAR sampling out of 422 (1.9%), but 4 of these days were usable for S1 calculations so only 4 days were eliminated from these analyses (0.9%). We also eliminated days when vigorous activity was reported prior to morning samples, which led to the removal of an additional 4 days from CAR analyses.

Timing is an important issue in CAR sampling. If the timing was off for more than 10 min between the waking and +30 sample, the day of sampling was eliminated. If the timing was off for more than 10 min between the +30 and the +60 sample, we noted this and examined the effects in analyses using a dummy variable (“TimingOFF”). If the timing was off for greater than 30 min between the second and third samples, the day of sampling was eliminated. This resulted in 24 days of sampling eliminated out of 422 (5.7%) for the CAR AUCi. Cortisol values at each sampling time were Winsorized to correct for extreme outliers (>3 SD; 5 data points for waking, 2 for +30 min, and 5 for + 60 min). After all data cleaning procedures, the final sample size was 196, which was used for all CAR analyses (N = 205 for S1 analyses). For non–cortisol-related analyses, the full sample size of 241 was used.

There were no differences between the cortisol sample and the 45 participants excluded from cortisol analyses on age, gender, or MDD symptoms, but participants in the cortisol sample showed lower BDI scores and were more likely to be White (ps < .05).

Results

Bivariate correlations and main effects

All analyses were conducted in IBM SPSS 24. Bivariate correlations among behavioral study variables are presented in Table 1. As shown there, all CA and episodic stress variables were significantly, positively correlated with each other (ps < .05), apart from CA and independent stress, which were only marginally correlated (p = .059). In addition, CA and all episodic stress variables were significantly correlated with current depressive symptoms. Significance of correlations was unchanged when controlling for sex, age, and race.

Table 1. Bivariate correlations and descriptive data for behavioral study variables

Note: CA, childhood adversity; MDD, major depressive disorder symptoms; BDI, Beck Depression Inventory.

To examine main effects of stress variables on the CAR, we conducted linear regression analyses, controlling for biobehavioral correlates. Consistent with our interaction models (see below), these models included the following covariates: sex, pubertal stage, follicular stage of menstrual cycle (boys were coded 0), hours slept the night before, and wake time (averaged across the 2 days of sampling). After accounting for these covariates, none of the study variables significantly predicted the CAR AUCi, including CA (b = 1.99, SE = 2.33, p = .393), total episodic stress severity (b = 472, SE = 3.76, p = .901), total independent severity (b = 1.93, SE = 5.21, p = .712), or total dependent severity (b = –1.59, SE = 6.49, p = .806). The CAR AUCi was also not significantly related to current self-reported depressive symptoms (b = –4.24, SE = 2.67, p = .113) and interview-assessed MDD symptoms (b = –37.05, SE = 23.76, p = .121). Additional covariates (including age, race, birth control use, the TimingOFF dummy varaible, and reports of eating or drinking during the 30 min prior to their morning saliva samples) were nonsignificant in models, and their inclusion did not impact results. We also examined the association between S1 cortisol and CA, episodic stress, dependent stress, independent stress, and depressive symptoms, controlling for key covariates, and found no significant associations (all ps > .05).

Episodic Stress × CA, predicting CAR

All interaction models were conducted using PROCESS macros for SPSS (Hayes, Reference Hayes2013). Our main outcome of interest was the CAR using the AUCi calculation method. In initial models, we included the following demographic and biobehavioral covariates: sex, age, race (dummy coded as White vs. non-White), follicular stage of menstrual cycle (boys were coded 0), current use of hormonal birth control, hours slept the night before, and wake time (averaged across 2 sample days), whether the day of cortisol sampling was a school day, the TimingOFF dummy code (averaged across 2 days), and reports of eating or drinking during the 30 min prior to their morning saliva samples (averaged across samples). To simplify models, we dropped highly nonsignificant covariates (ps > .15). Following this decision rule, the following covariates were retained: sex, pubertal status, follicular stage, wake time, and total sleep time.

An identical set of covariates emerged as significant across all interaction models with CAR AUCi as the outcome. This allowed us to use the same set of predictors across models, facilitating model comparison. Note that adding any of the excluded covariates did not substantially impact results.

We first tested the interaction between CA and overall episodic stress, predicting the CAR. We constructed a model including the main effects of CA and total episodic stress severity (both mean-centered) and their interaction, plus the covariates. Results are presented in Table 2. The interaction term was significant (p = .009). We decomposed the significant interaction by conducting simple slope tests at 1 SD above and below the mean of CA. At low levels of CA, there was a positive trending association between recent episodic stress and the CAR (b = 8.00, SE = 5.11, p = .119), 95% confidence interval (CI) [–2.09, 18.09]. At mean levels of adversity, the association was nonsignificant (b = –1.95, SE = 3.65, p = .59). In contrast, at high levels of CA, recent episodic stress significantly predicted lower levels of CAR, b = –11.90, SE = 5.38, p = .028, 95% CI [–22.52, –1.28]. This interaction is illustrated in Figure 1a. We used the Johnson–Neyman technique to determine region of significance; episodic stress predicted significantly decreased CAR at α = 0.05 when CA scores were above the 78th percentile of our sample.

Figure 1. The cortisol awakening response (CAR) as predicted by (a) overall, (b) independent, and (c) dependent episodic stress, at high and low levels of childhood adversity. The CAR was calculated as the area under the curve with respect to increase. Note that the interactions for (a) and (b) are significant (ps < .05), but the interaction for (c) is nonsignificant.

Table 2. Moderation of the association between total, independent, and dependent episodic stress and the cortisol awakening response by childhood adversity

Note: N = 196. CA, childhood adversity. CA and stress variables were mean centered. Covariates were standardized to facilitate intercept interpretability. Cortisol awakening response was calculated as the area

To examine whether this interaction held for dependent versus independent stress, we separately conducted models using total dependent stress severity and total independent stress severity as independent variables, moderated by CA. Models were analogous to the previous model, with identical covariates included. The Dependent Stress × CA interaction was not significant (p = .361). In contrast, the Independent Stress × CA effect, predicting the CAR, was significant (p = .013). At low levels of CA, there was a marginally significant, positive association between recent independent stress and CAR, b = 12.89, SE = 7.01, p = .068, 95% CI [–0.95, 26.72]. At mean levels of adversity, there was no association between independent stress and the CAR (b = 0.74, SE = 5.11, p = .885). In contrast, at high levels of CA, the association between recent independent stressors and CAR trended negative, b = –11.41, SE = 7.08, p = .109, 95% CI [–25.37, 2.56]. Region of significance analyses suggested independent stress significantly predicted increased CAR when adversity was below the 5th percentile, and predicted decreased CAR when adversity was above 91st percentile. Figure 1b and c illustrate these findings.

We also tested all of the above interactions with S1 (waking) cortisol as the outcome. There were no significant interactions between CA and episodic stress (including total, independent, and dependent stress) predicting S1 cortisol (all ps > .05).

Episodic Stress × CA, predicting depressive symptoms

We next examined interaction models with interview-assessed MDD symptoms as the outcome. Main effects for episodic stress variables and CA were entered along with their interaction. Demographic variables (sex, Caucasian race, and age) were entered as covariates. The results are provided in Table 3. Looking at overall episodic stress, the interaction term was significant (p = .048). Recent episodic stressors did not significantly predict MDD symptoms at low levels of CA, b = 0.02, SE = 0.01, p = .273, 95% CI [–0.01, 0.04], but did predict higher symptoms at mean, b = 0.03, SE = 0.01, p < .001, 95% CI [0.01, 0.05] and high, b = 0.05, SE = 0.01, p < .001, 95% CI [0.03, 0.08], levels of adversity. Region of significance analysis indicated that episodic stress significantly predicted MDD when CA was above the 31st percentile.

Table 3. Moderation of the association between total, independent, and dependent episodic stress and depressive symptoms by childhood adversity

Note: N = 241. CA, childhood adversity. CA and stress variables were mean centered. Covariates were standardized to facilitate intercept interpretability. K-SADS, Kiddie Schedule for Affective Disorders and Schizophrenia (Kaufman et al., Reference Kaufman, Birmaher, Brent, Rao, Flynn, Moreci and Ryan1997); MDD, major depressive disorder (dimensionally coded); BDI, Beck Depression Inventory

Next, we separately examined dependent and independent stress, revealing a pattern analogous to that observed for the CAR. Specifically, CA did not moderate the association between dependent stress and MDD symptoms (p = .661). As illustrated in Figure 2b, the association between dependent stress and MDD symptoms was significant at both high and low levels of CA. In contrast, when independent stress was entered as the independent variable, the interaction term approached significance b = 0.003, SE = 0.001, p = .071, 95% CI [0.00, 0.01]. Aligning with expectations, the association between independent stress did not predict MDD symptoms at low levels of CA, b = 0.00, SE = 0.02, p = .845, 95% CI [–0.03, 0.04], but significantly predicted MDD symptoms at mean, b = 0.03, SE = 0.01, p = .042, 95% CI [0.001, 0.054], and high levels of CA, b = 0.05, SE = 0.02, p = .005, 95% CI [0.02, 0.09]. Figure 2c illustrates this interaction. Johnson–Neyman analyses indicated that independent stress predicted depressive symptoms at above the 56th percentile of CA. Thus, for both CAR and depressive symptom outcomes, CA moderates the effects of independent but not dependent episodic stressors.

Figure 2. Symptoms of major depressive disorder as predicted by (a) overall, (b) independent, and (c) dependent episodic stress, at high and low levels of childhood adversity.

As an added test of this moderation finding, we retested these interaction models with self-reported depressive symptoms (BDI) as the outcome in place of interview-assessed depression. The pattern of results was identical (Table 3). Examining overall episodic stress, the interaction term was marginally significant, b = 0.02, SE = 0.01, p = .067, 95% CI [0.00, 0.05]. At low levels of CA, recent episodic stressors did not significantly predict BDI, b = 0.07, SE = 0.14, p = .606, 95% CI [–20, 0.35], but at high levels of CA, recent episodic stress strongly predicted BDI, b = 0.42, SE = 0.13, p = .002, 95% CI [0.16, 0.69], although this decomposition must be interpreted with caution given the marginal significance of the interaction term. Next, consistent with previously reported results, there was no significant interaction between dependent stress and CA, predicting BDI, b = 0.00, SE = 0.02, p = .982, 95% CI [–0.04, 0.04]. Finally, again aligning with previous findings, the independent Episodic Stress × CA Effect was significant (p = .039). Conforming with expectations, the association between independent stress did not predict depressive symptoms at low levels of CA, b = –0.01, SE = 0.19, p = .959, 95% CI [–0.39, 0.37], but significantly predicted depressive symptoms at high levels of CA, b = 0.53, SE = 0.18, p = .003, 95% CI [0.18, 0.89].

Discussion

The current study adds to a growing body of evidence supporting the stress sensitization model, showing that exposure to adversity over the course of childhood modifies the effects of continued exposure to stressful contexts later in development. Guided by a multiple levels of analysis approach, we found two intriguingly parallel sets of findings focused on two distinct outcomes, one neuroendocrinological (the CAR) and one behavioral (depressive symptoms).

Higher levels of CA predicted significantly altered associations between recent episodic stress and the CAR. Second, CA intensified the association between episodic stress and depressive symptoms. For both outcomes, stress sensitization effects were significant for independent but not dependent stress.

These parallel sets of findings may suggest that one way in which CA gets “under the skin” is by disrupting HPA axis functioning, consistent with the allostatic load framework (McEwen, Reference McEwen1998). Repeated activation of the HPA axis during childhood may culminate in allostatic load, and persistent exposure to excess cortisol during pivotal stages of development may alter neural circuits associated with the HPA axis (Heim et al., Reference Heim, Newport, Mletzko, Miller and Nemeroff2008), leading to sustained abnormalities in cortisol regulation. Looking at our specific pattern of results, among those with low levels of CA, there was a trend toward a positive association between recent episodic stress and CAR. We speculate that this may be indicative of optimal HPA axis functioning: recent episodic stressors signal to the adolescent that he or she may encounter continued challenges in the upcoming day, and the body mounts an increased CAR to marshal metabolic resources to cope with these expected challenges (Adam, Hawkley, Kudielka, & Cacioppo, Reference Adam, Hawkley, Kudielka and Cacioppo2006). In turn, the adolescent is protected from negative outcomes such as depression (in line with our finding that recent episodic stress is nonpredictive of depressive outcomes among those with low CA). Among those with high CA, however, this process may break down, as evidenced by a negative correlation between recent episodic stress and CAR, and a corresponding increased association between episodic stress and depression.

It is worth noting that although we found that CA predicted a negative association between episodic stress and the CAR, our results do not suggest a pervasive pattern of hypocortisolism (with respect to the CAR) among those with high levels of CA; there was no significant main effect of CA on the CAR. As illustrated in Figure 1a, at low levels of episodic stress, those with high CA showed significantly larger CARs than did those with low CA. This may suggest that youth with high CA experience elevated CARs regardless of the absence of recent stress (consistent with the stress autonomy model; see Monroe & Harkness, Reference Monroe and Harkness2005), potentially wasting metabolic resources. Alternatively, it may be that these youth have a very low threshold of recent stress for an elevated CAR (consistent with the stress sensitization model; see Monroe & Harkness, Reference Monroe and Harkness2005). Our analyses cannot distinguish between these possibilities; however, it is clear that the elevations in the CAR associated with CA vanish in the presence of episodic stress, corresponding with an increase in depression risk. Our findings may help reconcile seemingly inconsistent findings that link CA and depression to both smaller than average and larger than average CARs, as differences in recent episodic stress may alter these associations. It should also be noted, however, that these differences in findings are also likely a result of other factors, including methodological and demographic variations across studies (Chida & Steptoe, Reference Chida and Steptoe2009; Gustafsson et al., Reference Gustafsson, Anckarsäter, Lichtenstein, Nelson and Gustafsson2010; Miller et al., Reference Miller, Chen and Zhou2007; Quevedo et al., Reference Quevedo, Johnson, Loman, LaFavor and Gunnar2012). Clearly, HPA axis functioning is remarkably complicated, and far more research will be needed to fully understand its many nuances.

We also examined whether effects were found for independent (uncontrollable, fateful) versus dependent (controllable, self-generated) stress. Although previous findings have varied, we expected stronger effects for independent stress because of the preponderance of studies that have suggested that stress sensitization effects are specific to independent stress (Harkness et al., Reference Harkness, Bruce and Lumley2006). Here, we found that the interaction between CA and episodic stress was significant for independent stress, and not for dependent stress, in the prediction of both the CAR and depression. However, a visual inspection of the results (see Figures 1 and 2) adds a wrinkle to our interpretation. It appears that adolescents with low CA are protected against depressive symptoms following independent stress, but not dependent stress. All youth showed elevated depressive symptoms following dependent stress regardless of their CA level. This finding corresponds to a parallel result for the CAR: for adolescents with low adversity, high levels of recent independent stress predicted a higher CAR, while CAR was not influenced by level of recent dependent stress regardless of adversity level. In other words, youth with low CA were sensitive to dependent stress only, whereas youth with high CA were sensitive to both kinds of stress, as indicated by both outcomes.

In line with the hypothesized model we presented above, it is possible that adolescents with low adversity histories have a larger CAR following recent independent stress, and that this larger CAR protects them against negative emotional consequences by summoning metabolic resources to fuel coping efforts. However, this protective process appears to only occur for fateful, uncontrollable stress, and not for self-generated stress. It is not completely clear why this would be the case. Perhaps adolescents with low CA are less likely to engage in self-blame following independent events, allowing them to better focus on coping efforts. Moreover, an important developmental task of adolescence is to build greater autonomy from parental control, and high levels of self-generated stress may indicate that this process is going poorly. For example, common dependent stressors included peer-related events such as bullying, friendship losses, or romantic dissolutions. Given the high developmental salience of peer experiences (e.g., Hartup, Reference Hartup1996), stress in this domain may be problematic for all teens, regardless of CA history.

However, these ideas are fairly speculative, and more research is decidedly needed. It is also worth noting that independence was coded based on objective characteristics of the event, which may not exactly correspond with the adolescent's perception of the controllability of the event. More research should examine how subjective appraisals of event controllability affect cortisol secretion, above and beyond objective controllability.

A central tenet of developmental psychopathology is multifinality, or the acknowledgment that singular risk processes often result in divergent outcomes (Cicchetti & Rogosch, Reference Cicchetti and Rogosch1996). Although we have largely focused our discussion on depression, our results may be relevant to the development of other outcomes. Researchers have observed stress sensitization processes in the prediction of a wide range of disorders and problems other than depression, including alcohol consumption, episode recurrence in bipolar disorder, PTSD, and anxiety disorders (Dienes, Hammen, Henry, Cohen, & Daley, Reference Dienes, Hammen, Henry, Cohen and Daley2006; McLaughlin et al., Reference McLaughlin, Conron, Koenen and Gilman2010; Young-Wolff et al., Reference Young-Wolff, Kendler and Prescott2012). In addition, cortisol dysregulation is associated with multiple forms of psychopathology other than depression, including PTSD, anxiety disorders, disruptive behavior disorders, and substance abuse (e.g., Adam et al., Reference Adam, Vrshek-Schallhorn, Kendall, Mineka, Zinbarg and Craske2014; Delahanty, Raimonde, Spoonster, & Cullado, Reference Delahanty, Raimonde, Spoonster and Cullado2003; McBurnett, Lahey, Rathouz, & Loeber, Reference McBurnett, Lahey, Rathouz and Loeber2000; Moss, Vanyukov, & Martin).

Future research should examine whether cortisol dysregulation serves as a common pathway linking stress sensitization to multiple disorders. If so, stress sensitization processes via HPA axis disruptions may serve as a transdiagnostic process that partially explains high comorbidity across different forms of psychopathology.

Although in this study we examined the role of HPA axis alterations in stress sensitization and depression, CA has been shown to lead to alterations in other pathways that may interact with later stressful contexts in predicting depression. Studies on epigenetic processes have provided strong evidence that early experiences have the potential to alter gene expression, including RNA modification and DNA methylation (Heijmans et al., Reference Heijmans, Tobi, Stein, Putter, Blauw, Susser and Lumey2004, Reference Heijmans, Tobi, Stein, Putter, Blauw, Susser and Lumey2008; Heim & Binder, Reference Heim and Binder2012; Szyf et al., Reference Szyf, McGowan and Meaney2008). For example, one study of adolescents found that high levels of parental stress during the child's early life is associated with higher levels of methylation (Essex et al., Reference Essex, Thomas Boyce, Hertzman, Lam, Armstrong, Neumann and Kobor2013). Differential methylation profiles in stress-related genes have also been found for depressed versus nondepressed individuals, and are associated with altered stress reactivity (Fuchikami et al., Reference Fuchikami, Morinobu, Segawa, Okamoto, Yamawaki, Ozaki and Tsuchiyama2011; Oberlander et al., Reference Oberlander, Weinberg, Papsdorf, Grunau, Misri and Devlin2008; Unternaehrer et al., Reference Unternaehrer, Luers, Mill, Dempster, Meyer, Staehli and Meinlschmidt2012). These findings suggest another potential pathway through which early stress may lead to differential responses to proximal stress in individuals at risk for depression. In addition, findings from neuroimaging studies suggest that early CA may impair frontal brain regions critical for the development of inhibitory control and affective regulation (Carrion, Weems, Richert, Hoffman, & Reiss, Reference Carrion, Weems, Richert, Hoffman and Reiss2010; Veer et al., Reference Veer, Oei, Spinhoven, van Buchem, Elzinga and Rombouts2012). These neuroanatomical alterations are consistent with the large body of literature suggesting that children who have experienced early adversity exhibit impaired cognitive function, including problems with working memory, attention, and executive function (Hart & Rubia, Reference Hart and Rubia2012; Pechtel & Pizzagalli, Reference Pechtel and Pizzagalli2011). These neural changes may contribute to the development of information-processing biases that amplify the effect of stressors later in development. An examination of these alternate pathways to stress sensitization will be important to more clearly elucidate the process by which early adversity leads to increased risk for depression.

This study should be evaluated in the context of several important limitations. The study was cross-sectional. Longitudinal data would allow us to more directly test cascading effects of CA on HPA axis disruptions and, in turn, depression. As a result of the cross-sectional design, assessment of CA relied on retrospection, which may have introduced recall biases. In addition, because of time constraints, assessment of CA relied exclusively on parental report. On the one hand, parents may be more accurate reporters of events that occurred during early childhood, but on the other hand, there may be some adverse events that occurred outside of their awareness. In addition, our sample was recruited from the community, and consequently rates of current MDD were fairly low. Likewise, the majority of childhood adversities reported in our study represented significant but relatively commonplace stressors (e.g., grandparent death, parental divorce, and serious family illness). Much of the previous research on stress sensitization has focused on severe adversities where the child's safety is threatened, such as maltreatment, and although previous research has documented that more common adversities also predict stress sensitization (e.g., Hammen et al., Reference Hammen, Henry and Daley2000), there is also abundant evidence showing that effects on HPA axis functioning differ depending on the nature of the early adversity (Miller et al., Reference Miller, Chen and Zhou2007). Future research should determine whether results can be replicated in high-risk samples with higher rates of severe adversity such as maltreatment.

In addition, because of resource constraints, we utilized electronic compliance monitoring caps on only a subset of participants, and thus, compliance with cortisol sampling procedures cannot be verified in the majority of our participants. Within the subset who used monitoring caps, the intervals between their self-reported times and their electronically recorded times were comparable, suggesting reasonably good compliance, but tracking compliance of all participants would have allowed us to more precisely assess sample timing (e.g., Stalder et al., Reference Stalder, Kirschbaum, Kudielka, Adam, Pruessner, Wust and Clow2016).

Instead, we strongly emphasized to our participants the importance of collecting saliva immediately upon awakening, and relied on them to accurately do so. Issues with compliance are likely endemic to adolescent samples (Halpern, Whitsel, Wagner, & Harris, Reference Halpern, Whitsel, Wagner and Harris2012), in part because teenagers typically have demanding early morning schedules (e.g., preparing for school) that may conflict with sampling procedures. Given the importance of timing in properly capturing the CAR (Stalder et al., Reference Stalder, Kirschbaum, Kudielka, Adam, Pruessner, Wust and Clow2016), replication is needed.

These study limitations are balanced by important strengths. CAR was assessed using three data points (at awakening and 30 and 60 min postawakening), which is ideal for determining the CAR as it allows AUCi calculation and increases the chances of capturing peak cortisol secretion (Stalder et al., Reference Stalder, Kirschbaum, Kudielka, Adam, Pruessner, Wust and Clow2016). This practice is particularly unusual in adolescent samples of this size (see Chida & Steptoe, Reference Chida and Steptoe2009). We also assessed both CA and proximal episodic stress, occurring naturalistically in adolescents' lives, using gold-standard objective interviews that were team coded using the contextual threat method. This labor-intensive approach to the assessment of environmental stress has been shown to reduce bias related to cognitive vulnerability and more effectively predict outcomes, compared to more widely used checklists (Hammen, Reference Hammen2005; McQuaid, Monroe, Roberts, Kupfer, & Frank, Reference McQuaid, Monroe, Roberts, Kupfer and Frank2000).

This study examined two levels of analysis (behavioral and neuroendocrinological), while also studying interactive effects of stressors occurring across multiple developmental stages. To delve further into the complexities of risk and resilience, future researchers should examine additional levels of analysis. For example, some evidence suggests that genetic vulnerability increases vulnerability to stress sensitization processes. Starr et al. (Reference Starr, Hammen, Conway, Raposa and Brennan2014) found evidence for a Gene × Environment × Environment effect, where early adversity intensified the association between proximal stress and depression among those with risk alleles in the serotonin transporter linked polymorphic region (5-HTTLPR) or corticotropin releasing hormone receptor 1 (CRHR1) polymorphisms (see Grabe et al., Reference Grabe, Schwahn, Mahler, Schulz, Spitzer, Fenske and Freyberger2012). One plausible mechanism for this effect is that genetic risk confers neural plasticity and sensitivity to environmental input, which makes youth more vulnerable to disruptions in HPA axis development by CA exposure. HPA axis dysregulation persists across the life span, leaving the youth poorly equipped to manage later proximal stress. However, the role of HPA axis dysregulation in this Gene × Environment × Environment model has never been directly tested. Future research should examine whether current findings are further moderated by genetic risk, particularly by serotonergic and HPA axis-related genes.

Additional research should examine the impact of neural structures. Ample research has demonstrated that exposure to CA has detrimental effects on the development and plasticity of brain structures implicated in stress response and regulation, such as the hippocampus as well as other structures including areas of the prefrontal cortex (Gunnar & Nelson, Reference Gunnar and Nelson1994). Elevated cortisol and glucocorticoid levels have been shown to be associated with dampened hippocampal reactivity as well as reduced hippocampal and prefrontal cortical volume following exposure to early life stress (Carrion, Weems, & Reiss, Reference Carrion, Weems and Reiss2007; Carrion et al., Reference Carrion, Weems, Richert, Hoffman and Reiss2010; Teicher et al., Reference Teicher, Andersen, Polcari, Anderson, Navalta and Kim2003).

It is important that these structures are critically involved in HPA system regulation (see Dedovic, Duchesne, Andrews, Engert, & Pruessner, Reference Dedovic, Duchesne, Andrews, Engert and Pruessner2009; Diorio, Viau, & Meaney, Reference Diorio, Viau and Meaney1993; Jacobson & Sapolsky, Reference Jacobson and Sapolsky1991). Thus, understanding of the interplay between early stress associated alterations in neurobiological development and subsequent stressors is critical in disentangling the complex relationship between early stress exposure, proximal stress, and depression.

Finally, in addition to biological levels of analysis, researchers should consider broader, contextual factors that might impact the interactive effect of CA and proximal stress on cortisol regulation. For example, neighborhood effects may moderate findings. Research has previously demonstrated direct effects of neighborhood disadvantage on cortisol regulation (Rudolph et al., Reference Rudolph, Gary S., Stuart, Glass, Marques, Duncko and Merikangas2014). Neighborhood disadvantage also moderates risk and resilience processes among maltreated youth (Jaffee, Caspi, Moffitt, Polo-Tomás, & Taylor, Reference Jaffee, Caspi, Moffitt, Polo-Tomás and Taylor2007). It is also possible that neighborhood disadvantage itself constitutes a proximal stressor, to which those with higher CA are sensitized via HPA axis dysregulation.

Fortunately, neuroendocrine abnormalities related to CA are far from immutable; evidence suggests that cortisol regulation can be normalized through prevention and intervention programs (Cicchetti, Rogosch, Toth, & Sturge-Apple, Reference Cicchetti, Rogosch, Toth and Sturge-Apple2011; Fisher, Gunnar, Chamberlain, & Reid, Reference Fisher, Gunnar, Chamberlain and Reid2000), which may protect against negative outcomes. More precise understanding of the complex, interwoven biological and behavioral consequences of CA may lead to more effective treatments that promote resilience in at-risk youth.

Footnotes

This research was supported by funds from the University of Rochester. We thank the participating families for generously volunteering their time.

1. Note that we also assessed nonbinary gender identification, and three adolescents (1.2%) self-identified as gender fluid. Because of the relevance of sex hormones to cortisol regulation, these individuals were classified by biological sex for the present analyses.

References

Adam, E. K. (2006). Transactions among adolescent trait and state emotion and diurnal and momentary cortisol activity in naturalistic settings. Psychoneuroendocrinology, 31, 664679.CrossRefGoogle ScholarPubMed
Adam, E. K., Doane, L. D., Zinbarg, R. E., Mineka, S., Craske, M. G., & Griffith, J. W. (2010). Prospective prediction of major depressive disorder from cortisol awakening responses in adolescence. Psychoneuroendocrinology, 35, 921931. doi:10.1016/j.psyneuen.2009.12.007 CrossRefGoogle ScholarPubMed
Adam, E. K., Hawkley, L. C., Kudielka, B. M., & Cacioppo, J. T. (2006). Day-to-day dynamics of experience–cortisol associations in a population-based sample of older adults. Proceedings of the National Academy of Sciences, 103, 1705817063.Google Scholar
Adam, E. K., & Kumari, M. (2009). Assessing salivary cortisol in large-scale, epidemiological research. Psychoneuroendocrinology, 34, 14231436. doi:10.1016/j.psyneuen.2009.06.011 CrossRefGoogle ScholarPubMed
Adam, E. K., Vrshek-Schallhorn, S., Kendall, A. D., Mineka, S., Zinbarg, R. E., & Craske, M. G. (2014). Prospective associations between the cortisol awakening response and first onsets of anxiety disorders over a six-year follow-up. Psychoneuroendocrinology, 44, 4759.Google Scholar
Beck, A. T., Seer, R. A., & Brown, G. K. (1996). Manual for the Beck Depression Inventory—II. San Antonio, TX: Psycholigical Corporation.Google Scholar
Bifulco, A., Brown, G. W., Moran, P., Ball, C., & Campbell, C. (1998). Predicting depression in women: The role of past and present vulnerability. Psychological Medicine, 28, 3950.Google Scholar
Birmaher, B., Ryan, N. D., Williamson, D. E., Brent, D. A., Kaufman, J., Dahl, R. E., … Nelson, B. (1996). Childhood and adolescent depression: A review of the past 10 years: Part I. Journal of the American Academy of Child & Adolescent Psychiatry, 35, 14271439. doi:10.1097/00004583-199611000-00011 Google Scholar
Broderick, J. E., Arnold, D., Kudielka, B. M., & Kirschbaum, C. (2004). Salivary cortisol sampling compliance: Comparison of patients and healthy volunteers. Psychoneuroendocrinology, 29, 636650. doi:10.1016/s0306-4530(03)00093-3 CrossRefGoogle ScholarPubMed
Carrion, V. G., Weems, C. F., & Reiss, A. L. (2007). Stress predicts brain changes in children: A pilot longitudinal study on youth stress, posttraumatic stress disorder, and the hippocampus. Pediatrics, 119, 509516.Google Scholar
Carrion, V. G., Weems, C. F., Richert, K., Hoffman, B. C., & Reiss, A. L. (2010). Decreased prefrontal cortical volume associated with increased bedtime cortisol in traumatized youth. Biological Psychiatry, 68, 491493.Google Scholar
Chida, Y., & Steptoe, A. (2009). Cortisol awakening response and psychosocial factors: A systematic review and meta-analysis. Biological Psychology, 80, 265278. doi:10.1016/j.biopsycho.2008.10.004 CrossRefGoogle ScholarPubMed
Cicchetti, D., & Blender, J. A. (2004). A multiple-levels-of-analysis approach to the study of developmental processes in maltreated children. Proceedings of the National Academy of Sciences, 101, 1732517326.CrossRefGoogle Scholar
Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597600.CrossRefGoogle Scholar
Cicchetti, D., & Rogosch, F. A. (2001). The impact of child maltreatment and psychopathology on neuroendocrine functioning. Development and Psychopathology, 13, 783804.Google Scholar
Cicchetti, D., & Rogosch, F. A. (2012). Physiological measures of emotion from a developmental perspective: State of the science: Neuroendocrine regulation and emotional adaptation in the context of child maltreatment. Monographs of the Society for Research in Child Development, 77, 8795.CrossRefGoogle Scholar
Cicchetti, D., Rogosch, F. A., Toth, S. L., & Sturge-Apple, M. L. (2011). Normalizing the development of cortisol regulation in maltreated infants through preventive interventions. Development and Psychopathology, 23, 789800.Google Scholar
Clow, A., Thorn, L., Evans, P., & Hucklebridge, F. (2004). The awakening cortisol response: Methodological issues and significance. Stress, 7, 2937.Google Scholar
Collins, W. A. (1990). Parent-child relationships in the transition to adolescence: Continuity and change in interaction, affect, and cognition. In Montemayor, R. & Adams, G. R. (Eds.), From childhood to adolescence: A transitional period? Advances in adolescent development: An annual book series (pp. 85106). Thousand Oaks, CA: Sage.Google Scholar
Collins, W. A. (2003). More than myth: The developmental significance of romantic relationships during adolescence. Journal of Research on Adolescence, 13, 124.Google Scholar
Dedovic, K., Duchesne, A., Andrews, J., Engert, V., & Pruessner, J. C. (2009). The brain and the stress axis: The neural correlates of cortisol regulation in response to stress. NeuroImage, 47, 864871.Google Scholar
De Kloet, E. R. (2004). Hormones and the stressed brain. Annals of the New York Academy of Science, 1018, 115. doi:10.1196/annals.1296.001 CrossRefGoogle ScholarPubMed
Delahanty, D. L., Raimonde, A. J., Spoonster, E., & Cullado, M. (2003). Injury severity, prior trauma history, urinary cortisol levels, and acute PTSD in motor vehicle accident victims. Journal of Anxiety Disorders, 17, 149164.Google Scholar
Dickerson, S. S., & Kemeny, M. E. (2004). Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research. Psychological Bulletin, 130, 355391. doi:10.1037/0033-2909.130.3.355 Google Scholar
Dienes, K. A., Hammen, C., Henry, R. M., Cohen, A. N., & Daley, S. E. (2006). The stress sensitization hypothesis: Understanding the course of bipolar disorder. Journal of Affective Disorders, 95, 4349.CrossRefGoogle ScholarPubMed
Diorio, D., Viau, V., & Meaney, M. J. (1993). The role of the medial prefrontal cortex (cingulate gyrus) in the regulation of hypothalamic-pituitary-adrenal responses to stress. Journal of Neuroscience, 13, 38393847.Google Scholar
Doane, L. D., & Adam, E. K. (2010). Loneliness and cortisol: Momentary, day-to-day, and trait associations. Psychoneuroendocrinology, 35, 430441.CrossRefGoogle ScholarPubMed
Dressendörfer, R., Kirschbaum, C., Rohde, W., Stahl, F., & Strasburger, C. (1992). Synthesis of a cortisol-biotin conjugate and evaluation as a tracer in an immunoassay for salivary cortisol measurement. Journal of Steroid Biochemistry and Molecular Biology, 43, 683692.Google Scholar
Engert, V., Efanov, S. I., Dedovic, K., Dagher, A., & Pruessner, J. C. (2011). Increased cortisol awakening response and afternoon/evening cortisol output in healthy young adults with low early life parental care. Psychopharmacology, 214, 261268.Google Scholar
Espejo, E. P., Hammen, C. L., Connolly, N. P., Brennan, P. A., Najman, J. M., & Bor, W. (2007). Stress sensitization and adolescent depressive severity as a function of childhood adversity: A link to anxiety disorders. Journal of Abnormal Child Psychology, 35, 287299. doi:10.1007/s10802-006-9090-3 Google Scholar
Essex, M. J., Thomas Boyce, W., Hertzman, C., Lam, L. L., Armstrong, J. M., Neumann, S., & Kobor, M. S. (2013). Epigenetic vestiges of early developmental adversity: Childhood stress exposure and DNA methylation in adolescence. Child Development, 84, 5875.Google Scholar
Evans, G. W. (2003). A multimethodological analysis of cumulative risk and allostatic load among rural children. Developmental Psychology, 39, 924933.CrossRefGoogle ScholarPubMed
Evans, G. W., Kim, P., Ting, A. H., Tesher, H. B., & Shannis, D. (2007). Cumulative risk, maternal responsiveness, and allostatic load among young adolescents. Developmental Psychology, 43, 341351. doi:10.1037/0012-1649.43.2.341 CrossRefGoogle ScholarPubMed
Fisher, P. A., Gunnar, M. R., Chamberlain, P., & Reid, J. B. (2000). Preventive intervention for maltreated preschool children: Impact on children's behavior, neuroendocrine activity, and foster parent functioning. Journal of the American Academy of Child & Adolescent Psychiatry, 39, 13561364. doi:10.1097/00004583-200011000-00009 Google Scholar
Foti, D., Kotov, R., Klein, D. N., & Hajcak, G. (2011). Abnormal neural sensitivity to monetary gains versus losses among adolescents at risk for depression. Journal of Abnormal Child Psychology, 39, 913924. doi:10.1007/s10802-011-9503-9 Google Scholar
Fries, E., Dettenborn, L., & Kirschbaum, C. (2009). The cortisol awakening response (CAR): Facts and future directions. International Journal of Psychophysiology, 72, 6773. doi:10.1016/j.ijpsycho.2008.03.014 Google Scholar
Fries, E., Hesse, J., Hellhammer, J., & Hellhammer, D. H. (2005). A new view on hypocortisolism. Psychoneuroendocrinology, 30, 10101016. doi:10.1016/j.psyneuen.2005.04.006 Google Scholar
Fuchikami, M., Morinobu, S., Segawa, M., Okamoto, Y., Yamawaki, S., Ozaki, N., … Tsuchiyama, K. (2011). DNA methylation profiles of the brain-derived neurotrophic factor (BDNF) gene as a potent diagnostic biomarker in major depression. PLOS ONE, 6, e23881.CrossRefGoogle ScholarPubMed
Gartland, N., O'Connor, D. B., Lawton, R., & Bristow, M. (2014). Exploring day-to-day dynamics of daily stressor appraisals, physical symptoms and the cortisol awakening response. Psychoneuroendocrinology, 50, 130138.Google Scholar
Gonzalez, A., Jenkins, J. M., Steiner, M., & Fleming, A. S. (2009). The relation between early life adversity, cortisol awakening response and diurnal salivary cortisol levels in postpartum women. Psychoneuroendocrinology, 34, 7686.CrossRefGoogle ScholarPubMed
Goodyer, I. M., Bacon, A., Ban, M., Croudace, T., & Herbert, J. (2009). Serotonin transporter genotype, morning cortisol and subsequent depression in adolescents. British Journal of Psychiatry, 195, 3945. doi:10.1192/bjp.bp.108.054775 Google Scholar
Goodyer, I. M., Herbert, J., Tamplin, A., & Altham, P. M. (2000). Recent life events, cortisol, dehydroepiandrosterone and the onset of major depression in high-risk adolescents. British Journal of Psychiatry, 177, 499504.CrossRefGoogle ScholarPubMed
Grabe, H. J., Schwahn, C., Mahler, J., Schulz, A., Spitzer, C., Fenske, K., … Freyberger, H. J. (2012). Moderation of adult depression by the serotonin transporter promoter variant (5-HTTLPR), childhood abuse and adult traumatic events in a general population sample. American Journal of Medical Genetics, 159 B, 298309. doi:10.1002/ajmg.b.32027 Google Scholar
Granger, D. A., Fortunato, C. K., Beltzer, E. K., Virag, M., Bright, M. A., & Out, D. (2012). Focus on methodology: Salivary bioscience and research on adolescence: An integrated perspective. Journal of Adolescence, 35, 10811095. doi:10.1016/j.adolescence.2012.01.005 Google Scholar
Gunnar, M. R., & Fisher, P. A. (2006). Bringing basic research on early experience and stress neurobiology to bear on preventive interventions for neglected and maltreated children. Development and Psychopathology, 18, 651677. doi:10.1017/s0954579406060330 CrossRefGoogle ScholarPubMed
Gunnar, M. R., & Nelson, C. A. (1994). Event-related potentials in year-old infants: Relations with emotionality and cortisol. Child Development, 65, 8094.Google Scholar
Gunnar, M. R., Wewerka, S., Frenn, K., Long, J. D., & Griggs, C. (2009). Developmental changes in hypothalamus–pituitary–adrenal activity over the transition to adolescence: Normative changes and associations with puberty. Development and Psychopathology, 21, 6985.CrossRefGoogle ScholarPubMed
Gustafsson, P. E., Anckarsäter, H., Lichtenstein, P., Nelson, N., & Gustafsson, P. A. (2010). Does quantity have a quality all its own? Cumulative adversity and up- and down-regulation of circadian salivary cortisol levels in healthy children. Psychoneuroendocrinology, 35, 14101415.Google Scholar
Halligan, S. L., Herbert, J., Goodyer, I., & Murray, L. (2007). Disturbances in morning cortisol secretion in association with maternal postnatal depression predict subsequent depressive symptomatology in adolescents. Biological Psychiatry, 62, 4046. doi:10.1016/j.biopsych.2006.09.011 Google Scholar
Halpern, C. T., Whitsel, E. A., Wagner, B., & Harris, K. M. (2012). Challenges of measuring diurnal cortisol concentrations in a large population-based field study. Psychoneuroendocrinology, 37, 499508. doi:10.1016/j.psyneuen.2011.07.019 Google Scholar
Hammen, C. (1991). Generation of stress in the course of unipolar depression. Journal of Abnormal Psychology, 100, 555.Google Scholar
Hammen, C. (2005). Stress and depression. Annual Review of Clinical Psychology, 1, 293319. doi:10.1146/annurev.clinpsy.1.102803.143938 Google Scholar
Hammen, C., Henry, R., & Daley, S. E. (2000). Depression and sensitization to stressors among young women as a function of childhood adversity. Journal of Consulting and Clinical Psychology, 68, 782787.Google Scholar
Hankin, B. L., Abramson, L. Y., Moffitt, T. E., Silva, P. A., McGee, R., & Angell, K. E. (1998). Development of depression from preadolescence to young adulthood: Emerging gender differences in a 10-year longitudinal study. Journal of Abnormal Psychology, 107, 128140.Google Scholar
Harkness, K. L., Alavi, N., Monroe, S. M., Slavich, G. M., Gotlib, I. H., & Bagby, R. M. (2010). Gender differences in life events prior to onset of major depressive disorder: The moderating effect of age. Journal of Abnormal Psychology, 119, 791.Google Scholar
Harkness, K. L., Bruce, A. E., & Lumley, M. N. (2006). The role of childhood abuse and neglect in the sensitization to stressful life events in adolescent depression. Journal of Abnormal Psychology, 115, 730.CrossRefGoogle ScholarPubMed
Harkness, K. L., & Monroe, S. M. (2016). The assessment and measurement of adult life stress: Basic premises, operational principles, and design requirements. Journal of Abnormal Psychology, 125, 727745. doi:10.1037/abn0000178 Google Scholar
Harris, T. O., Borsanyi, S., Messari, S., Stanford, K., Cleary, S. E., Shiers, H. M., … Herbert, J. (2000). Morning cortisol as a risk factor for subsequent major depressive disorder in adult women. British Journal of Psychiatry, 177, 505510.Google Scholar
Hart, H., & Rubia, K. (2012). Neuroimaging of child abuse: A critical review. Frontiers in Human Neuroscience, 6, 52.Google Scholar
Hartup, W. W. (1996). The company they keep: Friendships and their developmental significance. Child Development, 67, 113.Google Scholar
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Press.Google Scholar
Heijmans, B. T., Tobi, E. W., Stein, A. D., Putter, H., Blauw, G. J., Susser, E. S., … Lumey, L. (2008). Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proceedings of the National Academy of Sciences, 105, 1704617049.Google Scholar
Heim, C., & Binder, E. B. (2012). Current research trends in early life stress and depression: Review of human studies on sensitive periods, gene–environment interactions, and epigenetics. Experimental Neurology, 233, 102111. doi:10.1016/j.expneurol.2011.10.032 CrossRefGoogle ScholarPubMed
Heim, C., & Nemeroff, C. B. (2001). The role of childhood trauma in the neurobiology of mood and anxiety disorders: Preclinical and clinical studies. Biological Psychiatry, 49, 10231039. doi:10.1016/S0006-3223(01)01157-X Google Scholar
Heim, C., Newport, D. J., Mletzko, T., Miller, A. H., & Nemeroff, C. B. (2008). The link between childhood trauma and depression: Insights from HPA axis studies in humans. Psychoneuroendocrinology, 33, 693710. doi:10.1016/j.psyneuen.2008.03.008 CrossRefGoogle ScholarPubMed
Heim, C., Plotsky, P. M., & Nemeroff, C. B. (2004). Importance of studying the contributions of early adverse experience to neurobiological findings in depression. Neuropsychopharmacology, 29, 641.CrossRefGoogle ScholarPubMed
Jacobson, L., & Sapolsky, R. (1991). The role of the hippocampus in feedback regulation of the hypothalamic-pituitary-adrenocortical axis. Endocrine Reviews, 12, 118134.Google Scholar
Jaffee, S. R., Caspi, A., Moffitt, T. E., Polo-Tomás, M., & Taylor, A. (2007). Individual, family, and neighborhood factors distinguish resilient from non-resilient maltreated children: A cumulative stressors model. Child Abuse & Neglect, 31, 231253. doi:10.1016/j.chiabu.2006.03.011 Google Scholar
Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., … Ryan, N. (1997). Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child & Adolescent Psychiatry, 36, 980988. doi:10.1097/00004583-199707000-00021 Google Scholar
Keeshin, B. R., Strawn, J. R., Out, D., Granger, D. A., & Putnam, F. W. (2014). Cortisol awakening response in adolescents with acute sexual abuse related posttraumatic stress disorder. Depression and Anxiety, 31, 107114. doi:10.1002/da.22154 Google Scholar
Kelly, S. J., Young, R., Sweeting, H., Fischer, J. E., & West, P. (2008). Levels and confounders of morning contisol collected from adolescents in a naturalistic (school) setting. Psychoneuroendocrinology, 33, 12571268 doi:10.1016/J.psyneuen.2008.06.010.Google Scholar
Kendler, K. S., Kuhn, J. W., & Prescott, C. A. (2004). Childhood sexual abuse, stressful life events and risk for major depression in women. Psychological Medicine, 34, 14751482.Google Scholar
Kessler, R. C., Avenevoli, S., & Ries Merikangas, K. (2001). Mood disorders in children and adolescents: An epidemiologic perspective. Biological Psychiatry, 49, 10021014.Google Scholar
Kim, J. H., Martins, S. S., Shmulewitz, D., Santaella, J., Wall, M. M., Keyes, K. M., … Hasin, D. S. (2014). Childhood maltreatment, stressful life events, and alcohol craving in adult drinkers. Alcoholism: Clinical and Experimental Research, 38, 20482055. doi:10.1111/acer.12473 Google Scholar
Kudielka, B. M., Hawkley, L. C., Adam, E. K., & Cacioppo, J. T. (2007). Compliance with ambulatory saliva sampling in the Chicago Health, Aging, and Social Relations Study and associations with social support. Annals of Behavioral Medicine, 34, 209216.Google Scholar
Kuehner, C., Holzhauer, S., & Huffziger, S. (2007). Decreased cortisol response to awakening is associated with cognitive vulnerability to depression in a nonclinical sample of young adults. Psychoneuroendocrinology, 32, 199209. doi:10.1016/j.psyneuen.2006.12.007 Google Scholar
La Rocque, C. L., Harkness, K. L., & Bagby, R. M. (2014). The differential relation of childhood maltreatment to stress sensitization in adolescent and young adult depression. Journal of Adolescence, 37, 871882. doi:10.1016/j.adolescence.2014.05.012 Google Scholar
Lewinsohn, P. M., Hops, H., Roberts, R. E., Seeley, J. R., & Andrews, J. A. (1993). Adolescent psychopathology: I. Prevalence and incidence of depression and other DSM-III-R disorders in high school students. Journal of Abnormal Psychology, 102, 133144.Google Scholar
Lu, S., Gao, W., Huang, M., Li, L., & Xu, Y. (2016). In search of the HPA axis activity in unipolar depression patients with childhood trauma: Combined cortisol awakening response and dexamethasone suppression test. Journal of Psychiatric Research, 78, 2430.Google Scholar
Lu, S., Gao, W., Wei, Z., Wu, W., Liao, M., Ding, Y., … Li, L. (2013). Reduced cingulate gyrus volume associated with enhanced cortisol awakening response in young healthy adults reporting childhood trauma. PLOS ONE, 8, e69350.Google Scholar
Lupien, S. J., Ouellet-Morin, I., Hupbach, A., Tu, M. T., Buss, C., Walker, D., … McEwen, B. S. (2006). Beyond the stress concept: Allostatic load—A developmental biological and cognitive perspective (Vol. 2). Hoboken, NJ: Wiley.Google Scholar
McBurnett, K., Lahey, B. B., Rathouz, P. J., & Loeber, R. (2000). Low salivary cortisol and persistent aggression in boys referred for disruptive behavior. Archives of General Psychiatry, 57, 3843. doi:10.1001/archpsyc.57.1.38 Google Scholar
McCrory, E., De Brito, S. A., & Viding, E. (2010). Research review: The neurobiology and genetics of maltreatment and adversity. Journal of Child Psychology and Psychiatry, 51, 10791095. doi:10.1111/j.1469-7610.2010.02271.x Google Scholar
McEwen, B. S. (1998). Stress, adaptation, and disease: Allostasis and allostatic load. Annals of the New York Academy of Sciences, 840, 3344.CrossRefGoogle ScholarPubMed
McEwen, B. S. (2004). Protection and damage from acute and chronic stress: Allostasis and allostatic overload and relevance to the pathophysiology of psychiatric disorders. Annals of the New York Academy of Sciences, 1032, 17. doi:10.1196/annals.1314.001 Google Scholar
McEwen, B. S., & Seeman, T. (1999). Protective and damaging effects of mediators of stress. Elaborating and testing the concepts of allostasis and allostatic load. Annals of the New York Academy of Sciences, 896, 3047.CrossRefGoogle ScholarPubMed
McGinnis, E. W., Lopez-Duran, N., Martinez-Torteya, C., Abelson, J. L., & Muzik, M. (2016). Cortisol awakening response and internalizing symptoms across childhood: Exploring the role of age and externalizing symptoms. International Journal of Behavioral Development, 40, 289295. doi:10.1177/0165025415590185 Google Scholar
McLaughlin, K. A., Conron, K. J., Koenen, K. C., & Gilman, S. E. (2010). Childhood adversity, adult stressful life events, and risk of past-year psychiatric disorder: A test of the stress sensitization hypothesis in a population-based sample of adults. Psychological Medicine, 40, 16471658. doi:10.1017/S0033291709992121 Google Scholar
McQuaid, J. R., Monroe, S. M., Roberts, J. E., Kupfer, D. J., & Frank, E. (2000). A comparison of two life stress assessment approaches: Prospective prediction of treatment outcome in recurrent depression. Journal of Abnormal Psychology, 109, 787.CrossRefGoogle ScholarPubMed
Meinlschmidt, G., & Heim, C. (2005). Decreased cortisol awakening response after early loss experience. Psychoneuroendocrinology, 30, 568576.Google Scholar
Miller, G. E., Chen, E., & Zhou, E. S. (2007). If it goes up, must it come down? Chronic stress and the hypothalamic-pituitary-adrenocortical axis in humans. Psychological Bulletin, 133, 2545.Google Scholar
Monroe, S. M. (2008). Modern approaches to conceptualizing and measuring human life stress. Annual Review of Clinical Psychology, 4, 3352. doi:10.1146/annurev.clinpsy.4.022007.141207 Google Scholar
Monroe, S. M., & Harkness, K. L. (2005). Life stress, the “kindling” hypothesis, and the recurrence of depression: Considerations from a life stress perspective. Psychological Review, 112, 417.Google Scholar
Moss, H. B., Vanyukov, M. M., & Martin, C. S. (1995). Salivary cortisol responses and the risk for substance abuse in prepubertal boys. Biological Psychiatry, 38, 547555. doi:10.1016/0006-3223(94)00382-D Google Scholar
Oberlander, T. F., Weinberg, J., Papsdorf, M., Grunau, R., Misri, S., & Devlin, A. M. (2008). Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics, 3, 97106.Google Scholar
Oldehinkel, A. J., Ormel, J., Verhulst, F. C., & Nederhof, E. (2014). Childhood adversities and adolescent depression: A matter of both risk and resilience. Development and Psychopathology, 26, 10671075. doi:10.1017/S0954579414000534 Google Scholar
Pechtel, P., & Pizzagalli, D. A. (2011). Effects of early life stress on cognitive and affective function: An integrated review of human literature. Psychopharmacology, 214, 5570. doi:10.1007/s00213-010-2009-2 Google Scholar
Pendry, P., & Adam, E. K. (2007). Associations between parents’ marital functioning, maternal parenting quality, maternal emotion and child cortisol levels. International Journal of Behavioral Development, 31, 218231.Google Scholar
Petersen, A. C., Crockett, L., Richards, M., & Boxer, A. (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence, 17, 117133.CrossRefGoogle ScholarPubMed
Post, R. M. (1992). Transduction of psychosocial stress into the neurobiology. American Journal of Psychiatry, 149, 9991010.Google Scholar
Powell, D. J., & Schlotz, W. (2012). Daily life stress and the cortisol awakening response: Testing the anticipation hypothesis. PLOS ONE, 7, e52067. doi:10.1371/journal.pone.0052067 Google Scholar
Pruessner, J. C., Kirschbaum, C., Meinlschmid, G., & Hellhammer, D. H. (2003). Two formulas for computation of the area under the curve represent measures of total hormone concentration versus time-dependent change. Psychoneuroendocrinology, 28, 916931.Google Scholar
Pruessner, J. C., Wolf, O. T., Hellhammer, D. H., Buske-Kirschbaum, A., von Auer, K., Jobst, S., … Kirschbaum, C. (1997). Free cortisol levels after awakening: A reliable biological marker for the assessment of adrenocortical activity. Life Sciences, 61, 25392549.Google Scholar
Quevedo, K., Johnson, A. E., Loman, M. L., LaFavor, T. L., & Gunnar, M. (2012). The confluence of adverse early experience and puberty on the cortisol awakening response. International Journal of Behavioral Development, 36, 1928.CrossRefGoogle ScholarPubMed
Raison, C. L., & Miller, A. H. (2003). When not enough is too much: The role of insufficient glucocorticoid signaling in the pathophysiology of stress-related disorders. American Journal of Psychiatry, 160, 15541565.Google Scholar
Rao, U. M. A., Daley, S. E., & Hammen, C. (2000). Relationship between depression and substance use disorders in adolescent women during the transition to adulthood. Journal of the American Academy of Child & Adolescent Psychiatry, 39, 215222. doi:10.1097/00004583-200002000-00022 Google Scholar
Rudolph, K. D., & Flynn, M. (2007). Childhood adversity and youth depression: Influence of gender and pubertal status. Development and Psychopathology, 19, 497521.Google Scholar
Rudolph, K. D., Hammen, C., Burge, D., Lindberg, N., Herzberg, D., & Daley, S. E. (2000). Toward an interpersonal life-stress model of depression: The developmental context of stress generation. Development and Psychopathology, 12, 215234.Google Scholar
Rudolph, K. E., Gary S., W., Stuart, E. A., Glass, T. A., Marques, A. H., Duncko, R., & Merikangas, K. R. (2014). The association between cortisol and neighborhood disadvantage in a U.S. population-based sample of adolescents. Health & Place, 25, 6877. doi:10.1016/j.healthplace.2013.11.001 Google Scholar
Saridjan, N. S., Velders, F. P., Jaddoe, V. W., Hofman, A., Verhulst, F. C., & Tiemeier, H. (2014). The longitudinal association of the diurnal cortisol rhythm with internalizing and externalizing problems in pre-schoolers. The Generation R Study. Psychoneuroendocrinology, 50, 118129. doi:10.1016/j.psyneuen.2014.08.008 Google Scholar
Saxbe, D. E. (2008). A field (researcher's) guide to cortisol: Tracking HPA axis functioning in everyday life. Health Psychology Review, 2, 163190.Google Scholar
Schlotz, W., Hellhammer, J., Schulz, P., & Stone, A. A. (2004). Perceived work overload and chronic worrying predict weekend–weekday differences in the cortisol awakening response. Psychosomatic Medicine, 66, 207214. doi:10.1097/01.psy.0000116715.78238.56 Google Scholar
Segerstrom, S. C., Sephton, S. E., & Westgate, P. M. (2017). Intraindividual variability in cortisol: Approaches, illustrations, and recommendations. Psychoneuroendocrinology, 78, 114124. doi:10.1016/j.psyneuen.2017.01.026 Google Scholar
Seidman, E., Allen, L., Aber, J. L., Mitchell, C., & Feinman, J. (1994). The impact of school transitions in early adolescence on the self-system and perceived social context of poor urban youth. Child Development, 65, 507522.Google Scholar
Shapero, B. G., Black, S. K., Liu, R. T., Klugman, J., Bender, R. E., Abramson, L. Y., & Alloy, L. B. (2014). Stressful life events and depression symptoms: The effect of childhood emotional abuse on stress reactivity. Journal of Clinical Psychology, 70, 209223. doi:10.1002/jclp.22011 Google Scholar
Shirtcliff, E. A., Allison, A. L., Armstrong, J. M., Slattery, M. J., Kalin, N. H., & Essex, M. J. (2012). Longitudinal stability and developmental properties of salivary cortisol levels and circadian rhythms from childhood to adolescence. Developmental Psychobiology, 54, 493502. doi:10.1002/dev.20607 Google Scholar
Shrout, P. E., Link, B. G., Dohrenwend, B. P., Skodol, A. E., Stueve, A., & Mirotznik, J. (1989). Characterizing life events as risk factors for depression: The role of fateful loss events. Journal of Abnormal Psychology, 98, 460467.Google Scholar
Sladek, M. R., & Doane, L. D. (2015). Daily diary reports of social connection, objective sleep, and the cortisol awakening response during adolescents’ first year of college. Journal of Youth and Adolescence, 44, 298316. doi:10.1007/s10964-014-0244-2 Google Scholar
Slavich, G. M., Monroe, S. M., & Gotlib, I. H. (2011). Early parental loss and depression history: Associations with recent life stress in major depressive disorder. Journal of Psychiatric Research, 45, 11461152. doi:10.1016/j.jpsychires.2011.03.004 Google Scholar
Stalder, T., Kirschbaum, C., Kudielka, B. M., Adam, E. K., Pruessner, J. C., Wust, S., … Clow, A. (2016). Assessment of the cortisol awakening response: Expert consensus guidelines. Psychoneuroendocrinology, 63, 414432. doi:10.1016/j.psyneuen.2015.10.010 Google Scholar
Starr, L. R., Davila, J., Stroud, C. B., Li, P. C. C., Yoneda, A., Hershenberg, R., & Miller, M. R. (2012). Love hurts (in more ways than one): Specificity of psychological symptoms as predictors and consequences of romantic activity among early adolescent girls. Journal of Clinical Psychology, 68, 403420. doi:10.1002/jclp.20862 Google Scholar
Starr, L. R., Hammen, C., Conway, C. C., Raposa, E., & Brennan, P. A. (2014). Sensitizing effect of early adversity on depressive reactions to later proximal stress: Moderation by polymorphisms in serotonin transporter and corticotropin releasing hormone receptor genes in a 20-year longitudinal study. Development and Psychopathology, 26, 12411254.Google Scholar
Stroud, C. B., Chen, F. R., Doane, L. D., & Granger, D. A. (2016). Individual differences in early adolescents’ latent trait cortisol (LTC): Relation to recent acute and chronic stress. Psychoneuroendocrinology, 70, 3846. doi:10.1016/j.psyneuen.2016.04.015 Google Scholar
Stroud, C. B., Davila, J., Hammen, C., & Vrshek-Schallhorn, S. (2011). Severe and nonsevere events in first onsets versus recurrences of depression: Evidence for stress sensitization. Journal of Abnormal Psychology, 120, 142.Google Scholar
Szyf, M., McGowan, P., & Meaney, M. J. (2008). The social environment and the epigenome. Environmental and Molecular Mutagenesis, 49, 4660.Google Scholar
Tarullo, A. R., & Gunnar, M. R. (2006). Child maltreatment and the developing HPA axis. Hormones and Behavior, 50, 632639.Google Scholar
Teicher, M. H., Andersen, S. L., Polcari, A., Anderson, C. M., Navalta, C. P., & Kim, D. M. (2003). The neurobiological consequences of early stress and childhood maltreatment. Neuroscience and Biobehavioral Reviews, 27, 3344.Google Scholar
Trickett, P. K., Negriff, S., Ji, J., & Peckins, M. (2011). Child maltreatment and adolescent development. Journal of Research on Adolescence, 21, 320.Google Scholar
Tyrka, A. R., Burgers, D. E., Philip, N. S., Price, L. H., & Carpenter, L. L. (2013). The neurobiological correlates of childhood adversity and implications for treatment. Acta Psychiatrica Scandinavica, 128, 434447.Google Scholar
Unternaehrer, E., Luers, P., Mill, J., Dempster, E., Meyer, A. H., Staehli, S., … Meinlschmidt, G. (2012). Dynamic changes in DNA methylation of stress-associated genes (OXTR, BDNF) after acute psychosocial stress. Translational Psychiatry, 2, e150.Google Scholar
Veer, I. M., Oei, N. Y., Spinhoven, P., van Buchem, M. A., Elzinga, B. M., & Rombouts, S. A. (2012). Endogenous cortisol is associated with functional connectivity between the amygdala and medial prefrontal cortex. Psychoneuroendocrinology, 37, 10391047.Google Scholar
Vrshek-Schallhorn, S., Doane, L., Mineka, S., Zinbarg, R., Craske, M., & Adam, E. (2013). The cortisol awakening response predicts major depression: Predictive stability over a 4-year follow-up and effect of depression history. Psychological Medicine, 43, 483493.Google Scholar
Wagner, B. M., Compas, B. E., & Howell, D. C. (1988). Daily and major life events: A test of an integrative model of psychosocial stress. American Journal of Community Psychology, 16, 189205. doi:10.1007/BF00912522 Google Scholar
Wilhelm, I., Born, J., Kudielka, B. M., Schlotz, W., & Wust, S. (2007). Is the cortisol awakening rise a response to awakening? Psychoneuroendocrinology, 32, 358366. doi:10.1016/j.psyneuen.2007.01.008 Google Scholar
Wilson, D. H., Starr, G. J., Taylor, A. W., & Dal Grande, E. (1999). Random digit dialling and Electronic White Pages samples compared: Demographic profiles and health estimates. Australian and New Zealand Journal of Public Health, 23, 627633.Google Scholar
Young-Wolff, K. C., Kendler, K. S., & Prescott, C. A. (2012). Interactive effects of childhood maltreatment and recent stressful life events on alcohol consumption in adulthood. Journal of Studies on Alcohol and Drugs, 73, 559569. doi:10.15288/jsad.2012.73.559 Google Scholar
Figure 0

Table 1. Bivariate correlations and descriptive data for behavioral study variables

Figure 1

Figure 1. The cortisol awakening response (CAR) as predicted by (a) overall, (b) independent, and (c) dependent episodic stress, at high and low levels of childhood adversity. The CAR was calculated as the area under the curve with respect to increase. Note that the interactions for (a) and (b) are significant (ps < .05), but the interaction for (c) is nonsignificant.

Figure 2

Table 2. Moderation of the association between total, independent, and dependent episodic stress and the cortisol awakening response by childhood adversity

Figure 3

Table 3. Moderation of the association between total, independent, and dependent episodic stress and depressive symptoms by childhood adversity

Figure 4

Figure 2. Symptoms of major depressive disorder as predicted by (a) overall, (b) independent, and (c) dependent episodic stress, at high and low levels of childhood adversity.