Psychiatric Symptoms in Adolescenceas Predictors of Obesity in EarlyAdulthood: A Longitudinal Study

Daniel S. Pine, MD, Patricia Cohen, PhD, Judith Brook, EdD,and Jeremy D. Coplan, MD

IntroductionConcem with medical correlates of

obesity has risen with accumulating evi-dence of associations between obesity andphysical health.'-" Renewed interest inpsychiatric correlates of obesity mayfollow recent research findings. Epidemio-logical studies have noted risk factors thatare common to psychopathology in youthand obesity in adulthood, including pov-erty, low IQ, and harsh parenting.'3Biological studies suggest that abnormali-ties in brain serotonin may result in apredisposition to some forms ofpsychopa-thology and obesity.'123 Epidemiologicalresearch, however, provides meager evi-dence of cross-sectional associations be-tween psychopathology and obesity.24 nthis report, we examine prospective rela-tionships between psychopathology inyouth and obesity in early adulthood.

There are disparities between clinic-based and epidemiological studies in thisarea. While clinic-based studies havefound an association between obesity andpsychopathology, particularly depres-sion,2527 these associations appear weakor inconsistent in epidemiological stud-ies.24 2>35 In fact, the only replicatedassociation in epidemiological research isan inverse relationship between currentdepressive symptoms and obesity, particu-larly among men.2>32 While differencesamong studies may derive from referralbiases, the cross-sectional nature of mostepidemiological studies could also beimportant. For example, acute depressionoften produces weight loss, and this mightexplain inverse cross-sectional relation-ships between depression and obe-

St33,36,37sity.3''3Because virtually all studies in this

area have been cross-sectional, prospec-tive epidemiological studies are needed.

Studies among youth may be particularlyimportant, since two correlates of psycho-pathology in youth, low social class of achild's parents and school failure inchildhood, consistently predict obesity inadulthood.10-12,38,39 The association be-tween obesity and school failure has beenattributed to other correlates of psychopa-thology in youth, including low IQ andchildhood aggression.8-Y1138'39 Inadequa-cies in children's social or physicalrearing environments may also predictadult obesity.10'13 For example, a recentstudy found that children who did notreceive harmonious support from parentsfaced a sevenfold increased risk forobesity as adults.13 Such environmentalrisk factors consistently relate to psycho-pathology, particularly conduct disorder,in youth.40'4 These suggestive prospec-tive findings have not been pursued inpsychiatric epidemiological studies ofyouth, which remain exclusively cross-sectional.

Methodological factors may contrib-ute to negative findings on the relationshipbetween psychopathology and obesity incross-sectional studies. First, smoking,alcohol use, and medical illness mayattenuate associations between obesityand psychiatric morbidity, much as theyattenuate associations with medical mor-bidity."l7'33'34 These variables may attenu-

Daniel S. Pine is with the Division of ChildPsychiatry, Patricia Cohen is with the Division ofChild Psychiatry and the Division of Epidemiol-ogy of Mental Disorders, and Jeremy D. Coplanis with the Biological Studies Unit, all at the NewYork State Psychiatric Institute and ColumbiaUniversity, New York, NY. Judith Brook is withthe Mt. Sinai School of Medicine, New York, NY.

Requests for reprints should be sent toDaniel S. Pine, MD, New York State PsychiatricInstitute, 722W 168th St, New York, NY 10032.

This paper was accepted November 21,1996.

American Journal of Public Health 1303

Pine et al.

ate associations because they relate topsychopathology and may producechanges in weight. Existing epidemiologi-cal studies rarely control for these con-

founders. Second, studies often rely on

brief rating scales to assess psychiatricsymptoms.9'2-35 At least in youth, stan-dardized multi-informant interviews pro-

vide more sensitive assessments.42Finally, interest in associations be-

tween psychopathology and obesity may

follow recent research on the serotonergicnervous system. Serotonin has been impli-cated in the development of both obe-sity'4-19 and psychiatric syndromes, suchas conduct disorder, which are character-ized by a pattern of recurrent impulsiveaggression.20-23 Few epidemiological stud-ies have examined associations betweenobesity and such syndromes; the onlyrelevant study in youth found relation-ships between teacher-rated aggressionand concurrent as well as later obesity.9Results in this study, however, were basedon brief global ratings by teachers, were

not adjusted for potential confoundingvariables, and were somewhat inconsis-tent across aggression measures.

In summary, prospective epidemio-logical studies have found that correlatesof youth psychopathology are associatedwith obesity, but, to our knowledge, no

epidemiological study has documenteddirect associations between psychopathol-ogy (defined according to the Diagnosticand Statistical Manual of Mental Disor-ders [DSM]) in youth and obesity, eitherconcurrently or prospectively. In thisstudy, we describe a prospective relation-ship between psychopathology in youthand obesity in early adulthood.

MethodsSample

Details of the sample used in thisstudy are described elsewhere."2-44 Thestudy is based on a cohort of 776adolescents who were psychiatrically as-

sessed in 1983 (time 1), when they were 9to 18 years old, and in 1992 (time 2),when they were 17 to 28 years old. Forconvenience, time 1 is referred to as

adolescence and time 2 is referred to as

young adulthood. Adolescents were se-

lected by means of a multistaged randomdesign with complete enumeration fromtwo semirural upstate New York counties.Complete information on sampling proce-dures and losses to follow-up is providedelsewhere.42

Assessment

Assessment protocols were ap-

proved by appropriate institutional reviewboards. All subjects provided informedconsent.

Obesity. Body mass index (BMI) isfrequenfly used to quantify obesity inepidemiological research, partially be-cause of the ease with which it ismeasured.Y-7'32'34 Despite some disadvan-tages as an obesity measure, BMI corre-

lates with more accurate measures ofobesity and is consistently related tomorbidity from illness associated withobesity. 1-7,45

Height and weight were assessed byself-report in young adulthood. BMI was

calculated (BMI = weight [kg] dividedby height [ml squared). At time 1, weightwas assessed only in a subset of the

sample who reported a recent loss or gainin weight. We therefore did not examineassociations using time 1 BMI. Althoughself-reported BMI is measured with somesystematic biases, validation studies sug-

gest that the magnitude of the bias is toosmall to affect conclusions about associa-tions in large-scale epidemiological stud-ies.4"48

Psychopathology. Interviews withparents and adolescents, administered bytrained lay interviewers, assessed psycho-pathology in youth at time 1. Only youthwere interviewed for psychopathology attime 2. Symptoms of DSM-Il or DSM-llI-R diagnoses were assessed with theuse of modifications of the DiagnosticInterview Schedule for Children, as de-scribed elsewhere.42-44 Psychopathologywas assessed both as disorders and bymeans of diagnostic criteria-based symp-

tom scales. Symptom scales incorporateditems on impairment associated with eachsyndrome. Evidence documenting thereliability and validity of this assessmentis presented elsewhere.42-44

We restricted our analyses to twospecific psychiatric domains. First, on thebasis of extensive clinical research, we

examined relationships between majordepressive disorder and BMI. Second,because epidemiological studies suggestthat adult obesity is predicted by corre-

lates of conduct disorder, including youthaggression, school failure, and parentalmaltreatment, we examined relationshipsbetween conduct disorder and BMI. Innonreferred samples, symptoms of depres-sion49'50 or conduct disorder4l"51'52 lie on a

continuum. Therefore, we used continu-ous symptom scales, rather than thediagnoses, for both major depression andconduct disorder.

Covariates

Age, gender, and ethnicity served as

covariates. Parental social class at time 1

served as a covariate, assessed with a

composite measure (described elsewhere)combining parental income and educa-tion.42 A social class measure was notused for young adults, who often were nolonger living with parents but were intransitional socioeconomic states. An in-dex of parental sociopathy was includedas a covariate, stimulated by recentresearch implicating this factor in the riskfor obesity.53 This measure was formed bycombining dichotomous measures foreach parent.44 Eight items on physicalhealth status, as described elsewhere,4344were summed to create continuous scales

1304 American Journal of Public Health

TABLE 1 -Sample Characteristics: 776 Adolescents Who WerePsychiatrically Assessed in Adolescence and Early Adulthood

Adolescent Young AdultFull Adolescent Sampleb Sampleb

Samplea (n = 776) (n = 644) (n = 644)

Mean age, y 14.1 13.8 22.0Age range, y 9-18 9-18 17-28Body mass index, kg/M2Males ... ... 24.6 ± 4.1Females ... ... 23.1 ± 4.4

Male, % 50 48 48IQ 98 ± 14.2 100.7 ± 14.8 ...

Positive parental sociopathy, % 17 18 ...

White, % 90 91 91

alncludes all subjects in time 1 (1983) sample.bSample used for body mass index analyses. Includes only subjects with complete time 1

(1983) and time 2 (1994) data.

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Psychiatric Symptoms and Obesity

that indexed physical health in adoles-cence and in early adulthood. Poorphysical health may predispose to obesitythrough inactivity but may also predis-pose toward thinness owing to illness-related weight loss. Since childhood IQhas been related to BMI, we also includedthis measure as a potential confounder,using a picture vocabulary test in adoles-cence. Finally, smoking and alcohol use

were assessed by self-report, as describedin detail elsewhere, and served as covari-

ates.424

Data Analysis

We examined associations both withBMI as a continuous variable and withobesity (defined by a cutoff on the BMIdistribution), since these two analyticapproaches answer conceptually differentquestions. The continuous approach exam-ines the association between psychiatricsymptoms and normal variations in rela-tive weight, as distributed throughout thepopulation. The categorical approach, incontrast, considers the association withobesity as an abnormal condition. Forcategorical analyses, subjects were di-chotomized at the 80th percentile of theBMI distribution for each gender (25.86kg/M2 in females, 27.31 kg/M2 in males).The 80th percentile was selected becauseBMI values approximated commonlyused cutoffs for definition of obesityamong young adults.57-"

Univariate associations with BMItreated continuously were quantified with

Pearson correlations. This procedure pro-

vides estimates of univariate associationsthat can be compared with findings fromprevious studies. The correlation matrixamong all variables used in this study isprovided to facilitate an appraisal of thedegree to which multicollinearity compli-

cates the results from multivariate analy-ses. Univariate associations with obesitywere quantified with odds ratios. Multivar-iate associations were examined by re-

gressing young adulthood BMI on a seriesof psychiatric scales and potential con-

founding variables, using both linear andlogistic regression.54 Various multivariatemodels were fitted to consider onlycross-sectional associations, to considerassociations with discrete psychiatricscales, to consider the influence of multi-collinearity, and to ensure that resultswere statistically stable.

Since gender differences are oftenfound in the correlates of obesity,7"12associations with BMI or obesity were

examined separately for males and fe-males on an a priori basis. Significantgender differences in the results are

indicated by interactions between sex andother independent variables. Similarly,interaction terms initially were fittedbetween age and each independent mea-

sure. Since no significant interactions withage were found, these terms were notincluded in our models.

To facilitate interpretations of the

regression coefficients, a number of the

scaled independent measures (smoking,

alcohol use, social class, physical health,and psychiatric scales) were converted tostandardized scores (mean = 0, SD = 1)before being entered into the regression.All linear regression models appearedsatisfactory after an examination of re-

sidual plots, a search for outliers, and an

examination of the overall fit. All tests are

two-tailed with a = .05. Trends to P =

.10 are also noted.

ResultsSample Characteristics

Of the 776 youth first assessed in1982, 716 had full psychiatric assess-

ments in 1992 (92% of living subjects).While detailed discussions of losses to

follow-up are found elsewhere,434 itshould be noted that the demographicfeatures of the sample remained represen-tative of the larger population from whichit was drawn. Complete data were avail-able for 644 subjects (83% of sample);most of the missing data were for IQ. Weexamined associations after excludingvariables with missing values. Becauseresults were similar across analyses, we

present data using an n of 644. Demo-

graphic and psychiatric characteristics of

the sample at each assessment have also

been reported elsewhere43 44but are sum-

marized in Table 1. Table 1 also presentssummary data for the 644 subjects used in

the current analyses.

American Journal of Public Health 1305

TABLE 2-Pearson Correlations among Adult Body Mass Index, Demographic Variables, Covariates, and Psychiatric

TABLE 2-Pearson Correlations among Adult Body Mass Index, Demographic Variables, Covariates, and PsychiatricScale Scores: 644 Subjects Assessed in Adolescence and Early Adulthood

BMI2 Age2 Sex Ethn SES1 Alc2 Tob2 Tob1 Socio1 1Q1 1112 1111 Dep2 CD2 Dep,Age2 +. 12**Sex +.17*** +.00Ethn +.13*** -.02 -.01SES1 -.10** +.03 +.06 -.27***Alc2 +.00 +.20*** +.28*** -.11** +.20***Tob2 -.01 +.02 -.02 +.03 -. 16*** +. 16***Tob1 -.02 +.34*** -. 13** -.04 -.14*** +.07 +.36***Socio1 -.02 + .00 -.05 +. 15*** -.25*** -.04 +. 15*** +.09*1Q1 +.00 +.03 +.13*** -.18*** +.40*** +.19*** -.11** -.11** -.14***1112 +.12*** -.06 -.24*** -.04 -.13*** -.11** +.08* +.11** -.02 -.01III, +. 15*** +.03 -.06 +.05 -.15*** -.04 +.05 +.10** +.05 -.06 +.26***Dep2 -.05 -.03 -. 18*** -.03 -.06 +. 12** +.11** +.11** +.06 +.00 +.21 *** +.23***CD2 -.01 -.17*** +.22*** +.08* -.05 +.31*** +.24*** +.01 +.10** +.03 -.01 +.04 +.26***Dep1 +.09* -.01 -.18*** +.09** -.21*** -.07 +.07* +.23*** +.16*** -.16*** +.17*** +.39*** +.30*** +.06CD1 +.17*** +.03 +.22*** +.19*** -.22*** +.13*** +.22*** +.30*** +.20*** -.14*** +.00 +.11** +.09* +.29*** +.37***

Note. BMI = body mass index; sex = gender (0 = female, 1 = male); Ethn = ethnicity (0 = White, 1 = other); SES = socioeconomic status; Alc =alcohol use; Tob = tobacco use; Socio = family history of sociopathy (0 = negative, 1 = positive); IQ = measured intelligence quotient; IlIl =physical health scale; Dep = major depressive symptoms; CD = conduct disorder symptoms. Subscript refers to adolescent (1) or adulthood (2)measure.

*P < .05; **P < .01; ***P < .005.

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Pine et al.

Univariate Associations

Continuous variables. As shown inTable 2, a higher BMI in early adulthoodwas correlated with eight independentmeasures: increasing age, male gender,non-White ethnicity, lower social classduring adolescence, increasing degrees ofphysical illness in both adolescence andadulthood, increasing depressive symp-toms in adolescence, and increasing con-

duct disorder symptoms in adolescence.While statistically significant, none ofthese correlations were large in magni-tude.

We examined the correlation matrixfor each gender and tested for interactions(data not shown). Similar associationswere found across gender with fourexceptions: BMI was inversely related todepression in adulthood among males(r =-.19) but not females, and BMI was

inversely related to adult alcohol use

(r = -.16), social class (r = -.20), and

IQ (r = -.14) among females but notmales. The partial correlation betweenBMI and IQ in females essentially disap-peared (r = -.05) when social class was

controlled for, but the partial correlationbetween BMI and social class remained(r = -.16) when IQ was controlled for.Table 2 also shows correlations among theindependent measures, many of whichwere significant, as discussed else-where.42-4 Finally, we examined poten-tial curvilinear relationships with BMI,using quadratic and cubic terms for eachindependent measure. No such relation-ships were found.

Univariate associations with obesity.As shown in Table 3, for males, forfemales, and for the entire sample, obesitywas inversely related to social class andpositively related to poor adult physicalhealth, poor adolescent physical health,and adolescent conduct disorder. Obesitywas related to race/ethnicity both in males

and in the entire sample but not in femalesalone. Obesity was related to adolescentdepression both in females and in theentire sample but not in males alone.Obesity was related to IQ only in females.While obesity was associated with depres-sion in adulthood in both genders, thisassociation was negative in males andpositive in females. The gender X adultdepression (X2 = 8.8, P < .001) and gen-

der X IQ (X2 = 4.7; P < .05) interactions,but no other interactions, were significantpredictors of obesity. When the analyseswere covaried for social class, the associa-tion between obesity and low IQ infemales disappeared (data not shown).Although these associations were consis-tently significant on statistical grounds,point estimates for most unadjusted oddsratios were not large (usually between 1.5and 2.5).

Multivariate Linear Regression

Because many independent variableswere correlated, various regression mod-els were fitted in an effort to evaluate theinfluence of multicollinearity. Results forpsychiatric scales were essentially un-

changed among analyses entering differ-ent combinations of covariates and psychi-atric scales; point estimates were very

similar, with somewhat different standarderrors and comparable levels of signifi-cance. For completeness, in Table 4 we

present results for separate linear regres-

sion models examining associations withadult BMI using only adult independentmeasures and using only adolescent inde-pendent measures. We also present resultsfor a model including both adolescent andadult measures, so that coefficients withadult measures represent associations forchange in the independent measure. Re-sults from other analyses are available onrequest.

Relationships with covariates. Asshown in Table 4, across analyses, eightcovariates were significantly related toBMI in young adulthood: age, gender,adolescent and adult health, adolescentsmoking, ethnicity, social class in fe-males, and adult alcohol use in females. Inthe model entering all adult and adoles-cent covariates simultaneously, the regres-sion step with covariates accounted for13% of the variance in adult BMI(F[12,631] = 7.7; P < .001). Poor physi-cal health at both ages, as indicated by a

higher score on the physical health scales,was associated with a higher BMI inadulthood. Smoking status in adolescencepredicted lower adult BMI. Increasingage, non-White ethnicity, and male gender

1306 American Journal of Public Health

TABLE 3-Unadjusted Odds Ratios (ORs) (95% Confidence Intervals [Cls])for Obesity in Adulthood: Associations with DemographicVariables, Covariates, and Psychiatric Scale Scores Measured inAdolescence and Early Adulthood

Males Females Total Sample(n = 310), (n = 334), (n = 644),OR (95% Cl) OR (95% Cl) OR (95% Cl)

Demographic predictorsAgea 1.35 (0.83, 2.20) 1.22 (0.75, 1.99) 1.28 (0.96,1.72)Non-White ethnicityb 2.48 (1.11, 5.55) 1.75 (0.77, 3.99) 2.10 (1.19, 3.70)

Predictors measured inearly adulthood

CovariatescPoor physical health 1.79 (1.03, 3.09) 2.32 (1.39, 3.86) 1.90 (1.33, 2.70)Smoking 0.83 (0.72,1.17) 1.08 (0.65, 1.80) 0.96 (0.68, 1.37)Alcohol use 0.79 (0.47,1.31) 0.43 (0.21, 0.72) 0.67 (0.45, 0.99)

Psychiatric scalescDepression 0.37 (0.16, 0.87) 1.65 (1.03, 2.64) 1.04 (0.70, 1.54)Conduct disorder 0.77 (0.46, 1.28) 1.72 (0.81, 3.61) 0.96 (0.70, 1.31)

Predictors measured inadolescence

CovanatesPoor physical healthc 2.01 (1.16, 3.49) 2.32 (1.39, 3.86) 2.05 (1.44, 2.92)Smokingc 0.82 (0.37,1.79) 1.11 (0.69, 1.45) 0.95 (0.65,1.42)Social class 0.55 (0.32, 0.95) 0.39 (0.23, 0.68) 0.47 (0.32, 0.69)IQc 1.35 (0.75, 2.43) 0.55 (0.31, 0.99) 0.74 (0.55, 1.33)Positive parental 1.79 (0.94, 3.41) 1.38 (0.75, 2.56) 1.54 (0.98, 2.41)

sociopathybPsychiatric scalesc

Depression 1.46 (0.78, 2.74) 3.06 (1.91, 4.91) 1.75 (1.23, 2.49)Conduct disorder 1.75 (1.09, 2.80) 2.32 (1.29, 4.17) 1.93 (1.36, 2.75)

aOdds ratio considers magnitude of association with obesity for one group of subjects who are5 years older than a second group of subjects (i.e., subjects who are 25 as opposed to 20years old).

bDichotomous measure, such that odds ratio is across the two groups.cContinuous measure, such that odds ratio is for a 2-SD difference on the continuous scale

(i.e., 1 SD above vs 1 SD below mean).

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Psychiatric Symptoms and Obesity

were each associated with a higher adultBMI.

In the model including all adult andadolescent covariates, the interaction be-tween social class and gender was signifi-cant only at the trend level (t626 = 1.8;P = .079). An association between in-creased BMI and lower social classspecific to women is consistently found in

epidemiological studies. We thereforeincluded gender-specific social class vari-ables in our models despite the marginalsignificance of this interaction. As shownin Table 4, social class was inverselyrelated to BMI in females but showed no

relationship to BMI in males.While the etiology of a female-

specific association between social classand BMI is poorly understood, earlypregnancy could play a role.55 Because

early pregnancy may be one step on the

path between conduct disorder and obe-

sity, pregnancy status was excluded from

our final models. However, number of

lifetime pregnancies was inversely corre-

lated with social class in our sample, and

when this variable was entered into the

model, number of pregnancies (B =1.3 ± 0.8; t628 = 2.6; P < .01), but not

social class, gender, or gender X social

class interactions, predicted young adult

BMI.There was also an interaction be-

tween time 2 alcohol use and gender0626 = 2.7; P < .01). As shown in Table

4, alcohol use in young adulthood was

associated with a lower BMI in women

but not in men. Finally, while there was a

significant interaction between IQ and

gender when only these variables were

fitted in the model, this interaction was not

included in the final model because it was

redundant with the gender X social class

interaction.

Relationships with psychiatric scale

scores. As shown in Table 4, results for

the psychiatric scale scores were consis-

tent, whether adolescent and adult scale

scores were entered alone in separatemodels or together in one model. Al-

though associations were statistically sig-nificant, they were not large in magnitude.For a model entering adult psychiatricmeasures after all adult and adolescent

covariates, adult psychiatric scales ac-

counted for 3% of the variance in adult

BMI (F[3,628] = 6.6; P < .001). As

shown in Table 4, significant associations

included the interaction between current

depressive symptoms and gender

American Journal of Public Health 1307August 1997, Vol. 87, No. 8

TABLE 4-Predictors of Body Mass Index among 644 Young Adults: Associations with Demographic Variables,Covariates, and Psychiatric Scale Scores Measured in Adolescence and Early Adulthood

Relationship Relationship Relationship withwith Adulthood with Adolescent Adulthood andPredictors Only Predictors Only Adolescent Predictors

Regression t Regression t Regression tCoefficient a (df = 633) Coefficienta (df = 632) Coefficienta (df = 626)

Demographic predictorsAge, y +0.22 ± 0.06 3.5t +0.27 ± 0.06 4.3t +0.25 ± 0.06 3.8tMale vs femaleb +1.93 ± 0.36 5.4t +1.23 ± 0.35 3.5t +1.48 ± 0.37 4.OtNon-White ethnicityb +1.94 ± 0.56 3.4t +1.22 ± 0.60 2.1* +1.39 ± 0.59 2.4*

Predictors measured in early adulthoodCovariatesc

Physical health +0.80 ± 0.17 4.7t ... ... +0.66 ± 0.17 3.8tSmoking -0.01 ± 0.16 0.1 ... ... +0.05 ± 0.18 0.3Alcohol use (females)d -1.01 ± 0.29 3.5t ... ... -0.85 ± 0.29 3.3tAlcohol use (males)d +0.31 ± 0.22 1.4 ... ... +0.27 ± 0.22 1.3

Psychiatric scalescDepression (females)e +0.40 ± 0.22 1.9 ... ... +0.25 ± 0.22 1.2Depression (males)e -0.92 ± 0.27 3.4t ... ... -1.15 ± 0.28 4.2tConduct disorder +0.01 ± 0.19 0.0 ... ... -0.06 ± 0.19 0.3

Predictors measured in adolescenceCovariates

Physical healthc ... ... +0.51 ± 0.17 3.0*** +0.44 ± 0.17 2.8**Smokingc ... ... -0.56 ± 0.19 2.9*** -0.55 ± 0.20 2.8***Social class (females)c f ... ... -0.77 ± 0.25 3.1*** -0.53 ± 0.25 2.2*Social class (males)cf ... ... -0.05 ± 0.25 0.2 +0.04 ± 0.25 0.2l0 ... ... +0.01 ± 0.01 0.6 +0.01 ± 0.01 0.9Parental sociopathyb ... ... -0.57 ± 0.42 1.3 -0.54 ± 0.42 1.3

Psychiatric scalescDepression ... ... +0.15 ± 0.20 0.7 +0.20 ± 0.20 1.0Conduct disorder ... ... +0.53 ± 0.21 2.6** +0.62 ± 0.21 3.0***

aBody mass index (BMI) is regressed on all predictors in column in one model. Regression coefficients refer to change in BMI (kg/M2) associatedwith each unit change in predictor. Coefficients for demographic predictors pertain to entire sample; coefficients for variables with gender qualifierspertain only to that gender.

bDichotomous variable.CScale is standardized (mean = 0, standard deviation = 1).dFor gender x adult alcohol use interaction, 4626 = 2.7, P< .01 .eFor gender x adult depression interaction, t628 = 4.1, P< .001.fFor gender x adolescent social class interaction, t26 = 1.8, P = .079.P < .05; **P< .01; ***P < .005; tP < .001.

Pine et al.

4628 = 4.1; P < .001). There was a nega-tive association between young adultdepression and BMI in males but apositive, nonsignificant, association infemales.

As shown in Table 4, adolescentpsychiatric scale scores related to adultBMI, whether entered only with adoles-cent covariates or with all covariates andadult psychiatric scale scores. In themodel entering adolescent depression andconduct disorder scale scores after allcovariates and adult psychiatric scalescores, the step accounted for an addi-tional 2% of the variance in young adultBMI (F[2,626] = 6.7; P = .001). AdultBMI was significantly related to adoles-cent conduct disorder but not depressivesymptoms. The adolescent depression andconduct scores were correlated in thesample (r = .37; P < .001). When thedepression score was entered alone in thefinal regression step, it also showed apositive association with BMI (B=.38 ± 0.19; t627 = 1.8; P < .05).

Multivariate Logistic Regression

Various logistic models were fitted toconsider the influence of multicollinearityand the statistical independence of associa-tions between obesity and correlatedpredictor variables. Gender-specific termsfor alcohol use and social class were notincluded in multivariate models, since thegender interactions were nonsignificant inlogistic models. Across analyses, obesitywas predicted by adolescent conductdisorder, the gender x adult depressioninteraction, and by both adolescent andadult physical health.

A logistic model was fitted to includecovariates related to obesity in eithergender. Adjusted odds ratios with obesitywere estimated for the differences inindependent measures shown in Table 3(i.e., groups differing in age by 5 years; 2-SD differences on psychiatric scale scores).Odds ratios, with 95% confidence inter-vals, excluded 1.0 for the followingvariables: age (1.65 [1.11, 2.44]), adoles-cent tobacco use (0.59 [0.46, 0.77]), poorphysical health in both adolescence (1.65[1.06, 1.99]) and adulthood (2.10 [1.35,3.23]), major depression in adulthoodamong men (0.27 [0.16, 0.76]), andconduct disorder in adolescence (2.14[1.34, 3.42]). There was also a trend for arelationship with social class (0.64 [0.38,1.07]). When the model was refitted withonly adolescent major depression but notadolescent conduct disorder, adolescentdepression predicted obesity at the trendlevel (1.49 [0.97, 2.30]).

Finally, we also considered associa-tions between obesity and number ofpregnancies, though we excluded thisvariable from other models as its role inthe causal pathway between conductdisorder and female obesity remainsunclear. Number of pregnancies predictedobesity in a univariate model, with anodds ratio of 3.60 (2.00, 6.48) for womenwith two as opposed to no pregnancies.This odds ratio was minimally changed(3.46 [1.71, 7.00]) when social class,ethnicity, and IQ were added to the model,none of which predicted obesity withpregnancy status in the model.

DiscussionWe found an association between

conduct disorder symptoms in adoles-cence and obesity in young adulthoodafter controlling factors that may affectthis association. These factors includephysical health, smoking, and alcohol use.Few other studies examining relationshipsbetween obesity and psychopathologyhave controlled for these variables, all ofwhich related to both BMI and psychopa-thology in our study.

A concurrent association betweensymptoms ofconduct disorder and obesitywas noted in studies that predated modemDSM nosology, though most of thesestudies relied on subjective indices ofobesity.41'56 While little effort has beenmade to replicate these findings usingcurrent anthropometric and psychiatricmeasures, Crisp et al.9 noted an associa-tion between teacher ratings of aggressionand an objective index of obesity. How-ever, this study used outdated measures ofaggression, and the results were not asclear when other potential indices ofconduct problems were employed. Usingcurrent DSM-oriented measures of con-duct problems in a cross-sectional high-risk study, we noted a concurrent associa-tion between obesity and parent-ratedsymptoms of conduct disorder.57 Thesefindings suggest that a prospective relation-ship between symptoms of conduct disor-der in adolescence and obesity in adult-hood may be preceded by a concurrentassociation between childhood conductdisorder and obesity.

Because we did not have suitabledata on adolescent BMI, it is unclearwhether the longitudinal association inthis study is also observable cross-sectionally in adolescence, as suggestedby these other findings.9'57 Researchersattempting a replication might considerwhether a relationship between adolescent

conduct disorder and adolescent BMIprecedes the longitudinal association de-scribed in the present paper. Such research-ers might also consider the importantissue of whether childhood conduct disor-der symptoms predict an increase in BMIbetween childhood and adulthood.

There are limitations to our findingsbeyond the lack of adolescent BMI data.First, we had no data on family history ofobesity. Because both psychopathologyand obesity are familial conditions, familyhistory of obesity could play a role inassociations between childhood psychopa-thology and adult obesity. Research onfamilial relationships between obesity andpsychiatric problems may therefore en-hance an understanding of the associa-tions found in the current report. Second,the overall amount of explained variancein early adult BMI that was due topsychopathology was small in our mod-els, and most odds ratios with obesitywere not large. As a result, our findingscarry unclear clinical implications. Third,there was some attrition in the samplewith complete data. However, analyseswere comparable after we eliminatedconfounding variables with missing data,and the sample studied at each assessmentremained demographically representativeof the population from which it wasdrawn. As discussed elsewhere,42-4 find-ings from this study should be interpretedagainst these losses to follow-up. Fourth,the wide age range in our sample is alimitation, as the relationship betweenobesity and psychopathology may varywith age. On the other hand, the wide agerange may carry some advantages.Namely, since we found no evidence ofage x psychopathology interactions, thedata provide some evidence that therelationship between adolescent psychopa-thology and adult obesity does not varybetween the ages of 9 and 18. Finally, asdiscussed elsewhere, the use of self-reported BMI and lay interviews to assesspsychopathology are other potential limi-tations.42-48 It might be possible to con-duct more accurate assessments ofpsycho-pathology by using experienced cliniciansas interviewers.

Despite these limitations, there arereasons to believe that our findings maybe replicable. Both the direction and themagnitude of associations between BMIand covariates in our study parallel resultsin other samples. These include thedifferential associations of BMI withalcohol use and social class across gen-ders as well as the association betweenBMI and smoking.7 Moreover, our results

1308 American Joumal of Public Health August 1997, Vol. 87, No. 8

Psychiatric Symptoms and Obesity

are consistent with recent neurobiologicalresearch suggesting that abnormalities inserotonin may relate to the pathophysiol-ogy of both obesity and psychiatricsyndromes that are characterized by ahigh degree of impulsive aggression, suchas conduct disorder.'123

In conclusion, in this study we foundrelationships between BMI and a group offactors associated with adolescent psycho-pathology, including low social class,poor physical health, smoking, and alco-hol use. After controlling these factors, aswell as concurrent depressive symptoms,we also documented a prospective relation-ship between symptoms of conduct disor-der in adolescence and obesity in earlyadulthood. Efforts to replicate these find-ings might be encouraged in light of thestrengths of this study, includingits prospective epidemiological designand comprehensive psychiatric assess-ment. C]

AcknowledgmentsThis work was supported by NIMH GrantMH-36971 to Dr Cohen (Epidemiology ofPersistent Child Psychopathology); a ResearchScientist Award (K05-DA-00244) and NIDAGrant DA-03188 to Dr Brook; NIMH ResearchTraining Grant MH-16432; NIMH CenterGrant MH-43878 (Center to Study YouthAnxiety, Suicide, and Depression); a ScientistDevelopment Award for Clinicians from NIMHto Dr Pine (K20-MH01391); and a grant fromthe Leon Lowenstein Foundation to the Lowen-stein Center for the Study and Prevention ofDisruptive Behavior Disorders.

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