Sex- and Subtype-Related Differences in the Comorbidity of Adult ADHDs

Abstract

Objective: Comorbidity in adult ADHD (aADHD) has been investigated in a large number of studies using varying research approaches with divergent results. In contrast, there is limited information about sex- or subtype-related differences from studies with small sample size. Method: A large sample of 910 individuals (458 males, 452 females) affected with aADHD was recruited at a tertiary referral center. All probands underwent a four-step procedure for diagnosing aADHD, including the Structured Clinical Interview of Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) Axis I disorders to assess comorbidity. This study will provide additional information regarding the co-morbidity of Axis I disorders in the currently largest clinical referral sample. However, the main objective of this study is to gain information about sex- or subtype-related differences. Results: Affected females show higher rates of mood (61% vs. 49%), anxiety (32% vs. 22%), and eating disorders (16% vs. 1%) than affected males, while substance use disorders were more frequent in affected males (45% vs. 29%), which mirrors sex differences in prevalence in the general population. There were hardly any relevant differences in comorbidities between subtypes, with the exception of the inattentive subtype having an especially low prevalence of panic disorder. Comorbidity in general and substance use disorders in particular, but not sex or subtype, were highly predictive of lower psychosocial status. Conclusion: Sex-related differences in the comorbidity of aADHD are more pronounced than subtype-related differences.

Keywords

aADHD comorbidity Axis I sex subtype

Introduction

Adult ADHD (aADHD) as the persistent manifestation of one of the most common psychiatric conditions of childhood and adolescence is observed with a prevalence of about 4.4% in the general population (National Comorbidity Survey Replication [NCS-R], Kessler et al., 2005). The disorder comprising symptoms of inattention, motor hyperactivity, and maladaptive impulsivity is complemented by substantial deficits in emotion regulation and social functioning, impacting on all areas of life (Biederman et al., 2006). The interpretation of past research efforts on psychiatric comorbidity in aADHD has been hampered by an inconsistency of results due to the heterogeneity in study design, diagnostic procedures, and limited sample sizes, indicating the need for further research. To our knowledge, ours is the largest clinical referral sample to date.

Comorbidity With Axis I Disorders

ADHD populations prominently feature a wide range of comorbid Axis I disorders, with anxiety (40%-60%; Biederman, Faraone, Monuteaux, Bober, & Cadogen, 2004; Biederman et al., 1994; Kooij et al., 2004; Sobanski et al., 2008; Spencer et al., 2005) or substance use disorders (41%-67%; Bernardi et al., 2012; Cumyn, French, & Hechtman, 2009; McGough et al., 2005; Sprafkin, Gadow, Weiss, Schneider, & Nolan, 2007) and depression (24%-60%; Barkley & Murphy, 2007; Biederman et al., 2004; Biederman et al., 1993; Kooij et al., 2004; Shekim, Asarnow, Hess, Zaucha, & Wheeler, 1990; Sobanski et al., 2008; Spencer et al., 2005) emerging as the most frequent comorbidities. Within the category of anxiety disorder, particularly social phobia is consistently reported (9.5%-34%; Biederman et al., 2004; Biederman et al., 1994; Kooij et al., 2004; Shekim et al., 1990; Sobanski et al., 2008; Spencer et al., 2005). However, prevalence estimates vary considerably between clinical referral studies.

Results from two U.S. catchment studies confirm the stability of the aforementioned comorbidities over a 12-month period (National Epidemiologic Survey on Alcohol and Related Conditions, [NESARC]; Bernardi et al., 2012; NCS-R, Kessler et al., 2006). However, results diverge regarding which diagnosis within the spectrum of anxiety disorders is most common in patients, with one sample confirming the primacy of social phobia, and the second sample suggesting specific phobias to be most strongly associated with ADHD. Interestingly, while data on substance use disorders are inconclusive, for early adulthood the majority of longitudinal studies does not suggest an increased risk for mood (Mannuzza, Klein, Bessler, Malloy, & LaPadula, 1998; Rasmussen & Gillberg, 2001; Weiss, Hechtman, Milroy, & Perlman, 1985) or anxiety disorders (Fischer, Barkley, Smallish, & Fletcher, 2002; Mannuzza et al., 1998; Rasmussen & Gillberg, 2001; Weiss et al., 1985), indicating that difficulties in these domains—putatively on the basis of the ADHD pathology—might take more time to pass the threshold to clinical significance.

Finally, there is limited evidence of aADHD patients showing higher rates of bulimia nervosa, and to a lesser degree anorexia nervosa, which fits with the character of ADHD as a disorder of compromised impulse control (Kooij et al., 2004; Shekim et al., 1990; Sobanski et al., 2008).

Sex- and Subtype-Related Differences

Analogous to the picture we see in childhood and adolescence, the gender ratio among aADHD patients is clearly skewed toward males (Barbaresi et al., 2013; Bernardi et al., 2012; Kessler et al., 2006). One study screening for the presence of ADHD symptoms in a nonclinical sample identified more females than males to be affected (Polanczyk et al., 2010). However, the authors expressed doubt regarding the screening instrument’s results translating into a clinical diagnosis of ADHD. It is conceivable that women on the whole show more subclinical symptoms without reaching the cut-off. The combined type appears to affect both sexes in equal measure (Biederman et al., 2004; Cumyn et al., 2009), whereas the inattentive type is more common in females (Biederman et al., 2004). While there are no significant sex differences in the co-occurrence of major depression and anxiety disorders (Biederman et al., 2004; Biederman et al., 1994), males are more likely to develop substance use disorders, presumably for self-medication purposes (Biederman et al., 2004; Biederman et al., 1994; Cumyn et al., 2009; Sobanski, 2006). Females, on the other hand, show a greater predisposition toward comorbid eating disorders compared to male ADHD patients (Cumyn et al., 2009).

Since ADHD is a highly heterogeneous condition, patients can be further assigned to one of three subtypes which are currently recognized: the inattentive subtype (I-type) with impairments mainly in attentional functioning, the hyperactive subtype (H-type) with difficulties in terms of impulse control and excess motor behavior, and the combined subtype (C-type) showing symptoms from both domains. Despite isolated reports of a predominance of the I-type (Cumyn et al., 2009), the majority of aADHD cases fall into the latter category of combined presentation followed by the inattentive subtype, while a purely hyperactive ADHD is fairly rare (Sobanski et al., 2008; Wilens et al., 2009).

Individuals of the combined type are oftentimes more broadly affected, which fits with evidence indicating that this subtype has more frequent and more severe comorbidity on Axis I than the others (Cumyn et al., 2009; Faraone, Biederman, Weber, & Russell, 1998; Sprafkin et al., 2007; Wilens et al., 2009). Despite their generally greater proneness to additional psychiatric diagnoses, data on subtype-related differences of comorbidities are inconsistent. Regarding mood disorders, higher, lower, or equal incidences are described for the C-type (Cumyn et al., 2009; Millstein, Wilens, Biederman, & Spencer, 1997; Sobanski et al., 2008), while this subtype consistently has higher rates of substance use disorders (Millstein et al., 1997; Murphy, Barkley, & Bush, 2002; Sobanski et al., 2008). With respect to anxiety disorders, while some studies did not detect differences between subtypes (Millstein et al., 1997; Sobanski et al., 2008), Cumyn and colleagues find C-type adults to be more likely to present with social or specific phobia compared to I-type ADHD (Cumyn et al., 2009). Furthermore, Sobanski et al. included a group of patients (I/C) who had experienced the characteristic decline in hyperactive-impulsive symptoms upon entering adulthood, thus effectively switching from the combined to the inattentive subtype, and this particular group more frequently suffers from a comorbid eating disorder than either I-type or C-type. Psychosocial impairment was comparable across ADHD group (Sobanski et al., 2008).

Objective of the Study

However, the main objective of this study is to expand the knowledge regarding sex- and subtype-related differences in comorbidity rates, with a focus on Axis I diagnoses. To this end, we examined the largest sample of adult ADHD patients to date, recruited mainly through our specialized outpatient unit.

Hypotheses

To reevaluate previous results about comorbid disorders and to obtain additional information about sex- and subtype-associated differences in aADHD, we collected an extended sample with more statistical power. We examined the following a priori hypotheses:

Hypothesis 1: Comorbidity with Axis I disorders. Psychiatric comorbidity on Axis I—specifically mood disorders, anxiety disorders, substance use disorders, and eating disorders—is highly prevalent in adult ADHD patients. Specifically, comorbidity in general and substance use disorders in particular are highly predictive of lower psychosocial status.

Hypothesis 2: Sex-related differences. In contrast to affected children and adolescents, the frequency of males and females is similar among aADHD patients. There are no relevant sex-related differences in the comorbidity with Axis I disorders in general, but we expect to find sex-dependent comorbidity profiles with specific Axis I disorders, that is, a predominance of affected males with substance use disorders and affected females with eating disorders according to the occurrence of these disorders in the general population. We do not expect sex to modify the clinical outcome of aADHD.

Hypothesis 3: Subtype-related differences. C-type is the most prevalent subtype of ADHD with more frequent comorbidity on Axis I than the other subtypes. There are subtype-related differences in the comorbidity with specific Axis I disorders, with a possible link between C-type and depression. We predict that subtype does not modify the clinical outcome of aADHD.

Hypothesis 4: Interaction of sex- and subtype. There are no relevant interactions between sex and subtype regarding the prevalence of comorbidities in aADHD.

Method

Participants

In- and outpatients of the Department of Psychiatry, Psychosomatics, and Psychotherapy (University of Wuerzburg) presenting with aADHD were extensively screened between 2003 and 2009. Data on an initial sample (N = 372; 173 females, 199 males; mean age 33.3 years, SD = 10.3) recruited between 2003 and 2005 have already been published elsewhere (Jacob et al., 2007). The final sample using identical inclusion and exclusion criteria comprised a total of 910 aADHD patients (452 females, 458 males; mean age 34.5 years, SD = 10.2) diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994), with an age range from 18 to 65 years. Briefly, inclusion criteria pertaining to the ADHD symptomatology were onset before the age of 7 years, chronic course of ADHD with life-long persistence, and current diagnosis (Jacob, Philipsen, Ebert, & Deckert, 2008). Most of the patients did not receive prior medication or other treatment for ADHD. Probands with current substance use problems underwent detoxification in an in-patient setting. Exclusion criteria were the occurrence of any organic disorder that may induce symptoms like ADHD or an IQ below 80 (Mehrfachwahl-Wortschatz-Intelligenztest-MWT-B < 13 points; Lehrl, 2005). Patients affected with bipolar affective disorder preventing clear differential diagnosis were excluded. Patients were furthermore excluded from participation if the ADHD symptoms occur exclusively during the course of a pervasive developmental disorder, schizophrenia, or other psychotic disorder, or symptoms are better accounted for by another psychiatric disorder (criterion E of DSM-IV; see Table 1).

Table 1.

Participants.

SCID1, n = 872	Male						Female
n	430						442
Axis I comorbidities	Combined		Inattentive		Hyperactive		Combined		Inattentive		Hyperactive
n	271		120		39		314		104		24
(Quantitative variables)	M	SD	M	SD	M	SD	M	SD	M	SD	M	SD
Number of comorbidities	2.3	2.3	1.5	1.7	2.2	2.3	2.1	2.0	1.9	1.6	1.4	1.5
Age	34.0	10.5	30.7	11.1	34.9	11.1	36.2	9.2	35.0	9.9	36.7	7.4
IQ (MWT)	110.7	15.1	110.6	13.9	109.0	13.8	112.3	13.1	112.9	13.1	113.0	16.3
Wender Utah Rating Scale	39.1	14.0	33.9	12.9	33.3	13.5	36.1	13.7	29.6	13.0	29.0	13.5
Psychosocial status	3.9	1.0	4.0	0.9	4.3	1.1	4.0	1.0	4.1	0.8	4.1	1.0

Note. SCID1 = Structured Clinical Interview for DSM-IV Axis I Disorders; MWT = Multiple Choice Vocabulary Intelligence Test; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994).

The study was approved by the Ethics Committee of the University of Würzburg. All participants granted a written informed consent after the procedures and aims of the study had been fully explained.

Instruments

Diagnosis of aADHD involved a four-step procedure, described in detail in previous publications (Jacob et al., 2008; Jacob et al., 2007). After ruling out other physical and mental conditions possessing higher explanatory value pertaining to the symptoms, lifetime comorbidity was assessed by means of the structured clinical interviews of Axis I (Structured Clinical Interview for DSM-IV Axis I Disorders [SCID I]; First, Spitzer, Gibbon, & Williams, 1996) and Axis II (Structured Clinical Interview for DSM-IV Axis II Personality Disorders [SCID II]; First, Gibbon, Spitzer, Williams, & Benjamin, 1997) disorders. Intellectual functioning was ascertained with the MWT-B (IQ total sample: M = 111.5, SD = 14.0; IQ females: M = 112.5, SD = 13.3; IQ males: M = 110.5, SD = 14.6). Subsequently, adult ADHD was assessed according to DSM-IV criteria. To ensure diagnostic validity, patients were examined a minimum of two times by more than one clinically experienced investigator. Supplementary information to corroborate diagnosis was obtained through self-report (Diagnostic Checklist of ADHD [ADHS-DC]; Rösler, Retz-Junginger, Retz, & Stieglitz, 2008) and interviews with partners, relatives, and friends, and we expressly probed the coherence of psychometric, psychopathological, and biographical information. In a third step, the presence of childhood ADHD was confirmed retrospectively by combining information from a structured clinical interview, and the Wender-Utah-Rating Scale (WURS-K; females: M = 34.2, SD = 13.9; males: M = 37.1, SD = 13.8; Rösler et al., 2008) as a self-report measure (Biederman et al., 1994). Additional information from school report cards/certificates and parents was included if available. Finally, at stage four anamnestic information served to confirm the persistent and uninterrupted presence of the disorder without phases of full remission.

Psychosocial status on the basis of a standardized biographical account was quantified on a scale ranging from 0 to 9, with low scores indicating better psychosocial functioning (total sample M = 4.0, SD = 1.0). Events contributing to this score were family status (divorced, separated, married two or more times), education (discontinued, two or more classes repeated), occupational qualification (unskilled, unemployed), psychiatric in-patient treatment, delinquency, suicidal and aggressive behavior.

Statistical Analysis

Fisher’s exact test was used to assess differences in sex ratio between ADHD subtypes, as well as differences in the frequency of comorbid disorders between groups (sex or subtype). We employed regression models to investigate interactions of sex and subtype in terms of comorbidity rates (logistic regression) and between the number of comorbid disorders and psychosocial status (linear regression). Psychosocial status was compared between subtypes in an analysis of variance (ANOVA). All reported p values reflect nominal significances.

Results

Comorbidity With Axis I Disorders

Adult ADHD patients show high rates of mood (focus on depression), anxiety (focus on social phobia), substance use (focus on cannabis, alcohol, and amphetamines), and eating disorders (anorexia and bulimia nervosa; see Table 2).

Table 2.

Comorbidity With Axis I Disorders.

SCID1 (n = 872)	Male						Female
SCID1 (n = 872)	430	49.3					442	50.7
	Combined		Inattentive		Hyperactive		Combined		Inattentive		Hyperactive
	N	%	n	%	n	%	N	%	n	%	n	%
Comorbidities lifetime	271	6.0	120	27.9	39	9.1	314	71.0	104	23.5	24	5.4
Mayor depressive disorder	102	37.9	38	31.7	11	28.2	153	49.2	52	50.0	6	25.0
Dysthymic disorder (current)	31	11.6	11	9.2	4	10.3	28	9.0	13	12.5	2	8.3
Depressive disorder NOS	36	13.6	16	13.3	6	15.4	50	16.1	22	21.1	4	16.7
Mood disorder due to a general medical condition	3	1.1	1	0.8	1	2.6	6	1.9	0	0	0	0
Substance-induced mood disorder	10	3.7	3	2.5	2	5.1	6	1.9	0	0	0	0
Any mood disorder	137	50.5	55	45.8	17	43.6	190	60.5	70	67.3	11	45.8
Alcohol abuse	36	13.3	7	5.8	4	10.3	31	9.9	4	3.8	1	4.2
Alcohol dependence	40	14.8	8	6.7	10	25.6	17	5.4	3	2.9	1	4.2
Sedative, hypnotic, or anxiolytic abuse	8	3.0	1	0.8	2	5.1	4	1.3	0	0	1	4.2
Sedative, hypnotic, or anxiolytic dependence	5	1.9	2	1.7	1	2.6	3	1.0	3	2.9	0	0
Cannabis abuse	35	13.0	12	10.0	10	25.6	24	7.6	12	11.5	1	4.2
Cannabis dependence	55	20.4	21	17.5	8	20.5	37	11.8	9	8.6	2	8.3
Amphetamine (or amphetamine-like) abuse	23	8.5	6	5.0	3	7.7	16	5.1	6	5.8	1	4.2
Amphetamine (or amphetamine-like) dependence	21	7.8	5	4.2	0	0	7	2.2	0	0	0	0
Opioid abuse	10	3.7	2	1.7	2	5.1	6	1.9	1	1.0	1	4.2
Opioid dependence	4	1.5	1	0.8	1	2.6	7	2.2	0	0	0	0
Cocaine abuse	23	8.5	4	3.3	1	2.6	11	3.5	3	2.9	2	8.3
Cocaine dependence	9	3.3	1	0.8	2	5.1	7	2.2	0	0	0	0
Halluzinogen abuse	24	8.9	6	5.0	2	5.1	9	2.9	2	1.9	1	4.2
Halluzinogen dependence	7	2.6	1	0.8	0	0	3	1	0	0	0	0
Polytoxikomania abuse	10	3.7	2	1.7	1	2.6	5	1.6	2	1.9	1	4.2
Polytoxikomania dependence	13	4.8	2	1.7	2	5.1	9	2.9	1	1	0	0
Substance abuse NOS	0	0	0	0	0	0	1	0.3	0	0	0	0
Substance dependence NOS	2	0.7	0	0	0	0	2	0.6	0	0	0	0
Any substance-related disorder	126	46.5	43	35.8	24	61.5	95	30.3	29	27.9	4	16.7
Panic disorder	17	6.3	0	0	2	5.1	29	9.3	3	2.9	2	8.3
Agoraphobia	11	4.1	1	0.8	1	2.6	16	5.1	2	1.9	2	8.7
Social phobia	30	11.1	12	10.1	4	10.3	39	12.5	13	12.5	0	0
Specific phobia	8	3.0	3	2.5	2	5.1	23	7.3	8	7.7	0	0
Obsessive-compulsive disorder	6	2.2	1	0.8	0	0	5	1.6	4	3.8	0	0
Post-traumatic stress disorder	10	3.7	3	2.5	1	2.6	11	3.5	5	4.8	0	0
Generalized anxiety disorder	5	1.8	2	1.7	1	2.6	20	6.4	4	3.9	1	4.2
Anxiety disorder due to a general medical condition	1	0.4	0	0	0	0	1	0.3	0	0	0	0
Substance-induced anxiety disorder	3	1.1	1	0.8	0	0	0	0	1	1	0	0
Any anxiety disorder	67	24.7	19	15.8	8	20.5	107	34.1	30	28.9	5	20.8
Somatization disorder (current)	1	0.4	1	0.8	0	0	1	0.3	1	1	0	0
Pain disorder (current)	1	0.4	1	0.8	0	0	6	1.9	2	1.9	1	4.2
Somatoform disorder NOS (current)	2	0.8	1	0.8	0	0	5	1.6	3	2.9	0	0
Hypochondriasis (current)	3	1.1	1	0.8	0	0	4	1.3	0	0	0	0
Dysmorphic disorder (current)	1	0.4	0	0	0	0	0	0	0	0	0	0
Any somatoform disorder	7	2.6	4	3.3	0	0	15	4.8	6	5.8	1	4.2
Anorexia nervosa	1	0.4	0	0	0	0	26	8.3	11	10.6	2	8.3
Bulimia nervosa	3	1.1	0	0	0	0	27	8.6	7	6.7	1	4.2
Binge eating	1	0.4	0	0	0	0	9	2.9	2	1.9	0	0
Any eating disorder	4	1.5	0	0	0	0	51	16.2	17	16.4	2	8.3

Note. SCID1 = Structured Clinical Interview for DSM-IV Axis I Disorders; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994), NOS= not otherwise specified. Boldface summarizes the results of the different categories of mental deseases.

In general, comorbidity is associated with a significantly lower psychosocial status. Pure aADHD patients report a mean of 3.7 (SD = 0.8) psychosocial problems, while patients with one or more comorbidities have a mean of 4.1 (SD = 1.0) psychosocial problems (p < .0001). Especially, major depression (p = .02), anxiety disorders (p = .01), and substance use disorders (p < .0001) are associated with lower psychosocial status. On average, each additional comorbidity increases the number of psychosocial problems by 0.1 (p < .0001 in linear regression).

Sex-Related Differences

Our study did not find sex differences in aADHD prevalence, psychosocial impairment, or number of comorbidities; however, the specific comorbid diagnoses are sex-dependent. Females compared with males present with higher rates of mood disorders in general (p < .001) and major depression in particular (p < .001), anxiety disorders (p < .001) with a focus on specific phobias (p < .001), and generalized anxiety disorder (p < .001) as well as all eating disorders (p < .001), anorexia nervosa (p < .001), bulimia nervosa (p < .001), and binge eating disorder (p = .006). In contrast to this, aADHD men are more likely than women to develop substance use disorders in general (p < .0001) and alcohol (p = .0005), cannabis (p = .0005), amphetamine (p = .0005), and hallucinogen (p = .002) abuse in particular. No significant differences emerged with regard to somatoform disorders (p = .08; see Table 3).

Table 3.

Sex-Related Differences.

SCID1, n = 872	Male		Female
SCID1, n = 872	430	49.3	442	50.7
Comorbidities lifetime	n	%	n	%
Mayor depressive disorder	151	35.3	211	48.1
Dysthymic disorder (current)	46	10.8	43	9.8
Depressive disorder NOS	58	13.7	76	17.4
Mood disorder due to a general medical condition	5	1.2	6	1.4
Substance-induced mood disorder	15	3.5	6	1.4
Any mood disorder	209	48.6	271	61.3
Alcohol abuse	47	10.9	36	8.1
Alcohol dependence	58	13.5	21	4.8
Sedative, hypnotic, or anxiolytic abuse	11	2.6	5	1.1
Sedative, hypnotic, or anxiolytic dependence	8	1.9	6	1.4
Cannabis abuse	57	13.3	37	8.4
Cannabis dependence	84	19.6	48	10.9
Amphetamine (or amphetamine-like) abuse	32	7.4	23	5.2
Amphetamine (or amphetamine-like) dependence	26	6.0	7	1.6
Opioid abuse	14	3.3	8	1.8
Opioid dependence	6	1.4	7	1.6
Cocaine abuse	28	6.5	16	3.6
Cocaine dependence	12	2.8	7	1.6
Halluzinogen abuse	32	7.4	12	2.7
Halluzinogen dependence	8	1.9	3	0.7
Polytoxikomania abuse	13	3.0	8	1.8
Polytoxikomania dependence	17	4.0	10	2.3
Substance abuse NOS	0	0	1	0.2
Substance dependence NOS	2	0.5	2	0.4
Any substance-related disorder	193	44.9	128	29.0
Panic disorder	19	4.4	34	7.7
Agoraphobia	13	3.0	20	4.5
Social phobia	46	10.7	52	11.8
Specific phobia	13	3.0	31	7.0
Obsessive-compulsive disorder	7	1.6	9	2.0
Post traumatic stress disorder	14	3.3	16	3.6
Generalized anxiety disorder	8	1.9	25	5.7
Anxiety disorder due to a general medical condition	1	0.2	1	0.2
Substance-induced anxiety disorder	4	0.9	1	0.2
Any anxiety disorder	94	21.9	142	32.1
Somatization disorder (current)	2	0.5	2	0.4
Pain disorder (current)	2	0.5	9	2.0
Somatoform disorder NOS (current)	3	0.7	8	1.8
Hypochondriasis (current)	4	0.9	4	0.9
Dysmorphic disorder (current)	1	0.2	0	0
Any somatoform disorder	11	26	22	5.0
Anorexia nervosa	1	0.2	39	8.9
Bulimia nervosa	3	0.7	35	8.0
Binge eating	1	0.2	11	2.5
Any eating disorder	4	0.9	70	15.8

Note. SCID1 = Structured Clinical Interview for DSM-IV Axis I Disorders; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994). NOS= not otherwise specified. Boldface summarizes the results of the different categories of mental deseases.

Subtype-related differences

As expected, the combined type emerged as the most prevalent subtype of aADHD (66.5%), followed by the primarily inattentive (25.8%) and the primarily hyperactive type (7.7%). The number of comorbidities differs by subtype (p = .006), with the combined type showing the highest comorbidity rates (M = 2.2, SD = 2.1), H-type has a mean of 1.9 comorbidities (SD = 2.1) and I-type has a mean of 1.7 comorbidities (SD = 1.7). Anxiety disorder prevalences differ between subtypes (p = .04), which is mostly due to the higher rates of panic disorder in (p = .001). Patients affected with the C-type and H-type show significantly higher comorbidity with alcohol use disorders than those with I-type (p = .001). The H-type is least likely to be diagnosed with major depression (p = .02). There are no significant differences between subtypes in mood, substance use, somatoform, or eating disorders (all ps > .13; see Table 4).

Table 4.

Subtype-Related Differences.

SCID1, n = 872	Combined		Inattentive		Hyperactive
SCID1, n = 872	585	67.1	224	25.7	63	7.2
Comorbidities lifetime	n	%	n	%	n	%
Mayor depressive disorder	255	44.0	90	40.2	17	27.0
Dysthymic disorder (current)	59	10.2	24	10.8	6	9.5
Depressive disorder NOS	86	15.0	38	17.0	10	15.9
Mood disorder due to a general medical condition	9	1.6	1	0.4	1	1.6
Substance-induced mood disorder	16	2.8	3	1.3	2	3.2
Any mood disorder	327	55.9	125	55.8	28	44.4
Alcohol abuse	67	11.4	11	4.9	5	7.9
Alcohol dependence	57	9.7	11	4.9	11	17.5
Sedative, hypnotic, or anxiolytic abuse	12	2.1	1	0.4	3	4.8
Sedative, hypnotic, or anxiolytic dependence	8	1.4	5	2.2	1	1.6
Cannabis abuse	59	10.1	24	10.7	11	17.5
Cannabis dependence	92	15.8	30	13.4	10	15.9
Amphetamine (or amphetamine-like) abuse	39	6.7	12	5.4	4	6.3
Amphetamine (or amphetamine-like) dependence	28	4.8	5	2.2	0	0
Opioid abuse	16	2.7	3	1.3	3	4.8
Opioid dependence	11	1.9	1	0.4	1	1.6
Cocaine abuse	34	5.8	7	3.1	3	4.8
Cocaine dependence	16	2.7	1	0.4	2	3.2
Halluzinogen abuse	33	5.6	8	3.6	3	4.8
Halluzinogen dependence	10	1.7	1	0.4	0	0
Polytoxikomania abuse	15	2.6	4	1.8	2	3.2
Polytoxikomania dependence	22	3.8	3	1.4	2	3.2
Substance abuse NOS	1	0.2	0	0	0	0
Substance dependence NOS	4	0.7	0	0	0	0
Any substance-related disorder	221	37.8	72	32.1	28	44.4
Panic disorder	46	7.9	3	1.3	4	6.3
Agoraphobia	27	4.6	3	1.3	3	4.8
Social phobia	69	11.8	25	11.2	4	6.3
Specific phobia	31	5.3	11	4.9	2	3.2
Obsessive-compulsive disorder	11	1.9	5	2.2	0	0
Post traumatic stress disorder	21	3.6	8	3.6	1	1.6
Generalized anxiety disorder	25	4.3	6	2.7	2	3.2
Anxiety disorder due to a general medical condition	2	0.3	0	0	0	0
Substance-induced anxiety disorder	3	0.5	2	0.9	0	0
Any anxiety disorder	174	29.7	49	21.9	13	20.6
Somatization disorder (current)	2	0.3	2	0.9	0	0
Pain disorder (current)	7	1.2	3	1.3	1	1.6
Somatoform disorder NOS (current)	7	1.2	4	1.8	0	0
Hypochondriasis (current)	7	1.2	1	0.4	0	0
Dysmorphic disorder (current)	1	0.2	0	0	0	0
Any somatoform disorder	22	3.8	10	4.5	1	1.6
Anorexia nervosa	27	4.6	11	4.9	2	3.2
Bulimia nervosa	30	5.2	7	3.1	1	1.6
Binge eating	10	1.7	2	0.9	0	0
Any eating disorder	55	9.4	17	7.6	2	3.2

Interaction of Sex- and Subtype

Subtype distribution differs between males and females (p = .003): both the inattentive (27.9% vs. 23.7%) and the hyperactive subtype (10% vs. 5.3%) are more common in males, however, the combined type is the most frequent subtype, irrespective of sex. No significant sex-by-subtype interactions were observed (all ps > .2) in the prevalence of diagnostic groups or single disorders.

Discussion

Understanding psychiatric comorbidity in adult ADHD is greatly hampered by the highly divergent findings reported in previous epidemiological studies. These inconsistencies are most likely based on differences in study design, recruitment strategy and setting, diagnostic procedures as well as inclusion and exclusion criteria, warranting further research on a large representative sample. We have reevaluated comorbidity in adult ADHD in, to our knowledge, the largest clinical referral sample to date. The size of our sample granted us the unique opportunity to observe even the rarer comorbid disorders. Furthermore, it allows for the investigation of the influence of sex and subtype, as well as the interaction of those two factors, which could partly explain heterogeneous findings.

Comorbidity With Axis I Disorders

Across the different types of studies, there is general agreement that comorbidity with Axis I disorders is a common feature in clinical and nonclinical samples of aADHD (Bernardi et al., 2012; Cumyn et al., 2009; Kessler et al., 2006; McGough et al., 2005). Still, prevalence estimates vary considerably due to the aforementioned reasons, but also depending on the disorders included in the screening.

Our study confirms in a much larger sample size the results of previous retrospective studies consistently demonstrating high comorbidity of mood disorders (55%) and especially major depressive episodes in aADHD (Barkley & Murphy, 2007; Biederman et al., 2004; Biederman et al., 1993; Kooij et al., 2004; Shekim et al., 1990; Sobanski et al., 2008; Spencer et al., 2005). It is also line with the results of the two prospective U.S. studies NCS-R (Kessler et al., 2006) and NESARC (Bernardi et al., 2012). One reason for the majority of the longitudinal studies failing to trace those elevated rates of mood disorders back into early adulthood may be the median age of onset (30 years) for those disorders (Kessler et al., 2005) and limited sample sizes (Fischer et al., 2002; Mannuzza et al., 1998; Rasmussen & Gillberg, 2001; Weiss et al., 1985).

We replicate the elevated lifetime comorbidity of anxiety disorders (27%), which is described in most of the retrospective clinical studies (Biederman et al., 2004; Biederman et al., 1994; Kooij et al., 2004; Spencer et al., 2005) and the catchment studies NCS-R (Kessler et al., 2006) and NESARC (Bernardi et al., 2012). Analogous to mood disorders, the vast number of longitudinal studies do not identify an elevated prevalence of anxiety disorders in early adulthood (Fischer et al., 2002; Mannuzza et al., 1998; Rasmussen & Gillberg, 2001; Weiss et al., 1985). However, since the median age of onset for anxiety disorders is 11 years, the time of evaluation in early adulthood is not relevant in this context (Kessler et al., 2005). More relevant might be a selection bias for externalization disorders at the time of recruitment due to the current diagnostic criteria that might have favored underdiagnosing of mood and anxiety disorders.

Our results are in line with retrospective and catchment studies consistently reporting that social phobia (11.3% in our sample) is one of the most common specific anxiety disorders in aADHD (Bernardi et al., 2012; Biederman et al., 2004; Biederman et al., 1994; Kessler et al., 2006; Kooij et al., 2004; Shekim et al., 1990; Sobanski et al., 2008; Spencer et al., 2005). We present corroborating evidence for high comorbidity with panic disorder (6.1%), which is the second most frequent anxiety disorder in our sample.

There is consensus in retrospective studies (Biederman et al., 2004; Sobanski et al., 2008), longitudinal studies (Gittelman, Mannuzza, Shenker, & Bonagura, 1985; Mannuzza, Klein, Bessler, Malloy, & LaPadula, 1993; Mannuzza et al., 1998; Rasmussen & Gillberg, 2001), and catchment studies (Bernardi et al., 2012; Kessler et al., 2006) that substance use disorders are highly frequent in aADHD (36.8% in our sample). Self-medication may be part of the explanation. Moreover, shared genetic susceptibility may be present in the pathogenesis of both aADHD and addiction vulnerability (Franke et al., 2012).

The results from the various studies are inconsistent with respect to the kind of substances which are more often used in aADHD. We find that cannabis dependence is the most frequent specific substance use disorders followed by cannabis abuse, alcohol abuse, and alcohol dependence. Amphetamine use disorders are less frequent. NCS-R (Kessler et al., 2006) and NESARC (Bernardi et al., 2012) do not differ between the various specific drug use disorders. Spatial or temporal preferences and availability of the different types of substances that can potentially be abused and diagnostic uncertainty between consumption and abuse especially of drugs impair the generalizability of the data.

We described evidence that the frequencies of anorexia nervosa and bulimia nervosa are similarly elevated in aADHD, while other studies found a higher comorbidity with bulimia than with anorexia nervosa (Kooij et al., 2004; Shekim et al., 1990; Sobanski et al., 2008). Sobanski and colleagues hypothesized that this is due to impulsive eating behavior (bulimia nervosa and binge eating disorders), while eating disorders with restrictive eating behavior (anorexia nervosa) are not found in their sample (Sobanski et al., 2008). Interestingly, the frequency of eating disorders is not mentioned in either of the two catchment study.

We confirm our previous findings that high rates of comorbid Axis I disorders and substance use disorders are associated with significantly lower psychosocial levels (Jacob et al., 2007) and find preliminary evidence that mood disorders and anxiety disorders might cause similar effects.

Sex-Related Differences

Our data are in line with several retrospective studies that showed a balanced distribution of females and males (Wilens et al., 2009). Boomsma et al. (2010) summarize that findings in genetic epidemiology suggest small differences between the sexes.

In the present sample, there is no association between the number of Axis I disorders and sex. Sex-specific differences in the comorbidity of certain disorders have similarities between affected with aADHD and general population. Mood disorders, anxiety disorders, and eating disorders are more frequent in affected females. Initial findings indicate higher rates of eating disorders (Cumyn et al., 2009; Nazar et al., 2008). Our data also confirm that substance use disorders are more frequent in affected males (Biederman et al., 2004; Biederman et al., 1994; Cumyn et al., 2009; Sobanski, 2006). We detect sex-related differences of specific anxiety disorders (specific phobia and generalized anxiety) in our sample, which does not confirm the results of two studies conducted by Biederman and colleagues (Biederman et al., 2004; Biederman et al., 1994).

Subtype-Related Differences

We replicate the finding that the C-Type is the most prevalent subtype of aADHD, followed by the I-type and the H-type. (Cumyn et al., 2009; Faraone et al., 1998; Sobanski et al., 2008; Sprafkin et al., 2007; Wilens et al., 2009). While the number of included probands in the preexisting studies is relatively low, we could find significant evidence for subtype-related differences in the comorbidity with Axis I disorders. In our sample, comorbid major depression is more frequent in C-type and I-type than in H-type, which is in line with the findings of Millstein et al. (Millstein et al., 1997). Sex-related differences in inattention or related symptoms might be involved.

The C-type has the highest comorbidity with anxiety disorders, which replicates the findings of Cumyn and colleagues (Cumyn et al., 2009), while Millstein et al. and Sobanski et al. do not detect subtype-related differences (Millstein et al., 1997; Sobanski et al., 2008). We replicated the low comorbidity of the I-type with panic disorder (Cumyn et al., 2009). There are no associations between the aADHD subtype and the psychosocial score. This is in contrast to the study of Sobanski et al., which only had limited sample size and might have caught a specific subset of patients and call into question the results’ generalizability (Sobanski et al., 2008).

Interaction of Sex- and Subtype

The observed differences in comorbidity between sex-subtype groups are as expected; there are no relevant interaction effects.

Limitations

A variety of reasons including methodological differences may have contributed to inconsistent findings among publications. Comorbidity of aADHD varies considerably, depending upon whether a prospective or retrospective design was used in clinical or nonclinical samples. Published prospective studies applied now outdated diagnostic nomenclatures, which lacked clear operational criteria or based inclusion on parents’ or teachers’ assessment of behavioral problems. Mean age of patients is also a relevant factor in the assessment of comorbidity, as examination at young adult age might be before the onset of a comorbid psychiatric disorder and thus miss the critical window. For example, the median age of onset is much earlier for anxiety (11 years) and impulse-control (11 years) disorders than for substance use (20 years) and mood (30 years) disorders according to NCS-R (Kessler et al., 2005). Most studies specify the comorbidity of aADHD during the assessed life span. There are age-dependent modifications of aADHD with a decline of hyperactivity and, to variable extent, impulsivity. Patients referred for treatment at an early age might be more severely affected or display more disruptive behavior in social contexts. This is contrasted by retrospective studies which evaluated a subgroup of childhood ADHD with persistent symptoms. Clinical referral studies comprise a selection of ADHD patients needing psychiatric care compared to general population, while catchment studies investigate prevalence rates and comorbidity in the general population. Hence, the consideration of retrospective, prospective, and catchment studies allows for different perspectives and capturing different aspects of ADHD across the life span.

One of the challenges consists in establishing whether symptoms are indeed due to aADHD and not another condition, or if another condition is comorbid with aADHD (Cumyn et al., 2009). Particular difficulties are to be expected in the field of comorbidity with bipolar disorder, substance use disorders, and personality disorders. As a result of this diagnostic gray area, these patients are underdiagnosed and undertreated. Optimal diagnosis, understanding and treatment of comorbid conditions are important, as comorbidity is crucially associated with functional impairment and suboptimal treatment responses. The above-described four-step procedure in diagnosing aADHD and the second opinion of another experienced investigator are established to increase diagnostic validity.

The diagnostic procedure of aADHD is not valid in probands affected with current substance use disorders. The larger the time gap between the end of the withdrawal and the diagnostic procedure of aADHD, the more accurate the differential diagnosis will be, and the less likely the patient will show up for assessment, since he now has to handle aADHD symptoms without self-medication and craving. Exclusion criteria concerning current alcohol and drug abuse or dependence influence sex discrepancies. In our study, the number of included men should be reduced by this exclusion criterion. The differences in the ranking of the specific disorders in probands affected with aADHD and general population might reflect different exclusion criteria.

Conclusion

Our data confirm that in adulthood, ADHD patients present with a high degree of comorbidity, and the presence of a comorbid disorder corresponds to lower levels of psychosocial functioning. This is particularly true for substance use, and to a lesser degree mood and anxiety disorders. There are substantial differences in comorbidity between male and female aADHD patients with males being more prone to substance use disorders and females leading in the area of mood, anxiety, and eating disorders, and our findings in this respect mirror sex differences observed in the general population. Subtype-related differences are comparatively minor. Results from the present study should be taken into account for the design of future epidemiological investigations, and their relevance for the therapeutic procedure needs to be examined.

Footnotes

Authors’ Note

Silke Groß-Lesch and Astrid Dempfle contributed equally.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: C.P.J. has received speaker’s honoraria by Medice and Novartis and is a member of the advisory boards of Medice

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the DFG (Grant RE1632/1-1, /1-3 and /5 to AR, KFO 125 to AR, CPJ, AW, SW, HS, and KPL; SFB 581 to KPL, SFB TRR 58 to AR and KPL; GR 1912/5-1, to HG), BMBF (01GV0605 to KPL), BMBF (IZKF Würzburg, 01KS9603, to KPL; IZKF N-27-N, to AR), and the EC (NEWMOODLSHM-CT-2003-503474, to KPL).

Author Biographies

Silke Groß-Lesch underwent medical training and specialization in Psychaitry at the Department of Psychiatry, University of Würzburg. She is now medical assistant in the clinic for Psychiatry and Psychotherapie Werneck.

Astrid Dempfle, PhD, is a biostatistician at the Institute of Medical Biometry and Epidemiology at the University of Marburg, Germany. Research interests include statistical methods for clinical trials and genetic epidemiology.

Susanne Reichert underwent medical training and specialization in Child and Adolescent Psychiatry at the Department of Child and Adolescent Psychiatry, University of Würzburg. She is now senior physician.

Thomas Jans underwent psychological training and specialization in Child and Adolescent Psychiatry at the Department of Child and Adolescent Psychiatry, University of Würzburg. He is senior psychologist of this department.

Julia Geissler underwent psychological training and specialization in Child and Adolescent Psychiatry at the Department of Child and Adolescent Psychiatry, University of Würzburg. She works now as psychologist in this department.

Sarah Kittel-Schneider works as a resident physician at the Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Germany. Her clinical focus is on bipolar-affective disorder and adult ADHD. She is actively involved in the field of biological psychiatric research with focus on genetics, biomarkers and cell models of bipolar disorder and ADHD.

Thuy Trang Nguyen, PhD, biostatistician, currently at the Stelle zur trägerübergreifenden Qualitätssicherung im Rettungsdienst Baden-Württemberg (SQR-BW) used to be biostatistician at the Institute of Medical Biometry and Epidemiology at the University of Marburg, Germany.

Andreas Reif underwent medical training and specialization in Psychiatry at the Department of Psychiatry, University of Würzburg. He is now Vice Chair at this Department, Full Professor of Psychiatry and heading the Laboratory for Psychiatric Neurobiology and the Bipolar Disorder Program.

Klaus-Peter Lesch underwent medical training and specialization in Psychaitry at the Department of Psychiatry, University of Würzburg. He is now Chair at the Division of Molecular Psychiatry (MP). He is heading the ADHD Clinical Research Unit and Laboratory of Translational Neuroscience (LTN).

Christian Peter Jacob underwent medical training and specialization in Psychaitry at the Department of Psychiatry, University of Würzburg. He is now chief medical in the clinic for Psychiatry and Psychotherapie Nürtingen.

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