Characterizing sleep disorders in an autism-specific collection of electronic health records

Protocol of a randomized controlled trial into guided internet-delivered cognitive behavioral therapy for insomnia in autistic adults (i-Sleep Autism)

BACKGROUND

Sleep problems, especially insomnia, are prevalent among autistic adults, affecting about 60 %, and significantly impact their quality of life. Internet-based cognitive behavioral therapy for insomnia (iCBT-I) could provide accessible and scalable treatment. Given the unique sensory- and information processing, and social challenges at play in autism, a tailored treatment approach may be essential to tackle sleep problems. Yet, interventions developed and tested specifically for autistic adults were scarce. Addressing this gap is crucial to meet the urgent need for effective insomnia treatments in this population.

METHODS

With this two-arm, parallel, superiority randomized controlled trial, we will assess the effectiveness of a guided iCBT-I intervention for adults (N = 160) with autism and insomnia (i-Sleep Autism). In co-creation, i-Sleep Autism has been adjusted from an existing intervention (i-Sleep). Inclusion criteria are: age ≥ 18, an ASD diagnosis, and at least sub-threshold insomnia (Insomnia Severity Index ≥10). Participants are randomly assigned to either i-Sleep Autism or an information only waitlist control condition (online psychoeducation and sleep hygiene). After 6 weeks, the control group receives the intervention. Insomnia severity is the primary outcome. Secondary outcomes include pre-sleep arousal, general mental health, depression, anxiety, daily functioning, and quality of life. Assessments will occur at baseline, mid-intervention (3 weeks), post-intervention (6 weeks), and at 6-month follow-up (the intervention group). Linear mixed-effect regression models are employed to evaluate the effectiveness of i-Sleep Autism, alongside exploration of potential moderators and mediators.

CONCLUSION

This trial can reveal whether autistic adults with insomnia benefit from a guided e-health intervention.

TRIAL REGISTRATION

NL-OMON56692.

Sex differences in sleep deficits in mice with an autism-linked Shank3 mutation

BACKGROUND

Insomnia is more prevalent in individuals with Autism Spectrum Disorder (ASD), can worsen core-symptoms and reduces quality of life of both individuals and caregivers. Although ASD is four times more prevalent in males than females, less is known about sex specific sleep differences in autistic individuals. Recent ASD studies suggest that sleep problems may be more severe in females, which aligns with the sex bias seen in insomnia for the general population. We have previously shown that male mice with a mutation in the high confidence ASD gene Shank3, Shank3∆C, recapitulate most aspects of the ASD insomnia phenotype. The objective of the present study was to leverage the Shank3∆C model to investigate sex-specific effects in sleep using polysomnography.

METHODS

Adult male and female Shank3∆C and wildtype (WT) littermates were first recorded for 24 h of baseline recordings. Subsequently, they were sleep deprived (SD) for five hours via gentle handling and allowed 19 h of recovery sleep to characterize the homeostatic response to SD. Vigilance states (rapid eye movement (REM) sleep, non-rapid eye movement (NREM) sleep and wake) were assigned by manual inspection using SleepSign. Data processing, statistical analysis and visualization were conducted using MATLAB.

RESULTS

Sex and genotype effects were found during baseline sleep and after SD. At baseline, male Shank3∆C mice sleep less during the dark period (active phase) while female Shank3∆C mice sleep less during the light period (rest phase) and sleep more during the dark period. Both male and female Shank3∆C mice show reduced spectral power in NREM sleep. We detect a significant effect of sex and genotype in sleep onset latency and homeostatic sleep pressure (sleepiness). In addition, while male Shank3∆C mice fail to increase sleep time following SD as seen in WT, female Shank3∆C mice decrease sleep time.

CONCLUSIONS

Overall, our study demonstrates sex differences in sleep architecture and homeostatic response to SD in adult Shank3∆C mice. Thus, our study demonstrates an interaction between sex and genotype in Shank3∆C mice and supports the use of the Shank3∆C model to better understand mechanisms contributing to the sex differences in insomnia in ASD in clinical populations.

Sleep Disturbances and Co-sleeping in Italian Children and Adolescents with Autism Spectrum Disorder

The current study aimed (1) to provide an analysis of the frequency and prevalence of sleep disturbances in a large Italian sample of children and adolescents with ASD, detecting specific predictors of the presence/absence of sleep disorders, (2) to examine the phenomenon of co-sleeping within a subgroup of participants with ASD. A total of 242 children and adolescents with ASD (194 males, mean age 5.03 ± 3.15 years) were included. After the diagnostic procedure, caregivers were requested to complete the Sleep Disturbance Scale for Children (SDSC) to assess sleep disorders among participants. The presence of co-sleeping was investigated in a subgroup of 146 children and adolescents with ASD. An elevated or clinically relevant global score for sleep disorders (≥ 60) was found in 33% of participants. The most prevalent sleep disorder in our group was related to difficulties with sleep onset and sleep maintenance (~ 41% of cases). Sleep disturbances were predicted by higher intelligence quotient (IQ)/developmental quotient (DQ), increased internalizing problems, and elevated parental stress. The subgroup of participants engaged in co-sleeping (N = 87) were younger and had lower IQ/DQ scores, reduced adaptive functioning, and diminished psychological wellbeing than the non-co-sleeping group. Our findings are consistent with the current literature highlighting that insomnia is the most widespread sleep problem associated with ASD. The relationship between IQ/DQ and sleep alterations is a crucial topic that deserves additional research. Future studies should assess sleep by objective measures such as EEG topography to better understand the mechanisms underlying sleep alterations in this neurodevelopmental disorder.

Developing a phenotype risk score for tic disorders in a large, clinical biobank

Tics are a common feature of early-onset neurodevelopmental disorders, characterized by involuntary and repetitive movements or sounds. Despite affecting up to 2% of children and having a genetic contribution, the underlying causes remain poorly understood. In this study, we leverage dense phenotype information to identify features (i.e., symptoms and comorbid diagnoses) of tic disorders within the context of a clinical biobank. Using de-identified electronic health records (EHRs), we identified individuals with tic disorder diagnosis codes. We performed a phenome-wide association study (PheWAS) to identify the EHR features enriched in tic cases versus controls (n = 1406 and 7030; respectively) and found highly comorbid neuropsychiatric phenotypes, including: obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, and anxiety (p < 7.396 × 10-5). These features (among others) were then used to generate a phenotype risk score (PheRS) for tic disorder, which was applied across an independent set of 90,051 individuals. A gold standard set of tic disorder cases identified by an EHR algorithm and confirmed by clinician chart review was then used to validate the tic disorder PheRS; the tic disorder PheRS was significantly higher among clinician-validated tic cases versus non-cases (p = 4.787 × 10-151; β = 1.68; SE = 0.06). Our findings provide support for the use of large-scale medical databases to better understand phenotypically complex and underdiagnosed conditions, such as tic disorders.

Stress in autism (STREAM): A study protocol on the role of circadian activity, sleep quality and sensory reactivity

Mental health issues are markedly increased in individuals with autism, making it the number one research priority by stakeholders. There is a crucial need to use personalized approaches to understand the underpinnings of mental illness in autism and consequently, to address individual needs. Based on the risk factors identified in typical mental research, we propose the following themes central to mental health issues in autism: sleep difficulties and stress. Indeed, the prevalence of manifold circadian disruptions and sleep difficulties in autism, alongside stress related to sensory overload, forms an integral part of autistic symptomatology. This proof-of-concept study protocol outlines an innovative, individualised approach towards investigating the interrelationships between stress indices, sleep and circadian activation patterns, and sensory sensitivity in autism. Embracing an individualized methodology, we aim to collect 14 days of data per participant from 20 individuals with autism diagnoses and 20 without. Participants' sleep will be monitored using wearable EEG headbands and a sleep diary. Diurnal tracking of heart rate and electrodermal activity through wearables will serve as proxies of stress. Those objective data will be synchronized with subjective experience traces collected throughout the day using the Temporal Experience Tracing (TET) method. TET facilitates the quantification of relevant aspects of individual experience states, such as stress or sensory sensitivities, by providing a continuous multidimensional description of subjective experiences. Capturing the dynamics of subjective experiences phase-locked to neural and physiological proxies both between and within individuals, this approach has the potential to contribute to our understanding of critical issues in autism, including sleep problems, sensory reactivity and stress. The planned strives to provide a pathway towards developing a more nuanced and individualized approach to addressing mental health in autism.

Assessment of autonomic symptom scales in patients with neurodevelopmental diagnoses using electronic health record data

Background

Sleep disturbances, gastrointestinal problems, and atypical heart rate are commonly observed in patients with autism spectrum disorder (ASD) and may relate to underlying function of the autonomic nervous system (ANS). The overall objective of the current study was to quantitatively characterize features of ANS function using symptom scales and available electronic health record (EHR) data in a clinically and genetically characterized pediatric cohort.

Methods

We assessed features of ANS function via chart review of patient records adapted from items drawn from a clinical research questionnaire of autonomic symptoms. This procedure coded for the presence and/or absence of targeted symptoms and was completed in 3 groups of patients, including patients with a clinical neurodevelopmental diagnosis and identified genetic etiology (NPD, n=244), those with an ASD diagnosis with no known genetic cause (ASD, n=159), and age and sex matched controls (MC, n=213). Symptoms were assessed across four main categories: (1) Mood, Behavior, and Emotion; (2) Secretomotor, Sensory Integration; (3) Urinary, Gastrointestinal, and Digestion; and (4) Circulation, Thermoregulation, Circadian function, and Sleep/Wake cycles.

Results

Chart review scores indicate an increased rate of autonomic symptoms across all four sections in our NPD group as compared to scores with ASD and/or MC. Additionally, we note several significant relationships between individual differences in autonomic symptoms and quantitative ASD traits.

Conclusion

These results highlight EHR review as a potentially useful method for quantifying variance in symptoms adapted from a questionnaire or survey. Further, using this method indicates that autonomic features are more prevalent in children with genetic disorders conferring risk for ASD and other neurodevelopmental diagnoses.

Steganography for medical record image

Medical record images in EHR system are users' privacy and an asset, and there is an urgent need to protect this data. Image steganography can offer a potential solution. A steganographic model for medical record images is therefore developed based on StegaStamp. In contrast to natural images, medical record images are document images, which can be very vulnerable to image cropping attacks. Therefore, we use text region segmentation and watermark region localization to combat the image cropping attack. The distortion network has been designed to take into account the distortion that can occur during the transmission of medical record images, making the model robust against communication induced distortions. In addition, based on StegaStamp, we innovatively introduced FISM as part of the loss function to reduce the ripple texture in the steganographic image. The experimental results show that the designed distortion network and the FISM loss function term can be well suited for the steganographic task of medical record images from the perspective of decoding accuracy and image quality.

A practical approach to identifying autistic adults within the electronic health record

The electronic health record (EHR) provides valuable data for understanding physical and mental health conditions in autism. We developed an approach to identify charts of autistic young adults, retrieved from our institution's de-identified EHR database. Clinical notes within two cohorts were identified. Cohort 1 charts had at least one International Classification of Diseases (ICD-CM) autism code. Cohort 2 charts had only autism key terms without ICD-CM codes, and at least four notes per chart. A natural language processing tool parsed medical charts to identify key terms associated with autism diagnoses and mapped them to Unified Medical Language System Concept Unique Identifiers (CUIs). Average scores were calculated for each set of charts based on captured CUIs. Chart review determined whether patients met criteria for autism using a classification rubric. In Cohort 1, of 418 patients, 361 were confirmed to have autism by chart review. Sensitivity was 0.99 and specificity was 0.68 with positive predictive value (PPV) of 0.97. Specificity improved to 0.81 (sensitivity was 0.95; PPV was 0.98) when the number of notes was limited to four or more per chart. In Cohort 2, 48 of 136 patients were confirmed to have autism by chart review. Sensitivity was 0.95, specificity was 0.73, and PPV was 0.70. Our approach, which included using key terms, identified autism charts with high sensitivity, even in the absence of ICD-CM codes. Relying on ICD-CM codes alone may result in inclusion of false positive cases and exclusion of true cases with autism.

Investigating the genetic pathways of insomnia in Autism Spectrum Disorder

BACKGROUND

Sleep problems are common in children with autism spectrum disorder (autism). There is sparse research to date to examine whether insomnia in people with autism is related to autism genetics or insomnia genetics. Moreover, there is a lack of research examining whether circadian-rhythm related genes share potential pathways with autism.

AIMS

To address this research gap, we tested whether polygenic scores of insomnia or autism are related to risk of insomnia in people with autism, and whether the circadian genes are associated with insomnia in people with autism.

METHODS AND PROCEDURES

We tested these questions using the phenotypically and genotypically rich MSSNG dataset (N = 1049) as well as incorporating in the analyses data from the Vanderbilt University Biobank (BioVU) (N = 349).

OUTCOMES AND RESULTS

In our meta-analyzed sample, there was no evidence of associations between the polygenic scores (PGS) for insomnia and a clinical diagnosis of insomnia, or between the PGS of autism and insomnia. We also did not find evidence of a greater burden of rare and disruptive variation in the melatonin and circadian genes in individuals with autism and insomnia compared to individuals with autism without insomnia.

CONCLUSIONS AND IMPLICATIONS

Overall, we did not find evidence for strong effects of genetic scores influencing sleep in people with autism, however, we cannot rule out the possibility that smaller genetic effects may play a role in sleep problems. Our study indicated the need for a larger collection of data on sleep problems and sleep quality among people with autism.