Affective, Biological, and Cognitive Predictors of DepressiveSymptom Trajectories in Adolescence

Amy Mezulis & Rachel H. Salk & Janet Shibley Hyde &

Heather A. Priess-Groben & Jordan L. Simonson

# Springer Science+Business Media New York 2013

Abstract Heterogeneity in the longitudinal course of depres-sive symptoms was examined using latent growth mixturemodeling among a community sample of 382 U.S. youth fromages 11 to 18 (52.1 % female). Three latent trajectory classeswere identified: Stable Low (51 %; displayed low depressivesymptoms at all assessments), Increasing (37 %; reported lowdepressive symptoms at age 11, but then significantly higherdepressive symptoms than the Stable Low class at ages 13, 15,and 18), and Early High (12 %; reported high early depressivesymptoms at age 11, followed by symptoms that declined overtime yet remained significantly higher than those of the StableLow class at ages 13, 15, and 18). By age 15, rates of MajorDepressive Disorder diagnoses among the Early High (25.0 %)and Increasing (20.4 %) classes were more than twice thatobserved among the Stable Low class (8.8 %). Affective(negative affectivity), biological (pubertal timing, sex) and

cognitive (cognitive style, rumination) factors were examinedas predictors of classmembership. Results indicated general riskfactors for both high-risk trajectories as well as specific riskfactors unique to each trajectory. Being female and high infantnegative affectivity predicted membership in the Increasingclass. Early puberty, high infant negative affectivity for boys,and high rumination for girls predicted membership in the EarlyHigh class. Results highlight the importance of examiningheterogeneity in depression trajectories in adolescence as wellas simultaneously considering risk factors across multipledomains.

Keywords Depression . Trajectories . Temperament .

Puberty . Cognitive risk factors . Adolescence

Cross-sectional and longitudinal studies have found that preva-lence rates of depressive disorders rise from 2 to 4 % inchildhood to nearly 20 % by age 18 (e.g., Cohen et al. 1993;Kessler et al. 2001). Adolescent-onset depression is associatedwith social impairment, recurrent depression in adulthood, andgreater risk for comorbid mental health problems includingsubstance use (e.g., Zisook et al. 2007). One important indicatorof risk for depressive disorders is depressive symptoms.Depressive symptoms are both normative in adolescence andpredictive of more severe symptoms and eventual depressivedisorders over time (Fergusson et al. 2005; Garber et al. 2002;Pine et al. 1999). While sample-wide analyses have clearlyidentified that, on average, depressive symptoms increase acrossadolescence, such analyses may mask important heterogeneityin the course of depressive symptoms. In addition, identifyingrisk factors that place youth on a high-risk trajectory is criticalfor understanding the onset, course, and prevention of depres-sion. In a recent review, Hankin (2012) noted “Despite consid-erable progress in distinct lines of vulnerability research, there isan explanatory gap in our ability to more comprehensively

A. Mezulis (*)Department of Clinical Psychology, Seattle Pacific University,Seattle, USAe-mail: mezulis@spu.edu

R. H. Salk : J. S. HydeDepartment of Psychology, University of Wisconsin – Madison,Madison, USA

R. H. Salke-mail: rsalk@wisc.edu

J. S. Hydee-mail: jshyde@wisc.edu

H. A. Priess-GrobenSimpson College, Indianola, Iowae-mail: heather.groben@simpson.edu

J. L. SimonsonShriever Air Force Base, United States Air Force,Colorado Springs, USAe-mail: jordan.simonson.1@us.af.mil

J Abnorm Child PsycholDOI 10.1007/s10802-013-9812-2

explain and predict who is likely to become depressed, whenand why.” (p. 695).

The purpose of the current study was to fill this gap. Ourfirst purpose was to examine whether there are subgroups ofadolescents who follow high-risk trajectories of depressivesymptoms and, if so, identify the age(s) at which these youthdiverge from a normal or low-risk trajectory and describe theirsubsequent risk for depression diagnoses. In addition, weemployed a multiple levels of analysis approach to identifyingpredictors of adolescent depressive symptom trajectories, test-ing affective, biological, and cognitive factors to predict mem-bership in distinct depressive symptom trajectories inadolescence.

Heterogeneity of Depressive Symptom Trajectories AcrossAdolescence

Most studies find that depressive symptom levels are lowest inthe late childhood/early adolescent period up to and includingage 11, then display an increasing trend starting at around age13, with a period of rapid increase occurring between ages 15and 18 (e.g., Garber et al. 2002; Hankin et al. 1998). After age18, rates of depressive symptoms in community samples tendto level off and remain relatively stable throughout most ofadulthood (Hankin et al. 1998; Yaroslavsky et al. 2013).However, sample-wide analyses of average depressive symp-toms mask important individual differences in depressivesymptom trajectories, and there is evidence of both continuityand change in depressive symptoms in adolescence. Themajority of adolescents in community samples display consis-tently low depressive symptoms across late childhood andadolescence (e.g. Costello et al. 2008; Frye and Liem 2011;Reinke et al. 2012; Sterba et al. 2007). Most trajectory analysesalso find evidence for a sizable minority who display a patternof low depressive symptoms initially, which increase dramati-cally over time (Brendgen et al. 2005; Costello et al. 2008;Dekker et al. 2007; Frye and Liem 2011; Reinke et al. 2012). Atleast three studies have also found that a sizable minority ofyouth display high depressive symptoms initially, which de-cline over time (Costello et al. 2008; Frye and Liem 2011;Reinke et al. 2012). Finally, some studies find a small group ofyouth with consistently high depressive symptoms (Brendgenet al. 2005; Frye and Liem 2011; Sterba et al. 2007; Reinkeet al. 2012; Rodriguez et al. 2005).

Prior studies have demonstrated specific and general riskfactors in predicting adolescent depression trajectories.Brendgen et al. (2005) reported that girls with a highly reactivetemperament who experienced rejection by same-sex peerswere more likely to follow the increasing trajectory of depres-sive symptoms. Some risk factors exert general influences on

high-risk depressive symptom profiles (e.g., increasing profile,stably high profile), including being female (Brendgen et al.2005; Costello et al. 2008; Frye and Liem 2011), trauma history(Frye and Liem 2011), and postpartum maternal depression(Sterba et al. 2007).

Although the extant literature on latent classes of depressivesymptoms and associated risk factors has contributed substantialknowledge, there are limitations to many prior depression tra-jectory analyses. Some are limited in age range, sampling youtheither prior to the adolescent transition (e.g. Sterba et al. 2007)or after the adolescent transition (e.g. Frye and Liem 2011).Others rely upon limitedmeasures of depressive symptoms (e.g.Costello et al. 2008). Some examine only one gender (e.g.Stoolmiller et al. 2005). Few have compared symptom trajecto-ries with depression diagnoses, which is important for under-standing the relation of symptoms to clinically significant psy-chopathology. The majority of studies have considered genderand stress as predictors of depression trajectories, with only ahandful examining other risk factors. The current study follow-ed youth from early to late adolescence (ages 11 to 18) using awell-validated measure of depressive symptoms, comparedsymptom trajectories with depression diagnoses, and examineda wide variety of theory-driven risk factors.

Potential Risk Factors for Distinct Depressive SymptomTrajectories

Numerous theories for adolescent depression have been pro-posed, covering a range of risk factors including genetics,pubertal hormones and timing, coping styles, emotional reac-tivity, negative cognitions, interpersonal relationships, andstress exposure. The ABC model of adolescent depressionoffers an integrated, developmentally sensitive model of howmultiple factors (affective, biological, and cognitive) may con-fer risk for depression in adolescence (Hyde et al. 2008). Usingthe ABC model as a theoretical framework, the current studyexamined multiple potential risk factors across developmentaldomains that may explain individual differences in depressivesymptom trajectories. Given the salience of the adolescentperiod for divergence of symptom trajectories, particular em-phasis was given to identifying childhood risk factorspremorbid to the onset of depressive problems.

Affective Risk for Depression Affective models of depressionsuggest that individual differences in emotional reactivity rep-resent an early temperamental risk factor for depressive disor-ders (Compas et al. 2004). A constellation composed of highnegative affect, high reactivity, high intensity of emotionalreactions, low adaptability, and low approach is typically la-beled “negative affectivity”. Extensive research links high

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childhood negative affectivity with depressive symptoms anddisorders in adolescence (Compas et al. 2004; Goodyer et al.1993). Given the relative stability of negative affectivity overtime and conceptual overlap between negative affectivity anddepressive symptoms, it is important to consider the extent towhich childhood negative affectivity predicts change in depres-sive symptoms, particularly increases in symptoms, as opposedto simply being associated with continuity of depressive symp-toms. Examining childhood negative affectivity as a predictor ofdivergent depressive symptom trajectories may clarify the roleof negative affectivity as a premorbid risk factor for adolescent-onset depression.

Biological Risk for Depression Pubertal timing (early, on time,or late) is crucial to understanding the emergence of depressionin adolescence. Early puberty may interfere with the child’sability to complete normative developmental tasks before beingfaced with the sociocultural demands of adulthood that accom-pany pubertal development, and may be accompanied bychanges in body image that make youth more susceptible topeer stressors (Ge and Natsuaki 2009). Early puberty is asso-ciated with depressive symptoms among both boys and girls(Ge et al. 2001; Graber et al. 1997; Kaltiala-Heino et al. 2003).

Cognitive Risk for Depression Cognitive models of depressionsuggest that individuals’ characteristic cognitive responses tostress or depressedmoodmay confer vulnerability to depression.Two of the most empirically supported cognitive vulnerabilitiesto depression are negative cognitive style and rumination. Thehopelessness theory of depression (Abramson et al. 1989) de-fines negative cognitive style as the trait tendency to makenegative inferences about causes, consequences, and self-characteristics of stressful events, and hypothesizes that thosewho encounter stressful events and exhibit this negative cogni-tive style are at elevated risk for depression. Strong prospectivesupport for negative cognitive style as a vulnerability factor fordepression has been shown among adolescents and adults (e.g.,Abela et al. 2011; Alloy et al. 2006). A second cognitive vul-nerability factor for depression is a ruminative response style.Nolen-Hoeksema (1991) described rumination as “focusing ondepressive symptoms and the possible causes and consequencesof those symptoms” (p. 569). Prospective studies have shownthat individuals who ruminate about their negative emotions areat increased risk for developing depressive disorders (e.g., Abelaand Hankin 2011; Nolen-Hoeksema et al. 2008).

Several researchers have hypothesized that cognitive vulner-ability to depression may be consolidating and stabilizing inearly-to-middle adolescence (e.g., Cole et al. 2008; Mezuliset al. 2006). The consolidation of cognitive vulnerability at thistime is consistent with the timing of the rise in depressivesymptoms. Thus, the transition from late childhood into adoles-cence may be an important developmental period in which

cognitive vulnerability to depression exerts its influence onsubsequent depression trajectories.

The Current Study

In the current study, we used data from the longitudinalWisconsin Study of Families and Work (Hyde et al. 1995).Our first aim was to identify heterogeneity in trajectories ofdepressive symptoms from age 11 to 18 using latent growthmixture modeling. The second aim was to associate distinctsymptom trajectories with depression diagnoses. Our thirdaim was to examine theory-driven predictors of depressionsymptom trajectories. We examined affective (infant temper-ament), biological (pubertal timing), and cognitive (cognitivestyle, rumination) predictors of trajectory group membership.We further tested whether the effects of these risk factorsvaried as a function of biological sex.

Based on prior trajectory analyses, we expected to find adistinct group of youth with stable low symptoms; a group ofyouth with increasing symptoms; and a group of youth withsymptoms that are high at the start of the study which remainstable and/or decline. Since the risk factors examined herehave all been associated with adolescent depression, we an-ticipated that negative affectivity, rumination, cognitive style,early pubertal timing, and being female would be associatedwith being on one or more of the high risk trajectories relativeto a normative, stable low group. However, we had no a priorihypotheses regarding which risk factors, if any, would differ-entially predict one high risk trajectory over another.

Methods

Participants

Participants were 382 youth (52.1 % female) who have partic-ipated in a longitudinal study of child development since birth.A total of 570 mothers were recruited during pregnancy forparticipation in the Wisconsin Study of Families and Work(formerly named the Wisconsin Maternity Leave and HealthProject; Hyde et al. 1995). Data were collected at age 1(N =480) and during the summer following Grades 5(N =306; mean age=11.5, SD =0.32), 7 (N =372; M =13.5,SD =0.33), 9 (N =337; M =15.5, SD =0.33), and 12 (N =324;M=18.5, SD =0.33). Every effort was made to retain all partic-ipants across the study from birth through the age 18 assess-ment. For the present study, 382 of the original 570 participants(67 %) were still participating at the time of the adolescentassessments. Of these 382 participants, 219 youth (57 %) par-ticipated in all four adolescent assessments, 105 (27 %)

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participated in three adolescent assessments, and 58 (15 %)participated in two adolescent assessments. Of these, 93%wereWhite, 3 % African American, 2 % American Indian, 1 %Asian/Pacific Islander, and 1 % Hispanic. Independent samplest-tests were used to compare the 382 participants included in theanalyses sample with the 188 participants from the originalrecruitment sample no longer participating by adolescence onthe following variables assessed in infancy: family income;maternal and paternal education; and maternal and paternaldepressive symptoms. The 188 participants from the originalrecruitment sample no longer participating by adolescence hadsignificantly lower paternal education at the start of the study(t =2.35, p <0.05) than the 382 participants included in thecurrent study; there were no significant differences on maternaleducation; family income; maternal depressive symptoms; orpaternal depressive symptoms (all p values>0.10).

Procedure

Mothers were enrolled in the study when pregnant with theparticipating child. The present study utilizes data from the12-month assessment (mother report of infant temperament)but otherwise focuses on the pre-adolescent and adolescentassessments at ages 11, 13, 15, and 18. When participantswere 12 months old, their mothers completed a questionnaireassessing infant temperament. At ages 11, 13, 15, and 18,participants completed a number of questionnaires adminis-tered on a laptop computer during in-home visits. Participantswho had moved out of the area completed paper question-naires by mail. Diagnostic interviews were conducted in per-son or by phone at the age 15 assessment. The study wasapproved by the University ofWisconsin Institutional ReviewBoard. Parents provided consent and children provided assentfor their participation until age 18, when participants providedconsent. At each wave of data collection participants receivedmonetary compensation.

Measures

Depressive Symptoms Depression symptoms were assessed atages 11, 13, 15, and 18 with the Children’s DepressionInventory (CDI; Kovacs 1981), a 27-item self-report scaledesigned for use with children between ages 8 and 17. Foreach item, participants identified one of three statements thatbest described themselves in the previous 2 weeks (e.g., “Ithought about bad things happening to me”). In the currentstudy, given that assessments were conducted in summer, weomitted three items that referenced school. Participants’ scoreson the remaining 24 items were averaged and then multipliedby 27 to create a total score that is comparable to the complete27-item CDI. The CDI has been widely used in depressionresearch (Sitarenios and Stein 2004) and has demonstratedgood internal consistency and test-retest reliability (Saylor

et al. 1984; Smucker et al. 1986). Internal consistencies were0.79 at age 11, 0.83 at age 13, and 0.86 at ages 15 and 18.

Depression Diagnoses Trained graduate students conducteddiagnostic interviews using the Kiddie-Schedule for AffectiveDisorders and Schizophrenia (K-SADS; Orvaschel 1995) whenparticipants were 15. The K-SADS is a semi-structured diagnos-tic interview administered to a child or adolescent (ages 6–18)and his or her primary caregiver. The interview provides DSM-IV diagnoses of a wide range of psychiatric disorders. Trainingin administration of the K-SADS was provided by Dr. HelenOrvaschel of Nova Southeastern University, FL, for the eightdiagnostic interviewers. To ensure the reliability of the diagnosticinterviews, the first 20 interviews were reviewed by Dr.Orvaschel and any discrepancies were resolved by consensus.fter that point, all interviewers participated in a weekly groupmeeting with audiotaped interviews being periodically reviewedby Dr. Orvaschel for accuracy and validity. Any interviews thatraised diagnostic issues or ambiguities were brought to the groupfor a consensus decision. These meetings were supervised by asenior faculty member.

Diagnostic interviewers first interviewed mothers abouttheir child’s symptoms, and then interviewed the adolescents.Interviews covered lifetime history of psychopathology, in-cluding unipolar depression diagnoses (major depressive dis-order; dysthymia; depressive disorder NOS; and adjustmentdisorder with depressed mood). The diagnostic interviewersscored the interview according to the K-SADS manual, usingboth parent and child information.

Negative Affectivity Infant negative affectivity was assessedwith the withdrawal negativity subscale of the Infant BehaviorQuestionnaire (IBQ; Rothbart 1981). Mothers completed theparent-report questionnaire when the children were 12 monthsold. The withdrawal negativity subscale consists of 21 itemsmeasuring distress to novelty/fear and startle (e.g. “How oftendid your baby fuss, cry, or show distress while waiting forfood?”). Mothers reported on each item across the prior 2-week time period using a 7-point Likert scale ranging from 1(never ) to 7 (always). Items were averaged across the respec-tive subscale (distress to novelty and startle) and then thesescale scores were averaged to compute overall withdrawalnegativity. Internal consistency of the withdrawal negativitysubscale was 0.76.

Rumination Depressive rumination was assessed at age 11using a short form of the Ruminative Response Scale (RRS)of the Response Style Questionnaire (RSQ; Nolen-Hoeksemaand Morrow 1991). In the original 22-item RRS, participantsindicate how frequently they engage in ruminative responseswhen they feel sad, down, or low, on a scale from 1 = almostnever to 4 = almost always . Based upon consultation withNolen-Hoeksema at the time of the study design (personal

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communication, 2001), our 5-item form of the RRS includedrumination items that emphasized rumination about sad, de-pressed, or down affect (e.g., “When I feel sad or down, I thinkabout how alone I feel”). The full RRS has been used withadolescents in several prior studies (e.g., Rood et al. 2009).Internal consistency was 0.68.

Negative Cognitive Style Negative cognitive style was assessedat age 11 with the Children’s Cognitive Style Questionnaire(CCSQ; Mezulis et al. 2006). On a Likert scale ranging from 1(don’t agree at all) to 5 (agree a lot), participants indicate theiragreement with statements regarding their attributions (1 itemeach for internality, stability, and globality), self-inferences(1 item), and anticipated consequences (1 item) for 4 hypotheticalnegative events. Responses to the 4 negative scenarios wereaveraged to compute three composite scores (negative attribu-tional style, negative self-inference, and negative consequence),which were then averaged to create a negative cognitive stylecomposite score. Higher scores on the CCSQ indicate morenegative cognitive styles. Internal consistencywas 0.86 at age 11.

Pubertal Timing Pubertal status was assessed at age 11 withline drawings of the 5 Tanner stages of pubertal status withinstructions to identify the pictures that looked most like theparticipant’s body (Marshall and Tanner 1970). Tannerratings based on line drawings are a widely used self-assessment of pubertal status and correlate adequately withphysical examination (Dorn and Biro 2011; physicalexamination was rejected as a method in this study because ofits intrusiveness). Both boys and girls indicated their pubic hairdevelopment, while girls additionally indicated breast growthand boys indicated genital growth. The stages ranged from 1 (apicture of a pre-pubertal body) to 5 (a picture of a mature body).The two Tanner ratings were averaged to form a compositescore for pubertal status (as is customary, Marshall and Tanner1970). Pubertal statuswas also assessed via age ofmenstruation,which we collected at age 15 via child self-report. Early Pubertywas defined, for girls, as Tanner stage 3 or higher at age 11 and/or menstruation prior to age 11.5 years old. For boys, EarlyPuberty was defined as Tanner stage 3 or higher at age 11. Wehad 308 participants with data from which to assign a pubertalstatus (Tanner stage and/or age of menstruation data).1 Thevariable was coded as 1 = Early Puberty (31.2 %) and 0 = NoEarly Puberty (i.e., on-time or late; 68.8 %).

Data-Analytic Plan

To identify heterogeneity in the patterns of depressive symp-toms over time, we performed growthmixture modeling usingMplus 6.0 software (Muthén and Muthén 1988-2009). Allstatistical analyses employed full information maximum like-lihood (FIML) estimation with robust standard errors to ac-count for the naturally skewed distribution of depressivesymptoms. Mplus also offers state-of-the-art methods for han-dling missing values, which allowed all participants to beincluded in latent growth analyses regardless of whether theyhad completed all depressive symptom assessments. Thenumber of latent trajectories was examined iteratively, startingwith the null hypothesis of only one latent class and specifyingan increasing number of classes. Evaluation of the output foreach subsequent iteration included interpretability of the re-sults, meaningfulness of the classes, and relevant model fitstatistics (see Table 1). To examine depression diagnosesacross latent growth trajectory classes, we employed chi-square tests of class by diagnosis frequency distributions usingSPSS 19.0. Results are reported as likelihood ratios. Finally,we examined predictors of trajectory class membership usingmultinomial logistic regression in Mplus 6.0. Predictors wereentered as centered, continuous variables for all variablesexcept child sex and pubertal status, which were categoricalpredictors. Significant interactions were interpreted by exam-ining each independent variable in the interaction at onestandard deviation above and below the mean and then thedistribution of class membership within each quadrant.

Results

Trajectories of Depressive Symptoms

Evaluation of the model statistics indicated that a three-classmodel provided the best fit to the data (see Table 1). Youth wereplaced into classes based upon most likely class membershipstatistics, and all subsequent analyses were based upon thisclass membership assignment. Average latent class probabilityfor most likely class membership ranged from 0.83 to 0.98. Welabeled the majority class (51 % of the sample) the Stable Lowclass (see Fig. 1). These adolescents had consistently very lowdepressive symptoms at all assessments. We labeled the nextlargest class (37 % of the sample) the Increasing class. Theseyouth displayed low depressive symptoms at the onset ofadolescence (age 11), which consistently increased over time.Finally, we labeled the smallest class (12 % of the sample) theEarly High class. These youth started the study at age 11 withthe highest depressive symptoms. Although their symptomsdecreased over time, they remained significantly higher thanthe Stable Low class at every assessment. Correlations amongst

1 Of the 382 participants, 224 participants completed the Tanner ques-tions at age 11. Given a 0.78 correlation (p<0.001) between child andmother Tanner report at age 11, we used the mothers’ Tanner scores for 65of the participants with missing data.We also used pubertal status data for19 participants who did not have Tanner data, resulting in a total N of 308for pubertal timing.

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study variables are shown in Table 2. Descriptive statistics andANOVA comparisons are shown in Table 3.

Depression Diagnoses by Depression Trajectory

We examined prevalence rates of major depressive disorder(MDD) as well as other depression diagnoses (dysthymia;depressive disorder NOS; and adjustment disorder with de-pressed mood) by class, and statistically compared the EarlyHigh and Increasing classes to the Stable Low class. Given thehigh early depressive symptoms among the Early High class,we also considered whether rates of depression diagnosesvaried if they were childhood-onset versus adolescent-onset.Results are shown in Table 4. Lifetime prevalence of MDDamong youth in the Increasing class was more than twice that

observed in the Stable Low class; prevalence of MDD amongyouth in the Early High class was nearly triple that of theStable Low class. These descriptive analyses also suggest that,across groups, most youth first met criteria for MDD at age 12or later. Thus, the depressive symptoms displayed at age 11 byyouth in the Early High class do not appear to be simply aproxy for childhood-onset depression but rather a potentialearly indicator of risk for adolescent-onset depression.

Affective, Biological, and Cognitive Predictors of DepressionTrajectories

Means, standard deviations, and frequencies of all predictors byclass are shown in Table 5.We conducted a multinomial logisticregression to examine predictors of class membership. Wemodeled child sex, negative affectivity, pubertal timing, cogni-tive style, and rumination, as well as two-way interactions withsex. Main effects and interactions were interpreted at a signifi-cance level of p <0.05. The Stable Low group served as thereference category. Regression results are presented in Table 6.

Membership in the Increasing class relative to the StableLow class was predicted by sex and infant negative affectivity.There was a trend (p =0.06) for cognitive style at age 11 topredict membership in the Increasing class as well. None ofthese main effects were moderated by sex.

Membership in the Early High class relative to the StableLow class was predicted by early puberty and rumination at age11. The main effect of rumination was moderated by sex.Examination of this interaction indicated that the effect ofrumination on membership in the Early High class was stron-gest for girls. There was also evidence for a significant inter-action between infant negative affectivity and sex. Examinationof this interaction indicated that the effect of infant negativeaffectivity onmembership in the Early High class was strongestfor boys.

To better specify distinct predictors of high risk trajectories,we conducted a final multinomial logistic regression examin-ing membership in the Early High class relative to member-ship in the Increasing class. This analysis confirmed that sex(β/SE= −2.55, p =0.01), rumination (β/SE=3.25, p <0.00);and negative affectivity (β/SE= −1.96, p =0.05) differentiallypredicted membership in the Early High class relative to theIncreasing class, such that being female and being high innegative affectivity were associated with greater likelihood ofbeing in the Increasing Class relative to the Early High classwhile being high in rumination was associated with greaterlikelihood of being in the Early High class relative to theIncreasing class. Although early puberty differentiated mem-bership in the Early High class relative to the Stable Low classin the prior analysis, it did not significantly differentiate mem-bership in the Early High class relative to the Increasing class(β/SE=1.23, p =0.22).

Table 1 Latent growth mixture model statistics

Number of classes AIC BIC Entropy LMR adjusted LRT

1 7964.33 7984.48 0.96 –

2 7816.50 7848.75 0.94 p=0.013

3 7775.76 7820.10 0.88 p=0.041

4 7766.15 7822.58 0.83 p=0.110

5 7727 7796.01 0.81 p=0.240

For the Bayesian Information Criterion (BIC) and the Akaike InformationCriterion (AIC), lower values typically indicate better fitting models.Model entropy is a measure of classification accuracy with values closerto 1 (range: 0–1) indicating greater precision of classification accuracy.The Lo-Mendell-Rubin adjusted likelihood ratio test (LMR AdjustedLRT) of model fit compares the estimated model with a model with onefewer class (Lo et al. 2001). The Lo-Mendell-Rubin Adjusted LRTyieldsa p-value that reflects whether the current model fits the data significantlybetter than a model with one less class. The three-class model (bold)displayed lower AIC and BIC compared to the two-class model whilemaintaining adequate entropy; the LMR Adjusted LRT indicated that thethree-class model was a significantly better fit to the data than the two-class model. Although the AIC decreased slightly from the three-class tothe four-class model, the BIC increased, entropy decreased, and the LMRAdjusted LRT indicated that the four-class model was not a significantlybetter fit than the three-class model

0

2

4

6

8

10

12

14

Increasing

Decreasing

Stable Low

11 13 15 18

Fig. 1 Depressive symptom trajectory classes

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Discussion

The current study examined the development of depression inadolescence by examining heterogeneity in symptom trajec-tories across adolescence using a multi-wave design and self-report measures, parental-report measures, and diagnosticinterviews.

Trajectories of Depressive Symptoms in Adolescence

We were particularly interested in identifying when and howhigh-risk youth diverge from their low-risk peers. Prior de-pression trajectory analyses have typically identified a largegroup of youth with stable low depressive symptoms, as wellas smaller groups with increasing, decreasing, and/or stablehigh depressive symptoms (Brendgen et al. 2005; Costelloet al. 2008; Dekker et al. 2007; Frye and Liem 2011; Reinkeet al. 2012; Sterba et al. 2007). Results from the current studywere largely consistent with prior depression trajectory anal-yses in adolescence. The majority of youth (51 %) displayedconsistently low depressive symptoms at all assessments. Wealso identified two high-risk trajectories. First, just over one-third of youth (36 %) displayed a pattern of increasing depres-sive symptoms. At age 11, youth in this Increasing class wereindistinguishable from youth in the Stable Low class based ondepressive symptoms, but by age 13 they had diverged

significantly and their symptoms increased steadily at eachsubsequent assessment. Second, we identified a small groupof youth (13 %) who displayed a pattern of high depressivesymptoms at age 11—markedly higher than those observed ineither the Increasing or Stable Low classes. Youth on thisEarly High trajectory reported declining symptoms over time,though it would not be accurate to characterize this pattern as areduction in overall depression risk. Despite net declines indepressive symptoms over time, Early High trajectory youthreported significantly higher depressive symptoms than theirpeers in the Stable Low trajectory at each assessment.

Although other studies have identified a group of youth withsteady high depressive symptoms across adolescence (e.g.,Reinke et al. 2012), our study did not identify such a group.This likely is due to our community sample and the relativelysmall number of youth (N =52) with high depressive symptomsat age 11 who were classified as being in the Early Hightrajectory. It is possible that some of the youth in the EarlyHigh trajectory actually displayed a stable high symptom tra-jectory but we lacked the statistical power to distinguish them.In a larger sample we would have had greater power to detect asmall group of youth with stable high depressive symptoms.

While describing these three depressive symptom trajecto-ries is interesting in and of itself and provides replication ofkey findings from prior trajectory analyses, the proximal goalsof this study were to both identify and predict depression

Table 2 Correlation matrix for all study variables

CDI 11 CDI 13 CDI 15 CDI 18 RUM CS NA Early Pub

CDI 13 0.49**

CDI 15 0.35** 0.56**

CDI 18 0.27** 0.33** 0.35**

RUM 0.44** 0.18** 0.16** 0.17**

CS 0.24** 0.15** 0.15** 0.17** 0.32**

NA 0.03 0.03 0.11+ 0.14** 0.01 0.02

Early Pub 0.21** 0.27** 0.23** 0.11 0.05 −0.05 0.03

Sex 0.00 0.14** 0.17** 0.06 0.01 0.13* 0.14* 0.31**

The number following CDI indicates the age of assessment. Early Puberty was coded 0 = No Early Puberty and 1 = Early Puberty. Sex was coded −1 =male and 1 = female

NA Negative affectivity

+ indicates correlation significant at p <0.10; * p <0.05; and ** p <0.01

Table 3 Depressive symptomsby trajectory class Age Total Depressive symptoms M (SD) F ANOVA

Stable Low (SL) Increasing (I) Early High (EH) Comparison

11 3.25 (4.44) 1.68 (1.80) 2.48 (3.02) 11.32 (6.27) 152.05** (SL=I)<EH

13 4.25 (4.76) 2.39 (2.47) 5.17 (4.86) 8.62 (6.81) 45.95** SL<I<EH

15 4.79 (5.44) 2.04 (2.10) 7.63 (6.33) 7.12 (6.18) 56.58** SL<(I=EH)

18 5.44 (5.78) 1.85 (1.96) 9.85 (5.93) 5.43 (5.66) 112.03** SL<EH<I

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trajectories in adolescence as necessary steps toward a moredistal goal of informing preventive and early interventionefforts by depression researchers and clinicians. Below weexamine the Increasing and Early High classes in terms ofdepression diagnoses and significant predictors.

The Increasing Trajectory: Emergent Risk for Adolescent De-pression from a Convergence of Risk Factors If it is reassuringto depression researchers and clinicians that the majority ofyouth (51 %) are on a relatively low-risk depression trajectoryin adolescence, it should be alarming that the next largest groupof youth (37 %) is on a trajectory of increasing symptoms andsteadily accumulating risk for depression diagnoses. By age 15,youth in the Increasing class had the highest level of depressivesymptoms in the entire sample and nearly a quarter of them hadalready met criteria for a major depressive episode. If weconsider all clinically relevant Axis 1 depressive disorders,the lifetime prevalence for depression among this group ofyouth was nearly 40 % by age 15. What is notable about thesehigh-risk youth, however, is that at age 11 they were indistin-guishable from their Stable Low peers in terms of early depres-sive symptoms. What, then, differentiates youth on the high-risk Increasing trajectory from youth on the low-risk Stable

Low trajectory even in the absence of identifiable differences indepressive symptoms? We identified three risk factors thatcontributed to emergent risk for adolescent depression: beingfemale; having high negative affectivity in infancy; and havingmore negative cognitive style in pre-adolescence.

The emergent sex difference in depression in adolescenceis a well-established and robust finding (Hankin et al. 1998).What is novel about this finding is the specificity of the riskassociated with being female—girls were more likely to be onthe Increasing high-risk trajectory, but not the Early Highhigh-risk trajectory. This is consistent with the depressivesymptom trajectories identified among adolescent girls byDekker and colleagues (2007), who found that among adoles-cent girls, depression symptom trajectories were characterizedby either stability of symptoms or increases in symptoms.These concordant results indicate that future research shouldidentify the mechanisms that propel girls onto an Increasingtrajectory. These might be factors that themselves increaseover the same period, such as stress or sexual victimization.

While extensive research has associated childhood nega-tive affectivity with depressive symptoms and disorders inadolescence (e.g., Compas et al. 2004), there has been debateover the extent to which childhood negative affectivity

Table 4 Depression diagnoses by trajectory class

Depression diagnosis Entire sample Stable low Increasing Early high

Major Depressive Disorder

Lifetime 15.2 % 8.8 % 20.4 %, LR=8.66** 25.0 %, LR=9.18*

Childhood Onseta 2.1 % 2.1 % 2.2 %, LR=0.01 1.9 %, LR=0.01

Adolescent Onsetb 13.1 % 6.7 % 18.2 %, LR=10.12** 23.1 %, LR=10.78**

Other Depressive Disorder

Lifetime 10.2 % 6.7 % 14.6 %, LR=4.48* 11.5 %, LR=1.10

Childhood Onseta 2.1 % 1.6 % 2.9 %, LR=0.55 0.0 %, LR=1.4

Adolescent Onsetb 8.4 % 5.2 % 11.7 %, LR=3.82* 11.5 %, LR=2.24

Other Depressive Disorder Dysthymia, Depressive Disorder NOS, and Adjustment Disorder with Depressed Mood. LR Likelihood Ratio compared toStable Low classa Less than 11 years, 11 monthsbGreater than or equal to 12 years, 0 months

*Indicates LR significant at p<0.05; **indicates LR significant at p <0.01

Table 5 Means, standard deviations, frequencies, percentages of predictor variables by class

Variable Increasing (I) Early High (EH) Stable Low (SL) Total F Increasing vs. SL Early High vs. SL

Frequency Sex (Female) 91 (66.4 %) 24 (46.2 %) 84 (43.5 %) 199 (52.1 %) 15.28** 0.013

Early Puberty 41 (33.6 %) 18 (42.9 %) 37 (25.7 %) 96 (31.2 %) 1.99 4.39*

Mean (SD) Negative Affectivity 3.08 (0.65) 2.86 (0.54) 2.75 (0.68) 2.89 (0.67) 8.88** 0.33 (0.08)** 0.11 (0.11)

Rumination 1.84 (0.51) 2.20 (0.58) 1.74 (0.50) 1.84 (0.53) 11.76** 0.10 (0.06) 0.46 (0.09)**

Cognitive Style 1.91 (0.50) 2.06 (0.58) 1.82 (0.43) 1.88 (0.48) 3.93* 0.09 (0.06) 0.24 (0.09)**

* = < 0.05; ** = <0.01. Class comparisons reported for Frequency (%) variables are Likelihood Ratios. Class comparisons reported for Mean (SD)variables are Mean Differences (Standard Error)

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predicts emergence of depressive symptoms and disorders asopposed to simply being an early form of depression. Thecurrent study clearly identifies infant negative affectivity as apremorbid risk factor for adolescent-onset depression. Youthwho were described by their mothers as being highly emo-tionally reactive and displaying more negative affect than thetypical child even as young as 12 months old were signifi-cantly more likely to be on the Increasing trajectory. However,these youth did not display greater depressive symptoms ordiagnoses at age 11 than youth on the Stable Low trajectory,suggesting that infancy negative affectivity is not simply aproxy for early depressive symptoms. Thus, negative affec-tivity prior to adolescence may be an easily identifiable andpowerful indicator of risk for depression in adolescence.

Finally, there has been considerable debate as to when cog-nitive risk factors begin to confer risk for depression, as theserely upon normative cognitive development in the transition toadolescence to emerge and consolidate (Mezulis et al. 2011).Researchers have also debated the direction of effects betweencognitive style and depressive symptoms, with some researcherssuggesting that negative cognitive style may actually emerge asa result of early depressive symptoms and functions instead as acognitive “scar” of early depression, which in turn predictsdepression recurrences (McCarty et al. 2007). In the currentstudy, we found a trend for negative cognitive style at age 11 topredict being on the Increasing trajectory in adolescence. Whilethis trend-level effect should be interpreted cautiously, this resultsupports other studies suggesting a prospective effect of nega-tive cognitive style on the emergence of adolescent depression(e.g. Abela et al. 2011).

The Early High Trajectory: Entering Adolescence on a High-risk Developmental Trajectory Created by Early RiskFactors Although common wisdom suggests that most youthenter adolescence with low depressive symptoms that thenincrease over time, youth on the Early High trajectory displayed

a very different pattern. They entered adolescence with highdepressive symptoms that declined somewhat over time. Thisclass might be interpreted in one of two ways: either as a groupof youth with childhood-onset depression rather thanadolescent-onset depression, or as a group of youth with de-creasing risk over time. Closer examination revealed that bothinterpretations were inaccurate. Total risk for depression did notappear to decrease over time for these youth. Their symptoms,while declining somewhat from age 11 to 18, remained signif-icantly higher than those observed in the Stable Low group at allassessments. Similarly, they accumulated depression diagnosesat a rate exceeding that of both the low-risk Stable Low andhigh-risk Increasing groups; nearly 30 % of youth in this classhad experienced a major depressive episode by age 15.However, this high rate of depression diagnoses does not appearto be explained by a high rate of childhood-onset episodes. Thehigh level of symptoms observed among these youth at age 11appear to precede and indicate risk for future depressive epi-sodes rather than simply being a marker of concurrent or priordepressive episodes. However, it will be critical to follow theseyouth across the transition to adulthood, as at least one otherstudy has found that the Early High group may become indis-tinguishable from the Stable Low group by age 25, at least interms of current symptoms (Costello et al. 2008).

Given the high depressive symptoms already beingdisplayed by these youth at age 11, it is difficult to interpretstatistically significant risk factors as “premorbid” and differen-tiate causal risk factors from correlate symptoms. However, weidentified both affective and biological risk factors for being onthe Early High trajectory that are likely premorbid to even thedepressive symptoms at age 11. Infant negative affectivity was arisk factor for being on the Early High trajectory, particularly forboys. This finding has clear clinical relevance, as research hasdemonstrated several etiological pathways to adolescent depres-sion among girls, but identifying high-risk boys has been moredifficult.

The other notable biological risk factor for being on theEarly High trajectory was early puberty. These are youth who,by age 11.5, had already attained Tanner Stage 3 and/or startedmenstruation, either of which would indicate a pubertal de-velopmental trajectory a year or more advanced than typicallydeveloping youth. The concordance between early pubertyand early depressive symptoms is consistent with prior studiessuggesting that early puberty puts youth at elevated risk formental health problems.While beyond the scope of this paper,other studies have found that the effects of early puberty ondepression outcomes may be mediated by changes in peersexual harassment and body image (e.g. Lindberg et al. 2007).

Finally, we found that one cognitive risk factor at age 11,rumination, predicted membership in the Early High class. Thisresult should also be interpreted very cautiously. Ruminationabout depressed affect has been criticized as having too muchconceptual overlap with depressive symptoms (Treynor et al.

Table 6 Multinomial logistic regressions predicting membership in in-creasing and early high classes

Predictor Increasing class Early high class

OR p-value OR p-value

Sex 0.41 0.00 1.06 0.60

Negative affectivity 1.93 0.00 4.12 0.25

Early Puberty 1.21 0.50 2.27 0.03

Rumination 1.45 0.14 4.07 0.00

Cognitive Style 1.68 0.06 2.90 0.28

Negative affectivity × Sex 0.84 0.34 2.63 0.04

Early Puberty × Sex 1.57 0.18 1.44 0.16

Rumination × Sex 0.88 0.65 0.51 0.02

Cognitive Style × Sex 1.40 0.34 1.60 0.31

Comparison group is Stable Low class for all analyses

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2003) and in the current study it was measured at age 11 whenyouth in the Early High trajectory were already displayingelevated depressive symptoms, so that it cannot clearly beinterpreted as a premorbid risk factor. However, several studieshave shown that rumination exacerbates and prolongs de-pressedmood and as such is an important cognitive mechanismin depression maintenance and recurrence (Nolen-Hoeksemaet al. 2008). In the current study, we also found a significantrumination by sex interaction, suggesting that rumination maybe a cognitive vulnerability factor that disproportionally makesgirls vulnerable to depressive symptoms.

Strengths, Limitations, and Future Directions

The latent class approach accounts for the heterogeneity in thelongitudinal course of depressive symptoms from early throughlate adolescence, and sheds light on a general risk factor for bothhigh-risk trajectories (negative affect) and specific risk factorsunique to each high-risk trajectory (early puberty, rumination,cognitive style). Importantly, our findings of Early High,Increasing, and Stable Low classes replicate those of other latentclass studies on depressive symptoms in adolescence (Brendgenet al. 2005; Costello et al. 2008; Frye and Liem 2011; Reinkeet al. 2012). While a strength of the current study is the com-munity sample of adolescents (who were experiencing stressorsnormative to the adolescent period), this study is limited in itsgeneralizability of the findings beyond non-Hispanic Whites.We also lacked data on depression intervention within oursample that may have impacted upon symptom trajectories;specifically, it is possible that one explanation for decliningsymptoms among the Early High group was early depressiontreatment which was not assessed in the study. Finally, we onlycollected depressive symptom data once every 2 years.Symptom measures such as the CDI only assess current symp-toms, and thus fluctuations in symptoms between assessmentsare not well-characterized by these analyses. Thus, we encour-age future research on the following: 1) latent class analysis ofdepressive symptoms every 3–6 months across adolescence; 2)risk factors assessed in childhood that predict membership in theEarly High group; 3) other risk factors that may predict classmembership, including HPA markers, pubertal hormones, im-mune markers, and life stressors; 4) latent classes of comorbidanxiety and depressive symptoms; and 5) research in morevulnerable populations (e.g., Ge et al. 2006; Repetto et al. 2004).

Clinical Implications for the Development of DepressionPrevention Programs

This study has several important implications for the effectivedevelopment and implementation of targeted depression preven-tion programs. Studies of adolescent depression demonstrating amean rise in depressive symptoms and diagnoses suggest a

clinical rationale for universal programs (Garber et al. 2012).However, our results clearly indicate that the majority of youthdo not need depression prevention programs. They enter ado-lescence on a low-risk trajectory characterized by consistentlylow depressive symptoms and relatively low depression diag-noses. Their resilience may come in the form of lower levels ofrisk factors for adolescent-onset depression—less negative af-fectivity, less negative cognitive style and rumination, and lesslikely to have early puberty—and/or in the form of higher levelsof protective factors not measured in the current study. It is likelythat this sizable group of youth contribute to the generally weakfindings for the effectiveness of universal prevention programsfor reducing depression risk.

Consistent with these findings, Horowitz and Garber (2006)stated that selective and indicated depression prevention pro-grams have the most promise for effectively altering the de-pression trajectories of at-risk youth. Most non-universal de-pression prevention programs are indicated prevention pro-grams, meaning they target youth already displaying signs ofdepression, e.g. high-symptom youth. These are differentiatedfrom selective depression prevention programs, which targetyouth who are high in one or more empirically supported riskfactors but who do not already display signs of depression—i.e.high-risk but low-symptom youth. It is important to observethat indicated prevention programs prior to age 13 wouldoverlook youth on the Increasing trajectory in our study.These youth do not display pre-adolescent high depressivesymptoms yet clearly they are on a high-risk trajectory. Youthon this Increasing trajectory would benefit most from selectiveprevention programs that target youth who are high in one ormore empirically supported risk factors but who do not alreadydisplay signs of depression.

If indicated depression prevention programs may inadver-tently overlook youth on the Increasing trajectory, it is likelythat they disproportionately target youth on the Early Hightrajectory. It is an open question as to whether indicateddepression prevention programs adequately link the preven-tive intervention with the empirically supported risk factorwithin that sample. Our findings suggest that youth with earlyhigh depressive symptoms may have a markedly differentpathway to adolescent depression than youth whose depres-sive symptoms emerge several years later. Here we see thatearly pubertal development may play a particularly salient rolein conferring risk for early depressive symptoms, suggestingthat preventive interventions that target the social and psycho-logical sequelae of early puberty may be particularly benefi-cial to these youth. Similarly, rumination appears to conferrisk at least for the maintenance of depressive symptomsamong youth with early symptom trajectories.

Taken together, our findings highlight the importance ofnuanced approaches to identifying heterogeneity in risk fordepression in adolescence across multiple levels of analysis(affective, biological, and cognitive). Our results guide future

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research by providing preliminary suggestions for tailoringinterventions to the empirically supported risk trajectory.

Author Note This material is based upon work supported by theNational Science Foundation Graduate Research Fellowship (DGE-071823 to Rachel Salk); the National Institute of Mental Health(F31MH084476 to Heather A. Priess-Groben and R01MH44340 to JanetShibley Hyde); and a University of Wisconsin Graduate School grant toJanet Shibley Hyde. The content is solely the responsibility of the authors,and any opinion, findings, and conclusions or recommendationsexpressed in this material are those of the authors and do not necessarilyreflect the views of the National Science Foundation or National Instituteof Mental Health.

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