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Predictors of Quality of Life in Youth with Fragile X Syndrome
Background
1. Bailey D, Raspa M, Olmsted M, Holiday D. “Co-occurring conditions associated with FMR1
gene variations: Findings from a national parent survey.” Am J Med Genet. 2008; 146A(16):
2060-2069.
2. Crawford D, Acuña J, Sherman S. “FMR1 and the Fragile X Syndrome: Human Genome
Epidemiology Review.” Genet Med. 2001; 3(5): 359–371.
3. Garber K, Visootsak J, Warren S. "Fragile X Syndrome." Eur J Hum Genet. 2008; 16(6):
666-72.
4. Kuhlthau, Karen, et al. "Health-related quality of life in children with autism spectrum
disorders: Results from the autism treatment network." Journal of autism and developmental
disorders 40.6 (2010): 721-729.
5. (PedsQL. (2016). The PedsQL Measurement Model for the Pediatric Quality of Life
Inventory. Retrieved from http://pedsql.org/about_pedsql.htm.
6. Turner G, Webb T, Wake S, Robinson H. “Prevalence of fragile X syndrome.” Am J Med
Genet. 1996; 64(1): 196–197.
7. [Untitled illistration of FXS inheritance]. Retrieved from
http://cornellbiochem.wikispaces.com/Fragile+X+Syndrome.
8. [Untitled illistration of FXS chromosome]. Retrieved from http://imgarcade.com/1/fragile-x-
syndrome-symptoms-in-girls/.
9. Varni, James W., Michael Seid, and Paul S. Kurtin. "PedsQL™ 4.0: Reliability and validity
of the Pediatric Quality of Life Inventory™ Version 4.0 Generic Core Scales in healthy and
patient populations." Medical care 39.8 (2001): 800-812.
10. Varni, James W., Tasha M. Burwinkle, and Mariella M. Lane. "Health-related quality of life
measurement in pediatric clinical practice: an appraisal and precept for future research and
application." Health and quality of life outcomes 3.1 (2005): 1.
Table 2. Partial Correlations between Mean Child and Family
Dimensions of HR-QOL and Outcome Measures Controlling for Age
and Sex. Numbers in purple text indicate strong significant correlations,
while numbers in green text indicate mild significant correlations.
This research is supported by Cincinnati Children’s Hospital
Medical Center (CCHMC) and was conducted within the
Behavioral and Developmental Neuropsychiatry (BDNP) group
within the Division of Psychiatry, CCHMC.
Emma Fox, Ryan Adams, PhD, Ernest V. Pedapati, MD, Logan K. Wink, MD, Hilary Meyer, BA, Kaela O’Brien, BA, Sihame Amlal, Catherine Buck, Craig A. Erickson, MD
Major Findings
• Social and School QOL are areas of reduced QOL in youth
with FXS compared to Physical and Emotional Domains.
• Mean Child QOL and each domain of HR-QOL are strongly
correlated in youth with FXS.
• High levels of irritability marked by aggression and self-
injury negatively correlate with Family QOL in families of
youth with FXS.
• Significant social impairment negatively correlates with QOL
in youth with FXS.
• Scores from phenotypic measures may be used to
identify HR-QOL-associated areas for targeted clinical
treatment.
Limitations
• Due to a small sample size (N=27), statistical results cannot
readily extrapolate to general population of FXS. However,
correlations found in analysis trend nicely with clinical
observations.
Further Experimentation
• Future studies should (1) include a larger sample size and
(2) be supplemented with phenotypic data such as IQ.
• Test-retest data is needed with the PedsQL in FXS before it
can be effectively used as a treatment outcome.
• Longitudinal PedsQL data is needed over time to
characterize any potential developmental trajectories of
QOL in persons with FXS and their families.
Purpose and Aims Results Discussion
Methods
References
Acknowledgements
Purpose and Immediate Aim:
To identify phenotypic predictors/correlates of QOL in youth with
FXS.
Long-Term Aim:
To gather a large body of data on phenotypic correlates of QOL in
FXS and use this to quantify treatment progress in areas of
impairments and behaviors known to negatively affect QOL in this
population.
Figure 2. Research plan with immediate and long-term aims.
Identify
Phenotypic
Predictors of
QOL
Gather
Longitudinal
PedsQL Data
Deliver
Outcome-
Driven and
Patient-
Centered Care
Figure 3. Mean Differences
Between the Four Domains
of HR-QOL in PedsQL
Parent Report for Children
Across Age and Sex.
“**” and “*” designate
significant differences (p <
.05) between means found in
follow-up analyses as a result
of significant main and
interaction effects from within-
subjects ANOVA. “**”
superscript is significantly
larger than “*” superscript.
Larger values indicate higher
QOL.
Data Acquisition
Data was extracted from the IRB-approved Developmental
Disabilities Clinical Repository (DDCR) RedCap database at
CCHMC. This repository holds phenotypic data and peripheral
biological samples for individuals diagnosed with a developmental
disability (DD), their first degree relatives, and non-DD control
subjects.
• Sample: 27 individuals (18 males, 9 females) with FXS were
pulled from the DDCR for analysis. Ages ranged from 2.92-
21.08 years (M=11, SD= 5.5).
• Measures: Parents of each individual completed several
phenotypic measures during participation in DDCR research:
• PedsQL Parent Report for Children survey
• PedsQL Parent Report Family Impact Module survey
• Social Responsiveness Scale (SRS)
• Aberrant Behavior Checklist (ABC)
• Vineland Adaptive Behavior Scale (VABS)
Data Analysis
Descriptive statistics were generated from the collected body of
data. Repeated measures ANOVA testing and partial correlation
analyses were run where designated.
• Variables:
• Totals for each of the PedsQL Parent Report for Children
score domains (Physical, Emotional, Social, and School)
• PedsQL Parent Report for Children total score (Mean Child
QOL)
• PedsQL Parent Report Family Impact Module total score
(Family QOL)
• VABS Adaptive Behavior Composite score (Vineland
Adaptive)
• Total SRS score
• Totals for each of the 5 ABC score domains (Irritability,
Lethargy, Stereotype, Hyperactivity, and Inappropriate
Speech)
What is Fragile X Syndrome (FXS)?
• FXS is an X-linked dominant developmental disorder that affects
approximately 1/4000 males and 1/4000 to 1/8000 females [2,6].
• Symptoms most commonly associated with FXS include:
• Anxiety
• Attention deficit/hyperactivity disorder (ADHD)
• Obsessive-compulsive disorder (OCD)
• Intellectual Disability (ID)
• Self-injurious behavior
• Aggression towards others [1]
• FXS is caused by a CGG repeat in the 5’ untranslated region of the
FMR1 gene on the X chromosome [3].
Figure 1. (Left) Diagram of genetic inheritance for FXS . (Right) X
chromosome with yellow arrow indicating site of FMR1 mutation at
Xq27.3.
Why is Quality of Life Important?
• Use of health-related quality of life (HR-QOL) measures in clinical
practice has grown significantly in the last ten years [10].
• Numerous studies have been conducted surrounding HR-QOL in
populations with ID, but none have focused explicitly on individuals
with FXS. This is of particular importance when the challenging
behavioral symptoms that accompany FXS are taken into
consideration [4].
Why Use Pediatric Quality of Life Inventory (PedsQL)?
• PedsQL is used for assessing HR-QOL in healthy children and
children with chronic health concerns [5].
• PedsQL proved to be reliable and valid in clinical trials of populations
with acute and chronic diseases, implying that it is a useful device in
the measurement and subsequent improvement in HR-QOL in these
populations [9].
Why Find Phenotypic Correlates?
• Due to the high degree of variance in clinical presentation, accurately
assessing HR-QOL in the FXS population is difficult.
• Clinicians want to accurately tell families which behaviors in FXS
significantly correlate with enhanced or diminished HR-QOL, and
then aggressively treat those issues that negatively impact HR-QOL.
Table 1. Partial Correlations between Each of the Four Dimensions of
HR-QOL and Other Dimensions of Quality of Life and Phenotyping
Measures Controlling for Age and Sex. Numbers in purple text indicate
strong significant correlations, while numbers in green text indicate mild
significant correlations.
Dimensions of Health Quality of Life
Physical Emotional Social School
Mean Child QOL .83*** .70*** .61** .67***
Family QOL .03 .44* .38* .50*
Vineland Adaptive .60** .08 -.05 .08
Total SRS -.32* -.32 -.59** -.46**
ABC Irritability -.13 -.44* -.27 -.45*
ABC Lethargy -.29 -.20 -.49** -.49**
ABC Stereotype -.25 -.15 -.39* -.42*
ABC Hyperactivity .07 -.38* -.19 -.33*
ABC Inappropriate
Speech
.38* -.11 -.43* -.08
* p < .05; ** p <.01; *** p < .001
Quality of Life
Mean Child Family
Vineland Adaptive .41* .14
Total SRS -.53** -.35
ABC Irritability -.36 -.66***
ABC Lethargy -.46* .06
ABC Stereotype -.40* -.25
ABC Hyperactivity -.20 -.43*
ABC Speech .05 -.04
*p < .05. ** p <.01; *** p < .001

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  • 1. Predictors of Quality of Life in Youth with Fragile X Syndrome Background 1. Bailey D, Raspa M, Olmsted M, Holiday D. “Co-occurring conditions associated with FMR1 gene variations: Findings from a national parent survey.” Am J Med Genet. 2008; 146A(16): 2060-2069. 2. Crawford D, Acuña J, Sherman S. “FMR1 and the Fragile X Syndrome: Human Genome Epidemiology Review.” Genet Med. 2001; 3(5): 359–371. 3. Garber K, Visootsak J, Warren S. "Fragile X Syndrome." Eur J Hum Genet. 2008; 16(6): 666-72. 4. Kuhlthau, Karen, et al. "Health-related quality of life in children with autism spectrum disorders: Results from the autism treatment network." Journal of autism and developmental disorders 40.6 (2010): 721-729. 5. (PedsQL. (2016). The PedsQL Measurement Model for the Pediatric Quality of Life Inventory. Retrieved from http://pedsql.org/about_pedsql.htm. 6. Turner G, Webb T, Wake S, Robinson H. “Prevalence of fragile X syndrome.” Am J Med Genet. 1996; 64(1): 196–197. 7. [Untitled illistration of FXS inheritance]. Retrieved from http://cornellbiochem.wikispaces.com/Fragile+X+Syndrome. 8. [Untitled illistration of FXS chromosome]. Retrieved from http://imgarcade.com/1/fragile-x- syndrome-symptoms-in-girls/. 9. Varni, James W., Michael Seid, and Paul S. Kurtin. "PedsQL™ 4.0: Reliability and validity of the Pediatric Quality of Life Inventory™ Version 4.0 Generic Core Scales in healthy and patient populations." Medical care 39.8 (2001): 800-812. 10. Varni, James W., Tasha M. Burwinkle, and Mariella M. Lane. "Health-related quality of life measurement in pediatric clinical practice: an appraisal and precept for future research and application." Health and quality of life outcomes 3.1 (2005): 1. Table 2. Partial Correlations between Mean Child and Family Dimensions of HR-QOL and Outcome Measures Controlling for Age and Sex. Numbers in purple text indicate strong significant correlations, while numbers in green text indicate mild significant correlations. This research is supported by Cincinnati Children’s Hospital Medical Center (CCHMC) and was conducted within the Behavioral and Developmental Neuropsychiatry (BDNP) group within the Division of Psychiatry, CCHMC. Emma Fox, Ryan Adams, PhD, Ernest V. Pedapati, MD, Logan K. Wink, MD, Hilary Meyer, BA, Kaela O’Brien, BA, Sihame Amlal, Catherine Buck, Craig A. Erickson, MD Major Findings • Social and School QOL are areas of reduced QOL in youth with FXS compared to Physical and Emotional Domains. • Mean Child QOL and each domain of HR-QOL are strongly correlated in youth with FXS. • High levels of irritability marked by aggression and self- injury negatively correlate with Family QOL in families of youth with FXS. • Significant social impairment negatively correlates with QOL in youth with FXS. • Scores from phenotypic measures may be used to identify HR-QOL-associated areas for targeted clinical treatment. Limitations • Due to a small sample size (N=27), statistical results cannot readily extrapolate to general population of FXS. However, correlations found in analysis trend nicely with clinical observations. Further Experimentation • Future studies should (1) include a larger sample size and (2) be supplemented with phenotypic data such as IQ. • Test-retest data is needed with the PedsQL in FXS before it can be effectively used as a treatment outcome. • Longitudinal PedsQL data is needed over time to characterize any potential developmental trajectories of QOL in persons with FXS and their families. Purpose and Aims Results Discussion Methods References Acknowledgements Purpose and Immediate Aim: To identify phenotypic predictors/correlates of QOL in youth with FXS. Long-Term Aim: To gather a large body of data on phenotypic correlates of QOL in FXS and use this to quantify treatment progress in areas of impairments and behaviors known to negatively affect QOL in this population. Figure 2. Research plan with immediate and long-term aims. Identify Phenotypic Predictors of QOL Gather Longitudinal PedsQL Data Deliver Outcome- Driven and Patient- Centered Care Figure 3. Mean Differences Between the Four Domains of HR-QOL in PedsQL Parent Report for Children Across Age and Sex. “**” and “*” designate significant differences (p < .05) between means found in follow-up analyses as a result of significant main and interaction effects from within- subjects ANOVA. “**” superscript is significantly larger than “*” superscript. Larger values indicate higher QOL. Data Acquisition Data was extracted from the IRB-approved Developmental Disabilities Clinical Repository (DDCR) RedCap database at CCHMC. This repository holds phenotypic data and peripheral biological samples for individuals diagnosed with a developmental disability (DD), their first degree relatives, and non-DD control subjects. • Sample: 27 individuals (18 males, 9 females) with FXS were pulled from the DDCR for analysis. Ages ranged from 2.92- 21.08 years (M=11, SD= 5.5). • Measures: Parents of each individual completed several phenotypic measures during participation in DDCR research: • PedsQL Parent Report for Children survey • PedsQL Parent Report Family Impact Module survey • Social Responsiveness Scale (SRS) • Aberrant Behavior Checklist (ABC) • Vineland Adaptive Behavior Scale (VABS) Data Analysis Descriptive statistics were generated from the collected body of data. Repeated measures ANOVA testing and partial correlation analyses were run where designated. • Variables: • Totals for each of the PedsQL Parent Report for Children score domains (Physical, Emotional, Social, and School) • PedsQL Parent Report for Children total score (Mean Child QOL) • PedsQL Parent Report Family Impact Module total score (Family QOL) • VABS Adaptive Behavior Composite score (Vineland Adaptive) • Total SRS score • Totals for each of the 5 ABC score domains (Irritability, Lethargy, Stereotype, Hyperactivity, and Inappropriate Speech) What is Fragile X Syndrome (FXS)? • FXS is an X-linked dominant developmental disorder that affects approximately 1/4000 males and 1/4000 to 1/8000 females [2,6]. • Symptoms most commonly associated with FXS include: • Anxiety • Attention deficit/hyperactivity disorder (ADHD) • Obsessive-compulsive disorder (OCD) • Intellectual Disability (ID) • Self-injurious behavior • Aggression towards others [1] • FXS is caused by a CGG repeat in the 5’ untranslated region of the FMR1 gene on the X chromosome [3]. Figure 1. (Left) Diagram of genetic inheritance for FXS . (Right) X chromosome with yellow arrow indicating site of FMR1 mutation at Xq27.3. Why is Quality of Life Important? • Use of health-related quality of life (HR-QOL) measures in clinical practice has grown significantly in the last ten years [10]. • Numerous studies have been conducted surrounding HR-QOL in populations with ID, but none have focused explicitly on individuals with FXS. This is of particular importance when the challenging behavioral symptoms that accompany FXS are taken into consideration [4]. Why Use Pediatric Quality of Life Inventory (PedsQL)? • PedsQL is used for assessing HR-QOL in healthy children and children with chronic health concerns [5]. • PedsQL proved to be reliable and valid in clinical trials of populations with acute and chronic diseases, implying that it is a useful device in the measurement and subsequent improvement in HR-QOL in these populations [9]. Why Find Phenotypic Correlates? • Due to the high degree of variance in clinical presentation, accurately assessing HR-QOL in the FXS population is difficult. • Clinicians want to accurately tell families which behaviors in FXS significantly correlate with enhanced or diminished HR-QOL, and then aggressively treat those issues that negatively impact HR-QOL. Table 1. Partial Correlations between Each of the Four Dimensions of HR-QOL and Other Dimensions of Quality of Life and Phenotyping Measures Controlling for Age and Sex. Numbers in purple text indicate strong significant correlations, while numbers in green text indicate mild significant correlations. Dimensions of Health Quality of Life Physical Emotional Social School Mean Child QOL .83*** .70*** .61** .67*** Family QOL .03 .44* .38* .50* Vineland Adaptive .60** .08 -.05 .08 Total SRS -.32* -.32 -.59** -.46** ABC Irritability -.13 -.44* -.27 -.45* ABC Lethargy -.29 -.20 -.49** -.49** ABC Stereotype -.25 -.15 -.39* -.42* ABC Hyperactivity .07 -.38* -.19 -.33* ABC Inappropriate Speech .38* -.11 -.43* -.08 * p < .05; ** p <.01; *** p < .001 Quality of Life Mean Child Family Vineland Adaptive .41* .14 Total SRS -.53** -.35 ABC Irritability -.36 -.66*** ABC Lethargy -.46* .06 ABC Stereotype -.40* -.25 ABC Hyperactivity -.20 -.43* ABC Speech .05 -.04 *p < .05. ** p <.01; *** p < .001