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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Neurobiol Aging. 2021 Mar 2;104:102–103. doi: 10.1016/j.neurobiolaging.2021.02.017

Sleep, rest-activity rhythms and aging: a complex web in Alzheimer’s disease?

Kun Hu 1,*, Peng Li 2, Lei Gao 3
PMCID: PMC8722769  NIHMSID: NIHMS1761528  PMID: 33902941

To the Editor,

We would like to congratulate Park and colleagues their recent study (Park et al., 2021). Compelling evidence indicates the links between Alzheimer’s disease (AD) and sleep/circadian disturbances (Musiek and Holtzman, 2016; Leng et al., 2019a). However, when AD starts to affect sleep and circadian function is unclear. A major barrier is due to the fact that AD may progress silently for many years prior to clinical diagnosis (Sperling et al., 2011). The work by Park et al. provided important pilot data for defining/distinguishing sleep/circadian alterations during normal aging and during the progression of AD, especially during preclinical AD. There are three highlights in their study design: (i) Participants were drawn from a wide range of ages (20-90 years old), and across different cognitive stages from normal cognition, mild cognitive impairment (MCI), to dementia. (ii) Positron emission tomography (PET) with Pittsburgh compound was used to assess AD pathology (amyloid beta deposition). (iii) Motor activity levels were continuously recorded for >7 days, starting from the same day as the PET scan (no time lag), and were used for objective assessment of sleep and daily activity rhythms.

Despite the strengths of Park’s study, there are a number of noticeable caveats including the cross-sectional design, a relatively small sample size, and certain inappropriate analytical approaches. Overall, the reported insignificant effects of age and cognitive status at very old ages are highly questionable, and we have serious concerns on their results and conclusions regarding the following three questions.

1. Age effect on sleep?

In contrast to previous studies, Park et al. did not find much by way of the effects of age on sleep measures. They speculated that the difference was due to uncontrolled neurodegenerative conditions in previous studies. One way to test this possibility is to re-examine the age effect by pooling all amyloid-negative and amyloid positive participants. Additionally, three important factors may complicate their results. (i) Sleep scoring in Park’s study was based on only activity counts in each epoch. This is oversimplified. It is better to determine sleep or wakefulness using a weighted sum of the current epoch and several epochs preceding and following the current epoch (Cole et al., 1992; Jean-Louis et al., 2001). (ii) Daytime napping is common in older adults; and excessive daytime napping is associated with cognitive impairment and predicts cognitive decline (Leng et al., 2018, 2019b; Li et al., 2020). It is unclear whether daytime napping was considered in Park’s study, especially when calculating sleep duration. (iii) Chronotype affects almost all sleep/circadian measures (Juda et al., 2013). It is important to adjust for chronotype when examining the age effect in cross-sectional studies.

2. Disappearance of the age effects on daily activity rhythms at very old ages?

Within those cognitively normal participants without amyloid beta deposition, Park et al. reported that the age effects on inter-daily stability (IS) and 24-h amplitude of became not significant at age >75 years old. They attributed the ‘nonlinear’ age effect to the dominant masking effect of daily schedules, that is, daily schedules changed when people retired but remained the same after-ward. However, since only 24 cognitively normal, amyloid-negative participants were older than 75, their cross-sectional study might have insufficient power to test the age effects in this age range. Indeed our recent longitudinal study of ~800 cognitively normal adults (most of them >75 years old; interquartile range: 76–86) showed significant, progressive decreases in both IS and 24-h amplitude over time during a follow-up period of up to 13 years (Li et al., 2020). The aging effects remained when including only 154 participants who had no AD pathology (based on the postmortem examination of the brain) [results not published]. The other factor is epoch length of activity recordings that was not specified in Park’s study. Originally IS was calculated using hourly sampled data (Van Someren et al., 1999), and IS values for different epoch lengths might be different (Gonçalves et al., 2014).

3. Effects of cognitive status on daily activity rhythms?

Our recent study showed that the longitudinal change of global cognition strongly correlated with the longitudinal changes in the amplitude, IS, and intradaily variability of daily activity rhythms (Li et al., 2019a; Li et al., 2020). However, Park et al. reported no such effects (except for acrophase) within those amyloid-positive participants. The negative results may indicate lack of power in their cross-sectional analysis of small samples. In addition, including both cognitive status and global cognitive function (two highly correlated variables) in the same regression model further casts doubt on their analysis.

The above caveats do not necessarily diminish the strengths and values of Park’s study. Here we would like to emphasize two of their important messages that were not elaborated enough in their paper:

4. Masking effect of daily schedules

Park et al. acknowledged the masking effect of daily schedules on activity-derived measures. They interpreted the observed higher IS in the older groups as a result of changes in daily schedules: “older adults are less flexible to environmental challenges …, they might try to adapt to … more regular physical activity, mealtimes, and social behaviors.” This is an excellent point. In fact, the masking effect may also affect young participants, for example, workday schedules impact bedtime/wake-up time and sleep duration and introduce social jet lag (Roenneberg et al., 2019). Thus, it is worth checking whether there are any differences in sleep/circadian measures between workdays and weekends. To ultimately address the masking problems, alternative measures that are more resilient to scheduled events are needed. For instance, fractal patterns in spontaneous fluctuations of motor activity (i.e., similar temporal structures at different time scales) were independent of mean activity levels and environmental conditions in healthy young adults (Hu et al., 2009, 2004); the changes in the fractal patterns can better reveal intrinsic neurological changes in the master circadian clock than traditional circadian measures (Hu et al., 2013); and fractal activity measures may help with prediction of dementia and monitoring of cognitive decline (Hu et al., 2016; Li et al., 2018, 2019b).

5. Interaction of AD and aging

One important conclusion of Park’s study is that “age-related earlier circadian phase is more prominent in older adults during the course of AD.” This was based on the comparison between amyloid-negative and amyloid-positive participants with normal cognition. Intriguingly, they found that the acrophase of daily activity rhythms and bedtime were advanced with age in amyloid-negative participants but were delayed or remained stable at older ages in amyloid-positive participants. To explain these findings, Park et al. proposed a novel concept: the age effect on daily activity rhythms may be reversed after AD pathology affects circadian regulation. This is different from our current belief that AD accelerates the aging process. As an example, our longitudinal study showed that the 24-h amplitude, acrophase, and IS progressively decreased and intradaily variability increased over time; and all the changes were accelerated after the onset of MCI and Alzheimer’s dementia (Li et al., 2020). The main limitation of our study is no assessment of AD pathology. Resolving the conceptual discrepancy is important for better understanding of the complex web in AD and its links to aging of sleep/circadian regulation. Future studies, ideally with longitudinal monitoring of sleep and circadian rhythms before and after amyloid beta deposition, are warranted.

Footnotes

Disclosure statement

All authors have no actual or potential conflicts of interest to disclose.

Contributor Information

Kun Hu, Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA; Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA.

Peng Li, Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA; Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA.

Lei Gao, Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA; Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

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