Hormonal and behavioral homeostasis in boys at risk for substance abuse

Drug and Alcohol Dependence 55 (1999) 165 – 176

Hormonal and behavioral homeostasis in boys at risk for substance abuse Michael A. Dawes a,*, Lorah D. Dorn b, Howard B. Moss a, Jeffrey K. Yao a, Levent Kirisci a, Robert T. Ammerman a,c, Ralph E. Tarter a a

Center for Education and Drug Abuse Research (CEDAR), Department of Psychiatry, Uni6ersity of Pittsburgh School of Medicine, 3811 O’Hara Street, Pittsburgh, PA 15213, USA b Uni6ersity of Pittsburgh, School of Nursing, and Department of Psychiatry, Pittsburgh, PA 15213, USA c Department of Psychiatry, Allegheny Uni6ersity of the Health Sciences, Pittsburgh Campus, Allegheny General Hospital, Pittsburgh, PA 15212, USA Received 5 June 1998; accepted 16 December 1998

Abstract This study modeled the influences of cortisol reactivity, androgens, age-corrected pubertal status, parental personality, family and peer dysfunction on behavioral self-regulation (BSR), in boys at high (HAR) and low average risk (LAR) for substance abuse. Differences between risk groups in cortisol and androgen concentrations, and cortisol reactivity were also examined. Subjects were 10- through 12-year-old sons of substance abusing fathers (HAR; n= 150) and normal controls (LAR; n =147). A multidimensional construct of BSR was developed which utilized multiple measures and multiple informants. Boys reported on family dysfunction and deviant behavior among their peers. Parents reported on their propensity to physically abuse their sons, and their own number of DSM-III-R Antisocial Personality Disorder symptoms. Endocrine measures included plasma testosterone, dihydrotestosterone, and salivary cortisol. HAR boys, compared to LAR boys, had lower mean concentrations for testosterone, dihydrotestosterone, salivary cortisol prior to evoked related potential testing, and lower cortisol reactivity. The number of maternal Antisocial Personality Disorder symptoms, parental potential for physical abuse, degree of family dysfunction, and peer delinquency were significantly associated with BSR. Parental aggression antisocial personality symptoms and parental physical abuse potential are likely to influence sons’ behavioral dysregulation and homeostatic stress reactivity. These key components of liability are posited to increase the likelihood of developing suprathreshold Psychoactive Substance Use Disorder (PSUD). © 1999 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Substance abuse liability; Hormonal and behavioral homeostasis; Personality; Family and peer dysfunction

1. Introduction Empirical findings (Reich et al., 1993; Martin et al., 1994; Dawes et al., 1997; Mezzich et al., 1997a,b), as well as theoretical literature (Gorrenstein and Newman, 1980; Tarter et al., 1989; Newman and Wallace, 1993) indicate that specific behavioral characteristics increase the likelihood of developing a Psychoactive Substance Use Disorder (PSUD; DSM-III-R, American Psychi* Corresponding author.

atric Association, 1987). Disorders of inattention, impulsivity, and hyperactivity (Barkley et al., 1990; Biederman et al., 1996), childhood aggressivity (Kellam and Brown, 1982; Brook et al., 1996), and conduct disorder (Kandel et al., 1986; Robins and McEvoy, 1990), have been shown to increase the risk for PSUD. Converging evidence suggests that Conduct Disorder often mediates the Attention Deficit Hyperactivity Disorder (ADHD)–PSUD relationship (Gittelman et al., 1985; Hechtman and Weiss, 1986; Barkley et al., 1990; Mannuzza et al., 1991; Babor et al., 1992; Biederman et

0376-8716/99/$ - see front matter © 1999 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 7 6 - 8 7 1 6 ( 9 9 ) 0 0 0 0 3 - 4

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al., 1997). It is posited that these behavioral characteristics constitute a core disorder of behavioral self-regulation (BSR). BSR is defined herein as the degree to which an individual can control their activity and reactivity to environmental challenges. Only recently has the extent to which BSR increases risk for PSUD and related behaviors been examined. Dawes et al. (1997), in an earlier study using the same subjects described in Section 2.1, demonstrated predictive validity of BSR for family, peer, and school problems in young adolescence at 2-year follow-up. Disturbances in these three domains of psychosocial functioning are posited to increase the risk for substance abuse (Hawkins et al., 1992). Mezzich et al. (1997a,b), employing definitions and procedures similar to those used by Martin et al. (1994), but in a separate sample of female adolescent substance abusers, developed a latent construct of behavioral dysregulation that included measures of hyperactivity, impulsivity, and inattention. Mezzich et al. (1997a) showed behavioral dysregulation, negative affectivity, and family impairment influence violence in substance abusing female adolescents. In a second study, Mezzich et al. (1997b) demonstrated that behavioral dysregulation, negative affectivity, and childhood victimization predicted substance use and risky sexual behavior in substance abusing female adolescents. Caspi et al. (1996), in a third study of a different sample, reported that undercontrolled 3-year-old boys were at greater risk of developing Antisocial Personality Disorder and Alcohol Dependence by age 21. Taken together, the concurrent and predictive validity of measures similar to BSR is supported by the three above described studies. Johnson et al. (1992), in their review of the hormonal and behavioral homeostasis (Greek for ‘steady state’) literature, define ‘stress’ as a state of threatened homeostasis. Hormonal and behavioral homeostasis can be perturbed by physical and psychological stressors, induced by family dysfunction, abusive parenting, and deviant peer affiliation (Johnson et al., 1992; Moss et al., 1995). Johnson et al. (1992) suggest that these chronic contextual stressors can suppress both androgen and cortisol concentrations. The mechanisms by which chronic environmental stress influences circulating levels of androgens and cortisol include direct modulation by the Hypopituitary Gonadal Axis (HPG) and indirect modulation by the Hypopituitary Adrenal Axis (HPA) (Bambino and Hsueh, 1981; Francis, 1981; Collu et al., 1984; Sapolsky, 1991, 1992; Johnson et al., 1992). Moreover, previous experience (Archer, 1991; Rubinow and Schmidt, 1996), social interactions (Mazur, 1985), and social rank (Schaal et al., 1996) are thought to influence androgen (Archer, 1991; Rubinow and Schmidt, 1996) and cortisol (Buchanan et al., 1992; Susman et al., 1997) levels, as well as hormone – behavior associations. Studies on non-human primates and

adult humans have shown that both the HPG and HPA axes, but particularly the HPA axis, are sensitive to physical and psychological stress. Studies on human youths, however, have generally shown hyporesponsivity of cortisol in chronic environmental stress situations. In a small sample of prepubertal youth, Constantino et al., (1993) failed to find abnormal levels of testosterone in aggressive subjects. Moss et al. (1995), in the same baseline assessment of preadolescent sample as described herein in Section 2.1, have shown that decreased cortisol reactivity to an anticipated stressor in sons of substance abusing fathers is associated with Child Behavior Checklist (CBCL; Achenbach and Edelbrock, 1983) problem behaviors, and commission error scores on a computerized task (Schneider and Detweiler, 1987). Moreover, Moss et al. (in press), also in longitudinal follow-up of the same sample as described in Section 2.1, have reported that lower preadolescent cortisol level prior to an anticipated stress was associated prospectively with regular monthly cigarette and marijuana use, but not with regular alcohol use during middle adolescence. Cross-sectional studies on hormone–behavior relations in peripubertal and pubertal youth have shown significant positive (Olweus, 1987), negative (Dabbs et al., 1991), and no (Susman et al., 1987; Inoff-Germain et al., 1988) associations. Positive associations have been shown between androgen concentration and level of verbal (Olweus, 1987) and physical (Olweus, 1987) aggression, as well as peer-nominated dominance (Schaal et al., 1996). Testosterone has been associated with other behaviors that are posited to be linked to BSR in adolescents, including smoking, drinking, and sex (Udry, 1990). Dabbs et al. (1991) reported a significant interaction between testosterone and cortisol levels; that is, they found that the testosterone–aggression association was greater among subjects having a low cortisol level. Scerbo and Kolko (1994) did not confirm this finding in a separate sample. The longitudinal relations among testosterone–aggression and low cortisol in these samples have not been reported. Inoff-Germain et al. (1988) and Susman et al. (1987), in a sample of peripubertal youth, failed to observe a significant correlation between plasma androgen level and negative affect, maternal reports of delinquency and oppositional behavior, and direct observation measures of irritability and assertiveness. Longitudinal data suggest that the hormone reactivity may be more sensitive to environmental stressors at particular developmental transitions, such as the peripubertal period (Johnson et al., 1992; Susman et al., 1997). For example, Susman et al. (1997) show that in a small sample of healthy adolescents, over a 1-year period, distress behavior for both sexes in a challenging situation decreases, whereas, for girls cortisol level decreases, while for boys, cortisol level increases. This

M.A. Dawes et al. / Drug and Alcohol Dependence 55 (1999) 165–176

study, however, does not relate the magnitude, timing, or chronicity of environmental stressors during the follow-up period. Hence, interpretation of these sex differences in cortisol level at longitudinal follow-up is problematic. In a separate sample of males, followed from ages 6 to 13, testosterone levels were positively associated with high social dominance and social success, whereas males with a history of high physical aggression had lower testosterone levels, compared to boys with no history of physical aggression (Schaal et al., 1996; Tremblay et al., 1997). Schaal et al. (1996) speculate that chronic activation of the adrenal axis may explain the relative suppression of the HPG axis in this sample. This study also does not relate the magnitude, timing, or chronicity of environmental stressors during the longitudinal follow-up. Although speculative, these chronically aggressive boys most probably matured in environments where they experienced high degrees of physical and psychological stressors, including family dysfunction, abusive parenting from angry and aggressive parents, and deviant peer affiliation. From a multifactorial epigenetic perspective of etiology of substance abuse, inclusion of measures of behavioral and hormonal homeostastis, and of the environmental stressors that may perturb homeostasis, in a longitudinal study of sons and daughters of substance abusing fathers is likely to help elucidate the mechanisms leading to PSUD outcomes (Tarter and Vanyukov, 1994; Vanyukov et al., 1994; Moss et al., in press). The extent to which measures of hormonal profile (e.g. androgens, cortisol) and hormonal homeostasis (e.g. cortisol reactivity), interact with concurrent environmental stressors (e.g. specific parental aggressive antisocial personality characteristics, as well as family and peer dysfunction), to modulate BSR in high risk youth (e.g. behavioral homeostasis) heretofore has not been investigated in sons of substance abusing fathers. It is posited that measures of hormonal and behavioral homeostasis have a greater magnitude of covariation in sons of substance abusing fathers, compared to normal control sons, during the peripubertal period. Empirical evidence has yet to document the preadolescent covariation of hormonal and behavioral homeostasis in sons of substance abusing fathers and in controls, or the direction and magnitude of these associations in these comparison groups. This investigation tested two cross-sectional hypotheses. The first hypothesis was that concentrations of plasma testosterone, plasma dihydrotestosterone, and salivary cortisol, as well as cortisol reactivity, were lower in preadolescent high average risk (HAR) boys, compared to preadolescent low average risk (LAR) boys; these associations are posited to be due in part to chronic homeostatic stress experienced from the family and peer environment prior to baseline assessment. The second hypothesis was that in these preadolescent boys,

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cortisol reactivity would be negatively associated with BSR (e.g. low cortisol reactivity would be associated with high behavioral dysregulation), while parent antisociality, family dysfunction and deviant peer affiliation would be significantly positively associated with BSR (e.g. high degree of environmental stressors would be associated with high behavioral dysregulation).

2. Methods

2.1. Subjects The sample consisted of baseline assessment of two groups of 10- through 12-year-old boys, classified according to their biological father’s diagnostic status of lifetime DSM-III-R Psychoactive Substance Use Disorder (PSUD). This baseline sample was accrued between 1989 and 1997, and is a portion of the first wave on an ongoing longitudinal study. One-hundred fifty high average risk (HAR) boys had fathers who qualified for PSUD. Low average risk (LAR) boys (n= 147) had fathers who were not qualified for PSUD, and had no other adult lifetime Axis I or Antisocial Personality Disorder. Chronic neurological illness, acute neurological injury, organic brain syndrome requiring treatment, uncorrected sensory incapacity (vision and hearing), intelligence quotient less than 75, and schizophrenia or other psychotic illness were exclusionary criteria. Current or lifetime comorbid psychiatric disorder in the PSUD fathers, other than psychosis, did not disqualify the son from the study. The two groups of boys were derived from intact, single parent, as well as reconstituted families, so as to obtain a representative sample of the range of family structures associated with PSUD in adult men. Recruitment sources of fathers included local outpatient and inpatient substance abuse treatment programs; radio, television, local newspaper advertisements; and other printed media. A market research group was also employed. Employing this recruitment strategy affords the opportunity of maximizing the generalizability of the HAR sample. Cash incentives were provided to participating fathers and family members who completed the protocol. The study has been approved by University of Pittsburgh Medical Center Institutional Review Board. Prior to entry, all participating parents granted informed consent and sons their assent. In order to control for sibling order effects, the oldest boy between ages 10 and 12 years in each family was selected. Comparison of differences in substance use patterns between HAR and LAR boys (Dawes et al., 1997) revealed very low rates of substance use experimentation that were unlikely to significantly influence differences in hormone levels between risk groups, or prediction of both risk status and BSR. In the baseline

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Table 1 Demographics, Behavioral Self-Regulation (BSR), and Lifetime DSM-III-R Diagnoses in HAR and LAR boysa Variable

HAR (n= 150) Mean (S.D.)

LAR (n =147) Mean (S.D.)

t-value

P-value

Age SES Full Scale IQ BSR

11.26 36.14 104.02 2.40

11.36 43.14 112.25 2.26

−0.91 −4.57 −4.28 3.81

NS B0.001 B0.001 B0.001

Ethnicity*

n (% of total)

x2

P-value

Caucasian African–American Other Diagnosis** ADHD CD ODD Childhood anxiety disorders Depressive disorders

(0.95) (12.85) (15.59) (0.32)

94 (31.8) 47 (15.9) 8 (2.7)

n (% of total) 122 (41.2) 20 (6.8) 5 (1.7)

n (%)

n (%)

31 10 21 19 7

15 2 7 11 1

(10.5) (3.4) (7.1) (6.4) (4.7)

(0.96) (13.46) (15.74) (0.29)

(5.1) (0.7) (2.4) (3.7) (0.7)

15.90

x2 6.47 5.50 7.63 2.32 4.58

0.001

P-value 0.01 0.02 B0.01 NS 0.03

a BSR, Behavioral Self-Regulation; ADHD, Attention Deficit Hyperactivity Disorder; CD, Conduct Disorder; ODD, Oppositional Defiant Disorder. * One missing value; ** Two missing values; (%) is the percentage of subjects in each column, for HAR and LAR groups.

assessment used for these analyses, neither the HAR nor the LAR boys reported a single DSM-III-R PSUD. Differences between HAR and LAR groups for demographics, behavioral self-regulation, and lifetime DSM-III-R Disorders are reported in Table 1. As can be seen, HAR boys, compared to LAR boys, had lower socioeconomic status (Hollingshead, 1990). HAR boys also had lower WISC-III intelligence quotients (Wechsler, 1991). These factors were thus controlled statistically in the analyses. The distribution of DSM-III-R lifetime psychiatric and PSUD disorders in fathers and mothers are reported in Tables 2 and 3. As can be seen, fathers had more extensive histories for PSUD and other comorbid psychiatric disorders, compared to mothers.

fasting venipuncture was performed at about 07:00 h. Following breakfast, the protocol was continued. All of the questionnaires and rating scales were administered and scored according to standard method. The boys, their parents, and their teachers were assessed using the following measures.

2.2. Assessment procedure

2.3.2. Family dysfunction The Dysfunctional Family Index (DFI) was obtained from summation of sons’ responses to seven scales in the General Section of the Family Assessment Measure (Skinner et al., 1983): Task Accomplishment, Role Performance, Communication, Affective Expression, Affective Involvement, Control, and Values and Norms. A high DFI score thus characterizes family dysfunction across a broad range of domains.

The protocol reported herein is a component of a 2-day comprehensive baseline evaluation in an ongoing longitudinal study. In this report, the assessment is conducted on 10- through 12-year-old boys and their nuclear families. Following informed consent from the parents, and assent from the boys, the protocol was administered in fixed order. Interviews were administered by trained masters level clinical associates. Demographic information was recorded first. Next, the parents and their sons were administered the psychiatric interviews. This was followed by completion of the self-report questionnaires. Thereafter, the boys slept overnight on an inpatient adolescent psychiatric unit. A

2.3. Measures 2.3.1. Demographics Information about age and household socioeconomic status (Hollingshead, 1990) was obtained using the CEDAR Demographic Form. Full scale intelligence quotient was obtained using the revised Wechsler Intelligence Scale for Children (WISC-III; Wechsler, 1991).

2.3.3. Parent characteristics Parents completed the Child Abuse Potential In6entory (Milner, 1980). The Abuse Potential Scale measured the parent’s propensity to employ severe punitive methods on their son. The algorithm described in Mil-

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Table 2 Lifetime DSM-III-R diagnoses of proband fathers in the PSUD (SA+) and control (SA−) groupsa DSM-III-R diagnoses*

SA+ (n =149)

SA− (n =147)

P-value

n

(%)

n

(%)

Psychoacti6e substance use disorders Amphetamine abuse Amphetamine dependence Alcohol abuse Alcohol dependence Cannabis abuse Cannabis dependence Cocaine abuse Cocaine dependence Nicotine dependence Opioid abuse Opioid dependence

6 9 76 93 39 40 18 48 27 4 26

(4.0) (6.0) (51.0) (62.4) (26.2) (26.8) (12.1) (32.2) (18.1) (2.7) (17.4)

0 0 3*** 0 0 0 0 0 6 0 0

(0) (0) (2.0) (0) (0) (0) (0) (0) (4.1) (0) (0)

0.03** 0.003** B0.001** B0.001** B0.001** B0.001** B0.001** B0.001** B0.001** NS B0.001**

Comorbid psychiatric disorders Major depression Adult Anxiety Disorder Antisocial Personality Disorder Attention Deficit Hyperactivity Disorder Oppositional Defiant Disorder Conduct Disorder

39 17 28 12 13 52

(26.2) (11.4) (18.8) (8.1) (8.7) (34.9)

0 0 0 1 0 6

(0) (0) (0) (0.7) (0) (4.1)

B0.001** B0.001** B0.001** 0.003** B0.001** B0.001**

a Adult Anxiety Disorder, Sum of Panic Disorder without Agoraphobia, Panic Disorder with Agoraphobia, Generalized Anxiety Disorder, Agoraphobia without history of Panic Disorder, Social Phobia, Obsessive Compulsive Disorder, Post-Traumatic Stress Disorder, Anxiety Disorder Not Otherwise Specified. * One missing subject. ** Significant Fisher’s Exact Tests, two-tail. *** These subjects had very mild alcohol abuse in young adulthood only, without any other psychiatric comorbidity.

ner (1980) was employed to obtain valid scores for the analyses reported herein. Twenty-three fathers of HAR sons (15.3% of substance abusing fathers) and nine fathers of LAR sons (6.1% of control fathers) were removed due to invalid scores. Twenty-eight mothers of HAR sons (18.7% of sample) and 16 (10.9% of sample) were also removed due to invalid scores. The number of lifetime DSM-III-R Antisocial Personality Disorder Symptoms was obtained from the Structured Clinical Interview for DSM-III-R (SCID; Spitzer et al., 1987).

2.3.4. Peer delinquency Sons completed the Peer Delinquency Scale (Loeber, 1989, unpublished). This scale rates delinquent activity of peers. The Cronbach’s a was 0.84, consistent with results reported in Loeber’s Pittsburgh Youth study (Loeber et al., 1998, p. 71). 2.3.5. Age-corrected Tanner staging Assessment of sexual maturation was determined during physical examination by a nurse practitioner. Staging of genital development employed the definitions of Marshall and Tanner (1970). Age-corrected Tanner stage was defined as the average of Tanner Stage for pubic hair and genital development, multiplied by 100, and divided by the boys’ ages in months.

2.3.6. Beha6ioral self-regulation scale (BSR; Dawes et al., 1997) A continuous measure of BSR was created from standardized (z-scored) subscales that were summed to develop aggregate observed measures of lifetime inattention, impulsivity/hyperactivity, and aggressivity. Higher scores on the construct of BSR indicated higher behavioral dysregulation, with the highest BSR scores meeting DSM-III-R criteria for Attention Deficit Hyperactivity Disorder (ADHD), Oppositional Defiant Disorder (ODD), and Conduct Disorder (CD) (Table 1). The observed measures included the K-SADS-E (Orvaschel et al., 1982), the Dimensions of Temperament Survey (DOTS-R, Windle, 1992), the Disruptive Behavior Disorder Rating Scale (Pelham and Murphy, 1987), and the Child Behavior Checklist, teacher version (Achenbach and Edelbrock, 1983). Specific items, and references for the observed measures have been reported previously (Dawes et al., 1997). The Cronbach’s a for inattention was 0.79, for impulsivity/hyperactivity was 0.66, and for aggressivity was 0.72. In the HAR group, the minimum value was 1.58, maximum was 3.19, with a range of 1.60. For the LAR group, the minimum was 1.43, maximum was 3.03, with a range of 1.59. Confirmatory factor analysis (CFA) was employed to fit a first-order factor, providing a good fit of the data (Dawes et al., 1997).

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Table 3 Lifetime DSM-III-R diagnoses of mothers in the PSUD (SA+) and control (SA−) groupsa DSM-III-R Diagnoses*

Psychoacti6e substance use disorders Amphetamine abuse Amphetamine dependence Alcohol abuse Alcohol dependence Cannabis abuse Cannabis dependence Cocaine abuse Cocaine dependence Opioid abuse Opioid dependence Nicotine dependence Comorbid psychiatric disorders Major depression Adult Anxiety Disorder Antisocial Personality Disorder Attention Deficit Hyperactivity Disorder Oppositional Defiant Disorder Conduct Disorder

SA+ (n = 143)

SA− (n = 144)

P-value

n

(%)

n

(%)

1 7 25 28 14 11 3 15 1 7 25

(0.7) (4.9) (17.5) (19.6) (9.8) (7.7) (2.1) (5.2) (0.7) (4.9) (8.7)

0 2 10 4 2 3 1 0 1 1 8

(0) (1.4) (6.9) (2.8) (1.4) (2.1) (0.7) (0) (0.7) (0.7) (2.8)

NS NS 0.007** B0.001** 0.002** 0.03** NS B0.001** NS 0.04** 0.002**

52 35 3 4 3 14

(36.4) (24.5) (2.1) (2.8) (2.1) (9.8)

38 22 1 0 0 1

(26.4) (15.3) (0.7) (0) (0) (0.7)

0.08 0.06 NS 0.06 NS B0.001

a Adult Anxiety Disorder, Sum of Panic Disorder without Agoraphobia, Panic Disorder with Agoraphobia, Generalized Anxiety Disorder, Agoraphobia without history of Panic Disorder, Social Phobia, Obsessive Compulsive Disorder, Post-Traumatic Stress Disorder, Anxiety Disorder Not Otherwise Specified; * Ten missing subjects. ** Significant Fisher’s Exact Tests, two-tail.

2.3.7. Determination of hormone concentrations in boys Salivary cortisol was obtained just prior to (at 09:00 h) and after (at 10:15 h) auditory evoked potential (ERP) testing, which is a mild anticipatory stressor for most subjects. The ERP task consisted of target (high) and standard (low) tones that were presented at 0.10 probability in an active-response condition, employing an auditory ‘oddball’ paradigm. Subjects mentally counted the number of target tones. Cortisol reactivity was defined as the difference between salivary cortisol concentrations after minus before ERP. Salivary cortisol concentrations were measured using dissociation-enhanced lanthanide fluoroimmunoassay (DELFIA) (Yao et al., 1999). Use of salivary cortisol avoids the potential induction of a stress response due to venipuncture. Salivary cortisol is highly correlated with serum cortisol concentration. It is a reliable and valid indicator of the unbound, biologically active fraction of circulating cortisol (Kirschbaum and Hellhammer, 1994; Kiess et al., 1995). The majority of cortisol in blood is bound to plasma proteins, including albumin and cortisol-binding protein. The biologically active fraction is not bound to plasma proteins. Intra-assay (within-run) coefficient of variation for salivary cortisol was 7.48 and 4.65% CV at 2.94 ng/ml (S.D.=0.22 ng/ml) and 6.23 ng/ml (S.D.= 0.29 ng/ml),

respectively. Inter-assay (between-run)% CV was 7.49 and 4.41 at 4.22 ng/ml (S.D.= 0.09 ng/ml) and 5.49 ng/ml (S.D.= 0.24 ng/ml), respectively. Plasma testosterone and dihydrotestosterone were determined by radioimmunoassay (RIA) using Amersham’s testosterone/dihydrotestosterone RIA kit. The kit employed tritiated dihydrotestosterone and an antibody that was specific to testosterone and dihydrotestosterone. The amount of labeled dihydrotestosterone bound to antiserum was inversely associated with the amount of testosterone and dihydrotestosterone available in the assay sample. Measurement of the antibody-bound radioactivity afforded the opportunity to quantify the amount of testosterone and dihydrotestosterone in the sample. This procedure provided a simple, sensitive, specific, and reproducible method for measurement of testosterone (T) and dihydrotestosterone (DHT). The intra-assay coefficient of variation (% CV) for T was 5.2 at 356 pg/ml and for DHT was 5.0 at 393 pg/ml. The intra-assay precisions for T were 5.2 and 5.0% CV at 356 pg/ml (S.D.=12.3 pg/ml) and 393 pg/ml (S.D.= 13.0 pg/ml), respectively. The inter-assay precisions for T and DHT were 10.8 and 9.4% CV at 347 pg/ml (S.D.= 25.0 pg/ml) and 396 pg/ml (S.D.= 24.9 pg/ml), respectively.

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Table 4 Differences in hormone levels between high average risk (HAR) and low average risk (LAR) boys, controlling for age-corrected pubertal status Hormone Testosterone1 (pg/ml) Dihydrotestosterone1 (pg/ml) Salivary cortisol before ERP2 (ng/dl) Salivary cortisol after ERP2 (ng/dl) Cortisol reactivity2,3

HAR (mean (S.D.)) 1026.39 238.43 262.57 246.27 −9.31

(1335.41) (160.31) (132.20) (108.21) (88.55)

LAR (mean (S.D.)) 1308.02 263.10 317.20 261.93 −52.43

(1757.61) (198.51) (161.30) (111.49) (90.10)

F-value

df

P-value

4.22 4.44 10.51 1.28 13.13

1,253 1,253 1,230 1,216 1,222

0.04 0.04 0.001 NS B0.001

1 Simple factorial ANOVAs (Grouping Variable, Risk Group Status; Covariates, Age-Corrected Pubertal Status, Household Socioeconomic Status, Full-Scale IQ). 2 Simple factorial ANOVA, controlling for Household Socioeconomic Status, and Full-Scale IQ; ERP, evoked related potential. 3 Cortisol reactivity was measured as the difference between after and before ERP cortisol concentrations.

2.4. Statistical analyses All positively skewed variables were logarithmic transformed to approximate normality. The first hypothesis was to test whether concentrations of plasma testosterone, plasma dihydrotestosterone, and salivary cortisol, as well as cortisol reactivity, were lower in high average risk (HAR) boys, compared to low average risk (LAR) boys, due to chronic homeostatic stress from the family and peer environment. Multivariate analysis of variance (MANOVA) was first used to compare whether hormone concentrations and cortisol reactivity between risk groups (HAR vs. LAR groups) were significantly different, controlling for Household Socioeconomic Status (SES) and age-corrected Tanner Stage. Simple factorial analysis of variances (ANOVAs) for individual hormone concentrations and cortisol reactivity between risk groups were also employed. For testosterone and dihydrotestosterone as the dependent variables, the factor was risk group, controlling for age-corrected Tanner stage, full-scale IQ, SES, family dysfunction, and deviant peer affiliation. For cortisol concentrations and cortisol reactivity, the factor was group status, controlling for full scale IQ, SES, family dysfunction and deviant peer affiliation. For the second hypothesis, that cortisol reactivity would be negatively associated with BSR, while parent antisociality, family dysfunction and deviant peer affiliation would be significantly positively associated with BSR, a regression model was developed in which behavioral and hormonal homeostatic factors increased the proportion of variance explained in association with BSR. Blocks of variables were entered in the following order, in order to model increasing levels of biological and homeostatic complexity (e.g. physiology, behavior, cognition, and environment), after first controlling for demographics and risk group status: (1) demographics and risk group; (2) hormones; (3) age-corrected Tanner stage; (4) parental abuse potential and number of parental lifetime Anti-

social Personality Disorder symptoms; (5) family dysfunction, and (6) peer delinquency. Interaction terms for age-corrected Tanner stage and demographics, with the parental characteristics, family dysfunction, and peer delinquency were tested in univariate relations with BSR.

3. Results

3.1. Test of differences in hormone le6els and risk group status MANOVA of hormone concentrations and cortisol reactivity between risk groups was significant (Wilk’s l= 0.93, F(5, 211) =3.28, P= 0.007, h 2 = 0.07), controlling for age-corrected Tanner stage and household SES. Table 4 presents results for simple factorial ANOVAs of differences in hormone levels in HAR and LAR boys. Age-corrected Tanner genital status was significantly positively correlated with testosterone level (T; r= 0.56, PB 0.001) and dihydrotestosterone (DHT; r=0.57, PB0.001). Cortisol prior to ERP (r= −0.15, PB0.05) and cortisol after ERP (r= − 0.15, PB0.05) were significantly negatively associated with family dysfunction. T (r= 0.13, PB 0.05) and DHT (r= 0.16, PB 0.05) were significantly positively associated with deviant peer affiliation. Therefore, agecorrected Tanner genital stage, family dysfunction, and peer delinquency were originally entered as covariates in the ANOVAs for testosterone and dihydrotestosterone. Family dysfunction and peer delinquency were also entered as covariates in the simple factorial ANOVAs for cortisol concentration, and for cortisol reactivity. Because family dysfunction and peer delinquency were not significant in these ANOVAs, the covariates were removed in the final models reported in Table 4. As can be seen, each of the hormone levels was lower for the HAR boys, compared to the LAR boys although salivary cortisol following ERP failed to reach statistical significance.

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Table 5 Regression model of BSR. Order of entry of independent variables Block number

Variables entereda

R2

R 2 change

F-value change

Significance of F-value change

1. 2. 3. 4. 5. 6.

ETHNIC, SA, SES, FSIQ T, DHT, C – DIFF ATANIND ABUSEFA, ABUSEMO, ASPLFFA, ASPLFMO DFI PDS

0.07 0.07 0.07 0.16 0.24 0.32

0.07 B0.01 B0.01 0.09 0.08 0.09

3.64 0.13 0.08 4.77 17.88 22.72

0.007 NS NS 0.001 B0.001 B0.001

a

ETHNIC, Ethnicity (dichotomized as Caucasian vs. Other); SA, (High Average Risk vs. Low Average Risk); SES, Head of Household Socioeconomic Status; FSIQ, Full Scale Intelligence Quotient; DHT, dihydrotestosterone; T, testosterone; C – DIFF, Cortisol Reactivity; ATANIND, Age-corrected Tanner Stage; ASPLFFA, fathers’ number of lifetime DSM-III-R Antisocial Personality Disorder Symptoms; ASPLFMO, mothers’ number of lifetime DSM-III-R Antisocial Personality Disorder Symptoms; ABUSEFA, father’s physical abuse potential; ABUSEMO, mother’s physical abuse potential; DFI, Dysfunctional Family Index; PDS, Peer Delinquency Scale.

3.2. Regression model of hormonal and beha6ioral homeostasis Ordinary least squares regression was employed to model measures of hormonal and behavioral homeostasis predicting BSR (Table 5). Demographics and risk group status were entered in the first block of variables, with R 2 of 0.7 and significant F change (P =0.007). The next two blocks of variables (hormones and age-corrected Tanner stage, respectively) did not produce significant changes in the R 2. In the fourth block, entry of parental abuse potential and parental Antisocial Personality Disorder symptoms, produced a 0.09 change in R 2 (F change=4.77, P =0.001). In the fifth block, entry of family dysfunction, R 2 change increased 0.08 (F change= 17.88, P B 0.001). In the final block, peer delinquency was entered, with R 2 change of 0.09 (F change= 22.72, P B 0.001). Only the interaction term for the univariate relation between age-corrected Tanner stage and peer delinquency was significant (r = 0.21, P=0.01). However, the interaction term between agecorrected Tanner stage and BSR was not significant in the final model and was removed. The final model overall R 2 was 0.32. All variation inflation factors were less than 10, which is the generally accepted maximum for this statistic (Neter et al., 1990). As can be seen in the final model in Table 6, family dysfunction and peer delinquency were highly significant in association with BSR. Fathers’ abuse potential was also highly significant, with a negative association with BSR. Mothers’ number of lifetime Antisocial Personality Disorder symptom were significantly associated with BSR, while mother’s abuse potential had a trend towards significance.

4. Discussion Our first hypothesis, that baseline assessment concentrations of testosterone, dihydrotestosterone, cortisol, and cortisol reactivity would be significantly lower in

HAR boys, compared to LAR boys, was supported. Our second hypothesis, that cortisol reactivity, parent personality characteristics, family dysfunction, and peer delinquency would be significantly associated with BSR was only partially supported. These findings nevertheless extend results reported by Vanyukov et al. (1993) and Moss et al. (1995) of a significant negative correlation between salivary cortisol level and number of conduct disorder symptoms in HAR boys, by also examining antisocial characteristics in both parents, while controlling for family dysfunction and peer delinquency. Moreover, the findings reported herein revealed that parental Antisocial Personality Disorder symptoms and parental abuse potential were strongly associated with both severity of family dysfunction and deviant peer affiliation. To our knowledge, this is the first report of a cross-sectional regression model employing BSR as the dependent variable, with independent variables of cortisol reactivity, and sex hormones, while controlling for age-corrected Tanner stage, parental abuse potential, parental Antisocial Personality Disorder symptoms, family dysfunction, and peer delinquency, in samples of sons of substance abusing fathers and controls. The findings reported herein are consistent with a multifactorial model of liability to PSUD (Tarter et al., 1998). This study extends previous studies on androgens, cortisol reactivity, pubertal development, and externalizing behavior in preadolescent boys, by using a sample size which afforded the opportunity to examine multiple measures of hormonal and behavioral homeostasis. Most previous studies of steroid hormones and problem behavior in children and adolescents have had smaller sample sizes which allowed only for conducting univariate correlations or group comparisons (Buchanan et al., 1992; Rubinow and Schmidt, 1996). The extant literature suggests that multiple homeostatic mechanisms may account for our results (Brann et al., 1995; Rubinow and Schmidt, 1996). Several biobehavioral factors can modulate the relations among androgen and cortisol concentrations, cortisol reactiv-

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173

Table 6 Final regression model of BSR Independent variablesa

b Coefficient

SE

Standardized b coefficient

t-value

P-value

VIFb

FSIQ SA SES ETHNIC DHT T C – DIFF ATANIND ASPLFFA ASPLFMO ABUSEFA ABUSEMO DFI PDS

B0.01 −0.05 B0.01 0.05 B0.01 0.01 B−0.01 −0.08 B0.01 0.02 −0.08 0.08 B0.01 0.12

0.01 0.05 B0.01 0.06 0.06 0.04 B0.01 0.09 B0.01 B0.01 0.04 0.04 B0.01 0.03

0.03 −0.08 0.09 0.07 −0.01 0.05 B−0.01 −0.08 0.04 0.17 −0.22 0.13 0.26 0.33

0.43 −0.96 1.19 0.84 −0.13 −0.01 −0.04 −0.93 0.52 2.32 −2.83 1.82 3.72 4.77

NS NS NS NS NS NS NS NS NS 0.02 0.005 0.07 B0.001 B0.001

1.71 2.03 1.39 1.70 3.36 3.36 1.17 1.77 1.94 1.40 1.70 1.41 1.27 1.26

a

ETHNIC, Ethnicity (dichotomized as Caucasian vs. Other); SA, (High Average Risk vs. Low Average Risk); SES, Head of Household Socioeconomic Status; FSIQ, Full Scale Intelligence Quotient; DHT, dihydrotestosterone; T, testosterone; C – DIFF, Cortisol Reactivity; ATANIND, Age-corrected Tanner Stage; ASPLFFA, fathers’ number of lifetime DSM-III-R Antisocial Personality Disorder Symptoms; ASPLFMO, mothers’ number of lifetime DSM-III-R Antisocial Personality Disorder Symptoms; ABUSEFA, father’s Physical Abuse Potential; ABUSEMO, mother’s Physical Abuse Potential; DFI, Dysfunctional Family Index; PDS, Peer Delinquency Scale. b VIF, Variance Inflation Factor.

ity, and BSR. First, differential sensitivity of the hypopituitary–adrenal axis and hypopituitary – gonadal axis to physical and psychological stressors can result in different hormone profiles. Manifest behaviors may represent the sum of individual steroid effects (Rubinow and Schmidt, 1996), or sums of the effects of individual steroids and ratios (Susman et al., 1987) of steroids. Second, factors such as age (Simon et al., 1992) and circadian rhythm (Winters, 1991) can influence androgen and cortisol levels. Third, previous experience (Rubinow and Schmidt, 1996), environmental stressors (Francis, 1981), social interactions (Mazur, 1985), and social rank (Schaal et al., 1996) are thought to influence androgen (Rubinow and Schmidt, 1996) and cortisol (Buchanan et al., 1992; Susman et al., 1997) levels and their concurrent effects on behavior. Additional hormone concentrations, such as adrenal androgens (Dorn and Chrousos, 1997), may influence androgen–behavior relationships directly or indirectly (Buchanan et al., 1992). Moreover, Dabbs et al. (1991) found that cortisol moderates the behavioral effects of testosterone. Moderation is consistent with the finding of Cumming et al. (1983) that cortisol can lower testosterone level. The specific neuroregulatory mechanisms linking the two hormone systems with BSR remain to be elucidated. Affect (Graber and Brooks-Gunn, 1995) and behavior (Paikoff and Brooks-Gunn, 1991) can influence hormone concentrations directly (e.g. from provoked aggressive behavior) or indirectly (e.g. through interpersonal states such as negative affect). Moreover, chronic family stressors can result from some forms of childhood psychiatric disorder (Lombroso et al., 1994) inter-

acting (Barkley et al., 1992; Anderson et al., 1994) or correlating (Scarr, 1992) with a harsh family environment. These family chronic stressors can lead to suppression of androgen and cortisol concentrations (Johnson et al., 1992). Two comments on the findings are indicated. First, the hypothesized relationship of a significant negative correlation between cortisol reactivity and BSR was not supported. It should be noted, however, that cortisol level prior to evoked potential was significantly negatively associated with BSR (r= −0.14, P B 0.05). The negative finding in the final regression model may be due in part to the fact that the BSR construct is for lifetime traits of inattention, impulsivity/hyperactivity, and aggressivity. A behavioral construct which measures behavioral distress is more likely to covary with cortisol reactivity (Susman et al., 1997). Moreover, lower preadolescent anticipatory cortisol responses have been shown to be significantly associated with regular cigarette use and regular marijuana use (Moss et al., in press). In summary, anticipatory cortisol responses and cortisol reactivity appear to have greater utility in explaining covariation with distress behavior and other health risk behaviors, and possibly as a predictor of intergenerational transmission of substance abuse liability. Second, the finding that BSR was significantly negatively associated with fathers’ abuse potential, but had a trend toward significant positive association with mothers’ abuse potential was unexpected. Post-hoc analyses revealed that 24.2% of HAR boys had lived a significant time with their mother only, whereas only 7.5% of LAR boys had lived with their mother only

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(x 2 =15.41, PB 0.001). Moreover, logistic regression analysis predicting living with the mother only for a significant period in the son’s lifetime revealed significant covariates of mother’s cocaine dependence (Odds Ratio =1.20, confidence interval (1.04, 2.19)) and father’s opiate dependence (Odds Ratio= 1.20, confidence interval (1.06, 1.83)). Although speculative, it may be the case that son’s who have lived with both parents their entire lives had the advantage of fathers who had effective parenting and discipline skills but rated themselves as somewhat rigid and distressed. Several caveats also need to be considered. First, this study was limited to sons of substance abusing fathers and sons of nonsubstance abusing fathers. Recruitment of daughters of PSUD fathers was initiated in 1994, and were not available in sufficient numbers to provide the statistical power needed to fulfill the aims of this study at this time. Second, a small literature on interview techniques for children younger than age 12 suggests that their responses may be unreliable (Achenbach et al., 1987). The assessment procedures described herein were designed to maximize the accuracy of the boys’ self-reports, by using trained interviewers and the ‘best estimate’ procedure (Leckman et al., 1982). Third, the authors acknowledge that serotonin dysfunction is present in some impulsive, aggressive individuals (Bernhardt, 1997; Coccaro et al., 1997a,b,c; Halperin et al., 1997; Pine et al., 1997; Brady et al., 1998), and that catecholamine dysfunction is present in some individuals with disorders of inattention and impulsivity/hyperactivity (Pliszka et al., 1996; Pliszka and McCracken, 1997; Tannock, 1998). However, peripheral measures of these neurotransmitter systems were not available for the analyses presented herein. Lastly, as noted throughout this manuscript, the results of this report are based on cross-sectional data. Hence, causal inferences at this point cannot be made regarding the mechanisms underlying the associations between hormone levels, family dysfunction, peer affiliation, and BSR in high risk boys based on the data reported herein. However, future reports of longitudinal follow-up of these same boys will allow causal inferences to be made. These limitations notwithstanding, the findings herein extend previous research by showing that boys’ perceptions of family dysfunction and peers delinquency, along with parental reports of their abuse potential and Antisocial Personality Disorder symptoms, are strongly associated with sons’ BSR. Group differences with lower androgen concentrations, and lower cortisol level and cortisol reactivity in HAR compared to LAR sons are consistent with the hypothesis that these sons are adapted to chronic stress imposed from a very dysfunctional family system. Interventions to prevent worsening of BSR should therefore address family dysfunction, deviant peer affiliation, as well as severity of BSR impairment. However, longitudinal research is

needed to elucidate the magnitude and timing of how parental personality characteristics influence developmental trajectories, to culminate in either good or adverse outcomes.

Acknowledgements This work was supported in part by a center grant from the National Institute on Drug Abuse (DA 05606), and a Mentored Clinical Scientist Development Award (1K08 DA-299) from the National Institute on Drug Abuse. We thank the families who participated in this research. Special appreciation is expressed to Dr Galina P. Kirillova for performing the hormone assays and Brion Maher for his technical assistance.

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