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. Author manuscript; available in PMC: 2023 Oct 1.
Published in final edited form as: Am J Med Genet A. 2022 Aug 5;188(10):3041–3048. doi: 10.1002/ajmg.a.62921

Sleep disturbance is a common feature of Kabuki syndrome

Tyler Rapp 1, Allison J Kalinousky 2, Jennifer Johnson 3, Hans Bjornsson 2,4,5, Jacqueline Harris 2,3,*
PMCID: PMC9474613  NIHMSID: NIHMS1825603  PMID: 35930004

Abstract

Kabuki syndrome (KS) is a rare epigenetic disorder caused by heterozygous loss of function variants in either KMT2D (90%) or KDM6A (10%), both involved in regulation of histone methylation. While sleep disturbance in other Mendelian disorders of the epigenetic machinery have been reported, no study has been conducted on sleep in KS. This study assessed sleep in 59 participants with KS using a validated sleep questionnaire. Participants ranged in age from 4 to 43 years old with 86% of participants having a pathogenic variant in KMT2D. In addition, data on adaptive function, behavior, anxiety, and quality of life were collected using their own respective questionnaires. Some form of sleep issue was present in 71% of participants, with night-waking, daytime sleepiness, and sleep onset delay being the most prevalent. Sleep dysfunction was positively correlated with maladaptive behaviors, anxiety levels, and decreasing quality of life. Sleep issues were not correlated with adaptive function. This study establishes sleep disturbance as a common feature of KS. Quantitative sleep measures may be a useful outcome measure for clinical trials in KS. Further, clinicians caring for those with KS should consider sleep dysfunction as an important feature that impacts overall health and wellbeing in these patients.

Keywords: epigenetics, neurodevelopmental, KMT2D, MLL2, KDM6A

INTRODUCTION

Kabuki syndrome (KS; Niikawa-Kuroki syndrome) is a Mendelian disorder of the epigenetic machinery (MDEM) characterized by intellectual disability, postnatal growth deficiency, and multisystem anomalies (Niikawa et al., 1988). KS can be diagnosed clinically or based on a pathogenic variant in either KMT2D (Kabuki syndrome type 1, KS1; NIM#147920) or KDM6A (Adam et al., 2019). KMT2D is a histone methyltransferase that adds mono- and trimethylation to the fourth lysine (K4) of histone 3, promoting open chromatin. KDM6A encodes a histone demethylase that removes H3K27me3, a closed chromatin mark (Bjornsson et al., 2014).

Kabuki syndrome is a quintessential example of a Mendelian disorder of the epigenetic machinery (MDEM), a rapidly emerging group of diseases that together account for a large proportion of genetic intellectual disability (Fahrner and Bjornsson, 2019). Individuals with KS have a very specific cognitive profile characterized by generally mild intellectual disability with visuospatial perception and memory far more impaired than other areas of cognition, while language is a relative strength (Harris et al., 2019). While we know more about the cognitive profile associated with KS, less is known about other issues associated with the syndrome.

Many individuals with a variety of specific genetic neurodevelopmental disorders experience higher rates of sleeping problems (Agar et al., 2021). It is known that some children with certain MDEMs have immense issues with sleep. For example, children with Rubinstein-Taybi syndrome or Wiedemann-Steiner syndrome have been reported to have high rates of obstructive sleep apnea (Sheppard et al., 2021; Zucconia et al., 1993), while individuals with KAT6A-associated neurodevelopmental disorder or Sotos syndrome have increased rates of behavioral sleep concerns (Smith and Harris, 2021; Stafford et al., 2021). However, no studies have been conducted specifically investigating sleep and sleep disorders in KS. Understanding sleep in this condition is important both for the clinical care of patients as well as for advancement of the field. Preclinical studies in Kabuki syndrome have shown that some neurological and functional deficits can be rescued postnatally (Bjornsson et al., 2014; Zhang et al., 2021). As such, if sleep disturbances were found to be a specific issue, this could be used as a potential outcome measure for targeted treatment. Our study therefore aimed to investigate the frequency and type of sleep issues in individuals with Kabuki syndrome. We also collected data on adaptive function, behaviors, anxiety, and quality of life (QOL) in these individuals given the interrelationship between all these domains and sleep in neurodevelopmental disorders.

METHODS

Participants

Fifty-nine participants with Kabuki syndrome completed this study. Participants were recruited from the Epigenetics Clinic at Kennedy Krieger Institute or the Epigenetics and Chromatin Clinic at Johns Hopkins Hospital, as well as those who previously consented to be contacted for further research. Participants were 4 years of age or older. Fifty-seven of the participants had genetic variants that were pathogenic or likely pathogenic by ACMG-AMP criteria. Two of the participants had genetic variants of uncertain significance that were confirmed to cause Kabuki syndrome by Episign Variant testing. The study was approved by the Johns Hopkins Medical Institutions Institutional Review Board. Each participant underwent a written informed consent process.

Sleep and Behavior Data Collection

All participants completed a Modified Simonds & Parraga Sleep Questionnaire (MSPSQ). The MSPSQ is a version of an original sleep questionnaire created by Sigmond and Parraga (Simonds and Parraga, 1982, 1984). This modified version has been expanded for use in individuals with intellectual and developmental disabilities (Wiggs and Stores, 1998). The survey consisted of open ended, dichotomous (“yes/no”), and Likert-scaled questions measuring sleep qualities and problems over the past one month. The first section focused on sleep environment, routines, and sleep quality. This was followed by 30-Likert scaled questions assessing sleep behaviors. The last section concerns questions about seeking treatment, perceived sleep problems, and family history of sleep issues. Previous studies have showcased the utility of the MSPSQ in assessing sleep in populations with neurodevelopmental disorders as well as established metrics for scoring. Consistent with these established parameters, the Likert-scaled questions were compiled to give a total sleep score that ranged from a possible minimum score of 36 to a maximum score of 180 (Johnson et al., 2012). The total score was subdivided into seven sleep domains: bedtime resistance, sleep onset delay, sleep anxiety, night-waking, parasomnias, sleep disordered breathing, and daytime sleepiness. Johnson et al. previously determined that a cutoff score of 56 was sufficiently sensitive and specific for identifying children with sleep problems (Johnson et al., 2012) and this cutoff was used in the present study as well.

For comparison, participants completed measures about adaptive functioning, behavior, anxiety, and quality of life. All participants completed an age-appropriate Child Behavior Checklist (CBCL) or Adult Behavior Checklist (ABCL) (Achenbach and Rescorla, 2000, 2001, 2003) and an Adaptive Behavior Assessment System-Third Edition (ABAS-3) (Harrison and Oakland, 2000). For child participants (age <18), parents/guardians also completed the SCARED assessment (Birmaher et al., 1997) and the PROMIS assessment (Pilkonis et al., 2011). For adult participants (age ≥18), the participant or their parents/guardians completed the Glasgow Anxiety Scale for adults with intellectual disability (GAS-ID) assessment (Espie et al., 2003) and a Health-Related Quality of Life questionnaire (Forrest et al., 2018).

Statistical Analysis

Differences between groups (i.e., those with variants in KMT2D versus those with variants in KDM6A and children versus adults) were performed using two-tailed unpaired t-tests at a significance level of 0.05. Correlation analyses were performed to analyze relationships between the various questionnaire results. These were performed using GraphPad Prism 9 (“GraphPad Prism,” n.d.). The hypothesis tests were two-sided. Reported p-values were corrected with a Bonferroni correction for multiple comparisons at a significance level of 0.05.

RESULTS

Participants

Fifty-nine participants with molecularly confirmed KS were included in this study. The median age was 13 years old (range 4-43 years). Twenty-eight participants (47.5%) were male, with the remaining females. Fifty-one participants have variants in the KMT2D gene, and eight participants have variants in the KDM6A gene (table 1). Detailed clinical and molecular information is included in Supplementary Table 1.

Table 1.

Characteristics of study participants.

All Participants
(n = 59)		 KMT2D
(n = 51)		 KDM6A
(n = 8)
n (%) n (%) n (%)
Sex
  Male 28 (47.5%) 27 (52.9%) 1 (12.5%)
  Female 31 (52.5%) 24 (47.1%) 7 (87.5%)
Age (median, range) 13 (4-43) 13 (4-43) 9 (6-19)
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Prevalence and Specificity of Sleep Disturbances in Individuals with KS

Sleep problems were highly prevalent among this cohort of participants with KS. The average overall score was 67.2 ± 16.2 with a range of 41-116. Forty-two (71.2%) participants scored above the cutoff threshold of 56, indicating clinically significant sleep dysfunction (figure 1).

Figure 1. Distribution of MSPSQ sleep scores by age of participants.

Figure 1.

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Forty-two participants scored at or above the threshold of 56. Triangles represent individuals with the KMT2D mutation (n = 51), circles represent individuals with the KDM6A mutation (n = 8), and the squares represent participants in which a mutation was not reported (n = 1).

Seven specific sleep domains were analyzed within the questionnaire at large (table 2). The average score for bedtime resistance was 10.2 ± 4.3 with a possible score of 5-25. Sleep onset delay had an average score of 2.2 ± 1.0 with a possible score of 1-5. Sleep anxiety scores averaged 10.2 ± 3.7 with a possible score of 5-25. Night-waking scores averaged 4.9 ± 2.2 with a possible score of 2-10. The average score for parasomnias was 18.4 ± 5.9 with a possible score of 11-55. Sleep disordered breathing had an average score of 9.1 ± 3.5 with a possible score of 5-25. Lastly, the average score of daytime sleepiness was 4.6 ± 2.5 with a possible score of 2-10. The average score for use of sleep medications in this population was 2.2 ± 1.8, corresponding to a little over once a month, with a possible score of 1 (never) to 5 (many times a week or daily).

Table 2.

Sleep characteristics among study participants.

All
Participants		 KMT2D
(n = 51)		 KDM6A
(n = 8)
Total Score (36-180) 1 66.9 ± 16.2 64.7 ± 14.1 75.5 ± 25.1
Bedtime Resistance (5-25) 10.2 ± 4.3 9.5 ± 3.5 13.2 ± 7.6
 Resists/struggles at bedtime 2.1 ± 1.5 2.2 ± 1.5 1.3 ± 0.5
 Afraid2 1.3 ± 0.9 1.4 ± 1.0 1.1 ± 0.4
 Insists on not sleeping in his/her bed2 1.4 ± 1.0 1.4 ± 1.1 1.3 ± 0.7
 Insists on bedtime rituals2 3.7 ± 1.8 3.9 ± 1.7 3.5 ± 2.1
 Reluctant to sleep 1.8 ± 1.3 1.9 ± 1.5 1.5 ± 0.8
Sleep Onset Delay (1-5) 2.2 ± 1.0 2.1 ± 1.1 2.6 ± 1.1
 Time to fall asleep 2.2 ± 1.0 2.1 ± 1.1 2.6 ± 1.1
Sleep Anxiety (5-25) 10.2 ± 3.7 9.9 ± 3.0 12.5 ± 5.9
 Afraid2 1.3 ± 0.9 1.4 ± 1.0 1.1 ± 0.4
 Fear he/she might die 1.0 ± 0.2 1.0 ± 0.4 1.0 ± 0.0
 Insists on not sleeping in his/her bed2 1.4 ± 1.0 1.4 ± 1.1 1.3 ± 0.7
 Needs security object 2.9 ± 2.0 3.0 ± 2.0 2.6 ± 2.0
 Insists on bedtime rituals2 3.7 ± 1.8 3.9 ± 1.7 3.5 ± 2.1
Night-Waking (2-10) 4.9 ± 2.2 4.6 ± 2.2 5.0 ± 2.1
 Frequency of night wakings 3.2 ± 1.6 3.2 ± 1.5 2.4 ± 1.7
 Time to fall back asleep 1.7 ± 1.0 1.7 ± 1.0 1.5 ± 1.1
Parasomnias (11-55) 18.4 ± 5.9 18.1 ± 5.7 18.9 ± 6.1
 Talks in sleep 1.7 ± 1.2 1.7 ± 1.3 1.3 ± 0.7
 Walks in sleep 1.1 ± 0.3 1.1 ± 0.4 1.0 ± 0.0
 Grinds teeth in sleep 1.9 ± 1.3 1.9 ± 1.3 1.6 ± 1.2
 Bangs head at night 1.1 ± 0.5 1.1 ± 0.5 1.1 ± 0.4
 Has quick movements of arms/legs 2.2 ± 1.6 2.3 ± 1.7 1.4 ± 0.7
 Moves around in sleep 3.0 ± 1.7 2.9 ± 1.6 3.0 ± 1.8
 Bites tongue during sleep 1.0 ± 0.0 1.0 ± 0.0 1.0 ± 0.0
 Wets bed 2.2 ± 1.7 2.3 ± 1.7 1.9 ± 1.8
 Wakes with nightmares 1.2 ± 0.4 1.2 ± 0.4 1.0 ± 0.0
 Wakes screaming in terror 1.1 ± 0.4 1.1 ± 0.4 1.1 ± 0.4
 Sweats a lot during sleep 2.2 ± 1.6 2.3 ± 1.7 1.6 ± 1.4
Sleep Disordered Breathing (5-25) 9.1 ± 3.5 8.7 ± 3.4 9.0 ± 3.5
 Snores loudly 1.7 ± 1.3 1.7 ± 1.3 1.3 ± 0.5
 Apneic episodes up to 30 seconds 1.3 ± 1.0 1.5 ± 1.0 1.0 ± 0.0
 Sleeps with head tipped back 1.6 ± 1.2 1.7 ± 1.2 1.5 ± 1.4
 Breathes through mouth 2.9 ± 1.7 2.8 ± 1.7 2.8 ± 1.7
 Complains of headaches upon waking 1.5 ± 0.9 1.5 ± 1.0 1.1 ± 0.4
Daytime Sleepiness (2-10) 4.6 ± 2.5 4.3 ± 2.4 5.9 ± 3.4
 Drowsy during day 2.1 ± 1.5 2.2 ± 1.5 1.1 ± 0.4
 More active than others during day 2.5 ± 1.9 2.4 ± 1.8 3.0 ± 2.1
Requires Sleep Medication 2.2 ± 1.8 2.4 ± 1.8 2.0 ± 1.9
Yes/No
 Self-Reported Sleep Problem 16/59 (27.1%) 14/51 (27.5%) 1/8 (12.5%)
 Sought Treatment for Sleep 21/59 (35.6%) 18/51 (35.2%) 3/8 (37.5%)
 Others Affected by Sleep Issues 22/59 (37.3%) 20/51 (39.2%) 2/8 (25.0%)
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1

1 = never, 2 = once a month, 3 = a few times a month, 4 = once or twice a week, 5 = many times a week or daily

2

Repeated across several sleep domains

Three other clinically important data points were analyzed from the questionnaire. Sixteen of the 59 respondents (27.1%) considered themselves to have a sleep problem. Even more, 21 out of the 59 participants (35.6%) have sought medical advice or treatment for sleep-related problems. Lastly, 22 out of the 59 participants (37.3%) denoted that others in the household are negatively affected by their sleep-related problems.

There was no correlation between the total sleep score and age. When dichotomized into children (<18 years old) and adults (≥18 years old), the average scores were 66.3 and 69.0, respectively. In addition, there was no difference between males (average of 67.4) and females (average of 66.0). There was also no significant difference between participants with KS1 (average of 64.7) and participants with KS2 (average of 75.5).

Sleep Correlations with Other Neurobehavioral Domains

Fifty-three participants completed the ABAS-3 questionnaire assessing adaptive function. Neither the total adaptive function score nor any individual domain of adaptive functioning was correlated with the total sleep score (figure 2). Additionally, none of the individual adaptive functioning subset scores (social, conceptual, and practical functioning) were correlated with the total sleep score. Neither the individual sleep domain scores nor the use of sleep medication were correlated with any of the ABAS-3 domain or composite scores.

Figure 2. Correlation plots between MSPSQ sleep scores and adaptive function scores.

Figure 2.

Figure 2.

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(a) Pearson correlation plot between total sleep scores and total ABAS-3 scores. No significant correlation was found (p = 0.67); (b) Pearson correlation plot between the total sleep score and the ABAS-3 social functioning score. No significant correlation was found (p = 0.51); (c) Pearson correlation plot between the total sleep score and the ABAS-3 practical functioning score. No significant correlation was found (p = 0.96); (d) Pearson correlation plot between the total sleep score and the ABAS-3 conceptual functioning score. No significant correlation was found (p = 0.80).

Fifty participants completed the CBCL or the ABCL depending on participant’s age. We found a positive correlation between the total sleep score and the total behavior score (r = 0.56, p = 0.001) with more behavioral problems correlated with increased sleep problems. The total sleep score was also positively correlated with each of the behavior subset scores, internalizing problems (r = 0.48, p = 0.005) and externalizing problems (r = 0.57, p = 0.001) (figure 3A-C). For the sleep domains, bedtime resistance was correlated with externalizing problems (r = 0.41, p = 0.03). The total night-waking score was correlated with the total behavior score (r = 0.42, p = 0.03). Parasomnias were correlated with the total behavior score (r = 0.42, p = 0.02), internalizing problems (r = 0.39, p = 0.05), and externalizing problems (r = 0.41, p = 0.03). For each of these correlations, more sleep dysfunction was correlated with more behavioral issues. In addition, the use of more sleep medications was correlated with externalizing problems (r = 0.36, p = 0.01).

Figure 3. Graphical representation of sleep survey results compared to behavioral survey results.

Figure 3.

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(a) Pearson correlation plot between the total MSPSQ sleep score and total CBCL/ABCL behavior score shows significant positive correlation (p = 0.001); (b) Pearson correlation plot between the total sleep score and the CBCL/ABCL internalizing problems score shows significant positive correlation (p = 0.005); (c) Pearson correlation plot between the total sleep score and the CBCL/ABCL externalizing problems score shows a significant positive correlation (p = 0.001).

It has been previously reported that anxiety is specific neurobehavioral feature of individuals with KS. When comparing the 56 participant’s SCARED/GAS-ID anxiety scores with their sleep scores, we found that sleep issues and anxiety were positively correlated (r = 0.40, p = 0.002). The relationship between the total sleep score and the anxiety score is shown in Figure 4A. The use of sleep medications and anxiety were not correlated. Fifty-five participants completed the QOL questionnaire. There was a negative correlation between total sleep behaviors and the QOL metric (r = −0.21, p = 0.14) (figure 4B); however, this was not a statistically significant finding.

Figure 4. Correlation plots between sleep scores and SCARED/GAS-ID anxiety scores and quality of life scores.

Figure 4.

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(a) Pearson correlation plot between the total MSPSQ sleep score and the SCARED/ GAS-ID anxiety score. Significant correlation was found (p = 0.002). (b) Pearson correlation plot between sleep and percent quality of life scores. Significant negative correlation was not found (p = 0.14).

DISCUSSION

It is known that children with MDEMs commonly have problems with sleep (Agar et al., 2021; Smith & Harris, 2021; Stafford et al., 2021; Zucconia et al., 1993). This is the first study to specifically evaluate the characteristics and prevalence of sleep disturbance in Kabuki syndrome. We have found that nearly three-quarters of individuals with KS have sleep issues, which is much higher than the expected prevalence in general intellectual disability (8.5%-42%) (Agar et al., 2021). Interestingly, the prevalence of sleep disturbances in this group of KS participants, as determined by the MSPSQ cutoff, is higher than the number of individuals who consider themselves to have a sleep problem (27.1%) or that have sought medical advice or treatment (35.6.%). This could potentially indicate that respondents have a prior expectation of sleep disturbances given being diagnosed with a MDEM or that they have a higher threshold for labeling something a problem given their child with a genetic disorder and neurodevelopmental disability.

Despite this, our data indicate that sleep problems have a major effect on individuals with KS. There was a strong positive correlation between total sleep problems and externalizing, internalizing and total behavior scores on the CBCL/ABCL in a population that overall has low rates of maladaptive behaviors. This suggests that sleep problems affect daytime behaviors in this population and that sleep should be carefully investigated in any child with KS presenting with major behavioral issues. These relationships have been demonstrated and explored in other studies in individuals with intellectual disabilities and genetic disorders (Bornstein et al., 2013; Stein et al., 2001). For example, in children with Down syndrome, sleep problems may exacerbate behavioral problems and affect quality of life among patients and their caregivers (Esbensen et al., 2018). In our population, while not statistically significant, sleep problems correlate with worsening QOL in these individuals, which once again suggests the need for careful attention to sleep by clinicians caring for those with KS. The increase in behavioral problems and reduction in QOL in these patients also correlates mainly with sleep problems aside from sleep-disordered breathing, which is a known problem in KS. This further emphasizes the need for a careful and comprehensive sleep history that goes beyond assessing for just sleep apnea.

Sleep problems in KS also strongly correlate with anxiety in this population which has recently been shown to be a specific feature of KS (Kalinousky et al., 2022). As anxiety in general is correlated with sleep dysfunction (Staner, 2003), it is difficult to know whether the anxiety is causing sleep problems in KS, especially given the high rates of sleep anxiety and bedtime resistance that this study found, or if sleep problems at night are contributing to the anxiety phenotype during the day. Either way, addressing both the sleep issues and the anxiety is very important for patients with KS. Additionally, both may prove to be important outcome measures for clinical trials in this syndrome.

Importantly, there was no difference in the total sleep score among variant nor sex. We found that there was no correlation with age, despite some studies having found that sleep disturbances decrease with age (Stein et al., 2001). Our data suggests that sleep disturbance in KS is a more uniform complication rather than an aspect that affects specific subpopulations within the disorder. What is more, there was no correlation between sleep problems and adaptive function, which suggests that the sleep disturbance is a function of having KS rather than being associated with the degree of cognitive impairment. Hippocampal involvement, specifically of the dentate gyrus, has been implicated in the pathophysiology of KS, with particularly marked impairment of visuospatial functioning and memory (Harris et al., 2019). While there are no known studies of visuospatial functioning and its relation to sleep in any genetic disorder population, one study did find a strong association between impaired visuospatial functioning and increased night-waking and difficulties falling asleep among patients with Parkinson’s disease (Specketer et al., 2019). In addition, it is well known that the hippocampus is vital in memory consolidation during sleep (Koyanagi et al., 2019). In patients with KS, sleep dysfunction could, in part, exacerbate their impaired memory profile, although further investigation would be required to say definitively.

While this study is important in establishing sleep as a problem among patients with KS, there are several limitations. For one, sleep was explored using a questionnaire. While this assessment is validated and used extensively among individuals with intellectual disability and genetic disorders, it is a subjective assessment. Also, this was completed largely by caregivers and as such could potentially miss some of the sleep problems in these individuals or some of the impact of the sleep dysfunction. Next, there was not a control group of neurotypical individuals to directly compare the scores. Since KS is a rare disease, the sample size was small, particularly among the KS2 subgroup. Future studies evaluating sleep in individuals with KS would benefit from the use of actigraphy and polysomnography, as well as more detailed and objective neuropsychological testing to best characterize these sleep disturbances. The authors are in possession of formal polysomnograms for five of the 59 individuals in this study. Those five individuals all scored above the 56 cutoff and were all above the median score (66.9 ± 16.2) in our cohort. They were all adult participants with the KMT2D mutation and four out of the five indicated they felt there were sleep problems. Two of the five individuals have obstructive sleep apnea based on their formal sleep studies; however, their total sleep survey scores were not particularly different from the others without the sleep apnea diagnosis. Two of the five had abnormal sleep cycling and excessive arousals and two others of the five studies were read as normal. A larger sample size would be necessary to deduce any statistical and clinically significant conclusions, but it is nonetheless interesting to know five individuals with above average sleep disturbance scores have sought clinical testing and supports the suggestion that further formal investigation is needed in this population and that actigraphy may be a good measure in addition to full polysomnograms.

Given the potential for the postnatal rescue of some deficits of KS, full understanding of the neurobehavioral phenotype in these individuals is key. This study demonstrated that sleep issues are prevalent among KS, with a high frequency of night-waking, daytime sleepiness, and sleep onset delay. As such, quantitative sleep parameters should be considered as an outcome measure in future studies assessing and attempting postnatal rescuing with treatment. In addition, given the relationship between sleep and behavioral problems, anxiety, and QOL, sleep issues among these patients are important for treating clinicians to consider. With improvements in sleep, there is the potential for enhancements in other significant areas of the lives of those with KS, as well as the QOL of their caregivers and households.

Supplementary Material

supinfo

ACKNOWLEGEMENTS

The authors express their gratitude to all the families that participated in this study. We also thank the Kabuki Syndrome Foundation and All Things Kabuki who helped promote for this study.

FUNDING

This research received no direct external funding. H.B. is supported by grants from the Louma G. Foundation, the Icelandic Research Fund (#217988, #195835, #206806) and the Icelandic Technology Development Fund (#2010588). J.H. is supported by grants from the NIH/NICHD K23HD101646, the Kabuki Syndrome Foundation, the Rubinstein-Taybi Syndrome Children’s Foundation, the Sekel-Breidenstein Family Fund, and the Kennedy Krieger IDDRC NIH P50HD103538.

Footnotes

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

ETHICS STATEMENT

Approval for this study was obtained from Johns Hopkins Medical Institutions Institutional Review Board. All procedures in this study were in compliance with the guidelines of the 1975 Helsinki Declaration.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supinfo
NIHMS1825603-supplement-supinfo.pdf (82KB, pdf)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon request.

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