Risk factors for cocaine-induced paranoia in cocaine-dependent sibling pairs

Drug and Alcohol Dependence 84 (2006) 77–84

Risk factors for cocaine-induced paranoia in cocaine-dependent sibling pairs Rasmon Kalayasiri a , Henry R. Kranzler b , Roger Weiss c , Kathleen Brady d , Ralitza Gueorguieva a,e , Carolien Panhuysen f , Bao-Zhu Yang a , Lindsay Farrer f,g , Joel Gelernter a , Robert T. Malison a,∗ a Department of Psychiatry, Yale School of Medicine, New Haven, CT 06520, USA Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT 06030, USA c Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA d Department of Psychiatry, Medical University of South Carolina, Charleston, SC 29425, USA e School of Epidemiology & Public Health, Yale University, New Haven, CT 06520, USA f Department of Medicine (Genetics Program), Boston University School of Medicine and Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA g Departments of Neurology and Genetics & Genomics, Boston University School of Medicine and Department of Epidemiology, Boston University School of Public Health, Boston, MA 02218, USA b

Received 15 June 2005; received in revised form 7 December 2005; accepted 7 December 2005

Abstract Objective: Cocaine-induced paranoia (CIP), an irrational intense suspicion of others, is a common manifestation of cocaine dependence. Both environmental and genetic factors are thought to play a role, but the specific nature of such contributions is poorly understood. Methods: Demographic, diagnostic, and cocaine-use data were obtained from 420 cocaine-dependent, genetically confirmed, full-sibling pairs (N = 840 subjects) interviewed with the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA). Probands with and without CIP were compared; then, factors associated with sibling CIP status were analyzed by logistic regression. Alcohol dependence, a known heritable phenotype, was analyzed as a positive control. Results: Of 420 probands, 273 (65%) experienced CIP. Probands with CIP were more severely dependent upon cocaine, had an earlier age of onset, were more likely to smoke cocaine, and used cocaine less frequently during the preceding year. Independent analyses of siblings replicated two of the former (i.e., dependence severity and age of onset). Probands with CIP also had a non-significantly higher proportion of siblings with the trait (66% versus 59%). Probands with concurrent alcohol dependence were confirmed to have significantly higher rates of alcoholism among their siblings. Conclusions: Severity of cocaine dependence and age of onset appear to be important risk factors for CIP. Concordance for CIP between siblings did not emerge as significant in our analyses. © 2006 Elsevier Ireland Ltd. All rights reserved. Keywords: Cocaine; Paranoia; Risk factor; Genetics

1. Introduction Cocaine is a potent, psychoactive drug with specific neuropharmacological and psychological effects (Gawin, 1991). In 2003, an estimated 2.3 million people (1.0% of the US pop∗ Corresponding author at: Clinical Neuroscience Research Unit, Yale University School of Medicine, 34 Park Street, New Haven, CT 06519, USA. Tel.: +1 203 974 7557; fax: +1 203 974 7662. E-mail address: [email protected] (R.T. Malison).

0376-8716/$ – see front matter © 2006 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.drugalcdep.2005.12.002

ulation aged 12 years or older) were current cocaine users, according to the National Household Survey on Drug Use and Health reported by Substance Abuse and Mental Health Services Association (SAMHSA). In addition to its potential to produce addiction and to result in medical complications (e.g., strokes, seizures, and arrhythmias), a host of psychological disturbances are observed among heavy cocaine users, including depression, anxiety, agitation, and psychosis. Psychosis is among the more severe psychiatric complications of cocaine use. First described by Mantegazza in 1859

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(Tueth, 1993), cocaine psychosis, a syndrome consisting of a range of psychotic symptoms (e.g., delusions, hallucinations, disorganized behaviors; Brady et al., 1991; Cubells et al., 2005), has been well documented as a behavioral manifestation of cocaine abuse, especially since the advent of smoked (i.e., “crack”) cocaine use in the 1980s (Manschreck et al., 1988). Cocaine-induced paranoia (CIP), a subjective intense feeling of distrust of others or fear of being harmed, despite knowing that it is irrational (Satel et al., 1991), is one of the most common psychosis-spectrum symptoms associated with cocaine intoxication (Brady et al., 1991; Cubells et al., 2005). Studies indicate that more than half of patients dependent upon cocaine experience CIP (Bartlett et al., 1997; Brady et al., 1991; Rosse et al., 1994a; Satel et al., 1991). The association of high-risk behaviors with CIP (e.g., self harm, arming oneself for protection against imagined enemies, homicide, etc.) makes it all the more serious (Lowenstein et al., 1987). Although CIP is most commonly time-limited and reversible – a manifestation of acute intoxication – some examples exist suggesting that some individuals may be vulnerable to protracted symptoms or autonomous (i.e., nondrug related) fully syndromal psychosis (Bowers et al., 1995; Satel and Edell, 1991). Despite the morbidity and mortality associated with CIP, risk factors for the trait are still largely unknown. Nearly 3 decades ago, Post (1975) put forth a continuum model of “cocaine psychosis,” in which he posited the importance of chronicity and dose of cocaine in combination with genetic and experiential predispositions. Surprisingly, few studies since then have allowed for the empirical testing of these ideas. Brady et al. (1991) (N = 55) reported that lifetime cocaine consumption (i.e., greater quantities), route of administration (i.e., intravenous), and sex (i.e., being male) were significantly associated with CIP, with a trend for an effect of race (i.e., being European-American (EA)). In addition, lifetime number of episodes of cocaine use (Cubells et al., 2005; N = 243) and cocaine smoking (Honer et al., 1987; N = 80) were reported to associate with CIP. However, other studies (Bartlett et al., 1997; Rosse et al., 1994b; Satel et al., 1991; N = 57, 62, and 50, respectively) failed to replicate these findings. A host of study-related differences, such as differences in patient populations and assessment methods, may account for the disparities. In addition, the literature is limited by few studies and small sample sizes. To our knowledge, no studies have examined whether the risk for CIP or cocaine psychosis is familial. If it is, such a finding would suggest the potential importance of heritable factors in modulating vulnerability to the trait. Prior studies of non-drug related psychosis (i.e., schizophrenia) have shown this trait to be familial (Kendler et al., 1985a,b). Moreover, significantly greater concordance rates in monozygotic (MZ) as compared to dizygotic (DZ) twin pairs (i.e., 50% versus 15%) (McGue, 1992; McGuffin et al., 1984) and evidence from adoption studies (Kety et al., 1971; Wender et al., 1974) have confirmed a genetic component to familial risk for schizophrenia. Such findings have guided efforts aimed at identifying psychosis vulnerability loci by genetic linkage or association techniques (Maier et al., 2003; O’Donovan et al., 2003). Recent studies in drugdependent populations have also implicated a familial risk for

substance dependence (Bierut et al., 1998), with genetic heritability confirmed by twin approaches (Kendler and Prescott, 1998; Tsuang et al., 1996). Thus, familial and genetic factors have been demonstrated to contribute independently to the risk of both non-drug related psychosis and substance dependence. Whether familial (or genetic) factors are important in the vulnerability to CIP or cocaine psychosis remains to be established. We report here on the first study of risk factors associated with CIP in the context of family data using an exploratory approach. We studied a total of 420 cocaine-dependent sibling pairs (N = 840 individuals) with sibling relationship confirmed by genetic markers, recruited in the course of a genetic linkage study of cocaine dependence (Gelernter et al., 2005), in order to assess the potential role of familial and non-familial factors in the risk for CIP. In parallel, we performed analyses for alcohol dependence as a positive control (i.e., to assess the adequacy of our sample size and methods for establishing familial effects in a well establish heritable trait) (Kendler et al., 1992, 1997; Sigvardsson et al., 1996). 2. Methods 2.1. Subjects and inclusion/exclusion criteria Family data were collected as part of a multi-site, collaborative study on the genetics of cocaine dependence conducted by researchers at Yale University School of Medicine (J.G.), University of Connecticut School of Medicine (H.R.K.), Medical University of South Carolina (K.B.), McLean Hospital (Belmont, MA; R.W.), and Boston University (L.F.). Subjects were recruited by local advertisement or referral. Only sibling pairs that met DSM-IV criteria for cocaine dependence were extracted from our full dataset and included in the present study. Voluntary written informed consent was obtained from all subjects prior to their research participation. We checked for inconsistencies in family relationships using Pedigree Relationship Statistical Test (PREST) (McPeek and Sun, 2000) based on genotype data from a ∼400-marker genomewide short tandem repeat marker linkage scan (Gelernter et al., 2005). This program detects genetic relationships by determining whether the pattern of estimated allele-sharing in the marker data (from identity-by-state observations) fits the expected pattern of allele-sharing based on proposed relationships. Only genetically confirmed full-sibling pairs were included in the study (i.e., half-sib pairs and unrelated subjects were excluded from consideration). In families where more than two, genetically confirmed, cocaine-dependent, full siblings were identified, one was selected at random for study inclusion. Probands were defined as the first member of the sibling pair to participate. Individuals with a primary psychotic disorder (e.g., schizophrenia) and bipolar disorder were excluded. 2.2. Assessments All subjects were administered the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA) (Pierucci-Lagha et al., 2005) by trained interviewers in order to

R. Kalayasiri et al. / Drug and Alcohol Dependence 84 (2006) 77–84

establish a variety of substance use and common co-occurring disorders based on DSM-IV criteria and to obtain information on variables of clinical relevance. It has shown a high degree of inter-rater and test–retest reliability with respect to the diagnosis of both cocaine dependence (κ = 0.83 and 0.92, respectively; Pierucci-Lagha et al., 2005) and CIP (overall reliability showed κ = 0.87; Gelernter et al., 2005). A rigorous quality-control program was implemented across sites to provide for uniformity of procedures and reliability of data collection. Specifically, data were first self-edited and then cross-edited by secondary interviewers within each of the study sites. Per the SSADDA, CIP was operationally defined as positive responses to both of the following questions: “Have you ever had a paranoid experience?” and “Have you ever had a paranoid experience when you were using cocaine?”; questions derived from the Cocaine Experience Questionnaire (Satel et al., 1991). These questions were posed only after an operational definition (and example) of ‘paranoia’ was provided to participants to help distinguish adaptive hypervigilance or anxiety in high-risk situations from the characteristically irrational nature of paranoia (e.g., the idea that a noise at a fourth floor window means someone is there, a shadow behind a door means someone is crouching there, or a trusted friend is planning to steal their drugs). The SSADDA provided extensive additional phenotypic information, including demographic (e.g., age, sex, marital status, race, years of education, and household gross income), diagnostic (e.g., major depressive disorder and marijuana, alcohol, opiate, stimulant, and sedative-hypnotic dependence), and cocaine-use related variables (e.g., age of first use, age of onset of cocaine dependence, dependence duration, daily amount and days per month of use during periods of heaviest use, times in one’s life using cocaine, times in last 12 months using cocaine, route of cocaine administration, severity of cocaine dependence, defined by DSM-IV symptom count, and abstinence ability, a binary variable, defined by the presence of one or more continuous cocaine-free intervals of at least 3 months duration). Population group (race) was ascertained genetically using an ancestry-informative marker panel (Yang et al., 2005) and, in cases of discordant reports of race between full siblings, categorized as “mostly African-American” (AA) or “mostly EuropeanAmerican” based on parameters derived by using the Structure program (Pritchard et al., 2000). Cocaine dependence duration was derived from two SSADDA variables (i.e., age of first and last dependence). We used reports of dollars spent on cocaine per day and grams used per day as estimates of drug consumption during periods of heaviest use. In instances where self-report measures elicited a significant number of “do not know” responses (e.g., daily amount in grams and dollars of heaviest cocaine use, number of lifetime cocaine-use episodes, and number of cocaine-use episodes in the last 12 months), data were expressed as categorized variable for purpose of analyses.

79

two-tailed χ2 (for categorical variables) or t-test (for continuous variables) statistics. Non-normally distributed data were either log transformed (e.g., age of first use and age of onset of cocaine dependence) or, when transformation was unsuitable, expressed as categorical variables (i.e., daily amounts and severity of cocaine dependence/DSM-IV symptom count). Factors identified by χ2 or t-test in our sample of probands, as well as previously reported factors (Brady et al., 1991), were then subjected to combined analysis by forward logistic regression methods to estimate the adjusted effects of these variables on CIP status of probands. Pearson’s product-moment correlation, analysis of variance (ANOVA), or χ2 tests were used to identify a priori variables that were highly correlated (p < 0.005, based on Bonferroni correction). Variables were entered individually into the logistic regression model, avoiding the simultaneous inclusion of significantly intercorrelated variables and retaining the statistically most robust variable at each step. Factors identified as significant (p < 0.05) by forward logistic regression were subsequently confirmed by backward logistic regression (i.e., by including all variables in the model initially and iteratively excluding the statistically weakest). To assess whether the presence of CIP in a proband influenced risk for the trait in the sibling, a similar logistic regression approach was applied to the siblings. The first factor entered into the analysis was proband CIP status. The second variable was a proband “propensity score” reflecting the tendency of probands to experience CIP derived from those variables that were significantly associated with the risk for CIP in probands, to control for potential differences deriving from the ‘non-random’ nature of the respective samples (i.e., due to potential systematic differences in non-family-related risk factors) (D’Agostino, 1998). After inclusion of proband CIP status and the proband propensity score into the model, additional demographic, diagnostic, and cocaine-use variables were analyzed by logistic regression as outlined above (including assessments for correlated variables and confirmation of identified factors by backward logistic regression). The influence of CIP status in the siblings to the presence of CIP in the probands was also assessed by identical methods described above. In addition, several other methods for assessing familial risk of CIP were employed, including simple analyses of agreement of CIP among probands and siblings, and generalized estimating equations (GEE) (i.e., to assess the association of CIP status within families using potential risk factors as covariates). The familial risk for alcohol dependence was also assessed by methods identical to those described for CIP. All analyses were performed using SPSS for Macintosh (v. 11) and SAS (v. 9.1.3). 3. Results 3.1. Demographic, diagnostic, and cocaine use variables

2.3. Data analysis Our data analysis focused initially on risks factors among probands. Probands with or without CIP were compared on demographic, diagnostic, and cocaine-use variables using either

Of 420 probands, 273 (65%) met criteria for paranoia related to cocaine use. Age and years of education were not significantly different between probands with CIP (38.5 ± 7.4 and 11.4 ± 2.0 years, respectively) and those without the trait (39.0 ± 7.5

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and 11.6 ± 2.1 years) (t = −0.6, d.f. = 418, p = 0.53; t = −1.3, d.f. = 417, p = 0.18). EAs had higher rates of CIP than AAs (χ2 = 4.1, d.f. = 1, p = 0.04), however, there were no statistically significant differences (i.e., p > 0.05) between groups for any other demographic or diagnostic variable, including sex, marital status, household gross income, co-morbid major depressive disorder, and other substance dependence diagnoses (Table 1). Probands with CIP were more severely dependent upon cocaine (i.e., met more DSM-IV criteria) and spent significantly more for cocaine (US$/day) during their periods of heaviest use than those without the trait (χ2 = 12.8, d.f. = 1, p < 0.001 and χ2 = 21.4, d.f. = 3, p < 0.001; Table 2). The latter measure (US$/day) was also positively and significantly correlated with grams of cocaine used (χ2 = 90.8, d.f. = 9, p < 0.001). Conversely, the presence of CIP was associated with significantly less cocaine consumption during the most recent 12-month period (χ2 = 7.9, d.f. = 2, p = 0.02). Probands with CIP had a younger age of first cocaine use (20.2 ± 5.6 years versus 22.1 ± 6.7 years; t = −3.0, d.f. = 418, p = 0.003) and a non-significant trend toward an earlier onset of cocaine dependence (26.2 ± 6.9 years versus 27.6 ± 7.8 years; t = −1.8, d.f. = 418, p = 0.08). In addition, probands with CIP more often reported a history of cocaine smoking and intranasal use than those without CIP (χ2 = 5.7, d.f. = 1, p = 0.02 and χ2 = 10.4, d.f. = 1, p = 0.001, respectively).

Table 1 Demographic and diagnostic characteristics of cocaine-dependent probands with and without cocaine-induced paranoia (CIP) Paranoid (N = 273)

Non-paranoid (N = 147)

n

%

n

Sex Male Female

140 133

51.3 48.7

61 86

41.5 58.5

0.06

Race White Black

147 126

53.8 46.2

64 83

43.5 56.5

0.04a

Marital status Married Widowed, separated, divorced Never married

35 74 164

12.8 27.1 60.1

21 43 83

14.3 29.3 56.5

0.77

Household gross income (US$/year)b 0–9999 120 10,000–19,999 62 20,000–29,999 33 ≥30,000 53

44.8 23.1 12.3 19.8

76 27 21 21

52.4 18.6 14.5 14.5

0.28

Psychiatric and substance related disorders Major depressive disorder 15 5.5 Tobacco dependence 182 66.7 Alcohol dependence 142 52.0 Opiate dependence 133 48.7 Marijuana dependence 78 28.6 Sedative dependence 24 8.8 Stimulant dependencec 18 6.6 Other substance dependence 51 18.7

6 100 65 75 36 9 10 27

4.1 68.0 44.2 51.0 24.5 6.1 6.8 18.4

0.53 0.78 0.13 0.65 0.37 0.33 0.94 0.94

a b c

p-Value

%

Table 2 Cocaine-use characteristics of cocaine-dependent probands with and without cocaine-induced paranoia (CIP)

Times used in lifetime Do not know >2000 1–2000

Non-paranoid (N = 147)

n

%

n

32 146 95

11.7 53.5 34.8

Times used in last 12 months Do not know 15 >200 48 0–200 210

p-Value

%

13 82 52

8.8 55.8 35.4

0.66

5.5 4 17.6 42 76.9 101

2.7 28.6 68.7

0.02a

Days per month using cocaine during period of heaviest use 26–30 176 64.7 88 59.9 20–25 51 18.8 29 19.7 1–19 45 16.5 30 20.4

0.55

Daily money spent for cocaine (US$) during period of heaviest use Do not know 26 9.5 10 6.8 <0.001a ≥100 178 65.2 84 57.1 50–99 51 18.7 21 14.3 1–49 18 6.6 32 21.8 Daily amount in grams during period of heaviest use Do not know 101 37.0 64 43.5 ≥2.50 88 32.2 49 33.3 1.01–2.49 37 13.6 11 7.5 0.01–1 47 17.2 23 15.6 Dependence duration (years) >6 164 2–6 78 0–1 31

60.1 28.6 11.4

80 48 19

0.23

54.4 32.7 12.9

0.53

Dependence severity (DSM-IV symptom count) 6–7 212 77.7 90 3–5 61 22.3 57

61.2 38.8

<0.001a

Cocaine smoking 241 Cocaine nasal use 228 Cocaine injection 89 Cocaine abstinence ability 244

79.6 70.1 34.0 84.4

0.02a 0.001a 0.77 0.14

a

Two-tailed χ2 test. N = 413; excluding 7 out of 420 probands who responsed “do not know”. Other than cocaine dependence.

Paranoid (N = 273)

88.3 117 83.5 103 32.6 50 89.4 124

Two-tailed χ2 test.

Among probands, no other cocaine-use variable significantly discriminated between CIP groups. Between probands and siblings, probands used more cocaine during the most recent 12month period (χ2 = 8.5, d.f. = 2, p = 0.01) and were more frequently dependent upon opiates (χ2 = 9.3, d.f. = 1, p = 0.002), however, the two cohorts were not significantly different with respect to other demographic, diagnostic, or cocaine-use variables (p > 0.05). When analyzed by logistic regression methods, severity of cocaine dependence (p = 0.004, odds ratio (OR) = 2.0), age of first cocaine use (p = 0.02, OR = 0.1), cocaine smoking (p = 0.01, OR = 2.2) and number of episodes of cocaine use in the preceding 12 months (p = 0.02, OR = 0.5) remained as significant risk factors for CIP, while sex was nearly significant (p = 0.08, OR = 1.5; Table 3); statistical significance for these variables was then confirmed by backward logistic regression (p = 0.002, OR = 2.0; p = 0.01, OR = 0.1; p = 0.01, OR = 2.1;

R. Kalayasiri et al. / Drug and Alcohol Dependence 84 (2006) 77–84 Table 3 Risk factors for cocaine-induced paranoia (CIP) in cocaine-dependent probands as identified by logistic regression analysis ORs

p-Value

1.3

3.3

Log age of first cocaine use Cocaine smoking

0.0 1.2

0.6 3.8

0.02 0.01

CIP in the proband Proband propensity score

Cocaine-use episodes in the last 12 months 0–200 >200 0.5 0.02b

0.3

0.9

Sex Female Male

0.9

2.2

1.5

0.08c

ORs

p-Value

Upper

Dependence severity (DSM-IV symptom count) 3–5 6–7 2.0 0.004a 0.1 2.2

Table 4 Influence of proband status and other risk factors on cocaine-induced paranoia (CIP) in cocaine-dependent siblings as established by logistic regression analysis

95% Confidence interval Lower

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Results of forward logistic regression; in instances where two or more variables were highly correlated (p < 0.005; Pearson’s product-moment correlation, analysis of variance (ANOVA), or χ2 tests), the statistically most robust variable was retained and is shown in the table. a Compared to less severe (three to five criteria met) cocaine dependence. b Compared to fewer (0–200) episodes of cocaine use in the last 12 months. c Compared to risk for CIP in females.

p = 0.02, OR = 0.5, respectively). Thus, these five variables were used to derive a propensity score summarizing proband risk for CIP. Race, age of onset of cocaine dependence, intranasal use, and money spent for cocaine were excluded from logistic regression analyses due to their strong association with the statistically more robust factors (e.g., age of first use (race: F = 18.3, d.f. = 1, p < 0.001; age of onset of cocaine dependence: Pearson’s r = 0.7, p < 0.001; snorting: F = 27.9, d.f. = 1, p < 0.001) or severity of cocaine dependence (money spent for cocaine: χ2 = 31.2, d.f. = 3, p < 0.001)). 3.2. Familiality of CIP With respect to familiality, 66.3% of probands with CIP (181 out of 273) had siblings with CIP, while only 58.5% of nonparanoid probands (86 out of 147) had CIP-positive siblings. This difference was not statistically significant either by Chi square analysis (χ2 = 2.5, d.f. = 1, p = 0.11) or logistic regression analysis for CIP status in siblings (p = 0.17, OR = 1.4; Table 4) or probands (p = 0.17, OR = 1.4). However, associations between CIP and severity of cocaine dependence (i.e., DSM-IV symptom count) and age of first cocaine use were replicated in siblings, both by forward (p < 0.001, OR = 2.8; p = 0.006, OR = 0.1, respectively) and backward (p < 0.001, OR = 2.8; p = 0.006, OR = 0.1, respectively) logistic regression. Interestingly, money spent for cocaine during periods of heaviest use and age of first cocaine use were significantly associated between probands and siblings (χ2 = 12.0, d.f. = 4, p = 0.02; Pearson’s r = 0.2, p < 0.001). The GEE model did not show significant associations between CIP status of probands and siblings (log odds ratio estimate = 0.3, S.E. = 0.2, p = 0.16) when controlling for variables that were found to be significant predictors of

1.4 0.7

0.17 0.63

95% Confidence interval Lower

Upper

0.9 0.1

2.2 3.2

Dependence severity (DSM-IV symptom count) 3–5 6–7 2.8 <0.001a

1.8

4.3

Log age of first cocaine use

0.0

0.5

0.1

0.006

CIP status of the proband and proband propensity score were entered first and fixed in the forward logistic regression model. In instances where two or more variables were highly correlated (p < 0.005; Pearson’s product-moment correlation, analysis of variance (ANOVA), or χ2 tests), the statistically most robust variable was retained and is depicted in the table. a Compared to less severe (three to five criteria met) cocaine dependence.

Table 5 Influence of proband alcohol dependence and other risk factors on alcohol dependence in cocaine-dependent siblings ORs

Proband alcohol dependence Proband propensity score Sedative dependence Tobacco dependence

2.2 1.4 3.4 1.9

p-Value

<0.001 0.64 0.004 0.007

95% Confidence interval Lower

Upper

1.5 0.4 1.5 1.2

3.4 4.9 7.8 2.9

Proband alcohol dependence and proband propensity score were entered first and fixed in the forward logistic regression model. In instances where two or more variables were highly correlated (p < 0.005; Pearson’s product-moment correlation, analysis of variance (ANOVA), or χ2 tests), the statistically most robust variable was retained and is depicted in the table.

CIP (e.g., severity of cocaine dependence, age of first cocaine use, cocaine smoking, and episodes of recent cocaine use). Nor were simple analyses of agreement for sibling/proband concordance for CIP significant (κ = 0.08, 95% CI = −0.02 to 0.17). 3.3. Familiality of alcohol dependence For alcohol dependence, 49.3% (207 out of 420) of cocaine-dependent probands were diagnosed with this trait. Of these 207 affected individuals, 127 (61.4%) had siblings with alcohol dependence, while only 39% (83 out of 213) of non-alcohol dependent probands had siblings with the trait (χ2 = 21.0, d.f. = 1, p < 0.001). This effect remained significant after formal logistic regression analysis (p < 0.001, OR = 2.2; Table 5). 4. Discussion Consistent with prior reports (Bartlett et al., 1997; Brady et al., 1991; Rosse et al., 1994a; Satel et al., 1991), our cocainedependent cohort demonstrated high rates (65%) of CIP. In probands, greater severity of cocaine dependence (i.e., as measured by DSM-IV symptom count), earlier onset of cocaine

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consumption (i.e., age of first use), route of administration (i.e., cocaine smoking), and recent reductions in cocaine use (i.e., as measured by frequency of use during the preceding 12 months) emerged as significant factors associated with vulnerability to CIP. The first two variables were independently replicated as significant predictors of CIP status in siblings. In contrast, demographic characteristics, concurrent substance dependence diagnoses, and sibling status did not significantly influence the risk for CIP in our sample. Several strengths of our study deserve mention. First, this is the largest study to examine risk for CIP to date. Analyses were conducted in a total of 840 cocaine-dependent individuals, a sample that is an order of magnitude larger than those of prior published studies (Bartlett et al., 1997; Brady et al., 1991; Honer et al., 1987; Rosse et al., 1994b; Satel et al., 1991). In addition, our cohort was extensively characterized and rigorously diagnosed using the SSADDA. Finally, ours is the first study of CIP to use a family-based design, one in which a host of demographic, co-morbid diagnostic, and cocaine-use factors were also carefully considered and controlled for. A particularly novel aspect of our design was the use of genotypic information to confirm the first-degree familial relationship of siblings. This approach constitutes a protection against occult errors in verbal reports of relatedness, an important issue in substance dependent populations. 4.1. Severity of cocaine use/dependence Severity of cocaine dependence, as measured by DSM-IV symptom count, was a strong predictor of the risk for CIP. The significance of this factor was confirmed independently in analyses of both probands and siblings. Severity of cocaine use, as measured by money spent on cocaine (US$/day) during periods of heaviest use, was highly associated with CIP in the initial analysis (p < 0.001, two-tailed χ2 test), however, was dropped from the forward logistic regression due to its strong and statistically significant correlation with dependence severity (p < 0.001). As such, our data are consistent with the hypothesis that CIP is the result of heavy cocaine use/dependence (Baker, 1989; Post, 1975). Moreover, measures of heavy cocaine use were significantly familial in our data. Therefore, to the extent that CIP might be observed to be heritable, it may be so as a proxy measure for cocaine use/dependence severity. It is important to note that other potential measures of severity of cocaine use (e.g., daily amount in grams, days per month during periods of heaviest use, lifetime cocaine-use episodes) did not emerge as significant predictors of CIP status in our analyses. One possible explanation may relate to the relative difficulty with which cocaine-dependent subjects were able to retrospectively recall such information (e.g., 39.3% of probands responded “do not know” when asked about amount used in grams, in contrast to only 8.6% of subjects queried about money spent). Thus, monetary information may have greater salience for cocaine-dependent subjects with respect to recalling and/or quantifying cocaine use. Importantly, our independent replication of severity of cocaine dependence as a risk factor for CIP in probands and siblings argues strongly against a Type-I error.

4.2. Age of first cocaine use Early-onset cocaine consumption, as measured by age of first use, also emerged as a risk for CIP in our study. This factor was significant in independent analyses of probands and siblings. In initial analysis, age of onset of cocaine dependence approached significance (p = 0.08). However, it was dropped from the regression analysis due to its strong and statistically significant (p < 0.001) correlation with age of first cocaine use. One interpretation of these findings is that early-onset drug use may reflect greater syndromal severity and/or greater cocaine exposure (although there were no significant correlations between this variable and other severity/quantity-of-use measures). Alternatively, early exposure to cocaine might coincide with a temporal window of ‘psychosis vulnerability’ consistent with neurodevelopmental models of schizophrenia risk/onset (Marenco and Weinberger, 2000; Morgan et al., 1987; Weinberger, 1996). Interestingly, our findings of an association between CIP and age-of-first use have been confirmed by Cubells et al. (2005) in an almost completely independent sample (N = 243, 5 of whom were also enrolled in the present study), who found an inverse correlation between age of onset of cocaine use and severity of cocaine-induced hallucinations and delusions as measured by a quantitative rating instrument (i.e., the Scale for Assessment of Positive Symptoms for Cocaine-Induced Psychosis). While these study designs preclude conclusions about causal relationships, the replication of age of onset variables in both cohorts points to the need for more research into the role of early drug use as a risk factor for cocaine-related psychotic symptoms. 4.3. Familiality of CIP Contrary to expectations, we did not find evidence for a familial component to the risk for CIP. Despite an increased prevalence of the trait, siblings of CIP-positive probands were not at statistically increased risk of CIP in comparison to siblings of non-paranoid probands. Our findings were negative both before and after carefully controlling for demographic and cocaine use factors. Regardless, it was not possible to control for the potentially confounding effects of ascertainment bias inherent in the study design—namely, sib-pairs were selfselected (i.e., responded to advertisements) for shared cocaine dependence as opposed to being ascertained epidemiologically. Neither can limitations of sample size or study design be definitively excluded. For these reasons, we assessed the power of our approach empirically, by studying a trait of known moderate heritability—alcohol dependence (Kendler et al., 1992, 1997; Sigvardsson et al., 1996). The latter was a convenient phenotype given its high prevalence among cocaine-abusing populations (Carroll et al., 1993; Regier et al., 1990). The familial component to risk for alcoholism was significant in our sample, lending support to the suitability of our methods for evaluating traits of at least moderate heritability. Based on this result, we hypothesize that if CIP is heritable and we did not detect this due to power limitations, the heritability of CIP is likely to be less than the heritability of alcohol dependence, or less than 0.50. Alternatively, under assumptions of equal or greater heritability, the

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diagnosis of alcohol dependence (i.e., one based on numerous syndromal criteria) may be a more stable phenotype and more robust to genetic study than CIP (i.e., definition of which was based on only a single symptom and two questions). Prior genetic studies have suggested allelic associations between psychosisrelated candidate genes (e.g., dopamine transporter, dopamine ␤-hydroxylase) using identical phenotypic definitions of CIP (Cubells et al., 2000; Gelernter et al., 1994) and more recently, provided evidence of linkage in specific population groups (i.e., AAs (Gelernter et al., 2005); although post hoc analyses in our AA cohort were negative; OR = 1.5, p = 0.2). Thus, future studies aimed at replicating each set of findings across psychiatric genetic approaches will be important (Kendler, 2005). 4.4. Other demographic and cocaine use variables Several other findings merit brief mention. Probands with CIP reported less recent cocaine use (i.e., fewer instances of use during the preceding 12 months) than non-paranoid probands—a finding consistent with observations of increasing aversive and decreasing pleasurable effects of the drug over time (e.g., Brady et al. (1991) noted that several subjects specifically sought treatment due to such cocaine effects). Consistent too with previous reports (Honer et al., 1987), probands with CIP were significantly more likely to smoke cocaine than those without. However, these findings (i.e., recent cocaine use, cocaine smoking) were not replicated as risk factors in siblings. Similarly, three other variables appeared to be statistically meaningful in probands, but not siblings, including race (EAs; p < 0.05), cocaine snorting (p < 0.05), and sex (male; p < 0.10). Interpretation of these results should, therefore, be made with caution, since they are based on unadjusted p-values derived from the initial analysis (i.e., two-tailed χ2 statistics). Race, and snorting were excluded from our final model due to their strong associations (p < 0.005) with age of first use. Such factors have nonetheless been suggested as important in a prior report (Brady et al., 1991). 4.5. Limitations Limitations of our study require careful consideration. First, our sample was not an epidemiologic one, and thus, sampling biases certainly have the potential to influence our findings. Conversely stated, the risk factors identified here may not apply to less severe, non-drug-dependent, non-self-selected cocaineusing populations. However, since inclusion criteria for sibling pairs were based on a diagnosis of expected cocaine dependence, and not CIP, we believe that gross ascertainment biases affecting this phenotype are unlikely. We can envision bias resulting, however, from the ascertainment method favoring recruitment of more severely affected subjects, who are also at higher risk for CIP. Similarly, the multi-site nature of the study sample makes it more likely that our findings will be applicable to other cocainedependent populations in the U.S. Secondly, although our cohort is the largest studied to date, the high frequency of CIP (i.e., 65%) among cocaine-dependent populations potentially reduced our statistical power to establish a familial effect. As such, future

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studies that assess a broader range of family members with less severe and narrowly defined cocaine use patterns and/or larger study samples might derive different conclusions. The relatively high severity of cocaine dependence in this sample may have been a factor in terms of increasing risk for CIP, even in subjects without a particular genetic predisposition. Lastly, our study is limited by the largely retrospective nature of our structured assessment instrument and our inability to infer causal associations between traits/variables of interest. Although considerable care was taken to orient subjects to an established and reliable definition of CIP, this phenotype was, nonetheless, based on retrospective and subjective responses to only two questions. More elaborately constructed questionnaires (e.g., that consider chronology of CIP onset, behavioral responses to CIP, etc.) and/or alternative research methods (e.g., controlled laboratory studies) may ultimately be required to begin to understand potential directional effects of our identified risk factors and the development of CIP. Acknowledgements We would like to thank Ye-te Wu, Ph.D., for help in the replication of statistical results using SAS. We also appreciate the help of Alisha Pollastri, Yari Z. Nunez, Michelle McKain, and Michelle D. Slivinsky, M.A. with respect to SSADDA interviewing and consultation. Greg Kay, Lingjun Zuo, Pavani Srimatkandada, and Ann Marie Lacobelle provided excellent technical assistance. Jennifer Hamilton and John Farrell provided excellent database support. We would also like to acknowledge comments by Joseph F. Cubells, M.D., Ph.D., and Atapol Sughondhabirom, M.D., on the final manuscript and methodology. This work was supported by NIH grants R01 DA12849, R01 DA12690, M01 RR06192, K24 DA15105, K24 AA13736, D43 A05028, and K02 DA00326. References Baker, F.M., 1989. Cocaine psychosis. J. Natl. Med. Assoc. 81, 987, 990, 993–996. Bartlett, E., Hallin, A., Chapman, B., Angrist, B., 1997. Selective sensitization to the psychosis-inducing effects of cocaine: a possible marker for addiction relapse vulnerability? Neuropsychopharmacology 16, 77–82. Bierut, L.J., Dinwiddie, S.H., Begleiter, H., Crowe, R.R., Hesselbrock, V., Nurnberger, J.I., Porjesz, B., Schuckit, M.A., Reich, T., 1998. Familial transmission of substance dependence: alcohol, marijuana, and habitual smoking. Arch. Gen. Psychiatry 55, 982–988. Bowers Jr., M.B., Imirowicz, R., Druss, B., Mazure, C.M., 1995. Autonomous psychosis following psychotogenic substance abuse. Biol. Psychiatry 37, 136–137. Brady, K.T., Lydiard, R.B., Malcolm, R., Ballenger, J.C., 1991. Cocaineinduced psychosis. J. Clin. Psychiatry 52, 509–512. Carroll, K.M., Rounsaville, B.J., Bryant, K.J., 1993. Alcoholism in treatmentseeking cocaine abusers: clinical and prognostic significance. J. Stud. Alcohol 54, 199–208. Cubells, J.F., Feinn, R., Pearson, D., Burda, J., Tang, Y., Farrer, L.A., Gelernter, J., Kranzler, H.R., 2005. Rating the severity and character of transient cocaine-induced delusions and hallucinations with a new instrument, the Scale for Assessment of Positive Symptoms for Cocaine-Induced Psychosis (SAPS-CIP). Drug Alcohol Depend. 80, 23–33. Cubells, J.F., Kranzler, H.R., McCance-Katz, E., Anderson, G.M., Malison, R.T., Price, L.H., Gelernter, J., 2000. A haplotype at the DBH locus,

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