Numerous studies show that Attention Deficit Hyperactivity

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1 Is Cigarette Smoking a Gateway to Alcohol and Illicit Drug Use Disorders? A Study of Youths with and without Attention Deficit Hyperactivity Disorder Joseph Biederman, Michael C. Monuteaux, Eric Mick, Timothy E. Wilens, Jessie A. Fontanella, Katrina M. Poetzl, Taralee Kirk, Joshua Masse, and Stephen V. Faraone Background: The goal of this study was to assess whether cigarette smoking is a gateway drug for subsequent alcohol and illicit drug abuse and dependence in youth with Attention Deficit Hyperactivity Disorder (ADHD), and to test if this association is stronger in ADHD youth relative to controls. Methods: We used data from a case-control family study of female youth with and without ADHD. We studied 97 ADHD and 203 control youth of both sexes, aged at least 12 years. We assessed ADHD, smoking, and substance use status using structured diagnostic interviews. We tested the association between cigarette smoking and subsequent substance use outcomes using Cox proportional hazard regression models. Results: ADHD youth who smoked cigarettes (n 15) were significantly more likely to subsequently use alcohol and illicit drugs as well as to develop abuse and dependence compared to ADHD youth who did not smoke (n 76; p.05). Conclusions: These results extend the gateway hypothesis to an ADHD sample and provide evidence that this effect may be particularly robust in ADHD youth. If replicated in larger, community-based samples, these findings have important public health consequences, and underscore the already pressing need to prevent smoking in ADHD children. Key Words: Gateway, smoking, ADHD, substance use, adolescents, substance use disorder Numerous studies show that Attention Deficit Hyperactivity Disorder (ADHD) increases the risk for nicotine use across the lifecycle. Milberger et al (1997a; 1997c) found significant associations between ADHD and cigarette smoking in both referred and nonreferred pediatric samples. ADHD increased the risk for cigarette smoking in early adolescence two fold and was associated with a two year earlier age at onset than controls. Likewise, Tercyak et al (2002) found that adolescents with ADHD were three times more likely to smoke than adolescents without ADHD and Pomerleau et al (1995) documented that adult ADHD is associated with an increased risk for tobacco addiction and, among smokers, increased difficulty quitting. Although the literature also supports an association between ADHD and other substance use disorders (SUDs) (alcohol or drug abuse or dependence), this association follows a different pattern than that observed for nicotine abuse and dependence. Milberger et al (1997b) showed that although nonnicotine SUDs were not associated with ADHD in early to mid adolescence, they differed significantly and robustly associated with ADHD by late adolescence. These findings bear remarkable congruence with retrospective data from adults with and without ADHD that show ADHD is associated with an increased risk for SUDs that emerges in late adolescence and young adulthood (Biederman et al 2004; Mannuzza et al 1998). From the Department of Psychiatry (JB, MCM, EM, TEW), Harvard Medical School; Psychiatry Department (JB, MCM, EM, TEW, JAF, KMP, TK, JM), Pediatric Psychopharmacology Unit, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry (SVF), State University of New York Upstate Medical University, Syracuse, New York. Address reprint requests to Joseph Biederman, M.D., Massachusetts General Hospital, Pediatric Psychopharmacology Program, Warren 705, 55 Fruit Street, Boston, MA 02114; jbiederman@partners.org Received November 30, 2004; revised April 7, 2005; revised June 16, 2005; accepted June 21, /06/$32.00 doi: /j.biopsych These findings suggest a developmental progression for addictive behaviors in ADHD that starts with cigarette smoking in early adolescence and follows into alcohol and drug abuse or dependence by late adolescence. To explain this proposed developmental progression, we can hypothesize that nicotine may be a gateway drug for subsequent alcohol and illicit drugs abuse or dependence in ADHD. However, it should be noted that observing ADHD youth who smoke are more likely to subsequently use or abuse alcohol and illicit drugs more often than expected by chance does not necessarily mean that smoking plays a causal role as the gateway hypothesis maintains. It is also possible that other factors, such as genetic vulnerabilities, environmental exposures, or combinations of these cause a generalized predisposition to substance use in youth. Although the traditional gateway hypothesis posits that individuals who use cigarettes or alcohol (licit drugs) are more likely to use marijuana or other illicit drugs (Kandel 2002), several studies examined tobacco as a gateway drug for other substances. These studies have produced convincing evidence that individuals who smoke cigarettes have an increased likelihood of engaging in other illicit drug use. For example, Torabi et al (1993) found that individuals who consistently smoked a pack of cigarettes a day were ten to thirty times more likely than nonsmokers to use illicit drugs and Lai et al (2000) reported that individuals who smoked cigarettes were significantly more likely to have progressed to marijuana, cocaine, crack and heroin use. Whether tobacco is a gateway drug for subsequent alcohol and illicit drug abuse and dependence in youths with ADHD has major clinical, scientific and public health implications. Establishing that cigarette smoking is a gateway drug for subsequent abuse and dependence of alcohol and illicit drugs can help clinicians identify those patients with ADHD at high risk for addictive behaviors. Considering that the average age at onset of ADHD is in the preschool years and the average age of onset of cigarette smoking is in early adolescence, there is an approximate 10 year window for the development of disorder-specific preventive and early intervention strategies to avert tobacco addiction outcomes in youths with ADHD. If, among youths with ADHD, cigarette smoking occurs first in the developmental BIOL PSYCHIATRY 2006;59: Society of Biological Psychiatry

2 J. Biederman et al BIOL PSYCHIATRY 2006;59: trajectory of substance use disorders, it is possible that the prevention of smoking could interrupt one pathway towards alcohol and illicit drug use disorders along with their associated morbidity, mortality and cost to society. Scientifically, information about developmental trajectories of substance use disorders may yield new insights into the neurobiology of addictions. The main goal of this study was to assess whether cigarette smoking is a gateway drug for subsequent alcohol and illicit drug abuse and dependence in youth with ADHD. Towards this end, we compared the risk for subsequent substance use disorders in a large sample of youths with and without ADHD stratified by smoking status. We tested the following hypotheses: 1) cigarette smoking will be associated with subsequent alcohol and illicit substance use disorders in ADHD youth; and 2) the magnitude of this association will be stronger in ADHD youth compared to nonadhd youth. To the best of our knowledge this study is the first to assess cigarette smoking as the gateway drug in ADHD youth. Methods and Materials Subjects We performed a secondary analysis on data from a casecontrol family genetic study of female youth with and without ADHD and their first-degree relatives. The present study consisted of 140 female ADHD probands and 122 female nonadhd comparisons, aged 6-17 at the time of ascertainment. We also included the 143 siblings (84 males, 59 females) of the ADHD probands and the 131 siblings (75 males, 56 females) of the control probands. Potential subjects were excluded if they had been adopted, or if their nuclear family was not available for study. We excluded probands if they had major sensorimotor handicaps (paralysis, deafness, blindness), psychosis, autism, inadequate command of the English language, or a Full Scale IQ (Wechsler 1974) less than 80. All of the ADHD probands met full DSM-III-R diagnostic criteria for ADHD according to clinical assessment at the time of the clinical referral; at the time of recruitment for this study they all had active symptoms of the disorder. Of the 536 potential subjects, we limited the present analysis to the probands and siblings who were 12 years of age or older (n 300). After a detailed description of the study procedures, written informed consent was obtained from all subjects. For subjects younger than 18, assent was also provided. This study was approved by the institutional review board of the Massachusetts General Hospital. Two independent sources provided the index children. We selected psychiatrically referred ADHD probands from consecutive referrals to a pediatric psychopharmacology clinic at a major academic center. The pediatrically referred ADHD subjects were consecutively ascertained outpatients from the pediatric clinics of a major health maintenance organization (HMO) identified from their computerized records as either having or not having ADHD. Within each setting, we selected nonadhd controls from outpatients at pediatric medical clinics. A three-stage ascertainment procedure was used to select the subjects. For ADHD subjects, the first stage was the patient s referral to a psychiatric or pediatric clinic. The second stage confirmed the diagnosis of ADHD by screening all children with a positive diagnosis at the first stage using a telephone questionnaire administered to the mother. The questionnaire asked about the 14 DSM-III-R symptoms of ADHD and questions regarding study exclusion criteria. The third stage further confirmed the diagnosis made via the telephone questionnaire with face-to-face structured interviews with the mother. Only patients who received a positive diagnosis at all three stages were included in the final analysis. For Control probands, we ascertained participants from referrals to medical clinics for routine physical examinations at both the psychiatric and pediatric ascertainment sources. In stage 2, the control mothers responded to the DSM-III-R ADHD telephone questionnaire. Eligible controls meeting study entry criteria were recruited for the study and received the third stage diagnostic assessment with a structured interview. Only subjects classified as not having ADHD at all three stages were included in the control group. Diagnostic Measures Psychiatric assessments of probands and their siblings were made with the Schedule for Affective Disorders and Schizophrenia for School-Age Children: Epidemiologic Version (K-SADS-E) (Epidemiologic Version) (Orvaschel and Puig-Antich 1987), except for siblings older than 18 years of age, who were assessed with the Structured Clinical Interview for DSM-III-R (SCID) (Spitzer et al 1990). Diagnoses were based on independent interviews with the mothers and direct interviews with the offspring. We combined data from direct and indirect interviews by considering a diagnostic criterion positive if it was endorsed in either interview. To assess childhood diagnoses in adult siblings, we administered modules from the K-SADS-E covering childhood diagnoses. Socioeconomic status (SES) was assessed with the Hollingshead four-factor scale (Hollingshead 1975). The instrument measures SES on a five-level ordinal scale, with larger numbers indicating lower SES and smaller numbers indicating higher SES. All assessments were made by raters who were blind to the ascertainment status of the child (ADHD or nonadhd control) and the child s ascertainment source (psychiatric or pediatric). Different interviewers met with mothers and children in order to maintain blindness to case-control status and in order to prevent information from one informant influencing the assessment of the other. Diagnoses were considered positive if, based on the interview results, DSM-III-R criteria were unequivocally met. All diagnostic uncertainties were resolved by a committee of boardcertified child and adult psychiatrists who were blind to the subject s ascertainment group, ascertainment site, all data collected from other family members, and all nondiagnostic data (e.g. cognitive functioning). Diagnoses presented for review were considered positive only if a consensus was achieved that criteria were met to a degree that would be considered clinically meaningful. Kappa coefficients of agreement (Cohen 1968) were computed between raters and three board certified psychiatrists who listened to audiotaped interviews made by the raters. Based on 173 interviews, the median kappa was.86; kappa was.99 for ADHD,.80 for multiple anxiety disorders, and.83 for major depression. Substance Use Measures Our diagnostic interviews collected data on the lifetime use of nicotine, alcohol, marijuana, cocaine, amphetamines, sedatives, hallucinogens, opiates, steroids, glue, Ecstasy and nonprescription sleeping or diet pills. Hereafter, all substances with the exception of alcohol and nicotine will be referred to as drugs. For every substance used by a given subject, we derived the age of first use, lifetime diagnosis of DSM-III-R abuse or dependence, and age of onset. For cigarette smoking, we also recorded the presence of subthreshold diagnoses, defined as smoking everyday for less than one month, or smoking less frequently for

3 260 BIOL PSYCHIATRY 2006;59: J. Biederman et al longer than one month. In all analyses, smoking is defined as the presence of the full or subthreshold diagnosis. Statistical Analyses We analyzed diagnostic outcomes as well as substance use outcomes to broaden the comparability of our results with the extant literature. The substance outcomes were: alcohol or drug abuse or dependence, alcohol abuse or dependence, drug abuse or dependence, alcohol use, drug use, and marijuana use. We created a binary smoking indicator variable for each substance outcome, defined as positive if: 1) subjects met criteria for full or subthreshold cigarette smoking; and 2) they did not meet criteria for the substance use outcome before the onset of smoking within one year after the onset of smoking. Thus, the smoking status variable for a given substance outcome was coded positive only for subjects who were free from that substance use outcome at the age when their smoking diagnosis began. Nonsmokers and subjects who began smoking after the onset of the substance use were defined as negative on this binary variable. Subjects whose smoking and substance outcome began at the same age were impossible to categorize and were dropped from the analysis of that outcome. Since we assessed multiple substance outcomes, each with its own age of onset, the number of subjects dropped varied from outcome to outcome, ranging from five for alcohol use disorders to 11 for alcohol first use. We compared smokers to nonsmokers on demographic factors. Any variables that were significantly related to smoking status at alpha.10 were controlled in the following models. Also, due to its strong correlation with smoking and substance use, all models were repeated with conduct disorder as an additional covariate to control for this potential confound. Because not all subjects were through the period of risk for substance use outcomes, we employed Cox proportional hazard survival models, which accommodate these subjects through censoring. To address our first hypothesis, we ran survival models with the time to substance outcome as the dependent variable and the corresponding smoking status indicator as the independent variable, restricted to the ADHD subjects. A significant smoking term would indicate a relationship between smoking and a subsequent substance use outcome. To address our second hypothesis, we ran a survival model with the substance use outcome as the dependent variable and the corresponding smoking status indicator, ADHD status, and their interaction as the independent variables. A significant interaction term would indicate that the association between smoking and the subsequent substance use outcome differed by ADHD status. Because we are analyzing probands and their siblings, the assumption that each observation is independent of all other observations is violated. To account for this, we used robust estimates of variance so that p -values would not be underestimated (Liang and Zeger 1986). Statistical models were fit with the statistical software package STATA (Stata Corporation 2001). For some substance use outcomes, there were strata in which no subjects reported a positive response, yielding a prevalence of zero. This sparse data situation prevented the fitting of the Cox proportional hazards models described above. In these cases, we estimated logistic models using conditional exact inference, as implemented in LogXact (Cytel, Cambridge, Massachusetts, 1996). The statistical significance of each covariate in these regression models was determined by Wald s test. Alpha was set at.05, and statistical trends were recognized at alpha.10. All tests were two-tailed. Results Among the 300 subjects available for this analysis, the overall rate of smoking was 15%. Among youth with (n 97) and without (n 203) ADHD, the rates of smoking were 22% and 12%, respectively ( 2 (1) 4.4, p.036). The average age of smoking onset in the ADHD group was , while the average in the control group was (t (44) 4.0, p.001). Demographic characteristics are presented in Table 1. For the purpose of presenting the demographic variables, we used the smoking status indicator defined vis-à-vis alcohol or drug abuse or dependence to stratify the sample (n 291; 9 subjects were not used for analyses of this outcome due to missing data (n 2) and our inability to determine the temporal sequence of smoking and the substance outcome (n 7)). As shown, among youth with ADHD, no significant differences were noted between the smoking groups on any demographic variable. In both in the nonadhd subjects and in the full sample, females were more common in the nonsmoking group. No other differences were noted in the nonadhd subjects or in the full sample. Because sex surpassed our statistical threshold for the identification of confounding variables, all models were adjusted for sex. To address our first hypothesis, we tested the association between smoking and subsequent substance use outcomes, restricted to subjects with ADHD (see Table 2). Smoking was a significant predictor of subsequent alcohol or drug abuse or dependence, alcohol abuse or dependence, drug abuse or dependence, alcohol use, drug use, and marijuana use (Table 2). We also assessed these outcomes while adjusting for conduct disorder. The pattern of statistical significance across all the substance outcomes did not change, except for alcohol use, which was no longer statistically significant ( 2 (1).4, p.535), and alcohol abuse or dependence, which was reduced to a statistical trend ( 2 (1) 3.2, p.072). To address our second hypothesis, we tested the interaction Table 1. Demographic Features in Youth with and without ADHD, Stratified by Smoking Status Demographic Feature Non n 180 Youth without ADHD Youth with ADHD Entire Sample n 20 Test Statistic (df), p Value Non n 76 n 15 Test Statistic (df), p Value Non n 256 n 35 Test Statistic (df), p Value Age t (198) 1.7, p t (89).3, p t (289) 1.0, p.33 Age Range SES Z.4, p Z.8, p Z.2, p.86 Sex a 126 (70) 10 (50) 2 (1) 3.3, p (79) 10 (67) 2 (1) 1.1, p (73) 20 (57) 2 (1) 3.6, p.06 ADHD 76 (30) 15 (43) 2 (1) 2.5, p.12 Values in table represent mean SD or frequency(percent). ADHD, attention-deficit hyperactivity disorder; SES, socioeconomic scale. a Females.

4 J. Biederman et al BIOL PSYCHIATRY 2006;59: Table 2. Smoking and Subsequent Substance Use Outcomes in Youth with Attention Deficit-Hyperactivity Disorder Substance Use Outcome Non s s (n 76) a (n 15) Hazard Ratio (95% CI) Test Statistic, p value Substance Use Disorders Any Substance Use Disorder b 4 (5) 9 (60) 18.6 (4.6, 75.1) 2 (1) 16.7, p.001 Alcohol Abuse or Dependence 4 (5) 6 (40) 8.5 (2.5, 29.2) 2 (1) 11.4, p.001 Drug Abuse or Dependence 0 (0) 8 (44) Not estimated c p Substance Use Alcohol Use 23 (30) 7 (78) 3.7 (1.8, 7.4) 2 (1) 13.4, p.001 Drug Use 6 (8) 10 (71) 9.0 (3.0, 26.8) 2 (1) 15.5, p.001 Marijuana Use 5 (7) 11 (69) 8.2 (2.9, 23.3) 2 (1) 15.8, p.001 Values in tables represent frequency(percent): all analyses adjusted for sex. a Sample sizes are for the any substance use disorder outcome; sample sizes vary slightly for each outcome. b Alcohol or drug abuse or dependence. c Test statistic and hazard ratio cannot be calculated due to sparse data; p value provided by exact logistic regression. between smoking exposure and ADHD status in the prediction of subsequent substance use outcomes. As shown in Figure 1, the association between cigarette smoking and all the subsequent substance outcomes under study was stronger in youth with ADHD relative to controls. The interaction term was significant in the models predicting alcohol abuse or dependence, alcohol or drug abuse or dependence, alcohol use, and drug use. The interaction term in the model predicting marijuana use approached statistical significance (p.074). We also estimated these models while adjusting for conduct disorder. The interaction term in the model predicting alcohol abuse or dependence remained significant, and the interaction term in the model predicting alcohol use was reduced to a statistical trend (p.086). The interaction terms in the remaining models were not significant. Discussion In a sample of adolescents with and without ADHD, we tested the hypothesis that cigarette smoking is a gateway drug to subsequent use and abuse of illicit drugs or alcohol. We found that smoking greatly and significantly increased the risk for subsequent use of alcohol and drugs as well as the full development of alcohol or drug use disorders in youth. This association was stronger in ADHD youth relative to controls. While these findings do not conclusively demonstrate that smoking played a causal role in the subsequent use of alcohol and drugs in ADHD youth, they are consistent with such a hypothesis and provide evidence for the extension of the gateway hypothesis (Kandel 2002) to an ADHD sample and indicate that cigarette smoking may help identify ADHD youth at the highest risk to progress into addictions. The rate of smoking in our high school aged (i.e., years) control youth (13%; 95% confidence interval [6%, 19%]) is consistent with rates reported in epidemiological samples of the same age group. In the Youth Risk Behavior Surveillance System report (Grunbaum et al 2002), the rate of lifetime daily cigarette smoking (defined as a lifetime history of smoking 1 cigarettes every day for 30 days) in high school students in Massachusetts and Boston were 19.5% and 9.1%, respectively. In the nationally representative Youth Tobacco Surveillance report (Centers for Disease Control and Prevention (CDC) 2001), the rate and 95% confidence interval for lifetime history of daily smoking was % for high school students. These rates are consistently lower than the rate of smoking in our high school aged youth with ADHD (35%; 95% confidence interval [22%, 48%]). Taken together, these data support the ecological validity of our findings. The association between cigarette smoking and subsequent substance use found in this sample is also consistent with the gateway effect of cigarettes documented in the literature. For example, Merrill and colleagues (1999), analyzing data from the 1995 Youth Risk Behavior Survey, found that among high school seniors, cigarette use before age 13 was associated with a significant three-fold increased risk for marijuana use. A study analyzing the data from the National Household Survey on Drug Abuse also found significant associations between cigarette use and the use of cocaine, heroin, crack and marijuana (Lai et al 2000). In another study of this dataset, Wagner and Anthony (2002) found that youth who had used tobacco or alcohol were more likely to experience an opportunity to use marijuana and more likely to actually use it given that an opportunity had occurred. Other studies also found that regular smoking was a risk factor for the use of alcohol and other drugs (Hanna et al 2001; Torabi et al 1993). In addition, our study provides evidence that the gateway effect of cigarette smoking is stronger in ADHD youth relative to controls. To our knowledge, this issue has not been addressed before. This finding is particularly relevant in the context of ADHD considering mounting evidence that ADHD is a risk factor for alcohol and drug use and abuse (Molina and Pelham 2003; Murphy and Barkley 1996; Wilens et al 1997). There are several plausible explanations for the differential association found here between cigarette smoking and subsequent alcohol and substance use outcomes in ADHD versus control samples. For example, nicotine has been shown to affect dopaminergic activity in the mesolimbic system (Brody et al 2004; Clarke 1990; Pich et al 1997; Shadel et al 2000; United States Department of Health and Human Services 2000). The activation of the mesolimbic dopaminergic pathway provides not only a positive reinforcement for smoking but may also in turn enhance the effect of other psychoactive drugs, increasing the likelihood that smokers will develop other drug use habits (Lindsay and Rainey 1997). Because ADHD has been associated with dysregulated dopaminergic systems (Faraone and Biederman 1998; Faraone et al 2001), it may be that the nicotinic effect on brain dopamine that enhances the reward to other drugs (Lindsay and Rainey 1997) is magnified by the pre-existing dopaminergic abnormalities associated with ADHD, leading to the increased magnitude of effect seen in our results. More work is needed to substantiate these intriguing hypotheses.

5 262 BIOL PSYCHIATRY 2006;59: J. Biederman et al Figure 1. The interaction between cigarette smoking and attention-deficit/hyperactivity disorder (ADHD) and the risk for subsequent substance use outcomes. (A) Substance use disorder (SUD); (B) Alcohol use disorder; (C) Drug use disorder; (D) Alcohol use; (E) Drug use; (F) Marijuana use. Our findings showing that smoking is a gateway drug for subsequent substance use outcomes is particularly relevant in the context of ADHD considering that ADHD is a risk factor for both cigarette smoking (Molina and Pelham 2003; Tercyak et al 2002) and alcohol and drug abuse and dependence (Biederman et al 2004; Mannuzza et al 1998; Milberger et al 1997b). If smoking could be prevented in ADHD youth, a large amount of morbidity and mortality associated with tobacco, alcohol and drugs could be prevented. Preliminary evidence suggests that effective ADHD treatment has the added, serendipitous benefit of preventing smoking in ADHD youth. In a longitudinal study of youth with ADHD, we found that subjects treated with stimulants for 30% or less of their time in the study had a significantly higher rate of smoking

6 J. Biederman et al BIOL PSYCHIATRY 2006;59: initiation than youth treated for longer than 30% of the study (Monuteaux 2004). In a community sample of adolescents with ADHD, Whalen et al (2003) found that subjects receiving pharmacotherapy for ADHD smoked significantly less than those who went untreated. If confirmed, these findings can have an important public health impact. While these results are consistent with the gateway hypothesis, they could also be accounted for by the alternate notion that genetic risk, environmental exposures, or a combination of these factors predispose youth to using substances in general, including both nicotine and other drugs. In this model, the use of one substance (e.g., nicotine) does not causally affect the subsequent use of other substances (e.g., alcohol). The relative ease of access and availability to cigarettes could explain the observed temporal sequence of nicotine use followed by the use of other substances. Nevertheless, at the least our results provide valuable clinical information by identifying a strong risk factor (i.e., smoking) for future alcohol and drug use, abuse and dependence, especially in ADHD youth. These results should be considered in the light of some methodological limitations. Our sample was predominantly Caucasian and the probands were referred for ADHD. As such, our findings may not generalize to community samples and ethnic minorities. Future studies should attempt to replicate these findings in community samples. Also, our sample consisted of a preponderance of females. While this feature is a strong point since most studies of ADHD focus on males, future studies should replicate these findings in samples that are sex balanced. Another constraint on our generalizability is the limited range of SES in our sample. Although all five of the Hollingshead categories were represented in our sample, very few subjects were in the lowest categories (n 17 in category four and n 1in category five). Also, the size of our smoking sample was small. Although the results were mostly significant nonetheless, replication in a larger sample would be beneficial. We combined data from both parent and child reports to make diagnostic classifications. While there is some evidence that ADHD diagnoses based on child-only reports have questionable validity (Jensen et al 1999), our proband ADHD diagnoses was not subject to this problem, as they was made in three stages, relying on a clinician s judgment and maternal reports. Other studies have lended support for the use of multiple informants in child psychiatry research, such as in studies of prepubertal and early adolescent bipolar disorder (Tillman et al 2004) and disruptive behavior disorders (Hart et al 1994). Also, we depended on the retrospectively reported ages of onset for smoking and substance use to establish the temporal sequence of smoking as an exposure. To the degree that these ages were incorrectly recalled, our results suffer from misclassification and thus a reduction in precision. However, while the exact ages may not have been recalled accurately, the relative ordering of the ages of smoking and substance use initiation are likely to be correct, so that any misclassification of our exposure and outcome variables should be minimal. Also, there may be additional, unmeasured confounders that may account for our findings. While it is unlikely that an unmeasured confounder could account for the magnitude of effect found here, future studies should include other predictors of adolescent substance use. Finally, most of our subjects were not completely through the period of risk for substance use. While our survival models account for this problem, additional follow-up may be necessary to refine our estimates. Despite these concerns, our results extend the gateway hypothesis to an ADHD sample, demonstrating how cigarette smoking is a significant risk factor for subsequent substance use, abuse, and dependence. Also, these findings provide preliminary evidence that this effect in ADHD youth is significantly stronger than in controls. If replicated, these findings have public health consequences for ADHD youth, and underscore the already pressing need to prevent smoking in ADHD children. This work was supported, in part, by United States Public Health Service (National Institute of Mental Health [NIMH]) grant RO1 MH A2 (JB) and RO1 DA14419 (TW). JB receives research support from the following sources: Shire Laboratories, Eli Lilly & Company, Pfizer Pharmaceutical, Cephalon Pharmaceutical, Janssen Pharmaceutical, Neurosearch Pharmaceuticals, Stanley Medical Institute, Lilly Foundation, Prechter Foundation, NIMH, National Institute of Child Health and Human Development (NICHD) and National Institute on Drug Abuse (NIDA). JB is a speaker for the following speaker s bureaus: Eli Lilly & Company, Pfizer Pharmaceutical, Novartis, Wyeth Ayerst, Shire Laboratories, McNeil Pharmaceutical, and Cephalon Pharmaceutical. JB is on the advisory board for the following pharmaceutical companies: Eli Lilly & Company, CellTech, Shire Laboratories, Novartis Pharmaceutical, Noven Pharmaceutical, McNeil Pharmaceuticals, Janssen, Johnson & Johnson, Pfizer, and Cephalon Pharmaceuticals. TEW receives research support, is on the speaker s bureau, and is a consultant for: Abbott, Celltech Medieva, Eli Lilly & Company, McNeil, NIDA, NIMH, Novartis, Pfizer, Shire, Glaxo Klein Smith. SVF receives research support from the following sources: McNeil Consumer & Specialty Pharmaceuticals, Shire, Eli Lilly & Company, NIMH, NICHD, and National Institute on Neurological Disorders and Stroke (NINDS). SVF is a speaker for the following speaker s bureaus: Eli Lilly & Company, McNeil and Shire. SVF has had an advisory or consulting relationship with the following pharmaceutical companies: McNeil, Noven, Shire, and Eli Lilly & Company. Biederman J, Faraone SV, Monuteaux M, Spencer T, Wilens T, Bober M, et al (2004): Gender effects of attention deficit hyperactivity disorder in adults, revisited. Biol Psychiatry 55: Brody AL, Olmstead RE, London ED, Farahi J, Meyer JH, Grossman P, et al (2004): Smoking-induced ventral striatum dopamine release. 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