J Autism Dev Disord DOI 10.1007/s10803-014-2225-4

COMMENTARY

Autism Biomarkers: Challenges, Pitfalls and Possibilities George M. Anderson

 Springer Science+Business Media New York 2014

Abstract Network perspectives, in their emphasis on components and their interactions, might afford the best approach to the complexities of the ASD realm. Categorical approaches are unlikely to be fruitful as one should not expect to find a single or even predominant underlying cause of autism behavior across individuals. It is possible that the complex, highly interactive, heterogeneous and individualistic nature of the autism realm is intractable in terms of identifying clinically useful biomarker tests. It is hopeful from an emergenic perspective that small corrective changes in a single component of a deleterious network/configuration might have large beneficial consequences on developmental trajectories and in later treatment. It is suggested that the relationship between ASD and intellectual disability might be fundamentally different in single-gene versus nonsyndromic ASD. It is strongly stated that available biomarker ‘‘tests’’ for autism/ASD will do more harm than good. Finally, the serotonin–melatonin-oxidative stress-placental intersection might be an especially fruitful area of biological investigation. Keywords Autism  Conceptualization of autism  Biomarker  Mutualism  Emergenesis  Network

Introduction Over the past 50 years much effort has been expended in the search for biomarkers for autism. The term ‘‘biomarker’’ has usually been practically defined as a biological measure that

G. M. Anderson (&) Yale Child Study Center and the Departments of Child Psychiatry and Laboratory Medicine, Yale University School of Medicine, 230 S. Frontage Road, New Haven, CT 06519, USA e-mail: [email protected]

differs across groups or is associated with some aspect of a condition. The hope has been that biomarkers would be informative regarding mechanisms of the atypicalities associated with autism, might be useful in early identification, predicting risk and course, might define subgroups, and might index or predict treatment response. Genetic, biochemical, neuropsychological, neurophysiological and neuroimaging measures have been investigated and, at times, proposed as biomarkers for autism. Underlying the biomarker research are three crucial issues: (1) just how should ‘‘autism’’ be conceptualized; (2) what are the relative genetic and environmental contributions to autism; and (3) how should phenotypic heterogeneity in the autism realm be dealt with. A consideration of past and potential conceptualizations of autism leads to implications regarding how autism biomarker research might be best approached. Conceptual considerations appear especially relevant to an increased understanding of the relationships between autism and the intellectual disability and epilepsy that often co-occur. The gene versus environment debate also is of fundamental importance to the questions of where and how one looks for autism biomarkers. Included in ‘‘environmental’’ influences are endogenous gestational factors, experiential factors throughout development, and exogenous exposures such as pollutants. Following examination of these underlying issues, basic problems with using putative biomarkers as tests for autism are discussed. Finally, research on selected biochemical biomarkers is overviewed.

Conceptualization of Autism The existing, though often implicit, conceptualizations of autism include models that view autism as a Disorder, a

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J Autism Dev Disord Table 1 Conceptual models in the autism realm Defining term

Major assumptions

Apparent/assumed genetics

Explanation for phenotypic heterogeneity

Approach, examples & parallels

Disorder

Underlying unity: latent entity

Heterogenetic and polygenetic with convergent neurobiology

Divergent expression of underlying genetics and neurobiology, GxE effects

Categorical: DSM-IV’s ‘‘autistic disorder’’

Spectrum disorder

Underlying unity: latent entity

Heterogenetic and polygenetic with convergent neurobiology

Gene dose effects, influence of genetic background, GxE effects

Categorical with severity dimension(s): DSM-50 s ‘‘autism spectrum disorder’’

Umbrella term

Related latent entities

Heterogenetic with semiconvergent neurobiology

Related, but distinct neurobiological subtypes

Subtyping: As for cerebral palsy, as in the PDD category of the DSM-IV, and as denoted in the term ‘‘autism spectrum disorders’’

Dimensional

Multiple relevant traits and endophenotypes

Complex genetics of each component trait

Multitude of possible combinations of traits and atypicalities

Endophenotypic, dimensional & fractionable; part of the BAP concept; Exemplified by the RDoC

Mutualistic network

Multiple highly interacting traits

Complex genetics of each trait, with major role for epistasis

Multitude of possible combinations of traits and atypicalities, and their complex interaction

Mutualistic theory of IQ: A positive manifold of associated traits without an underlying cause

Emergenic network

Multiple highly interacting traits

Complex and epistatic genetics of each trait, with a major role for ‘‘submergence’’ of social behaviors

Complex interactive effects, emergenic non-familial phenomena including seizures and ID.

Mutualistic approaches of systems biology; Systemist theory of M. Bunge; Emergenesis concept of D. Lykken

DSM-IV and DSM-5, The Diagnostic and Statistical Manual of Mental Disorders, Fourth and Fifth Editions, respectively GxE gene-environment interaction, PDD pervasive developmental disorders, RDoC Research Domain Criteria, ID intellectual disability, BAP broader autism phenotype

Spectrum Disorder, an Umbrella Term, or from a Dimensional Perspective. Two additional conceptualizations offered here are the Mutualistic Network and the Emergenic Network models. As overviewed in Table 1., all of the models have major assumptions regarding the fundamental nature of autism, as well as explanations for the observed phenotypic heterogeneity and for the apparent heterogenetic (differing across individuals) and polygenetics (multiple genes involved, see Gaugler et al. 2014) of autism. The categorical Disorder perspective is typified by The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) (APA 2000) classification ‘‘Autistic Disorder’’ and assumes an underlying unity or latent entity, and a common final cause or pathway. Although the genetics are generally acknowledged to be heterogenetic and polygenetic, the neurobiology is presumed to be convergent. Phenotypic heterogeneity is attributed to effects of environment and to heterogenetics. The Spectrum Disorder model of the DSM-5 (APA 2013) also assumes an underlying unity, with two spectra of severity in the domains of social communication and of restricted, repetitive behaviors. Phenotypic heterogeneity and especially the variations in severity are thought to be due to gene dosage effects as well as genetic background and environmental influences. A third categorical perspective views autism(s) or autism spectrum disorders as an

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Umbrella Term. Here it is assumed that a number of related latent entities exist that are similar due to their semi-convergent neurobiology. Phenotypic variation is thought to be a reflection of these related, but distinct, neurobiological subtypes. The pervasive developmental disorders (PDD) category of DSM-IV was an umbrella term that included autism, Asperger’s and PDD Not Otherwise Specified, along with Rett syndrome and Childhood Disintegrative Disorder. In the years leading up to DSM-5, the plural neologism ‘‘autism spectrum disorders’’ was often used as an umbrella term in referring to the first three categories. Cerebral palsy and epilepsy are two other relevant examples of the Umbrella Term nosology. It should be noted that some take a more agnostic constructivist approach to these fundamentally categorical approaches, seeing the categories merely to represent groups of individuals who are relatively similar phenotypically (Borsboom 2008; Volkmar et al. 2014). In contrast to these categorical models, the Dimensional Perspective does not assume that an underlying unity or latent entity is present. Instead, this model proposes that multiple relevant traits and atypicalities can occur and combine in a multitude of ways in individuals that meet criteria for autism (and in their relatives and the general population) (Maxwell et al. 2013; Sucksmith et al. 2011). The observed heterogenetics and polygenetics of the autism

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realm are an expected correlate of the complex genetics underlying the relevant component traits. The Dimensional (actually multidimensional) approach is operationalized by metrification of phenotypes and endopheontypes, and, in its individualistic and idiographic methodology, is more psychologically based. More broadly, the approach is exemplified by the Research Domain Criteria (RDoC) promoted by the National Institute of Mental Health (www.nimh.nih. gov/research-priorities/rdoc/index.shtml). The approach has a long history of consideration in neuropsychiatry as reflected in the summarizing statement that ‘‘Psychological dysfunctions are the basic units of psychopathology.’’ (van Pragg 2003). The dimensional approach has long been discussed in autism, beginning with the statement that ‘‘…multiple impairments [of autism] which can vary in severity and…can occur independently in various childhood conditions.’’ (Wing and Wing 1971). McBride and colleagues suggested that ‘‘Progress [in autism research] will come from careful description and metrification, and from thorough consideration of the interactions between domains.’’ (McBride et al. 1996). The most thorough promotion of the dimensional and ‘‘fractionable’’ approach was provided by Happe´ and colleagues in their review entitled ‘‘Time to give up on a single explanation for autism’’ (Happe´ et al. 2006) where it was said that ‘‘Heterogeneity within the autism spectrum… is an unavoidable consequence of variation along at least three largely independent (although of course interacting) dimensions of impairment.’’. It should be acknowledged that seemingly simpler dimensions or endophenotypes can themselves be quite complex genetically and biologically (e.g. Fisher 2006; Naples et al. 2012; Skuse and Gallagher 2011). However, one can attribute at least some of the difficulties experienced when attempting the endophenotype approach in autism to the paucity of validated behavioral metrics (Dawson et al. 2007; Losh and Piven 2007). Two additional perspectives offered here are the Mutualistic Network and Emergenic Network models (see Table 1.). Both extend the multidimensional approach and propose a network of multiple interacting and mutually reinforcing traits and factors. The Mutualistic Network model posits a multitude of possible combination of traits and emphasizes the importance of their interaction during development. The model very much parallels the mutualism explanation for the positive manifold of correlations (statistical g) seen for cognitive tasks/processes contributing to IQ (van der Maas et al. 2006). Using a dynamical systems approach to psychological development (Spencer et al. 2011), van der Mass and colleagues proposed that a network of mutually facilitating cognitive processes with reciprocal causation abrogated the necessity for postulating a unifying underlying cause (or physiological g). As an

aside, a prior explanation of g based on the pleiotropic sharing of genetic factors across cognitive processes (Anderson 2001) can be combined with the mutualism explanation and would provide an even greater basis for a positive manifold. The network perspective and approach to neuropsychiatric problems and mental health have been discussed in detail in the paper of Cramer and colleagues, in the 26 associated open peer commentaries, and in the authors’ response (Cramer et al. 2010a, 2010b). The proposed Emergenic Network model incorporates the multi-dimensional and mutualistic approaches while emphasizing to an even greater extent the importance of interactions between traits and factors, and hypothesizing a central role for emergent phenomena. The concept of emergence has a long history and the term has had a range of definitions (Anderson 2008). The philosopher, physicist and self-described systemist, Mario Bunge, uses the term’’strong emergence’’ to refer to novel phenomena of complex systems having to do with the whole being very different than the parts (Bunge 2003). One pertinent example is the emergence of consciousness from neural activity. Emergent or emergenic processes are very dependent upon a particular configuration of factors, and Bunge has used the term ‘‘submergence’’ to refer to the loss of emergent phenomena that can occur when an aspect of a system or configuration is lost or altered. The term ‘‘emergenesis’’ has been used by David Lykken and he offered genius as a prime example of such a phenomenon in behavioral genetics (Lykken 2006). Lykken defined an emergenic trait as one that does not run in families, but, rather, one that appears to depend upon a crucial configuration of co-occurring factors within the affected individual. On the genetic level, emergenic phenomena can be viewed as arising from synergistic epistasis. Two autismassociated deleterious phenomena that appear to meet the non-familial criterion are seizures and intellectual disability. We have previously considered the evidence suggesting that when occurring in association with autism these two phenomena can be viewed in this manner (Anderson 2008). There are some recent data to bolster this contention with respect to seizures in autism (Cuccaro et al. 2012). The Emergenic Network model further hypothesizes that the development and expression of typical ‘‘social relatedness’’ can be conceptualized as a beneficial emergent phenomenon requiring an interacting configural network of attributes/processes. Deficient social relatedness can be conceptualized as a submergenic process resulting from the loss of critical component(s) of a social relatedness configuration. As with the mutualistic explanation of ‘‘g’’, it is quite difficult to specify exactly the components of the emergenic network. However, it is clear that a number of traits and factors must interact synergistically during

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development for typical social relatedness to emerge and that day-to-day social interaction requires a similar interacting network of capacities. By way of example, one could propose that emotional empathy, expressive and receptive skills, flexibility, and social motivation form such a network. The loss of, or deficiency in, a single aspect of the network could be sufficient to cause submergence of social relatedness. Conversely, once submergence occurs during development, a mutualistic network of social atypicality can arise where, again for example, low emotional empathy, deficient expressive and receptive skills, reduced social motivation, and rigidity are reciprocally reinforcing (Anderson 2009).

Implications of a Network Perspective Viewing autism from a network perspective leads to several important implications for biomarker research. Firstly, it underlines the importance of examining component traits, both during development and afterwards. Viewing autism categorically might well be futile when looking for genetic or biological markers as the crucial submergenic factors and, perhaps, the very nature and components of emergenic networks can be expected to differ across individuals. Attempts to identify converging genetic networks and biological pathways of autism per se, though taking a network and systems biology approach (Baraba´si et al. 2011), would seem better focused on components of the autism realm. It also strongly suggests that the nature of the relationships between component traits and the interactions between their underlying factors are extremely important and worthy of investigation. Whether one is examining components of autism at the level of behavior, neurocognition, neurophysiology, neural circuitry, neuron, metabolism, transcriptome or gene, a network perspective within and across levels should increase insight and yield (Cicchetti and Dawson 2002; Cicchetti and Toth 2009; Gottlieb 2007; Yordanova et al. 2010). In a recent study, network analysis and visualization has been applied to medical and psychiatric conditions associated with autism (Lyalina et al. 2013). Parallel analyses at other levels—especially at the behavioral level—should prove informative. If social relatedness is fundamentally an emergenic process, etiological and phenotypic heterogeneity in the core aspects of the autism realm are to be expected, as there are many ways that ‘‘submergence’’ can occur and be expressed. This view is quite consistent with the ‘‘equifinality’’ and ‘‘multifinality’’concepts of developmental psychopathology and with that field’s warnings not to assume common causes in phenotypically similar individuals (Cicchetti and Toth 2009; Richters 1997). Furthermore, the network perspective provides a non-environmental explanation for discordant

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monozygotic (MZ) twin pairs (Anderson 2012). If the intellectual disability and seizures often associated with autism, and social relatedness itself, arise from sensitive configurations of factors, MZ discordance in these areas could be due to subtle epigenetic differences that can arise during the twinning process (Haque et al. 2009; Stromswold 2006). Reported copy number variant (CNV) differences in twin-pairs (Bruder et al. 2008) and variation in genetic mosaicism (Abyzov et al. 2012) that can be expected to occur within twin-pairs could also contribute to discordance. Potentially important differences in MZ twins are also seen in placentation status (mono or dichorionic, placement, size) and amniotic cavity. Although these latter in utero differences can be considered environmental, they are probably to a large extent stochastic, having little to do with exogenous factors. An emergenic network perspective also might inform the puzzling relationship between intellectual disability (ID) and autism (Srivastava and Schwartz 2014). It can be hypothesized that the initial direction of causality of the non-familial ID often associated with idiopathic (‘‘non-syndromic’’) ASD is from the co-occurring risk factors for atypicalities in the components of social functioning to ID. If, as has been suggested (Skuse 2007), the directionality was from ID to ASD, one would expect increased family loading of ID in ASD families, but this has not been seen. Once established, the ID does undoubtedly adversely affect social functioning: ID and atypical social relatedness can then become mutualistically reinforcing during and after development. It is also useful to consider the ID-autism relationship in syndromic/single-locus causes of autism. Importantly, the known human mutations reported to be associated with autism (e.g. Fragile X, Rett syndrome, tuberous sclerosis, and PTEN mutations, as well as CNVs at 1q21.1, 7q11.23, 15q11-13, 16p11.2 and 22q11.2) frequently do not produce autism-like atypicalities and, when present, the atypicalities often only superficially resemble those seen in autism (Carter and Scherer 2013; Moss and Howlin 2009). However, when expressed phenotypically, the associated mutations do invariably involve ID and cognitive deficits. Similarly, animal models of the mutations display cognitive deficits as well as any, often less than convincing, social deficits. This suggests that the genetics and biology of the single-locus mutations associated with autism have more to do with ID than with the factors contributing to the ‘‘core’’ social aspects of ASD. That is to say, in this circumstance, the causality appears to be from ID to autism. Although such mutation-directed research is important, I would second prior suggestions that it appears to tell us much more about ID than about autism (Skuse 2007; Rutter 2011, 2014). To summarize, biological measures and genetic variation might be best related to specific components/dimensions of the emergenic social relatedness network and to the interactions of the components. It should be fruitful to

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identify autism-associated deleterious emergent phenomena, of which ID and seizures appear to be the best examples, and to identify the specific configurations of factors that lead to deleterious emergent phenomena. The sensitivity of configural emergenic phenomena to subtle changes provides further impetus to epigenetic research in the autism realm.

Problems with Reported Biomarker Tests for Autism The foregoing discussion raises basic issues concerning the quest for autism biomarkers, risk factors and ‘‘tests’’. Even if one puts aside the theoretical issues associated with trying to test for or predict a complex and heterogeneous condition, there remain two serious problems with the reported autism tests. First, most reported ASD or autism tests do not use separate development and test sets. Thus, the reported sensitivities and specificities for the tests are often unsubstantiated. A second and more fundamental problem has to do with the poor positive predictive values (PPVs) of the reported tests. The PPV is the number of true positive tests divided by the total number of positive tests and it is very dependent on the prevalence of the condition in the sample being tested. As an example, consider a test with a quite respectable specificity of 90 % and sensitivity of 90 % applied to 1,000 subjects and testing for a condition with a prevalence of 1 %. In this group of 1,000 subjects, 10 will have the condition and be true positives. Of these 10, the sensitivity of 90 % indicates that 9 will receive a positive test. However, the specificity of 90 % indicates that 10 % of the subjects without the condition will receive a (false) positive test: this will amount to 99 of the 990 subjects without the condition. The calculated PPV is thus 9 divided by 99 plus 9, or 0.08 (8 %). In other words, if applied to the general population with an estimated 1 % prevalence of autism, 92 % percent of the positive tests would be in error and would misidentify a typically developing individual as being positive for autism. If the test were to be used to help determine whether early intervention should be implemented, such a test would do much more harm than good. One might defend some of the tests on the basis that they will be applied to pre-screened samples where the prevalence of autism is expected to be substantially higher than the 1–2 % reported for the general population. However, it should be noted that such pre-screened samples also probably would be highly enriched in individuals with intellectual disability, language delay and other non-autistic developmental disorders. There is little or no information on how the reported tests would perform in such a sample, but it can be presumed that specificity would suffer markedly. On the subject of screening tests more generally, it is clear that

risk factors (as opposed to early manifestations) must be very highly associated with a condition before they provide clinically useful screening tests (Wald et al. 1999). We have previously pointed out the distressingly low predictive values (6 and 17 %) of two of the reported tests that already have been commercialized (Anderson and Stahl 2014). The marketing and clinical application of the placenta (Walker et al. 2013) and maternal autoantibody (Braunschweig et al. 2013) tests will have serious detrimental effects due to their poor PPVs. Typically developing children will be misidentified as being at risk and might be subjected to unnecessary and intrusive interventions. The misidentified children might suffer from labeling effects and altered intra-family dynamics, and their families would experience needless anxiety and stress. In addition, given the relatively low sensitivities of the tests, a large proportion of the families of children who go on to develop autism would have been falsely reassured that their child was not at risk. Similar concerns are warranted regarding the clinical application of reported genetic tests (Kong et al. 2012; www.integragen.com/185-the-arisk-test. htm), neuroimaging tests (Anderson et al. 2011; Wang et al. 2012) and eye-tracking measures (Jones and Klin 2013; Pierce et al. 2011). At this time, it is clear that the available autism or ASD ‘‘tests’’ are not clinically useful and would do substantial harm if applied clinically (Anderson and Stahl 2014; Rossi et al. 2013). Available ASD biomarkers, defined as measures that have been consistently shown to be different across autism or ASD groups and controls, might lead to more specific biomarkers/tests. They might also provide leads to relevant pathophysiology. However, as mentioned, the markers are probably best studied in a way that allows for the identification of specific behavioral associations.

Biochemical Biomarker Research in Autism Although many functional, structural, physiological, molecular and genetic measures have been investigated, the following discussion is limited to selected biochemical biomarkers. Prior reviews of the biochemical research of autism have tended to focus on the neurochemical (e.g. Anderson 2014; Cook 1990; Young et al. 1982; Yuwiler et al. 1985). A number of recent reviews and ethical and theoretical considerations of the broad area of biomarkers in autism/ASD are also available (Ratajczak 2011; Hu 2012; Mizejewski 2012; Rossi et al. 2013; Ruggeri et al. 2014; Walsh et al. 2011; Wang et al. 2011; VeenstraVanderWeele and Blakely 2012; Yerys and Pennington 2011). At this point, the most well replicated biochemical findings in autism include higher group mean platelet serotonin (hyperserotonemia), lower melatonin secretion/

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excretion, and increased oxidative stress or altered redox status. These three specific areas will be discussed following a consideration of several broad areas of interest including oxytocin, immune measures, placental research and ‘‘omic’’ studies. Oxytocin The neurohormone oxytocin (OT) is involved in social learning and affiliative behavior in animals and humans (Hammock 2014; Insel 2010; Ross and Young 2009). In humans, OT has been reported to facilitate eye gaze, enhance activation to biological motion, and increase social motivation, and reduce amygdala activation to emotional faces. The OT receptor gene OXTR has been reported to be associated with ASD and an association between the degree of OXTR methylation and brain activity evoked by the perception of animacy has been reported (Jack et al. 2012; Jacob et al. 2007). Urinary oxytocin excretion has been reported to increase in response to several forms of affiliative interaction (Feldman et al. 2011; Nagasawa et al. 2009). Plasma levels of OT have been reported to be altered in autism and in social anxiety, though findings are not entirely consistent (Hoge et al. 2008; Jansen et al. 2006). More recently it has been reported that mean plasma oxytocin levels do not differ between autism and comparison groups; rather, levels were observed to be positively associated with social functioning across groups (Parker et al. 2014). While the Parker et al. study is commendable in its dimensional/correlative approach, one must guard against an ‘‘affirming the consequent’’ error in logic and determine whether oxytocin is cause, consequence, or spurious correlative of altered social functioning (Richters 1997). Several promising studies using intranasal OT to treat ASD-associated symptoms have been reported (reviewed in Anagnostou et al. 2014; Preti et al. 2004), although a recent large study did not observe benefits of sub-acute intranasal oxytocin (Dadds et al. 2014). Immune Measures Immune system alteration in autism/ASD is indicated given epidemiological evidence of increased immunerelated problems in mothers of ASD children, observations of neuroinflamatory states in small-n studies of postmortem ASD brain, reports of altered levels of cytokines, associations between ASD and immune-related genes, and studies of animal models with altered immune response (Onore et al. 2012). Most of the biochemical research has focused on the interleukin and interferon families of cytokines. In general, the cytokine research suggested that ASD is associated with an elevated immune response. However,

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the cytokine research has not been consistent enough to nominate specific cytokines or patterns of alteration as potential biomarkers. The potential implications for ASD and neuropsychiatry of the close connection between serotonin and the immune system have been recently discussed (Baganz and Blakely 2013). Going forward, it will be necessary to resolve the discrepant results, to determine whether subtypes based on specific types of altered immune function can be defined, and to see if alterations are related to particular dimensions of ASD (Gesundheit et al. 2013). Placental Research The placenta is crucially important in embryonic and fetal development and recent research has revealed that serotonin produced by the placenta is necessary for proper brain and body development (Bonnin et al. 2011). There have been several autism-relevant studies of human placental tissue. In the first study of human placental tissue in autism, a significantly greater rate of occurrence of a microscopic abnormality the trophoblast inclusion (TI; 38.5 % versus 13.1 %) was found in placentas of individuals who developed ASD compared to controls (Anderson et al. 2007). In a larger follow-up study, the TI occurrence rate in placentas of at-risk siblings (individuals with an older sibling with ASD) was found to be greater compared to controls (Walker et al. 2013). The initial study included only 13 ASD individuals and the second study did not report on the outcomes of the subjects. Several recent studies have been presented on gross morphological and vascular abnormalities of the placenta in ASD (e.g. Salafia et al. 2014). Future human studies will need to carefully assess and compare behavioral outcomes in individuals with normal and abnormal placental morphologies (microscopic and macroscopic). Recent animal studies have examined effects of maternal stress and illness on placental function and integrity in an attempt to understand the possible role of altered placental function in ASD etiology (Carpentier et al. 2013; Kirsten et al. 2013). The role of placental serotonin in neurodevelopmental disorders is of special interest and might be of particular relevance to ASD (Bonnin and Levitt 2012; Velasquez et al. 2013). Omic Research ‘‘Omic’’ research involves the simultaneous measurement of a large number of analytes. Transcriptomics, proteomics, and metabolomics concern the measurement of RNA species, of peptides/proteins, and of small molecules (respectively). The approach offers an unbiased appraisal of many possible associations and group differences, the ability to discern patterns of alterations, and the potential for

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unanticipated discovery. The unfocused nature of the approach can be criticized and it can be difficult to analyze the large amounts of data produced. An early proteomics effort in autism involving two-dimensional gel electrophoresis was mentioned over two decades ago (Anderson et al. 1990). The initial productive proteomic application in autism was the work of Pevsner and colleagues (Purcell et al. 2001) and the potential utility of proteomics in autism was reviewed at that time (Junaid and Pullarkat 2004). The pace of omics research in autism/ASD has accelerated with improved methodologies and the increased application of omics research in biomedicine. The potential of the omic approach in ASD research has been more recently reviewed (Dudley et al. 2011; Maurer 2012; Voineagu 2012). There are several published transcriptomic studies in ASD examining brain tissue as well as blood elements, and proteomic studies have also studied both brain and blood elements. The metabolomic research has compared urinary excretion of metabolites and has examined plasma lipid profiles (lipidomics) in ASD and typically developing individuals. At this early stage, the omics research needs replication both in terms of the specific analytes that have been reported to be altered in autism and regarding claims of discriminant power. It has been pointed out that it might be especially powerful to integrate genomic, transcriptomic, proteomic and metabolomic observations (Voineagu (2012). Serotonin There has been a longstanding interest is the possible role of serotonin in autism and ASD. Much of the interest can be traced back to the initial 1,961 report of elevated levels of platelet serotonin in autism (Schain and Freedman 1961). That study was prompted in part by a consideration of serotonin’s role in mediating the effects of hallucinogens. Interest has been augmented by an increasing awareness of serotonin’s role in neurodevelopment, its growth factor effects, its involvement in ASD relevant behaviors, and treatment effects of serotonergic agents (Anderson et al. 2009; Bonnin and Levitt 2012; Fukumoto et al. 2005; Posey et al. 2008; Scahill and Anderson 2010; Young et al. 2014). The basic finding of platelet hyperserotonemia is perhaps the best replicated biochemical finding in neuropsychiatry with studies consistently observing group mean elevations in autism or ASD of 20–50 % (Anderson 2002; Anderson et al. 1990; Gabriele et al. 2014). The elevation appears specific to autism/ASD as it is not seen in intellectual disability (McBride et al. 1998; Mulder et al. 2004) or in other neuropsychiatric disorders. The distribution of the measure in the autism/ASD group is usually right-skewed. One larger study of an ethnically homogenous Dutch sample observed a bimodal distribution of platelet serotonin concentrations (Mulder et al. 2004).

Disappointingly, a number of crucial questions remain unanswered. Thus, the mechanism of the elevation remains undetermined, there have been no consistently observed specific behavioral associations, and the genetic determinants have not been identified. It appears likely, though less than certain, that the platelet is not exposed to more serotonin, but instead that there is some alteration in the platelet’s handling of serotonin (Anderson et al. 2012). Although several animal models of hyperserotonemia has been proposed (Flood et al. 2012; McNamara et al. 2008; VeenstraVanderWeele et al. 2012), the mechanism and the relationship to central serotonergic functioning remain elusive. Melatonin Melatonin is produced mainly in the pineal gland where its production has a marked circadian variation, with blood levels typically peaking around 2 AM. In humans, this diurnal rhythm is usually present by 3–4 months of age. Melatonin is also produced in the gut wall and the placenta. Melatonin reduces sleep latency, reinforces biorhythms, is a powerful antioxidant, has a role in neurodevelopment and plasticity, and might be important in placental homeostasis (Galano et al. 2011; Reiter et al. 2014), and is reported to affect gut tone, regeneration and immunity (Bubenik 2002). Initial interest in melatonin in autism came mainly from its role in sleep and it potential use as a treatment for sleep problems associated with autism and, perhaps, from its close metabolic relationship to serotonin. Increasing recognition of its other functions and properties has further increased interest. As recently reviewed (Tordjman et al. 2013), nearly all prior studies of melatonin in ASD have found lower levels of plasma melatonin or lower urinary excretion rates for its principal metabolite melatonin sulphate. Investigation of melatonin production in infants and toddlers, research relating the measure to specific behaviors, and studies ascertaining the value of the biochemical measures in predicting sleep problems and treatment effects of melatonin all seem warranted. Oxidative Stress/Redox Status Over fifty papers have reported that redox status is altered or oxidative stress is increased in ASD (Frustaci et al. 2012; Rossignol and Frye 2011; Villagonzalo et al. 2010). Few negative or non-replicating studies have been reported. Much of the work has focused on measuring plasma glutathione and most investigators have observed lower levels of reduced glutathione (GSH) and higher oxidized glutathione (GSSG) levels in ASD. Plasma indices of oxidative stress have also been consistently reported to be increased in ASD. Several studies of postmortem brain tissue have also observed higher oxidative stress markers in

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ASD. As mentioned, the reported group mean deficit in the powerful antioxidant melatonin deserves some consideration in terms of melatonin’s possible role in the apparent altered redox status and increased oxidative stress of ASD. The overlaps of serotonin and melatonin neurobiology, placental physiology, and oxidative stress are intriguing.

Summary Network perspectives, in their emphasis on components and their interactions, might afford the best approach to the complexities of the ASD realm. Categorical approaches are unlikely to be fruitful as one should not expect to find a single or even predominant underlying cause of autism behavior across individuals. It is possible that the complex, highly interactive, heterogeneous and individualistic nature of the autism realm is intractable in terms of identifying clinically useful biomarker tests (Buchanan et al. 2006). At best, one could hope that biomarkers might be useful in predicting or explaining certain aspects in the autism realm. As discussed, the few observed group differences should not be taken to indicate similar alterations or causal mechanisms across all members of the group, rather they provide a starting point for trying to associate the measure with specific aspects. Similarly, researchers using animal models should be content to study specific behaviors rather than attempting to model autism in its entirety. It is hopeful from an emergenic perspective that small corrective changes in a single component of a deleterious network/configuration might have large beneficial consequences on developmental trajectories and in later treatment. It is suggested that the relationship between ASD and ID might be fundamentally different in single-mutation (syndromic) versus non-syndromic ASD. It is strongly stated that available biomarker ‘‘tests’’ for autism/ ASD will do more harm than good. Finally, the serotonin– melatonin-oxidative stress-placental intersection might be an especially fruitful area of biological investigation. Acknowledgments We thank James McPartland, PhD, Sherin Stahl, PhD, and an anonymous reviewer for their helpful comments on the manuscript. This work was supported by the Mindworks Charitable Lead Trust.

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