Abstract
Background and Objectives:
There is a paucity of research on the role of circadian rhythm disruption in Alzheimer disease (AD)-related cognitive impairments in adults with Down syndrome (DS). The aim of this study was to examine the association of the 24-hour rest-activity rhythm with cognition, dementia symptoms, and clinical AD status in adults with DS.
Methods:
In this cross-sectional study, adults with DS aged 25–61 years in the Alzheimer’s Biomarkers Consortium–Down Syndrome underwent wrist-worn actigraphy (≥4 days) and cognitive assessment. Primary variables included interdaily stability, intradaily variability, relative amplitude, most active 10-hour period (M10), and least active 5-hour period (L5). Secondary measures included coefficient of variation of total sleep time, sleep midpoint, sleep efficiency, and the sleep regularity index. Cognitive outcomes included modified Cued Recall Test (mCRT), Wechsler Block Design with Haxby Extension (Block Design), Purdue Pegboard, Cat and Dog Modified Stroop Task, DS Mental Status Examination (DSMSE), National Task Group-Early Detection Screen for Dementia (NTG-EDSD), Dementia Questionnaire for People with Learning Disabilities (DLD), and clinical AD status based on a case consensus process (stable vs mild cognitive impairment [MCI]/dementia). Linear and logistic regression models were adjusted for age, sex, intellectual disability level, site, and obstructive sleep apnea severity, with false discovery rate (FDR) correction.
Results:
Of 115 participants (mean age 40.0 ± 9.2 years; 43.5% female), higher interdaily stability was associated with higher DSMSE scores B = 20.6 (95% CI 5.0–36.2). Higher intradaily variability was associated with worse cognitive performance and increased dementia symptoms: mCRT B = −9.2 (95% CI −15.2 to −3.1), Block Design B = −11.0 (95% CI −19.0 to −3.0), DSMSE B = −12.0 (95% CI −20.1 to −3.9), and DLD-cognitive B = 6.3 (95% CI 3.0–10.5). Lower M10 was associated with increased dementia symptoms: NTG-EDSD B = −0.004 (95% CI −0.008 to −0.001); DLD-cognitive B = −0.004 (95% CI −0.006 to −0.001), and DLD-social B = −0.003 (95% CI −0.005 to −0.0008). All associations remained significant after FDR correction (p < 0.05). Fifteen participants had MCI/dementia. Higher intradaily variability was associated with increased odds of MCI/dementia (OR: 1.45; 95% CI 1.04–2.29) although this was not significant after FDR correction.
Discussion:
Fragmentation and low amplitude of the 24-hour rest-activity rhythm are associated with AD-related cognitive impairment, dementia symptoms, and increased odds of MCI/dementia in adults with DS. Circadian rhythm disruption may contribute to AD-related outcomes in adults with DS and potentially serve as a modifiable risk factor.
Introduction:
The circadian system regulates nearly all physiological and behavioral processes including the sleep-wake cycle and cognitive function. These processes are highly rhythmic and regulated by a central pacemaker in the suprachiasmatic nucleus of the hypothalamus, approximating 24-hour oscillations or circadian rhythms (circa = about, diem = day). Consequently, circadian disruption can lead to fragmentation of the 24-hour rest-activity rhythm, which has been shown to increase the risk of adverse health outcomes including cognitive impairment and Alzheimer disease (AD) dementia.1
A growing number of studies have used actigraphy to evaluate the 24-hour rest-activity rhythm and cognitive outcomes in the general population. For example, in older adults, rest-activity fragmentation was associated with deficits in perceptual speed, semantic and working memory, and visuospatial tasks.2 Longitudinal studies show that fragmentation and lower amplitude of the 24-hour rest-activity rhythm are associated with a higher incidence of mild cognitive impairment (MCI) and dementia.3,4 In addition, sleep-wake timing and sleep-wake regularity (day-to-day stability of sleep-wake patterns) are associated with cognitive function.5
Adults with DS have a >90% lifetime risk of AD dementia because of the triplication of the amyloid precursor protein (APP) gene located on chromosome 21.6 Although the mean age at onset is 53.8 years, variability in the age at onset is large,7 indicating that a potential modifiable factor such as circadian disruption could be influencing the development and progression of AD dementia. Sleep-wake disturbances are highly prevalent in adults with DS. Compared with the general population, adults with DS have poor sleep efficiency, increased wake after sleep onset, and decreased nightly total sleep time with increased daytime napping.8 Although obstructive sleep apnea (OSA) is highly prevalent in individuals with DS,9 there is evidence suggesting that OSA alone does not fully account for these disturbances.10 Circadian and sleep dysregulation likely contribute to the disturbances but their role on cognitive function in adults with DS is poorly understood.
Evidence from animal studies and studies in people without DS indicate that circadian disruption exacerbates amyloid beta deposition and tau pathology indirectly through its effect on the sleep-wake cycle11,12 and potentially directly through a circadian and sleep-independent mechanism.13,14 Given that AD dementia in DS is primarily driven by the overproduction of APP, there is a need to characterize markers of the circadian system and its effect on cognition and AD dementia in adults with DS.
Therefore, the primary aim of this study was to characterize parameters of the 24-hour rest-activity rhythm of adults with DS and to evaluate their associations with cognition and AD clinical status. Our hypothesis was that fragmentation and low amplitude of the 24-hour rest-activity rhythm would be associated with worse cognitive performance, greater dementia symptoms, and increased prevalence of clinical AD status. A secondary aim of this study was to evaluate associations between the timing and regularity of sleep with cognition in adults with DS.
Methods:
Participants
This cross-sectional study included adults with DS enrolled in the Alzheimer’s Biomarkers Consortium–Down Syndrome (ABC-DS) who participated in an auxiliary lifestyle study at four sites (University of Wisconsin–Madison, University of Pittsburgh, Washington University–St. Louis, and University of California–Irvine). The ABC-DS is a longitudinal study of AD biomarkers in adults with DS and has been previously described.15 For this cross-sectional study, data collected at one timepoint were analyzed.
Inclusion criteria for ABC-DS and the auxiliary lifestyle study included (1) age ≥25 years, (2) karyotype to confirm DS (full trisomy, mosaicism, or translocation), and (3) mental age ≥3 years. The primary exclusion criterion was that participants do not have untreated medical or psychiatric conditions that affect cognition. A study partner who is a family member or staff person with regular contact with the adult with DS was asked to participate. Study partners report on the participant’s sociodemographic, medical history, and dementia symptoms.
Standard Protocol Approvals, Registrations and Patient Consents
Consent and/or assent (with consent from a legally authorized representative) was obtained from all participants. This study has internal review board approval and each site has obtained regulatory approval which has been previously described.15
Actigraphy protocol and processing
As part of the lifestyle study, participants with DS wore the Actigraph GT9X (Actigraph, Pensacola, FL) for 7 consecutive 24-hour periods. Participants with DS were sent home with the device at the end of the study visit where collection of all other measures were completed. The study partner and participant with DS also completed a 7-day diary that included a sleep log. Time in bed and time out of bed was calculated using sleep logs and confirmed with movement data by a trained study staff member. Otherwise, movement data were used to estimate time in bed and time out of bed.
Primary variables were the nonparametric measures: interdaily stability, intradaily variability, relative amplitude, most active 10-hour period (M10), and least active 5-hour period (L5).16 Interdaily stability provides an index of how consistently rest-activity rhythms are maintained day-to-day across the data collection period and ranges from a score of 0.0 (no consistency) to 1.0 (perfect consistency). A higher value for interdaily stability indicates greater stability across days. Intradaily variability provides information on rest-activity fragmentation and is an index quantifying the frequency of transitions between low-activity periods and high-activity periods in a 24-hour day. Intradaily variability ranges from 0.0 (indicating that transitions are few) to 2.0 (indicating that transitions are highly fragmented). Lower values for intradaily variability indicate more stable and consistent rest-activity rhythm within a 24-hour period. M10 is the mean activity of the most active 10 hours of the day and L5 is the mean activity of the least active consecutive 5 hours of the day. Relative amplitude is the difference between nighttime and daytime activity and is calculated as a ratio of the difference between M10 and L5 divided by their sum.
Nonparametric analyses were used because of limited previous data on circadian rhythms in adults with DS and the duration of actigraphy in our cohort (4–7 consecutive 24-hour periods). Parametric analyses, most commonly using a cosinor model, apply a cosine curve to data (actigraphy) that are often nonsinusoidal and require several harmonics to acquire a better fit to the actigraphy data.
Secondary measures included total sleep time, sleep midpoint, sleep efficiency; the coefficient of variation (CV) of total sleep time, sleep midpoint, and sleep efficiency; and the sleep regularity index. Sleep midpoint was defined as the time halfway between sleep onset and final wake time. Sleep efficiency was computed as the ratio of total sleep time to time in bed, expressed as a percentage. The CV is a normalized measure of dispersion and allows comparison of the intraindividual sleep variability. For each participant, nightly values were extracted across all valid nights of actigraphy recording. The CV was calculated as the SD divided by the mean of each metric, multiplied by 100, yielding a unitless measure of relative variability: CV = (SD/Mean)x100.17 Higher CV values indicate greater night-to-night variability in the corresponding sleep parameter. The sleep regularity index captures probability of an individual being in the same state (asleep or awake) at any two time points 24 hours apart.18 A higher sleep regularity index indicates more regular sleep patterns.
Sleep variables were processed using the Cole-Kripke algorithm in ActiLife 6 software (Ametris, Pensacola, FL).19 The sleep regularity index was analyzed with “GGIR” package (version 3.2-6) for R using 1 second bins.20 For nonparametric measures of the 24-hour rest-activity rhythm, nonwear time was evaluated for each participant. Nonwear time was ~3 hours for three participants, ~7 hours for one participant, ~10 hours for two participants, and ~16 hours for one participant. Activity counts of nonwear time for these 7 participants were imputed using the median time of day method.21 All other participants had <2 hours of nonwear time. The nonparametric measures were then obtained using the “nparACT” package (version 0.8) for R.22
Sleep-disordered breathing
For the covariate of OSA severity, participants underwent 1 night of home sleep apnea testing with the WatchPAT-300 device (WatchPAT; Itamar Medical Inc., Atlanta, GA). Scoring of hypopneas required SpO2 drop ≥4% from baseline. The peripheral Apnea-Hypopnea Index (pAHI) was obtained to evaluate OSA severity. Severity ranged from no sleep apnea (pAHI <5.0) to mild (pAHI: 5.0–14.9), moderate (pAHI: 15.0–29.9), and severe (pAHI ≥30.0).
Premorbid intellectual disability level
The covariate premorbid intellectual disability (ID) level was based on standardized IQ scores obtained in ABC-DS using the Stanford-Binet Intelligence Scales, Fifth Edition abbreviated IQ23 or the Kaufman Brief Intelligence Test, Second Edition24 before any concerns about AD-related cognitive decline. Premorbid ID was coded as: mild (mental age: 9–14 years), moderate (mental age: 4–8 years), and severe/profound (mental age: <4 years). Mental age was used as a proxy for ID level rather than standardized IQ scores given the limited range in standard scores on the IQ tests which do not allow for differentiation of moderate and severe/profound ID level.
Cognitive Functioning and Dementia Symptoms
Memory
The modified Cued Recall Test (mCRT) was used to assess episodic memory.25 In this test, participants are asked to learn and recall 12 pictures across 3 trials. In each trial, participants first attempt to recall as many pictures as they can without cues (free recall) and are then given a category cue (e.g., “tell me the fruit”; cued recall) for any item not freely recalled. The free and cued scores are summed up to yield a total score that ranges from 0 to 36. The mCRT has high sensitivity and specificity for detecting MCI and dementia status in adults with DS.26
Visuospatial
Visuospatial construction was assessed using the Block Design subtest from the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV) Block Design with Haxby Extension.27,28 In this task, participants replicate patterns using red and white blocks. This task has been shown to be sensitive in detecting MCI and AD dementia in adults with DS.29 Three participants did not complete the Block Design, and their scores were treated as missing.
The Purdue Pegboard assesses fine motor speed by having participants place as many small pegs as possible into a board within 30 seconds with their dominant and then their non-dominant hands.30 The dominant hand score was used for analyses. This has been shown to decline after the presence of elevated PET Aβ and to be indicative of MCI and dementia in adults with DS.31
Executive Functioning
The Cat and Dog Stroop Task assesses executive function using a modified Stroop paradigm that consists of 16 dog and cat pictures.32 The naming trial instructs participants to name the presented pictures of cats and dogs as quickly as possible. A naming trial time and trial error score (the number of incorrect answers) are obtained. The switch trial condition instructs participants to say “dog” when they see a cat (and vice versa). A switch trial time and switch trial error score (the number of incorrect answers) are obtained. Although standard scores are not available for this measure, the rate-correct score (RCS), which combines speed and accuracy, was used.33 RCS = #Correct/(naming trial time+switch trial time). The Cat and Dog Stroop Task has been shown to correlate with biomarkers of early AD pathology (e.g., PET Aβ and tau).34
General Cognition
The Down Syndrome Mental Status Examination (DSMSE) assesses a variety of cognitive domains including the ability to recall personal information, memory, apraxia, language, and visuospatial ability.28 The DSMSE total score was used for analysis and is able to differentiate adults with DS who are cognitively stable from those with MCI and dementia.35 Higher scores generally indicate better cognitive function.
Dementia Symptoms
The National Task Group-Early Detection Screen for Dementia (NTG-EDSD) is an informant-based measure of dementia symptoms.36 It includes 6 domains and yields a total score ranging from 0–61, with a higher score indicating more pronounced dementia symptoms. The total score has been shown to yield greater than 0.80 sensitivity and specificity for detecting symptomatic AD in adults with DS.37
The Dementia Questionnaire for People with Learning Disabilities (DLD) is an informant-based measure of dementia symptoms.38 The Sum of Cognitive Score (DLD–SCS) evaluates short-term and long-term memory as well as orientation. The Sum of Social Score (DLD–SOS) assesses changes in speech, everyday function, mood, engagement in activities, and behavior. High scores indicate greater impairment for both the SCS and SOS. The DLD has been found to be sensitive for detecting AD dementia in adults with DS.39
Clinical Alzheimer Disease Status
Clinical AD status was determined through a case consensus process involving at least 3 study staff (e.g., psychologist, physician, and study coordinator) who participated in the assessment of the individual with DS. All available information from directly administered cognitive tests, informant reports on functioning and dementia symptoms, medical history, recent life events, and clinical blood laboratory tests were reviewed in the context of premorbid ID level. Reviewers were blinded to AD imaging and biofluid biomarker data and actigraphy data. Clinical AD status groups were cognitively stable (no evidence of cognitive or functional decline), mild cognitive impairment (MCI; evidence of cognitive and/or functional decline that is limited in scope), AD dementia (marked cognitive and functional decline), and unable to determine (it was either not clear if declines had occurred or declines had occurred but it was unclear if recent life events or changes in medical history other than AD had caused the decline).
In this analysis, none of the participants had an “unable to determine” clinical AD status. Owing to the limited number of MCI and AD dementia participants (n=10 and 5, respectively), analysis was performed by grouping MCI and AD dementia together.
Statistical Analyses
Statistical analyses were performed with R version 4.4.1. All tests were considered significant at p<0.05 and included 95% CIs. Continuous variables were summarized using means, SD, and ranges. Descriptive statistics were calculated for the overall sample.
Based on a priori hypothesis, primary analysis focused on the nonparametric measures of the rest-activity rhythm: interdaily stability, intradaily variability, relative amplitude, M10, and L5. Unadjusted and adjusted linear regressions were performed with cognitive tests and dementia symptom assessments as outcomes. Covariates included age, sex, premorbid ID level, site, and OSA severity. For clinical AD status, Firth logistic regressions were performed for unadjusted and adjusted models. To facilitate clinical interpretation of effect sizes, Firth logistic regression models used scaled versions of the primary analysis. Interdaily stability, intradaily variability, and relative amplitude range from 0.0–1.0 and 0.0–2.0. Therefore, each variable was multiplied by 10 before modeling such that a 1-unit change in the model corresponds to a 0.1 unit change on the original scale. M10 and L5 were multiplied by 0.01 given count ranges of 100s to 1000s. Each 1-unit change reflects a 100-unit increase on the original scale. All analyses were false discovery rate (FDR) corrected using the Benjamini-Hochberg method.40 Unadjusted and adjusted linear regressions and Firth logistic regressions with FDR correction were repeated for secondary variables: total sleep time, sleep midpoint, sleep efficiency; the CV for these sleep variables; and the sleep regularity index.
Results:
Of the 166 participants with DS enrolled in the lifestyle study, 115 had cognitive data available for analysis and a minimum of 4 24-hour periods of actigraphy data (92.2% with 6-7 days and 7.8% with 4-5 days).41 Mean age was 40.0±9.2 years (range 25–61 years) and there were 50 female participants (43.5%). See Table 1 for a summary of demographic information. Table 2 summarizes the 24-hour rest-activity rhythm variables, sleep variables, cognitive tests and dementia symptom assessments.
Table 1:
Demographic Information
| n = 115 | |
|---|---|
|
| |
| Age, years | |
| Mean ± SD Range |
40.0 ± 9.2 25 – 61 |
|
| |
| Sex, % | |
| Male Female |
65, (56.5) 50, (43.5) |
|
| |
| Race/Ethnicity, % | |
| White White, Hispanic/Latino Black Biracial |
99, (86.1) 11, (8.0) 1, (0.9) 4, (3.5) |
|
| |
| Residence, % | |
| With Family Group Home Independent Living |
86, (74.8) 11, (9.6) 18, (15.7) |
|
| |
| Activity/Work, % | |
| Postsecondary Program Vocational Training Program Adult day program Working in the community Volunteering in community Sheltered workshops |
10, (8.7) 8, (7.0) 22, (19.1) 55, (47.8) 30, (26.1) 19, (16.5) |
|
| |
| Obstructive Sleep Apnea Severity, % | |
| No (AHI < 5) Mild (AHI 5.0 – 14.9) Moderate (AHI 15.0 – 29.9) Severe (AHI ≥ 30) Not Completed |
21, (18.3) 36, (31.3) 24, (20.9) 26, (22.6) 8, (7.0) |
|
| |
| Intellectual Disability Level, % | |
| Mild Moderate Severe |
56, (48.7) 49, (42.6) 10, (8.7) |
Table 2:
Descriptive summaries of non-parametric rest-activity rhythm variables, sleep variables, cognitive tests, and dementia symptom assessments. DLD: Dementia Questionnaire for People with Learning Disabilities, DSMSE: Down Syndrome Mental Status Exam, NTG – EDSD: National Task Group-Early Detection Screen for Dementia, WISC-IV: Wechsler Block Design with Haxby Extension
| n = 115 | ||
|---|---|---|
| Mean ± SD | Range | |
| Interdaily Stability | 0.62 ± 0.12 | 0.33 - 0.85 |
| Intradaily Variability | 0.74 ± 0.24 | 0.29 - 1.67 |
| Relative Amplitude | 0.87 ± 0.09 | 0.53 - 0.99 |
| L5 Activity Counts | 94.19 ± 66.72 | 7.38 - 360.32 |
| M10 Activity Counts | 1359.39 ± 457.67 | 128.33 - 2464.52 |
| Total Sleep Time (minutes) | 399.22 ± 75.53 | 234.29 - 615.57 |
| Sleep Midpoint (hh:mm, hours) | 03:04 ± 1.25 | 00:40 - 08:18 |
| Sleep Efficiency (SE), % | 72.50 ± 10.79 | 32.04 - 97.40 |
| Total Sleep Time – CV | 15.18 ± 8.76 | 2.51 - 49.80 |
| Sleep Midpoint – CV | 2.40 ± 1.82 | 0.00 - 10.18 |
| Sleep Efficiency - CV | 12.85 ± 7.84 | 1.99 - 43.72 |
| Sleep Regularity Index | 56.99 ± 11.19 | 19.21 - 79.61 |
| Modified Cued Recall Test | 30.02 ± 8.77 | 0.00 - 36.00 |
| Block Design | 22.80 ± 11.63 | 0.00 - 47.00 |
| Cat and Dog Stroop Task | 0.42 ± 0.23 | 0.00 - 1.03 |
| Purdue Pegboard | 6.63 ± 2.28 | 0.00 - 11.00 |
| DSMSE | 63.89 ± 14.52 | 15.00 - 83.00 |
| NTG – EDSD | 4.87 ± 8.41 | 0.00 - 41.00 |
| DLD - Cognitive Score | 4.71 ± 6.71 | 0.00 - 27.00 |
| DLD - Social Score | 4.73 ± 5.02 | 0.00 - 23.00 |
In the adjusted model, interdaily stability was positively associated with DSMSE (unstandardized regression coefficient (B) = 20.61 [95% CI 5.01–36.20], standardized beta coefficient (β) = 0.18, and FDR p = 0.046) (Table 3, Figure 1). Intradaily variability was negatively associated with mCRT (B = −9.17 [95% CI −15.23 to −3.11], β = −0.25, FDR p = 0.035), Block Design (B = −11.04 [95% CI −19.03 to −3.04], β = −0.23, FDR p = 0.041), and DSMSE (B = −12.01 [95% CI −20.06 to −3.95], β = −0.20, FDR p = 0.035), and positively associated with DLD–SCS (B = 6.26 [95% CI 2.05–10.47], β = 0.22, FDR p = 0.035) (Table 3, Figure 2).
Table 3:
Linear regression of primary variables, cognitive tests, and dementia symptom assessments.
| Interdaily Stability | Intradaily Variability | Relative Amplitude | M10 | L5 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Unadjusted | Adjusted | Unadjusted | Adjusted | Unadjusted | Adjusted | Unadjusted | Adjusted | Unadjusted | Adjusted | ||
| Modified Cued Recall Test | B (95% CI) | 7.32 (−5.79, 20.44) | 9.65 (−2.34, 21.63) | −8.67 (−15.29, −2.05)a | −9.17 (−15.23, −3.11)a | 23.99 (6.83, 41.15)a | 17.73 (1.72, 33.75) | 0.01 (0.00, 0.01)a | 0.00 (0.00, 0.01) | 0.00 (−0.02, 0.03) | 0.00 (−0.03, 0.02) |
| SE | 6.62 | 6.04 | 3.34a | 3.06a | 8.66a | 8.08 | 0.00a | 0.00 | 0.01 | 0.01 | |
| Block Design | B (95% CI) | 16.09 (−1.15, 33.32) | 10.71 (−5.04, 26.46) | −14.20 (−22.84, −5.55)a | −11.04 (−19.03, −3.05)a | 19.44 (−3.81, 42.69) | 6.92 (−14.51, 28.35) | 0.01 (0.00, 0.01)a | 0.00 (0.00, 0.01) | 0.00 (−0.03, 0.04) | 0.00 (−0.03, 0.03) |
| SE | 8.70 | 7.94 | 4.36a | 4.03a | 11.74 | 10.81 | 0.00a | 0.00 | 0.02 | 0.01 | |
| Cat and Dog Stroop Task | B (95% CI) | 0.07 (−0.27, 0.41) | 0.03 (−0.28, 0.34) | −0.08 (−0.25, 0.10) | −0.04 (−0.20, 0.12) | 0.45 (0.00, 0.91) | 0.29 (−0.13, 0.70) | 0.00 (0.00, 0.00)a | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) |
| SE | 0.17 | 0.16 | 0.09 | 0.08 | 0.23 | 0.21 | 0.00a | 0.00 | 0.00 | 0.00 | |
| Purdue Pegboard | B (95% CI) | 3.28 (−0.10, 6.66) | 2.74 (−0.25, 5.74) | −1.44 (−3.20, 0.31) | −1.19 (−2.76, 0.38) | 7.19 (2.77, 11.61)a | 4.57 (0.56, 8.58) | 0.00 (0.00, 0.00)a | 0.00 (0.00, 0.00) | 0.00 (−0.01, 0.00) | 0.00 (−0.01, 0.00) |
| SE | 1.71 | 1.51 | 0.89 | 0.79 | 2.23a | 2.02 | 0.00a | 0.00 | 0.00 | 0.00 | |
| DSMSE | B (95% CI) | 27.42 (6.18, 48.65)a | 20.61 (5.01, 36.20)a | −16.15 (−27.02, −5.27)a | −12.01 (−20.06, −3.95)a | 42.71 (14.43, 70.99)a | 25.54 (4.36, 46.73) | 0.01 (0.01, 0.02)a | 0.00 (0.00, 0.01) | 0.00 (−0.04, 0.04) | −0.01 (−0.04, 0.02) |
| SE | 10.72a | 7.86a | 5.49a | 4.06a | 14.27a | 10.68 | 0.00a | 0.00 | 0.02 | 0.01 | |
| NTG - EDSD | B (95% CI) | −8.86 (−21.40, 3.68) | −10.88 (−23.65, 1.89) | 5.61 (−0.84, 12.06) | 6.89 (0.27, 13.50) | −21.34 (−37.88, −4.80)a | −19.83 (−36.89, −2.76) | −0.01 (−0.01, 0.00)a | 0.00 (−0.01, 0.00)a | 0.00 (−0.02, 0.02) | 0.00 (−0.02, 0.02) |
| SE | 6.33 | 6.44 | 3.26 | 3.33 | 8.35a | 8.60 | 0.00a | 0.00a | 0.01 | 0.01 | |
| DLD - SCS | B (95% CI) | −8.25 (−18.22, 1.72) | −8.61 (−16.85, −0.36) | 7.05 (2.01, 12.09)a | 6.26 (2.05, 10.47)a | −16.39 (−29.61, −3.17) | −12.83 (−23.93, −1.74) | −0.01 (−0.01, 0.00)a | 0.00 (−0.01, 0.00)a | −0.01 (−0.03, 0.01) | 0.00 (−0.02, 0.01) |
| SE | 5.03 | 4.16 | 2.55a | 2.12a | 6.67 | 5.59 | 0.00a | 0.00a | 0.01 | 0.01 | |
| DLD - SOS | B (95% CI) | −5.05 (−12.53, 2.44) | −4.79 (−12.37, 2.79) | 4.35 (0.54, 8.16) | 4.49 (0.61, 8.37) | −9.34 (−19.33, 0.66) | −8.18 (−18.38, 2.01) | 0.00 (−0.01, 0.00)a | 0.00 (−0.01, 0.00)a | 0.00 (−0.02, 0.01) | 0.00 (−0.02, 0.01) |
| SE | 3.78 | 3.82 | 1.92 | 1.96 | 5.05 | 5.14 | 0.00a | 0.00a | 0.01 | 0.01 | |
Adjusted = adjusted for age, sex, premorbid intellectual disability level, site, and obstructive sleep apnea severity. Some significant values may show 0.00 due to rounding.
FDR adjusted p < 0.05.
B = unstandardized regression coefficient, DLD = Dementia Questionnaire for People with Learning Disabilities – SCS = sum of cognitive score and SOS = sum of social score, DSMSE = Down Syndrome Mental Status Exam, NTG – EDSD = National Task Group-Early Detection Screen for Dementia, Block Design = Wechsler IV Block Design with Haxby Extension.
Figure 1: Interdaily Stability, Cognitive Function and Dementia Symptoms.
Scatter plots with regression line and 95% CI of interdaily stability and cognitive tests (first 5 panels), and dementia symptom assessments (last 3 panels) with standardized β and false discovery rate (FDR) corrected p values. The y-axes are outcome scores, and the x-axes are units of interdaily stability which ranges from 0.0 to 1.0. All models were adjusted for age, sex, premorbid intellectual disability level, site, and obstructive sleep apnea severity. DLD = Dementia Questionnaire for People with Learning Disabilities; DSMSE = Down Syndrome Mental Status Exam; NTG-EDSD = National Task Group-Early Detection Screen for Dementia.
Figure 2: Intradaily Variability, Cognitive Function and Dementia Symptoms.
Scatter plots with regression line and 95% CI of intradaily variability and cognitive tests (first 5 panels), and dementia symptom assessments (last 3 panels) with standardized β and false discovery rate (FDR) corrected p values. The y-axes are outcome scores, and the x-axes are units of intradaily variability which ranges from 0.0 to 2.0. All models were adjusted for age, sex, premorbid intellectual disability level, site, and obstructive sleep apnea severity. DLD = Dementia Questionnaire for People with Learning Disabilities; DSMSE = Down Syndrome Mental Status Exam; NTG-EDSD = National Task Group-Early Detection Screen for Dementia.
Relative amplitude was positively associated with mCRT (B = 22.10 [95% CI 6.02–38.19], β = 0.26, FDR p = 0.025), Purdue Pegboard (B = 6.58 [95% CI 2.31–10.86], β = 0.29, FDR p = 0.011), and DSMSE (B = 42.71 [95% CI 14.43–70.99], β = 0.27, FDR p = 0.035), and negatively associated with NTG-EDSD (B = −21.34 [95% CI −37.88 to −4.80], β = −0.23, FDR p = 0.048) for the unadjusted model only (Table 3, Figure 3). M10 was positively associated with mCRT (B = 0.006 [95% CI 0.003–0.010], β = 0.32, FDR p = 0.019), Block Design (B = 0.006 [95% CI 0.002–0.011], β = 0.25, FDR p = 0.043), Cat and Dog (B = 0.0001 [95% CI 0.000–0.0002], β = 0.24, FDR p = 0.046), Purdue Pegboard (B = 0.002 [95% CI 0.001–0.003], β = 0.33, FDR p = 0.014), and DSMSE (B = 0.012 [95% CI 0.006–0.017], β = 0.37, FDR p = 0.010) in the unadjusted model only, and negatively associated with NTG-EDSD (adjusted model: B = −0.005 [95% CI −0.008 to −0.001], β = −0.26, FDR p = 0.046), DLD–SCS (adjusted model: B = −0.004 [95% CI −0.006 to −0.001], β = −0.25, FDR p = 0.031), and DLD–SOS (adjusted model: B = −0.003 [95% CI −0.005 to −0.001], β = −0.27, FDR p = 0.041) for both the unadjusted and adjusted models (Table 3).
Figure 3: Relative Amplitude, Cognitive Function and Dementia Symptoms.
Scatter plots with regression line and 95% CI of relative amplitude and cognitive tests (first 5 panels), and dementia symptom assessments (last 3 panels) with standardized β and false discovery rate (FDR) corrected p values. The y-axes are outcome scores, and the x-axes are units of relative amplitude which ranges from 0.0 to 1.0. All models were adjusted for age, sex, premorbid intellectual disability level, site, and obstructive sleep apnea severity. DLD = Dementia Questionnaire for People with Learning Disabilities; DSMSE = Down Syndrome Mental Status Exam; NTG-EDSD = National Task Group-Early Detection Screen for Dementia.
There were 15 participants with MCI/dementia. In the adjusted model, higher intradaily variability was associated with higher odds of MCI/dementia status (OR = 1.45 [95% CI 1.04–2.29], p = 0.018) but was no longer significant after FDR correction of p values (FDR p = 0.33) (Table 4). There were no significant associations for interdaily stability, relative amplitude, M10, and L5 in the adjusted model (see Table 4).
Table 4:
Clinical Alzheimer disease status based on binary outcome of stable vs MCI/dementia adjusted for age, sex, premorbid intellectual disability level, site, and obstructive sleep apnea severity. All Firth logistic regression models used scaled versions of primary predictors. Odds ratio from all Firth logistic regression represent the change in odds of MCI/dementia per 0.1 unit increase in interdaily stability, intradaily variability, and relative amplitude and per 100-unit increase for M10 and L5. MCI n = 10, Dementia n = 5. Firth logistic regression p values and FDR corrected p values are reported. B = unstandardized regression coefficient, OR = odds ratio.
| Clinical Alzheimer Disease Status | |||||
|---|---|---|---|---|---|
|
| |||||
| n = 115 | |||||
|
| |||||
| B (95% CI) | SE | OR (95% CI) | p value | FDR p value | |
| Interdaily stability | −0.43 (−1.03, 0.07) | 0.24 | 0.65 (0.36, 1.07) | 0.09 | 0.34 |
| Intradaily variability | 0.37 (0.04, 0.83) | 0.16 | 1.45 (1.04, 2.29) | 0.02 | 0.33 |
| Relative amplitude | −0.55 (−1.44, 0.12) | 0.33 | 0.58 (0.24, 1.12) | 0.11 | 0.34 |
| Least active 5-h | 0.30 (−0.56, 1.22) | 0.40 | 1.35 (0.57, 3.40) | 0.49 | 0.51 |
| Most active 10-h | −0.14 (−0.37, 0.03) | 0.08 | 0.87 (0.69, 1.03) | 0.11 | 0.34 |
Of the sleep variables, the sleep regularity index was negatively associated with DLD–SCS (B = −0.200 [95% CI −0.305 to −0.94], β = −0.33, FDR p = 0.036) in the unadjusted model only (eTables 1 and 2). The other sleep variables and CV of these sleep variables were not associated with any cognitive test, dementia symptom assessment, and clinical AD status (eTables 1 and 2).
Discussion:
We found that higher fragmentation and lower amplitude of the 24-hour rest-activity rhythm are present in adults with DS and are associated with worse cognitive performance and increased prevalence of dementia symptoms. This was most notable for intradaily variability, which represents rest-activity fragmentation within a 24-hour period, and M10, the most active 10-hour period, after controlling for age, sex, premorbid ID level, site, and OSA severity. No significant associations were found for L5 or for the secondary measures (total sleep time, sleep efficiency, sleep midpoint, CV of these variables, and the sleep regularity index) after controlling for covariates. Although only 13% of participants were categorized as MCI/dementia, the odds of MCI/dementia were higher in those with higher intradaily variability although this was not significant after FDR correction.
Only one prior study has included young adults with DS when examining the 24-hour rest-activity rhythm, focusing on 3 age groups with DS: children 6–10 years, adolescents 11–18 years, and young adults 19–26 years.42 The interdaily stability and intradaily variability in their 10 young adults with DS were similar to our findings. Of interest, they showed worsening of interdaily stability and intradaily variability in a linear trajectory from childhood to adolescence and adulthood. However, we did not find significant differences when evaluating interdaily stability and intradaily variability between age groups stratified by decades (25–61 years; data not shown) suggesting that age-related changes may occur earlier in development. Subsequently, there may be other factors strongly influencing the 24-hour rest-activity rhythm in older adults with DS that are potentially modifiable, such as light/dark exposure.
Although there is a relative lack of studies evaluating the 24-hour rest-activity rhythm and cognitive outcomes in adults with DS, our findings are consistent with studies evaluating individuals who are neurotypical.1 These studies show that higher intradaily variability and lower interdaily stability and relative amplitude are associated with worse performance on cognitive tests assessing several domains including memory, processing speed, and executive function. Similar to our study, higher intradaily variability was most consistently associated with worse performance on cognitive tests. The association of higher intradaily variability with worse scores on the mCRT and Block Design, which represent memory and visuospatial ability, in our study may possibly be specific to AD in adults with DS, which often involves early changes in memory and visuospatial ability.43
In addition to intradaily variability, M10 was associated with a higher level of dementia symptoms as measured by the NTG-EDSD, DLD–SCS, and DLD–SOS. M10 represents activity counts from the most active 10-hour period with a higher M10 indicating a more active lifestyle. The latter assessments are an important part of dementia screening and diagnosis because they include social and behavioral aspects that are not fully captured by cognitive tests. Because caregivers observe individuals with DS throughout the day, M10 and intradaily variability may be good indicators of subtle changes in 24-hour behavior and function related to MCI and AD dementia. Conversely, we did not find an association with L5 because, in our study, L5 typically occurred during the subjective night. Caregivers do not always observe nightly behavior, and there is evidence of discordance between caregiver report and objective measures of sleep in adults with DS.8
Although higher intradaily variability was associated with an increased odds of MCI/dementia before FDR correction of p values, our results should be interpreted with caution owing to the small number of participants with MCI and AD dementia. Still, our findings are consistent with previous cross-sectional and longitudinal studies of the neurotypical population evaluating cognitive status and incident MCI/dementia.1,3,4 In contrast, our study did not find an association of interdaily stability, relative amplitude, or M10 with clinical AD status, which was observed in other studies.1,3,4 The differences could be attributed to the small number of participants with MCI/dementia in our study. Another possibility is the method used to categorize cognitive status: our study determined clinical AD status through a case consensus process which may have been more rigorous compared with other studies. In addition, given their genetic predisposition, individuals with DS could be viewed as an enriched population for early-onset AD dementia. Therefore, our findings may be more reflective of genetically driven AD dementia, those with intellectual disabilities, and/or a combination of these.
A previous smaller study of the ABC-DS (n = 47) investigated the association of several sleep parameters with cognitive function and found a negative correlation between average length of awakenings and executive functioning after adjusting for age; increased awakenings were linked to poorer executive function.44 We attempted to build on these findings by examining sleep variability including the CV of total sleep time and the sleep regularity index. The lack of significant associations between sleep variables (total sleep time, sleep midpoint, and sleep efficiency; CV of these variables; and the sleep regularity index) and cognitive outcomes in the adjusted model was unexpected, especially given the evidence in the neurotypical population showing the negative impact of increased sleep variability on daytime function, executive function and markers of AD.45–47 It may be that these sleep variables only represent a part of the complex 24-hour rest-activity rhythm and that fragmentation of the full 24-hour period, not just limited to the sleep period, may be a stronger factor influencing cognitive impairment and AD dementia in adults with DS. However, further studies are needed to investigate whether this is specific to individuals with DS or is applicable to the broader population at risk for AD dementia.
Our study is an important step toward the understanding of how circadian rhythms affect cognitive functioning and AD in adults with DS. Drawing from the neurotypical population, potential mechanisms linking circadian and sleep disruption with AD in adults with DS include decreased glymphatic clearance and circadian dysregulation of astrocyte/microglia function, leading to increased amyloid beta deposition and higher permeability of the blood-brain barrier.48,49 Although most individuals with DS have the characteristic neuropathologic changes of AD by the age of 40 years, the age at onset of clinical AD dementia occurs more than a decade later and is highly variable across individuals.7,50 Our study suggests that fragmentation and low amplitude of the 24-hour rest-activity rhythm can negatively affect cognitive function in adults with DS and could represent a modifiable factor for improving cognitive performance and potentially delay clinical AD dementia. One potential circadian-based intervention is bright light therapy which has been shown to increase interdaily stability and reduce intradaily variability in AD, but studies are needed to investigate its effect on cognition in DS. Future studies should incorporate (1) circadian rhythm synchronizing agents such as light exposure, (2) biomarkers and imaging markers of AD dementia to better understand the mechanistic relationship of circadian disruption on AD dementia, and (3) longitudinal analyses to assess directionality.
Our study has several strengths. This is one of the largest cohorts of adults with DS with good representation of male and female sex. We used actigraphy rather than self- or informant-reports to objectively measure the 24-hour rest-activity rhythm. We not only evaluated objective measures of cognitive function but also included informant-based assessments of dementia symptoms and clinically adjudicated AD status. Finally, we controlled for several covariates that may have confounded our results including premorbid ID level and OSA severity.
There are also several limitations. First, participants were predominantly White and therefore, our results may not be generalizable to the general population. Second, the cross-sectional analysis did not allow inference into directionality of the effects. Third, there were a small number of participants (13%) who had MCI and AD dementia and although our analyses were consistent with other studies in the general population, this needs to be taken into consideration when interpreting our findings. Fourth, we did not have a neurotypical control group. Finally, there may have been other residual confounding factors that were not accounted for in this study. These include depression, medications that affect sleep, BMI, total daytime activity, and seasonal effects. We did not have measurements of the endogenous circadian system such as melatonin levels, light exposure data, or assessment of other sleep abnormalities with in-laboratory polysomnography. We also did not evaluate biomarkers or imaging markers of AD dementia.
In conclusion, our study provides support for the role of circadian disruption in cognitive impairment and AD dementia in adults with DS. Specifically, fragmentation and low amplitude of the 24-hour rest-activity rhythm are associated with more AD-related cognitive impairment and dementia symptoms, and increased odds of MCI/Dementia in adults with DS. Future studies should (1) examine the relationship between circadian disruption and AD biomarkers, (2) perform longitudinal analyses to evaluate directionality and (3) evaluate for modifiable factors of circadian disruption that have the potential to improve circadian health and attenuate the development and progression of AD dementia in adults with DS.
Supplementary Material
Appendix: Coinvestigators
| Site | Name | Role | Contribution |
|---|---|---|---|
| University of Pittsburgh (Coordinating Center), PA | Cathy Wolfe | Study Coordinator | Collected Data |
| Columbia University Irving Medical Center, NY | Joseph Lee, DrPH | Site PI | Acquired Funding Supervised Personnel Collected Data |
| Deborah Pang | Project Coordinator | Collected Data | |
| The New York State Institute for Basic Research in Developmental Disabilities, NY | Sharon Krinsky-McHale, PhD | Site PI | Acquired Funding Supervised Personnel Collected Data |
| Deborah Pang | Study Coordinator | Collected Data | |
| Harvard Medical School, Massachusetts General Hospital, MA | Florence Lai, MD | Site PI | Acquired Funding Supervised Personnel Collected Data |
| Diana Rosas, MD | Site PI | Acquired Funding Supervised Personnel Collected Data |
|
| Gabriella Leka | Study Coordinator | Collected Data | |
| University of Wisconsin-Madison, Waisman Center, WI | Renee Makuch | Study Coordinator | Collected Data |
| University of Cambridge, UK | Shahid Zaman, MD, PhD | Site PI | Acquired Funding Supervised Personnel Collected Data |
| Malwina Filipczuk | Study Coordinator | Collected Data | |
| Jessica Beresford-Webb | Study Coordinator | Collected Data | |
| University of California-Irvine, CA | Ira T. Lott, MD | Site PI | Acquired Funding Supervised Personnel Collected Data |
| Eric Doran | Study Manager | Collected Data | |
| University of Kentucky: Sanders-Brown Center on Aging and the Kentucky Neuroscience Institute, KY | Frederick Schmitt, PhD | Site PI | Acquired Funding Supervised Personnel Collected Data |
| Roberta Davis | Study Coordinator | Collected Data | |
| Washington University in St. Louis School of Medicine, MO | Brittany Nelson | Study Coordinator | Collected Data |
| University of Kansas Medical Center, KS | Lauren Ptomey, PhD | Site PI | Acquired Funding Supervised Personnel Collected Data |
| Jeffrey Burns, MD | Site PI | Acquired Funding Supervised Personnel Collected Data |
|
| Keri Cox | Study Coordinator | Collected Data |
Data Availability
Anonymized data not published within this article will be made available by request from any qualified investigator.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Anonymized data not published within this article will be made available by request from any qualified investigator.