Addictive Behaviors 31 (2006) 948 – 961 Psychiatric comorbidity and progression in drug use in adult male twins: Implications for the design of genetic association studies Alexandre A. Todorov a,*, Michael T. Lynskey a, Julia D. Grant a, Jeffrey F. Scherrer a,b, Richard D. Todd a, Kathleen Keenan Bucholz a a Department of Psychiatry, Midwest Alcoholism Research Center, Washington University School of Medicine, 660 South Euclid Avenue, Box 8134, St. Louis, MO 63110-1502, United States b Research Service, St. Louis VAMC, St. Louis, MO, United States Abstract Psychiatric comorbidity with drug dependence has been widely documented. In the present study, we reanalyze DSM-III-R diagnostic data on middle-aged male twin pairs from the VETR study using latent class methods. We identify four subtypes based on 15 diagnostic categories. We then show that these subtypes are strongly associated with differential rates of transitions in drug use histories, with increased risks in relatives for depression, alcohol, drug and ASPD, as well as with a variety of non-normative and deviant behaviors in youth and in adulthood. We use the result of these analyses to show how the use of a particular drug disorder phenotype for selecting cases could impact final sample composition. D 2006 Published by Elsevier Ltd. Keywords: Alcohol use disorder; Substance use disorder; Psychiatric co-morbidity; Genetic association study 1. Introduction Substance dependence is a complex disorder with multiple causes and manifestations. Psychiatric comorbidity is common, including antisocial personality disorder, depression and anxiety disorders, and has been correlated with poorer treatment outcome (e.g., Grant, 1995; Grant & Harford, 1995; Helzer & * Corresponding author. E-mail address: todorov@wustl.edu (A.A. Todorov). 0306-4603/$ - see front matter D 2006 Published by Elsevier Ltd. doi:10.1016/j.addbeh.2006.03.046 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 949 Pryzbeck, 1988; Kessler et al., 1997; Merikangas, Mehta et al., 1998; Merikangas, Stolar et al., 1998; Myrick & Brady, 1997; Regier et al., 1990; Rounsaville et al., 1991a,1991b, 1998; Swendsen et al., 1998; Tomasson & Vaglum, 1998a,1998b; Ulzen & Hamilton, 1998). Extensive polysubstance use has been reported in multiple populations, including, for example, the somewhat paradoxical simultaneous dependence on both opioids and stimulants (e.g., Conway, Cane, Ball, Poling, & Rounsaville, 2003; Hopfer, Khuri, Crowley, & Hooks, 2002; Kaye & Darke, 2000; Kosten, Rounsaville, & Kleber, 1998; Lauzon et al., 1994). These observations have led to the postulate of a common liability for substance use disorders (Glantz & Pickens, 1992; Vanyukov et al., 2003) and to the suggestion that this liability may overlap with that for other psychiatric disorders, such as antisocial personality disorder and depression (e.g., Helzer & Pryzbeck, 1988; Kendler, Jacobson, Prescott, & Neale, 2003,Kendler, Prescott, Myers, & Neale, 2003; Merikangas, Mehta et al., 1998; Merikangas, Stolar et al., 1998; Merikangas, Risch, & Weissman, 1994; Vanyukov et al., 2003). Kendler, Jacobson et al. (2003) for example, argue in their analysis of twin data there may be four general genetic risk factors: liability to externalizing disorders (e.g., adult antisocial behavior, and conduct disorder), liability to internalizing disorders (major depression, generalized anxiety disorder, and phobia), a factor specific for alcohol dependence and one for drug abuse/ dependence). Nurnberger et al. (2004) show aggregation of antisocial personality disorder, drug dependence, anxiety disorders, mood disorders and alcohol dependence within some families. Previous analyses of data from male twins who served in the military during the Vietnam era suggested significant genetic correlations between the liabilities for generalized anxiety disorder and panic disorder (Scherrer et al., 2000) and between these syndromes and posttraumatic stress disorder (Chantarujikapong et al., 2001). Others have reported that the overlap may be explained, at least in part, by measures of personality (neuroticism in particular) (Bienvenu et al., 2001; Clark, Watson, & Mineka, 1994; Khan, Jacobson, Gardner, Prescott, & Kendler, 2005; Krueger, 2005; Markon & Krueger, 2005; Sher & Trull, 1994). The question of psychiatric comorbidity among those with substance use disorders is especially salient given the strong momentum to map genes associated with substance use risk. It is now well established that genetic factors are important determinants of the susceptibility to substance use disorders. Indeed, family histories of substance abuse problems are reliably associated with an increased risk of the disorder in close relatives (Hill, Cloninger, & Ayre, 1977; Luthar, Anton, Merikangas, & Rounsaville, 1992a, Luthar, Anton, Merikangas, & Rounsaville, 1992b; Maddux & Desmond, 1989; Merikangas et al., 1992; Pickens et al., 2001; Rounsaville et al., 1991) as well as with poorer substance abuse treatment outcomes (Pickens et al., 2001) Adoption studies (Cadoret, Yates, Troughton, Woodworth, & Stewart, 1995; Cadoret, Yates, Troughton, Woodworth, & Stewart, 1996; Langbehn, Cadoret, Caspers, Troughton, & Yucuis, 2003; Troughton, O’Gorman, & Heywood, 1986) and twin studies (Jang, Livesley, & Vernon, 1995; McGue, Elkins, & lacono, 2000; Newlin, Miles, van den Bree, Gupman, & Pickens, 2000; Prescott & Kendler, 1999; Rhee et al., 2003; Tsuang et al., 1998; van den Bree, Johnson, Neale, & Pickens, 1998) which have the ability to some extent to separate the effects of gene and environment, demonstrate that this familiality is to a large degree determined by genetic factors, with estimates of the overall heritability of substance abuse vulnerability typically ranging from 40% to 60% (Kendler, Karkowski, Corey, Prescott, & Neale, 1999; Kendler, Jacobson et al., 2003; Kendler, Prescott et al., 2003; Tsuang et al., 1998). While no single linkage study alone provides conclusive results, a comparison of the results of several studies suggests 15 reproducible chromosomal loci (Uhl, Li, Walther, Hess, & Naiman, 2001, Uhl, Liu, & Naiman, 2002). Of special significance is the 950 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 recent report of linkage between conduct disorder symptoms and substance dependence vulnerability to the same region on chromosome 9q34 (Stallings et al., 2005). Animal studies provide a firm scientific basis for several candidate genes, with some emerging association findings in humans (reviewed in Kreek, Bart, Lilly, LaForge, & Nielsen, 2005). Given the current ability to collect sufficiently large samples for genetic epidemiological studies of substance use disorders, we now have the prospect of not only acknowledging the complexity of substance use disorders in genetic epidemiological studies, but modeling it as well. For practical reasons, participants are typically selected on one particular phenotype, such as severe alcohol, opioid or cocaine dependence. Given the high level of comorbidity with other psychiatric disorders, many of which have themselves been shown to be modulated by genetic factors, the question remains as to what population is studied when selecting on one phenotype, as well as whether other genetic traits are unknowingly being enriched for at the same time (for example, genes for IQ; Todd, 2005). In the present study, we examine this issue through a reanalysis of an unusually comprehensive data set that is based on information obtained from over 8000 middle aged male twins in a telephone administration of a structured psychiatric interview that covered the full range of substance and nonsubstance use disorders. These data afford certain advantages. Lifetime diagnostic criteria for DSM-3R abuse and dependence are obtained for a full range of licit (alcohol and nicotine) and illicit (marijuana, cocaine, other stimulants, sedatives, opiates, hallucinogens/PCP) drugs. Moreover, stages of use of each illicit drug are also obtained, permitting several transitions in drug use progression to be studied. A wide range of non-substance psychiatric disorders are covered, including depression, anxiety, and PTSD, among others, the full range of which is rarely included in a national community sample. For example, both large-scale national surveys–the National Longitudinal Alcohol Epidemiology Survey (NLAES, conducted in 1991) and National Epidemiologic Survey of Alcohol and Related Disabilities (NESARC, conducted in 2001)–have limited coverage of non-substance psychiatric disorders. Therefore, these data present a rare opportunity to investigate the full extent of psychiatric comorbidity in tandem with substance use disorders, and progression of substance use disorders. In the present, we adopted a categorical approach in our application of latent class analysis to these data to determine if subtypes of individuals can be clearly distinguished with distinctive profiles on 15 psychiatric and substance use diagnoses. Using the subtypes obtained, we examined them in association with various transitions in drug use; increased risks to relatives (fathers, mothers, siblings, and children) of depression, alcohol, drug and antisocial problems, and with an array of deviant youthful and adult behaviors. Finally, we use the results of these analyses to address the impact of selecting individuals affected with a particular substance use disorder on the final composition of the sample, using the example of opiate dependence versus general drug dependence (i.e. dependence on any illicit drug). 2. Methods 2.1. Sample The Vietnam Era Twin Registry (VETR) is a national sample of male–male twin pairs both of whom served in the military during the Vietnam War era (1965–1975). Approximately 1/3 of participants actually served in South East Asia. From Department of Defense files of about 5.5 million men who were on active duty during that time, 7369 twin pairs were identified using algorithms which matched 951 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 subjects on similarity of social security number, date of birth, and first and last names. Construction of the Registry and method of determining zygosity are described in detail elsewhere (Eisen et al., 1989; Eisen et al., 1987; Henderson et al., 1990). Twins were recruited for a mailed questionnaire study in 1987, to which 10,300 individuals responded. Data in the present report are from a 1992 follow-up interview of this twin cohort, in which all twin pairs where at least one member participated in the 1987 questionnaire were invited to participate. Of the 10,300 twins from the 1987 study, 8169 (79.3%) participated in the 1992 follow-up (3372 complete twin pairs; 66.1% pair-wise response rate) and 1425 singletons. (Tsuang et al., 1998). 2.2. Psychiatric assessment The structured interview used in the 1992 study was a computerized telephone version of the Diagnostic Interview Schedule, Version III Revised (DIS-3R) (Robins et al., 1989), a comprehensive structured interview used to derive psychiatric diagnoses according to DSM-III-R lifetime criteria. A standard research instrument in the psychiatric field, the DIS has been shown to be highly reliable and valid (Robins, Helzer, Croughan, & Ratcliff, 1981; Robins, Helzer, Ratcliff, & Seyfried, 1982; Robins et al., 1985). In the present report, 15 lifetime diagnoses are considered (Table 1) in four general domains (mood, personality, anxiety, and substance dependence). Twins were also asked to report on the presence of depression, alcoholism, drug dependence and ASPD in their first-degree relatives (mothers, fathers, children and sibs), based on thumbnail sketches of these disorders. Interviews were administered by experienced staff from the Institute for Survey Research at Temple University, who were trained in the telephone administration of the interview. This work was conducted with approvals from the Institutional Review Boards of participating universities. All subjects gave informed consent prior to their participation. Table 1 DSM-III-R lifetime considered in these analyses Diagnosis N % Major depressive disorder (excludes bereavement) Mania Dysthymia Panic disorder Generalized anxiety disorder Post-traumatic stress disorder Antisocial personality disorder Alcohol dependence Nicotine dependence Cannabis dependence (marijuana, hashish, etc.) Stimulant dependence (amphetamines, khat, ice, etc.) Opiate dependence (heroin, morphine, opium, methadone, codeine, etc.) Hallucinogen dependence (LSD, PCP, mescaline, peyote, etc.) Sedative dependence (sleeping pills, tranquilizers, barbiturates, valium, etc.) Cocaine dependence (Cocaine, crack, coca leaves, etc.) 8150 8146 8151 8149 8149 8107 8147 8148 8169 8135 8135 8135 8135 8134 8135 10.2 0.8 2.5 1.8 2.6 10.1 2.9 35.8 47.6 6.5 2.6 1.3 1.0 1.4 2.6 Prevalence for descriptive purposes only. Both twins included. The mild, moderate and severe dependence categories specified by DSM-III-R were combined. Except for nicotine dependence, baseline category includes individuals with a DSM-III-R diagnostic of babuseQ. 952 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 2.3. Transitions in drug use Because the focus of the data collection effort was on progression to illicit substance dependence, questions were added to the DIS to obtain use patterns, and the ages at which each usage pattern began. As well, opportunity to use each drug type (termed bexposureQ) was assessed for all. Ever use, use more than 5 times, daily use for several weeks (bregular useQ), and drug problems that reflected DSM-III-R criteria for dependence and abuse were elicited. This information was used to define transitions—from ever use (bexperimentationQ) to more than experimental use (N5 times), to regular use, to dependence. 2.4. Statistical methods Latent class analysis (LCA; McCutcheon, 1987) is a special form of mixture modeling that can be used to investigate patterns of association among sets of categorical responses. It is predicated on the assumption that a relatively small number of mutually exclusive classes exist, each class having a distinctive profile of item endorsement probabilities. The parameters of the latent class model are the number of classes, the class membership probabilities, which may be thought of as prevalence, and, for each class, the symptom endorsement probabilities given membership in that class. On the assumption of local independence, the conditional probabilities of endorsing a set of items are independent within a given latent class. We verified the validity of this assumption through an examination of residuals (roughly, comparison of the predicted and observed joint distribution for two items). In the present analyses, we fitted LCA models with 2 to 6 classes and selected the model with the lowest value of the Bayesian Information Criterion (Biernacki et al., 1998). The latent class analyses were conducted using LatentGold, with several restarts to minimize the problems of local minima and boot-strapping for the determination of empirical p-values to further substantiate the choice of a K-class model. Follow-up analyses were performed using SAS and S-Plus. The VETS sample consists of twin pairs which are not independent for the traits considered in this analysis. Since the primary focus of these analyses presented here is on modeling the joint distribution of the 15 diagnoses at the individual level, rather than on modeling the joint distribution in twin pairs, we randomly assigned one member of each twin pair to a btrainingQ set and the other to a breplicationQ set. Latent class analyses were run on both the training and the replication sets (and reported separately) in order to ensure that the solution obtained was not a result of the non-independent nature of the twins. The large sample number of pairs ensures adequate power even when only one twin is considered. At this stage of the analyses, it would have been premature to bring in the additional complexity, and associated modeling assumptions, of multivariate twin data analysis. This is reserved for future investigations. 3. Results 3.1. Sample The mean age of respondents was 44.6 years (S.D. + 2.8, range 36 to 55 years); 90.4% were nonHispanic white, 4.9% African-American, 2.7% Hispanic, 1.3% Native American/ Alaskan Native, and 953 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 Table 2 Summary statistics for the latent class analyses Number classes Number parameters BIC (Twin 1, N = 4044) BIC (Twin 2, N = 4006) 2 3 4 5 6 31 47 63 79 95 22,533 22,268 22,096 22,143 22,236 23,614 23,412 23,244 23,321 23,404 Bootstrap p-value for a test of the significance of the difference in BIC value for the 3 and 4 classes models: p b 0.0001, each, for analyses using only twin 1 data and analyses using only twin 2 data. Empirical p-values for the difference for 4 and 5 classmodels: p b 0.01 for twin 1 data and p = 0.12 for twin 2 data. 0.7% botherQ; 72% were at least high school graduates with 38.6% being college graduates. At the time of interview, 92.6% were employed full-time and 1.8% part-time. 3.2. Latent class analyses The latent class analyses suggest that a four class solution was the most parsimonious (Tables 2 and 3). Class I, with an estimated frequency of 56.2%, is essentially a baseline class characterized by low rates of psychiatric disorders and substance dependence (licit or illicit). Class II (33.6%) is characterized by elevated prevalence of alcohol and nicotine dependence (67% and 75%), but the rates of illicit substance dependence remain low when compared to Classes III and IV. Class III (6.8%) and Class IV (3.4%) are distinguished by high rates of depression, PTSD, alcohol and nicotine dependence. When Table 3 Estimated class prevalences and diagnostic probabilities within each class Class I II Class prevalence Psychiatric comorbidity PTSD Depression Panic Anxiety Mania Dysthymia ASPD 56.23 33.55% III 4.7 2.2 0.2 ~0 0.1 ~0 ~0 21.0 4.8 1.2 ~0 0.9 ~0 ~0 75.1 77.0 13.7 8.5 7.8 1.8 1.5 55.1 46.5 8.9 6.0 5.3 1.5 1.7 Substance dependence Alcohol Tobacco Cannabis Stimulants Cocaine Sedatives Hallucinogens Opiates 10.8 25.3 0.3 ~0 0.1 ~0 ~0 0.1 67.0 75.1 9.1 1.5 3.6 0.7 0.3 1.2 61.8 71.9 10.9 ~0 3.6 2.0 0.2 0.6 88.6 88.9 74.5 62.2 30.8 29.9 26.4 23.9 6.81% IV 3.40% 954 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 compared to Classes I and II, these two classes also exhibited elevated rates of antisocial personality disorder, anxiety and panic disorders, as well as higher prevalence estimates of mania and dysthymia. However, Classes III and IV are strikingly different in the prevalence of illicit substance dependence. In this respect, Class IV clearly stands out from all other classes. 3.3. Characterization of the four latent classes We further characterized the four classes according to specific indicators of drug use patterns; conduct and/or personality disorders symptoms in both youth and adulthood (for which we used the standard DIS/DSM conceptualization of adult behaviors as those occurring at or after age 15) We also examined prevalence of depression, alcohol and drug dependence and ASPD in first-degree relatives. These were based on affirmative responses to thumbnail sketches of these disorders from the twins for mothers, fathers, siblings, and own children. For the purpose of these analyses, individuals were assigned to the most likely class given their observed diagnostic profiles. The posterior probabilities of class membership were calculated using the estimated four-class model parameters. Overall (all twins considered), 4860 individuals were assigned to Class I, 2077 to class II, 468 to class 3, and 226 to Class IV. The ability to assign an individual to a particular class was generally high. The proportion of subjects for which the ratio of the largest to next most largest posterior class probabilities was greater than two ranged from a low of 82% (individuals assigned to Class II) to a high of 99% (Class I). By definition, Class IV is defined as one with a high prevalence of dependence on one or more substances. However, the other classes are not and yet, we observe that substantial numbers of individuals in each class have experimented with illicit drugs (Table 4). Thus, differences in the prevalence of substance dependence between classes are not due to abstinence alone. Cannabis experimentation was most common. One third of Class I, the unaffected class, experimented with cannabis, while over 70% of the other classes did so. Although experimenting with substances other than cannabis was less common in Class I, this behavior was much more common in Classes II and III and nearly universal in Class IV. For example, about 35% of Classes II and III reported experimenting with stimulants, compared to 97% in Class IV. Experimentation with sedatives, cocaine and hallucinogens was reported by 20–28% of Classes II and III, but by over 80% of Class IV. Opiate experimentation was lowest among the substances. The classes do differ in the proportions of individuals who have progressed from experimentation to dependence. For the purposes of the study, an episode of bregular useQ was defined as a period where the substance was used daily for two weeks or more. Generally, compared to Classes II and III, a lower proportion of individuals from Class I will progress from using a substance 6 times or more to regular use. However, the differences are not particularly striking. In contrast, there are sharp between-class differences in the probabilities to progress from regular use to dependence, with Class I the lowest, roughly comparable proportions in Classes II and III, and the highest proportion in Class IV. Using opiates as an illustration, the probability of progressing from regular use to dependence is 5.3% in Class I, 37.9% in Class II, 15.4% in class III and 76.7% in Class IV. In Class II compared to Class III, the transition probabilities may be higher for stimulants and opiates, and lower for cocaine. Keeping in mind that these transition probabilities are conditional on the prior use level, the transition to dependence was very low among Class I—under 5% across all substances. In contrast, a very high 955 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 Table 4 Transition probabilities, by drug and class Class assignment I II Cannabis Ever use Used N5 times Regular use Dependence 36.8 53.5 59.1 1.4 (4329) (1590) (850) (501) Stimulants Ever use Used N5 times Regular use Dependence 9.6 48.3 38.5 0 Sedatives Ever use Used N5 times Regular use Dependence 72.7 70.72 73.6 31.1 III IV (1969) (1431) (1012) (744) 72.9 68.8 69.4 38.7 (431) (314) (216) (150) 100.0 97.8 95.9 82.6 (226) (226) (221) (212) (4328) (414) (200) (77) 35.2 (1967) 62.6 (693) 54.3 (433) 11.9 (235) 34.6 61.1 52.8 2.1 (431) (149) (91) (48) 97.4 95.9 89.5 83.5 (226) (219) (210) (188) 5.1 40.0 36.4 0 (4328) (220) (88) (32) 21.8 58.6 48.2 12.5 (1967) (428) (251) (120) 24.4 51.4 63.0 29.4 (431) (105) (54) (34) 81.0 79.2 73.1 66.0 (226) (183) (145) (106) Cocaine Ever use Used N5 times Regular use Dependence 7.3 41.6 40.2 5.7 (4326) (317) (132) (53) 27.1 53.8 55.1 55.1 (1968) (534) (287) (158) 24.6 50.0 56.6 53.3 (431) (106) (53) (30) 81.9 70.8 68.7 80.0 (226) (185) (131) (90) Opiates Ever use Used N5 times Regular use Dependence 2.3 29.6 65.5 5.3 (4326) (98) (29) (19) 12.7 41.2 64.1 37.9 (1968) (250) (103) (66) 14.9 32.8 61.9 15.4 (431) (64) (21) (13) 70.4 57.9 80.4 76.7 (226) (159) (92) (73) Hallucinogens Ever use Used N5 times Regular use Dependence 6.0 34.1 27.3 0 (4326) (258) (88) (24) 23.0 44.4 42.3 8.24 (1968) (453) (201) (85) 20.7 36.0 43.8 7.1 (431) (89) (32) (14) 85.0 74.5 70.6 61.0 (226) (192) (143) (100) Transition probabilities, conditional upon preceding stage having been met. The number of observations on which the probability is calculated is in parentheses. bRegularQ use is defined as daily use for a minimum of 2 weeks. percentage of Class IV transitioned to dependence, (greater than 75% for 4 substances, and over 60% for 2 substances), suggestive of a higher susceptibility, not only to experimentation with drugs but also to progression to dependence. Different patterns among Classes II and III were evident. Transitions to dependence were moderate for cannabis for both classes–about one third, and quite high for cocaine– over one half. In comparison, the probability of transition to dependence for stimulants was quite low (12% or less in Classes I to III). Also of note was the sharp difference in transition probabilities observed 956 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 Table 5 Twin report of problems in relatives, within each class Problem Depression Alcohol problems Drug problems ASPD Father Mother Siblings Children Father Mother Siblings Children Father Mother Siblings Children Father Mother Siblings Children Class I, N = 4860 Class II, N = 2077 Class III, N = 468 Class IV, N = 226 18.5 19.9 16.1 9.1 23.3 5.4 9.2 3.4 0.88 1.2 3.8 2.4 3.96 0.4 4.0 3.9 29.5 30.7 24.8 15.4 36.1 8.0 18.4 5.1 1.94 2.7 8.8 2.6 6.1 1.0 7.8 4.9 44.8 52.3 52.7 40.8 35.2 12.1 22.3 6.7 0.48 5.1 12.6 7.7 9.1 0.5 14.6 10.6 46.3 44.4 40.7 32.5 58.3 23.3 34.6 7.5 2.4 7.8 28.1 10.0 15.1 2.2 15.9 17.5 for opiate dependence between Class II and Class III—38% of Class II, but only 15% of Class III, progressed to dependence. Second, we observed a nearly linear response relationship between class membership and the prevalence of symptoms of antisocial personality disorder and social maladjustment, both in youth and in adulthood. For example, the proportion of respondents reporting having committed acts of vandalism before age 15 are 2.7%, 7.8%, 11.5% and 19.5% in Classes I, II, III and IV, respectively. Even though Classes III and IV have comparable proportions of antisocial personality disorder (1.5% versus 1.6%, Table 3), overall, symptom endorsement probabilities tend to be higher in Class IV. Third (Table 5), we observed that class membership is associated with an elevated rate of depression, alcohol, drug, and legal problems in first degree relatives, suggesting, if not heritability, at least familiality. Additional analyses suggest that class membership appears substantially heritable. Indeed, the polychoric correlations for alcohol dependence were 54% for monozygotic twins and 32% for dizygotic twins, compared to 56% and 37% for membership into Class IV (versus all others). Table 6 Distribution of cases of DSM-III-R dependence on particular substance, across the classes Selection dependence phenotype, DSM-III-R N Alcohol Tobacco Cannabis Stimulants Sedatives Cocaine Opiates Hallucinogens 2486 3678 483 196 108 188 93 80 Proportion of cases of dependence in each class Class I Class II Class III Class IV 15.1 34.5 1.5 0 0 1.6 1.1 0 64.8 51.0 49.1 15.3 16.7 50.0 30.1 8.8 11.9 9.0 12.2 0 11.1 8.5 3.2 1.3 8.3 5.6 37.3 84.7 72.2 39.9 65.6 90.0 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 957 3.4. Implications for sample selection Finally, we considered the implication of this class structure for epidemiological studies that select probands based on substance dependence diagnosis (Table 6). In this sample, if we select cases to be dependent on any illicit substance (one or more), then most of the observations would come from the lower progression risk Class II (54.1%) and fewer from the high progression risk Class IV (31.9%). However, if we select cases to be opioid dependent, then these proportions are reversed: 65.6% from the high-risk Class IV and 30.1% from the low-risk Class II. In general, the results suggest that most of the sample would come from Classes I and II if selected on alcohol or nicotine dependence; from Classes II and IV if selecting on cannabis or cocaine dependence; and primarily from Class IV if selecting on opiate, hallucinogen, stimulant or sedative dependence. 4. Discussion Much progress has been made in the last few years in the understanding of the substance dependence phenotype. There are several lines of research in this regard. In addition to detailed work that examines the structure of narrow phenotypes, such as developmental trajectories for specific drug classes, there is another rapidly developing branch of study that takes a broader perspective in a consideration of substance dependence in context of other psychiatric and personality related factors. The present study falls in this latter category. We adopted a categorical approach and focused on modeling psychiatric and substance use comorbidity within individuals, rather than between twins. Our results suggest four distinctive groups of psychiatric comorbidity. One class is composed of essentially unaffected individuals (Class I). Another reflects dependence on licit substances (alcohol and nicotine). Two additional classes both show elevations of mood and anxiety disorders but very different prevalence of illicit drug dependence. Rates of illicit drug dependence across all drug categories are very high in Class IV, but in contrast, are negligible in Class III. For the most part, the observed classes are suggestive of a latent trait model. This is especially salient when one considers the relationship between class membership and the selected symptoms of personality disorder and social maladjustment, both in youth and in adulthood. However, we do not observe a regular increase in the prevalence of all symptoms across classes. These symptom endorsement profiles are consistent with the hypothesis of two broad groups of diagnostic comorbidity, one comprised of internalizing disorders (Class III), the other with a trend to externalizing disorder and illicit drug dependence (Class IV) or to licit drug dependence (Class II). Whether the underlying process is due to the existence of distinct bsyndromesQ as opposed to a continuum in liability (along one or more dimensions) is of lesser interest for the purpose of designing a sampling scheme for a genetic epidemiological studies of substance use disorders. Indeed, the key consideration there is not how these phenotypes co-segregate but rather the end result—the expected profile of study participants. In the present study population, sampling on stimulant, sedative, opiate or hallucinogen dependence would likely result in a sample that is characterized by elevated rates of depression and PTSD, and which exhibits a strong tendency to transition from experimental drug use to dependence. On the other hand, sampling on alcohol, tobacco, cannabis or cocaine dependence, or sampling on any drug dependence, would result in 958 A.A. Todorov et al. / Addictive Behaviors 31 (2006) 948–961 sample with slightly elevated depression and PTSD rates, but otherwise not altogether different from baseline. There are several limitations to the present study. Because the sample was composed only of middleaged males, who are primarily Caucasian, the degree to which the findings reported here apply to women, to younger individuals, or to a more ethnically diverse sample is not known. The current study examined DSM-III-R diagnoses only, since data collection predated DSM-IV. Whether the change in nosology would affect the results is uncertain. However, despite the fact that this is a sample of military veterans, which were likely screened for the most extreme externalizing disorders, we still observe an association between externalizing behaviors and substance dependence. At the same time, there are several advantages to the sample used. The exposure of the men to illicit drugs was pervasive, by virtue of the era and place of military duty for some. In particular, experimentation with opiates is far higher than reported in other studies (e.g., NESARC), resulting in greater expression of an underlying vulnerability to opioid dependence. All participants, being in their 40s when interviewed, are past the crucial stages of drug problem development, so that we can expect little censoring in the observations. Despite the limitations of the male-only nature of the sample and the availability of DSM-III-R (not DSM-IV) disorder, the results reported here are broadly consistent with those of previous studies of comorbidity of common psychiatric conditions and substance dependence. We report here on lifetime diagnoses. Even though the overall sample is large, given the relative rarity of each phenotype (e.g., ~ 10% for major depressive disorder), there was relatively little information to order events, which would have allowed us to assess, for example, if a depressive episode was substance related. Lastly, the intriguing differences in transition probabilities to dependence across classes and types of substances may suggest different propensities to addiction based on comorbidity profiles. It was particularly striking for opiates. However, the data do not permit distinguishing among different types of opiates. For example, individuals reporting use of prescription medications may have a lower probability of becoming dependent compared to their counterparts who use heroin. Observed differences might be due to different forms of the drug taken. Thus, even though much of the liability to substance dependence may be shared, it appears that the choice of selection phenotype (e.g., opioid dependence versus cannabis dependence) will strongly affect the ultimate sample composition, not only in terms, e.g., the propensity to transition to dependence from experimental use, but also in terms of psychiatric comorbidity as well. Analyses that focus on any substance dependence as the phenotypic endpoint may be introducing substantial heterogeneity in the analyses. Our results suggest that a more controlled sampling scheme should ascertain samples for genetic association studies based on diagnostic profiles rather than individual diagnoses. 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