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. 2023 Oct 5;28(7):1654–1666. doi: 10.1177/13623613231200081

Differentiating early sensory profiles in toddlers at elevated likelihood of autism and association with later clinical outcome and diagnosis

Elena Maria Riboldi 1, Elena Capelli 1, Chiara Cantiani 1, Carolina Beretta 1, Massimo Molteni 1, Valentina Riva 1,✉
PMCID: PMC11191663  PMID: 37795823

Abstract

Sensory features are included in the diagnostic criteria of autism spectrum disorder, and sensory responsiveness may produce “cascading effects” on later development. However, the relation between early sensory profiles and later skills has yet to be defined. This study aims to characterize sensory subgroups in 116 toddlers at elevated likelihood for autism spectrum disorder and test their association with later autistic traits and diagnosis. We used latent class analyses to assess individual differences across sensory patterns, grouping individuals with similar sensory profiles together. The final model was chosen based on a stepwise procedure, starting with a one-class solution, and then adds one class at a time. The Sensory Profile-2 Questionnaire measured clinical sensory features, and four sensory patterns were evaluated (seeking, avoiding, sensitivity, and registration). We investigated sensory subgroups concerning socio-communication skills and restricted/repetitive behaviors at 24 months and the clinical best-estimate diagnosis at 3 years. A three-class solution was favored, and toddlers can be characterized into three homogeneous sensory groups: low seeking, sensory balanced, and high sensitivity. The results showed that the high sensitivity group showed later socio-communicative difficulties and restricted/repetitive behaviors. Children in this class were those with the highest percentage of diagnosis at 3 years (57.9%). These findings provide new insights into the nature of sensory processing and may have implications for personalized support needs.

Lay abstract

Early sensory responsiveness may produce cascading effects on later development, but the relation between sensory profiles and autistic diagnosis remains unclear. In a longitudinal sample of toddlers at elevated likelihood for autism, we aimed to characterize sensory subgroups and their association with clinical outcomes later on. Three sensory subgroups were described and early sensory sensitivity plays a significant role in later development and diagnosis. This study supported the importance of examining different levels of sensory patterns to dissect the phenotypic heterogeneity in sensory processing. As sensory differences are associated with later developmental outcomes, these results may be critical when designing intervention needs and support for children at increased likelihood for neurodevelopmental disorders.

Keywords: autism spectrum disorder, development, infancy, sensory, siblings

Sensory processing refers to how we perceive, process, filter, and react to sensory stimuli in the environment. Atypical sensory reactivity is now considered a diagnostic criterion of autism spectrum disorder (ASD) under the heading of restricted and repetitive behaviors, according to the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association [APA], 2013). Atypical sensory responsiveness is estimated to occur in 90% of autistic individuals (Baranek et al., 2006; Ben-Sasson et al., 2009; Leekam et al., 2007) compared to 5%–15% of nonautistic individuals (Tomchek & Dunn, 2007) and affects all sensory modalities (auditory, visual, touch, smell, and taste; Bonnel et al., 2003; Puts et al., 2014; Rozenkrantz et al., 2015; Simmons et al., 2009; Tavassoli & Baron-Cohen, 2012). A previous review focused on different sensory domains such as auditory, visual, and tactile, reporting atypical sensory-related behaviors across sensory modalities in autistic children compared to neurotypical peers (Marco et al., 2011). For example, previous neurophysiological studies showed that reduced habituation to repeated auditory stimuli is associated with ASD in school-age autistic children (Whitehouse & Bishop, 2008) and enhanced neural response to auditory stimuli has been found in 12-month-old infant siblings of autistic children (Riva et al., 2018). In the tactile domain, previous studies reported mixed results in autistic individuals, ranging from typical tactile seeking to greater tactile perception, such as rubbing objects (Blakemore et al., 2006; Ide et al., 2019). Similarly, atypical visual processing has been widely documented in the autism field and autistic individuals seem to seek out or avoid visual stimulation (Simmons et al., 2009).

Other than single sensory modalities, previous literature showed differences in sensory responsiveness between autistic and nonautistic individuals in terms of three empirically derived constructs (Ben-Sasson et al., 2009): hyperresponsiveness, hyporesponsiveness, and sensory seeking. An exaggerated response to stimuli characterizes hyperresponsiveness, whereas hyporesponsiveness is characterized by delayed, absent, or more attenuated responses to stimuli than expected. In addition, sensory seeking encompasses behaviors that reflect a fascination with sensory stimulation. A recent meta-analysis (Ben-Sasson et al., 2019) focused on 55 research studies. It supported that autistic individuals showed higher hyperresponsiveness levels than individuals with other clinical conditions (e.g. developmental delay) and neurotypical individuals. In contrast, hyporesponsiveness and sensory seeking patterns differentiated autistic and neurotypical individuals, but no differences were found with individuals under other clinical conditions. Notably, these three patterns of sensory responsiveness are not mutually exclusive and may co-occur in individuals with different combinations of sensory patterns (Ben-Sasson et al., 2009). This reflects the high level of heterogeneity in sensory responsiveness in ASD (Tillmann et al., 2020; Uljarević et al., 2017), and a typical autistic sensory profile has yet to be established (Feldman et al., 2022).

More importantly, it has been reported that atypical sensory responsiveness emerges during the first months of life (Bryson et al., 2007) and early patterns of sensory processing may produce cascading effects on later social communication skills (Brandwein et al., 2015; Cascio et al., 2016; Watson et al., 2011) and restricted and repetitive behaviors at age 2 (Wolff et al., 2019). Most of the previous literature on the role of early sensory processing on later outcomes has used parent-report data; however, some studies considered different measures to examine sensory processing, including behavioral and brain techniques (Ramappa et al., 2022). For example, differences in parent-report sensory responsiveness were associated with brain development in 6-month-old infant siblings of autistic children later diagnosed with ASD (Wolff et al., 2017), and an observational measure of sensory seeking at 18 months of age is associated with neural responses (frontal asymmetry) and later social skills at 3 years (Damiano-Goodwin et al., 2018). A multifaceted approach, using observational, behavioral, and neurophysiological techniques, may yield more informative and complementary results to understand individual differences in sensory processing (Burns et al., 2017).

This evidence suggests that the study of sensory responsiveness in the early phases of life is relevant and may be an early predictor of autism. However, only a handful of longitudinal infant studies explored early sensory processing and its association with later development, and inconsistent results were reported. Using a parent report of the sensory profile, Mulligan and White (2012) found that 12-month-old infant siblings of autistic children had fewer sensory seeking behaviors than neurotypical infants, especially in the auditory processing domain. Feldman et al. (2021) found significant correlations between sensory responsiveness and concurrent communication skills between 12 and 18 months of age. In addition, a follow-up study of the same group (Feldman et al., 2022) examined the longitudinal associations between early sensory responsiveness (measured at 12–18 months) and later communication skills in 40 siblings of autistic children. Both hyporesponsiveness and hyperresponsiveness predicted lower receptive communication outcomes 9 months later (at 21–27 months), and only hyperresponsiveness predicted lower expressive communication skills, especially in the visual, tactile, and vestibular sensory domains. More recently, a longitudinal study on 218 infant siblings (Worthley et al., 2023) found significant associations between 12-month-old sensory responsiveness (sensory seeking and hyperresponsivity) and later adaptive behavior (socialization scores) at 3 years.

Interestingly, further evidence found that sensory processing in autism varies during development (Ausderau et al., 2014; Ben-Sasson et al., 2009). For example, it seems that sensory hyperresponsiveness decreases with age (Baranek et al., 2007; Kern et al., 2007), and this reduction is associated with mental age and development of cognitive skills (Baranek et al., 2007). On the other hand, Ben-Sasson and colleagues (2009) reported that sensory over-responsivity increases until the school-aged period and later decreases. Focusing on the early stages of life, an infant sibling longitudinal study (Wolff et al., 2019) showed that levels of hyperresponsiveness became more pronounced between 12 and 24 months of age in those children with a later diagnosis of ASD and decreased in children without a later autistic diagnosis. At the same time, hyporesponsiveness and sensory seeking behaviors decreased in neurotypical infants and infant siblings of autistic children, not themselves diagnosed with ASD, between ages 12 and 24 months.

Taken together, different patterns of sensory responsiveness seem to be predominant in autistic individuals from the early phases of life. However, more studies are required to better understand the impact of early sensory skills on later development. High variability has been reported, and more studies are needed to parse heterogeneity across different sensory patterns. Due to the fact sensory patterns may impact later development, characterizing groups of children based on differences in co-occurring sensory patterns is extremely challenging. Research has begun to characterize groups of children based on differences in co-occurring sensory patterns to examine how the co-occurrence of sensory patterns affects development (Little et al., 2016; Tillmann et al., 2020). However, no study has examined the impact of sensory subgroups on later diagnosis in individuals with an elevated likelihood of being diagnosed as autistic. Longitudinal research in the first years of life and looking at siblings of autistic children is therefore relevant, given that they are at elevated likelihood of receiving a diagnosis of ASD (hereafter EL-ASD). Longitudinal studies on EL-ASD children may better understand developmental trajectories from early sensory skills and later autistic features (Messinger et al., 2015; Ozonoff et al., 2011; Riva et al., 2022).

Purpose of the present study

Overall, our study aimed to examine early sensory subgroups in a longitudinal cohort of toddlers at elevated likelihood of being autistic and to further verify whether identified sensory subgroups differ in later social-communicative skills, restricted/repetitive behaviors, and autistic diagnosis.

To best capture behaviors that occur in response to sensory stimuli in various contexts, we used a parent report of the sensory profile, the Sensory Profile -2 (Dunn, 2014). Based on Dunn’s (2014) model, sensory processing was described as the interaction between an individual’s neurological threshold for sensory input and self-regulatory behavioral responses. Sensory profiles are divided depending on whether passive and active self-regulation strategies are used to respond to sensory stimulation and on high and low neurological thresholds for sensory stimuli. As a result, four sensory processing patterns are distinguished: sensory seeking, sensory avoiding, sensory sensitivity, and registration. All four sensory patterns have been reported as present in autistic individuals (Kern et al., 2007). Sensory seeking behaviors consist of active involvement in activities that provide intense sensory input (high threshold and active response). Registration, usually referred to as hyporesponsiveness, is characterized by a delayed response to sensory stimuli (high threshold and passive response). Finally, sensory sensitivity and avoidance, usually referred to as hyperresponsiveness, are characterized by an aversion to sensory stimuli. In particular, low thresholds characterize sensory sensitivity and passive response, whereas avoiding is characterized by low thresholds and active response.

In this longitudinal study, we tried to address the lack of literature by (1) investigating differences in co-occurring sensory features in a sample of EL-ASD toddlers across the 12- to 18-month period and (2) by associating early sensory subtypes with later autistic features at 24 months (communication and social interaction and restricted/repetitive behaviors) and clinical best estimate diagnosis at 36 months (autism vs nonspectrum). Specifically, latent class analyses were used to assess individual differences across sensory patterns, grouping individuals with similar sensory profiles together. We hypothesized to identify different sensory profiles among EL-ASD toddlers. Differences and specificities in co-occurring sensory patterns are expected between siblings later diagnosed with ASD compared to siblings without a later diagnosis of ASD.

Methods

Participants

The sample was recruited within an ongoing longitudinal project to identify early autistic signs in infants at elevated likelihood for ASD (EL-ASD; Riva et al., 2018, 2019, 2021, 2022) and to monitor them in the first years of life. In this study, EL-ASD toddlers have at least one sibling with a clinical diagnosis of ASD (APA, 2013). They were recruited at the Scientific Institute IRCCS Medea, Italy. They have been included in the study thanks to a collaboration with the Italian Network for Early Detection of Autism Spectrum Disorders (NIDA Network; Caruso et al., 2021; Riva et al., 2021). The participants’ sample comprises 116 EL-ASD toddlers (56 males and 60 females; mean age = 16.48 months; SD = 2.13).

Inclusion criteria were (1) gestational age was ⩾35 weeks; (2) birth weight was ⩾2000 g; (3) Griffiths developmental quotient was > 70 (Griffiths, 1996); (4) the absence of known medical, genetic, or neurological conditions; and (5) absence of major complications in pregnancy and/or delivery likely to affect brain development.

The present study was conducted according to the Declaration of Helsinki guidelines, with written informed consent obtained from a parent for each child before any assessment or data collection. The Ethical and Scientific Committee approved all procedures involving human subjects in this study at the Scientific Institute IRCCS Medea.

Materials

Toddler Sensory Profile-2

The Toddler Sensory Profile-2 (SP-2, Dunn, 2014) is a 54-item scale assessing sensory processing in the context of everyday life in toddlers aged 7–35 months. For each item, caregivers were asked to describe their child’s response to a sensory experience on a 5-point Likert scale ranging from “almost never” to “almost always”.

Dunn’s sensory processing framework has two core constructs: thresholds and self-regulation. Thresholds range from high to low, and self-regulation ranges from passive to active. Thresholds refer to the nervous system’s responsiveness to stimuli. With a low threshold, the nervous system is easily activated; with a high threshold, a more intense stimulus is required for the nervous system to respond. Moreover, the self-regulation continuum is anchored at one end by passive responding and at the other end by active responding.

Four sensory processing patterns are identified when these constructs intersect: registration (high threshold and passive self-regulation), seeking (high threshold and active self-regulation), sensitivity (low threshold and passive self-regulation), and avoiding (low threshold and active self-regulation). Moreover, it recognizes five sensory modalities (i.e. auditory, visual, touch, movement, and oral). The SP-2 classifies toddlers as having “typical performance”; “less/more” than others (between 1 and 2 SD); or a “much less”/”much more” than others (>2 SD; Dunn, 2014). Good reliability and validity values of Toddler SP are reported in the published manual: alpha di Cronbach’s are mostly in the adequate (>0.70) to excellent (>0.90) range, the test–retest reliability coefficient ranges from 0.83 to 0.97 supporting good stability in the caregiver questionnaires over time (Dunn, 2014).

We administered an Italian adaptation of the SP-2 for all the participants. The forward and backward translation from the English version was made according to the guidelines for cross-cultural adaptation of self-report measures provided by Beaton et al. (2000). Furthermore, the final version was administered to an independent sample of 46 neurotypical toddlers (24 males; M = 16.02; SD = 0.91 months) recruited at the Scientific Institute IRCCS Medea, and standardized z-scores (M = 0; SD = 1) were provided to evaluate overlap with English-speaking published norms for age 12–18 months (Dunn, 2014). No differences were found, and English-published norms were used in the analyses (data available on request).

Based on potential age differences in sensory patterns, all four sensory pattern measures were standardized on age-based general population norms (Crippa et al., 2022; Dunn, 2014; Riva et al., 2021), to control for potential age effect.

Autism Diagnostic Observation Schedule-Second Edition

To evaluate autistic features (Gotham et al., 2009), we used the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2), a semistructured assessment of communication, social interaction, imaginative play, and restricted and repetitive behaviors, designed to detect putative autistic signs (Lord et al., 2012). The ADOS-2 includes five modules, each requiring 40–60 min to administer. In this study, we used the ADOS Toddler Module (Lord et al., 2012; Luyster et al., 2009) to evaluate social-communication outcomes and the presence of autistic traits at 24 months. Two separate algorithms are provided for Module Toddler based on age and language level. Esler et al. (2015) published the Calibrated Severity Scores (CSS) for the total ADOS score and subdomains of the Toddler Module. For the purpose of this study, since the CSS is less influenced by child characteristics (i.e. verbal level and age), total score CSS and subdomains CSS (Social Affect-SA and Restricted and Repetitive Behaviors-RRB) have been calculated to provide quantitative estimates of autistic characteristics (Esler et al., 2015).

Statistical analysis

To identify similar sensory subgroups in EL-ASD toddlers, we used latent class analysis (LCA) estimated in Mplus (Muthén & Muthén, 2017). Maximum likelihood with robust standard errors (MLR) was used as a method of estimation. This approach combines both continuous (dimensional trait variability) and categorical factors (subgroups) and allows for heterogeneity within groups using continuous latent variables (Muthén, 2008). The final model was chosen based on a stepwise procedure (Jung & Wickrama, 2008). This procedure starts with a one-class solution and then adds one class at a time. In addition, to determine the final model, we used several statistical fit indices based on previous literature (Nylund et al., 2007). First, we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), which were calculated to assess how well each model fitted the data. The most parsimonious model is the one with the lowest AIC and BIC. Likelihood-based tests (Lo–Mendell–Rubin test-LMR and bootstrap likelihood ratio test–BLRT) are also reported to explore best-fitting indexes among models further, comparing k-class and k-1 class models. Entropy values are also considered. Entropy values, ranging from 0 to 1 with higher values, indicate a more accurate classification of individuals into classes. Entropy values greater than 0.70 indicate acceptable classification (Wang & Wang, 2012). In LCA, the BIC is more reliable in obtaining the best-fitting models than AIC and SSABIC (Nylund, 2007). Therefore, the BIC was used to determine the best-fitting model in LCA analyses. The LMR test and entropy were calculated to determine the best number of latent classes. An LMR value that is not statistically significant indicates that a model with one less class better fits the data (Lo et al., 2001). Higher entropy indicates a better model (Ramaswamy et al., 1993).

We then examined the posterior probabilities (Jung & Wickrama, 2008) that provide information on the likelihood of that subject belonging to each of the obtained classes. The probability of the class to which a certain subject is ultimately assigned should be considerably higher than the probability of belonging to another group and at least 0.70 (Lanza et al., 2003). Probabilities of at least 0.816 or higher determined subjects assigned to each latent class, as these probabilities are considered key characteristics of that class. In this way, the classes are distinguishable from each other. Classification of toddlers into their best-fitting class was carried out using the SAVEDATA option in Mplus (MPLUS, Version 6.11) (Muthén & Muthén, 2017). The variables were then imported into SPSS for further analysis.

Repeated measures (Within Subjects) ANOVA was used to compare different sensory subscales in each sensory class. Post hoc analyses were computed to identify significant differences and Bonferroni correction was applied.

Moreover, the ANOVAs and Bonferroni correction for multiple comparisons were used to compare sensory classes on later ASD-related traits at 24 months. In particular, the association between ADOS CSS (social affect, restricted and repetitive behaviors, and total score) at 24 months in the different sensory classes was analyzed. Finally, we examined the association (chi-square statistics) between sensory subgroups and clinical best estimate diagnosis at 36 months (autism: toddlers who later received a diagnosis of autism; no-autism: toddlers who appeared to be neurotypical).

Results

Descriptive statistics of individual, demographic, and clinical measures are shown in Table 1.

Table 1.

Descriptive statistics on individual, demographic, and clinical characteristics.

N Range Mean SD
Age (months) 116 [12, 18] 16.48 2.13
Gestational age (weeks) 111 [36, 42] 39.26 1.37
Socio-economic status a 115 [20, 90] 55.19 21.26
Developmental quotient b 114 [74, 125] 100.45 10.77
Sensory Profile-2 c 116
 Seeking [−4.73, 2.98] −1.49 1.57
 Avoiding [−1.97, 2.26] −0.44 0.80
 Sensitivity [−1.30, 2.03] −0.20 0.81
 Registration [−1.26, 1.51] −0.36 0.68
ADOS-2 calibrated severity scores 100
 Social affect [1, 9] 3.11 2.20
 Restricted and repetitive behaviors [1, 9] 3.46 2.35
 Total [1, 9] 2.82 2.46
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ADOS-2: Autism Diagnostic Observation Schedule-2 (Lord et al., 2012).

a

Socio-economic status was scored according to Hollingshead 90-point scale, whereby a score ranging 10–90 was assigned to each parental job and the higher of two scores was used when both parents were employed (Hollingshead, 1975). Scores ranged between 10, corresponding to unskilled workers; 50, corresponding to sales workers; and 90, corresponding to major professionals.

b

Age-standardized IQ scores (M = 100; SD = 15) score in Griffiths Mental Development Scales-Extended Revised (Griffiths, 1996).

c

z-scores are reported for SP-2 (Dunn, 2014).

Using LCA and maximum likelihood with robust standard errors method, a three-class solution was the model of best fit. This model had the lowest BIC; the LMR likelihood ratio test (LMR p values) showed that the four-class solution was not significantly better than the three-class solution. Furthermore, the entropy value (0.951) also showed a classification of cases. Table 2 shows model fit statistics for one-, two-, three-, four-, and five-class as measured by LCA. The three classes represent three underlying subgroups of EL-ASD toddlers with different sensory responsiveness and processing profiles. The three classes did not differ for sex (X2(2) = 0.833, p = 0.659). Table 3 shows descriptive statistics of individual and cognitive variables (age, gestational weeks, socio-economic status, and developmental quotient) in the three classes. No significant differences between subgroups were found.

Table 2.

LCA fit statistics for the sensory profile in EL-ASD toddlers (n = 116).

Model Fit statistics Sample size (n)
AIC BIC SS-ABIC Entropy LMR
p value 		BLRT p value 1c 2c 3c 4c 5c
1c 1246.553 1268.582 1243.294 — — — 116
2c 1149.928 1185.724 1144.632 0.900 0.0004 <0.0001 66 50
3c 1079.477 1129.041 1072.144 0.951 0.0138 <0.0001 65 29 22
4c 1074.424 1137.757 1065.055 0.948 0.2072 <0.0001 65 7 29 15
5c 1053.512 1130.613 1042.106 0.941 0.3881 0.0128 60 17 8 15 16
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AIC: Akaike information criterion; BIC: Bayes information criterion; BLRT: Bootstrapped likelihood ratio test; c: class; EL-ASD: elevated likelihood for autism spectrum disorder; LCA: latent class analyses; LMR: Lo–Mendell–Rubin likelihood ratio test; SS-ABIC: sample size adjusted BIC.

Best performing model (three-class solution) in font bold.

Table 3.

Descriptive statistics of the classes’ individual, demographic and cognitive characteristics.

Class 1 Class 2 Class 3 Group differences
M (SD) Range M (SD) Range M (SD) Range F (p)
Age (months) 16.16 (2.40) [12, 18] 17.01 (1.64) [12, 18] 16.74 (1.73) [12, 18] 1.866 (0.156)
Gestational age (weeks) 39.23 (1.48) [36, 42] 39.42 (1.10) [37, 41] 39.16 (1.34) [36, 42] 0.219 (0.803)
Socio-economic status a 54.25 (20.44) [20, 90] 55.77 (22.66) [30, 90] 57.37 (22.81) [20, 90] 0.165 (0.848)
Developmental quotient b 100.53 (10.20) [76, 116] 98.13 (12.15) [74, 112] 102.56 (11.11) [86, 125] 0.675 (0.512)
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a

Socio-economic status was scored according to Hollingshead 90-point scale, whereby a score ranging 10–90 was assigned to each parental job and the higher of two scores was used when both parents were employed (Hollingshead, 1975). Scores ranged between 10, corresponding to unskilled workers; 50, corresponding to sales workers; and 90, corresponding to major professionals.

b

Age-standardized intelligence quotient scores (M = 100; SD = 15) score in Griffiths Mental Development Scales-Extended Revised (Griffiths, 1996).

In particular, the model consisted of “Low seeking” group (Class 1; n = 65), “Sensory-balanced” group (Class 2; n = 29), and “High Sensitivity” group (Class 3; n = 22). Repeated measures ANOVAs in the three classes revealed significant differences in sensory subscales for Class 1 “Low seeking” (F(1,64) = 113.01; p < 0.001) and Class 3 “High sensitivity” (F(1,21) = 49.48; p < 0.001). No significant differences in sensory subscales were found in Class 2 “Sensory balanced” (F(1,28) = 1.49; p = 0.233). This Class (25% of the total sample) is characterized by average-range levels in all four sensory patterns (seeking: M = 0.06; SD = 1.34; avoiding: M = 0.12; SD = 0.73; sensitivity: M = 0.17, SD = 0.22, and registration: M = −0.28; SD = 0.49). Post-hoc analyses (Bonferroni correction applied) showed that Class 1 “Low seeking” (56% of the total sample) is characterized by lower levels of sensory seeking (M = −2.27; SD = 1.15) compared to other sensory patterns (avoiding: M = −0.69; SD = 0.64, sensitivity: M = −0.81; SD = 0.29, and registration: M = −0.70; SD = 0.39; all Bonferroni corrected ps < 0.001). Class 3 “High sensitivity” (19% of the total sample) is characterized by significantly higher levels of sensory sensitivity (M = 1.13; SD = 0.28) compared to all other subscales (seeking: M = −1.24; SD = 1.38; avoiding: M = −0.44; SD = 0.95; registration: M = 0.53; SD = 0.75; Bonferroni corrected p-values range: <0.001–0.015). Furthermore, the registration subscale is significantly higher compared to seeking (Bonferroni p-value < 0.001) and avoiding (Bonferroni p-value = 0.001). Figure 1 shows the three-class solution.

Figure 1.

Figure 1.

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Three sensory profile latent class solutions.

Note. Class 1 “low seeking” (n = 65); Class 2 “sensory-balanced” (n = 29); Class 3 “high sensitivity” (n = 22). Lines represent sensory profile plots in each class.

Sensory subgroup differences in autistic traits at 24 months

ANOVA analyses were conducted to determine whether toddlers assigned to separate classes differed in autistic-related traits at 24 months. A total of 100 children (86.2% of the initial sample) had complete information on ADOS-2 at 24 months. Follow-up missing data were controlled and were missing completely at random (Little’s MCAR X2 = 3.66; p = 0.455). In the analysis, class membership was the categorical independent variable, and ADOS scores (CSS) at 24 months were the dependent variables. The ANOVA revealed a significant main effect of class membership (F(2,99) = 8.78; p < 0.001). Post hoc analyses showed that children in Class 3 “High sensitivity” obtained higher ADOS-2 total scores compared to both Class 1 “Low seeking” (Bonferroni corrected p-value < 0.001) and Class 2 “Sensory balanced” (Bonferroni corrected p-value = 0.047).

Considering ADOS-social affect and ADOS-restricted and repetitive behaviors separately, the ANOVAs revealed a significant main effect of class membership on ADOS-social affect (F(2,99) = 8.82; p < 0.001), whereby Class 3 “High sensitivity” obtained higher ADOS-social affect scores compared to Class 1 “Low seeking” (Bonferroni corrected p-value < 0.001).

Finally, the ANOVA revealed a main effect of class membership on ADOS-restricted and repetitive behaviors (F(2,99) = 4.10; p = 0.020), whereby Class 3 “High sensitivity” obtained higher ADOS-restricted and repetitive behaviors scores compared to Class 2 “Sensory balanced” (Bonferroni corrected p-value = 0.017). Table 4 shows descriptive statistics and post-hoc analyses of ADOS CSS at 24 months across different sensory subgroups. Figure 2 shows ADOS CSS at ages 24 months as a function of different sensory subgroups.

Table 4.

Descriptive statistics and post hoc analyses of ADOS CSS at 24 months across different sensory classes.

ADOS CSS—total
Mean (SD) Significant post hoc analyses
Class 1 (“Low seeking”) 2.17 (1.68) Class 3 > Class 1
Class 3 > Class 2
Class 2 (“Sensory balanced”) 2.92 (2.10)
Class 3 (“High sensitivity”) 4.45 (2.93)
ADOS CSS—social affect
Mean (SD) Significant post hoc analyses
Class 1 (“Low seeking”) 2.39 (1.52) Class 3 > Class 1
Class 2 (“Sensory balanced”) 3.50 (2.23)
Class 3 (“High sensitivity”) 4.55 (2.87)
ADOS CSS—restricted and repetitive behaviors
Mean (SD) Significant post hoc analyses
Class 1 (“Low seeking”) 3.37 (2.23) Class 3 > Class 2
Class 2 (“Sensory balanced”) 2.73 (2.27)
Class 3 (“High sensitivity”) 4.65 (2.43)
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ADOS: autism diagnostic observation schedule; CSS: calibrated severity scores.

Figure 2.

Figure 2.

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Violins plots comparing EL-ASD toddlers assigned to different classes and ADOS calibrated severity scores at age 24 months.

CSS: calibrated severity scores.

Sensory subgroup differences on clinical best estimate diagnosis at 36 months

Toddler siblings were classified as autistic if they met or exceeded the algorithm cutoffs for autism on scores on the Autism Diagnostic Observation Schedule–2 (CSS > 6) “and” they received a clinical best-estimate diagnosis of ASD (APA, 2013). Children underwent a comprehensive evaluation at our laboratory and all standardized tests were administered by licensed clinicians. After this evaluation, a clinical diagnosis of autism was determined by a licensed clinical team (psychologists and child neuropsychiatrists). The autistic diagnosis was established using clinical history, and DSM-5 criteria.

Based on these criteria, 25 toddlers (25.50%; 17 boys) were autistic, and 69 toddlers (70.40%; 27 boys) were classified as non-autistic/neurotypical. Four children (4.10%; 2 males) showed atypical development, including language and developmental delay. The small sample size limited our study to a specific diagnostic group, and we decided to exclude children with atypical development from the analyses. Differences between sensory subgroups based on the presence of later diagnosis were calculated. A crosstabulation (chi-square) analysis found differences in distribution by diagnosis across subgroups (X2(98) = 12.885; p = 0.002). In particular, we found significant differences in distribution by diagnosis across Class 1 “Low seeking” (autistic: n = 8, 15.4%; no-autistic: n = 44, 84.6%) and Class 3 “High sensitivity” (autistic: n = 11, 57.9%; no-autistic: n = 8, 42.1%; X2(71) = 12.830; p < 0.001), and across Class 2 “Sensory balanced” (autistic: n = 6, 26.1%; no-autistic: n = 17, 73.9%), and Class 3 “High sensitivity” (X2(42) = 4.369; p = 0.037). No differences in the distribution of diagnosis were found between Class 1 “Low seeking” and Class 2 “Sensory balanced.”

Discussion

This study aimed to characterize sensory profiles in a longitudinal cohort of toddlers at elevated likelihood of being autistic and to further verify whether the sensory subgroup differs regarding social-communicative skills, restricted/repetitive behaviors, and autistic diagnosis at 36 months. The present study used latent class analyses to assess the child’s combination of scores across the four sensory patterns. LCA is an appropriate statistical approach to form subtypes within the group and uses probabilities of group membership for latent variables, rather than predetermined distances between traits or variables (DeBoth & Reynolds, 2017). This approach more fully describes the individual differences in sensory processing.

We found that EL-ASD toddlers can be characterized into three more homogeneous sensory subgroups: low seeking group (56%), sensory balanced group (25%), and high sensitivity group (19%). Each class showed differences in the level of sensory pattern scores (seeking, avoiding, sensitivity, and registration; Dunn, 2014). In addition, the sensory classes showed differences in their associations with later autistic features and with the clinical best estimate diagnosis at 36 months. These associations provided insight into the clinical utility of the three identified subgroups.

Our results showed that the majority of siblings showed low sensory scores in all patterns, especially low sensory seeking, suggesting that this group is more unaware, does not seek additional sensory experiences, and plays more passively than other children. Moreover, the lowest percentage of autistic children (15.4%) was represented in this class, and no association with later autistic features was found. These results suggest that lower scores in seeking behaviors at 12–18 months do not seem to be associated with later social communication skills. These findings align with previous literature (Wolff et al., 2019) reporting that siblings who do not receive a diagnosis of autism obtained lower scores in sensory seeking than siblings who receive a diagnosis of autism and neurotypical individuals. In contrast, Mulligan and White (2012) found that 12-month-old infants with a later diagnosis of autism had fewer sensory seeking behaviors than their neurotypical counterparts. It should be noted that several previous studies found an association between behavioral patterns of sensory seeking and increased autistic features in autistic children (Ben-Sasson et al., 2009; Liss et al., 2006; Watson et al., 2011). However, two meta-analyses (Ben-Sasson et al., 2009, 2019) reported that sensory seeking was associated with age, with more pronounced behaviors from 6 to 9 years old. It is plausible that a link between sensory seeking and autistic characteristics emerged in different periods of child development, as reported in previous studies (Baranek et al., 2007; Wolff et al., 2019). It is worth noting that this group unexpectedly showed sensory seeking mean scores in the “clinical” range (more than 2 SD below the mean) compared to the normative sample (Dunn, 2014). It may be an adaptive behavior to avert sensory stimulation that may cause distress for children (Mulligan & White, 2012). Alternatively, an interpretation of this result may be related to a potential rater bias. Parents of EL-ASD toddlers have at least one older autistic sibling, and it is likely that these parents have a different experience of ASD compared to controls and may under-rate the sensory seeking behaviors of their children.

Children in class 2 (25% of the total sample) are characterized by a balanced sensory profile with typical range scores across all patterns. Children in this group seem to explore different stimuli and easily engage in different sensory experiences. 26.1% of these children received a diagnosis of autism at 36 months. These results highlighted the high heterogeneity of sensory features in autism and suggested that different levels of sensory processing are not observed in all autistic children (Kadlaskar et al., 2022).

Finally, toddlers in the high sensitivity group (19% of the total sample) showed significantly higher sensory sensitivity scores than other sensory patterns. This group may notice sensory input more than others and is more easily distracted by events. Notably, only this group showed later socio-communicative difficulties and restricted/repetitive behaviors. The highest percentage of children with later diagnosis of ASD is represented in this class (i.e. 57.9% of the children received a diagnosis at 36 months). These results align with previous studies supporting that early sensory differences may have a cascading effect on later development (Baranek et al., 2018; Thye et al., 2018). In particular, our data supported that hyperresponsiveness profiles are associated with social difficulties and more restricted/repetitive behaviors. Hyperresponsiveness is well documented in the ASD literature (Schulz & Stevenson, 2019; Tomchek & Dunn, 2007) and was found to be related to poor communicative skills (Ben-Sasson et al., 2019), restricted interests and repetitive behaviors in autistic populations (Schulz & Stevenson, 2019). Consistently with our results, one infant siblings study (Wolff et al., 2019) found that sensory scores in siblings who received a diagnosis of autism increased from age 12 to 24 months (elevated hyperresponsiveness) while decreasing for those who did not receive a diagnosis of autism. Children with early hyperresponsiveness to sensory input may be hyperselective to specific sensory stimuli that may limit the ability to take advantage of communicative and social learning opportunities. It also may be that children with hyperresponsiveness are more distracted by irrelevant information in the social environment, contributing to fewer opportunities for social interaction. It is important to note that this group is also characterized by co-occurring registration scores compared to avoiding and sensory seeking behaviors. Registration usually referred to hyporesponsiveness patterns (Dunn, 2014) and reflected the degree to which children “tune into” environmental stimuli. Our results supported the hypothesis that different sensory responsiveness may co-occur in the same individual (Ben-Sasson et al., 2019). Even if further research is needed, the combination of sensitivity (hyperresponsiveness) and registration (hyporesponsiveness) may be related to overall difficulties in sensory modulation (Little et al., 2016), which increased the likelihood of affecting development.

In contrast, several studies reported that higher levels of sensory sensitivity in the early stages of life were associated with higher level social skills (Green et al., 2015; Jones et al., 2018). For example, Jones and colleagues (2018) found that sensory hypersensitivity at 2 years predicted greater attention skills and social approach behaviors at 4 years. However, a direct comparison with our study is not possible due to different methodological aspects: only sensory hypersensitivity was measured, and potential effects of co-occurring sensory patterns may influence the results. Furthermore, we tested a specific age range (i.e. 12–18 months), and it may be the case that younger infants have different sensory patterns with different values on the outcome (Feldman et al., 2022; Wolff et al., 2019). Due to the fact the relationship between sensory processing and age is currently unclear, more in-depth research is needed to fully understand the nuances of this association and the direction of this relationship.

Interestingly, we found that toddlers with higher sensory sensitivity also showed higher levels of restricted and repetitive behaviors compared to toddlers with balanced sensory patterns. This result is consistent with previous literature (Schulz & Stevenson, 2019) that found sensory hypersensitivity as a good predictor of stereotypies and sameness behaviors in a sample of 114 children aged from 6 to 20 years old. In contrast, individuals with balanced sensory profiles showed relatively low levels of restricted/repetitive behaviors (Tillmann et al., 2020). Even if replications are needed, we may speculate that hyperresponsiveness accounted for unique variance in restricted/repetitive behaviors and showing higher levels of sensory sensitivity specifically increased the probability of using restricted and repetitive behaviors to overcome overwhelming sensory inputs (overarousal hypothesis; Boyd et al., 2010; Rogers & Ozonoff, 2005).

Our study has some limitations that should be mentioned. First, it focused on toddlers with an older sibling with ASD, and future work should be devoted to extending the present findings in individuals with a low likelihood of developing ASD. The present study supports an individual differences perspective that emphasizes the need to understand the variability of early sensory processing that occurs between siblings of autistic children. However, responses to sensory stimuli occur on a continuum for children with and without developmental conditions (Little et al., 2017; Uljarević et al., 2017) and all individuals may have different thresholds and self-regulation strategies for their sensory experiences. It may be the case that some sensory subgroups occur also in general population samples during early development, supporting the view of common mechanisms across sensory subtypes that may crosscut both diagnostic categories and typical development (Kadlaskar et al., 2022). The clinical implication of these results may influence the development of future early screening and the selection of appropriate and individualized support. Second, there are some limitations related to using a parent report (SP-2) to evaluate sensory processing. A direct assessment of sensory responsiveness would be important to consider in future studies. Previous research found limited consistency between observational assessments and parental reports (Ramappa et al., 2022), and both measures seem to provide different but potentially complementary information. Future work, including additional reports and observational and experimental measures (e.g. EEG), would be necessary to better understand the nuances of our results (Jones et al., 2018; Riva et al., 2022).

Implications for intervention

In this study, we demonstrate that different co-occurring patterns of sensory processing characterize siblings of autistic children and early sensory sensitivity seems to have a significant role for later development and diagnosis in terms of social interaction and restricted/repetitive behaviors.

This study supported the importance of examining different sensory patterns to dissect the phenotypic heterogeneity in sensory processing. The identification of more homogeneous subgroups with similar sensory characteristics can serve as indicators of later outcomes and facilitate the development of personalized supports (Tillmann et al., 2020; Uljarević et al., 2017). It is plausible that children in a specific sensory-based subtype may have similar developmental trajectories and underlying etiological mechanisms (Gottesman & Gould, 2003; Green et al., 2015).

As sensory differences are associated with later developmental outcomes, these results may be critical when designing intervention needs and support for children at increased likelihood for neurodevelopmental disorders. For example, practitioners may guide parents during interaction with their child, providing the child with an adequate amount of sensory experiences, eliminating distractors, and adapting the environment to meet the child’s sensory needs (e.g. parents may adopt highly structured routines or avoid a highly stimulating environment). Further studies assessing the effect of intervention strategies on sensory responsiveness in the early phases of life should be considered with the long-term goal of improving the quality of life of autistic individuals and reducing the impact on the National Health System.

Footnotes

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We are grateful to all participants’ families. This study was supported by the Italian Ministry of Health (Ricerca corrente) a, by “5 per mille” funds for biomedical research and by Fondazione Italiana per l’Autismo (Italian Autism Foundation, FIA). APC funded by Bibliosan.

ORCID iD: Valentina Riva Inline graphic https://orcid.org/0000-0001-7959-0354

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