Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 May 1.
Published in final edited form as: Am J Intellect Dev Disabil. 2023 May 1;128(3):254–268. doi: 10.1352/1944-7558-128.3.254

Health Effects of Sleep Quality in Premutation Carrier Mothers of Individuals with Fragile X Syndrome

Robert S Dembo 1,*, Jinkuk Hong 1, Leann Smith DaWalt 1, Elizabeth M Berry-Kravis 2, Marsha R Mailick 3
PMCID: PMC10506164  NIHMSID: NIHMS1924933  PMID: 37104861

Abstract

Sleep plays an integral role in supporting well-being, and sleep difficulties are common in mothers of individuals with developmental disabilities, including fragile X syndrome (FXS). This study assessed whether the effects of sleep quality on physical health and depression are exacerbated by genetic risk factors (CGG repeats) in FMR1 premutation carrier mothers of individuals with FXS. Poor sleep quality predicted a greater number of physical health conditions for mothers with CGG repeats in the mid-premutation range (90–110 repeats), but not for those in the lower (< 90 repeats) or higher (> 110 repeats) ends of the range. A significant association between poor sleep quality and maternal depressive symptoms was also observed, but there was no evidence that this effect varied by level of genetic vulnerability. This research extends our understanding of individual differences in the effects of sleep quality among mothers of individuals with FXS.

Keywords: Fragile X syndrome, FMR1 premutation, sleep quality, physical health conditions, depressive symptoms

Introduction

Sleep plays an integral role in supporting well-being. Previous research links sleep problems to a range of adverse physical health outcomes, including dementia (Sabia et al., 2021), cardiovascular disease (Sofi et al., 2014), obesity (Cappuccio et al., 2008), systemic inflammation (Irwin et al., 2016), as well an increased risk of multimorbidity (Nicholson et al., 2020). Longitudinal associations between poor sleep and depression (Zhai et al., 2015), anxiety (Jackson et al., 2014), and suicidal ideation (Pigeon et al., 2012) have also been reported, underscoring the influence of poor sleep in the development of psychopathology. Efforts to improve sleep quality have thus been recognized as an important public health priority (Hale et al., 2020).

Parents of children with intellectual and developmental disabilities (IDDs) report significantly worse sleep quality than other parents (da Estrela et al., 2021; Hodge et al., 2013) – a disparity that appears, in part, due to differential exposure to parenting stress (e.g., da Estrela et al., 2021). Past research indicates that variation in genetic liability may also help to explain differences in the effects of risk factors on health outcomes among mothers of children with certain IDDs, such as fragile X syndrome (FXS) (Hodge et al., 2011; Seltzer et al., 2012). The present study considers whether genetic vulnerability contributes to varying health impacts of sleep problems among mothers of individuals with FXS who themselves are premutation carriers of the FMR1 gene. Specifically, we examine whether the effects of poor sleep quality on health outcomes are moderated by genetic factors in a longitudinal cohort of premutation carrier mothers of adolescents and adults with FXS.

FXS and the FMR1 Premutation

Fragile X syndrome is a neurodevelopmental disorder that involves an expansion of more than 200 repeats of the cytosine-guanine-guanine (CGG) sequence of nucleotides comprising the 5’ untranslated region of the FMR1 gene on the X chromosome [formerly referred to as Fragile X Mental Retardation 1; recently renamed to Fragile X Messenger Ribonucleoprotein 1] (Brown, 2002; National Library of Medicine, 2022). The full mutation of the FMR1 gene causes FXS, which is the most prevalent hereditary cause of intellectual disability (Hagerman et al., 2009). Mothers of children with FXS may themselves have the full mutation, though they are much more frequently ‘premutation’ carriers of the FMR1 gene (defined as 55–200 CGG repeats). Premutation carriers were originally thought to be clinically unaffected, but there is increasing evidence that at least some carriers of the FMR1 premutation exhibit signs of impairment. Two widely recognized phenotypes associated with the premutation are Fragile X-associated Primary Ovarian Insufficiency (FXPOI), characterized by hot flashes, early menopause, and infertility, and Fragile X-associated Tremor Ataxia Syndrome (FXTAS), which involves executive function deficits, tremor, ataxia, and neuropathy (Wheeler et al., 2014). Up to 20% of female carriers under the age of 40 experience FXPOI (Allen et al., 2007). FXTAS is estimated to occur in 16% of female premutation carriers aged 50 or older (Rodriguez-Revenga et al., 2009). Beyond FXTAS and FXPOI, premutation carriers have been found in some studies to experience an elevated incidence of other conditions, such as migraines, immune and thyroid disorders (Wheeler et al., 2014), mental health difficulties (Roberts et al., 2016), executive function deficits (Famula et al., 2022), language dysfluencies (Bredin-Oja et al., 2021), and sleep problems (Chonchaiya et al., 2010; Hamlin et al., 2011; Summers et al., 2014).

CGG repeat length has been found in some studies to account for differential health risks among premutation carriers. While there is some evidence of a linear association, primarily with respect to FXTAS (e.g., Willemsen et al., 2011), several studies suggest that the relationship with CGG repeat length and other physical and psychological outcomes may be non-linear. In particular, CGG repeats in the mid-range (e.g., ~90 to 110 repeats) have been described as a zone of heightened vulnerability to stress and poor health relative to the lower (e.g., < 90 repeats) or higher (e.g., > 110 repeats) ends of the premutation range. For example, premutation carriers with mid-range CGG repeats have been found to be at a greater risk for depression (Roberts et al., 2016), FXPOI (Allen et al., 2021), earlier age of menopause (Mailick et al., 2014), and inhibition deficits (Klusek et al., 2020). Further, premutation carriers with mid-range CGG repeats may be more sensitive to the adverse effects of other risk factors, including challenging life events (Seltzer et al., 2012), perceived stress (Sansone, 2015), and older age (Klusek et al., 2020). In this study, we consider whether the health consequences of poor sleep quality differ by CGG repeat length among FMR1 premutation carrier mothers of individuals with FXS.

Sleep and Health in Parents of Children with IDDs

The challenges of caring for a child with an IDD are well-documented, and behavioral problems are cited as a uniquely difficult source of stress that can adversely affect parents’ physical and mental health (Bailey et al., 2012; Fielding-Gebhardt et al., 2020; Hartley et al., 2019). Many individuals with IDDs such as fragile X syndrome continue to display maladaptive behaviors into adolescence and adulthood (Smith et al., 2012), exposing parents to heightened stress over the life course. Prior research suggests that parents, especially mothers, of children with FXS have worse depression (e.g., Roberts et al., 2016) and face an elevated risk of physical health conditions and symptoms compared to the general population (e.g., Smith et al., 2012). Sleep problems are another common consequence of stress exposure (da Estrela et al., 2021), and some evidence suggests that mothers of children with FXS have worse sleep quality relative to controls (Chonchaiya et al., 2010). Although there is limited research examining the health impacts of sleep quality among mothers of children with FXS specifically, several prior studies have shown sleep quality to be a significant determinant of physical and mental health among mothers of children with other IDDs (Bourke-Taylor et al., 2012; Chu & Richdale, 2009; Hodge et al., 2013; Lovell et al., 2021).

Not all premutation carrier mothers of children with FXS exhibit health difficulties or sleep problems (Allen et al., 2020), however, and not all studies find an association between poor sleep and adverse outcomes (Meltzer, 2011). Among premutation carrier mothers of children with FXS, genetic differences related to CGG repeat length have been shown to be an important source of variation in their susceptibility to poor health (Wheeler et al., 2014) and in the negative health effects of risk factors (Hartley et al., 2012; Seltzer et al., 2012). Building on this past research, the goal of this study was to examine whether such genetic vulnerability moderates the association between poor sleep quality and health outcomes among premutation carrier mothers of children with FXS.

This Study and Hypotheses

Most prior research concerning sleep and health in mothers of children with IDDs is cross-sectional (e.g., Chu & Richdale, 2009; da Estrela et al., 2021). In the present study, we utilize longitudinal data collected 4 years apart to assess whether the effects of sleep quality on maternal health persist over time. After controlling for characteristics of the mothers (age, education) and the individuals with FXS (behavior problems, sleep difficulties) that have been shown to affect maternal sleep and health, we hypothesized that: (1) poor sleep quality would be associated with worse physical health and depressive symptoms in premutation carrier mothers of individuals with FXS; and (2) the effects of poor sleep quality on physical health and depressive symptoms would be worse for those with mid-range CGGs as compared to those with CGG repeats in the lower or higher end of the premutation range.

Methods

Participants

The sample for the present analysis was drawn from an ongoing longitudinal study of family adaptation to FXS (n=147); most participants (n = 135) were premutation carriers (Mailick et al., 2014). Recruitment criteria in the FXS study required mothers to be the biological parent of an adolescent or adult (≥ 12 years of age) with the FMR1 full mutation, and to either live with or have at least weekly contact with their child with FXS. Mothers provided medical records with genetic verification of their child’s full mutation of FXS. Participants lived in 38 states and were recruited through advertisements, support groups, disability organization listservs, and a research registry.

All mothers included in this report were carriers of the FMR1 premutation. The analysis utilized two time points of data, collected in 2012/2013 and 2016/2017. These waves were the third and fourth time points in the ongoing longitudinal study. We selected these time points to capture mothers at midlife and beyond, as many premutation-related health challenges are experienced with advancing age. At Time 3, all participants in the analytic sample were at least 40 years of age, more than half were over the age of 50, and nearly 20% were over the age of 60. The average duration between Time 3 and Time 4 was 3.9 years. The analytic sample was further restricted to co-residing dyads of premutation carrier mothers and their adolescent or adult children with FXS, which represented 84% of cases. Prior research has shown that maternal sleep quality and health are impacted by caregiving-related stressors, including child sleep difficulty and behavior problems (Bailey et al., 2012; Hodge et al., 2013). Hence, we focus on co-residing dyads because these risk factors are more proximal and relevant to mothers who live with their child with FXS than those who live in separate households. The final sample comprised 84 co-residing dyads of premutation carrier mothers and their adolescent or adult children with FXS.

Procedure

Data were collected from mothers via self-administered questionnaires and telephone interviews. The CGG repeat assay was conducted at the Rush University Medical Center Molecular Diagnostics Laboratory under the supervision of Elizabeth Berry-Kravis, MD, PhD. Mothers were provided with buccal swab kits and instructions to obtain their own genetic samples (swabs were labeled “R” and “L” for each cheek). The kits were then returned to the laboratory via prepaid courier package. Standard methods were employed to isolate DNA from buccal samples, and CGG repeat length was ascertained using Asuragen AmplideX® Kits (Grasso et al., 2014). Institutional Review Boards from the University of Wisconsin-Madison and the Marshfield Clinic Research Institute approved the data collection protocols. All participants provided written consent.

Measures

Sleep quality.

Mothers’ sleep quality over the past month was assessed using the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989). The PSQI is the most commonly used instrument for measuring sleep health in clinical and non-clinical samples (Manzar et al., 2018), and is the only standardized protocol to capture a wide array of constructs pertaining to sleep quality (Mollayeva et al., 2016). Prior research demonstrates significant correlations between the PSQI and objective measures of sleep quality (Chung, 2017; Lemola et al., 2013; Zak et al., 2022), although not all studies report these associations (Landry et al., 2015). The PSQI contains 19 items which generate 7 subscales scores (sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, sleep medication use, daytime dysfunction). Each score has a range of 0 (better) to 3 (worse). The 7 scores were summed to create a global PSQI measure; higher scores reflect worse sleep quality. The PSQI has demonstrated strong reliability and validity (Mollayeva et al., 2016).

CGG repeat length.

CGG repeat length was operationalized as a categorical variable, with values grouped as low- (<90), mid- (90 – 110), and high-range (>110). These thresholds are consistent with research on differential risk across the CGG repeat range, although endpoints of the categories vary slightly in different studies (Allen et al., 2007; Klusek et al., 2020).

Physical health conditions.

Mothers were asked if they had been diagnosed with or treated for any of 55 physical health conditions in the prior 12 months. Conditions included diabetes, migraine headaches, sciatica, hypertension, heart disease, arthritis, fibromyalgia, and others. Following previous studies examining the effects of sleep on health (Lee & Lawson, 2021; Sindi et al., 2020), we constructed an index of the number of physical health conditions that mothers experienced in the past year. There was moderate temporal stability in the number of health conditions experienced by respondents (r=.62) (Harrison et al., 2021). A complete list of the physical health conditions is included in Table S1 in the supplemental materials; the index of conditions did not include mental health diagnoses or developmental disorders.

Depressive symptoms.

The 20-item Center for Epidemiologic Studies–Depression Scale (CES-D; Radloff, 1977) measured respondents’ depressive symptoms. For each item (e.g., “I felt sad”; “I felt that everything I did was an effort”), mothers reported the frequency with which they experienced the symptom in the past week, from 0 (never) to 3 (5–7 days) (α=.92). There was strong stability in depressive symptoms over time (r=.77) (Harrison et al., 2021).

Covariates.

Regression models controlled for mothers’ age and educational attainment. Age was controlled because of its association with sleep (Gadie et al., 2017), depression (Kessler et al., 2010), and physical health conditions (Latham & Peek, 2013). The ages of mothers and children were highly correlated (r=.77). Therefore, we controlled for the former as it relates to the unit of analysis in this study. Educational attainment (1=high school degree or less, 2=some college education, 3=college degree or more) was included due to its correlation with sleep and health in the general population (Kawachi et al., 2010; Patel et al., 2010).

Behavior problems and sleep difficulties exhibited by the individuals with FXS were also included, as exposure to these stressors has been linked to maternal sleep quality (Chu & Richdale, 2009; Hodge et al., 2013) and health (Bailey et al., 2012; Hartley et al., 2012). Mothers completed the Child Behavior Checklist (CBCL) for sons or daughters younger than 18 years of age and the Adult Behavior Checklist (ABCL) for sons or daughters aged 18 years or older (Achenbach & Rescorla, 2001, 2003). The C/ABCL assessed the extent to which the adolescent/adult exhibited behavior symptoms in the last 6 months, ranging from 0 (not true) to 2 (very or often true). A total behavior problems score was computed by summing all behavior items. Age-normed T-scores were used in this analysis. The C/ABCL’s validity and reliability are well-established (Achenbach & Rescorla, 2001, 2003). Child sleep difficulty was assessed based on the following survey question: “In a typical month, how often does your son or daughter have difficulty getting to sleep or staying asleep?” Response options ranged from 0 (never) to 6 (daily).

In preliminary models, we explored the effects of controlling for other potential confounders, including the mother’s marital status, the number of other co-residing children, as well as the number of children with disabilities in the family. These additional variables did not alter the pattern of findings and, in the interest of parsimony, were not included in the final models.

Statistical Analysis

We first provide descriptive statistics, including sociodemographic and other background characteristics. For the multivariate analyses, ordinary least squares regression was used to predict depressive symptoms and Poisson regression was used to predict the number of health conditions. The predictors in the models were either time-invariant (educational attainment; CGG repeat length) or measured at Time 3 (sleep quality; adolescent/adult behavior problems; adolescent/adult sleep difficulties; maternal age) and the health outcomes were assessed almost four years later at Time 4. In the first set of models, we examined the independent effects of sleep quality and the categorical CGG repeat variable on maternal health outcomes. The second set of models included an interaction term (CGG repeats x PSQI) to assess whether the association between sleep quality and health varied for mothers across the categorical levels of CGG repeat length. To avoid multicollinearity, behavior problems and sleep difficulties exhibited by the individuals with FXS were controlled in separate models as these characteristics were significantly correlated (r=.50, p<.0001). Because depressive symptoms had a right-skewed distribution, we took its logarithm and re-ran the OLS models as a robustness check. This approach yielded similar results to those from our initial models, and thus we continued with the analysis of the untransformed depressive symptoms measure.

Between 0.2 and 12% of cases had missing data. To reduce possible bias, and to include all available information from respondents in the regression models, missing data were multiply imputed with 10 data sets using chained equations (Royston & White, 2011). All analyses were carried out using Stata 16 (StataCorp, 2019).

Results

Most mothers in the sample were white, non-Hispanic (95.24%), married (84.52%), and employed (66.67%). The median household income was $90,000-$100,000. Mothers had, on average, between 2 and 3 children, including their son or daughter with FXS (mean: 2.56; range: 1–6). The individuals with FXS were predominately male (85.71%) and were aged 22.73 years on average (range: 15–43 years).

Table 1 presents descriptive information for the variables included in the regression analysis. Mothers averaged 52.93 years of age (range: 40–71 years) at Time 3, and most had at least a college education (63.10%). The mean behavior problems score for the individuals with FXS was 57.59 (range: 39.0–75.0). On average, mothers reported that their child with FXS had difficulty getting to sleep or staying asleep “about once a month”, though there was considerable variability; nearly 40% of mothers reported that their child never had sleep difficulties and nearly 30% reported that their child had sleep difficulties at least once per week.

Table 1.

Characteristics of FMR1 premutation carrier mothers and adolescent and adult children with FXS

Full sample Low-range CGG repeat length Mid-range CGG repeat length High-range CGG repeat length F or χ2
Maternal characteristics
Age in years (M, SD) 52.93 (7.03) 55.0 (7.22) 51.5 (7.07) 50.25 (5.01) 3.70*
Educational attainment (%) 3.11
 High school degree or less 13.10 10.00 21.43 6.25
 Some college education 23.81 27.50 17.86 25.00
 College degree or more 63.10 62.50 60.71 68.75
CGG repeat length (M, SD) 94.18 (16.30) -- -- --
Adolescent/adult child characteristics
Behavior problems (M, SD)a 57.59 (7.28) 58.81 (6.86) 57.48 (7.84) 54.94 (6.94) 1.61
Sleep difficulty (%) 1.11
 Never 39.47 37.14 34.62 53.33
 Less than once a month 21.05 20.00 23.08 20.00
 About once a month 1.32 2.86 0.00 0.00
 A few times a month 9.21 5.71 11.54 13.33
 About once a week 5.26 0.00 15.38 0.00
 A few times a week 14.47 20.00 7.69 13.33
 Daily 9.21 14.29 7.69 0.00
Sleep quality and health outcomes
Global PSQI (M, SD) 5.79 (3.31) 5.94 (3.14) 6.00 (3.71) 5.13 (3.10) 0.40
Depressive symptoms (M, SD) 11.74 (10.15) 11.55 (10.72) 12.68 (10.61) 10.57 (8.07) 0.23
No. physical health conditions (M, SD) 1.82 (2.57) 2.35 (2.46) 1.5 (2.91) 1.06 (2.05) 1.79
N 84 40 28 16
Open in a new tab

Notes:

*

p<.05.

(a)

CBCL/ABCL t-score; Abbreviations: Fragile X syndrome (FXS); mean (M); standard deviation (SD); Pittsburgh Sleep Quality Index (PSQI).

Table 1 also provides descriptive statistics on mothers’ CGG repeat length, sleep quality, health conditions, and depressive symptoms. The mean CGG repeat length was 94.18 (range: 67 – 138) among the premutation carrier mothers; 47.62% were in the low premutation range (< 90), 33.33% were in the mid premutation range (90–110), and 19.05% were in the high premutation range (> 110). The average global PSQI score was 5.79 (range: 0–14). A threshold of 5.0 on the global PSQI is sometimes used to differentiate “poor sleepers” (i.e., > 5) from “good sleepers” (i.e., ≤ 5) (Buysse et al., 2008; Mollayeva et al., 2016). Using this cut-off, 42.67% of mothers in the sample would be categorized as poor sleepers and 57.33% as good sleepers. The mean score for depressive symptoms was 11.74 (range: 0–49). CES-D scores ≥ 16 are generally considered clinically significant (Lewinsohn et al., 1997); 32.14% of mothers met or exceeded this threshold. The average number of physical health conditions was 1.82 (range: 0–13); 30.95% of the sample had ≥ 2 conditions. Prevalent conditions among respondents included: high blood pressure or hypertension (15.48%); allergies (14.29%); high cholesterol (14.29%); recurring stomach problems (13.10%); and arthritis or bone disease (10.71%). With the exception of maternal age, the study variables did not significantly differ across the three CGG repeat categories (p’s > .27). Mothers in the low-premutation range were 4.75 years older, on average, than mothers in the high-premutation range (p=.02).

Sleep, CGG Repeats, and Health in Premutation Carrier Mothers

The effects of CGG repeats and sleep quality (and their interaction) on maternal health outcomes were similar when controlling for adolescent/adult behavior problems or adolescent/adult sleep difficulties. To avoid redundancy, we present the models controlling for adolescent/adult sleep difficulties in Table 2. In a subsequent section, we describe the results of for behavior problems.

Table 2.

Regression models predicting number of physical health conditions and depressive symptoms among FMR1 premutation carrier mothers of adolescents and adults with fragile X syndrome (n=84)

Physical health conditions Depressive symptoms
Model 1 Model 2 Model 3 Model 4
Main effects Interaction Main effects Interaction
b (SE) b (SE) b (SE) b (SE)
Age (years) 0.05 ***
(0.01) 		0.05 ***
(0.01) 		−0.21
(0.15)		 −0.20
(0.15)
Educationa
 High school or less −0.47
(0.31)		 −0.60
(0.32)		 4.29
(2.97)		 4.74
(3.12)
 Some college 0.18
(0.20)		 0.14
(0.21)		 6.42 **
(2.40) 		6.04 *
(2.44)
Adolescent/adult sleep difficulties 0.13 **
(0.04) 		0.12 **
(0.04) 		1.17 *
(0.48) 		1.09 *
(0.48)
CGG repeat lengthb
 < 90 0.17
(0.20)		 1.42 **
(0.52) 		−0.59
(2.26)		 −1.07
(4.65)
 > 110 −0.14
(0.30)		 1.19
(0.64)		 −0.53
(2.81)		 4.29
(5.49)
PSQI 0.04
(0.04)		 0.13 **
(0.05) 		0.70 *
(0.32) 		0.83
(0.47)
PSQI x CGG repeat lengthb
 PSQI x < 90 -- −0.17**
(0.06) 		-- 0.09
(0.68)
 PSQI x > 110 -- −0.22*
(0.10) 		-- −0.90
(0.87)
Open in a new tab

Notes:

***

p<.001

**

p<.01

*

p<.05 (in bold).

Models 1 and 2 were fit with Poisson regression; Models 3 and 4 were fit with OLS.

(a)

ref: college degree or more;

(b)

ref: CGG 90–110. Abbreviations: Standard error (SE); Pittsburgh Sleep Quality Index (PSQI).

Physical health conditions.

As shown in Model 1, maternal age was significantly associated with having a greater number of physical health conditions (b=0.05, p<.001), whereas educational attainment was not. Sleep difficulties exhibited by individuals with FXS was a significant predictor of the number of physical health conditions experienced by premutation carrier mothers (b=0.13, p=.003). The main effect of sleep quality on physical health conditions was not significant, in contrast to our first hypothesis. Physical health conditions also did not vary by CGG repeat categories.

The results of Model 2 indicate a significant interaction between CGG repeats and the PSQI, specifically for mothers with repeats between 90 and 110, which was consistent with our second hypothesis. The effect of poor sleep quality on physical health conditions was significantly smaller for mothers with CGG repeats in the lower- (< 90) and higher-ends (> 110) of the premutation range than it was for mothers with CGG repeats in the mid-premutation range, or between 90 and 110 repeats (b=−0.17, p=.005 and b=−0.22, p=.031, respectively).

Figure 1 plots the differential health effect of sleep across the CGG repeat length categories, illustrating a significant relationship between poor sleep and the predicted number of conditions for mothers with mid-range CGG repeats. Of note, poor sleep quality was not associated with physical health conditions for mothers with less than 90 (b=−0.08, p=.38) or more than 110 CGG repeats (b=−0.11, p=.31).

Figure 1.

Figure 1.

Open in a new tab

Differential effects of sleep quality by CGG repeat length on the predicted number of physical health conditions

Note: Figure 1 plots the interaction from the model controlling for sleep difficulties exhibited by the individual with FXS. The corresponding effects from the model controlling for behavior problems are: [CGG < 90: b = −0.06, p = .51], [CGG 90–110: b = 0.19, p = .009], [CGG > 110: b = −0.11, p = .32]

Depressive symptoms.

Table 2 also presents the results of the regression models predicting depressive symptoms. As shown in Model 3, participants with a “college degree or more” had fewer depressive symptoms than those with “some college education” (b=6.42, p=.009). Sleep difficulties exhibited by individuals with FXS was a significant predictor of maternal depressive symptoms (b=1.17, p=.017). Consistent with our first hypothesis, there was a significant main effect of the PSQI in Model 3, indicating that poor sleep at Time 3 predicted more depressive symptoms at Time 4 (b=0.70, p=.031). The association between CGG repeat length and depressive symptoms was not significant. Model 4 assessed the differential effect of sleep quality on depressive symptoms across the CGG repeat length categories. The CGG x PSQI interaction was not significant, in contrast to our second hypothesis. Thus, while premutation carriers with CGG repeats between 90 and 110 may be more vulnerable to adverse physical health effects of poor sleep quality, the negative impacts on depressive symptoms do not vary by CGG repeat length.

Effects of adolescent and adult behavior problems.

The results of the models controlling for behavior problems are included in Table S2 in the supplemental materials. Behavior problems exhibited by individuals with FXS were significantly associated with the number of physical health conditions (b=0.05, p<.001) and depressive symptoms experienced by premutation carrier mothers at follow-up (b=0.36, p=.016). Consistent with the results presented in Table 2, the interaction between CGG repeats and sleep quality was significant in predicting physical health conditions but not maternal depressive symptoms when controlling for behavior problems (rather than child sleep difficulties).

Discussion

There has been increasing interest in the quality of sleep among parents of children with developmental disabilities (da Estrela et al., 2021; Lovell et al., 2021). Many studies have focused on the impacts of child- or family-level stressors on parents’ sleep and well-being. Limited research, however, has examined whether genetic risk factors also contribute to within-group heterogeneity in the effects of sleep quality on parents’ health. We address this gap by focusing on the associations between poor sleep quality and negative health outcomes in a cohort of 84 FMR1 premutation carrier mothers of adolescents and adults with fragile X syndrome. This study contributes to the literature by illustrating how mothers’ sensitivity to poor sleep quality may be conditioned by their level of genetic vulnerability.

Among mothers of individuals with FXS, the effect of poor sleep quality on physical health conditions was significantly moderated by CGG repeat size. Specifically, poor sleep quality was associated with a greater number of physical health conditions 4 years later for those with CGG repeats in the mid-range category (i.e., between 90 and 110 CGG repeats) but not for those with CGG repeats in the lower or higher ends of the premutation range. These findings are broadly aligned with evidence documenting a non-linear relationship between FMRI premutation expansions and many adverse health or functional outcomes (Allen et al., 2007; Klusek et al., 2020; Seltzer et al., 2012), where mothers who have mid-range CGG repeats are more vulnerable than those with fewer or a greater number of repeats. It is also worth underscoring that the mean PSQI scores did not significantly differ across the categorical levels of CGG repeat length (see Table 1). Hence, the findings of our analysis do not reflect gradients in sleep quality across levels of genetic risk. Instead, we provide evidence of a moderating process, wherein genetic vulnerability amplified the association between poor sleep and physical health conditions for a sub-group of mothers of individuals with FXS. However, the hypothesized main effect of poor sleep quality on physical health conditions for all mothers in the sample, including those with CGG repeats in the lower- and higher-ends of the premutation range, was not supported by the data. Previous research has found age-related differences in the associations between CGG repeat length and health outcomes (Kraan et al., 2014; Maltman et al., 2022). Thus, it is possible that the relationship between sleep quality and physical health conditions varies by age among premutation carriers in different CGG repeat length groups. It should also be noted that not all studies about parents of children with IDDs report statistically significant effects of sleep quality on health (Hoffman et al., 2008; Meltzer, 2011). In this respect, the results for mothers with CGG repeats in the lower- and higher-ends of the premutation range mirror these previous findings.

We also examined the relationship between sleep quality and maternal depressive symptoms, and whether this association similarly varied by genetic factors. In support of our first hypothesis, there was a significant main effect of sleep quality and depressive symptoms, such that worse sleep quality at Time 3 was associated with more depressive symptoms at follow-up. However, in contrast to our second hypothesis, the effect of sleep quality on maternal depressive symptoms was not moderated by the categorical CGG repeat length variable. Mothers with CGG repeats in the mid-premutation range were thus no more vulnerable to the negative effects of poor sleep on depressive symptoms compared to those with CGG repeats in other parts of the premutation range or, potentially, than those in the general population (Y. Huang & Zhu, 2020). It is unclear why premutation carriers with CGG repeat lengths in the mid-range exhibited greater sensitivity to the effects of poor sleep on physical health but not on depressive symptoms. Future research is needed to further clarify this question, for example, by utilizing a larger sample of premutation carriers and data collected over a longer period of time.

Understanding the mechanisms that contribute to the differential genetic sensitivity to poor sleep among mothers of individuals with FXS will require additional research. Indicators of biological dysregulation that correspond with impaired sleep may be a promising direction of inquiry. For example, Seltzer et al. (2012) found that the physiological effects of life stressors were greater among premutation carrier mothers with mid-range CGG repeats, as evidenced by flatter cortisol awakening responses. Hypocortisolism induced by chronic stress is thought to result from down-regulation of the HPA axis and diminished cortisol secretion (Fries et al., 2005). Notably, dysregulated diurnal cortisol rhythms are correlated with poor sleep (T. Huang et al., 2017) and health problems (Adam et al., 2017). Other physiological mechanisms should be considered as well. For example, CGG repeat length was shown to be correlated with vagal control in an exploratory analysis of a small sample of premutation carriers (Klusek et al., 2019). Parasympathetic activation, of which vagal control is a measure, has been linked to both sleep quality (Werner et al., 2015) and an array of other health outcomes (e.g., Gidron et al., 2018; Williams et al., 2019), suggesting a possible mechanism to be explored in future research. More broadly, parameters that tap into biological regulation may elucidate pathways between genetic risk, sleep, and health in mothers of children with FXS as well as other developmental disabilities.

The findings from this study have implications for future research and practice. There is a need for intervention studies focused on improving sleep quality in premutation carriers. A few prior intervention studies have assessed whether reducing challenging behaviors and sleep problems in children with IDDs can lead to indirect improvements in parents’ sleep (e.g., McLay et al., 2021; Tsai et al., 2020; Wiggs & Stores, 2001). Our findings, however, indicate that poor maternal sleep quality is associated with negative health outcomes over and above the impacts of child sleep difficulties and behavior problems. Sleep interventions that directly target mothers are thus warranted. Additionally, by taking genetic liability into account (i.e., mid-range CGG repeats), providers can offer support and sleep interventions to mothers who may be more vulnerable to the physical health risks associated with poor sleep quality.

This study had several limitations. The PSQI is a self-reported index, and it is unclear whether a similar pattern of findings would be observed using data from objective sleep assessments. Several studies have shown the PSQI to be correlated with objective indicators of sleep quality (Chung, 2017; Lemola et al., 2013; Zak et al., 2022); other research, however, reports weaker relationships between subjective and objective sleep quality measures (see Mollayeva et al., 2016 for a review). Replication studies using actigraphy or polysomnography would be informative. Although the PSQI includes a question about the use of sleep medications, we did not have information about how other medications might have affected the study participants. Understanding the impact of different medications on sleep quality, health conditions, and depression among premutation carriers should be investigated in future studies. Additional research is also needed to understand whether differential vulnerability to poor sleep is a direct genetic effect or due to indirect effects of other vulnerabilities associated with mid-range CGGs. The physical health outcome measure used in this study is a count of the number of conditions experienced by participants. We did not have data describing the onset, severity, or impairment caused by these conditions. It is possible that the analysis of such details would reveal other negative impacts of poor sleep quality, particularly among women with CGG repeats in the lower- and higher-ends of the premutation range. Finally, we are unable to provide a causal interpretation of the findings as the results of the regression models are associational in nature. Additional studies are needed to ascertain the direction of effects, including whether physical health conditions or depression influence sleep quality in premutation carriers, or if such relationships are bidirectional.

This study also has several strengths. Our examination of genetic vulnerability sheds new light on individual differences in the associations between sleep quality and physical health among premutation carrier mothers of individuals with FXS. Controlling for behavior problems and sleep difficulties exhibited by individuals with FXS allowed us to account for proximal child-level exposures previously shown to impact maternal sleep and health. Finally, the longitudinal findings of this study build on prior, largely cross-sectional research concerning sleep and health in mothers of children with IDDs.

In conclusion, this study extends our understanding of the interplay between genetic factors and sleep quality in mothers of adolescents and adults with fragile X syndrome. Among premutation carrier mothers, the longitudinal effect of sleep quality on physical health appears to depend upon CGG repeat length. This evidence can be used to individualize sleep interventions for mothers who have different profiles of genetic liability.

Supplementary Material

Supplementary Material

References

  1. Achenbach TM, & Rescorla LA (2001). Manual for the ASEBA School-Age Forms & Profile: An integrated system of multi-informant assessments. University of Vermont, Research Center for Children, Youth, and Families. [Google Scholar]
  2. Achenbach TM, & Rescorla LA (2003). Manual for the ASEBA Adult Forms & Profiles. University of Vermont, Research Center for Children, Youth, and Families. [Google Scholar]
  3. Adam EK, Quinn ME, Tavernier R, McQuillan MT, Dahlke KA, & Gilbert KE (2017). Diurnal cortisol slopes and mental and physical health outcomes: A systematic review and meta-analysis. Psychoneuroendocrinology, 83, 25–41. 10.1016/j.psyneuen.2017.05.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Allen EG, Charen K, Hipp HS, Shubeck L, Amin A, He W, Hunter JE, & Sherman SL (2020). Clustering of comorbid conditions among women who carry an FMR1 premutation. Genetics in Medicine, 22(4), 758–766. 10.1038/s41436-019-0733-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Allen EG, Charen K, Hipp HS, Shubeck L, Amin A, He W, Nolin SL, Glicksman A, Tortora N, McKinnon B, Shelly KE, & Sherman SL (2021). Refining the risk for fragile X–associated primary ovarian insufficiency (FXPOI) by FMR1 CGG repeat size. Genetics in Medicine, 1–8. 10.1038/s41436-021-01177-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Allen EG, Sullivan AK, Marcus M, Small C, Dominguez C, Epstein MP, Charen K, He W, Taylor KC, & Sherman SL (2007). Examination of reproductive aging milestones among women who carry the FMR1 premutation. Human Reproduction, 22(8), 2142–2152. 10.1093/humrep/dem148 [DOI] [PubMed] [Google Scholar]
  7. Bailey DBJ, Raspa M, Bishop E, Mitra D, Martin S, Wheeler A, & Sacco P (2012). Health and economic consequences of fragile X syndrome for caregivers. Journal of Developmental & Behavioral Pediatrics, 33(9), 705–712. 10.1097/DBP.0b013e318272dcbc [DOI] [PubMed] [Google Scholar]
  8. Bourke-Taylor H, Pallant JF, Law M, & Howie L (2012). Predicting mental health among mothers of school-aged children with developmental disabilities: The relative contribution of child, maternal and environmental factors. Research in Developmental Disabilities, 33(6), 1732–1740. 10.1016/j.ridd.2012.04.011 [DOI] [PubMed] [Google Scholar]
  9. Bredin-Oja SL, Warren SF, Swinburne Romine RE, Fleming KK, Brady N, & Berry-Kravis E (2021). Word retrieval difficulty in adult females with the FMR1 premutation: Changes over time and across contexts. Brain and Cognition, 148, 105694. 10.1016/j.bandc.2021.105694 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Brown WT (2002). The molecular biology of fragile x mutation. In Hagerman RJ & Hagerman PJ (Eds.), Fragile X syndrome: Diagnosis, treatment, and research (3rd ed., pp. 110–135). John Hopkins University Press. [Google Scholar]
  11. Buysse DJ, Hall ML, Strollo PJ, Kamarck TW, Owens J, Lee L, Reis SE, & Matthews KA (2008). Relationships between the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and clinical/polysomnographic measures in a community sample. Journal of Clinical Sleep Medicine, 4(6), 563–571. [PMC free article] [PubMed] [Google Scholar]
  12. Buysse DJ, Reynolds CF, Monk TH, Berman SR, & Kupfer DJ (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. 10.1016/0165-1781(89)90047-4 [DOI] [PubMed] [Google Scholar]
  13. Cappuccio FP, Taggart FM, Kandala N-B, Currie A, ChB M, Peile E, & Miller MA (2008). Meta-Analysis of Short Sleep Duration and Obesity in Children and Adults. 31(5), 619–626. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Chonchaiya W, Nguyen DV, Au J, Campos L, Berry‐Kravis EM, Lohse K, Mu Y, Utari A, Hervey C, Wang L, Sorensen P, Cook K, Gane L, Tassone F, & Hagerman RJ (2010). Clinical involvement in daughters of men with fragile X-associated tremor ataxia syndrome. Clinical Genetics, 78(1), 38–46. 10.1111/j.1399-0004.2010.01448.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Chu J, & Richdale AL (2009). Sleep quality and psychological wellbeing in mothers of children with developmental disabilities. Research in Developmental Disabilities, 30(6), 1512–1522. 10.1016/j.ridd.2009.07.007 [DOI] [PubMed] [Google Scholar]
  16. Chung J (2017). Social support, social strain, sleep quality, and actigraphic sleep characteristics: Evidence from a national survey of US adults. Sleep Health, 3(1), 22–27. 10.1016/j.sleh.2016.10.003 [DOI] [PubMed] [Google Scholar]
  17. da Estrela C, McGrath J, Booij L, & Gouin J-P (2021). Heart rate variability, sleep quality, and depression in the context of chronic stress. Annals of Behavioral Medicine, 55(2), 155–164. 10.1093/abm/kaaa039 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Famula J, Ferrer E, Hagerman RJ, Tassone F, Schneider A, Rivera SM, & Hessl D (2022). Neuropsychological changes in FMR1 premutation carriers and onset of fragile X-associated tremor/ataxia syndrome. Journal of Neurodevelopmental Disorders, 14(1), 23. 10.1186/s11689-022-09436-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fielding-Gebhardt H, Warren SF, & Brady NC (2020). Child challenging behavior influences maternal mental health and relationship quality over time in fragile X syndrome. Journal of Autism and Developmental Disorders, 50(3), 779–797. 10.1007/s10803-019-04308-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Fries E, Hesse J, Hellhammer J, & Hellhammer DH (2005). A new view on hypocortisolism. Psychoneuroendocrinology, 30(10), 1010–1016. 10.1016/j.psyneuen.2005.04.006 [DOI] [PubMed] [Google Scholar]
  21. Gadie A, Shafto M, Leng Y, & Kievit RA (2017). How are age-related differences in sleep quality associated with health outcomes? An epidemiological investigation in a UK cohort of 2406 adults. BMJ Open, 7(7), e014920. 10.1136/bmjopen-2016-014920 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gidron Y, Deschepper R, De Couck M, Thayer JF, & Velkeniers B (2018). The vagus nerve can predict and possibly modulate non-communicable chronic diseases: Introducing a neuroimmunological paradigm to public health. Journal of Clinical Medicine, 7(10), 371. 10.3390/jcm7100371 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Grasso M, Boon EMJ, Filipovic-Sadic S, van Bunderen PA, Gennaro E, Cao R, Latham GJ, Hadd AG, & Coviello DA (2014). A novel methylation PCR that offers standardized determination of FMR1 methylation and CGG repeat length without southern blot analysis. The Journal of Molecular Diagnostics, 16(1), 23–31. 10.1016/j.jmoldx.2013.09.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hagerman RJ, Berry-Kravis E, Kaufmann WE, Ono MY, Tartaglia N, Lachiewicz A, Kronk R, Delahunty C, Hessl D, Visootsak J, Picker J, Gane L, & Tranfaglia M (2009). Advances in the treatment of fragile X syndrome. Pediatrics, 123(1), 378–390. 10.1542/peds.2008-0317 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hale L, Troxel W, & Buysse DJ (2020). Sleep health: An opportunity for public health to address health equity. Annual Review of Public Health, 41(1), 81–99. 10.1146/annurev-publhealth-040119-094412 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hamlin A, Liu Y, Nguyen DV, Tassone F, Zhang L, & Hagerman RJ (2011). Sleep apnea in fragile X premutation carriers with and without FXTAS. American Journal of Medical Genetics Part B, 156(8), 923–928. 10.1002/ajmg.b.31237 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Harrison V, Kemp R, Brace N, & Snelgar R (2021). SPSS for Psychologists (Seventh edition). Red Globe Press. [Google Scholar]
  28. Hartley SL, DaWalt LS, Hong J, Greenberg JS, & Mailick MR (2019). Positive emotional support in premutation carrier mothers of adolescents and adults with fragile X syndrome: Gene by environment interactions. American Journal on Intellectual and Developmental Disabilities, 124(5), 411–426. 10.1352/1944-7558-124.5.411 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hartley SL, Mailick Seltzer M, Hong J, Greenberg JS, Smith L, Almeida D, Coe C, & Abbeduto L (2012). Cortisol response to behavior problems in FMR1 premutation mothers of adolescents and adults with fragile X syndrome: A diathesis-stress model. International Journal of Behavioral Development, 36(1), 53–61. 10.1177/0165025411406857 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hodge D, Hoffman CD, & Sweeney DP (2011). Increased psychopathology in parents of children with autism: Genetic liability or burden of caregiving? Journal of Developmental and Physical Disabilities, 23(3), 227–239. 10.1007/s10882-010-9218-9 [DOI] [Google Scholar]
  31. Hodge D, Hoffman CD, Sweeney DP, & Riggs ML (2013). Relationship between children’s sleep and mental health in mothers of children with and without autism. Journal of Autism and Developmental Disorders, 43(4), 956–963. 10.1007/s10803-012-1639-0 [DOI] [PubMed] [Google Scholar]
  32. Hoffman CD, Sweeney DP, Lopez-Wagner MC, Hodge D, Nam CY, & Botts BH (2008). Children with autism: Sleep problems and mothers’ stress. Focus on Autism and Other Developmental Disabilities, 23(3), 155–165. 10.1177/1088357608316271 [DOI] [Google Scholar]
  33. Huang T, Poole EM, Vetter C, Rexrode KM, Kubzansky LD, Schernhammer E, Rohleder N, Hu FB, Redline S, & Tworoger SS (2017). Habitual sleep quality and diurnal rhythms of salivary cortisol and dehydroepiandrosterone in postmenopausal women. Psychoneuroendocrinology, 84, 172–180. 10.1016/j.psyneuen.2017.07.484 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Huang Y, & Zhu M (2020). Increased global PSQI score is associated with depressive symptoms in an adult population from the United States. Nature and Science of Sleep, 12, 487–495. 10.2147/NSS.S256625 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Irwin MR, Olmstead R, & Carroll JE (2016). Sleep disturbance, sleep duration, and inflammation: A systematic review and meta-analysis of cohort studies and experimental sleep deprivation. Biological Psychiatry, 80(1), 40–52. 10.1016/j.biopsych.2015.05.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Jackson ML, Sztendur EM, Diamond NT, Byles JE, & Bruck D (2014). Sleep difficulties and the development of depression and anxiety: A longitudinal study of young Australian women. Archives of Women’s Mental Health, 17(3), 189–198. 10.1007/s00737-014-0417-8 [DOI] [PubMed] [Google Scholar]
  37. Kawachi I, Adler NE, & Dow WH (2010). Money, schooling, and health: Mechanisms and causal evidence: Money, schooling, and health. Annals of the New York Academy of Sciences, 1186(1), 56–68. 10.1111/j.1749-6632.2009.05340.x [DOI] [PubMed] [Google Scholar]
  38. Kessler RC, Birnbaum H, Bromet E, Hwang I, Sampson N, & Shahly V (2010). Age differences in major depression: Results from the National Comorbidity Surveys Replication (NCS-R). Psychological Medicine, 40(2), 225. 10.1017/S0033291709990213 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Klusek J, Fairchild AJ, & Roberts JE (2019). Vagal tone as a putative mechanism for pragmatic competence: An investigation of carriers of the FMR1 premutation. Journal of Autism and Developmental Disorders, 49(1), 197–208. 10.1007/s10803-018-3714-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Klusek J, Hong J, Sterling A, Berry-Kravis E, & Mailick MR (2020). Inhibition deficits are modulated by age and CGG repeat length in carriers of the FMR1 premutation allele who are mothers of children with fragile X syndrome. Brain and Cognition, 139, 105511. 10.1016/j.bandc.2019.105511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kraan CM, Hocking DR, Georgiou-Karistianis N, Metcalfe SA, Archibald AD, Fielding J, Trollor J, Bradshaw JL, Cohen J, & Cornish KM (2014). Age and CGG-repeat length are associated with neuromotor impairments in at-risk females with the FMR1 premutation. Neurobiology of Aging, 35(9), 2179.e7–2179.e13. 10.1016/j.neurobiolaging.2014.03.018 [DOI] [PubMed] [Google Scholar]
  42. Landry G, Best J, & Liu-Ambrose T (2015). Measuring sleep quality in older adults: A comparison using subjective and objective methods. Frontiers in Aging Neuroscience, 7, 166. 10.3389/fnagi.2015.00166 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Latham K, & Peek CW (2013). Self-rated health and morbidity onset among late midlife U.S. adults. The Journals of Gerontology: Series B, 68(1), 107–116. 10.1093/geronb/gbs104 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lee S, & Lawson KM (2021). Beyond single sleep measures: A composite measure of sleep health and its associations with psychological and physical well-being in adulthood. Social Science & Medicine, 274, 113800. 10.1016/j.socscimed.2021.113800 [DOI] [PubMed] [Google Scholar]
  45. Lemola S, Ledermann T, & Friedman EM (2013). Variability of sleep duration is related to subjective sleep quality and subjective well-being: An actigraphy study. PLOS ONE, 8(8), e71292. 10.1371/journal.pone.0071292 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Lewinsohn PM, Seeley JR, Roberts RE, & Allen NB (1997). Center for Epidemiologic Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychology and Aging, 12(2), 277–287. 10.1037/0882-7974.12.2.277 [DOI] [PubMed] [Google Scholar]
  47. Lovell B, Elder GJ, & Wetherell MA (2021). Sleep disturbances and physical health problems in caregivers of children with ASD. Research in Developmental Disabilities, 113, 103932. 10.1016/j.ridd.2021.103932 [DOI] [PubMed] [Google Scholar]
  48. Mailick MR, Hong J, Greenberg J, Smith L, & Sherman S (2014). Curvilinear association of CGG repeats and age at menopause in women with FMR1 premutation expansions. American Journal of Medical Genetics Part B, 165(8), 705–711. 10.1002/ajmg.b.32277 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Maltman N, Klusek J, DaWalt L, Hong J, Sterling A, Berry-Kravis E, & Mailick MR (2022). Verbal inhibition declines among older women with high FMR1 premutation expansions: A prospective study. Brain and Cognition, 159, 105851. 10.1016/j.bandc.2022.105851 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Manzar MD, BaHammam AS, Hameed UA, Spence DW, Pandi-Perumal SR, Moscovitch A, & Streiner DL (2018). Dimensionality of the Pittsburgh Sleep Quality Index: A systematic review. Health and Quality of Life Outcomes, 16(1), 1–22. 10.1186/s12955-018-0915-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. McLay LL, France KG, Blampied NM, Hunter JE, van Deurs JR, Woodford EC, Gibbs R, & Lang R (2021). Collateral child and parent effects of function-based behavioral interventions for sleep problems in children and adolescents with autism. Journal of Autism and Developmental Disorders. 10.1007/s10803-021-05116-3 [DOI] [PubMed] [Google Scholar]
  52. Meltzer LJ (2011). Factors associated with depressive symptoms in parents of children with autism spectrum disorders. Research in Autism Spectrum Disorders, 5(1), 361–367. 10.1016/j.rasd.2010.05.001 [DOI] [Google Scholar]
  53. Mollayeva T, Thurairajah P, Burton K, Mollayeva S, Shapiro CM, & Colantonio A (2016). The Pittsburgh Sleep Quality Index as a screening tool for sleep dysfunction in clinical and non-clinical samples: A systematic review and meta-analysis. Sleep Medicine Reviews, 25, 52–73. 10.1016/j.smrv.2015.01.009 [DOI] [PubMed] [Google Scholar]
  54. National Library of Medicine. (2022). FMR1 fragile X messenger ribonucleoprotein 1 [Homo sapiens (human)]. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/gene/2332 [Google Scholar]
  55. Nicholson K, Rodrigues R, Anderson KK, Wilk P, Guaiana G, & Stranges S (2020). Sleep behaviours and multimorbidity occurrence in middle-aged and older adults: Findings from the Canadian Longitudinal Study on Aging (CLSA). Sleep Medicine, 75, 156–162. 10.1016/j.sleep.2020.07.002 [DOI] [PubMed] [Google Scholar]
  56. Patel NP, Grandner MA, Xie D, Branas CC, & Gooneratne N (2010). “Sleep disparity” in the population: Poor sleep quality is strongly associated with poverty and ethnicity. BMC Public Health, 10(1), 475. 10.1186/1471-2458-10-475 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Pigeon WR, Pinquart M, & Conner K (2012). Meta-analysis of sleep disturbance and suicidal thoughts and behaviors. The Journal of Clinical Psychiatry, 73(9), 0–0. 10.4088/JCP.11r07586 [DOI] [PubMed] [Google Scholar]
  58. Radloff LS (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. 10.1177/014662167700100306 [DOI] [Google Scholar]
  59. Roberts JE, Tonnsen BL, McCary LM, Ford AL, Golden RN, & Bailey DB (2016). Trajectory and predictors of depression and anxiety disorders in mothers with the FMR1 premutation. Biological Psychiatry, 79(10), 850–857. 10.1016/j.biopsych.2015.07.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Rodriguez-Revenga L, Madrigal I, Pagonabarraga J, Xunclà M, Badenas C, Kulisevsky J, Gomez B, & Milà M (2009). Penetrance of FMR1 premutation associated pathologies in fragile X syndrome families. European Journal of Human Genetics, 17(10), 1359–1362. 10.1038/ejhg.2009.51 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Royston P, & White IR (2011). Multiple imputation by chained equations: Implementation in Stata. Journal of Statistical Software, 45(4), 1–20. 10.18637/jss.v045.i04 [DOI] [Google Scholar]
  62. Sabia S, Fayosse A, Dumurgier J, van Hees VT, Paquet C, Sommerlad A, Kivimäki M, Dugravot A, & Singh-Manoux A (2021). Association of sleep duration in middle and old age with incidence of dementia. Nature Communications, 12(1), 2289. 10.1038/s41467-021-22354-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Sansone SM (2015). The FMR1 Gene and Differential Susceptibility to HPA-axis and Immune Dysfunction: Examination of a Gene-Environment Interaction with Stress. University of California, Davis. [Google Scholar]
  64. Seltzer MM, Barker ET, Greenberg JS, Hong J, Coe C, & Almeida D (2012). Differential sensitivity to life stress in FMR1 premutation carrier mothers of children with fragile X syndrome. Health Psychology, 31(5), 612–622. 10.1037/a0026528 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Sindi S, Pérez LM, Vetrano DL, Triolo F, Kåreholt I, Sjöberg L, Darin-Mattsson A, Kivipelto M, Inzitari M, & Calderón-Larrañaga A (2020). Sleep disturbances and the speed of multimorbidity development in old age: Results from a longitudinal population-based study. BMC Medicine, 18(1), 382. 10.1186/s12916-020-01846-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Smith LE, Seltzer MM, & Greenberg JS (2012). Daily health symptoms of mothers of adolescents and adults with fragile x syndrome and mothers of adolescents and adults with autism spectrum disorder. Journal of Autism and Developmental Disorders, 42(9), 1836–1846. 10.1007/s10803-011-1422-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Sofi F, Cesari F, Casini A, Macchi C, Abbate R, & Gensini GF (2014). Insomnia and risk of cardiovascular disease: A meta-analysis. European Journal of Preventive Cardiology, 21(1), 57–64. 10.1177/2047487312460020 [DOI] [PubMed] [Google Scholar]
  68. StataCorp. (2019). Stata Statistical Software: Release 16. StataCorp LLC. [Google Scholar]
  69. Summers SM, Cogswell J, Goodrich JE, Mu Y, Nguyen DV, Brass SD, & Hagerman RJ (2014). Prevalence of restless legs syndrome and sleep quality in carriers of the fragile X premutation. Clinical Genetics, 86(2), 181–184. 10.1111/cge.12249 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Tsai S-Y, Lee W-T, Lee C-C, Jeng S-F, & Weng W-C (2020). Behavioral-educational sleep interventions for pediatric epilepsy: A randomized controlled trial. Sleep, 43(1), zsz211. 10.1093/sleep/zsz211 [DOI] [PubMed] [Google Scholar]
  71. Werner GG, Ford BQ, Mauss IB, Schabus M, Blechert J, & Wilhelm FH (2015). High cardiac vagal control is related to better subjective and objective sleep quality. Biological Psychology, 106, 79–85. 10.1016/j.biopsycho.2015.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Wheeler AC, Bailey DB, Berry-Kravis E, Greenberg J, Losh M, Mailick M, Milà M, Olichney JM, Rodriguez-Revenga L, Sherman S, Smith L, Summers S, Yang J-C, & Hagerman R (2014). Associated features in females with an FMR1 premutation. Journal of Neurodevelopmental Disorders, 6(1). 10.1186/1866-1955-6-30 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Wiggs L, & Stores G (2001). Behavioural treatment for sleep problems in children with severe intellectual disabilities and daytime challenging behaviour: Effect on mothers and fathers. British Journal of Health Psychology, 6(3), 257–269. 10.1348/135910701169197 [DOI] [PubMed] [Google Scholar]
  74. Willemsen R, Levenga J, & Oostra B (2011). CGG repeat in the FMR1 gene: Size matters. Clinical Genetics, 80(3), 214–225. 10.1111/j.1399-0004.2011.01723.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Williams DP, Koenig J, Carnevali L, Sgoifo A, Jarczok MN, Sternberg EM, & Thayer JF (2019). Heart rate variability and inflammation: A meta-analysis of human studies. Brain, Behavior, and Immunity, 80, 219–226. 10.1016/j.bbi.2019.03.009 [DOI] [PubMed] [Google Scholar]
  76. Zak RS, Zitser J, Jones HJ, Gilliss CL, & Lee KA (2022). Sleep self-report and actigraphy measures in healthy midlife women: Validity of the Pittsburgh Sleep Quality Index. Journal of Women’s Health, jwh.2021.0328. 10.1089/jwh.2021.0328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Zhai L, Zhang H, & Zhang D (2015). Sleep duration and depression among adults: A meta-analysis of prospective studies. Depression and Anxiety, 32(9), 664–670. 10.1002/da.22386 [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary Material
NIHMS1924933-supplement-Supplementary_Material.docx (25.7KB, docx)

RESOURCES