Exploring Social Biomarkers in High-Functioning Adults with Autism and Asperger’s Versus Healthy Controls: A Cross-Sectional Analysis

Baseline characteristics for all participants are shown in Table 1. Recruitment was intended to include IQ-matched HCs, however, although mean age was similar, mean IQ was higher in the HC group (Table 1). Median FSIQ score was 116.0 in the HC group (range 101.0–140.0) and 100.0 in the ASD group (range 71.0–136.0).

This study sought to identify discriminant properties of putative surrogate markers relating to social dysfunction in adults with ASD. Measures differentiating participants with ASD from HCs were pupillometry, quantifying arousal during task performance, and three of seven eye tracking paradigms, (preference for heads in activity monitoring, preference for biological motion compared with synthetic movements, and preference for videos of human movements compared with geometric shape videos). However, looking at the head during the activity monitoring task of the eye tracking was the only measures that survived Bonferroni correction, and no group difference in pupil size remained significant. Our findings are consistent with the majority of ASD literature, which relies heavily on studies of younger subjects. Our report is unique in that a few studies have applied such an extensive and broad battery of potential surrogate markers of ASD in adults, with the potential exception of consortia focused on this topic (EU-AIMS) (Loth et al. 2017), The Autism Biomarkers Consortium for Clinical Trials (ABC-CT), (Foundation for the National Institues of Health 2018), InFoR-Autism) (Fondation Fondamental 2018) and industry efforts i.e. JAKE® (Ness et al. 2017). One of the most novel findings relates to our identification of a high proportion of adults with ASD with evidence of impaired olfaction. Although it could be argued that the data may be skewed, since there appears to be a ceiling effect in the HC more than the ASD group, this 'compresses' the normal score. Hence a difference to an overall population of ASD displaying a larger variability is more difficult to demonstrate. A dichotomization by the olfactory status offers a solution. Among the general population, the prevalence of olfactory impairments seems to be age-related and has been reported to be between 19 and 22% in individuals between 16 and 55 years of age (Bramerson et al. 2004; Hummel et al. 2007; Vennemann et al. 2008). Doubling the normal rates, 42% of the participants with ASD showed olfaction dysfunction in our study. Despite small samples, differing olfaction test procedures, and non-standard scoring, it is notable that impairments in identification of odorants as well as differences in the rating of intensity and pleasantness/unpleasantness have been reported in adults with Asperger’s syndrome (Suzuki et al. 2003) and ASD (Wicker et al. 2016). Nevertheless, negative results have been reported as well: One used fewer stimuli and did not score per convention (Addo et al. 2017). Another study identified no differences in olfaction detection thresholds or adaptation to continued stimulus presentation in adults with ASD but did not test for accuracy (Tavassoli and Baron-Cohen 2012). However, our results were confirmed in the recently completed phase 2 study VANILLA (NCT01793441) (data on file, Roche) (Bolognani et al. 2019) in which the same olfaction test was assessed at baseline in 191 high-functioning male adults with ASD and 48.17% showed olfaction dysfunction. Taken together, our data coupled with prior reports provides significant support for an increased prevalence of olfaction dysfunction in ASD.

There is an increasing recognition that olfactory problems may be predictive of social impairment in children with ASD (Kumazaki et al. 2018; Lane et al. 2010; Hilton et al. 2007). Olfaction identification scores have been moderately correlated with reciprocal conversation skills (r = − 0.56) and social chatting scores (r = − 0.44) from the Autism Diagnostic Interview-Revised test (Bennetto et al. 2007) and taste/smell sensitivity has been identified as a predictor of maladaptive behaviors (r = − 0.53) measured by the VABS (Lane et al. 2010). Rozenkrantz et al., observed a significant association between sniff response to odor valence and the social affect component of the ADOS in children, together with an association between olfaction and FSIQ, thereby suggesting a mechanistic link between the response to olfactory stimuli and ASD through impaired sensory-motor systems that modulate social communication (Rozenkrantz et al. 2015). In the assessment of concurrent validity of this study, we observed that reduced olfaction was associated with worse emotion recognition ability on both RMET (r = 0.54) and ASR (r = 0.40), possibly indicating greater impairments in their TOM capacity, as well as communication deficits in the ADOS communication domain (r =  − 0.34) and in the inappropriate speech subscale of the ABC (r = − 0.32). Olfaction identification also correlated with the VIQ (r = 0.47) and FSIQ (r = 0.40) (Del Valle Rubido et al. 2018). These subanalyses did not control for differences in IQ, thus, the contribution of IQ differences to these associations is unknown. However, given the links between olfaction and the development of social cognition and the fact that olfactory identification also relies upon intact orbitofrontal cortical (OFC) functioning, further research is warranted to clarify both the potential of olfaction as a biomarker for social deficits in ASD and the underlying biological mechanisms.

Our findings are not surprising, as olfaction has been established as a critical element in affective matching after the age of 5 years in typically developing children (Cavazzana et al. 2016). It plays a key role in bonding (Bowlby 1980; Sullivan et al. 2011; Wedekind and Penn 2000) and highly influences interpersonal relationships (Huttenbrink et al. 2013). Research has already identified olfaction as an indicator of neuronal, social and cognitive development (Rozenkrantz et al. 2015), and it may also be a marker for severe central nervous pathology affecting social communication (Amaral et al. 2008; Huttenbrink et al. 2013). Krajnik et al. suggested a relationship between olfactory dysfunction and interoceptive awareness (Krajnik et al. 2015). Recent research has also drawn attention to the association between interoceptive abnormalities and ASD (Barttfeld et al. 2012; Elwin et al. 2012; Fiene and Brownlow 2015; Garfinkel et al. 2016; Hatfield et al. 2017; Noel et al. 2018) as well as other psychiatric disorders characterized by emotional impairment (Furman et al. 2013; Pollatos et al. 2009; Stevens et al. 2011). Interoception could also be associated with the sensory processing abnormalities found in ASD which are now an important aspect of the ASD diagnosis criteria per the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (American Psychiatric Association 2013). Correlations between olfactory dysfunction, sensory processing and interoception in ASD remain yet to be further elucidated.

Notably, another consistent finding were the larger pupil sizes during the eye-tracking assessments in the ASD group compared with the HC group, with moderate to large effect sizes (0.60 to 0.85) suggesting a dysregulated autonomic arousal in response to environmental stimulus as a prominent phenotype in ASD (Anderson and Colombo 2009; Kushki et al. 2013; Hirstein et al. 2001; Anderson et al. 2013; Corbett et al. 2010). We did not assess pupillometry using standardized stimuli (e.g. flashes of light, as seen in (Nystrom et al. 2015)) or baseline pupil measurements outside of the eye tracking experiments. In addition, this study was not designed to test pupil response but rather to provide a straightforward comparison of pupil sizes during tasks. Therefore, it is unknown whether the larger pupil sizes are a baseline characteristic, a reaction to the task or to specific social or non-social stimuli within each task. Increased tonic pupillary size noted in children with ASD with evidence of lower sympathetic tone, (Anderson et al. 2013), and lower electrodermal activity and responses (Kushki et al. 2013) support the position of abnormal autonomic nervous system response in pathophysiology of ASD. In our correlation analysis of these measures, pupillometry was mostly unrelated to ASD severity and core social deficits, except for the biomotion task that correlated with the ADOS total score (r = − 0.33) and the communication subdomain of the Vineland (r = 0.36). However, larger pupil size was consistently related to lower behavioral ratings of hyperactivity on the ABC (r values ranging from − 0.36 to − 0.43) and higher FSIQ (ranging from 0.38 to 0.45) and PIQ scores (ranging from 0.36 to 0.44) while the VIQ remained unrelated (Del Valle Rubido et al. 2018). Thus, in ASD, better functioning is associated with larger pupil sizes.

Pupil dilation is known to be modulated by the brain’s locus coeruleus-norepinephrine system (Rajkowski et al. 1993), which controls physiological arousal (Samuels and Szabadi 2008) and cognitive functioning (Ramos and Arnsten 2007; Sara 2009) and has been used as a measure of subjective task difficulty, mental effort, and neural gain (Eckstein et al 2017). As a reflection of greater arousal or effort while engaged in task performance, pupil size may indicate the ability to better marshal effortful attention during the eye tracking as a sign of greater cognitive or inhibitory control and prove its utility in studying this separate important dimension of co‐occurring inattentive and disruptive behavior symptoms in ASD (McCracken 2011) or intellectual disability. While this explanation would appear to be inconsistent with the between-group difference observed, where ASD participants were shown to have larger pupil sizes than HCs, it is important to note that all eye-tracking tasks presented were fundamentally implicit or explicit tasks of social cognition. It is possible that HCs required less effortful attention to complete these tasks due to an inherently greater facility in social information processing. Another possibility is a proposed model of chronic autonomic nervous system hyperarousal in ASD, which describes chronic biological threat response, forwarded by Patriquin et al. based on a review of cardiac literature in adults and children with ASD (Patriquin et al., 2019; Edmiston et al. 2016; Guy et al. 2014; Bal et al. 2010; Van Hecke et al. 2009; Ming et al. 2005; Denver 2004). Based on the Polyvagal Theory (Porges 1995), Patriquin et al. suggest a potential difference of the information flowing from the brain to periphery in individuals with ASD due to differences in the neuroception of safety versus threat, resulting in greater autonomic hyperarousal in ASD. Latent hyperarousal differences between ASD and HCs could explain between-group pupil size differences observed, with this effect modulated by differences in autonomic flexibility observed between individuals with ASD with and without intellectual impairment (Van Hecke et al. 2009; Cohen and Johnson 1977; Goodwin et al. 2006; Miller and Bernal 1971; Palkovitz and Wiesenfeld 1980; Sigman et al. 2003; Sheinkopf et al. 2013). Although, the precise determinants of increased pupillary size in ASD remain to be clarified, pupillometry could also be informative for subject stratification efforts, depending on intervention.

Extending the results of previous work, we demonstrated atypical gaze patterns in eye tracking in the activity monitoring, biological motion preference and human preference tasks (Annaz et al. 2012; Chawarska et al. 2013; Frederick Shic et al. 2014) in the ASD group. However, no differences were observed in the remaining four out of seven eye-tracking paradigms (biodetection, WAVW, gaze discrimination and gender discrimination). This contrasts with results of many previous studies in younger subjects which showed that the best predictor of autism was reduced eye region fixation time (Auyeung et al. 2015; Klin et al. 2002). Moreover, despite the association found between looking at the mouth and social communication skills (Del Valle Rubido et al. 2018), there was no difference in fixation in the mouth between groups.

The failure to replicate previous eye tracking findings may be explained by several factors: firstly, potential under-reporting of negative and inconclusive results, because of the dearth of studies investigating eye pattern differences in adults with and without ASD or subgroups within the ASD population (Zamzow et al. 2014); divergent eye gaze patterns may depend on the nature of the stimuli presented (dynamic or static, real-life and naturalistic or non-naturalistic, social or non-social) (Hanley et al. 2015; Hanley et al. 2013; Speer et al. 2007; Manyakov et al 2018). More likely, however, is the possibility that high-functioning adults with ASD might ultimately succeed in reaching the developmental level of neurotypicals with overall minor differences in eye gaze patterns (Baez et al. 2012; Ullman and Pullman 2015) by developing compensatory mechanisms, or implementation of strategies to read faces (Bauminger 2002; Dawson et al. 2005; Hwang and Hughes 2000) and/or detect biological motion.

Our study also showed little relationship between eye tracking measures, adaptive behaviors measured by the Vineland, other measures of social perception and olfaction. Nonetheless, small to moderate correlations were found between activity monitoring, WAVW, and gender discrimination tasks with the severity of ASD symptoms and behavior measured by the ADOS and ABC (Del Valle Rubido et al 2018). Of all these tasks, the only paradigm for which there were consistent findings between correlation results (Del Valle Rubido et al. 2018) and the between-group differences highlighted here were in looking at the people in Activity Monitoring (greater looking at people associated with lower autism symptom severity in ASD, and less looking at people, especially the head, in ASD as compared to HCs). Associations with phenotype within ASD and ASD-HC between group differences were in an opposite-to-expected direction for human activity preference, with poorer adaptive communication associated with greater human looking within ASD, but less looking at the human versus geometric shape observed here in ASD as compared to HC. Other tasks showed significant findings for one of either correlations (Del Valle Rubido et al 2018) or between-group comparisons, but not both. These patterns highlight the complexity of straightforward extensions of between-group comparisons of ASD and HC groups to relationships within ASD. Factors which may impact the directionality and strength of effects could include reduced dynamic range within the ASD or HC groups, comorbid psychiatric features such as anxiety or depression in ASD, as well as fundamentally different mechanisms impacting social scene gaze patterns within ASD as compared to across groups, similar to that for which we have forwarded for pupil size relationships. Further studies are necessary to clarify these complex relationships.

Perhaps somewhat surprising was the lack of group differences observed between the ASD and HC groups for two measures, the RMET and the ASR, contrary to prior studies (Baron-Cohen et al. 2001a, b, 2015; Holt et al. 2014; Kaland et al. 2008). A review by Sivaratnam et al. found inconsistent reports of ToM impairments in structured test settings in high-functioning ASD groups (Happe 1995; Bauminger 2002), in contrast to clear impairments revealed in naturalistic test settings (Rump et al. 2009; Dziobek et al. 2006) and in everyday functioning (Rieffe et al. 2000). Suggesting that paradigms measuring ToM in non-naturalistic social settings may not provide an accurate pattern of functioning in ASD groups (Sivaratnam et al. 2015; Adolphs 2001; Klin 2000; Baron-Cohen et al. 1985; Leslie and Frith 1990; Weeks and Hobson 1987). Klin et al. (2003) theorized that due to the differences in learning, individuals with ASD may develop compensatory strategies which help them score well on standardized tests. Yet, difficulties may remain when the applying the cognitive potential and the appropriate set of social skills in naturalistic contexts (Klin et al. 2007, 2003). Our findings also reflect this contradiction. On the one hand, despite the lack of group differences, both the ASR and RMET demonstrated significant relationships with each other (r = − 0.64) but neither did they correlate with the ADOS communication and reciprocal social interaction domains. On the other hand, both the ASR and RMET correlated with the Inappropriate Speech subscale of the ABC (ASR r = − 0.66, RMET r = − 0.52) and the ASR with the Vineland communication subdomain and the adaptive behavior composite score (r = 0.46 and r = 0.40 respectively). It remains unclear whether the lack of group differences despite existing correlations between the ASR and RMET and the Vineland and ABC is due to the non-naturalistic test setting. In addition, the difference in how the concepts of socialization and communication are measured with the various clinical assessments (symptomatology/ disability in ADOS vs. ability in Vineland (Klin et al. 2007) and problematic behaviors in ABC) could be an additional confounding factor to be taken into consideration. When looking at the individual emotions in the ASR, ASD participants did not identify disgust and happiness as easily as healthy controls, whereas they were able to identify fearfulness and surprise. This over-responsiveness for fearfulness and surprise observed in with the ASD group, is perhaps indicative of higher levels of anxiety or a lack of understanding and inappropriate expression of emotions in ASD (Shields et al. 1994; Sigman et al. 1992). A plausible mechanism for the higher level of anxiety could be an increased activation of subcortical brain regions (i.e., amygdala) involved in the processing of fearful faces differs in subjects with ASD compared with HCs in functional magnetic resonance imaging (Kleinhans et al. 2015, 2011). These findings in ToM warrant further research to understand the underlying mechanisms. The higher level of complexity and effort required of both the RMET and ASR compared to passive viewing of faces in the eye tracking and pupillometry, may have led to the lack of differences.

The included studies both enrolled a relatively small sample size of all-male, high-functioning adults, limiting the generalizability of these findings. Participants with ASD were required to have ABC irritability subscale scores ≤ 13 and to undergo a 2-h infusion in Study 2, which may further limit applicability to lower-functioning, more severe and disruptive ASD phenotypes. The time required to complete the assessments was long (8 h), which may have created substantial burden of cognitive load on the participants. It can also be argued that the ASD and HC groups, while matched on age, were not well matched on other potential confounding factors. For instance, the race characteristics of the participants in ASD and HC groups were different i.e., a preponderance of Caucasian participants in the ASD group) and socioeconomic aspects were not considered. These fundamental differences between groups limit the applicability of these results to broader ASD populations. The analyses performed are exploratory. These include the post-hoc analysis of the ASD population by olfaction status based on the odor identification subtest of the Sniffin’ Sticks Screening 12 olfaction identification test. As such, between-group differences and p-values should be interpreted with caution and used as a guidance for selection of assessments in future studies. Finally, despite the selection of Screening 12 version of the Sniffin’ sticks test for its convenient administration in everyday clinical practice, other versions of the Sniffin’ Sticks test may allow a more precise testing and therefore an improved characterization of olfactory performances.

Our results suggest a potential use of specific eye tracking tasks, pupillometry and olfaction tests for stratification and response sub-analyses outcome-prediction in ASD trials. They also highlight the fact that abnormalities reported in young individuals with ASD may no longer be present to the same extent or with the same profile in adults with ASD. This points towards the view that the profile of abnormalities and hence characteristics of potential markers may change with development. The eye tracking, activity monitoring, biological motion, human activity preference and pupillometry tasks differentiated the best between paticipants with ASD and HCs. Our results implicate olfaction as a factor in the development of social cognition. It may be a simple and useful assessment for characterization of disease severity and for stratification in clinical trials. However, replication is needed for confirmatory purpose, and additional research should clarify sensitivity to change and links to functional outcomes.