Podcast: A potential biomarker to AID, not MAKE, a diagnosis

The media has just called another biological marker a “diagnostic test”, when in this case, it was always intended to be an aid, not a test itself. It involves using baby hair strands to look a variation in metabolism of certain chemical elements across time. Remarkably, it showed similar results in autistic children in Japan, the US and Sweden. It’s not ready to be used as a diagnostic test, so what is it supposed to do? Listen to an interview with the inventor and researcher, Dr. Manish Arora from The Icahn School of Medicine at Mt. Sinai School here.

The full article (open access) can be found here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740182/

Many of the existing tools to identify autism cost money or are not specific for ASD, and they are hidden behind paywalls and are hard to obtain. A group of scientists led by Tom Frazer at John Caroll University put together a 39 questionnaire called the Autism Symptoms Dimensions Questionnaire to be filled out by parents of children. It’s free and open source! But that’s just the first step. The media got the intent wrong, yet again.

It should not replace a full diagnosis. Autism is complex, and even those with genetic forms of autism show heterogeneity in symptoms. They each need comprehensive evaluations. But this is a good start. Listen to the podcast and check out the ASDQ here! It’s open source!

References below:

https://pubmed.ncbi.nlm.nih.gov/36670671/

https://onlinelibrary.wiley.com/doi/epdf/10.1111/dmcn.15497

https://pubmed.ncbi.nlm.nih.gov/36639821/

People tend to go towards a “strengths only” or “weaknesses only” approach to describing autism. But even if you think about a single aspect of autistic challenges – social communication – autistics can show both. How can you measure this, and even more importantly, document it to play to someone’s strengths while addressing their impairments at the same time? Special guests Dr. Matthew Lerner and Jacquelyn Gates from Stony Brook University explain how this can be done by clinicians in our latest podcast.

https://pubmed.ncbi.nlm.nih.gov/36573397/

Like ASD, the prevalence of ADHD has increased significantly in the past 2 decades. A critical analysis examines the factors, and many of them can be applicable to the increase in the rise of autism diagnoses: increased diagnosis in adults, looser diagnostic criteria, and untrained professionals making the diagnoses. While they are not of course the same, listen to some of their arguments and read their comments (link below) to see if you agree with my assessment.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/jclp.23348

Background: Canonical babbling-producing syllables with a mature consonant, full vowel, and smooth transition-is an important developmental milestone that typically occurs in the first year of life. Some studies indicate delayed or reduced canonical babbling in infants at high familial likelihood for autism spectrum disorder (ASD) or who later receive an ASD diagnosis, but evidence is mixed. More refined characterization of babbling in the first year of life in infants with high likelihood for ASD is needed.

Methods: Vocalizations produced at 6 and 12 months by infants (n = 267) taking part in a longitudinal study were coded for canonical and non-canonical syllables. Infants were categorized as low familial likelihood (LL), high familial likelihood diagnosed with ASD at 24 months (HL-ASD) or not diagnosed (HL-Neg). Language delay was assessed based on 24-month expressive and receptive language scores. Canonical babble ratio (CBR) was calculated by dividing the number of canonical syllables by the number of total syllables. Generalized linear (mixed) models were used to assess the relationship between group membership and CBR, controlling for site, sex, and maternal education. Logistic regression was used to assess whether canonical babbling ratios at 6 and 12 months predict 24-month diagnostic outcome.

Results: No diagnostic group differences in CBR were detected at 6 months, but HL-ASD infants produced significantly lower CBR than both the HL-Neg and LL groups at 12 months. HL-Neg infants with language delay also showed reduced CBR at 12 months. Neither 6- nor 12-month CBR was significant predictors of 24-month diagnostic outcome (ASD versus no ASD) in logistic regression.

Limitations: Small numbers of vocalizations produced by infants at 6 months may limit the reliability of CBR estimates. It is not known if results generalize to infants who are not at high familial likelihood, or infants from more diverse racial and socioeconomic backgrounds.

Conclusions: Lower canonical babbling ratios are apparent by the end of the first year of life in ASD regardless of later language delay, but are also observed for infants with later language delay without ASD. Canonical babbling may lack specificity as an early marker when used on its own.

While previous work has identified the early predictors of language skills in infants at elevated familial risk (ER) and low familial risk (LR) for autism spectrum disorder (ASD), no studies to date have explored whether these predictors vary based on diagnostic outcome of ASD or no ASD. The present study used a large, multisite dataset to examine associations between a set of commonly studied predictor variables (infant gesture abilities, fine motor skills, nonverbal cognition, and maternal education level), measured at 12 months, and language skills, measured at 3 years, across three diagnostic outcome groups-infants with ASD (“ASD”), ER infants without ASD (“ER-no ASD”), and LR infants without ASD (“LR-no ASD”). Findings revealed that the predictors of language skills differed across groups, as gesture abilities were positively associated with language skills in the ER-no ASD group but negatively associated with language skills in the ASD group. Furthermore, maternal education level was positively associated with language skills in the ASD and LR-no ASD groups only. Variability in these early predictors may help explain why language skills are heterogeneous across the autism spectrum, and, with further study, may help clinicians identify those in need of additional and/or specialized intervention services that support language development. LAY SUMMARY: The present study identified predictors of language skills in infants with and without autism spectrum disorder (ASD). Maternal education level and 12-month gesture abilities predicted 3-year language skills in infants with ASD. Measuring these predictors early in life may help identify infants and families in need of additional and/or specialized intervention services that support language development.

Keywords: gesture; infant sibling; language; maternal education; motor; nonverbal cognition.

Assessment of autism spectrum disorder (ASD) relies on expert clinician observation and judgment, but objective measurement tools have the potential to provide additional information on ASD symptom severity. Diagnostic evaluations for ASD typically include the autism diagnostic observation schedule (ADOS-2), a semi-structured assessment composed of a series of social presses. The current study examined associations between concurrent objective features of child vocalizations during the ADOS-2 and examiner-rated autism symptom severity. The sample included 66 children (49 male; M = 40 months, SD = 10.58) evaluated in a university-based clinic, 61 of whom received an ASD diagnosis. Research reliable administration of the ADOS-2 provided social affect (SA) and restricted and repetitive behavior (RRB) calibrated severity scores (CSS). Audio was recorded from examiner-worn eyeglasses during the ADOS-2 and child and adult speech were differentiated with LENA SP Hub. PRAAT was used to ascertain acoustic features of the audio signal, specifically the mean fundamental vocal frequency (F0) of LENA-identified child speech-like vocalizations (those with phonemic content), child cry vocalizations, and adult speech. Sphinx-4 was employed to estimate child and adult phonological features indexed by the average consonant and vowel count per vocalization. More than a quarter of the variance in ADOS-2 RRB CSS was predicted by the combination of child phoneme count per vocalization and child vocalization F0. Findings indicate that both acoustic and phonological features of child vocalizations are associated with expert clinician ratings of autism symptom severity. LAY SUMMARY: Determination of the severity of autism spectrum disorder is based in part on expert (but subjective) clinician observations during the ADOS-2. Two characteristics of child vocalizations-a smaller number of speech-like sounds per vocalization and higher pitched vocalizations (including cries)-were associated with greater autism symptom severity. The results suggest that objectively ascertained characteristics of children’s vocalizations capture variance in children’s restricted and repetitive behaviors that are reflected in clinician severity indices.

Keywords: audio processing; objective measurement; vocalization.

Large-scale genomic studies have identified over 100 genes associated with autism spectrum disorder (ASD); however, important phenotypic variables are captured inconsistently. In many cases, the resources required for comprehensive characterization hinder the feasibility of collecting critical information, such as intellectual ability. Thus, electronic collection of important phenotypes would greatly facilitate large-scale data collection efforts. This study assessed the utility of two electronic assessments as a proxy of cognitive ability relative to clinician-administered cognitive assessments. Ninety-two participants completed the study, including individuals with ASD (probands, n = 19), parents of probands (n = 46), and siblings without ASD (n = 27). Participants were administered the electronic-Peabody Picture Vocabulary Test, Fourth Edition (e-PPVT-4), an electronic visual reasoning (VR) test, and a clinician-administered Wechsler Abbreviated Scales of Intelligence, Second Edition (WASI-II). Probands also completed a full, in-person, cognitive assessment and Vineland Adaptive Behavior Scales, 2nd Edition. Correlations between scores on electronic and clinician-administered measures were examined. Classification accuracy of individual scores based on 95% confidence intervals and score range (below average, average, above average) were also assessed. Moderate to strong correlations were identified between both electronic measures and the clinician-administered WASI-II (ρ = 0.606–0.712). Mean difference between standard scores ranged from 10.7 to 14.8 for the cohort. Classification accuracy based on WASI-II 95% confidence interval was consistently low (27.5%–47.3%). Classification accuracy by score range (below average, average, above average) was variable, ranging from 33% to 86% for probands. All participants unable to complete the electronic assessments met DSM-5 criteria for intellectual disability. e-PPVT-4 and VR scores were strongly correlated with scores on the WASI-II full-scale IQ (ρ = 0.630, 0.712), indicating utility of these measures at the group level in large-scale genomic studies. However, the poor precision of measurement across both measures suggests that the e-PPVT-4 and VR are not useful alternatives to in-person testing for the purpose of clinical assessment of an individual’s IQ score.

Lay Summary

Large-scale studies designed to identify genes associated with autism have been successful in identifying over 100 genes. However, important clinical information about participants with autism and their family members is often missed—including cognitive functioning. Cognitive testing requires in-person administration by a trained clinician and therefore can be burdensome and often reduces feasibility of diverse samples. Here, we assessed whether electronic assessments could take the place of in-person cognitive testing. We found that at the group level, for large-scale studies, electronic measures added valuable information; however, they were not accurate enough to be used on an individual level (i.e., to offer feedback about an individual’s predicted IQ score).

Background: Differences in face processing in individuals with ASD is hypothesized to impact the development of social communication skills. This study aimed to characterize the neural correlates of face processing in 12-month-old infants at familial risk of developing ASD by (1) comparing face-sensitive event-related potentials (ERP) (Nc, N290, P400) between high-familial-risk infants who develop ASD (HR-ASD), high-familial-risk infants without ASD (HR-NoASD), and low-familial-risk infants (LR), and (2) evaluating how face-sensitive ERP components are associated with development of social communication skills.

Methods: 12-month-old infants participated in a study in which they were presented with alternating images of their mother’s face and the face of a stranger (LR = 45, HR-NoASD = 41, HR-ASD = 24) as EEG data were collected. Parent-reported and laboratory-observed social communication measures were obtained at 12 and 18 months. Group differences in ERP responses were evaluated using ANOVA, and multiple linear regressions were conducted with maternal education and outcome groups as covariates to assess relationships between ERP and behavioral measures.

Results: For each of the ERP components (Nc [negative-central], N290, and P400), the amplitude difference between mother and stranger (Mother-Stranger) trials was not statistically different between the three outcome groups (Nc p = 0.72, N290 p = 0.88, P400 p = 0.91). Marginal effects analyses found that within the LR group, a greater Nc Mother-Stranger response was associated with better expressive language skills on the Mullen Scales of Early Learning, controlling for maternal education and outcome group effects (marginal effects dy/dx = 1.15; p < 0.01). No significant associations were observed between the Nc and language or social measures in HR-NoASD or HR-ASD groups. In contrast, specific to the HR-ASD group, amplitude difference between the Mother versus Stranger P400 response was positively associated with expressive (dy/dx = 2.1, p < 0.001) and receptive language skills at 12 months (dy/dx = 1.68, p < 0.005), and negatively associated with social affect scores on the Autism Diagnostic Observation Schedule (dy/dx = – 1.22, p < 0.001) at 18 months.

Conclusions: In 12-month-old infant siblings with subsequent ASD, increased P400 response to Mother over Stranger faces is positively associated with concurrent language and future social skills.

This week’s #ASFpodcast highlights a few articles from the Journal of Autism and Developmental Disorders this week which examined the tolerability and efficacy of online diagnostic procedures and interventions, from the perspective of both parents and clinicians. They seem to work about the same, although there were some caveats. For many reasons, online and Telehealth options are here to stay, and more needs to be done to improve their accuracy, acceptability, feasibility and effectiveness. These early studies are promising though, and lead the way to even more improvements to help make them a viable option for families in the future. Listen to the podcast here.

https://link.springer.com/article/10.1007/s10803-022-05435-z

https://link.springer.com/article/10.1007/s10803-022-05576-1

https://link.springer.com/article/10.1007/s10803-022-05554-7

https://link.springer.com/article/10.1007/s10803-022-05580-5

https://link.springer.com/article/10.1007/s10803-022-05607-x

Outcome measures for clinical trials and understanding and determining gene x environment interactions have been two (of many) challenging questions for scientists. In the first study, we explain a new study that looks at the feasibility of three potential biomarkers that have the potential to look at presence of a diagnosis as well as effectiveness of an intervention. In the second half, we describe some new research that shows novel approaches to better understand the presence of an environmental factor with genetic influences, or a new method to describe them in different communities. You can read the studies at the links below and you can listen to the podcast here:

https://pubmed.ncbi.nlm.nih.gov/36000217/

https://pubmed.ncbi.nlm.nih.gov/35974006/

https://pubmed.ncbi.nlm.nih.gov/35992618/