ADHD Symptoms and Cognitive Abilities in the Midlife Cohort of the PATH Through Life Study

Abstract

Objective: ADHD is a lifelong condition, but it remains understudied in older adults. We examined the effects of ADHD-related inattention and hyperactivity symptoms on cognitive abilities in middle-age adults. Method: ADHD symptoms and cognitive abilities were accessed in a population-based sample (N = 2,091). Multiple regression analyses evaluated the effect of dimensional and categorical measures of ADHD on performance in cognitive tests. Results: ADHD symptoms are negatively associated with measures of reaction time, processing speed, task-switching, mental flexibility, and an aggregate measure of cognition. Inattention and hyperactive symptoms have reciprocal effects on performance in some cognitive tasks. Significant subclinical effects are present. Conclusion: Our demonstration that cognitive effects of ADHD symptoms are present in the middle-age population provides the impetus to investigate whether these effects contribute to cognitive decline in late age and whether identification and treatment of ADHD symptoms in middle age might be effective in reducing late-age cognitive decline.

Keywords

inattention hyperactivity cognition middle-age population-based sample

ADHD is a common neuropsychiatric childhood disorder that often persists across the life span. ADHD is characterized by a persistent pattern of age-inappropriate level of inattention and/or excessive motor activity and impulsivity (Biederman & Faraone, 2005). Based on the presentation of these symptoms, three diagnostic subtypes are identified in the Diagnostic and Statistical Manual of Mental Disorders (5th ed., DSM-V; American Psychiatric Association, 2010): hyperactive-impulsive, inattentive, and combined hyperactive-impulsive and inattentive. The prevalence of ADHD in children and adolescents is ~5% (Polanczyk & Rohde, 2007) and between 1% and 7% in adults (Fayyad et al., 2007). Symptom patterns of ADHD change with age and are less specific in adults than in children. Most adults predominantly exhibit problems with inattention, which manifest as disorganization, forgetfulness, unreliability, and poor performance in planning, task completion, task shifting, and time management (Barkley, 2010; Rösler, Casas, Konofal, & Buitelaar, 2010). These symptoms can often lead to employment and financial difficulties and interpersonal problems. Adults with ADHD also experience a range of other conditions, including substance abuse and mood disorders (Karam et al., 2009; Rösler et al., 2010). The direct medical costs of adult ADHD as well as indirect costs associated with workplace productivity loss, accidents, and so on place a substantial economic burden on patients, families, and the society (Bernfort, Nordfeldt, & Persson, 2008; Birnbaum et al., 2005; Matza, Paramore, & Prasad, 2005; Secnik, Swensen, & Lage, 2005; Swensen et al., 2003).

Impaired frontal lobe function is thought to be the primary cause of cognitive deficits associated with ADHD (Barkley, Grodzinsky, & DuPaul, 1992; Spencer, Biederman, Wilens, & Faraone, 2002). The frontal lobe plays a critical role in regulating executive functions such as planning, working memory, attention, inhibition, problem solving, mental flexibility, initiation, and monitoring of actions (Chan, Shum, Toulopoulou, & Chen, 2008). Hence, executive functions have been the main focus of investigations of neurocognitive functioning in ADHD patients. Poor performance is observed in tasks that measure response and behavioral inhibition, attention, working memory, and planning (Bálint et al., 2009; Boonstra, Oosterlaan, Sergeant, & Buitelaar, 2005; Leth-Steensen, Elbaz, & Douglas, 2000; McLean et al., 2004; Rapport, Van Voorhis, Tzelepis, & Friedman, 2001; Seidman, Biederman, Weber, Hatch, & Faraone, 1998; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Based on a meta-analysis, Hervey, Epstein, and Curry (2004) concluded that in adults with ADHD cognitive differences are observed across multiple domains of functioning, with notable differences in attention, behavioral inhibition as well as nonexecutive functioning aspects of memory, processing speed, and motor speed (Hervey et al., 2004).

The cognitive domains affected in ADHD (such as attention, memory, and processing speed) are also subject to significant age-related decline, which is strongly linked with ill-health, frailty, dependence, and loss of quality of life in old age leading to substantial personal, societal, and financial costs. However, few studies have investigated cognitive characteristics of ADHD in middle-age and old adults. From a cross-sectional study of 116 nonmedicated ADHD patients 19 to 55 years of age, Biederman et al. (2010) concluded that the negative impact of ADHD on cognition remains constant across the life span. Association between adult ADHD and dementia has been investigated in two studies, both reporting no significant association between ADHD and Alzheimer’s disease. However, Golimstok et al. (2011) found a higher risk of dementia with Lewy bodies in individuals (51-89 years of age) with preceding adult ADHD. Ivanchak et al. (2011) reported significant associations between childhood ADHD and cognitive test profiles in older adults (62-91 years of age) but not with a diagnosis of mild cognitive impairment.

In a recent study on a large representative population-based sample, we reported that middle-age adults with ADHD symptoms are significantly impaired in several life domains (including health, work, relationship, social interactions, and well-being; Das, Cherbuin, Butterworth, Anstey, & Easteal, 2012). Importantly, substantial impairment is observed in individuals with symptom scores that are below the indicative cutoff for clinical diagnosis and the observed impairments are independent of co-occurring anxiety/depression symptoms. Extending these observations, in this study, we report relationships between ADHD symptoms and cognitive abilities of these participants. Specifically, we investigate the relative impact of the two core symptom dimensions of inattention and hyperactivity, the effect of ADHD symptom scores below the level indicative of clinical diagnosis, and the effect of co-occurring anxiety/depression symptoms on cognitive domains such as attention, information processing speed, mental flexibility, and working memory, assessed using psychometric tests. To the best of our knowledge, this is the first study to investigate the relationship between ADHD, cognitive abilities, and co-occurring anxiety/depression symptoms in a large population-based sample of middle-age adults.

Method

Participants

The study sample was drawn from the PATH Through Life Project, a large longitudinal study of mental health and aging (Anstey et al., 2011) in participants across three age groups (20-24, 40-44, and 60-64 years at baseline) with a 4-yearly follow-up for up to 20 years. The baseline sample comprised individuals randomly selected from the electoral roll from the city of Canberra and the adjacent town of Queanbeyan, Australia (which provides a good representative population sample because enrolment to vote is a legal requirement for all adult Australian citizens). All participants gave written informed consent to be included in the PATH project and the study was approved by the ethics committee of The Australian National University. Participants were surveyed for information on physical and mental health, lifestyle, and social factors (for details, see Anstey et al., 2011).

The present study used data from the third wave of assessment (the adult ADHD Self-Report Scale [ASRS] was introduced in this wave) of the middle-age cohort, which includes 2,182 individuals aged 47 to 54 years (M = 50.7, SD =1.5). Twenty-six participants (1%) did not complete the ASRS-6 questionnaire. These nonresponders did not differ significantly from responders with respect to any of the sociodemographic variables. Participants who reported having epilepsy (n = 21) or brain tumor (n = 4) or brain infection (n = 10) or severe head injury (n = 18) in the last 4 years and with missing data for these measures (n = 23) were excluded from the analyses (results did not change significantly when participants reporting ever having epilepsy or brain tumor/infection or head injury were excluded). In addition, a participant who reported taking medically prescribed dexamphetamine, which is used to treat ADHD, was excluded resulting in a sample of n = 2,091 (47% male; Figure 1).

Figure 1.

Sample selection.

Measures

The adult ASRS was developed by the World Health Organization. The short form of the screener consists of a checklist of six questions regarding symptoms of ADHD based on the diagnostic criteria of DSM-IV (Kessler et al., 2005; Kessler et al., 2007). Each item requires respondents to rate how frequently a particular symptom of ADHD occurred over the past 6 months on a 5-point response scale from 0 = never to 4 = very often. A summary score (ASRS-6 score) with a possible range of 0 to 24 was obtained as an equally weighted sum of response scores for all questions. Higher scores indicate increased risk of ADHD. Based on recommended classification methods (Kessler et al., 2005; Kessler et al., 2007), participants were grouped into four strata with the following score ranges: 0 to 9 (Stratum I), 10 to 13 (Stratum II), 14 to 17 (Stratum III), and 18 to 24 (Stratum IV). The first four items of the screener capture inattention-related symptoms, and the remaining two items assess hyperactivity-related symptoms. To explore these dimensions separately, we used an equally weighted sum of response scores for the four items related to inattention as the Inattention Trait Score (ITS) and of response scores for the two items related to hyperactivity as the Hyperactivity Trait Score (HTS). This approach of creating and analyzing trait scores separately is supported by a recent study, which reported that the ASRS-6 measures two separate structures—inattentiveness and hyperactivity—rather than one unitary construct (Hesse, 2012). The distributions of ITS and HTS in this sample are reported in Das et al. (2012).

Depression and anxiety symptoms were assessed using the Patient Health Questionnaire (PHQ). This is a short version of the patient questionnaire component of the Primary Care Evaluation of Mental Disorders (PRIME-MD) instrument (Martin, Rief, Klaiberg, & Braehler, 2006; Spitzer, Kroenke, & Williams, 1999). We generated measures of depression and anxiety-related disorders from the nine items related to depression symptoms (rated on a 4-point scale from 1 = not at all to 4 = nearly every day), seven items related to anxiety symptoms (rated on a 3-point scale from 1 = not at all to 3 = more than half the days), and five items related to panic disorder (rated on a 2-point scale from 1 = no to 2 = yes) following the coding algorithm provided in the PHQ instruction manual (available from Patient Health Questionnaire Screeners; http://www.phqscreeners.com/overview.aspx). Variables for panic disorder and other anxiety syndromes were combined to generate a binary categorical variable for anxiety symptoms (both panic disorder and other anxiety syndrome absent = 0, either panic disorder or other anxiety syndrome present = 1).

Participants also reported use of medication prescribed to treat anxiety and depression. Alcohol consumption was assessed using the Alcohol Use Disorders Identification Test (Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). Participants reported on cigarette use by answering questions such as, “Do you currently smoke?” “Have you ever smoked regularly?” “On an average how many cigarettes have you smoked each day over the time you were smoking?” “At what age did you start smoking?” and “At what age did you stop smoking?”

Cognitive Tests

Participants performed the following cognitive tests during the course of the interview: the Spot-the-Word test (STW), which is a measure of verbal ability (Baddeley, Emslie, & Nimmo-Smith, 1993); the Trail Making Tests A and B (TMT-A and TMT-B) for visual attention and task-switching (Sánchez-Cubillo et al., 2009); the Symbol-Digit Modalities Test (SDMT) for information processing speed and attention (Smith, 1982); the first trial of the California Verbal Learning Test (CVLT) for immediate and delayed recall (Delis, Kramer, Kaplan, & Ober, 1987); and the digits span backward (DSB) task from the Weschler Memory Scale for verbal working memory (Wechsler, 1945). These tests are widely used as a part of neuropsychological examinations and their test–retest reliabilities have been reported in previous studies (Cangoz, Karakoc, & Selekler, 2009; Hinton-Bayre & Geffen, 2005; Iverson, 2001; Paolo, Tröster, & Ryan, 1997).

Simple and choice reaction time (RT) tasks were administered using a hand-held box with two depressible buttons (left and right), and two red stimulus lights and one get-ready light. For the simple RT (SRT) task, one of the stimulus lights was activated and participants were instructed to press the right hand button (regardless of dominance). For the choice RT (CRT) tasks, participants had to press the button corresponding to the left or the right stimulus light. SRT was measured first using four blocks of 20 trials followed by two blocks of 20 trials for CRT, and the average time for each task was used as a measure of RT.

Statistical Analysis

All statistical analyses were conducted using SPSS 18 (SPSS Inc., Chicago). Means and standard deviations for the ASRS-6 score, ITS/HTS, and cognitive tests and zero-order correlations for ADHD and cognitive variables were computed. For each cognitive test, z scores (individual test score minus mean test score divided by the standard deviation) were generated. Higher scores on TMT and RT tasks indicate a worse performance, whereas a higher score on all other tests indicate a better cognitive function. An overall measure of cognition (aggregate cognition score) was computed by averaging the z scores of all cognitive tests. Completion times for TMT-A and TMT-B were used to compute the TMT (B:A) ratio (a measure of mental flexibility; Oosterman et al., 2010) and the TMT (B-A) difference (a measure of information processing speed; Salthouse, 2011). Because the scores for immediate and delayed recall were highly correlated, r(2104) = 0.82, p < .001, in our sample, we created an index of episodic memory using the average z scores of the two tests. Associations between cognitive test measures and continuous or categorical measures of ADHD symptoms were analyzed using linear regression. Age, sex, and total years of education were covariates in all models. Depression and anxiety symptoms, smoking (number of cigarettes smoked per day) and alcohol intake (number of alcoholic drink consumed per week), use of antipsychotics, antidepressants, and sedatives were additional covariates in models, as indicated. Regression models were generated by entering the covariates first, followed by the predictors. Change in R² value between each step and the p value associated with R² change was noted. Given the exploratory nature of the study, results significant at the commonly used threshold of p ≤ .05 and a more conservative threshold of p ≤ .01 are indicated.

Results

Demographic details, ASRS-6 score, ITS, HTS, depression and anxiety symptom measures, and mean performance scores for cognitive tests are shown in Table 1. Applying the four-strata classification scheme for the ASRS-6 recommended by Kessler et al. (2007), we observed that 67%, 27%, 5%, and 1% of participants were present in Strata I, II, III, and IV, respectively. Good concordance has been demonstrated between the four-strata classification and clinical diagnosis, with Stratum IV most likely to include ADHD cases (Kessler et al., 2007). Individuals in Strata II and III are unlikely to meet clinical criteria for ADHD, but they are likely to have subclinical levels of ADHD symptoms. Using the ASRS-6, it is not possible to identify ADHD subtypes (hyperactive-impulsive, inattentive, and combined hyperactive-impulsive and inattentive). However, our separate analysis of ITS and HTS indicates the presence of all three subtypes in this sample. Although ITS and HTS are correlated, we observed that some participants with high inattention scores have low hyperactivity scores and vice versa (Das et al., 2012).

Table 1.

Demographic, ADHD-Related Traits and Cognitive Performance in Middle-Age Adults.

	M	±SD	n
Age (years)	50.7	1.5	2,091
Education (years)	15.0	2.2	2,088
ASRS-6 score	8.15	3.41	2,091
Inattention trait score	5.32	2.66	2,091
Hyperactivity trait score	2.83	1.69	2,091
ASRS strata
I			1,407
II			555
III			109
IV			20
PHQ depression
Present			286
Absent			1,800
PHQ anxiety
Present			120
Absent			1,969
Spot-the-Word test^a	51.73	4.83	1,990
TMT-A^b	25.88	7.43	2,030
TMT-B^b	57.61	19.73	2,027
Simple reaction time^b	0.24	0.05	1,974
Choice reaction time^b	0.29	0.04	1,973
Symbol-Digit Modalities Test^a	59.56	8.72	2,032
Immediate recall^a	8.14	2.22	2,031
Delayed recall^a	7.33	2.43	2,031
Digits span backward^a	5.71	2.32	2,001

Note: ASRS = ADHD Self-Report Scale; PHQ = Patient Health Questionnaire; TMT-A = Trail Making Test A; TMT-B = Trail Making Test B.

Total number of correct responses.

Time in seconds.

Zero-order correlations for all cognitive variables, ITS, and HTS are presented in Table 2. For ITS, significant correlations were observed with the STW, TMT-A, CRT, and DSB tests. For HTS, fewer significant correlations were observed, which included the STW, TMT-B, and DSB tests. Results from regression analyses on cognitive variables using continuous or categorical measures of ADHD symptoms as predictors are presented in Tables 3 and 4. When the ASRS-6 score was the predictor (Table 3, Model I), we observed that, after controlling for age, sex, and education, a higher ASRS-6 score is associated with (a) greater time taken to complete both parts of the TMT, although no significant associations were observed for the derived scores—TMT (B:A) and TMT (B-A); (b) significantly slower CRT and a trend was observed for SRT; and (c) lower accuracy in SDMT. No significant associations were observed with STW test, episodic memory, or the DSB test. There is a significant negative association between ASRS-6 score and the aggregate cognition score. Scatterplots for the distribution of cognitive performance measures and ASRS-6 scores are presented in Figure 2.

Table 2.

Pearson Correlations for Age, Education, ITS, HTS, and Cognitive Variables.

	Age	Education	ITS	HTS	STW	TMT-A	TMT-B	SRT	CRT	EM	SDMT	DSB
Age	—	−.03	<.01	<−.01	<.01	.09**	.05*	.03	.05*	<−.01	−.06**	.01
Educ		—	.11**	−.03	.39**	−.09**	.20**	−.11**	−.05*	.17**	.14**	.20**
ITS			—	.20**	.14**	.05*	<.01	.03	.09**	.02	−.03	.04*
HTS				—	−.08**	.03	.07**	.01	.04	−.02	−.03	−.06*
STW					—	−.21**	−.35**	−.12**	−.13**	.28**	.28	.36**
TMT-A						—	.50**	.25**	.27**	.19**	.44	.23**
TMT-B							—	.21**	.24**	.26**	.51	.37**
SRT								—	.74**	−.13**	−.20	−.12**
CRT									—	−.12**	−.03	−.13**
EM										—	.30	.23**
SDMT											—	.31**
DSB												—

Note: Educ = total years of education; ITS = Inattention trait score; HTS = Hyperactivity trait score; STW = Spot-the-Word test; TMT-A = Trail Making Test A; TMT-B = Trail Making Test B; SRT = simple reaction time; CRT = choice reaction time; EM = episodic memory; SDMT = Symbol Digit Modality Test; DSB = digit span backward.

p < .05. **p < .01.

Table 3.

Associations Between Cognitive Test Scores and ASRS-6 Score or ITS and HTS.

	Model I			Model II
	ASRS-6 score			ITS		HTS
	β	p	R² (change)	β	p	β	p	R² (change)
Spot-the-Word test	.036	.084	.156 (.001)	.113	<.001**^a	−.096	<.001**^a	.172 (.018)
TMT-A	.061	.005**	.020 (.004)	.062	.006**^b	.012	.606	.021 (.004)
TMT-B	.050	.023*	.044 (.002)	.012	.588	.060	.007**^a	.045 (.004)
TMT (B:A)	<.001	.994	.016 (.000)	−.047	.040*^a	.062	.006*^a	.020 (.007)
TMT B-A	.033	.133	.035 (.001)	−.012	.598	.066	.003*^a	.038 (.004)
Simple reaction time	.041	.066	.036 (.002)	.053	.021*	−.006	.807	.036 (.003)
Choice reaction time	.097	<.001**^a	.026 (.009)	.102	<.001**^a	.014	.536	.027 (.011)
Episodic memory	.004	.863	.095 (.000)	.023	.286	−.025	.242	.096 (.001)
Symbol Digit Modality Test	−.049	.024*	.041 (.002)	−.040	.075	−.023	.307	.041 (.002)
Digit span backward	−.009	.691	.040 (.000)	.033	.146^a	−.057	.011*^a	.043 (.004)
Aggregate cognition score	−.049	.027*	.090 (.002)	−.010	.655	−.062	.006**^b	.091 (.004)

Note: ASRS = ADHD Self-Report Scale; ITS = Inattention Trait Score; HTS = Hyperactivity Trait Score; TMT-A = Trail Making Test A; TMT-B = Trail Making Test B. Models are adjusted for age, sex, and total years of education.

Significant after adjusting for depression and anxiety symptoms.

Trend observed after adjusting for depression and anxiety symptoms.

p < .05. **p < .01.

Table 4.

Associations Between Cognitive Test Scores and ASRS-6 Strata.

	II^a		III^a		IV^a
	β	p	β	p	β	p	R² (change)
Spot-the-Word test	.005	.818	.025	.224	.001	.991	.155 (.001)
TMT-A	.037	.099	−.007	.758	.076	.001**^a	.023 (.007)
TMT-B	.060	.007**^c	.013	.550	.064	.003*^a	.048 (.007)
TMT (B:A)	.031	.168	.018	.411	−.001	.963	.017 (.001)
TMT B-A	.053	.016*^c	.019	.392	.045	.038*	.039 (.005)
Simple reaction time	.006	.787	.047	.036*	.038	.084	.035 (.004)
Choice reaction time	.037	.102	.078	.001**^a	.018	.420	.023 (.007)
Symbol Digit Modality Test	−.035	.116	−.022	.328	−.040	.069	.042 (.003)
Episodic memory	−.034	.116	.009	.667	−.028	.190	.097 (.002)
Digit span backward	.018	.428	−.011	.613	−.018	.416	.040 (.001)
Aggregate cognition score	−.039	.082	−.020	.356	−.061	.006**	.092 (.015)

Note: TMT = Trail Making Test. Models adjusted for age, sex, and total years of education.

ASRS-6 strata I is the reference category.

Significant after adjusting for depression and anxiety symptoms.

Trend observed after adjusting for depression and anxiety symptoms.

p < .05. **p < .01.

Figure 2.

Scatterplots for distribution of cognitive scores and ASRS-6 score of male (dashed line) and female (solid line) participants. Values on Y-axes are z-scores for the cognitive indices. Values on X-axes are ASRS-6 scores. (A) Aggregate cognition score; (B) TMT-A; (C) TMT-B; (D) SRT; (E) CRT.

To examine the effects of ADHD dimensions of inattention and hyperactivity on cognitive performance, we generated regression models with ITS and HTS as predictors (Table 3, Model II). Both trait scores were significantly associated with the number of words correctly identified in the STW test. Interestingly, the association is positive for ITS and negative for HTS (Figure 3). Because these effects cancel each other out, there is no significant association between STW task performance and the overall ASRS-6 score (Table 3, Model I). Higher ITS is significantly associated with greater time taken to complete TMT-A, and there is a similar association between HTS and TMT-B. Reflecting these differences, ITS and HTS are associated with TMT (B:A), but the associations are in opposite directions. Only HTS is significantly associated with TMT (B-A). Both SRT and CRT are associated with ITS but not HTS. Episodic memory, SDMT, and DSB are not significantly associated with either trait scores. There is a significant negative association between the aggregate cognition score and HTS, but not ITS.

Figure 3.

Scatterplots for distribution of STW task score and ASRS-6, ITS and HTS scores of male (dashed line) and female (solid line) participants. Values on Y-axes are z-scores for the STW task. Values on X-axes are continuous ADHD measures: (A) ASRS-6; (B) ITS; (C) HTS.

We also examined the relationship between ADHD symptoms and cognition at the group level using ASRS-6 strata as the predictor in the regression analysis (Table 4). Compared with Stratum I, Stratum II scores were significantly worse for TMT-B and TMT (B-A), and Stratum III had longer RT scores. No significant differences were observed for the other measures. A comparison between Strata I and IV revealed significantly worse scores for TMT-A, TMT-B, TMT (B-A), and the aggregate cognition score. For the other cognitive tests, the difference between these two strata did not reach significance possibly reflecting the relatively small number of participants in Stratum IV.

In our sample, ADHD and depression/anxiety symptoms were significantly correlated (Das et al., 2012). As depression and anxiety symptoms could potentially affect performance in cognitive tests, we generated regression models controlling for depression and anxiety measures in addition to age, sex, and years of education for all cognition variables that were significantly associated with ADHD symptoms. For both continuous and categorical measures of ADHD symptoms, the majority of the associations remained significant or demonstrated a trend after controlling for depression/anxiety symptoms, suggesting a unique contribution of the ADHD symptoms to the variance of the cognitive performance measures (Tables 3 and 4).

Similarly, smoking and alcohol intake, psychotropic medications, and illicit drug use could affect performance in cognitive tests. We controlled for use of cigarettes, alcohol, antipsychotics, antidepressants, and sedatives in addition to age, sex, and education in regression analysis. The relationships between ADHD symptoms and cognition described above remained mostly unchanged when controlling for these additional covariates (results are shown for the traits scores in Table 5). Furthermore, controlling for use of marijuana, ecstasy, and amphetamines in the past year did not alter these results (data not shown). The relationships between ADHD symptoms and cognitive abilities were similar for males and females (Figures 2 and 3).

Table 5.

Associations Between Cognitive Test Scores and ITS/HTS (Adjusted for Use of Cigarette, Alcohol, Antipsychotics, Antidepressants, and Sedatives).

	ITS		HTS
	β	P	β	p	R² (change)
Spot-the-Word test	.117	<.001**	−.094	<.001**	.175 (.018)
TMT-A	.056	.015*	.006	.785	.026 (.003)
TMT-B	.0105	.810	.057	.010*	.051 (.003)
TMT (B:A)	−.047	.040*	.063	.006*	.022 (.005)
TMT B-A	−.017	.467	.065	.004**	.042 (.004)
Simple reaction time	.043	.064	−.003	.881	.042 (.003)
Choice reaction time	.097	<.001**	.012	.610	.031 (.009)
Episodic memory	.025	.258	−.018	.413	.107 (.001)
Symbol Digit Modality Test	−.034	.138	−.014	.520	.050 (.002)
Digit span backward	.033	.146	−.058	.010*	.045 (.004)
Aggregate cognition score	−.002	.942	−.053	.018*	.099 (.003)

Note: ITS = Inattention Trait Score; HTS = Hyperactivity Trait Score; TMT = Trail Making Test. Models adjusted for age, sex, education, and use of cigarette, alcohol, antipsychotics, antidepressants, and sedatives.

p < .05. **p < .01.

Discussion

In this study, we have evaluated the effect of ADHD symptoms on the cognitive performance of healthy middle-age adults from a large community-based sample. Age differences in cognition were largely removed by the use of the narrow age-cohort design of the study. We examined performance in a battery of widely used neurocognitive tests. We used both continuous and categorical classifications of ADHD symptoms to explore effects of the core symptom traits of inattention and hyperactivity as well as combined symptoms. From our analysis, it is evident that in this middle-age cohort (a) ADHD symptoms are consistently associated with worse performance on TMT and RT tasks irrespective of whether continuous or categorical classifications are used; (b) the symptom traits of inattention and hyperactivity (ITS and HTS, respectively) have reciprocal effects on performance in some domains; (c) the effects on cognitive performance extend to participant groups with symptom scores below the threshold of likely clinical diagnosis, indicating that subclinical levels of ADHD symptoms affect cognitive performance; and (d) co-occurring depression and anxiety symptoms cannot completely explain the observed association between ADHD symptoms and cognitive abilities. Our results closely follow previous reports of cognitive deficit in ADHD patients of younger age groups (Bálint et al., 2009; Boonstra et al., 2005; Hervey et al., 2004; Leth-Steensen et al., 2000; McLean et al., 2004; Rapport et al., 2001; Seidman et al., 1998; Willcutt et al., 2005), suggesting that ADHD-associated performance differences in these cognitive domains persist into middle age.

Separate analyses of inattention and hyperactivity traits revealed distinct cognitive profiles associated with ADHD symptom dimensions. These associations remain significant after removing the variance explained by depression and anxiety symptoms. High ITS is related to low attentional task scores, while HTS is associated with low scores in working memory tasks. Processing speed is affected by both ITS and HTS. Interestingly, ITS and HTS were found to have opposite associations with both STW performance and the TMT (B:A) ratio. This suggests that inattention symptoms are correlated positively with verbal ability but negatively with mental flexibility and that, reciprocally, hyperactivity symptoms are correlated positively with mental flexibility but negatively with verbal ability. Because these cognitive measures are associated with ITS and HTS in opposite directions, the associations tend to cancel each other when the ITS and HTS are combined in the overall ASRS-6 score so that no associations are apparent between these two cognitive measures and overall ADHD symptoms. These results indicate that predominantly inattentive, predominantly hyperactive, and combined subtypes of ADHD most likely have different effects on cognitive abilities of middle-age adults.

We have previously reported that individuals experiencing subclinical ADHD symptom levels (ASRS-6 Strata II and III) have significant functional impairment compared with those without these symptoms (ASRS-6 Stratum I; Das et al., 2012). In the analysis using this categorical measure of ADHD symptoms, we observed worse performance in individuals with few and/or less severe ADHD symptoms, although associations with few cognitive measures reached statistical significance. ASRS-6 Strata II and III, which are below the suggested cutoff for clinical diagnosis, have reduced scores for TMT (TMT-B and TMT B-A) and RT measures, respectively, when compared with Stratum I, which supports the proposition that a milder form of ADHD with subthreshold symptoms should be recognized, as suggested by Biederman and Faraone (2006). This further emphasizes the value of a dimensional approach to symptom assessment.

Verbal memory assessed using immediate and delayed recall measures of the CVLT was the only domain that was not associated with either continuous or categorical measures of ADHD. This is in contrast with previous studies, which reported notable performance differences between adults with ADHD and control adults in the CVLT (Hervey et al., 2004). The reason for this apparent discrepancy is not clear. However, it is important to note that (a) our results refer specifically to inattention and hyperactivity symptoms and not clinically diagnosed ADHD cases, and (b) our sample represents an age group not extensively studied for ADHD and associated impairment, and hence, there is limited scope for direct comparison with existing studies.

The main strength of this study is that it was conducted in a large representative population sample, and hence it is likely to be free of the biases that can be associated with clinical and convenience samples. In addition, the narrow age range of the sample removes the possible confounding effects of age. Consequently, our results are likely to be generalizable to middle-age populations. In addition, we report results for both continuous and categorical measures of ADHD symptoms and, most importantly, our analysis of trait scores allows us to distinguish between inattention and hyperactivity-related effects. Finally, we demonstrate the effect of confounding variables such as depression/anxiety symptoms by reporting how regression results were affected by the inclusion of depression and anxiety symptoms as covariates in the model.

Limitations of the study include lack of clinical assessments for ADHD, depression, and anxiety. Furthermore, symptom measures are based on self-reports, which may not be completely accurate (e.g., social desirability and current emotional state could introduce biases; Brewin, Andrews, & Gotlib, 1993; Neugebauer & Ng, 1990). However, the assessment instruments we used have been shown to have good sensitivity and specificity, and they have been used in a number of previous studies and validated in different cultural settings (Kessler et al., 2007; Manea, Gilbody, & McMillan, 2012; Martin et al., 2006; Ramos-Quiroga et al., 2009; Zohar & Konfortes, 2010). Their validity is supported by our observation of effects in cognitive domains reported to be affected in both patient and population samples of younger adults with ADHD.

In conclusion, these results represent a significant addition to existing literature on ADHD, particularly for an age group that has not previously been well studied but which is critically important in the context of promoting healthy aging. We have demonstrated that cognitive performance in specific domains is affected in middle-age people with ADHD symptoms, in ways that are similar to younger adults, and that males and females are similarly affected. The cognitive domains affected by ADHD are sensitive to age-related decline, and hence there is a compelling need to develop greater understanding of the presentation and impact of ADHD in later life. ADHD symptoms are responsive to interventions in children, adolescents, and young adults (Antshel et al., 2011), and hence improvement of age-appropriate approaches to diagnosis and treatment may help alleviate late-age impairment and ill health in individuals with ADHD symptoms.

Footnotes

Acknowledgements

The authors are grateful to Anthony Jorm, Bryan Rodgers, Helen Christensen, Peter Butterworth, Perminder Sachdev Andrew Mackinnon, Chantal Reglade-Meslin, Patricia Jacomb, Karen Maxwell, and PATH interviewers.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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 National Health and Medical Research Council of Australia (NHMRC: Unit Grant 973302, Program Grant 179805, Project Grant 418039). DD is funded by NHMRC Capacity Building Grant No. 418020 in Population Health Research, NC is funded by NHMRC Research Fellowship No. 471501, and KA is funded by NHMRC Research Fellowship No. 366756.

Author Biographies

Debjani Das, PhD, works as a postdoctoral researcher with Kaarin Anstey at the Centre for Research on Ageing, Health and Wellbeing at the Australian National University. She has a background in molecular-genetics and her research interest is the investigation of the genetic and environmental factors for cognitive aging.

Nicolas Cherbuin, PhD, is a psychologist and NHMRC research fellow who leads the Neuroimaging and Brain Lab at the Centre for Research on Ageing, Health and Wellbeing at the Australian National University. His main research interests are the investigation of risk factors for dementia and cognitive aging, early identification of individuals at risk of developing dementia, and the investigation of neuroanatomical changes associated with cognitive decline and dementia using brain imaging techniques.

Kaarin J. Anstey, PhD, FASSA, is Director of the Centre for Research on Ageing, Health and Wellbeing at the Australian National University and the Principal Investigator of the PATH Through Life Project which focuses on the development of common mental disorders and cognitive function over the adult life course. Her research focuses on risk and protective factors for cognitive aging, longitudinal studies, healthy aging and older drivers.

Simon Easteal, PhD, MBA, is Head of the Genome Diversity and Health Group in the John Curtin School of Medical Research at the Australian National University. His research focuses on the evolution of human diversity and the influence of our evolutionary past on current heath and well-being. He has served on numerous editorial boards, advisory committees, government taskforces, working parties and other bodies in public and private organizations, and he has provided expert opinion in relation to genetic evidence used in court cases in most Australian jurisdictions.

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