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Maternal Prenatal Stress and Other Developmental Risk Factorsfor Adolescent Depression: Spotlight on Sex Differences
Seth D. Maxwell1 & Anna M. Fineberg1 & Deborah A. Drabick1& Shannon K. Murphy1 &
Lauren M. Ellman1
# Springer Science+Business Media New York 2017
Abstract Maternal stress during pregnancy has been linkedto premorbid abnormalities associated with depression (e.g.,difficult temperament, cognitive deficits) in offspring.However, few studies have looked across developmental pe-riods to examine maternal stress during pregnancy and off-spring depression during adolescence and whether these asso-ciations differ by sex. The current study used data from 1711mother-offspring dyads (offspring sex: 49.8% male) in a lon-gitudinal birth cohort study. Maternal narratives collected dur-ing pregnancy were qualitatively coded for stress-relatedthemes by independent raters. Latent class analysis (LCA)identified distinct subgroups of offspring based on exposureto maternal prenatal stress and other developmental factorsfrom the prenatal, childhood, and adolescent periods that havebeen associated with depression and/or maternal prenatalstress. LCA identified subgroups that were compared to deter-mine whether and to what extent they differed on adolescent
depressive symptoms. LCA revealed a subgroup of Bhigh-risk^ individuals, characterized by maternal factors duringpregnancy (higher ambivalence/negativity and lower positiv-ity towards the pregnancy, higher levels of hassles, lower ma-ternal education and higher maternal age at birth, higher pre-pregnancy BMI) and offspring developmental factors (de-creased cognitive functioning during childhood and adoles-cence, lower perceived parental support during adolescence,and higher levels of maternal depression during adolescence).High-risk females exhibited elevated conduct symptoms andhigher birth order, while high-risk males exhibited decreasedinternalizing symptoms and lower birth order. Both high-riskmales and females reported elevated depressive symptomsduring adolescence relative to their Blow-risk^ counterparts.
Keywords Prenatal stress . Adolescence . Depression . Sexdifferences . Developmental risk factors
A small but growing literature suggests an associationbetween maternal psychological experiences duringpregnancy and risk for depression among offspring dur-ing adolescence (Betts et al. 2014; Pawlby et al. 2009).While the preponderance of prenatal studies have shownsex differences between maternal stress during pregnan-cy and offspring developmental outcomes, some studieshave found no association (Agnafors et al. 2013;Agnafors et al. 2016). Despite the associated high dis-ease burden, risk of recurrence, and risk of suicide(Stikkelbroek et al. 2013), depression often goes undi-agnosed in adolescence (Fletcher 2008). Research hasshown that depression rates spike during adolescence,particularly in females (Hankin and Abramson 2001;Nolen-Hoeksema 2001), and that adolescent onset ofclinical depression is associated with more persistent
Electronic supplementary material The online version of this article(doi:10.1007/s10802-017-0299-0) contains supplementary material,which is available to authorized users.
* Lauren M. [email protected]
Seth D. [email protected]
Anna M. [email protected]
Deborah A. [email protected]
Shannon K. [email protected]
1 Department of Psychology, Temple University, 1701 N. 13th Street,Philadelphia, PA 19122, USA
DOI 10.1007/s10802-017-0299-0J Abnorm Child Psychol (2018) 46:
Published online: 10 April 2017
–381 397
and more severe depressive symptoms (Harrington et al.1990; Kim-Cohen et al. 2003; Wickramaratne et al.2000). Early intervention is essential, as the durationof untreated depression is associated with a worsenedclinical course (Fletcher 2008). Enhancing our under-standing of the influence of early developmentalfactors—beginning as early as the prenatal period—onrisk for subsequent depression represents a promisingavenue for early identification of at-risk individuals.
Maternal Stress during Pregnancy and Riskfor Offspring Depression
Although few studies have directly examined the associationbetween prenatal maternal stress and adolescent depression,previous research has linked maternal stress during pregnancyto adverse childhood outcomes often found in the premorbidperiod of depression (Hankin and Abramson 2001; Koenenet al. 2009; Lahey et al. 2002), including: shyness/inhibition(Talge et al. 2007), decreased intelligence scores (Slykermanet al. 2005); and increased behavioral/emotional problems(O’Connor et al. 2002), such as childhood anxiety (Davisand Sandman 2012), attention deficit hyperactivity disorder(ADHD; Loomans et al. 2011), and conduct problems(Rodriguez and Bohlin 2005). These findings underscore theimportance of not just examining a link between prenatal ma-ternal stress and offspring depression, but also consideringfactors across multiple developmental periods that are associ-ated with both prenatal maternal stress and offspring depres-sion that may contribute to this association.
Among ecologic studies, one study found that adult off-spring of women who were pregnant during Holland’sBhunger winter^ were at increased risk of developing unipolarand bipolar major affective disorder during adulthood (Brownet al. 2000). Another study of women who were pregnantduring a severe earthquake found that 13.3% of their 18-year-old offspring had severe depression, compared with only5.5% of offspring born a year later (Watson et al. 1999).Nevertheless, the implications and generalizability of thesestudies are limited by their examination of severe and relative-ly rare traumatic stressors, as well as a presumed, but notassessed, uniform level of stress based on an adverse eventor exposure experienced by the population. Recent work in alarge, prospective Australian longitudinal birth cohort foundthat the combination of maternal anxiety, depression, andstress during pregnancy, assessed using self-report measures,was associated with increased risk of internalizing behaviorproblems in adolescence (Betts et al. 2014) and adulthood(Betts et al. 2015). Although evidence for a link betweenprenatal maternal stress and offspring depression is mounting,there are still unexplored gaps in the literature, including
whether a combination of prenatal and childhood risk factorsincreases risk for depressive symptoms in adolescence.
Offspring Sex Differences in Response to MaternalStress during Pregnancy
Human studies suggest that males may be more vulnerablethan females to the deleterious influences of maternal stressduring pregnancy, as they appear to be at increased risk formortality, morbidity, reduced viability, delayed neonatal ma-turity, neurological problems, and perhaps even risk of schizo-phrenia spectrum disorders (Ellman et al. 2008; Fineberg et al.2016; Sandman et al. 2013). Although these findings werepreviously thought to suggest that females are protectedagainst the adverse influences of early life stress, emergingevidence suggests a Bviability-vulnerability tradeoff,^ suchthat for females, prenatal maternal stress is associated withsubtle but persistent influences on temperament, behavior,and brain development that may predispose females to lateraffective problems (Sandman et al. 2013). In support of thistheory, a growing body of work suggests that fetal exposure tomaternal psychosocial distress and cortisol during pregnancyis linked to temperaments associated with affective disorders,childhood anxiety, and changes in brain regions associatedwith mood and emotional problems that could selectivelyput females at risk for future psychopathology, such as depres-sion (Sandman et al. 2013). Despite known sex differences inthe prenatal stress literature (Sandman et al. 2013) and in ratesof depression during adolescence (Alloy and Abramson2007), the literature is agnostic about whether there are sexdifferences in risk for offspring depression after exposure toprenatal maternal stress.
Developmental Models of Maternal Stressduring Pregnancy and Offspring Depression
The question of how prenatal maternal stress exposure relatesto adolescent depression is complex, given the myriad influ-ences across developmental periods (i.e., in utero, childhood,adolescence) that could influence this association and the po-tentially differing characteristics that increase risk for adoles-cent depression among individuals or groups of individuals(Cicchetti and Rogosch 1996). One possibility is that thesedevelopmental risk factors work in conjunction to influencelater risk for depression. Support for this possibility comesfrom the relatively small effect sizes of many of these devel-opmental variables, suggesting that their individual impact onthe risk for the disorder is small, but may additively or inter-actively influence the etiology for depression (Gale andMartyn 2004; Patton et al. 2004). Another possibility (thatdoes not preclude the first possibility) is that maternal stress
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during pregnancy leads to a cascade of developmental diffi-culties that begin in early life (Davis and Sandman 2012;Sandman et al. 2013; Talge et al. 2007). While both modelsare feasible, the present study sought to analyze a largepregnancy/birth cohort to identify distinct profiles of individ-uals who might be at increased risk for adolescent depressionbased on exposure to a variety of distal and proximal devel-opmental risk factors, as a first step to understanding impor-tant developmental precursors for the disorder.
Present Study Aims and Hypotheses
Given our interest in characterizing developmental pat-terns associated with risk of depression among adoles-cents, we used latent class analysis (LCA)—a person-centered approach that groups individuals based on sim-ilarities and differences across a number of indicatorvariables—to explore patterns among known correlatesof risk for depression across childhood and adolescence,as well as contemporaneously reported prenatal maternalstress and other obstetric variables that have been linkedto risk of offspring depression. Although LCA is anexploratory data analysis technique, we anticipatedbased on previous studies that the following variableswould collectively identify a subgroup at increased riskfor adolescent depression: pregnancy and other maternalfactors, such as maternal stress during pregnancy (Bettset al. 2014; Brown et al. 2000; Buss et al. 2011; DaCosta et al. 1998; Pritchard and Teo 1994; Shah et al.2011; Watson et al. 1999), obstetric risk factors (e.g.,lower birth weight), increased maternal age (Braithwaiteet al. 2014; Cleary-Goldman et al. 2005), increased ma-ternal worry during the offspring’s childhood (Entringeret al. 2015), and higher levels of maternal depressivesymptoms during offspring adolescence (Murray et al.2011); and offspring developmental factors, such aschildhood internalizing symptoms and conduct problems(Talge et al. 2007), decreased cognitive functioning dur-ing childhood and adolescence (Koenen et al. 2009),and lower levels of perceived parental support in ado-lescence (Stice et al. 2004). Given known sex differ-ences in the prenatal stress literature (Sandman et al.2013) and research suggesting that pathways to depres-sion differ for males and females (Piccinelli andWilkinson 2000), we chose to examine males and fe-males separately in supplementary analyses afterconducting LCA of the entire analytic sample. We hy-pothesized that females would be particularly at risk forsymptoms of adolescent depression, given past researchsuggesting that the incidence of depression is higheramong female adolescents (Alloy and Abramson 2007)and a recent study that found an association between
prenatal maternal anxiety and offspring depressivesymptoms during adolescence that was mediated by off-spring cortisol in females, but not in males (Van denBergh et al. 2008).
Method
Participants
The study was approved by the Institutional ReviewBoards of the New York State Psychiatric Institute,Public Health Institute, and Temple University. Datafor the current study were drawn from the ChildHealth and Development Study (CHDS), a large preg-nancy cohort comprised of nearly all pregnant womenreceiving health care through the Kaiser FoundationHealth Plan (KFHP) at its hospital and clinics inAlameda County, California from 1959 to 1966 (livebirths N = 19,044) (van den Berg et al. 1988).Approximately 30% of the population of AlamedaCounty, including the cities of Oakland, Berkeley, andHayward, received care through KFHP at the time.KFHP membership was demographically similar to theregion’s population, albeit with slightly elevated socio-economic status, as eligibility the KFHP typicallydepended on employment (Krieger 1992). To encourageparticipation, routine prenatal lab work was providedfree of charge to participants.
CHDS has continued to follow these mothers andtheir offspring over multiple time points throughout de-velopment. The present study analyzed data from Kaisermedical records and from interviews at three study timepoints (described further in Measures): 1) interviews ofthe mother during pregnancy, completed in or aroundthe second trimester, as well as extensive medical infor-mation collected about the mother just before, during,and after the pregnancy (e.g., during the early neonatalperiod), 2) interviews and questionnaires given to themother when her offspring was aged 9 to 11, as wellas cognitive testing administered to the child and, 3)separate interviews with the mother and child whenthe child was aged 15 to 17, as well as cognitive testingadministered to the child. Data were included in thepresent analyses only if the mother and child participat-ed in all three time points. Of the 2518 offspring eligi-ble for the Adolescent Study—by virtue of continuedresidence in the San Francisco Bay area, participationin all previous study time points, and birth dates be-tween March 1, 1960 and March 31, 1963—the CHDSenrolled 2020 (80.2%; see Online SupplementaryFigure). In cases where financial hardship would haveprevented a family from bringing a child to an
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examination, the CHDS paid the cost of the visit.Compared to the initial CHDS pregnancy cohort, theoffspring in the Adolescent Study had a smaller propor-tion of firstborn offspring and a greater proportion ofoffspring whose mothers were 1) married and livingwith a husband at the original intake, 2) white, and 3)high school graduates, though these differences weresmall (see van den Berg et al. 1988 for details).
Of the 2020 adolescent cohort members, 1964 (97.2%) haddata on the pregnancy variables of interest (see OnlineSupplementary Fig. 2 for comparative demographicsbetween our sample of 1964 and the remainder CHDS livebirths). For mothers who had more than one child in theAdolescent Study, we selected one child at random using arandom number generator to avoid potential statistical com-plications associated with non-independent observationsamong siblings. As such, 253 siblings were removed fromthe analytic dataset, leaving a final analytic sample of 1711mother-offspring dyads with data available at all relevant timepoints (see Table 1 for sample demographics).
Measures (See Online Supplementary Materials forExamples of Questionnaire Items)
Pregnancy Interviews Pregnancy interviews included a de-tailed reproductive history and questions about the expectantmother’s health, the health of her husband and children, herattitudes toward the current pregnancy, and her health-relatedbehaviors. While these interviews often took 4–6 h to com-plete, the questions pertaining to the stress themes analyzed inthe present study typically were completed in 10 min or less.The majority of the interviews were conducted during thesecond trimester (M = 15.32, SD = 6.76), as calculated fromgestational age (GA) at the time of interview. (SeeSupplementary Fig. 1 for full study timeline.)
Maternal Psychosocial Stress during Pregnancy Maternalpsychosocial stress during pregnancy was measured byextracting stressful life events and stress-related themes fromnarratives generated in response to interview questions such asBWhat kinds of things have been worrying you recently?^ Formost of the interviews (n = 1440, or 84.2%), interviewers wereinstructed to BEncourage gravida to talk freely^ and gentlyprobe for detail, but there were no specific follow up ques-tions. For the remaining interviews (n = 271, or 15.8%), inaddition to the Bworrying^ question, women were askedwhether they had experienced Bany nervousness, anxiety, ordepression^ or Bany family troubles.^ Given that the presenceof these additional stress-theme-related questions could leadto greater endorsement of stress themes, a dichotomous inter-view version variable was created for analyses.
Typical responses in the maternal narratives included de-scriptions of financial and relationship stress and stressful life
events (see Online Supplementary Table 2 for examples ofstress coding and Online Supplementary Manual for ourstress theme coding manual). Two independent raters follow-ed a detailed coding manual to extract stress-related themes.To minimize bias, inter-rater reliability was established aftercoders analyzed 50 maternal interviews chosen at randomfrom a previous CHDS subsample that did not overlap withparticipants from the current study. Reliability was evaluatedusing intraclass correlations (ICCs) calculated from frequencyof stress-related themes (e.g., the number of narrative refer-ences to each theme), which are the most appropriate statisticfor continuous data (Shrout and Fleiss 1979). ICCs of .70 orabove on the aforementioned test cases were required before acoder began to code study interviews; ICCs ranged from .72 to1.00 (mean ICC = 0.89; substantial to almost perfect reliabil-ity). To assess the possibility of coder drift during coding, 75interviews from the analytic sample were selected at randomto be coded by both coders independently throughout the cod-ing process. ICCs for this second reliability check ranged from.76 to 1.00 (mean ICC = .81; almost perfect reliability). Afterall interviews were coded initially, an additional 325 inter-views were selected at random to be double coded to furtherto assess the possibility of coder drift; this third reliabilitycheck yielded ICCs ranging from .71 to .87 (meanICC = 0.76; substantial reliability).
Raters, blind to offspring characteristics, coded interviewsfor the following themes: reported anxiety, reported depres-sion, perceived stress, pregnancy-specific anxiety, positive/negative/ambivalent feelings about pregnancy, traumatic/severe life events, non-traumatic life events, routine daily has-sles, medical life events, ailments of pregnancy, and negativeaffect about pregnancy ailments. These themes were chosenbecause they are representative of constructs that previouslyhave been associated with depression or with obstetric/childhood risk factors associated with depression (Betts et al.2014, 2015; Brown et al. 2000; Buss et al. 2011; Da Costaet al. 1998; Huizink et al. 2002, 2003; Pritchard and Teo 1994;Shah et al. 2011; Watson et al. 1999). Stress-related themeswere operationally defined and coded based on well-established definitions of the constructs (Cohen et al. 1997;Khashan et al. 2008; Serido et al. 2004) and/or by using val-idated measures of the construct as a guide (Cohen et al. 1983;Radloff 1977; Rini et al. 1999; Spielberger 1983).
Reported depression and anxiety were coded based on themother’s use of words consistent with depression or anxiety,using the CES-D scale and the State-Trait Anxiety Inventoryas models (Radloff 1977; Spielberger 1983). Perceived stresswas coded if the woman made appraisals of situations in herlife as stressful, as defined in the Perceived Stress Scale(Cohen et al. 1983). Pregnancy-specific anxiety was coded ifthe woman reported worries or concerns about the pregnancy,such as concerns about labor and delivery or health of thebaby (Rini et al. 1999). Positive, negative, and ambivalent
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feelings about the pregnancy were coded based on responsesaddressing wantedness of the pregnancy (Herman et al. 2006).Non-traumatic life events were coded as present if the preg-nant woman reported discrete and observable events that re-quired an adjustment in life routines and were impactfulenough to not be considered routine daily hassles (Cohenet al. 1997). Routine daily hassles were coded if a womenreported relatively minor events arising out of day-to-day
living and/or small, unexpected events that disrupt daily life(Serido et al. 2004). Medical life events were coded if a con-cern related to an ongoing medical condition was endorsed.Ailments of pregnancy were coded when women reportedphysical discomfort related to pregnancy (e.g., morning sick-ness), and negative affect about pregnancy ailments was cod-ed when women reported negative feelings towardspregnancy-related physical discomfort. Severe/traumatic life
Table 1 Demographic Characteristics of the Sample (N = 1711)
Demographics Overall Males Females p-value
Offspring sex, n (%)
Male 859 (49.8)
Female 852 (50.2)
Maternal race, n (%) 0.55
Caucasian 1304 (76.3) 659 (76.7) 645 (75.8)
African American 317 (18.5) 152 (17.7) 165 (19.4)
Asian 89 (5.2) 48 (5.6) 41 (4.8)
Maternal education, n (%) 0.99
Less than or equal to H.S. 782 (45.8) 393 (45.8) 389 (45.8)
Greater than H.S. 927 (54.2) 466 (54.3) 461 (54.2)
Birth weight (g), M (SD) 3365.1 (516.3) 3444.5 (514.8) 3285.0 (505.62) <0.01*
Gestational age at interview (weeks), M (SD) 15.3 (6.8) 15.2 (6.7) 15.4 (6.9) 0.45
Gestational age at birth (weeks), M (SD) 40.0 (2.2) 40.0 (2.1) 40.1 (2.4) 0.78
Maternal age, M (SD) 29.0 (6.0) 29.0 (5.9) 29.0 (6.2) 0.86
Maternal smoking during pregnancy n (%) 589 (34.4) 316 (36.8) 273 (32.0) 0.04*
Clinical Characteristics
Ambivalent feelings About pregnancy, n (%) 205 (12.0) 99 (11.5) 106 (12.4) 0.55
Ailments of pregnancy, n (%) 63 (3.7) 33 (3.8) 30 (3.5) 0.72
Medical Life events, n (%) 55 (3.2) 27 (3.1) 28 (3.3) 0.87
Negative affect about preg ailments, n (%) 40 (2.3) 19 (2.2) 21 (2.5) 0.73
Negative feelings about pregnancy, n (%) 189 (11.1) 102 (11.9) 87 (10.2) 0.27
Non-traumatic life events, n (%) 432 (25.3) 211 (24.6) 221 (25.9) 0.51
Positive feelings about pregnancy, n (%) 756 (44.2) 373 (43.4) 383 (45.0) 0.52
Perceived stress, n (%) 365 (21.3) 189 (22.0) 176 (20.7) 0.50
Pregnancy-specific anxiety, n (%) 251 (14.7) 126 (14.7) 125 (14.7) 0.99
Reported anxiety, n (%) 571 (33.4) 286 (33.3) 285 (33.5) 0.94
Reported depression, n (%) 117 (6.8) 60 (7.0) 57 (6.7) 0.81
Routine daily hassles, n (%) 570 (33.3) 269 (31.3) 301 (35.3) 0.08
Traumatic life events, n (%) 96 (5.6) 52 (6.1) 44 (5.2) 0.42
Childhood internalizing symptoms,M (SD) 2.7 (2.3) 2.6 (2.3) 2.8 (2.3) 0.20
Childhood conduct symptoms, M (SD) 51.3 (9.7) 51.4 (9.5) 51.3 (9.9) 0.87
Childhood peabody scores, M (SD) 23.4 (5.1) 23.2 (5.1) 23.6 (5.1) 0.12
Adolescent depression scores, M (SD) 114.8 (15.4) 116.5 (14.3) 113.1 (16.2) <0.01*
Adolescent peabody scores, M (SD) 205.0 (12.0) 99.0 (11.5) 106.0 (12.4) 0.55
For males, the following variables had missing data: gestational at interview (n = 7); gestational age (n = 7); maternal age (n = 2); childhood inhibitionsymptoms (n = 518); childhood Peabody scores (n = 14); adolescent depression scores (n = 70), adolescent Peabody scores (n = 21)
For females, the following variables had missing data: maternal education (n = 2); gestational age at interview (n = 3); gestational age (n = 3); maternalage (n = 2); childhood inhibition symptoms (n = 517); childhood Peabody scores (n = 18); adolescent depression scores (n = 41); adolescent Peabodyscores (n = 8)
P-values correspond to tests of sex differences (t-tests for continuous or count variables and chi-square tests for categorical variables)
J Abnorm Child Psychol (2018) 46: –381 397 385
events were coded as present if there was actual or threateneddeath or serious injury, threat to physical integrity of self orothers, and/or loss or diagnosis with cancer, acute myocardialinfarction, or cerebrovascular accident of a close relative,based on previous research of traumatic life events duringpregnancy (Khashan et al. 2008) (See Online SupplementaryTable 2 for examples of stress themes).
Although interview coding documented the frequency withwhich women reported a given stress theme (e.g., how oftendaily stress was mentioned in a given narrative), the stressthemes were reduced to dichotomous variables (present/ab-sent) prior to analyses because of limited variability in fre-quency data found in a previous study of CHDS women (dataavailable upon request) (Fineberg et al. 2016). Stress themeswere coded using ATLAS.ti 6 (ATLAS.ti Scientific SoftwareDevelopment GmbH, Berlin, Germany).
Other Prenatal Factors Of the potential socioeconomic sta-tus (SES) variables, maternal education had the most completedata (n = 1709, or 99.88%), has been correlated with othermeasures of socioeconomic status in this sample, and is oftenused to account for the potential contributions of postnataladversity to risk of negative developmental sequelae(Schlotz and Phillips 2009); therefore, maternal educationwas treated as representative of SES for the present study.Although a variety of pre- and perinatal factors are thoughtto influence offspring development (Siegel 1982), variablesthat have previously been associated with depression, or withobstetric risk factors associated with depression, were chosenfor inclusion in the present study (Braithwaite et al. 2014),specifically: maternal age (Cleary-Goldman et al. 2005), ma-ternal pre-pregnancy bodymass index (BMI) (Abenhaim et al.2007), maternal smoking during pregnancy (Menezes et al.2013), gestational age (Patton et al. 2004), and birth weight(Gale and Martyn 2004).
Age 9–11 Childhood Visit At the child’s age 9–11 year visit,mothers answered true, not true, or uncertain to 100 questionsabout their child’s behavior, including behaviors related tointernalizing symptoms and conduct problems. The 100-itemquestionnaire was based on a 46-question inventory of childbehavior that had been used in previous studies at theUniversity of California, Berkeley (see Tuddenham et al.1974 for additional detail). Using as a guide the AchenbachChild Behavior Checklist (CBCL), a well-validated and com-monly used measure among children (Achenbach 1991), weidentified items that corresponded to childhood internalizingsymptoms and conduct symptoms as follows (see OnlineSupplementary Table 3 for items from unpublished scales).
Childhood Internalizing Symptoms Thirteen items relatedto internalizing symptoms (depression and anxiety) weredrawn from the 100-item questionnaire and summed to create
a scale with items broadly representative of the internalizingbehaviors on the CBCL (Achenbach 1991). The resultant in-ternalizing symptoms scale has moderate internal consistency(KR-20 α = 0.70) and was correlated with adolescent depres-sion scores in our sample (p < 0.001), suggesting that it rep-resents a uniform construct and that it is predictive of relatedoutcomes found in previous studies (described below). Thisvariable was treated as continuous.
Childhood Conduct Symptoms Fifteen items related to con-duct problems were drawn from the 100-item maternal reportquestionnaire and summed to create a conduct symptom scalewith items broadly representative of the externalizing behav-iors on the CBCL (Achenbach 1991). The conduct problemsscale has good internal consistency (KR-20 α = 0.73). Thisvariable was treated as continuous.
Childhood conduct symptoms were included in the analy-ses given associations with both prenatal maternal stress(Loomans et al. 2011) and later development of depression(Lahey et al. 2002). Further, in another study of the samecohort, conduct problems predicted alcohol abuse in adoles-cence, suggesting good external validity (Keyes et al. 2011).
Maternal Worry during Childhood At the 9–11 visitmothers were asked to answer BYes^ or BNo^ to whetherany or all of their finances, marital relations, employment, orhealth had been worrying them recently. These variables wereincluded to account for the influence of postnatal stress onoffspring development (Entringer et al. 2015). Birth order,which was assessed at the 9–11 visit and is highly correlatedwith family size in this sample (Keyes et al. 2011), and wheth-er the child lived in a single parent household were included totap variables related to family structure during childhood, ascertain aspects of family structure (e.g., smaller family size,two parents) have been positively associated with offspringwell-being (Amato 2005). Because firstborn status has beenassociated with lower depression scores (e.g., Gates et al.1988), and because there is evidence that birth order can havenon-normal relationships with developmental outcomes, wedichotomized birth order into firstborn (n = 427) and secondborn or later (n = 1264) (Haukka et al. 2004).
Childhood and Adolescent Cognitive Variables ThePeabody Picture Vocabulary Test (PPVT) is a commonly usedreceptive vocabulary measure that can be administered to in-dividuals aged 2.5 years and above and is correlated with IQ(Lezak et al. 2004). The PPVT has high test-retest reliability(>0.90) and does not require reading, writing, or verbal re-sponses. These data were included based on past researchlinking prenatal maternal stress exposure to cognitive prob-lems, including decreased IQ (Slykerman et al. 2005), whichhas been found to occur during the premorbid period of de-pression (Koenen et al. 2009; Zammit et al. 2004). PPVT
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standardized scores from the 9–11 and adolescent visits wereused.
Adolescent Depressive SymptomsAdolescents, ages 15–17,completed a self-report inventory about their lives and beliefsabout themselves, which included depressive symptom items.The adolescent depression symptom scale included 14 itemsrelated to depressive symptomatology and assessed affectiveand cognitive symptoms of depression on a 4-point Likertscale. The internal consistency of the depression scale is high(Cronbach’s α = 0.81); the depression scale was treated as acontinuous variable.
A number of follow-up studies of the CHDS cohort haveused semi-structured interviews, including the StructuredClinical Interview for DSM-IV-TR Disorders and theDiagnostic Interview for Genetic Studies (First et al. 1994;Nurnberger et al. 1994; Parboosing et al. 2013; Susser et al.2011) to ascertain diagnoses of mental disorders. While mostof the participants of these follow-up studies were not over-lapping with the Adolescent Study, we sought to determinewhether scoring high on the depression scale was predictive ofmajor depressive disorder (MDD) diagnoses among thosewho participated in both the Adolescent study and one of thesefollow up studies. Analyses suggest that the adolescent de-pression scale has excellent predictive validity with semi-structured interviews conducted in 20–30 year follow-up stud-ies, as well as with well-known questionnaires administered inan ongoing follow-up study. Adolescent Study members whoreceived a MDD diagnosis in these follow-up studies (n = 13)had significantly higher scores on the adolescent depressionscale compared to those who did not receive aMDD diagnosis(n = 39), and these differences were associated with a largeeffect size (p = 0.04; Cohen’s d = 0.62, unpublished data,available upon request). Additionally, there was 72% specific-ity in predicting MDD among those who scored in the upperquartile on the adolescent depression scale (46% sensitivity).Although the adolescent depression scale does not captureeveryone who will later develop depression, those scoringhigh on the scale appear to be at increased risk for a depressiondiagnosis.
Further, during the adolescent visit maternal responses tothe question, BOverall, how happy would you say your childis?^ were significantly related to their adolescent’s overalldepression score, F(3, 1520) = 39.95, p < 0.001. Motherswho responded with answers of not too happy or veryunhappy had adolescents with significantly higher depressionscores, M = 25.95, SD = 5.15 and M = 30.63, SD = 5.29,respectively, than adolescents of mothers who reported theirchild was fairly happy or very happy, M = 23.98, SD = 5.00and M = 21.59, SD = 4.75, respectively.
Maternal Depressive Symptoms during AdolescenceMothers completed a self-report inventory of their own
depressive symptoms that mirrored the self-report inventorycompleted by adolescents at the same time point. The scalehas good internal consistency (Cronbach’sα = 0.76). Previouswork has linked maternal depression to increased rates of de-pressive symptoms in childhood and adolescence (Murrayet al. 2011).
Perceived Maternal and Paternal Support duringAdolescence Adolescents were asked a number of questionsabout family support. The Adolescent Personal Questionnaireasked questions about the adolescent’s relationship with theirparents along with a variety of statements related to their re-lationship with their parents that were scored on a 4-pointLikert scale. Each scale consisted of 5 items; the PerceivedMaternal and Paternal Support scales have moderate internalconsistency (Cronbach’s αs = 0.77 and 0.72, respectively).Perceived maternal and paternal support during adolescencewere included in the analyses because they have been identi-fied as protective factors associated with decreased risk fordepression (Stice et al. 2004).
Statistical Analyses
Preliminary analyses, including descriptive and bivariate anal-yses, were conducted using SAS version 9.4. Primary analy-ses were conducted using Mplus statistical software package,Version 7.3 (Muthén and Muthén 2012). Mplus addressesmissing data using Full-Information Maximum Likelihood(FIML) estimation, a robust and commonly accepted way tohandle missing data (Graham 2009; Muthén and Shedden1999; Schafer and Graham 2002). See Table 1 for informationabout missing data. LCA, which classifies individuals from aheterogeneous population into smaller, more homogenoussubgroups (Nylund et al. 2007b), was conducted with the full,analytic sample to determine whether there were profiles ofmaternal stress during pregnancy and other developmentalvariables that were associated with increased risk of offspringdepression. Percentages of sexes within LCA classes wereexamined to ascertain whether there were descriptive differ-ences in the proportions of females andmales between classes.Next, the hypothesis that there are sex-specific profiles thatincrease risk for offspring depression was tested using LCAanalyses conducted separately for male and female offspring.Prior to running LCA, continuous variables were converted toz-scores (M = 0.00, SD = 1.00), first in the full analytic sampleand then separately for males and females, to put all indicatorvariables on the same scale.
In LCA, selection of the best-fitting model in LCA is basedon both statistical indicators and conceptual (i.e., practical)considerations (Nylund et al. 2007a). While there is no defin-itive fit index to determine the best-fitting model, a simulationstudy suggested that the Bayesian Information Criterion (BIC)and bootstrap likelihood ratio test (BLRT) were the most
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consistent statistical indicators of relative fit across models(Nylund et al. 2007a, b). The BLRT evaluates whether addingan additional class significantly improves model fit by com-paringmodel fit with k classes to the model fit with k-1 classes(Nylund et al. 2007a, b). Other fit indices—the AkaikeInformation Criterion (AIC) and the sample-size adjustedBIC (ABIC) were also evaluated (Muthén and Muthén2000). Both entropy and posterior probability values provideinformation about the precision of assigning latent class mem-bership, with values closer to 1 indicating greater precision(Muthén and Muthén 2000). Statistical measures of fit wereassessed accordingly and models were evaluated to determinewhether adding a class was both substantively meaningful andconsistent with the underlying conceptual model.
Once the best-fitting models were selected, we applied amodel-based approach to LCA with distal outcomes (Lanzaet al. 2013) to assess class differences in levels of adolescentdepressive symptoms. Specifically, we added as auxiliary var-iables the adolescent depressive symptoms score and a dichot-omous variable indicating whether an individual’s depressivesymptoms score was above the 75th percentile of the sample;equality of means chi-square tests and chi-square tests of in-dependence were used to compare the latent classes on thesevariables. Finally, an auxiliary variable was added to assesspotential differences by interview version. To assess differ-ences potentially related to verbosity, we added word countof the maternal stress narratives during pregnancy as an aux-iliary variable in our analyses.
Results
Demographics and mean scores for clinical characteristics aredisplayed in Table 1 (full analytic sample and by sex).
LCA for Overall Sample
For the overall sample, LCAs were conducted for one, two,three, and four class models. LCA statistics are presented inOnline Supplementary Table 4. Although the four-class modelminimized the BIC, the smallest class in that model comprisedonly 2.6% of the sample, suggesting that the model wasoverfit to the sample. Statistical indices (i.e., lower BIC andsignificant BLRT) indicated that the three-class model was abetter fit than the two-class model; however, conceptually, thethree-class model did not differentiate the groups on a numberof important variables (Masyn et al. 2010). Specifically, theadded class in the three-class model seemed to track closelywith the larger class in the two-class model, particularlyamong maternal pregnancy stress characteristics, and the de-crease in BIC between the two- and three-class models wasmarginal. The two-class model had higher entropy comparedto the three-class model (0.765 vs. 0.652), indicating better
precision of individuals being placed into latent classes.Relatedly, posterior probabilities were higher for the two-class model (range: 0.943 to 0.892) relative to the three-classmodel (range: 0.863 to 0.807), indicating clearer class separa-tion for the two-class model. The two-class model producedclasses that were not only substantively distinct but also con-ceptually meaningful; therefore this model was used in thepresent study (Masyn et al. 2010).
As Figs. 1 and 2 show, the two-class model for the overallsample identified two subgroups that we have characterized asBlow-risk^ (n = 943) and Bhigh-risk^ (n = 768) groups. Ascompared to the low-risk group, the high-risk group was char-acterized by mothers with ambivalent or negative feelingsabout the pregnancy, mothers with an absence of positivefeelings about the pregnancy, high maternal age at pregnancy,high pre-pregnancy BMI, and elevated maternal depressivesymptoms during offspring adolescence. Further, high-riskoffspring were characterized by elevated conduct symptomsin childhood, low offspring Peabody scores in childhood andadolescence, low perceivedmaternal and paternal support dur-ing, and a low probability of being firstborn. Post hoc analysesindicated that the high-risk group had significantly higherlevels of adolescent depressive symptom scores than thelow-risk group (X2(1) = 9.78, p = .002, Mhigh = 23.91,SEhigh = 0.22, Mlow = 22.95, SElow = 0.18, Cohen’sd = 0.165). Further, offspring in the high-risk group were 1.4times more likely to have adolescent depressive symptomscores above the 75th percent i le for the sample(X2(1) = 6.23, p = 0.013, OR = 1.41). The two risk groupsdid not differ significantly by sex.
Sex-Specific LCA
LCA statistics for females (n = 852) and males (n = 859) aredisplayed in Online Supplementary Table 2. For females, al-though the four-class model minimized the BIC, the smallestclass in that model comprised only 3.2% of the sample, sug-gesting that the model was overfit to the sample. Statisticalindices (i.e., lower BIC and significant BLRT) indicated thatthe three-class model for females was a better fit than the two-class model (see Online Supplementary Table 2); however, thethree-class model added a class that seemed to track closelywith the larger class from the two-class model, particularlyamong maternal pregnancy stress characteristics, and the de-crease in BIC between the two- and three-class models wasmarginal. The three-class model had higher entropy comparedto the two-class model (0.723 vs. 0.654), indicating betterprecision of individuals being placed into latent classes, butthe posterior probabilities were similar for the two-class(range: 0.912 to 0.873) and three-class models (range: 0.931to 0.843), indicating similar class separation in each.Compared to the three-class model, the two-class model
J Abnorm Child Psychol (2018) 46: –381 397388
produced not only substantively distinct but also conceptuallymeaningful classes; therefore this model was used.
As Figs. 1 and 2 show, the two-class model for thefemales identified a low-risk (n = 495) and a high-risk(n = 357) group. The female high-risk group was charac-terized by increased probability of the mother reportingambivalent or negative feelings about the pregnancy, lowprobability of the mother reporting positive feelings aboutthe pregnancy, high pre-pregnancy BMI, elevated offspringinternalizing and conduct symptoms in childhood, low off-spring Peabody scores in childhood and adolescence, lowperceived maternal and paternal support as perceived bytheir offspring during adolescence; and a low probabilityof offspring being firstborn. Post hoc analyses indicatedthat high-risk females had significantly higher levels ofadolescent depressive symptom scores than the low-riskfemales, X2(1) = 7.91, p = 0.005, Mhigh = 24.33,SEhigh = 0.33, Mlow = 23.018, SElow = 0.26, Cohen’sd = 0.22. Further, females in the high-risk group were
1.8 times more likely to have adolescent depressive symp-tom scores above the 75th percentile for the sample,X2(1) = 8.05, p = 0.005, OR = 1.81.
For males, the three-class model minimized the BIC andhad a significant BLRT, indicating that the three-class modelwas the best statistical fit. Also, the three-class model hadhigher entropy compared to the two-class model (0.644 vs.0.578), indicating better precision of individuals being placedinto latent classes, and the posterior probabilities were similarfor the two-class (range: 0.881 to 0.861) and three-classmodels (range: 0.869 to 0.819), indicating clearer class sepa-ration in the three-class model. However, the three-classmodel added a class that was qualitatively similar, par-ticularly on pregnancy stress indicators, to the low-riskgroup in the two-class model, and the decrease in BICbetween the two- and three-class models was marginal.Because the two-class model produced classes that weresubstantively distinct and conceptually meaningful, thismodel was used for males in the sample.
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Fig. 1 Proportion of individualsin each latent class with presence(vs. absence) of eachdichotomous variable by class.Note. All variables refer tocharacteristics of the mother.Prenatal period: PFP = PositiveFeelings about Pregnancy;AFP = Ambivalent Feelingsabout Pregnancy;NFP = Negative Feelings aboutPregnancy; PS = PerceivedStress; RA = Reported Anxiety;PSA = Pregnancy-SpecificAnxiety; RD = ReportedDepression; TLE = TraumaticLife Events; NTLE = Non-traumatic Life Events; MSLE =Medical Self Life Events;RDH = Routine Daily Hassles;AOP = Ailments of Pregnancy;NAPA = Negative Affect AboutPregnancy Ailments; ED = Morethan high school education;SMK = Maternal smoking duringpregnancy. Childhood:WF = Worry about Finances;WM = Worry about Marriage;WE = Worry about Employment;WH = Worry about Health.Adolescence: Single parent =Single parent home
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The high-risk male group (n = 424) was characterized byincreased probability of the mother reporting ambivalent ornegative feelings about the pregnancy; low probability of themother reporting positive feelings about the pregnancy, highpre-pregnancy BMI, increased probability of the motherreporting stress, anxiety, and non-traumatic life events duringpregnancy, elevated offspring internalizing symptoms inchildhood, low offspring Peabody scores, particularly in ado-lescence, low maternal and paternal support as perceived bytheir offspring during adolescence, elevated probability of ma-ternal depression during offspring adolescence, and a low
probability of offspring being firstborn. Post hoc analyses in-dicated that high-risk males had significantly higher levels ofadolescent depressive symptom scores than the low-risk(n = 435) males (X2(1) = 8.54, p = 0.003, Mhigh = 23.92,SEhigh = 0.33, Mlow = 22.49, SElow = 0.28, Cohen’sd = 0.23). Further, males in the high-risk group were 2.8 timesmore likely to have adolescent depressive symptom scoresabove the 75th percentile for the sample (X2(1) = 4.77,p = 0.031, OR = 2.81).
Auxiliary analyses showed that risk classes in the overallsample, in females, and in males did not significantly differ by
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Fig. 2 Standardized means ofeach continuous variable byclass. Note. Prenatal period(Maternal) variables: MatAge =Maternal age at birth; BMI = Pre-pregnancy BMI;GA = Gestational Age;BWT = Birth weight. Childhood(Offspring variables): IntSxs =Childhood Internalizingsymptoms; CondSxs = ChildhoodConduct symptoms; PB-C = Childhood Peabody Score.Adolescent (Offspring variables):PB-A = Adolescent PeabodyScore; FaSupp = Perceivedpaternal support; MaSupp =Perceived maternal support;MatDep = Maternal depressionduring offspring adolescence
J Abnorm Child Psychol (2018) 46: –381 397390
interview version (i.e., whether or not specified probes wereincluded). Risk classes differed on mean word count (naturallog transformed; raw word count range: 1–287) of the mater-nal pregnancy narratives in the overall sample ((X2(1) = 69.08,p < 0.001, Cohen’s d = 0.44).
Discussion
The present study is the first (to our knowledge) to identifydistinct profiles of individuals at increased risk for depressionin adolescence, characterized by known developmental riskfactors for depression, while including maternal stress indica-tors during pregnancy. LCA identified a high-risk group in theoverall sample and within males and females that were char-acterized by factors that have been associated with increasedrisk for psychopathology, with more maternal negativity/lessmaternal positivity about the pregnancy (McNeil et al. 2009),more routine stressors during pregnancy (Fineberg et al.2016), high pre-pregnancy BMI (Abenhaim et al. 2007), lowprobability of being firstborn (e.g., Gates et al. 1988), de-creased offspring cognitive functioning during childhoodand adolescence (Koenen et al. 2009), and elevated levels ofmaternal depression during adolescence (Murray et al. 2011)common to all high-risk groups. Offspring in both the maleand female high-risk groups were characterized by elevatedinternalizing symptoms in childhood (Rosenbaum et al.1993), although this difference did not manifest in the overallsample. High-risk profiles also showed different patterns bysex. The male high-risk group was additionally characterizedby greater likelihood of the mother reporting perceived stress,anxiety, and non-traumatic life events during pregnancy, anddecreased levels of perceived paternal and maternal supportduring offspring adolescence (Kaslow et al. 1994); while thefemale high-risk profile was additionally characterized by off-spring with elevated conduct symptoms during childhood(Lahey et al. 2002). All high-risk groups had significantlyhigher levels of depressive symptoms during adolescenceand a higher probability of having adolescent depressivescores above the 75th percentile, relative to the low-riskgroups, with small effects for each.
Our findings suggest that examining multiple indicatorsacross domains and developmental periods may help to identifya subset of individuals who are at risk for future depression, andthat while the profiles are similar for males and females, thepredictive roles of some risk factors vary by sex. It is importantto note that unlike previous studies (e.g., Betts et al. 2014), therewere no main effects of maternal stress during pregnancy,evidenced by the lack of significant zero-order correlationsbetween maternal stress during pregnancy and adolescentdepression (see Online Supplementary Table 5), though onepregnancy theme appeared to be operating through childhoodconduct problems to influence risk of depression in the overall
sample (see Supplementary Fig. 2). Rather, findings from thepresent study are likely linked to multiple development factors.As such, analyses such as a mediational model were notappropriate; however, future studies using alternative analytictechniques are necessary to help clarify the interlinked nature ofdevelopmental risk factors for adolescent depression.
To date, few studies have examined the association be-tween prenatal maternal stress and offspring depression duringadolescence in a longitudinal birth cohort (Betts et al. 2014;Brown et al. 2000; Watson et al. 1999). Many of these studiesaimed to disentangle pre- and postnatal influences (e.g., Bettset al. 2014; Pearson et al. 2013) to determine which develop-mental periods were most influential in conferring risk of off-spring psychopathology. We sought instead to characterizeprofiles of offspring development from the prenatal to adoles-cent period. One major contribution of our findings is an un-derstanding of how different risk factors may operate in con-cert to predict adolescent depression, and how these risk fac-tors may differ depending on sex. Linking these risk factorsacross development is the first step in developing strategies forearly identification and prevention efforts. Although not di-rectly tested, a dynamic cascade model of adolescent depres-sion, in which earlier occurring events underpin a Bcascade^of incremental factors contributing to larger effects over time,may help explain our findings.
For example, some evidence suggests that stress duringpregnancy may Bprime^ the fetus to experience more fearfultemperaments and anxiety risk throughout development(Davis and Sandman 2012; Sandman et al. 2013; Talge et al.2007). Increased internalization symptoms throughout child-hood may lead to poorer academic performance and cognitivefunctioning over time (Koenen et al. 2009; Slykerman et al.2005; van den Bergh et al. 2006), potentially contributing totransactional processes between parent and child (less per-ceived parental support, increased maternal depression) thatincrease risk for adolescent depression (Murray et al. 2011;Stice et al. 2004). Further, research suggests that pre-pregnancy BMI is associated with a number of adverse out-comes during pregnancy, including higher levels of inflamma-tion, which could be increased by increases in maternal stress(Abenhaim et al. 2007; Coussons-Read et al. 2005; Madanet al. 2009). Maternal inflammation during pregnancy hasbeen associated with a number of adverse outcomes in off-spring (Fineberg and Ellman 2013), suggesting thatneurodevelopment could be disrupted early on by a numberof pre- and perinatal risk factors that were either directly testedin the present study or associated with study variables.
Some of these processes appeared to be more subtle formales, as high-risk males exhibited fewer signs of difficultiesduring childhood as compared to females, despite havingsimilarly high risk for depression during adolescence. Onepossible explanation is that the high-risk males may havehad a higher genetic or familial loading for depression than
J Abnorm Child Psychol (2018) 46: –381 397 391
the high-risk females. Specifically, mothers of high-risk malesreported more stress, anxiety, and non-traumatic life eventsduring pregnancy compared to mothers of low-risk males (apattern not observed among females). Similarly, mothers ofhigh-risk males reported high levels of depression duringadolescence coupled with adolescent reports of low perceivedmaternal support compared to low-risk males. The possibilitythat the high-risk developmental profile among males reflectspotential genetic contributions should be followed up byfuture studies.
While our analyses targeted variables that have beenprimarily associated with prenatal stress, depression, andobstetric risk factors related to later psychopathology, we didnot consider other potential risk factors for adolescentdepression (e.g., peer factors, adolescent health behaviors)due to lack of available data. Information about paternaldepression and/or genetic variations associated withdepression was unavailable, but we attempted to account forsome measure of genetic risk by including maternaldepressive symptoms during offspring adolescence in ourmodeling. However, maternal depressive symptoms duringoffspring adolescence likely tap into both risk conferred bygenetics and risk conferred by exposure to parentalpsychopathology, and, therefore, future studies should assesshow genetic risk, combined with other developmental riskfactors, contributes to the etiology of adolescent depression.Nevertheless, results from the present study may be a first stepin developing algorithms to identify individuals at risk fordepression prior to the onset of symptomatology, which canhave major implications for early intervention and preventionstrategies. For instance, King et al. (2008) emphasize thatestimating overall risk across a range of possible factors playsan essential role in depression prevention efforts.
While we have interpreted our findings as identifying riskprofiles for adolescent depression, the high comorbidity ofdepression and anxiety calls into question whether these riskprofiles are specific to depression or whether they indicaterisk for a broader category of disorders, potentially withshared behavioral phenotypes. For instance, Clauss andBlackford (2012) have found that childhood inhibitionconfers a sevenfold increased risk for developing socialanxiety, and Davis and Sandman (2012) have found thatprenatal stress increases risk for developing anxiety.Further, previous work has found that prenatal maternalstress increases risk for schizophrenia spectrum disordersamong males but not females (Ellman et al. 2008; Fineberget al. 2016), which was not assessed in the present study.Although there is a vast literature that characterizes riskfactors and sex differences in rates and course of mooddisorders, as well as a substantial body of work on theinfluences of prenatal maternal stress, the question ofwhether there are sex-dependent routes to different kinds ofpsychopathology has been underexplored and should
continue to be examined in future work. Of the studies thathave examined maternal psychological distress duringpregnancy in relation to adolescent depression (Betts et al.2014; Pawlby et al. 2009; Pearson et al. 2013), few, if any,have specifically examined sexdifferences. Inkeepingwith agrowing literature on sex differences in the influence ofmaternal stress on offspring development (Sandman et al.2013) and in the depression literature (Alloy and Abramson2007), we identified differences between the subgroups ofmales and females who may be particularly at risk fordeveloping a depressive disorder and/or significant orpotentially impairing symptoms. The high-risk groups donot necessarily represent a clinical sample, and themagnitudes of the differences in depressive symptom scoresbetween the high- and low-risk groups appeared to be small.Nevertheless, results held for analyses that used 75thpercentile cutoff scores for our depression scale, which hasbeen validated against future diagnoses of depression,indicating that our findings may have clinical relevance.Our results are consistent with previous work indicating thatdecreased IQ commonly occurs in the premorbid period ofdepression (Koenen et al. 2009; Zammit et al. 2004). Ourresults also are consistentwith an emerging bodyof evidencesuggesting that prenatal stress may exert a subtle butpersis tent influence on development, influencingtemperament, behavior, and brain development in ways thatmay predispose individuals to later affective problems(Sandman et al. 2013).
LCA results suggest that some stress-related themes ap-peared to be less influential indicators of class membershipthan others. For both males and females, these themes includ-ed traumatic life events, ailments of pregnancy, medical lifeevents, and negative affect about pregnancy ailments, all ofwhich occurred at low base rates in our sample (2.3–5.6%).Although the sample size was relatively large, traumatic lifeevents may have been too rare to detect significant results,without access to population-level data (Abel et al. 2014).However, given the prevalence of the other stress themes, itseems unlikely that we were underpowered to detect results,and instead, the lack of findings may suggest that these stress-related themes may not have the same public health impact asother aspects of stress during pregnancy. Maternal reports ofdepressive symptoms during pregnancy also occurred at a lowbase rate in our sample (6.8%), which may stem from thewording of the interview questions related to recent worries,potentially diminishing spontaneous reports of depressivesymptoms. Future research should continue to investigatethe contribution of prenatal maternal depressive symptomsto risk for offspring depression in adolescence, as two recentstudies have found support for an increased risk of adolescentdepression among offspring of mothers who experienced de-pressive symptoms during pregnancy (Betts et al. 2014;Pearson et al. 2013). Although there is growing support for a
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link between pregnancy-specific anxiety and a variety of ad-verse developmental outcomes (reviewed in Buss et al. 2011),our results did not identify pregnancy-specific anxiety as animportant indicator of high-risk class membership or as pre-dictive of offspring depressive symptoms. Although our cod-ing of pregnancy-specific anxiety was based on a reliable, 10-item pregnancy-related anxiety scale developed for use inpregnancy research (Rini et al. 1999), it is possible that thefull measure would more accurately capture women’s experi-ences of pregnancy-specific anxiety. It should be noted thatwhile the risk groupings did not differ by interview version(i.e., whether specified probes about stress, anxiety, etc., wereincluded in the maternal pregnancy interview), the risk groupsin the overall sample were differentiated by verbosity of themother in the interview, as measured by word count.However, it stands to reason that mothers who endorsed preg-nancy stress themes would have higher word counts.
Although maternal smoking during pregnancy did not dif-ferentiate subgroups, it is important to note that the frequencyof smoking during pregnancy was higher in the 1960s whenthese pregnancies occurred (34.4% of mothers in the studysample smoked during some or all of pregnancy). Presentday cohorts might detect an effect of prenatal maternalsmoking on adolescent depression, as women who smokeduring pregnancy despite health warnings may differ in im-portant ways from women who smoked during pregnancyprior to these warnings. Another possibility is that maternalsmoking during pregnancy may be more related to other typesof offspring psychopathology, such as externalizing problems(Rodriguez and Bohlin 2005).
A major strength of our study is that the cohort includescontinuous follow-up data on childhood and contextual fac-tors throughout multiple periods of development, which isrelatively rare, even in the prenatal stress literature. Further,data on different aspects of maternal stress during pregnancywere collected directly and prospectively from women duringtheir pregnancies. Even so, unavailability of data on additionalcontextual and resilience factors meant that important vari-ables that may have influenced our findings (e.g., detailedinformation on caregiving during childhood, etc.) could notbe examined. Another important limitation to note is thatmuch of the data used to characterize the risk profiles andthe data used to characterize adolescent depressive symptomswere obtained via self-report (with some exceptions, e.g., cog-nitive testing, BMI, birth order, maternal age, gestational age,birth weight etc.). While self-reported data can be problematic(Gerald and George 2010), we were able to corroborate ado-lescent self-reported depression with maternal report of thesame construct (e.g., maternal report of adolescent happinesswas significantly related to adolescent self-report of depres-sive symptoms; See Method). An additional complication inanalyzing self-report data is created by missing values, whichoccurred most acutely in our data for childhood internalizing
and conduct symptoms. If any of the underlying scale itemswere missing (most often because the mother answeredBuncertain^ to an item), that scale score was consideredmissing. While FIML is robust even at high levels ofmissingness, especially when auxiliary correlates are includedin the analysis (Graham 2009), any inferences to be drawnfrom our study about the roles of internalizing and conductsymptoms during childhood could be limited by the amount ofmissing data for those variables. Another potential limitationof the study is that qualitative coding of narrative themescarries the possibility of introducing subjectivity into the data.We attempted to safeguard against potential bias in coding byrequiring coders to: follow a detailed coding manual, achievehigh inter-rater reliability on representative narratives prior tocoding study narratives, remain blind to offspring depressivesymptoms, and maintain reliability throughout the coding pro-cess. Additionally, other studies have analyzed stress-relatedthemes from open-ended narratives, providing support for thefeasibility of our methods (Duggan et al. 2008). A limitationof the LCA approach is that we are not able to draw conclu-sions about the relative contributions of pre- and postnatalfactors to any association between high-risk subclasses andsubsequent depressive symptoms; as such, this current studymust be viewed as exploratory. Future studies should use avariable-centered analytic approach to determine whether spe-cific variables and/or periods of development are particularlyimportant in conferring risk for the development of depressionin adolescence. Another limitation of the current study is thatwe could not determine exact timing of prenatal stress expo-sure; however, the majority of our interviews took place dur-ing second trimester of pregnancy, which previous work sug-gests is a key period for the influence of maternal stress onfetal development (Ellman et al. 2008).
The present study’s measure of adolescent depressivesymptoms had both strengths and limitations. The scale hadgood reliability and excellent predictive validity. Additionally,the scale taps into many of the themes associated with adepressogenic cognit ive style (e .g. , inadequacy,worthlessness, failure; Alloy et al. 1999). However, the scaleis limited by a lack of items assessing other symptoms ofdepression (e.g., sleep, appetite). Other limitations of the studyare shared by many longitudinal studies, including potentialbias due to attrition (though the use of FIML somewhat re-duces this concern) and cohort effects. Despite the continualfollow up of nearly 2000 birth cohort members of the CHDSthrough adolescence, an important caveat to consider wheninterpreting the generalizability of study results is that theadolescent sample represents only a portion of the originalcohort and differed from that original cohort on some keyvariables (though the magnitudes of the differences weresmall). It is a limitation of the study that we could not compareour analytic sample to the entire cohort on all variables in theLCA, given that many in the overall cohort did not complete
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these assessments (see Online Supplementary Table 1, foravailable comparative demographics between the analyticsample and the remainder of the CHDS cohort). Further, oursample developed within a distinct sociocultural environment(the San Francisco Bay Area during the 1960s and 1970s),which may limit generalizability to present day cohorts withdifferent demographic and cultural (e.g., parenting practices,mental health literacy) characteristics.
This study is among the first, to our knowledge, to examinematernal stress during pregnancy and postnatal influences inrelation to adolescent depressive symptoms in a prospective,longitudinal, population-based design. Our findings character-ize the developmental profiles of subgroups of adolescentmales and females who exhibit increased depressive symp-toms following exposure to prenatal maternal stress and otherpostnatal influences, and underscore the importance of con-sidering pre- and postnatal contributors to offspring risk forpsychopathology. Future research should continue to charac-terize developmental profiles from prenatal maternal stress tooffspring psychopathology in an effort to better understandsex differences in risk factors and disorder onset and course.Our findings also highlight potential targets for early interven-tion that include both distal and proximal variables, whichmay allow clinicians to combine current and past informationto target individuals who are most at risk during adolescence.Finally, the results from the present study support the neces-sity of taking a lifespan approach to studying risk for psycho-pathology, as multiple indicators across development wereassociated with risk for future depression.
Acknowledgements The authors thank Barbara A. Cohn, Piera Cirillo,Lauren Zimmermann, and Nickilou Y. Krigbaum, our collaborators at theChild Health and Development Studies, for their thoughtful contributionsto the manuscript.
Compliance with Ethical Standards
Funding This study was supported by funding to LME (R01MH096478, Temple University start-up award, Schizophrenia researchfellowship – 5 T32 MH018870–20) and AMF (National ScienceFoundation Graduate Research Fellowship awarded to AMF – DGE-1144462). This study was also supported by the Department of Healthand Human Services Contract HHSN275201100020C from the EuniceKennedy Shriver National Institute of Child Health and Development,National Institutes of Health. Any opinion, findings, and conclusions orrecommendations expressed in this material are those of the author (s) anddo not necessarily reflect the views of the National Science Foundation.
Conflicts of Interest The authors declare that they have no conflict ofinterest.
Ethical Approval All procedures performed in studies involvinghuman participants were in accordance with the ethical standards of theinstitutional and/or national research committee and with the 1964Helsinki declaration and its later amendments or comparable ethicalstandards.
Informed Consent Informed consent was obtained from all individualparticipants included in the study.
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