Developmental Course of Autistic Social Impairment In Males

Abstract

Background

Recent research has suggested that autistic social impairment (ASI) is continuously distributed in nature, and that subtle autistic-like social impairments aggregate in the family members of children with pervasive developmental disorders (PDDs). This study examined the longitudinal course of quantitatively-characterized ASI in 3 to 18 year old boys with and without PDD.

Methods

We obtained assessments of 95 epidemiologically ascertained male-male twin pairs and a clinical sample of 95 affected children using the Social Responsiveness Scale (SRS), at two time points, spaced 1–5 years apart. Longitudinal course was examined as a function of age, familial loading for PDD, and autistic severity at baseline.

Results

Inter-individual variation in SRS scores was highly preserved over time, with test-retest correlation of 0.90 for the entire sample. SRS scores exhibited modest general improvement over the study period; individual trajectories varied as a function of severity at baseline and were highly familial.

Conclusion

Quantitative measurements of ASI reflect heritable trait-like characteristics. Such measurements can serve as reliable indices of phenotypic severity for genetic and neurobiologic studies, and have potential utility for ascertaining incremental response to intervention.

INTRODUCTION

The Pervasive Developmental Disorders (PDDs) are a group of conditions primarily characterized by specific impairments in reciprocal social behavior, the most common of which are Autistic Disorder, Asperger’s Disorder and Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS). Previous research examining the longitudinal course of PDD has often focused on the substantial degree to which categorical “caseness” is preserved over time. It has become increasingly apparent, however that autistic social impairment (ASI) is continuously distributed in nature (Constantino & Todd, 2003); and that ASIs that fall below the level of severity observed in categorically diagnosed PDD aggregate in the family members of autistic probands (Constantino et al, 2006; Dawson, Webb, et al, 2005; Pickles et al., 2000; Piven et al., 1997;). In exploring such quantitative symptoms as either targets of treatment or as markers of genetic risk, it is important to establish whether measurements of such traits are actually stable over time in the same way that categorical PDD diagnoses are stable over time.

In recent years, the life course of ASI in affected children has been approximated by repeated measures using instruments that were originally designed to establish PDD “case-ness” in a categorical sense. These include prospective studies using the 15-item CARS (Eaves & Ho, 1996; Mesibov et al., 1989), parental reports of DSM-IV criteria (Piven et al., 1996), and the Autism Diagnostic Interview-Revised (McGovern & Sigman, 2005; Szatmari et al., 2003). Each of these studies has revealed a high degree of stability of categorical designation of affected status (ranging from 90% retention of diagnosis among higher-functioning PDD subjects to 100% among low-functioning PDD subjects), as well as evidence for some improvement in various autistic symptoms over the course of childhood. We recently observed developmental improvements (unrelated to intervention effects) in quantitative measurements of symptoms in autistic preschoolers followed prospectively for a 6 month period (Pine et al., 2006). This highlighted the importance of a) using reliable quantitative methods for measuring change over time—similarly supported by the experience of psychopharmacologic research groups (see McDougle et al., 2005) and b) controlling for a possible naturalistic tendency toward improvement over the course of early childhood when interpreting outcome data from early intervention studies.

In this study, we examined the first wave of data from an ongoing longitudinal study of ASI in both clinically-ascertained and general population subjects. The study encompasses the age range from 3–18 years and the full range of severity of ASI that occurs in nature (see Constantino & Todd, 2003), from unaffected (highly socially-competent) to severely socially impaired. It involves both epidemiologically ascertained sibling (twin) pairs and sibling pairs in which one or both subjects are clinically affected with a PDD. Although the twin method allows powerful resolution of genetic and environmental influences on change over time, it is important to determine whether longitudinal trajectories for subtle ASIs match those for clinical level symptoms when equivalent measurement methods are used.

Congruence in the longitudinal course of ASIs across the entire range in which they are manifested in nature has important implications for ongoing research on the causes and neurobiology of autism. We have recently shown that subthreshold ASIs are genetically linked to clinical level symptomatology by demonstrating the following: a) pronounced aggregation of subthreshold ASI in the family members of autism and PDD probands in comparison to the family members of non-PDD child psychiatric controls (Constantino et al., 2006); and b) enhancement of linkage signals for autism in a molecular genetic study utilizing the quantitative trait data from whole families rather than affected individuals (only) within those families (Duvall et al., 2007).

Demonstration of uniformity in longitudinal trajectories of both clinically-affected and subtly affected individuals would further support a common underlying biological structure, and provide critical information for studies that are utilizing samples representing broader ranges of severity in order to increase statistical power to identify or confirm associations between symptomatology and underlying biological variables (genotype, neurobiological markers, etc.). To our knowledge, this is the first prospective study of ASI to incorporate validated quantitative measurement methods. We hypothesized that ASI would exhibit a high degree of stability over time across the range in which it manifests in nature, for which we examined both the longitudinal course of children with clinical level ASI and that of twins in the general population.

METHODS AND MATERIALS

Sample

Subjects for this study are participants in an ongoing longitudinal study of autistic social impairment (ASI). This report concerns the first two subject groups for whom systematic prospective data has been collected.

The first group of subjects were twins who were recruited for enrollment into this study from a sample of 232 male pairs age 8 to 15, who had originally been epidemiologically ascertained from the general population for the Missouri Twin Study (children who were non-verbal or who carried diagnoses of autistic disorder or mental retardation were excluded from this sample), and assessed in 1999 using the SRS (see Constantino & Todd, 2000 for a more detailed description of the sample). It should be stated that there are numerous research groups following the twins in this sample. Research access to the twins is governed by an oversight committee whose charge is to minimize the risk of the families being overburdened by multiple research requests. Research contacts are allowed to occur no more frequently than every six months; thus, there are constraints on access to various sets of twins at various points in time.

Over the 2004–2005 year, we were able to contact the families of 123 of the original 232 male twin pairs in order to collect 5–6 year follow-up data. Of those, 28 declined participation, and 95 (comprising 40 identical twin pairs and 55 non-identical twin pairs) enrolled in the current study. There were no statistically significant differences in mean age or SRS score between enrollees, those whose families declined participation, and the remainder of the previously assessed sample.

In addition to obtaining follow-up SRS assessments, we conducted additional laboratory assessments on twins who had scored in the top 10% of the distribution (at the time of the original assessment) in order to verify that elevated scores in a general population “screening” were consistent with near clinical level symptomatology. PDD’s are believed to affect up to one per cent of males in the general population (Fombonne et al., 2005), however children who carried formal diagnoses of autism had been excluded from the Missouri Twin Study Sample. The laboratory assessments were completed according to the Autism Diagnostic Observation Schedule (ADOS), an established semi-structured diagnostic measure for Autism (Lord et al., 1999) which measures social impairment, communicative impairment, and sterotypic behaviors that are specific to PDD. Of 7 high-scoring SRS twins who participated in this observational assessment, all had appreciable deficits in at least 1 domain on the ADOS, four exceeded the cutoff score of 4 for deficits in reciprocal social interaction toward a clinical PDD diagnosis—the mean for the group was 4.3 +/− 2.7.

The second subject group was comprised of 85 boys with pervasive developmental disorders (PDDs) consecutively recruited by their physicians (in 2003–2005) from either a) the Washington University Child and Adolescent clinics or b) from outpatient child psychiatry practices in the greater St. Louis metropolitan area. Any child with a PDD diagnosis documented by a child psychiatrist was eligible for inclusion in the study, for the Washington University sub group, an additional inclusion criteria was that index PDD subjects had at least one male full sibling (whether co-affected or unaffected by PDD). 10 of the male siblings were co-affected with a PDD. Families were excluded from the study if the index PDD subject carried a diagnosis of a comorbid psychiatric disorder or if there was any sustained ambiguity with respect to a singular primary diagnosis. All index cases, affected sibs, and any undiagnosed male sibs were assessed using the Social Responsiveness Scale (SRS—see below) by parent- and teacher-report at baseline. All clinically affected subjects (85 index PDD cases and 10 affected male sibs) were subsequently re-assessed with the SRS at one year follow-up. Table 1 summarizes the assessment schedule and selected sample characteristics, as function of specific grouping of subjects.

Table 1.

Schedule of Study Assessments and Selected Characteristics of Study Groups

Study Group	PDD Subjects	Undiagnosed Male Sibs	General Population Twins
n	95	66	190
Baseline Assessment	SRS-p, SRS-t, ADI-R, ADOS	SRS-p, SRS-t	SRS-p
Age (SD) at baseline	8.0 ± 4.0	7.0 ± 3.4	11.6 ± 1.8
SRS-p mean at baseline	102.8 ± 26.2	35.0 ± 31.3	38.1 ± 24.8
Follow Up Assessment	SRS-p, SRS-t, DIGS/FIGS*	-	SRS-p, ADOS**
Time of Follow-Up Assessment	1 year	-	5 years

^*: 16 high functioning verbal adolescents

^**: Subjects who scored in top 10% of distribution at baseline

SRS-p: Parent-Report Social Responsiveness Scale

SRS-t: Teacher-Report Social Responsiveness Scale

For diagnostic confirmation all clinically-affected subjects were assessed with the Autism Diagnostic Interview Revised (ADI-r) and the Autism Diagnostic Observation Schedule (ADOS). Of the total 95 PDD subjects, 71 scored at or above the clinical cutoff for a full DSM-IV diagnosis of Autistic Disorder on either the ADI-r, the ADOS, or both; the remainder had substantially elevated scores on these measures and were diagnosed clinically with either Asperger’s Disorder or Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS) by their respective clinicians. There was no change in clinical diagnosis for any of the participants over the course of the one-year follow-up period. Current IQ scores were available for 38 of the subjects; mean full scale IQ for this group was 93.2, S.D. =24.7.

MEASURES

Social Responsiveness Scale

The SRS is a 65-item quantitative measure of autistic social impairment (ASI), which capitalizes on observations of children in naturalistic social contexts by either parent- or teacher-report. Scores on the SRS are highly heritable (Constantino & Todd, 2000, 2003, 2005), continuously distributed in the general population (Constantino & Todd, 2003), exhibit a unitary factor structure (Constantino et al., 2004), and distinguish children with autism spectrum conditions from those with other child psychiatric conditions (Constantino et al., 2000; Constantino & Gruber, 2005). In addition, quantitative ASIs measured by the SRS have been shown to aggregate in the unaffected siblings of children with autism spectrum disorders (Constantino et al., 2006). Both the 3 year old version of the SRS (Pine et al., 2006) and the 4–18 year old version (Constantino & Gruber, 2005) were implemented in this study; the number of items and range of scores are identical for the two instruments; wording of the items differ only where developmentally appropriate.

The SRS was obtained exclusively by maternal-report for the twins, and by both maternal-report and teacher-report for the clinically-ascertained sample of sibling pairs (all at baseline and clinically affected children at 1-year follow-up). For teacher reports, parents were asked to request that an SRS form be completed by a current classroom teacher who had known the child for a minimum of 2 months and whom they felt knew their child best. The SRS generates a singular total score for ASI empirically validated via factor, cluster, and latent class analysis (Constantino et al., 2004), as well as treatment scale scores, which (though not empirically-derived) represent relevant target domains for intervention. The SRS exhibits non-significant correlations with IQ (Constantino et al., 2006), substantial agreement with the ADI-r (Constantino et al., 2004), and an absence of age effects in the range of ages represented by this study (Constantino & Gruber 2005). For the analyses in this study, raw (non-transformed) scores on the SRS were used—the scores range from 0 to 195, with higher scores indicating more severe levels of social impairment. For the clinically-ascertained sibling sample, the correlation between parent and teacher-report SRS score at baseline was robust; intraclass correlation 0.66. The familiality of SRS reports in the clinical sample was documented by calculating the average of raters intraclass correlation coefficient for sibling pairs (one pairing involving the index case and closest-in-age male sib for clinical subjects with 1 or more male sibs); the intraclass correlation coefficient (ICC) was 0.25. This value was expected to be somewhat less than the sibling correlation of 0.35 that we have observed in the general population (Constantino & Todd, 2003), since the range of SRS scores encompassed by the clinically affected index cases was restricted (within the pathological end of the distribution) in comparison to the range represented by the general population.

Intervention History

Extensive treatment records were available (at or near the time of enrollment) from the medical records of PDD subjects who had been recruited from the Washington University Child and Adolescent Psychiatry service (n=40). Data abstracted from the records included presence or absence of history of treatment with Applied Behavior Analysis, presence or absence of current occupational therapy, and presence or absence of current pharmacotherapy (categorically subdivided into atypical neuroleptics, stimulants, selective serotonin reuptake inhibitors).

Diagnostic Interview for Genetic Studies/Family History Interview for Genetic Studies (DIGS/FIGS)

Since it is rare for adults to carry PDD diagnoses, and rare for children to carry a diagnosis of Schizoid Personality Disorder, we were interested in the question of whether milder PDD syndromes in adolescence might underlie the development of schizoid psychopathology. For this reason, adolescent PDD subjects who were willing and capable of completing the schizoid personality disorder sections of the DIGS (self-report, Nurnberger et al., 1994) were asked to do so and their parents completed the parallel section of the FIGS (parent-report, Maxwell, 1992). Sixteen subjects completed the assessments (mean age 15.2 +/− 2.5 years); one of these subjects carried a clinician diagnosis of Autistic Disorder; the remainder carried clinical diagnoses of Asperger’s Disorder or PDD-NOS.

Familial loading for PDD

For each clinical subject, parents were interviewed according to a uniform protocol for inquiring about any known (diagnosed) or strongly suspected case of a pervasive developmental disorder within the extended family. First, the pedigree was reviewed out to 3^rd degree relatives, and for each family member, the parent was asked whether a diagnosis of pervasive developmental disorder, Asperger’s disorder, autism, autism spectrum disorder or Schizoid Personality Disorder had ever been made. Next, the parent was asked whether any undiagnosed relatives had constellations of behavior that he/she (the parent) recognized in retrospect as similar in nature to core pervasive developmental disorder symptoms (for which the list of DSM-IV criteria for autism was reviewed with the informant)—if such behaviors were viewed by the parent as significantly limiting that relative’s functioning, that relative was designated as “suspected” of having a pervasive developmental disorder. A reliability sub sample of these family members (n = 35) revealed that when the index subject’s parent completed an SRS report on that family member, mean score was 81.8 +/− 35.6. In this sample, when comparing PDD subjects who have at least one other family member affected to those with none, there was no significant difference in mean SRS score (t=1.3; df=69; p=.20).

Data Analysis

Univariate analyses of the stability of SRS scores over time were conducted separately for the twin and clinical samples using a) Intraclass coefficients of correlation (ICC) between baseline and follow-up scores and b) paired t-tests.

In order to examine the genetic-environmental structure of change over time in the twin data, variance in change scores for first-born twins, variance for second-born twins, and within-pair covariance were calculated separately for the respective groups of monozygotic (n=40 pairs) and dizygotic (n=55 pairs) male twins. The resulting covariance matrices were fit to models of genetic and environmental causation using the structural equation modeling software Mx (Neale & Cardon, 1992). Univariate models followed the classic twin design, as adopted and described in our previous work (Constantino & Todd, 2000).

For the clinical sample, linear regression analysis was used to assess the effects of familial loading, (presence or absence of a second PDD–affected individual among 1^st–3^rd degree relatives), baseline severity, and age, on change in SRS scores. We separately examined effects of specific treatments (which have significant associations with age and baseline severity) on change in SRS scores. Principal components factor analysis was subsequently implemented to determine whether specific subsets of symptoms might exhibit independent patterns of change over time, among clinically-affected subjects.

Finally, data common to the twin and clinical samples (maternal SRS scores at baseline and follow-up) were pooled and analyzed for within-subject contrasts using GLM repeated measures analysis, executed in SPSS.

RESULTS

Twins (General Population)

First, we describe univariate analyses of the longitudinal course of Social Responsiveness Scale scores in the twin sample. Over time, inter-individual differences were highly preserved, as depicted for maternal-report SRS scores in the scatterplot in Figure 1. Intraclass coefficient of correlation (between baseline and follow-up maternal SRS score) was 0.71 (selecting one twin per family at random).

Figure 1. Scatter plot of maternal SRS Scores at baseline and follow-up.

This plot incorporates both groups of study subjects (general population and clinic subjects) in order to represent the full range of SRS scores that occur in nature. When calculating the intraclass coefficient of correlation for the whole sample, icc = .90; when calculated separately for each study group, for twins (one per family, as incorporated in the scatter plot), icc = .71; for clinic subjects, icc = .76. Lower coefficients of correlation are expected when the range of trait variation within a sub sample is narrower.

Despite a lack of cross-sectional age effects on SRS scores in the twin sample, there were modest improvements in mean scores over time, on the order of 0.5 SD over the course of the 5-year follow-up. When examining differences between baseline and follow-up for the twins, the most conservative approach is to include one twin per family (n=95): for twin 1 mean change=9.7 points on the SRS, t=5.15, df=94, p<.001; for twin 2 mean change=13.0, t=6.44, df=94, p<.001.

We next explored the genetic and environmental structure of time-rated change in SRS scores in twins. The difference in SRS total score between baseline assessment and 5-year follow-up was calculated for each twin, covariance matrices were separately constructed for monozygotic and dizygotic twins, and the data were subjected to univariate structural equation modeling using the statistical software Mx (Graphic User Interface, Neale, 2004). Structural equation modeling incorporates variance/covariance data into models that consider causal influence on latent variables that underlie (but do not equate with) phenotypic measurements (for a description of this method of analysis, see Neale & Cardon, 1992; Constantino & Todd, 2003). The results revealed that the most parsimonious model for causal influence on change over time was an AE model, which revealed a predominant effect of additive genetic influences (explaining 73% of variance) and modest unique environmental influences (explaining 27% of variance–this subsumes measurement error) on time-rated change in the latent variable represented by SRS scores. Results of model fitting are summarized in Table 2.

Table 2.

Genetic and Environmental Influences On Change Over Time.

Model	Maximum Likelihood Analysis			AIC	RMSEA	Parameter Estimates for Causal Influence
	X2	Df	p			a2 (95% c.i.)	c2	e2 (95% c.i.)
ACE	6.08	3	0.07	0.83	0.13
AE	7.06	4	0.13	−0.94	0.10	0.73 (.64–.79)	-----	0.27(.20–.37)
CE	12.7	4	0.01	4.70	0.20

• a² additive genetic influence (also subsumes interactions between unmeasured environmental influences and genetic factors)

• c² common environmental influence

• e² unique environmental influence (also subsumes measurement error)

• AIC—Akaike Information Criterion; RMSEA—Root Mean Square Error Approximation: for both of these indices, (lower values represent improved fit to the data).

It is important to note that any common environmental influence interacting with genetic factors to bring about a reduction in SRS scores would be subsumed under the parameter for additive genetic influences. The presence of such an interaction would be consistent with the finding that the most pronounced improvements occurred among children with the most impairment at baseline.

Given the results of pronounced effects of additive genetic influence on change, as well as our previously published results documenting substantial heritability of cross sectional SRS ratings (Constantino & Todd, 2003), we proceeded with an additional analysis to explore whether separate sets of genetic influences might be operating during earlier versus later stages of development (within the same children). To do this, we attempted a set of bivariate analyses (the two variables being SRS score at baseline and SRS score at follow-up) involving children in the sample who were 8–12 years of age at baseline and greater than 13 years at follow-up (n=30 MZ pairs and 41 DZ pairs from the total of 95 male pairs in the sample). We compared models for complete overlap, partial overlap (Cholesky decomposition), and non-overlap of genetic influences using bivariate (SRS baseline, SRS follow-up) models depicting each respective level of genetic overlap, in the manner described in Constantino, Hudziak, & Todd, 2003. These models incorporated parameters for additive genetic (A) and unique environmental influence (E) as substantiated by the results of univariate analysis of change scores (described above) and by previously published univariate analyses of baseline SRS scores in males (Constantino & Todd, 2000).

Although the power of the subsample is extremely limited for differentiating the 3 bivariate models, the goodness-of-fit of the non-overlap model was much poorer (Akaike’s Information Criterion [AIC] = 95.6) than that of either the complete overlap model (AIC=26.1) or the partial overlap model (AIC=1.8, 95% confidence interval − 10.2 to + 21.4)—lower value indicates superior fit. Parameter estimates derived from the partial overlap (best-fitting) model indicated that although a majority of additive genetic influences on ASI in childhood overlapped with those operating in adolescence, up to 33% (based on 95% confidence limits for parameter estimation) of the total additive genetic influence (0.80) were specific to developmental stage (childhood versus adolescence).

Clinical Sample

As was the case for twins in the general population, inter-individual differences among PDD subjects were highly preserved over time by maternal SRS report (ICC=0.76). There was substantial agreement between mothers and teachers on SRS score at baseline (ICC=0.66) and follow-up (ICC=0.63). When averaging SRS scores from parent and teacher report at baseline and follow-up, the coefficient of correlation between baseline and follow-up scores was 0.63.

There were modest improvements in mean scores over time, which reached statistical significance by maternal-report (as observed in twins) but not by teacher-report, as depicted in Table 3. In most cases, teacher-reports at follow-up were provided by different teachers than those who provided baseline reports, which may, in part, explain the discrepancy in magnitude of trends for change over time, between parent- and teacher-report data. None of the children in the clinical group experienced a magnitude of reduction in maternal SRS scores over the one-year period that would have been consummate with a “loss” of a PDD diagnosis.

Table 3.

Mean SRS Scores By Rater.

	PDD Subjects (n=95)
Rater	Mother		Teacher
	Mean	SD	Mean	SD
Baseline SRS	102.8	26.2	92.4	26.9
1 Year Follow-Up	95.6	26.7	89.7	32.4
paired t	3.87		.73
Df	94		92
P (2-tailed)	0.0002		0.47

Linear regression analysis, examining the effects of age, baseline SRS, and familial loading, revealed only an effect of severity at baseline on time-rated improvements in SRS scores (F = 3.31; df = 3.79; R² = .11; baseline SRS effect t = −2.85, p = .006, β = 18.2). Higher level of severity at baseline was associated with treatment with atypical neuroleptic medication and occupational therapy, so it is possible that these contributed to the subsequent trend toward improvement (in more severely affected subjects in the clinical group), however when the effects of the various treatment modalities were examined in relation to change over time, no statistically significant predictors emerged among the 40 subjects for whom data was available (F = 0.94; df = 7.32).

In order to determine whether specific subsets of SRS items might exhibit independent patterns of correlated changes over time, principal components factor analysis was conducted on change scores and revealed no evidence of independent change in specific symptom clusters. This supported the validity of examining naturalistic change on the basis of a singular (total) SRS score.

Given the high level of congruence between the respective developmental trajectories of these traits in our clinically and epidemiologically ascertained samples, we pooled maternal SRS report data from the clinical and twin samples in order to derive the largest available sample for longitudinal data analysis. Test- retest reliability (ICC) for total SRS scores across the entire range of scores represented by the pooled sample was 0.90. We conducted a GLM repeated measures analysis for within-subject contrasts, which revealed that time-rated improvements as reported by parents, though modest in magnitude, achieved a high level of statistical significance (p<.001). The trend for improvement, though most pronounced in more severely affected children (see above regression analysis) spanned the entire range of the SRS distribution, and therefore was not entirely explainable as a straightforward function of regression toward the mean.

Finally, we explored the possible continuity between autistic social impairments (ASIs) and schizoid personality disorder symptoms in higher functioning adolescent ASD subjects at 1-year follow-up. On average, parents reported 2.8 schizoid personality disorder symptoms in their ASD children, which was substantially higher than what was endorsed by self-report among the subjects themselves (four symptoms are required for a DSM-IV diagnosis of Schizoid Personality Disorder). The most common symptoms endorsed by parents were: “has no one to be really close to or confide in, or just one person, outside of the immediate family” and “acts cold or distant, hardly ever smiles or nods back at people”. By parent-report, 5 of 16 ASD subjects met full DSM-IV criteria for Schizoid Personality Disorder; an additional 3 subjects met 3 of 4 criteria required for the diagnosis. Quantitative trait scores on the SRS did not significantly differ between those who met criteria for Schizoid PD and those who did not.

DISCUSSION

Consistent with the results of previous longitudinal studies of autism, we observed a high degree of preservation of inter-individual differences in autistic social impairment (ASI) over time, but also subtle improvements over the course of the study period in both clinically-affected and “unaffected” children. In this first wave analysis of our longitudinal study of quantitative ASI, inter-individual variation, as reported by mothers using the Social Responsiveness Scale (SRS), exhibited a baseline to follow-up correlation exceeding 0.70 for both twins from the general population and for a clinically-ascertained sample of boys with PDD. Congruence in the longitudinal course of autistic traits across the range of severity observed in nature extends findings from our previous family/genetic studies supporting a biological link between clinical and subthreshold levels of symptomatology (Constantino et al., 2006, Duvall et al., 2007). Furthermore, the level of stability in inter-individual differences over time indicates that quantitative measurements of ASI can serve as extremely reliable markers of symptomatology, which might relate to neurobiological and genetic determinants of autism, and which might be useful for ascertaining the response of core symptoms to successful intervention.

The changes that we observed over time were very gradual and, on average, would only be construed as clinically significant when allowed to accumulate over years of time. There was a clear (statistically significant) tendency in both the clinical and general population samples for more severely affected children to exhibit greater reduction in impairment scores over time. Although the prediction of improvement by baseline severity is consistent with some influence of regression toward the mean on our results, baseline severity accounted for only 11 percent of the variance in change in our clinical sample, and the overarching trend for improvement extended to even the most socially-competent group of children at baseline. Moreover, the nature of that improvement in the clinical group encompassed the entire constellation of symptoms observed in autism. As we have observed for the factor structure of autism itself (Constantino et al., 2004), the factor structure of improvement over time is consistent with a singular underlying component, best characterized quantitatively by a total impairment score.

If it is true (as our data suggests) that general improvements are occurring in the absence of age (developmental) effects on cross-sectional measurements of ASI—in essence that all children, irrespective of age, are getting better (albeit slowly)—this would raise the possibility of subtle period effects on autistic social impairment. Period effects are influences on an entire population (in this case, children across the entire range of ages represented by the sample) for a specific period of time (in this case, the years over which the study was conducted). The identification of true period effects—possible examples would be beneficial effects of new treatments across the age distribution, or recent improvements in the capacity of the educational system to assist children affected by a wide range of autistic traits—would have potentially important implications for public health strategies for improving the outcomes of children with pervasive developmental disorders.

One would expect that period effects, if present, would result from environmental (not genetic) influences on the course of ASI over time. Our analysis of the genetic and environmental structure of change over time among twins revealed strong evidence for the importance of genetic factors in determining the longitudinal course of symptoms and the interesting possibility that different sets of genetic factors might account for some of the heritable influences on ASI in childhood versus adolescence. The caveat, however, is that in the analysis of twin data, any interaction (or correlation) between genetic factors and unmeasured environmental factors is subsumed under the parameter for genetic influence. Thus, if there is a socio-environmental change that is resulting in the observed improvements in symptomatology over time, it is likely interacting with genetic factors to exert such an influence on outcome. A possible candidate for such an interaction with the environment is inherited deficiency in social behavior itself—in this study, we directly observed that the children who experienced the most improvement over time were the ones who were most affected by ASI at baseline.

Finally, we observed that among higher-functioning adolescents with PDD, a high proportion (50%) meet or approach DSM-IV diagnostic criteria for Schizoid Personality Disorder. It is rare for non-retarded adults to carry PDD diagnoses, in spite of the fact that these are common childhood conditions with enduring symptomatology over time; this suggests a possible lack of systematic recognition of the impairments that are carried forward into adulthood by individuals who were diagnosed with PDD-NOS, Asperger’s Disorder or “High-Functioning Autism” in childhood. One hypothesis supported by our exploratory accrual of data on schizoid personality disorder symptoms is that a sizeable portion of higher-functioning PDD subjects become diagnosable with Schizoid Personality Disorder in adulthood. Elucidating the continuity and discontinuity between these two conditions warrants further study and remains a goal of our ongoing longitudinal research. Since the subjects themselves had minimal insight into the presence of schizoid personality disorder symptoms (that their parents identified in them), it will be important for future research efforts to incorporate observations of parents or other informants, rather than relying exclusively on reports by the subjects themselves.

There are some limitations of this analysis of first-wave data from our ongoing longitudinal study; the sample size, though the largest to date for tracking the longitudinal course of quantitative ASI, warrants efforts at replication in still larger samples. In addition, our clinical subjects (for whom there has been minimal attrition in the sample to date) have been studied at only two time points thus far. Nevertheless, the convergence of findings across two disparate study groups—with respect to both stability of inter-individual differences and change over time—supports the reliability of the findings in each respective sample. Reliance on maternal report SRS scores is potentially vulnerable to rater bias; however the absence of age effects at either baseline or follow-up, make it less likely that the findings regarding change are substantively influenced by systematic maternal reporting bias. We have previously reported an absence of evidence for maternal rating bias on SRS scores obtained in considerably larger samples of twins (Constantino & Todd, 2003). Continued follow-up of this sample, which will include yearly reassessments of the clinical subjects described herein, and every-other-year assessments of the clinical subjects’ male siblings (see Constantino et al., 2006), by both parent- and teacher-report, will help elucidate any ongoing effects of reporting bias. An additional limitation was that current IQ data was available for only a minority of clinical subjects and none of the twins. Furthermore, this report is limited to an analysis of male sibling pairs; parallel data collections involving female subjects are in progress.

In this study we showed that the average SRS score recorded by parent and teacher exhibits very high levels of consistency (r=0.63) over time even when two different teachers provide ratings at respective baseline and follow-up assessments. Use of multi-informant data from parent- and teacher-report on a given child may more comprehensively represent the social functioning of children across home and school settings than when relying on information from a single source. This issue is being addressed in an ongoing psychometric study of the SRS.

CONCLUSIONS

Quantitative measurements of autistic social impairment (ASI) are both highly heritable and extremely stable over time; they reflect trait-like characteristics which can serve as reliable markers of core components of the autistic syndrome for genetic and biological studies, and for evaluation of the effects of intervention. Our observation of subtle improvement over time is consistent with findings of a number of previous research studies. In addition, these data indicate that naturalistic improvements over time exhibit a unitary factor structure (across all symptom domains in the autistic syndrome) and involve children whose social impairments fall above or below the threshold for a clinical diagnosis. The improvements appear most pronounced among children with more severe levels of autistic social impairment at baseline. It will be important for naturalistic studies, treatment studies, and genetic studies to continue to explore causal influences on change over time, as this will have important implications for educational and public health interventions for affected children. The possibility that different sets of genetic factors might predominate in childhood versus adolescence in influencing ASI warrants further study and warrants consideration of segregating child and adolescent subjects in studies of gene expression in autism.

Further exploration of the continuity between a childhood diagnosis of PDD-NOS and an adult diagnosis of Schizoid Personality Disorder requires additional study. This may lead to better understanding those patterns of social behavior in childhood that predict enduring conditions construed as personality disorder in our current nomenclature. It is always important to note that quantitative characteristics (traits) of any psychiatric condition which fall below the threshold for clinical diagnosis may for many individuals, and under many conditions, be adaptive. The continued study of such traits may lead to greater insights not only into disease processes but also into the biology of normal human social development.