Age differences in the association between sleep and Alzheimer's disease biomarkers in the EPAD cohort

Abstract Introduction We aimed to determine the independent association between sleep quality and Alzheimer's disease (AD) biomarkers, and whether the associations differ with age. Methods We included 1240 individuals aged ≥50, without dementia from the European Prevention of Alzheimer's Disease v1500.0 dataset. Linear regression was used to examine Pittsburgh Sleep Quality Index (PSQI) scores against cerebrospinal fluid (CSF) phosphorylated tau/β‐amyloid ratio (p‐tau/Aβ42) for the entire sample and via age tertiles. Models controlled for demographic, clinical, genetic, vascular, and neuroimaging variables. Results For the youngest age tertile, shorter sleep duration and higher sleep efficiency were associated with greater p‐tau/Aβ42 ratio. For the oldest tertile, longer sleep latency was associated with greater p‐tau/Aβ42. Discussion Differential relationships between sleep and AD pathology depend on age. Short sleep duration and sleep efficiency are relevant in middle age whereas time taken to fall asleep is more closely linked to AD biomarkers in later life. Highlights This study shows age differences in the link between sleep and AD biomarkers. Shorter sleep was associated with greater p‐tau/Aβ42 ratio in middle age. The association was independent of genetic, vascular, and neuroimaging markers of AD.


INTRODUCTION
The mechanisms by which sleep disturbances may contribute to future dementia risk are not yet known. For Alzheimer's disease (AD) specifically, prior work using positron emission tomography (PET) in relatively small cognitively intact samples has shown that greater sleep latency 2 and duration 3 are linked to higher brain Aβ deposition. Using cerebrospinal fluid (CSF), a recent analysis of 736 cognitively intact participants in their sixties showed that both short and long selfreported sleep duration were associated with greater levels of Aβ (CSF Aβ42 and Aβ42/40). 4 While amyloid deposition is associated with low CSF Aβ42, 5 the ratio of CSF phosphorylated tau (p-tau)/Aβ  has also been associated with increased brain amyloid 6 and is superior to single biomarkers at predicting risk of decline and conversion to dementia. 7 Whereas prior biomarker studies have variously controlled for contributors to Aβ accumulation such as age, sex, education, and apolipoprotein E ε4 (ApoE4) status, they have not simultaneously adjusted for concomitant factors linked to both sleep and dementia such as depression, cardiovascular risk, body mass index (BMI), smoking. or psychotropic use, as well as other known neurobiological correlates of dementia including white matter lesions and hippocampal volumes. In determining the specific and unique association of sleep with AD and dementia pathology, it is important to glean a clearer insight of the relative contribution of sleep disturbance beyond these other confounds and/or markers of disease, which in turn will advance our understanding of the pathophysiology of disease and also assist with directing screening approaches and the design of longitudinal studies to confirm these associative relationships.
Given dementia pathology begins to accumulate in the brain up to two decades before clinical symptoms, 8 it is important to elucidate if there are differential associations between sleep, age, and the presence of dementia biomarkers in midlife compared to in older aged or elderly samples. With regard to other key risks, systolic blood pressure 9 appears to be linked to cognitive decline only in the 6th decade (ages 50-59, but not in other age bands), the risk of atrial fibrillation is most pronounced in those younger than 67 years 10 and hypercholesterolemia also carries the strongest risk in midlife. 11 Prior work examining sleep, however, has tended to focus on older samples in their seventies, and no known studies have examined the sleep, age, and biomarker inter-relationships within the one sample. Longitudinal data from the Whitehall study 12 showed that the link between sleep duration and dementia 25 years later was evident from midlife and recent analysis of UK Biobank participants 13 found that higher genetic liability to AD influenced sleep duration only among those aged 55 years and older, implying potential age differences in the mechanistic pathways linking sleep disturbances and AD. Understanding the differential age associations between sleep and AD will have important implications for sleep screening and targeted interventions during midlife, potentially slowing cognitive decline and reducing the burden of dementia in later life.
In this study, we aimed to determine in a large sample of middleto older-aged individuals without dementia, whether self-reported sleep duration, latency, and efficiency were associated with CSF AD biomarkers, and whether such relationships might differ by age.

Measures
CSF samples were collected according to harmonized preclinical protocol for measurement of p-tau and Aβ42 (analysis by automated Roche Elecsys System 14 ), from which a p-tau/Aβ42 ratio was calculated.
Self-reported sleep quality was first assessed with the PSQI 16 total score, where a score > 5 is considered to be indicative of poor sleep quality (score range 0-21). We also examined the PSQI component scores for sleep duration, latency, and efficiency.

Statistical analysis
For linear regressions, the skewed p-tau/Aβ42 ratio was transformed to a normal distribution with ordered quantile normalization. 22  In all models, the p-tau/Aβ42 ratio was included as the outcome variable, with age, sex, depressive symptoms, ApoE4 status, vascular risk, hippocampal volume, and WMH volume included as covariates. We first performed multiple linear regressions for the PSQI total score and p-tau/Aβ42 ratio ( Table 2, Model 1) and repeated this with the inclusion of the interaction between age and the PSQI total score ( Table 2, Model 2). We then examined the association between the component scores for sleep duration, latency, and efficiency and p-tau/Aβ42 ratio (Model 3, Table 3), followed by a model with all age × component score interactions (Model 4, Table 3).
Next, in order to examine the associations between sleep and AD pathology across age bands, the sample was split into age tertiles, and the relative contribution of each component was modeled after adjustment for other confounders or causal contributors to AD pathology (Models 5, 6, 7 Table 4). Although we a priori selected model variables based on their relationship to AD risk, sleep, or both, we considered that some of the variables (e.g., depressive symptoms or vascular risk) may be on the causal path between sleep and AD. To test if inclusion of these variables attenuated the relationships between sleep and AD, we performed the same analyses with only age, sex, and ApoE4 status as covariates.
The relative importance of the sleep variables and covariates was investigated by decomposition 23

Participants
After applying the study specific exclusion criteria, 1240/1500 participants were included for analysis. Characteristics of the included participants are presented in Table 1.

Sleep quality
For the entire sample, Table 2 shows that the PSQI total score was not a significant predictor of p-tau/Aβ42 ratio after controlling for the covariates (Model 1) and there was no interaction between age and PSQI total score (Model 2).

Component scores of sleep duration, latency, and efficiency
For the entire sample, Table 3  To further explore the effect of age, we stratified the cohort by age into tertiles and again tested each component score and the covariates. These models are shown in Table 4 and Figure 1 displays relative proportions of each variable expressed as explained variance only (normalized to sum to 1).
For the youngest tertile (Model 5), aged 50-62 years, shorter sleep duration and higher sleep efficiency were significantly associated with higher p-tau/Aβ42 ratio (i.e., greater AD pathology). In this group, sleep efficiency had high relative importance or R 2 -greater than age, TA B L E 1 Baseline characteristics for all participants included in analysis

DISCUSSION
In this study, we assessed whether self-reported sleep quality, and specific aspects of sleep disturbance are associated with AD pathology in a large sample of older adults without dementia while accounting for several factors including age, sex, depression, ApoE4, vascular risk, BMI, smoking status, use of psychotropics, white matter lesions, and hippocampal volumes. While there was no association between overall perceived sleep quality and p-tau/Aβ42 ratio, we found that in the overall sample of 1240 participants, the associations between sleep latency, duration, and sleep efficiency and AD pathology were dependent on age.
We examined these age interactions further with three specific analyses per age tertile. For the youngest tertile (age 50-62 years) of the EPAD cohort, both shorter sleep duration and higher sleep efficiency were associated with greater p-tau/Aβ42 ratio. However, for the oldest (age 70-88 years) tertile, longer sleep latency was associated with greater p-tau/Aβ42. There were no significant associations  Table 4). See Statistical Analysis for full description and references of R 2 decomposition. Duration, efficiency, latency, Pittsburgh Sleep Quality Index (PSQI) components for sleep duration, sleep efficiency, and sleep latency, respectively; GDS-30, Geriatric Depression Scale -30 -item; ApoE4, apolipoprotein E ε4; Hippo vol, hippocampal volume, mm 3 .
between sleep components and p-tau/Aβ42 ratio for the middle tertile, aged 63-69 years.
Our findings regarding sleep duration are consistent with recent large prospective studies which have shown short sleep in mid-life is linked to increased dementia risk 25 years later. 12 Although both short and long sleep duration have been linked to cognitive decline, 25,26 the follow-up period of these studies has been approximately 10 years or less. This area warrants further study. The findings regarding sleep efficiency in our study, were unexpected, as higher sleep efficiency was linked to greater AD pathology. It is possible that for middle aged adults with low sleep duration, their sleep deprivation is linked with more time spent in bed actually sleeping, however, this remains to be empirically examined or tested. For older adults aged over 69 years, our data suggest that sleep duration is no longer a predominant factor but instead the latency of sleep appears to be more critically linked to AD pathology. However, it is noted that longer sleep latency may be associated with daytime napping, 27 which is also associated with dementia 28 and napping was not examined in the EPAD cohort.
In addition to sleep, the association of other AD risk factors to ptau/Aβ42 also varied by age. For instance, depressive symptoms and vascular risk showed larger relative associations in the youngest group, which is aligned with neurobiological work linking these two risks to dementia. 29 By contrast, age, ApoE4 status, and hippocampal volumes showed larger relative associations with p-tau/Aβ42 ratio in the oldest group.
Overall, our results suggest that after controlling for many confounding factors, the relationship between self-reported sleep and markers of AD pathology may depend on age and this has implications for using sleep parameters as markers for AD risk or intervention targets in AD intervention trials. For example, short sleep duration at younger age may convey increased risk of developing AD while long sleep latency at older age may be a marker for ongoing AD pathogenesis. Establishing the contribution of various aspects of sleep disturbance to AD pathology relative to other AD risk factors will allow for a more personalized approach to both assessing AD risk and appropriately selecting individuals for sleep interventions. In addition, longitudinal studies measuring sleep, cognitive function, and AD biomarkers as well as other emerging biomarkers of neurodegeneration (e.g., neurofilament light, glial fibrillary acidic protein, or vascular dementia biomarkers) are critical to improving our understanding of these inter-relationships, and how they contribute uniquely to AD and other forms of dementia. Indeed, it is noted that for all models, with comprehensive modelling of a number of known risk and causal factors, explained variance was in the range of 15%-22%. Hence, there remains a large proportion of variance in AD pathology unexplained by these models. Additionally, the relative contribution of self-reported sleep to AD pathology was very small (up to a maximum of 2.4% across models), but notably was similar to other potentially modifiable risk factors such as depressive symptoms and vascular risk.

LIMITATIONS AND FUTURE DIRECTIONS
The current study is limited by the use of self-report data, which can be impacted by recall inaccuracies. Subjective sleep measures, particularly of perceived sleep quality, have limited association with more objective sleep measures collected by actigraphy or polysomnography 30 and they do not capture key neurophysiological events integral for optimal sleep and which may provide greater insights into potential disease mechanisms. Additionally, our dataset did not capture obstructive sleep apnea, which is known to be prevalent in older people and linked to AD biomarkers and dementia risk. 31 It is noted that the data pertaining to sleep efficiency may have been driven by the inclusion of unbalanced groups. Finally, it is noted that these data are cross-sectional in nature. Future analyses seeking to support causal inferences may wish to consider the prospective associations between sleep duration, efficiency and latency in relation to the biomarkers for AD and other neurodegenerative diseases, and longitudinal cognitive decline or dementia trajectory.