Neurobiology of Aging︱ Sleep and Rhythm Aging Variations and Pathological Variations

And using rigorous academic and logical thinking to explore the mysteries of neuroscience.
Written by Ning Zi, edited by Wang Sizhen, "Whether it’s spring and autumn, the moon is missing the full moon, or the sunset, the sunrise, the ebb and flow of the tide, they all portray the nature of our planet.
Laws.
The
earth’s operating mode is endowed by hundreds of millions of years of cosmic evolution, and this physical property has been imprinted on all aspects of earth’s organisms in the long-term natural evolution.

This imprint has been passed down on the microscopic level of life to this day through complexity.
The biological processes of life are reflected by regulating life behaviors, such as the nocturnal or nocturnal laws of different species.

The study of chronobiology shows us the interaction between nature and the micro and macro states of life.
Explains the biological rhythm mechanism of the circadian activities of living organisms, sleep and other physiological processes.

Ageing is almost an inevitable process of all living organisms.
The biological evolution of ageing attracts many scientists.
Between the ageing process and the disease process The interaction of many researchers is also one of the objects that many researchers pay attention to.

” In the appreciation of a paper published in Logic Neuroscience last year (for details, please click "The Lancet · Health and Longevity" | Biorhythm Disorders and Alzheimer's Disease, hereinafter referred to as "The "Liu" article"), the author used the above paragraphs as an introduction; when drafting this article, the author brewed a number of opening sentences.
After thinking about it, I still feel that the above paragraph is more accurate.In the last article appreciation, we understood the biological rhythm changes in the process of aging and how these biological rhythm changes are intertwined with the clinical process of neurodegenerative diseases; in this article, the author will interpret the "Neurobiology of Aging" "(Neurobiology of Aging) recently published an article titled "Differential associations of age and Alzheimer's disease with sleep and rest-activity rhythms across the adult lifespan" [1].

In this paper, the researchers showed the similarities and differences between sleep and rhythm changes caused by the pathology of Alzheimer's disease and their changes during healthy aging.

The long evolutionary process makes most of the earth's creatures, including human beings, perfectly adapt to about 24 hours of day and night changes.
This synchronization is coordinated by the body's clock center-the biological clock system [2].

Most biological or physiological activities, such as sleep rhythm and Rest-Activity Rhythm, are controlled by the biological clock system.

The normal process of aging (or aging) will trigger changes in these rhythms.
Similarly, these rhythm characteristics will also change in many disease states, such as Alzheimer's disease (AD).

A very important and unanswered scientific question is whether the changes in sleep and quiet-motion rhythms produced by the normal aging process are similar to those caused by pathological processes.
Park (article 1 [1]) and others from South Korea and their KBASE research group (Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease) have carried out a cohort study since 2014, as of March 2017 , They have enrolled 758 volunteers.

The main purpose of KBASE is to find markers of AD and early life factors related to AD pathology [3].

In order to study sleep and static-motion rhythms, these researchers screened KBASE from subjects (251 people in total) who did not suffer from mental or neurological disorders that may affect cognitive function and completed the daily activity signal (actigraphy) monitoring.
The baseline study was completed.

 As the standard detection module of KBASE, these subjects have completed PiB-PET in vivo pathological imaging ([11C] Pittsburgh compound B-position emission tomography; indicator PiB is used to mark beta amyloid plaque Aβ-the pathology of AD One of the markers).

Among 251 people, 180 people were Aβ negative (PiB absorption value [retention value] was below a predetermined threshold), and the remaining 71 people were Aβ positive.

These subjects also completed the clinical dementia rating (CDR), and were classified into cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer's based on clinical symptoms.
Three groups of Alzheimer's disease dementia (ADD).

In order to study the effect of normal aging, the researchers further divided the subjects in the CN group into the young-middle-aged group (20-54 years old), the destiny group (near-old, 55-64 years old), and the sub-aged group ( Early-old, 65-74 years old) and late-old, 75-90 years old.

The number of persons in each group is shown in Table 1.

 Based on daily activity signals, this article studies the following sleep characteristics: sleep time, wake time, total sleep time, sleep onset latency, sleep efficiency, wake time After sleep onset) and the following static-motion rhythm characteristics: average exercise level, peak exercise phase, rhythm intensity, rhythm robustness, rhythm fragmentation, and relative amplitude.

Although the paper introduces the calculation methods of these features in detail in the appendix, in order for readers to better understand this research, the author will briefly introduce the meaning of these sleep and rhythm features here.

Among the 6 sleep characteristics, sleep time and awakening time, as the name suggests, indicate the time of sleep and waking; total sleep duration is the length of sleep of the subject per day; sleep latency refers to the time required for the subject to fall asleep completely; sleep efficiency refers to The ratio between the length of sleep and the total length of time lying in bed; the awake time after sleep is the awakening time experienced during sleep after falling asleep.

The author has explained the calculation and meaning of the three indicators of peak exercise phase, rhythm robustness, and rhythm fragmentation in the interpretation article "The Willow" article.
Among the remaining three indicators, the average exercise level is It is an attribute used to express the average amount of exercise; the rhythm intensity is similar to the amplitude parameter mentioned by the author in the "Liu" article, and both represent the strength of the static-movement rhythm.
In this paper, the researcher uses the cosine fitting F In the "Liu" article, the rhythm intensity uses the amplitude of the fitted cosine signal; finally, the relative amplitude is a non-parametric indicator, which represents the average amount of the highest amount of exercise in ten hours (M10) The relative difference ((M10-L5)/(M10+L5)) from the average amount (L5) in the five hours of the lowest amount of exercise.

 By analyzing these data, the researchers found that sleep and static-motion rhythm changes related to aging include advancement of phases (including sleep moments, awakening moments, and peak motor phases), increased rhythm robustness, and relative In addition, the awake time after going to bed showed an inverted V-shaped change, which had the highest value in the destiny group.

Aβ pathology in the pre-clinical phase (Aβ+ and CN) wake up time is about 1 hour ahead of Aβ- and CN group; in Aβ+ group, MCI patients have a postponed phase compared to CN, which is in ADD The patient also showed performance but was not statistically significant.

 These results reflect the different sleep and static-motion rhythm changes caused by normal aging and pathology, and help to find the potential pathological process early in people with normal cognition.

The publication of this paper has also aroused heated debate.
For example, Kun Hu, Peng Li and Lei Gao from Brigham and Women's Hospital and Harvard Medical School published a review article in the journal "Sleep, Static-Motion Rhythm and Ageing: the complex network of Alzheimer's disease" (Letter to the editor: Sleep, rest-activity rhythms and aging: A complex web in Alzheimer's disease?) [4].

This review article affirmed the importance of Park et al.
's research (including a wide age span, in vivo pathology imaging data and long-term daily activity monitoring data), and also put forward unique insights into its research conclusions and explanations: 1.
Park Et al.
found no change in sleep characteristics due to age, which is different from many previous studies; Park et al.
attributed these differences in results to previous studies that did not consider the underlying pathological process.

Hu et al.
believe that if it is necessary to confirm whether this explanation is reasonable, Park et al.
can combine their Aβ+ and CN subjects and Aβ- and CN subjects and re-analyze them.

In addition, Hu et al.
also commented on Park's article on the method of extracting sleep from daily activity data and whether factors such as daytime sleep and chronotype are considered.

 2.
Park et al.
found no further static-motion rhythm changes in the elder group.
Hu et al.
believed that the interpretation of the result should take into account the small sample problem (24 people), and the statistical power was limited; in addition, the study was a cross-sectional study , Longitudinal data may be more sufficient to better explain the variability caused by the process of aging.

For example, Hu et al.
analyzed the long-term data analysis of 154 Aβ- (based on autopsy data) subjects during their lifetime and found that rhythm robustness and rhythm intensity gradually decreased with age (the results have not yet been published).

 3.
Park et al.
did not find the difference between static-motion rhythm at different clinical cognitive stages, which is different from the existing research results (such as [5]), and the difference may come from multiple perspectives, such as cross-sectional research and Longitudinal research attributes, statistical power and statistical method considerations of existing results.  In addition, Hu et al.
also elaborated on the issues that need to be considered in the follow-up research: One is the analysis method.
The existing analysis methods based on daily activity signals are extremely susceptible to external factors, such as illusions caused by study or work arrangements.
The masking effect is extremely strong in young people, while the daily activities of the elderly after retirement mainly reflect their spontaneous tendency and are not easily affected by the masking effect.
So how to optimize or minimize this masking effect, establish Better mathematical analysis methods require special attention in follow-up research; the second is about the hypothetical model proposed by Park et al.
-"AD pathology affects the biological clock system, and reverses the influence of age on the rhythm of daily activities (the age).
effect on daily activity rhythms may be reversed after AD pathology affects circadian regulation)”, Hu et al.
believe that this model requires more clinical data testing, especially the support of longitudinal cohort research data combined with in vivo pathology imaging and daily activity monitoring . Review articles (Letter) Author: Kun Hu (fourth from left), Peng Li (Li) (third from left), Lei Gao (left) (Source: https: //sleep.
hms.
harvard.
edu/research/labs- divisions/medical-biodynamics-program-mbp) Original: Park el al.
, "Differential associations of age and Alzheimer's disease with sleep and rest-activity rhythms across the adult lifespan," Neurobiology of Aging 2021.
Link: https://doi .
org/10.
1016/j.
neurobiolaging.
2021.
01.
006 References (slide up and down to view) [1] Park JE, Lee YJ, Byun MS, et al.
Differential Associations of Age and Alzheimer's Disease with Sleep and Rest-Activity Rhythms Across the Adult Lifespan.
Neurobiology of Aging.
Published online January 22, 2021.
doi:10.
1016/j.
neurobiolaging.
2021.
01.
006【2】Van Someren EJW, Riemersma-Van Der Lek RF.
Live to the rhythm, slave to the rhythm.
Sleep Med Rev.
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Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease: Methodology and Baseline Sample Characteristics.
Psychiatry Investig.
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851【4】Hu K, Li P, Gao L.
Sleep, Rest-Activity Rhythms and Aging: A Complex Web in Alzheimer's Disease? Neurobiology of Aging.
Published online March 2, 2021.
doi:10.
1016/j.
neurobiolaging.
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017【5】Li P, Gao L, Gaba A, et al.
Circadian disturbances in Alzheimer's disease progression: a prospective observational cohort study of community-based older adults.
Lancet Healthy Longev.
Published online 2020.
doi:10.
1016/ S2666-7568(20)30015-5︱ Wang Sizhen end of this articleLi P, Gao L.
Sleep, Rest-Activity Rhythms and Aging: A Complex Web in Alzheimer's Disease? Neurobiology of Aging.
Published online March 2, 2021.
doi:10.
1016/j.
neurobiolaging.
2021.
02.
017【5】Li P, Gao L, Gaba A, et al.
Circadian disturbances in Alzheimer's disease progression: a prospective observational cohort study of community-based older adults.
Lancet Healthy Longev.
Published online 2020.
doi:10.
1016/ S2666-7568(20)30015-5 ︱Wang Sizhen end of this articleLi P, Gao L.
Sleep, Rest-Activity Rhythms and Aging: A Complex Web in Alzheimer's Disease? Neurobiology of Aging.
Published online March 2, 2021.
doi:10.
1016/j.
neurobiolaging.
2021.
02.
017【5】Li P, Gao L, Gaba A, et al.
Circadian disturbances in Alzheimer's disease progression: a prospective observational cohort study of community-based older adults.
Lancet Healthy Longev.
Published online 2020.
doi:10.
1016/ S2666-7568(20)30015-5 ︱Wang Sizhen end of this article10.
1016/ S2666-7568(20)30015-5 plate making ︱ Wang Sizhen end of this article10.
1016/ S2666-7568(20)30015-5 plate making ︱ Wang Sizhen end of this article