Measuring autistic traits in the general population: a systematic review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females

Autism was traditionally considered as a clinical condition distinct from the general population, but recent evidence suggests autistic traits are continuously distributed across the population [1–3]. From observed data of measured autistic traits, people with a diagnosis of an autism spectrum condition (ASC) - at least those who have average IQ or above - score at the extreme end of this distribution [4]. It may be that ‘syndromic’ forms of autism, which often entail comorbid learning disability (or below average IQ) and a known genetic mutation, are discontinuous with autistic traits in the general population, but here the focus is on the general population without learning disability. The Autism-Spectrum Quotient (AQ) is widely used in research and clinical practice to quantify autistic traits. The AQ was first developed as a self-report measure for adults [5] and subsequently as a parent-report measure for adolescents (aged 12 to 15 years) [6] and for children (aged 4 to 11 years) [7]. A toddler version also exists (Q-CHAT (Quantitative Checklist for Autism in Toddlers) [8]). The AQ has 50 items, which are divided into five subscales consisting of 10 items each that assess domains of cognitive strengths and difficulties related to ASC: communication, social skills, imagination, attention to detail and attention switching. While the AQ is not the only research tool used to measure autistic traits (for example, see the SRS (Social Responsiveness Scale [9]), it has several advantages over other measures, including subscales for both social and nonsocial aspects of behavior and cognition and a format that is brief, self-administered, and forced-choice.

The AQ was designed for adults with average IQ or above [5], who comprise at least 50% of the autism spectrum [10]. Individuals are instructed to respond to each of the 50 items with one of four responses: ‘definitely agree’, ‘slightly agree’, ‘slightly disagree’, and ‘definitely disagree’. Responses are scored using a binary system, where an endorsement of the autistic trait (either mildly or strongly) is scored as a +1, while the opposite response is scored as a 0, leading to a maximum score on the AQ of 50. An alternative scoring system has also been employed that uses a 4-point Likert scale [11]. AQ items are counterbalanced to avoid a response bias, so that half of the ‘agree’ responses and half of the ‘disagree’ responses endorse the autistic trait. The AQ includes questions about both ability and preference. The questionnaire is not suitable for individuals with low IQ, low verbal ability, or language impairment, as it relies on receptive understanding of the 50 questions.

The AQ was originally validated in 2001 in adult males and females with Asperger Syndrome (AS) and high-functioning autism (HFA), in scientists versus nonscientists in Cambridge University students, in winners of the mathematical Olympiad (because of the finding that autism may be genetically linked to an aptitude for ‘systemizing’ [12–14]), and nonstudent individuals drawn from the general population. This study found that the total AQ score and its five subscale scores are normally distributed and have demonstrated good test-retest reliability, good internal consistency [5], and that the measure has acceptably high sensitivity and specificity: at a cut-off score of 26, 83% of patients were correctly identified (sensitivity 0.95, specificity 0.52, positive predictive value 0.84, negative predictive value 0.78), while a cut-off score of 32 correctly identifies 76% of patients (sensitivity 0.77, specificity 0.74) [11, 15] when the AQ is used in a referred clinical sample.

These results indicate that the AQ is a sensitive measure of autistic traits in the general population, implying that traits reaching a clinical level in autism also exist to a lesser degree in nonclinical counterparts [5]. Within families, AQ score has shown heritability, which is in line with genetic evidence suggesting the heritability of autism [16]. Further, some (but not all) parents of children with autism show a subclinical set of characteristics or traits that index familiarity and/or genetic liability to autism [17, 18]. This is referred to as the ‘Broader Autism Phenotype’ (BAP). There is a consistent sex difference in mean AQ score, such that typical males score significantly higher than typical females, while people of both sexes with ASC score at the extreme high end of the scale, in line with the extreme male brain (EMB) theory of autism [19, 20].

The AQ is also widely referenced: a recent search of Google Scholar indicated that the original publication has been cited over 1,250 times. The present study reports the first large-scale systematic review of published AQ data over the last 13 years from adults with and without a diagnosis of ASC, in order to characterize the distribution of autistic traits in adult males and females and to contrast scores from clinical versus nonclinical samples. The specific goal is to establish a reliable mean AQ score in nonclinical controls, which can then be used as a guideline for researchers to define their control groups in future studies that compare people with and without a clinical diagnosis of ASC, as well as to other specially selected groups.

This is the first systematic review of the AQ, with several findings emerging. First, the mean AQ score in a typical sample drawn from a nonclinical population is approximately 17 (CI 16.4 to 17.4) (for those explicitly excluding BAP, the mean is approximately 15 (CI 13.0-17.1)), with a narrow confidence interval of one to two points. In addition, the mean AQ score in individuals with ASC is approximately 35, nearly 20 points above that found in the general population. Second, control males and females have significantly different average AQ scores, with males scoring higher, confirming earlier reports. Third, from 2001 onwards, there is considerable fluctuation in reported mean AQ scores, but scores have not appreciably drifted in one direction or another within the general population.

Several rationales were employed by researchers for using the AQ. Many of the studies administered the AQ not as a central variable correlated with the outcome measure, but as an accessory measure for characterizing the population or defining the experimental groups. Further, a number of articles used the AQ as a proxy for diagnosis, using the cut-off scores of either 32 or 26 to exclude individuals either from the clinical or from the nonclinical control group. (These articles were not included in the final analysis). However, caution is recommended when using the AQ in this way, as the AQ was designed to be a descriptive, rather than a diagnostic, measure of autistic traits. While, perhaps due to it being freely available, easy to administer, and widely precedented in the literature, the AQ is used as a screening instrument (such as for patients referred to a diagnostic clinic for a detailed assessment for ASC [93]), it has been argued that the AQ does not have the sensitivity and specificity for population screening with an eye to diagnosis [94–96]. This follows logically from the fact that the AQ is a brief self-report, reliant upon the individuals’ own self-awareness, and from the self-imposed limitations of age (16+) and IQ (85+). As discussed in the original publication, the AQ was developed from a theoretical understanding of autism, and therefore has not necessarily undergone the rigorous psychometric evaluation procedure that diagnostic screening tools must pass for inclusion in clinical practice. A more conservative use for the AQ is to segment the population into bands of autism phenotypes (broad, medium and narrow) as in the method of Wheelwright and colleagues [17], or as a descriptive quantitative measure of autistic traits.

Strengths of the current review include the exhaustive search criteria, especially the citation search for relevant papers, followed by the rigorous selection process. In addition, the total number of individuals (N = 8,897 clinical cases and nonclinical controls) examined by this review lends weight to the findings. The study was limited by a risk of bias, at the outcome level, the selection level and at the level of the review, though an effort was made to mitigate possible disproportionate effect of means from studies of varying samples through weighting by group size. Limitations also exist in the review procedure, in that each study included in the review was not judged for methodological rigor, rather a holistic evaluation was made of study methodology in an effort to report trends in items such as recruitment strategies, participant inclusion, and AQ data psychometric properties. Second, the number of participants from each study was relatively small (minimum N was set at 10); this is balanced by the large overall sample size derived from summing all studies together. More broadly, an ideal investigation of AQ score distribution would evaluate the raw data from each of the included studies in order to also measure data spread and subscale scores. However, this was not feasible for the current study. Finally, not every included article verified that the control group did not have ASC. Therefore there may have been incomplete information on how representative the demographic distribution of the nonclinical sample that make up this analysis may be.

We recommend that future researchers should think carefully when planning a recruitment strategy, both for nonclinical and clinical participants in order to be able to clearly define participants in each group. Furthermore, the field would greatly benefit if researchers better described the control participants, stating the method of recruitment in the methods. While healthy, typically-developing participants are often taken for granted, the considerable variability found in this review indicates that the method of recruitment of a ‘true’ representative sample - either of the general population or a specific patient group - may significantly impact results. This could have implications when examining group differences on dependent variables if the groups have not been carefully defined, potentially leading to attenuation of real groups differences.

We hope the current review holds value in the light of the considerable range of research types under which the AQ has been used. This dimensional approach to quantifying autistic traits has been found to correlate with a range of biological measures, including instances of brain activity [97], brain structure [98], social perception using gaze-tracking [42], prenatal testosterone [99], candidate genes and epigenetics [100]; clinical screening [44] and autism genetic risk [17]. Thus, although it is a self-report instrument, it correlates with a large number of more objective measures, suggesting that autistic traits are measurable aspect of personality, independent of the Big 5 [101].

Future research might consider a similar investigation of other versions of the AQ. Aside from the AQ-Adolescent and -Child, widely-used cross-cultural and foreign-language versions of the AQ exist, including translations into Chinese [102], Dutch [103], French [104], Italian [105], Japanese [106], Persian [107] and Polish [108], among others. On the whole, the results from studies that utilize these versions demonstrate analogous findings to those found using English-language versions of the AQ; however, validation by systematic review has not been done. In addition, a future study might attempt to undertake a whole population survey of autistic traits using the AQ, with more detailed information about the respondents collected in order to make stronger claims about generalizability. The underlying structure of taxa leading to AQ score distribution could be assessed using a number of modelling solutions, including latent class, taxometric, or factor mixture modelling. Perhaps, using these techniques in a population sample of individuals along the spectrum might help elucidate the apparent gap between clinical and nonclinical scores, despite the apparent continuity of autistic traits.

The AQ continues to be a useful brief assessment instrument for measuring autistic traits in adults of normal intelligence. By determining the distribution of the AQ in the nonclinical population, the AQ can now be used more definitively to assess the extent to which other specialist populations exhibit autistic traits.