Associations Between Amyloid Burden,Hypoxemia,Sleep Architecture,and Cognition in Obstructive Sleep Apnea

Abstract

Background:

Obstructive sleep apnea (OSA) is associated with an increased risk of amyloid-β (Aβ) burden, the hallmark of Alzheimer’s disease, and cognitive decline.

Objective:

To determine the differential impacts of hypoxemia and slow-wave sleep disruption on brain amyloid burden, and to explore the effects of hypoxemia, slow-wave sleep disruption, and amyloid burden on cognition in individuals with and without OSA.

Methods:

Thirty-four individuals with confirmed OSA (mean±SD age 57.5±4.1 years; 19 males) and 12 healthy controls (58.5±4.2 years; 6 males) underwent a clinical polysomnogram, a NAV4694 positron emission tomography (PET) scan for Aβ burden, assessment of APOE ɛ status and cognitive assessments. Linear hierarchical regressions were conducted to determine the contributions of demographic and sleep variables on amyloid burden and cognition.

Results:

Aβ burden was associated with nocturnal hypoxemia, and impaired verbal episodic memory, autobiographical memory and set shifting. Hypoxemia was correlated with impaired autobiographical memory, and only set shifting performance remained significantly associated with Aβ burden when controlling for sleep variables.

Conclusions:

Nocturnal hypoxemia was related to brain Aβ burden in this sample of OSA participants. Aβ burden and hypoxemia had differential impacts on cognition. This study reveals aspects of sleep disturbance in OSA that are most strongly associated with brain Aβ burden and poor cognition, which are markers of early Alzheimer’s disease. These findings add weight to the possibility that hypoxemia may be causally related to the development of dementia; however, whether it may be a therapeutic target for dementia prevention in OSA is yet to be determined.

Keywords

Alzheimer’s disease amyloid-β apolipoprotein E gene cognition hypoxemia positron emission tomography sleep apnea syndrome slow wave sleep

INTRODUCTION

Obstructive sleep apnea (OSA) is characterized by cyclic apneas and hypopneas throughout sleep, leading to repeated episodes of hypoxia, followed by subsequent arousals and a return to normal breathing. The prevalence of OSA (defined as an apnea-hypopnea index [AHI] of ≥5) is estimated to be 22% in men and 17% in women, with the risk increasing with age and obesity [1]. OSA is a major public health concern due to its association with higher mortality and an increased risk of cardiovascular disease, including stroke [1, 2].

Persons with untreated OSA commonly exhibit impairments in the cognitive domains of vigilance, psychomotor speed, attention and executive function; meta-analyses are not entirely in agreement as to whether memory and language are also affected [3, 4]. While the underlying mechanisms have not been fully delineated, the cognitive impairments observed in OSA may be due to disrupted sleep, leading to reduced slow wave sleep and excessive daytime sleepiness, and/or intermittent hypoxemia, which is associated with dysfunction and death of neurons [5, 6]. These changes are associated with a range of downstream processes affecting inflammation and cerebral perfusion [7].

A potential mechanism underpinning the cognitive impairment in OSA is the accumulation of intracerebral amyloid-β (Aβ), a peptide associated with Alzheimer’s disease (AD) pathogenesis. High cortical burdens of Aβ in apparently cognitively healthy older people are associated with reduced cognitive function when compared to similar persons without a high Aβ burden [8], with impairments being found in episodic memory, working memory, and language [9 –12]. Older individuals with OSA experience higher rates of cognitive decline and have an increased risk of developing AD [13 –15]. Furthermore, human studies have demonstrated associations between sleep disruption, including OSA, and increased Aβ accumulation in the blood, cerebrospinal fluid (CSF) [16], and brain [17 –19].

A community-based study of cognitively unimpaired older adults demonstrated that those with sleep-disordered breathing had higher levels of cerebral Aβ, which was associated with nocturnal hypoxemia [20]. In healthy older cohorts, poor quality sleep (i.e., lower sleep efficiency and a lower proportion of slow-wave activity) [21] and excessive daytime sleepiness [22] were associated with the subsequent deposition of greater amounts of Aβ, as determined by PET imaging. However, neither sleep quality nor Aβ burden were correlated with cognitive performance [20] or a decline in cognitive performance over time [21]. These findings suggest that while sleep quality and Aβ are linked, it is not a simple linear relationship. Research using animal models indicates that the relationship between Aβ burden and sleep quality is bidirectional. Sleep deprivation, particularly slow-wave sleep disruption, is associated with increased accumulation of Aβ, whereas the presence of Aβ plaques is linked to subsequent impairment in slow-wave activity during sleep [23 –25].

Beyond older age, the strongest risk factor for high Aβ burden and memory decline is the expression of the ɛ4 allele of the apolipoprotein E gene (APOE ɛ4) in both older adults with late onset AD, and in those who are cognitively normal [11]. While some early reports suggested that APOE ɛ4 carriers may also be at higher risk of developing OSA, this was not supported by meta-analysis [26]. However, when ɛ4 carriers do have OSA they tend to have greater levels of cognitive impairment than counterparts lacking APOE ɛ4 [27]. It is not known whether OSA carriers of APOE ɛ4 have heavier burdens of Aβ.

To summarize, a strong body of evidence has established that OSA is associated with impaired cognition and more extensive accumulation of Aβ [15, 28]. However, it is not known whether the Aβ burden of middle-aged individuals with OSA is associated with more extensive cognitive impairments, nor is it known whether Aβ burden in OSA is associated with the extent of nocturnal hypoxemia or sleep disruption. An understanding of these relationships would help to elucidate how OSA increases the risk of AD as well as point to potential therapeutic options. The present study aimed to investigate these relationships by determining whether:

the severity of nocturnal hypoxemia and/or disrupted sleep (arousals and reduced slow wave sleep) are correlated with Aβ burden;

a) Aβ burden is associated with the extent of cognitive impairment; and b) if so, whether such associations remain after considering the independent effects of nocturnal hypoxemia, arousals, and slow-wave sleep disruption.

We hypothesized that: 1) greater severity of nocturnal hypoxemia will be associated with higher Aβ burdens in OSA; 2a) Aβ burden will be significantly associated with impairments in verbal and working memory, and information processing speed; and 2b) Aβ burden will continue to be associated with impaired cognitive performance, even after accounting for the independent effects of slow wave sleep, nocturnal hypoxemia, and arousal index.

METHODS

35 patients with moderate-to-severe untreated OSA (AHI > 15; 16 females) and 12 healthy controls (6 females) participated in the study. Participants were eligible if they were aged 35–75 years, had sufficient English to undertake cognitive testing, and were able to provide informed consent. OSA participants were recruited from the Department of Respiratory and Sleep Medicine, at Austin Health, Melbourne. Eligible OSA participants included consecutive patients who had completed a recent diagnostic sleep study with confirmed OSA (apnea-hypopnea index (AHI) >10 events/hour) and were OSA treatment-naïve. Healthy controls were asymptomatic for sleep disorders (confirmed via polysomnography), and recruited from the community through advertisements, and involvement in previous research studies, using a similar age range and educational attainment as OSA participants. The research was approved by the Austin Health Human Research Ethics Committee (reference number: HREC/16Austin/134) and the study conformed to the Declaration of Helsinki. All participants provided written informed consent.

Exclusion criteria for all participants included major psychiatric conditions (but not a history of depression or anxiety if well managed), a history of neurological conditions that may affect cognition, uncontrolled hypertension, learning disorder, pregnancy, substance abuse, diagnosed cognitive impairment or dementia. An additional exclusion criterion for the control group was having a sleep disorder (i.e., AHI > 10).

Participants underwent a telephone screening interview and were then sent questionnaires to complete on demographics, health, sleep, and mood. They were scheduled to have: i) a neuropsychological assessment; ii) a PET scan; iii) a blood test. Control participants completed a night of laboratory-based or at-home polysomnography to confirm the absence of any sleep-disordered breathing (AHI < 5).

OSA diagnosis (or confirmation of the absence of OSA for controls) was made following clinical overnight polysomnography which was conducted either in the laboratory using the Compumedics E-Series (Abbottsford, Victoria, Australia) or at home using the Compumedics Somté (31% of the OSA group and 25% of the control group conducted in-home studies). Acquired signals were electroencephalogram (F4-M1, C4-M1, O2-M1), left and right electrooculogram, chin electromyogram, electrocardiogram, piezo movement or electromyogram sensors, a nasal pressure cannula, an oronasal thermistor, chest and abdominal respiration bands, body position sensor, and pulse oximetry. Sleep stages, respiratory events and arousals were scored according to American Academy of Sleep Medicine v2.3 criteria and the 2010 Australasian Commentary [29, 30]. Polysomnographic variables derived from the clinical diagnostic report included AHI, non-Rapid Eye Movement (NREM) AHI, REM AHI, oxygen saturation (SaO₂) nadir, oxygen desaturation index 3% (ODI 3%), Arousal Index, Stage 1 Sleep (N1 min and %), Stage 2 Sleep (N2 min and %), Slow-wave sleep (SWS min and %), REM sleep (min and %), and REM onset latency (min). ODI was calculated as the number of oxygen desaturations ≥3% ×60 / total sleep time (min). Sleep studies were staged and scored using the same method and scoring criteria for both groups by a single clinical sleep technician at the Austin Hospital.

PET scans were acquired on a Philips TF64 PET/CT scanner (Philips Healthcare) at the Molecular Imaging Department at Austin Health, Melbourne. Participants received intravenous injection with 185 MBq of the amyloid plaque imaging tracer ¹⁸F-NAV4694. After resting for 50 min, the participant was placed in a supine position in the PET scanner while a 20-min image was acquired. PET scans were analyzed using Computational Analysis of PET from AIBL (CapAIBL) software [31], which allows quantitative PET measurements without relying on magnetic resonance imaging. A continuous measure of global neocortical amyloid burden expressed as the average cortical standardized uptake value to cerebellar cortex ratio (SUVR) was recorded. Participants blood was immunoassayed to determine presence of APOE ɛ4.

The Mini-Mental State Examination was used as a measure of global cognitive status [32]. The Wechsler Test of Adult Reading provided a measure of estimated premorbid intellectual function [33]. The Digit Span Backwards subtest [33] was used as a measure of working memory span. The Logical Memory task was used as a measure of verbal episodic memory [33]. The Rey Complex Figure Test [34] was used as a measure of visual memory. The 30-min delayed score was used as the outcome for both memory tasks. Autobiographical memory was measured using the Autobiographical Memory Test [35], number of specific memories recalled. Information processing speed was assessed by Digit Symbol Coding task. Delis-Kaplan Executive Function System Verbal Fluency Test provided a measure of letter fluency and semantic (category) verbal fluency and switching [36]. Trail Making Test (B minus A) provided a measure of set shifting ability [37]. A 30-min computerized CogState (Cogstate Ltd., Australia) battery included the Groton Maze Test (executive functioning, error monitoring and processing speed), Identification Task (attention), One Card Learning (complex working memory and visual learning), One Back Working Memory Task (simple working memory), and Continuous Paired Associate Learning Task (visuo-spatial associative memory).

Sleepiness, fatigue, and mood were assessed using the Epworth Sleepiness Scale (ESS) [38], the Samn Perelli Fatigue Scale [39], and the Hospital Anxiety and Depression Scale [40], respectively. Sleepiness and mood scales were administered to the OSA participants on the evening prior to the PSG study (as per clinical protocols) and the fatigue scale was administered at the end of the cognitive test battery. All three questionnaires were administered to the control group on the cognitive testing day.

Statistical analyses

All data were analyzed using SPSS 25 (IBM Corp., Armonk, USA). Large outliers on TMT-B-A and ODI 3% (i.e., median±interquartile range×3) were winsorized at one unit outside the next most extreme score in the distribution [41]. ODI 3% was then square-root transformed to normalize the distribution. Independent samples t-tests and chi squared analyses were used to examine group differences in demographic, sleep, and cognition outcomes. Mann Whitney tests were used as a non-parametric alternative, and effect sizes were calculated using Hedge’s g.

While AHI is a clinical measure of OSA disease severity, it does not accurately reflect nocturnal hypoxic burden [42] nor sleep disruption [43], two variables of interest in the current study. Therefore, we instead used the oxygen desaturation index of 3% (ODI 3%) and Arousal Index in predictive models as indices of nocturnal hypoxemia and sleep disruption, respectively. These measures were chosen because of their wide use and availability in clinical sleep scoring software, and because they provide an indication of disease severity. To address Aim 1, a series of separate linear regressions models were conducted across the combined sample to determine the contribution of ODI 3%, Arousal Index, ESS (scores >10) and N3% to the prediction of SUVR, after controlling for age.

To address the Aim 2, linear regression models were conducted to determine whether SUVR is correlated with cognitive performance, after accounting for age and education, which are confounders of cognitive performance. This model (Model 1) was chosen because the association between Aβ and cognition has not yet been explored in OSA. Following this, models accounting for age and education, plus measures of sleep quality (ODI 3%, Arousal Index, N3%), were conducted to determine whether these factors accounted for any additional variance in cognition (Model 2). Significance was set at p < 0.05 for all analyses, and the False Discovery Rate (FDR) correction was applied to minimize the likelihood of Type 1 errors arising from multiple comparisons.

RESULTS

OSA and control participants were equivalent in age, gender, and estimated premorbid intellectual function (Table 1). There were no group differences in global cognitive function, with both groups performing close to ceiling (OSA M (SD) = 28.9 (1.5); healthy controls M (SD) = 29.5 (0.5)). All participants performed at or above the established dementia cut-off of 24, although there was more spread in the OSA group (range = 24–30) compared to the healthy control group (range = 29–30). In contrast, OSA participants had greater BMI, AHI, and ESS, as expected as a function of their diagnosis, and they had a slightly lower education. This group also reported more fatigue following the cognitive test battery and reported higher levels of depressive symptoms. OSA participants had a significantly higher ODI 3% and arousal index, and lower REM% and SWS% when compared to controls (Table 1).

Table 1

Means and standard deviations (SD) of group demographics and sleep variables

	OSA (N = 35)		HC (N = 12)
	Mean	(SD)	Mean	(SD)	p	g
Demographics
Age (y)	57.6	(8.5)	57.2	(8.1)	0.88	0.05
Sex (F %)	45.7		50.0			1.00^a
Education (y)	13.5	(2.7)	14.9	(2.2)	0.113	0.53
Estimated Premorbid IQ	106.1	(9.2)	109.4	(6.0)	0.254	0.38
BMI m/kg²	36.0	(8.7)	24.1	(3.1)	<0.001	1.52
ESS	9.2	(5.6)	4.6	(2.9)	0.001	0.89
Samn Perelli Fatigue Scale	3.6	(1.5)	2.5	(1.3)	0.029	0.74
MMSE	28.9	(1.5)	29.5	(0.5)	0.18	0.44
HADS – depression	5.1	(3.9)	1.4	(1.6)	<0.001	1.04
APOEɛ4^b (N; % positive)	7; 25.9		5; 41.7		0.46^a
SUVR^d	1.25	(0.11)	1.19	(0.14)	0.14	0.50
Sleep variables
TST (min)	331.2	(95.3)	358.5	(78.8)	0.37	0.30
Sleep efficiency (%)	66.1	(19.9)	74.6	(13.7)	0.18	0.45
N1 (%)	13.7	(9.1)	8.9	(4.5)	0.18	0.57
N2 (%)	55.6	(11.0)	55.3	(6.1)	0.93	0.03
N3 (%)	17.4	(13.5)	17.0	(6.3)	0.94	0.03
REM (%)	14.5	(7.2)	17.9	(6.5)	0.09	0.47
AHI	39.8	(18.4)	5.4	(4.4)	<0.001	2.10
AHI – NREM	36.2	(16.9)	4.1	(4.3)	<0.001	2.13
AHI – REM	50.6	(20.9)	11.0	(11.3)	<0.001	2.05
ODI 3% ^c	4.8	(1.5)	1.1	(0.9)	<0.001	2.64
Arousal Index	30.0	(10.3)	16.3	(7.3)	<0.001	1.40

^aFisher’s Exact test, 2-sided. ^bN = 27 in OSA group; ^cODI 3% transformed data; ^dN = 34 in OSA group. AHI, apnea hypopnea index; BMI, body mass index, kg/m²; ESS, Epworth Sleepiness Scale; g, Hedge’s g; HADS, Hospital Anxiety and Depression Scale; HC, healthy controls; MMSE, Mini-Mental State Exam; N1, stage N1 sleep; N2, stage N2 sleep; ODI 3%, oxygen desaturation index ≥3% per hour; OSA, Obstructive Sleep Apnea; REM, percentage time spent in rapid eye movement sleep; SD, standard deviation; %, percentage time spent in stage N3 sleep; SUVR, standardized uptake value ratios; TST, total sleep time.

The PET scan results from one participant were compromised by high tracer retention in the cerebellar cortex reference region and were removed from the analyses, leaving N = 34 OSA participants for the SUVR analyses. There was no mean difference in SUVR between groups. Using an SUVR cut-off for early Aβ accumulation of 1.25 [44], two healthy controls and 14 OSA participants met this criterion. Details of the SUVR results have been reported previously [19]. For the APOE ɛ4 results, data for 8 of the OSA participants was unavailable, leaving N = 27 in the APOE ɛ4 analyses. Seven OSA and 5 controls were APOE ɛ4 positive (Table 1).

Participants with OSA had significantly poorer performance on digits backward (p = 0.003), logical memory delay (p = 0.001), RCFT long delay (p = 0.043), autobiographical memory test (p = 0.001), letter fluency (p = 0.045); category fluency (p = 0.041), and identification speed (p = 0.01), but not on the Trail Making Task or any of the other CogState computerized measures (p > 0.05; see Table 2). Given the lack of group differences in all but one of the computerized CogState measures, these measures were not included in the regression analyses.

Table 2

Cognitive outcomes compared between the OSA and healthy control groups

	OSA (N = 35)		HC (N = 12)
	Mean	(SD)	Mean	(SD)	p	g
Traditional Tests
Logical Memory Delayed Recall	17.37	(6.70)	23.83	(3.93)	0.003	1.21
RCFT Delayed Recall	16.60	(6.49)	21.25	(5.00)	0.029	0.73
Autobiographical Memory Test	6.66	(2.91)	10.25	(2.09)	<0.001	1.39
Digits backward	6.63	(2.51)	8.58	(2.61)	0.026	0.75
Letter Fluency	34.54	(11.79)	42.33	(9.71)	0.045	0.72
Category Fluency	37.03	(6.55)	44.92	(11.10)	0.005	1.06
Digit Symbol Coding	56.74	(12.31)	75.83	(16.20)	<0.001	1.50
Trail Making Test (B-A)	34.62	(22.81)	26.00	(17.15)	0.239	–0.43
Computerized Tests
Detection Speed^**	329.31	(73.42)	361.08	(156.45)	0.378	0.33
Identification Speed^***	583.90	(97.92)	502.42	(66.94)	0.012	–0.91
One Card Learning Accuracy^***	69.46	(7.73)	70.93	(7.71)	0.579	0.17
GML Errors Score^***	99.47	(7.79)	101.58	(6.01)	0.403	0.33
GML Total Errors^***	59.97	(20.40)	54.25	(15.66)	0.389	–0.34
CPA Accuracy^***	0.69	(0.20)	0.79	(0.17)	0.120	0.52

OSA, Obstructive Sleep Apnea; HC, healthy controls; SD, standard deviation; g, Hedge’s g; MMSE, Mini-Mental State Exam; RCFT, Rey-Osterreith Complex Figure Test; GML, Groton Maze Learning; CPA, Continuous Paired Associate Learning Task. ^*OSA N = 27, HC N = 11 due to software issues; ^**OSA N = 29 due to test being added to protocol after data collection commenced, and software issue; ^***OSA N = 30 due to test being added to protocol after data collection commenced.

Prediction of SUVR by sleep outcomes and daytime sleepiness

After accounting for age (R² = 0.28, F(1, 44) = 17.22, standardized β= 0.53, SE < 0.01, p < 0.001), ODI 3% explained an additional 7% of the variance in SUVR (R² = 0.35, F(1, 43) = 4.76, p = 0.035; standardized β= 0.27, SE = 0.01, p = 0.035; Table 3). In contrast, Arousal Index (standardized β= 0.13, SE < 0.01, p = 0.34), N3% (standardized β= –0.21, SE < 0.001, p = 0.11) and ESS (standardized β= 0.02, SE = 0.04, p = 0.88) did not explain any significant variance in SUVR.

Table 3

Summary of linear regression analyses for SUVR by sleep outcomes and daytime sleepiness

	Predictors	β	SE	p
SUVR	ODI 3%	0.27	0.01	0.035
	N3%	0.21	<0.001	0.11
	Arousal Index	0.13	<0.01	0.34
	ESS	0.02	0.04	0.88

ESS, Epworth Sleepiness Scale; ODI 3%, oxygen desaturation index; N3%, percentage time spent in stage 3 sleep; β, standardized beta. Age was controlled for in all models.

Prediction of cognition by sleep and SUVR

For verbal episodic memory, in Model 1, after accounting for demographics (R² = 0.21, F(2, 43) = 5.77, p = 0.006), SUVR explained an additional 8.7% of the variance in Logical memory performance (ΔR² = 0.09, ΔF(1, 42) = 5.18, standardized β= 0.35, SE = 8.3, p = 0.028). In Model 2, after accounting for demographics, sleep variables and Aβ burden did not explain significant additional variance in verbal memory scores (ΔR² = 0.14, ΔF(4, 39) = 2.15, p = 0.093; Table 4).

Table 4

Summary of linear regression analyses for cognitive variables predicted by demographics, disease severity, SWS%, and SUVR

Dependent Variable	Model	Predictors	β	SE	p
Digit Span Backwards	1.	SUVR	–0.31	3.53	0.064
	2.	SUVR	–0.34	3.96	0.074
		ODI 3%	0.01	0.24	0.962
		Arousal Index	–0.19	0.04	0.299
		SWS%	–0.09	0.03	0.574
Logical Memory Delayed Recall	1.	SUVR	–0.35	8.31	0.028
	2.	SUVR	–0.29	9.11	0.091
		ODI 3%	–0.05	0.54	0.784
		Arousal Index	–0.14	0.09	0.404
		SWS%	0.13	0.08	0.362
RCFT Delayed Recall	1.	SUVR	–0.30	8.91	0.089
	2.	SUVR	–0.33	9.93	0.090
		ODI 3%	0.035	0.59	0.861
		Arousal Index	–0.23	0.10	0.225
		SWS%	–0.09	0.09	0.592
Autobiographical Memory Test	1.	SUVR	–0.23	3.90	0.140
	2.	SUVR	–0.06	3.91	0.680
		ODI 3%	–0.47	0.24	0.055
		Arousal Index	0.07	0.04	0.635
		SWS%	0.02	0.03	0.903
Letter Fluency	1.	SUVR	0.03	15.89	0.882
	2.	SUVR	0.18	17.11	0.322
		ODI 3%	–0.39	1.02	0.042
		Arousal Index	0.23	0.17	0.198
		SWS%	–0.09	0.15	0.561
Category Fluency	1.	SUVR	–0.12	11.84	0.503
	2.	SUVR	0.001	12.81	0.996
		ODI 3%	–0.29	0.76	0.132
		Arousal Index	–0.03	0.13	0.864
		SWS%	0.05	0.11	0.328
Digit Symbol Coding	1.	SUVR	–0.37	20.59	0.028
	2.	SUVR	–0.38	21.61	0.031
		ODI 3%	–0.19	1.28	0.300
		Arousal Index	–0.20	0.21	0.244
		SWS%	–0.27	0.19	0.071
Trail Making Test B-A°	1.	SUVR	0.46	23.43	0.007
	2.	SUVR	0.62	25.16	<0.001
		ODI 3%	–0.31	1.50	0.092
		Arousal Index	0.18	0.25	0.306
		SWS%	0.20	0.22	0.183

ODI 3%, oxygen desaturation index; RCFT, Rey-Osterreith Complex Figure Test; N3%, percentage time spent in stage 3 sleep; β, standardized beta. In Model 1, age and education are controlled for prior to including SUVR in a subsequent step. In Model 2, age and education are controlled for prior to including SUVR, ODI 3%, Arousal Index and SWS%.

For autobiographical memory, in Model 1, after accounting for demographics (R² = 0.26, F(2, 43) = 7.35, p = 0.002), SUVR alone did not account for significant additional variance (ΔR² = 0.04, ΔF(1, 42) = 2.26, standardized β= –0.23, SE = 3.90, p = 0.14). In Model 2, after accounting for demographics, sleep variables and SUVR explained an additional 20.1% of the variance (ΔR² = 0.20, ΔF(4, 39) = 3.55, p = 0.015), with ODI 3% accounting for significant unique variance (standardized beta β= –0.47, SE = 0.23, p = 0.005) while SUVR did not (standardized beta β= –0.06, SE = 3.90, p = 0.68).

For digit symbol coding, in Model 1, after accounting for demographics (p > 0.05), SUVR explained an additional 9.7% of the variance in performance (ΔR² = 0.10, ΔF(1, 42) = 5.17, standardized beta β= –0.37, SE = 20.59, p = 0.028). In Model 2, after accounting for demographics, sleep variables and SUVR explained an additional 22% of the variance (ΔR² = 0.22, ΔF(4, 39) = 3.20, p = 0.023; Table 4), with SUVR accounting for significant unique variance (standardized beta β= –0.38, SE = 21.62, p = 0.031).

For Trail Making Test scores, in Model 1, after accounting for demographics (p > 0.05), SUVR explained an additional 15% of the variance (ΔR² = 0.15, ΔF(1, 42) = 7.99, standardized beta β= 0.46, SE = 23.43, p = 0.007). In Model 2, after accounting for demographics, sleep variables and SUVR explained an additional 24% of the variance (ΔR² = 0.24, ΔF(4, 39) = 3.36, p = 0.019; Table 4), with SUVR accounting for significant unique variance (standardized β= 0.62, SE = 25.16, p < 0.001).

Neither SUVR nor any of the sleep variables were significantly associated with Digit Span Backwards, Rey-Osterreith Complex Figure Test delayed recall or Letter or Category fluency (Table 4). Additionally, none of the associations in the cognitive analyses remained significant following FDR correction for multiple comparisons, although we note the heightened risk of Type II errors with this approach.

DISCUSSION

The present study investigated a sample of middle-aged persons with moderate-to-severe untreated OSA and compared them to age-matched healthy controls, with the aim of determining whether nocturnal hypoxemia or indices of sleep quality can account for variance in cortical Aβ burden. The extent of nocturnal hypoxemia accounted for a significant proportion of the variance in Aβ burden, after controlling for age, whereas sleep quality and daytime sleepiness were not predictive of Aβ burden. We also investigated whether the extent of impairment on cognitive tests is associated with Aβ burden or indices of sleep quality. Our findings demonstrated that Aβ burden was associated with impairments in verbal episodic memory, information processing speed and set shifting. Aβ burden continued to account for variance in information processing speed and set shifting performance after controlling for sleep variables. In addition, ODI was associated with poorer autobiographic memory performance. Variance in working memory, verbal fluency or visual memory were not associated with Aβ burden or any indices of sleep quality. Previous studies of OSA have reported that the APOE ɛ4 allele is linked to poorer spatial working memory [45], verbal memory and executive function performance, but this relationship was not observed in those without the allele [27, 46]. Twenty-seven OSA participants were screened for the APOE ɛ4 allele in the current study, and it was present in only 7 OSA patients and 5 controls.

Studies of elderly persons without OSA who suffer from disrupted sleep have reported increased levels of Aβ in the CSF [47, 48], however, those studies did not examine brain burdens of Aβ. The present study demonstrated that nocturnal hypoxemia is associated with Aβ burden. This supports our first hypothesis and is consistent with a postmortem study which demonstrated that Aβ burden in the hippocampus of OSA patients is correlated with ODI [49]. Interestingly, animal studies have reported an equivocal relationship, where chronic intermittent hypoxia has been reported to increase [50] or decrease [51] cerebral and hippocampal burdens of Aβ in transgenic mouse models of AD. Similarly, slow-wave sleep was not a predictor of Aβ burden in the current study, contrary to animal studies that have reported Aβ accumulation during SWS deprivation (for review, see [25]). This difference could be explained by the OSA group experiencing levels of sleep deprivation or SWS restriction that are not as severe as achieved in animal models, and therefore, the association may only be observed in the extreme.

Our study demonstrated that Aβ burden was associated with impairments in verbal episodic memory, information processing speed and set shifting. These findings support our second hypothesis, and are consistent with a meta-analysis of 38 studies which found that Aβ burden in cognitively normal older adults is associated with poorer episodic memory, reduced speed of information processing and impaired executive function [12]. Our study extended these findings by examining the contribution of sleep to these associations. When controlling for these aspects of sleep, verbal episodic memory performance was no longer predicted by Aβ burden; however, Aβ burden remained significantly associated with set-shifting performance and information processing in these adjusted models.

A further novel element of this study was the assessment of episodic autobiographical memory, which was found to be associated with ODI when accounting for sleep variables and Aβ burden. Impaired autobiographical specificity has been previously observed in both OSA [52] and individuals with cognitive impairment [53]. Hippocampal function has been implicated in autobiographical memory retrieval [54]. Given that OSA patients show grey matter atrophy in this brain region [55], coupled with the susceptibility of the hippocampus to hypoxia [56], it is perhaps not surprising that the measure of hypoxemia in the current study was uniquely predictive of impaired autobiographical memory.

A limitation of the present study was the lack of information on the chronicity of OSA. This limitation is common to most studies because individuals can suffer from OSA for many years without being aware of their condition and they typically obtain a clinical diagnosis only when their symptoms have begun to impact on the activities of daily living, although some algorithms to estimate age of onset are emerging [57]. We included standard measures of sleep and respiratory events derived from the PSG, however there are limitations to the robustness of some outcomes, such as the arousal index as a measure of sleep fragmentation. In addition, EEG recording in the at-home remote setting may be less reliable than that recorded in-laboratory, and thus combining both in-laboratory and at-home PSG in the same study is not without its limitations. There may also be some biases arising from comparing community-based controls to a clinical sample; however, clinic-based controls can also carry limitations and biases if they have presented to the laboratory with a sleep issue. We did not examine tau burden, which may play an important role in the degeneration of wake-promoting neurons in AD [58]; nor biomarkers of neurodegeneration which are features of the latest diagnostic criteria for AD [59]. Additionally, the cross-sectional nature of this study precludes us from determining the direction of the associations observed; for instance, nocturnal hypoxemia was associated with Aβ burden, but we cannot be certain whether hypoxemia causes Aβ deposition or whether Aβ deposition increases the severity of nocturnal hypoxemia. Both the nocturnal hypoxemia and sleep disturbances in OSA are treatable, and consistent treatment has been shown to improve cognitive performance and memory deficits. It will be important to investigate whether long-term treatment of OSA can slow or halt the accumulationof Aβ.

The findings of the current study, while preliminary, offer important insights into the relationship between OSA and AD, and highlight hypoxemia as an important correlate of Aβ burden. While sleep disturbances in OSA are treatable, whether hypoxemia and reduced slow-wave sleep are causally related to higher Aβ burden and lower cognition is an important question for future research as the field moves toward targeted interventions for modifiable risk factors to slow the early pathogenesis of AD and the associated declines in cognition.

Footnotes

ACKNOWLEDGMENTS

We thank the participants of this study for their time and effort; Dr. Rachel Schembri, Dr. V Vien Lee, and Ms. Emily Pattison for their assistance with data collection; Miss Veronica Odeke for her assistance with scoring of the PSG data; and A/Prof. Victor Villemagne and Dr. Vincent Dore for assistance with imaging analysis. This research was presented as the Australasian Sleep Association Conference in 2019, with the following published abstract:

Cavuoto, M., Robinson, S., Rowe, C., O’Donoghue, F., & Jackson, M. L. (2019). Amyloid burden and less slow-wave sleep are associated with poor cognition in obstructive sleep apnoea. In Journal of Sleep Research (28) S111.

FUNDING

This research has been funded by RMIT University, the Institute of Breathing and Sleep, The Mason Foundation, and Brain Foundation. We also thank Cogstate for in-kind research support.

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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