S0033291721004190jra 2352..2360

Background. Risk factors for depressive disorders (DD) change substantially over time, but the prognostic value of these changes remains unclear. Two basic types of dynamic effects are possible. The ‘Risk Escalation hypothesis’ posits that worsening of risk levels predicts DD onset above average level of risk factors. Alternatively, the ‘Chronic Risk hypothesis’ posits that the average level rather than change predicts first-onset DD. Methods. We utilized data from the ADEPT project, a cohort of 496 girls (baseline age 13.5– 15.5 years) from the community followed for 3 years. Participants underwent five waves of assessments for risk factors and diagnostic interviews for DD. For illustration purposes, we selected 16 well-established dynamic risk factors for adolescent depression, such as depressive and anxiety symptoms, personality traits, clinical traits, and social risk factors. We conducted Cox regression analyses with time-varying covariates to predict first DD onset. Results. Consistently elevated risk factors (i.e. the mean of multiple waves), but not recent escalation, predicted first-onset DD, consistent with the Chronic Risk hypothesis. This hypothesis was supported across all 16 risk factors. Conclusions. Across a range of risk factors, girls who had first-onset DD generally did not experience a sharp increase in risk level shortly before the onset of disorder; rather, for years before onset, they exhibited elevated levels of risk. Our findings suggest that chronicity of risk should be a particular focus in screening high-risk populations to prevent the onset of DDs. In particular, regular monitoring of risk factors in school settings is highly informative. Identification of risk factors for psychopathology is essential for prevention efforts (e.g. defining the group to receive preventive intervention) and etiological models (e.g. providing insights about the processes leading toward psychopathology). The search for risk factors for mental disorders has identified numerous predictors but has generally assumed that risk is static, in that risk factors are typically assessed only once, rather than considering how risk changes with time (Fusar-Poli et al., 2013; Hankin, 2012; Klein, Kotov, & Bufferd, 2011; Nelson, McGorry, Wichers, Wigman, & Hartmann, 2017). However, many risk factors have been shown to change substantially over time (e.g. Roberts, Walton, & Viechtbauer, 2006). It is largely unknown what pattern of change indicates risk for psychopathology. At least two basic types of dynamic relationship are possible between risk factors and onset of psychopathology. The ‘Risk Escalation hypothesis’ posits that worsening of risk levels predicts disorder onset above the average level of the risk factor. In other words, among people with the same level of risk currently, those who were previously at low risk but worsened are more likely to experience onset than those who were at elevated risk all along. Alternatively, the ‘Chronic Risk hypothesis’ posits that average risk over time predicts DD onset, and fluctuations around the average are not informative for prediction. These hypotheses have not been systematically compared for any mental disorders. In this study, we seek to demonstrate a strategy for testing these hypotheses on a number of risk factors for adolescent-onset depressive disorders (DD; i.e. major depressive disorder, dysthymic disorder, and depressive disorder not otherwise specified). Many risk factors have been identified for DD, including malleable vulnerabilities such as symptoms of anxiety and subclinical depression (Klein et al., 2013; Wang et al., 2014), certain personality traits (Bagby, Quilty, & Ryder, 2008; Jeronimus, Kotov, Riese, & Ormel, 2016), and social risk factors (Stice, Ragan, & Randall, 2004). Indeed, these characteristics have been found to change substantially over time (e.g. Roberts et al., 2006; Hankin, 2008; Nocentini, Menesini, & Salmivalli, 2013; Yaroslavsky, Pettit, Lewinsohn, Seeley, & Roberts, 2013; Nelemans, Hale, Branje, Hawk, & Meeus, 2014; Kopala-Sibley, Zuroff, Hankin, & Abela, 2015; Kendler & Aggen, 2017; Bleys, Soenens, Claes, Vliegen, & Luyten, 2018; Fernandes, Davidson, & Guthrie, 2018), especially during adolescence (e.g. Klimstra, Hale, Raaijmakers, Branje, & Meeus, 2010). Hence, it is important to consider how change in risk factors predicts DD onset. The ‘Risk Escalation hypothesis’ has received support in several longitudinal studies of depression. These studies found that increases in risk levels predict subsequent increases in depression symptoms (Mu, Luo, Rieger, Trautwein, & Roberts, 2019; Steiger, Allemand, Robins, & Fend, 2014) or DD onset (e.g. Laceulle, Ormel, Vollebergh, Van Aken, & Nederhof, 2014). However, these and most other studies tested escalation by analyzing baseline level and subsequent change in risk. This analytic approach cannot compare the two competing hypotheses, because high proximal risk should positively predict depression onset under both Risk Escalation and Chronic Risk scenarios. It would be more informative to compare the change in risk to the risk level most proximal to onset rather than to the distal baseline. The proximal assessment conveys more information about risk than the baseline assessment, which is often years before the proximal assessment. Indeed, past research has shown that the most recent assessment is most predictive of onset when multiple timepoints are available for a risk measure (e.g. Shanahan, Copeland, Costello, & Angold, 2011). In addition, modeling change while controlling for the proximal level of the risk factor is not only a sound analytic practice, but also aligns with clinical decisionmaking. When forecasting prognosis, clinicians first consider present illness and then its history, a practice best captured in models that include both the proximal assessment and change since baseline. The alternative ‘Chronic Risk hypothesis’ has been tested only indirectly. First, research has consistently shown that chronic stressors (e.g. chronic marital stress, chronic illness) are potent predictors of subsequent depression onset (e.g. Bey, Waring, Jesdale, & Person, 2018; Cuijpers, Van Straten, & Smit, 2005; Hammen, Hazel, Brennan, & Najman, 2012). Also, one study reported that adolescents with subclinical depressive symptoms at multiple waves are more likely to develop DD than adolescents with subclinical depressive symptoms at only one assessment (Klein, Shankman, Lewinsohn, & Seeley, 2009). Moreover, some studies have separated the stable portion of risk from temporary fluctuations around it and found that the stable fraction predicted subsequent change in depression (Naragon-Gainey, Gallagher, & Brown, 2013; Kendall, & Langer, 2015) and suicidality (Young et al., 1996). However, these studies did not directly compare the Chronic Risk v. Risk Escalation hypotheses. Moreover, most previous studies included only a small number of follow-ups, or failed to distinguish first onsets of depression from recurrent episodes, which confounds vulnerabilities to developing depression with processes that maintain depression after onset (Wilson, Vaidyanathan, Miller, McGue, & Iacono, 2014). The current study aimed to provide the first direct test of these competing hypotheses – Risk Escalation and Chronic Risk – to predict the first onset of DD, addressing the aforementioned methodological limitations. We utilized data from a richly characterized sample of adolescent girls from the community who underwent five waves of assessment. We did not consider fixed and relatively fixed risk factors, such as childhood maltreatment and parental psychopathology, respectively, and discrete experiences (e.g. life events) which, by definition, cannot evolve. Indeed, most parents who are ever going to develop depression have already done so as most parents were in their 40s when they entered the study. We focused on well-established malleable risk factors for adolescent depression: symptoms of anxiety and subclinical depression (Klein et al., 2013; Wang et al., 2014), three personality traits (neuroticism, conscientiousness, and extraversion; Jeronimus et al., 2016; Mu, Luo, Nickel, & Roberts, 2016), three clinical traits indexing depressogenic cognitive or interpersonal styles (rumination, self-criticism, and dependency; Klein et al., 2011; Mahaffey, Watson, Clark, & Kotov, 2016), and four social risk factors (social support, school engagement, being bullied, and parental criticism; Burkhouse, Uhrlass, Stone, Knopik, & Gibb, 2012; Sachs-Ericsson, Verona, Joiner, & Preacher, 2006; Starr & Davila, 2008; Stice et al., 2004; Swearer, Song, Cary, Eagle, & Mickelson, 2001; Van Voorhees et al., 2008; Wilson et al., 2014).