Identification of Essential, Equivocal and Complex Autism by the Autism Dysmorphology Measure: An Observational Study

The Autism Dysmorphology Measure is designed for non-expert clinicians. It uses an algorithm to assess 12 body regions and categorizes Autism on the number of dysmorphic regions identified; Essential (≤ 3), Equivocal (4–5) or Complex (≥ 6). We evaluated 200 Indian children with Autism (mean age 3.7 years) in a hospital-based cross-sectional study and compared inter-group profiles. We found 31% Essential, 49% Equivocal and 20% Complex Autism. On comparing results with existing literature, it appeared that genetic ancestry and age significantly influenced dysmorphism and hence categorization. No significant differences were observed between complex and essential autism in epilepsy, severity of autism or development, as reported earlier. These shortcomings make the present tool unsuitable for use in young Indian children with Autism.

Keywords: Autism spectrum disorder; Congenital abnormalities; Dysmorphism

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10803-020-04641-x) contains supplementary material, which is available to authorized users.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder of childhood that is characterized by impairments in social communication and social interaction, in the presence of restricted and repetitive patterns of behavior, interests or activities. The etiopathogenesis of ASD still remains uncertain. A popular hypothesis is that some common biochemical, structural or developmental pathway gets impacted by yet-to-be-identified genetic, environmental, or epigenetic agents during pregnancy, which results in the characteristic manifestations of ASD. Just as there is wide variability in behavioural phenotype within the spectrum, there is also considerable heterogeneity in clinical and physical phenotypes. Individuals with ASD have higher rates of physical anomalies than the normo-typical population (Hultman et al. [12]; Lauritsen et al. [18]; Wier et al. [49]; Timonen-Soivio et al. [43]). This makes them appear overtly or subtly (to the discerning eye) dysmorphic. Some of them have underlying genetic disorders like Fragile X syndrome, Cornelia-de-Lange syndrome, Tuberosis Sclerosis Complex, etc., while in the others the underlying etiology remains obscure. However, the presence of dysmorphism does suggest that whatever the nature, some insult did occur between the 2nd and 12th weeks of gestation, that affected embryogenesis.

Conventionally, physical anomalies are classified as major or minor. The former have significant functional and cosmetic impact on the affected individual requiring urgent medical intervention, whereas the latter do not. Despite their innocuity, minor physical anomalies (MPA) can be considered as biological indicators since they are associated with increased risk of major physical anomalies, when multiple (Marden et al. [21]; Leppig et al. [19]), abnormal brain development, schizophrenia, intellectual disability and ASD (Rodier et al. [34]; Miles and Hillman [22]). It has been observed that MPA are commonly seen in embryologically complex structures (i.e. ears, face, hands and feet), which share embryonic origin with the central nervous system (Smalley et al. [40]). A meta-analysis comparing MPA in 330 pooled individuals with ASD (mean age 9.75 years, 80% males) with 388 pooled controls (mean age 10.3 years, 70% males) revealed significantly higher rates of MPA in the former with a large pooled effect size of 0.84 (Ozgen et al. [31]).

Continuing with their earlier research on dysmorphism in ASD, Miles et al. ([23]) categorized the endophenotypes as 'Complex Autism' and 'Essential autism'. This was based on the presence or absence of microcephaly and the number of MPA (≥ 6 or ≤ 3). Complex autism was associated with lower developmental/intelligence quotients, more seizures, more abnormal EEGs and brain MRIs, and sporadic inheritance. Essential autism was associated with more heritability, higher recurrence rates and higher male to female ratio. The authors felt that this MPA based clinical categorization would be important for planning management and prognostication. However, it was an exhaustive process that involved evaluation of 556 physical structures by experts that were according to the London Dysmorphology Database (LDDB) classification (involving the primary, secondary and tertiary levels). Since the 1970′s, the 'Waldrop weighted scoring scale' (Waldrop and Halverson, [47]) was being used for assessing MPA in schizophrenia in adults (Ismael et al. [14]; Sivkov and Akabaliev [39]) and intellectual disability and behavioural problems in children (Quinn and Rapoport [32]; Steg and Rapoport [42]; Firestone et al. [9]). Some authors had used it to evaluate children with ASD (Walker [48]; Links et al. [20]; Rodier et al. [34]; Tripi et al. [44]), but their results were inconclusive. The goal of the research team subsequently became the development of an ASD specific tool that would enable the clinician to identify the same categories by an easier strategy which involved evaluation of lesser MPA.

In those days it was thought that the definition of homogeneous subgroups within ASD would provide exciting insights in genetic linkage and association studies (Miles and Hillman [22]). With genetic and epigenetic hypotheses in the etiopathogenesis of ASD becoming more convincing and the advent of genetic technology, research in functional genomics is now focussing on the identification of ASD genes or 'hot spots' that display convergence of neural pathways and morphogenesis of the brain, face, cranium and other body structures (Ozgen et al. [31], [30]; Krishnan et al. [17]). Recent practice parameters recognize that dysmorphism in ASD has a higher likelihood of genetic etiology, and if identified can direct further testing, interventions, parental counselling and monitoring of co-morbid medical conditions (Hyman et al. [13]). Evaluation of dymorphism is no longer 'to be considered' but should be included in the comprehensive assessment of individuals with ASD (Hyman et al. [13]). This has conventionally been the purview of dysmorphologists and/ or clinical geneticists who establish clinical diagnosis based on gestalt recognition, supported by standard criteria and definitions and then confirmed with specific genetic tests (Carey, [7]). Unfortunately, both experts and economical testing are scarce in low- and middle-income countries (LMIC). Since most professionals and paraprofessionals dealing with ASD lack expertise in dysmorphology, many dysmorphic children go unrecognized, unless a high index of suspicion is kept or children are proactively sent for genetic referrals, which is not the usual practice.

The Autism Dysmorphology Measure (ADM) is a tool that was designed for non-expert clinicians to evaluate dysmorphism in ASD in a simplified and non-invasive way (Miles et al. [25]). Its development started with the 34 LDDB primary regions that were brought down to 12 regions by the Classification and Regression Tree (CART) statistical method. It was a complicated process that involved a trade-off between frequently observed dysmorphic features (in the original study population of 222 children with Autism) and best possible sensitivity and specificity when validated against expert diagnoses.

When administering the tool, the clinician uses the ADM algorithm by starting with the first body region (the ear), decides whether it is dysmorphic or non-dysmorphic (according to operationalized definitions) and then progresses to the next area. This continues until all 12 body regions have been labelled accordingly. Individuals are then categorized based on the number of dysmorphic regions; Essential Autism (≤ 3), Equivocal Autism (4–5) and Complex Autism (≥ 6). The following concerns regarding ADM were raised by Miles et al. ([25]); reliability of a dichotomous label (dysmorphic or non-dysmorphic) in regions in which the structure and/or size of the concerned region is along a continuum, disregarding MPA that are present in a subject but not included in the 12 regions of the algorithm, and validity of its performance when used by non-experts with varied expertise in a non-research setting. It seems there is an ongoing multi-centric ADM validity study in children from different ethnic backgrounds across America, which received sponsorship by the Simons Foundation (Miles [24]) and started in 2011, but the paper based on its results is still awaited.

Despite the high prevalence and clinical implications of dysmorphism in ASD, and availability of the ADM, its routine use in evaluation of ASD is not yet established. This is probably because there is limited published research on MPA based endophenotypes and there is considerable heterogeneity in results (Table 1). In addition, most studies have focussed on Essential and Complex autism and disregarded Equivocal autism as a separate entity (including them with Essential autism in analysis).

Table 1 Details of studies of Essential, Equivocal and Complex Autism based on evaluation by Autism Dysmorphology Measure (+) or other methods (−)

ADM utism Dysmorphology Measure, DD developmental delay, MA mean age, TD typically developing #Male–female ratio *Significant difference with respect to essential autism **Cases with genetic syndromes excluded

This study was planned to evaluate Indian children with ASD using the ADM, categorize them into essential, equivocal and complex autism and compare their clinical and psychometric profiles (severity of autism, developmental status and adaptive function).

A hospital-based, cross sectional observational study was conducted in a tertiary level children's hospital in India over 17 months (November 2016–March 2017), after obtaining Ethics Committee approval. Figure 1 depicts the flow of participants from recruitment to data analysis.

Graph: Fig. 1 Flow diagram depicting study methodology

The inclusion criteria were children between 2 and 12 years who were registered in the hospital's Autism Clinic and satisfied the diagnostic criteria for ASD according to the Diagnostic and Statistical Manual of Mental Disorders, 5th ed., (DSM -5; American Psychiatric Association [1]). Children in whom photography could not be done (due to feasibility issues or parental refusal), whose parents or their photographs were unavailable for comparison, or whose data was incomplete were excluded. The calculated sample size was 196, taking the prevalence of dysmorphism in ASD as 15%, precision as 5% and type I error as 5%.

As per international recommendations, the DSM 5 diagnostic criteria for ASD can be used to establish a clinical diagnosis of ASD (Johnson et al. [15]; Volkmar et al. [46]; Hyman et al. [13]). These comprise of five criteria (current or in the past) that should exist across multiple settings and are elicited by parental report and observation of the evaluator. All four pertaining to persistent difficulty in social communication and social interaction, at least 2 of the 4 related to restrictive and repetitive patterns of behavior, interests or activities and all the obligatory criteria (onset in the early developmental period, causing functional impairment and not better explained by other childhood mental health conditions) need to be fulfilled.

The Autism Dysmorphology Measure (ADM) kit comprises of an operational manual (Miles et al. 2015), ADM worksheet and ADM algorithm. The manual outlines the method of evaluation of 12 body regions (short stature; hair growth patterns; ear structure, size and placement; nose size; face size and structure; philtrum; mouth and lips; teeth; hand size; fingers and thumbs; nails; and feet structure and size) in a manner that avoids unclothing the child. This includes measurement of 9 parameters (height, weight, head circumference, ear length, inner canthal distance, outer canthal distance, hand length, palm length, and foot length) and photography of the face, abnormal hair whorls (if present), hands and feet.

The operational definitions of normal, abnormal, and grey areas for each of these parameters is detailed with photographs and illustrations and/or compared with age-specific charts of typically developing individuals. In case any doubt persists, the assessor should refer to standard dysmorphology references like the LDDB or an expert. If a feature is deemed dysmorphic, it is compared with the corresponding feature of both parents or their photographs (when parents are unavailable). If it is present in the parent and the parent does not have ASD, the particular feature is considered non-dysmorphic, otherwise it is deemed dysmorphic. Based on these criteria, each feature is judged as dysmorphic, non-dysmorphic, or equivocal (considered non-dysmorphic for the ADM algorithm, but nonetheless warrants evaluation by an expert). These decisions are entered in the ADM worksheet and applied to the ADM algorithm (as described earlier). The final morphological classification is based on the total number of dysmorphic features identified; essential autism (0–3 dysmorphic features), equivocal (4–5 dysmorphic features) and complex autism (≥ 6 dysmorphic features). It is recommended that irrespective of the ADM classification, all children should undergo an evaluation by an expert for identification of syndromes known to have a high association with ASD (i.e. Fragile X syndrome, Timothy Syndrome, etc.), since these may not be captured by the ADM. The sensitivity of ADM is reportedly 81%, specificity 99%, inter-rater reliability 0.88 and inter-class correlation 0.65 (Miles et al. [25]).

The Developmental Profile, 3rd edition [DP-3] (Gerald, [11]), a parental questionnaire for children from birth to 12 years was used to assess five domains; physical, adaptive, social-emotional, cognitive and communication. Raw scores based on parental responses are converted into standard scores (SS) for each domain and an overall General Development Score (GDS) is computed. Performances with SS or GDS < 70 indicates delay [< − 2 standard deviations (SD)]. The internal consistency ranges from 0.81 to 0.97 and test–retest reliability 0.81 to 0.92.

The Vineland Adaptive Behavioral Scale-II [VABS-II] (Sparrow, Cicchetti and Balla 2005) was used to measure adaptive function (the skills required to perform and cope with activities of daily living). This tool assesses four domains (communication, daily living skills, socialization and motor skills) and corresponding sub-domains, based on parental responses. VABS -II is used in individuals ranging from birth to 90 years. The software VABS ASSIST generates electronic records and converts raw score to standard scores (SS) for domains, v scale scores (VSS) for sub-domains and an overall Adaptive Behavior Composite [ABC]. The scores correlate with the level of adaptive function. Performance is rated as low (< -2 SD) when ABC/ SS is < 70. The test–retest reliability is 76–92%, inter-rater reliability 71–81% and internal consistency is 93–97%.

The Childhood Autism Rating Scale 2 [CARS-2] (Schopler and Van Bourgondien [36]) is a tool that identifies autism and rates severity of symptoms in individuals ≥ 2 years of age. It comprises of 15 items that are according to frequency, intensity, peculiarity, and duration of each item. In children below 13 years of age a total score of 15 – 29.5 denotes 'Minimal-to-No-Symptoms of ASD', between 30 and 36.5 'Mild-to-Moderate Symptoms of ASD' and > 37 'Severe Symptoms of ASD'. The sensitivity and specificity of CARS-2 is 0.81 and 0.87 respectively.

A non-expert clinician was trained by experts (with experience in LDDB classification) to differentiate between dysmorphic and non-dysmorphic in each of the 12 body regions, fill the ADM worksheet and apply the ADM algorithm to arrive at the final ADM classification. This was in accordance with the protocol outlined in the ADM operational manual.

Figure 1 depicts the flow of participants during the study. Children diagnosed as ASD were consecutively recruited from the Autism clinic. Informed consent was taken from the parents of all eligible children. Study relevant demographic data was collected. Subsequently, the ADM was administrated, digital photographs taken and observations recorded in the ADM worksheet by the trained investigator. Enrollment continued until the pre-decided sample size of 200 was attained. To avoid information bias, clinical details (presentation, behavioral issues, co-morbid conditions and family history with construction of three generation pedigree tree) were elicited and psychometric testing scores of DP-3, VABS II and CARS II (performed by a clinical psychologist or developmental pediatrician in the previous month) noted from records afterwards. The ADM algorithm was applied at the end and the final classification documented. The parents of children identified with any dysmorphic feature underwent a comparative evaluation as per ADM protocol. Each child underwent an expert evaluation that included general physical and systemic examination and comprehensive assessment of dysmorphism for identification of any recognizable syndrome. Digital photographs of the patients were made anonymous and evaluated by experts. The non-expert clinician, clinical psychologist and experts were blinded to each other's reports.

Primary outcome variables included the number of children with ADM score of 0–3 (Essential autism), 4–5 (Equivocal autism) and ≥ 6 (Complex autism). Secondary outcome variables included the identified syndromes, number of children with various grades of severity of autism (CARS-II), delay/ low adaptive function in overall and various domains (DP3 and VABS-II respectively) and total CARS-II scores, domain-wise SS, GDS and ABC.

Data was analyzed by SPSS Version 20. Descriptive statistics were used. Inter-group differences between essential, equivocal and complex autism were assessed by unpaired student's t-test or ANOVA (for the continuous variables) and Chi square test (for the categorical variables).

Out of the 210 eligible children recruited, 10 were excluded; 8 parents or their photographs were unavailable and 2 children lacked the complete set of photographs. The final study population consisted of 200 children with ASD. The age ranged from 2 to 5.9 years with a mean age ± standard deviation (SD) of 3.7 ± 1.1 years. There were 158 (79%) boys and 42 (21%) girls with a boy-girl ratio of 3.8:1.

The most common regions which were labelled dysmorphic were hair growth patterns 155 (77.5%), ear structure, size and placement 143 (71.5%), and fingers and thumbs 136 (68%). The frequency of involvement and ranking of other regions is depicted in Table 2. The ADM algorithm identified 62 (31%) Essential Autism, 98 (49%) Equivocal Autism and 40 (20%) Complex Autism. The distribution of each ADM body region in each group is compared in Table 3.

Table 2 Inter-study comparison of different racial populations with Autism by distribution of the 12 body regions evaluated in Autism Dysmorphology Measure

aMiles et al. ([25]) bWong et al. ([50]) cPresent study *The rank of each parameter is rated according to the prevalence of dysmorphic features (in descending order) in each study

Table 3 Inter-group distribution of Dysmorphic ADM body regions in Essential (n = 62), Equivocal (n = 98) and Complex (n = 40) Autism

Significant difference of p value * (< 0.05)/ ** (< 0.0001) with respect to essential Autism Significant difference of p value#(< 0.05) between equivocal & complex Autism

The demographic profiles of each group are given in Table 4. There was no significant inter-group difference in the mean age. There was a higher male to female ratio in Essential autism with respect to Equivocal autism, but not with Complex autism (Table 4).

Table 4 Demographic and clinical profile in Essential (n = 62), Equivocal (n = 98) and Complex (n = 40) Autism

Significant difference of p value * (< 0.05)/ ** (< 0.0001) with respect to essential Autism Significant difference of p value # (< 0.05)/ ## (<0.0001) between equivocal & complex Autism aDuchenne muscular dystrophy (DMD)-1, Prader-Willi syndrome-2, and Tuberous Sclerosis Complex (TSC)-1 bAngelman syndrome-1 cWilliams syndrome-1 dAngelman syndrome-1, Gorlin syndrome-1, Kabuki syndrome-1, Mucopolysaccharidosis type 4-2, Rubenstein-Taybi (RT) syndrome-1, Russell-Silver (RS) syndrome-1 and TSC-1 eWilliams syndrome-1, Fragile X syndrome-1 fAarskog syndrome-2. Angelman syndrome-2, Cornelia de Lange syndrome-2, Kabuki syndrome-2, OMENS syndrome (each letter depicting grades of Orbital asymmetry, Mandibular hypoplasia, Ear deformity, 7th Nerve involvement and Soft tissue deficiency)-6, RT-2, RS-1, and Sotos syndrome −1

The distribution of co-morbid medical condition (developmental regression, sleep issues, feeding issues, and seizures), anthropometric abnormalities (underweight, stunting, severe stunting, microcephaly and macrocephaly) and syndromes (diagnosed by genetic testing and on clinical phenotype) as well as their inter-group differences are given in Table 4. Developmental regression was comparable in all three groups. The semiology of seizures observed across groups were focal motor seizures, generalized tonic–clonic seizures and West Syndrome. Children with Equivocal autism had significantly more seizures (specifically West syndrome) than Essential autism. There was no significant difference in seizures between Complex and Essential autism or Equivocal and Complex autism. There was no significant inter-group difference in parental report of sleep issues (interrupted sleep, decreased sleep, and/or early morning awakening) or feeding issues. The most common complaint across all groups was difficulties in chewing and decreased food intake.

There were significantly more children with abnormalities in weight, height and head circumference (except macrocephaly) in Equivocal and Complex autism compared to Essential autism. Microcephaly and severe stunting was significantly higher in Complex autism compared to Equivocal autism. Details of the identified genetic syndromes are given in Table 4. There were significantly more syndromes in Equivocal and Complex autism compared with Essential autism and in Complex autism compared to Equivocal autism.

Though 88.5% of the study population had severe ASD, there was no significant inter-group difference in the severity of autism in terms of proportions of children with no to minimal, mild to moderate and severe autism as well as the mean CARS-2 scores (Table 5).

Table 5 CARS-2 assessment of severity of Autistic symptoms in Essential (n = 62), Equivocal (n = 98) and Complex Autism (n = 40)

There was no significant inter group difference in any parameter CARS−2 Childhood Autism rating Scale, 2nd edition, SD standard deviation

Almost all (98.5%) of the children had co-morbid developmental delay (i.e. low functioning). However, the proportion of children with delay was comparable in all three groups, though the overall GDS score was significantly more in equivocal autism with respect to essential autism (Table 6). No significant difference was observed between equivocal and complex Autism.

Table 6 DP3 assessment: Inter-group comparison of domain-wise developmental profile in Essential (n = 62), Equivocal (n = 98) and Complex (n = 40) Autism

No significant difference was observed between equivocal and complex Autism Comm. Communication; DP3 Developmental Profile, 3rd edition; GDS Global Developmental score; SD standard deviation; SS Standard score *Significant p value (< 0.05) when compared with essential Autism

Not surprisingly, the majority of the children (89.5%) had low adaptive function. The proportion of children with low adaptive function was significantly more in equivocal autism with respect to essential autism, but the ABC was comparable in all three groups (Table 7). The individual SS of the DP3 and VABS-II domains are presented in Tables 6 and 7. Significant differences was observed only in the Physical (DP3) and Motor (VABS-II) domains of equivocal and complex autism with essential autism. No significant difference was observed between equivocal and complex Autism.

Table 7 VABS-II assessment: Inter-group comparison of domain-wise adaptive function in Essential (n = 62), Equivocal (n = 98) and Complex (n = 40) Autism

No significant difference was observed between equivocal and complex Autism ABC Absolute Behavior Composite, Comm. communication, DLS daily living skills, Soc. socialization, SD standard deviation, SS standard score, VABS II Vineland adaptive Behavior Scale, 2nd edition *Significant p value (< 0.05) when compared with essential Autism

Since the presence of dysmorphism is common in ASD, and has clinical implications in further management, counseling and prognostication, its evaluation should be included in the comprehensive assessment of any individual diagnosed with ASD. However, as most professionals dealing with ASD (even developmental pediatricians and other clinicians) lack this expertise and there is paucity of dysmorphologists and clinical geneticists in India and other LMIC, this is not universally practiced. ADM was designed for non-dysmorphologist clinicians to categorize ASD into Essential, Equivocal or Complex Autism, based on the number of MPA. Complex Autism reportedly differs from Essential autism in terms of co-morbid conditions, heritability and prognosis. The existing literature on ADM is limited, most studies have not studied Equivocal autism and there is heterogeneity in results. Since Autism is usually identified in early childhood when further management is planned and parents counselled, the scope of clinical application of an easy-to-use, non-invasive and inexpensive tool like ADM is vast, provided it found to be reliable across populations, and inter-group differences do actually exist between Essential, Equivocal and Complex Autism.

The primary objective of this hospital-based study was to evaluate Indian children with ASD using the ADM algorithm. The secondary objectives were to compare the clinical and psychometric profiles of each sub-group identified (including Equivocal autism which has been understudied earlier). When compared with the lacunae identified in earlier research, the strengths of our study included adequate power, the ADM protocol was followed completely, and the research team (clinical psychologist, non-expert clinician and experts in clinical genetics) were blinded. We purposely did not include typically developing children as controls, as ADM is specifically designed for ASD. A major limitation of the study was that though most syndromes were diagnosed by experts by gestalt recognition and clinical diagnostic criteria (when applicable), confirmation by genetic investigations was possible in only a select few, due to financial restraints and/or inaccessibility to tests. This is a common occurrence in India and other LMIC. Also, despite our inclusion criteria being 2–12 years, all our study participants were less than 6 years old. This was probably because we did not plan a priori age stratification during recruitment and though our autism clinic caters to adolescents, our hospital primarily offers early intervention until l 5 years. Since older patients receive intervention elsewhere, they start getting lost to follow up from the Autism Clinic.

We identified 31% essential, 49% equivocal and 20% complex autism in 200 children ranging from 2 to 5.9 years. The proportion of essential was considerably lower and equivocal autism significantly higher than earlier studies, whereas complex autism was comparable to a few studies (Table 1). A critical appraisal of all these studies led us to arrive at three probable explanations for this variability.

The first explanation is related to issues identified in methodology. The evaluation of MPA varies across studies in several aspects; method (ADM, clinical examination or only photographs), type of MPA involved in assessment (selective 12 ADM regions or all that are present in an individual), levels of expertise in dysmorphology of assessors (clinicians or geneticists), categorization (inclusion or exclusion of Equivocal autism), and inclusion or exclusion of genetic syndromes. We decided to exclude two studies from further synthesis, as we felt that the lacunae identified in their methods rendered their results unreliable: Angkustsiri et al. ([2]) in which ≥ 3 MPA were considered dysmorphic (effectively combining Equivocal and Complex autism), and Zachariah et al. ([51]) in which the sample size of ASD was inadequate (26 children between 2 and 5 years). Though 140 neurotypical children were mentioned as controls in the paper, their data was not disclosed, and in any case would have been irrelevant.

The second probable explanation is differences of the study population with respect to race/ethnicity or common genetic ancestry, a term preferred by geneticists (Fujimura and Rajagopalan [10]). Conventional global nomenclature of race/ethnicity includes caucasoid, negroid, mongoloid, Australoid and mixed. The original ADM norms (Miles et al. [25]) were generated from a population described as predominantly white (Caucasoid) with some black (negroid), multi-racial (mixed) and Asians (may be mongoloid or mixed) individuals. The genetic ancestry of the other study populations are given in Table 1, though details were sketchy in a few. The frequency of occurrence and rank of dysmorphic features in each of the 12 ADM regions are compared in the three studies in which we were able to retrieve the relevant data (Table 2). Dysmorphic 'hand size' and 'feet size and structure' are similar in both rank and prevalence in each population (Predominantly white, purely Chinese and purely Indian). Dysmorphic 'Face' is similar in prevalence and rank in Indian and white populations. Dysmorphic 'Philtrum' are similar in prevalence in white and Chinese populations but differ in rank. These inter-racial differences may be the reason why Equivocal Autism was much higher (49%) in our study compared to others; 10% (Miles et al. [23]), 21.3% (Wong et al. [50]) and 13% (Shapira et al. [37]). The ADM validity multi-centric study is apparently focusing on the evaluation of ADM across races (genetic ancestry) in American populations. The aforementioned points that we have made suggests that its performance will need to be assessed in the different global genetic ancestries before it can be recommended for universal use. Country-wise norms of non-dysmorphic and dysmorphic features for each of the 12 regions may need to be developed and validated in target populations.

The last and equally important probable reason is the influence of age on dysmorphism. Since physical features changes with growth as a child becomes older, several authors think that it also affects the appreciation of dysmorphism (Allanson et al. 1989; Cole and Hughes [6]; Braddock et al. [5] and Shapira et al. [37]). The craniofacial shape alters in childhood, adolescence (especially in boys) and adulthood. The variations that are apparent in childhood and adolescence have been attributed to physical forces like biochemical stress and gravity, and those that occur later in adulthood are due to textural variations and wrinkles (Ramanathan and Challapa 2006). More recently, Tripi et al. ([45]) proposed an 'ASD facial phenotype' based on the increased orbital hypertelorism and decrease of height of the facial midline that they observed in individuals with ASD. They hypothesised that these were due to aberrant optical differentiation from the neural crest and maldevelopment of the midline part of the face during embryogenesis, respectively. Since 6 of the 12 ADM body regions are craniofacial, the clinical implications of these observations on ADM scoring are enormous.

We would like to emphasize that the ADM norms were developed in a population with a mean age 8.1 ± 7.1 years in Essential Autism and 13.4 ± 9.8 years in Complex Autism. The ages of the participants in the other studies range from toddlers to mid-adulthood. In the study by Flor et al. ([8]) in which study population ranged from 2 to 17 years, the overall prevalence of Complex autism was 5.6%. This exhibited a decreasing trend with increasing age on subgroup analysis; 41.4% (2–4 years), 30% (5–7 years), 18.6% (8–11 years), 8.6% (12–14 years) and 1.4% (15–17 years). Similar data are not available from the other studies (including ours). Future research employing longitudinal studies to determine the effect of age on the ADM algorithm is definitely warranted.

Finally, we will discuss the inter-group comparisons between Essential, Equivocal and Complex autism in our study. In contrast to Essential autism, children with Complex autism displayed significantly more genetic syndromes, underweight, stunting, and microcephaly, and also had lower DP3 motor and VABS-II physical mean standard scores. All of these are inter-connected. Genetic syndromes are known to be more common in complex autism. We presume our high detection rate was comparable with Shapira et al. ([37]) due to the inclusion of experts in dysmorphology in our research team. Higher prevalence of underweight, short stature and microcephaly is common in syndromes, and the first two anthropometric abnormalities are known to result in lower physical and motor scores.

Sacco et al. ([35]) did a systematic review on macrocephaly and brain overgrowth in ASD. They found a prevalence of 15.9% in the former and 9.1% in the latter. Both were seen to occur in early childhood. In contrast, microcephaly has been comparatively less researched, but studies have shown prevalence ranging from 5.9% to 15.1% in ASD (Zachor and Ben-Itzchak [52]). We compared our results with four earlier Indian studies in which data on head circumference was available. Microcephaly was observed in 6.3% (Singhi and Malhi [38]), 56.6% (Juneja et al. [16]), 30% (unpublished data, Mukherjee et al. [28]), 25.6% (Mukherjee et al. [27]) and 17% (present study). In contrast macrocephaly was present in 0, 0, 5.6%, 2.8% and 6.5% respectively. Genetic syndromes have a higher association with microcephaly. However, it has recently been discovered that duplication/deletion of genetic loci like 19p13.12 (Nebel et al. [29]) and 1q21.1 (Bernier et al. [4]) can lead to both microcephaly and macrocephaly, and neurodevelopmental disorders (including cognitive impairment and ASD). Given the clinical profile that Indian children with ASD exhibit, it may be speculated that some undiscovered genetic hot spot(s) exist in the Indian population, the dysregulation of which results in ASD, cognitive impairment (accounting for the high proportion of low functioning ASD), dysmorphism (higher proportion of Equivocal Autism) and microcephaly (rather than macrocephaly).

We did not find any significant difference in regression, seizures, developmental delay or impaired cognition between complex and essential autism as reported earlier by Miles et al. ([25]). A higher male to female ratio was found in essential autism compared to equivocal autism, but not with complex autism (Miles et al. [25]). Similar results were not found in the two studies that had published data on Equivocal autism (Miles et al. [25]; Shapira et al [37]). Results are inconclusive (Table 1) regarding significant differences between complex and essential autism with respect to gender as there is evidence both for (Miles and Hillman [22]; Miles et al. [23], [25]), and against (Wong et al. [50]; Flor et al. [8]; Shapira et al. [37]; present study) this. The aforementioned discrepancies in ADM categorization may be the reason for the inter-group heterogeneity in this as well as other parameters. The study by Shapira et al. ([37]) employed expert evaluation in MPA assessment (rather than ADM) and categorized Autism according to the same number of MPA. However, since clinical and psychometric data was not collected, no concrete conclusion can be drawn regarding the existence of inter-group differences.

Based on our study results and review of earlier research, we conclude that the existing ADM norms need to be re-examined, not only with respect to genetic ancestry but also with age. It is imperative that this be preceded by multi-centric studies in different ethnicities to determine whether significant differences in clinical, psychometric, (and possibly genetic) profiles of essential, equivocal and complex Autism exist in early childhood, based solely on assessment of MPAs by expert clinical evaluation. Only if significant differences are found, would it be plausible to modify ADM, or develop similar models, keeping into consideration the influence of both these factors. Another option could be to generate target population-based norms for each parameter and construct a tool accordingly. Until then, we recommend that personnel managing children with ASD ensure that clinical geneticists are proactively involved in their evaluation, specifically to exclude syndromes, identify MPA, plan a search for concealed major physical anomalies (if multiple MPA present), and rationally plan further genetic testing. Only then will management and parental counseling be truly holistic.

The paper is based on the M.D. thesis of Dr Neelam that was conceptualized, designed and supervised by Dr S. B. Mukherjee (as thesis supervisor) and submitted to Delhi University in April 2017 on completion. It was a non-funded study. A paper based on the study was presented at the International Developmental Pediatrics Association Congress 2017, held in Mumbai, India from 8th to 10th December, 2017.We acknowledge the contributions of Ms Priyanka Thagela (clinical psychologist), Ms. Meenakshi Sharma (psychiatric medical social worker) and Ms. Chetna Pal (data entry operator) for helping in data management.

Identification of Essential, Equivocal and Complex Autism by the Autism Dysmorphology Measure: an Observational Study. SBM conceived of the study, planned the study design, coordinated its execution, provided technical and academic expertise (related to neuro-development and dysmorphology) during collection of data and drafted the manuscript. NK participated in review of literature, collection and interpretation of the data. SK participated in the design of the study and provided technical and academic expertise (related to genetics and dysmorphology) during data collection. SS participated in the coordination and execution of the study and provided technical and academic expertise (related to neurology) during data collection. All authors gave intellectual input during the drafting of the paper, read and approved the final manuscript.

None.

The authors declare that they have no conflict of interest.

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