Figure 1. Diagram of a participant during the monitoring of Aβ levels in cerebrospinal fluid (CSF) samples. These samples were collected from a lumbar intrathecal catheter approximately every hour for 36 hours from each participant, and electroencephalograms (EEGs) and video recordings were obtained continuously from a subset of study patients. The concentrations of CSF Aβ40 and Aβ42 were measured using an enzyme-linked immunosorbent assay and were analyzed over time for Aβ dynamics.
Figure 2. Mean-adjusted Aβ42 levels over time in 20 participants younger than 60 years of age from the younger normal control (YNC) group. A linear increase and a circadian pattern in Aβ42 levels over the duration of our study were observed (A), and the Aβ42 circadian patterns remained after the linear trend was removed (P < .05) (B).
Figure 3. Mean percent change with respect to the linear increase in Aβ42 level per 24 hours, by mean cortical binding potential (MCBP) of carbon 11–labeled Pittsburgh Compound B. In general, individuals without amyloid deposition (young normal controls [YNCs] and older cognitively normal controls who tested negative for amyloid plaque [amyloid−]) had significant increases in Aβ42 level, independent of age, whereas participants with amyloid deposition (older cognitively normal controls who tested positive for amyloid plaque [amyloid+]) had lower increases in Aβ42 level (P < .05).
Figure 4. Decreased Aβ42 circadian amplitude with increasing age. An Aβ42 circadian amplitude was calculated for each participant and correlated with the participant's age. Age and Aβ42 circadian amplitude were negatively correlated (P < .01). Young normal control (YNC) participants had the highest circadian amplitudes, older participants who tested negative for amyloid plaque (amyloid−) had decreased circadian amplitudes, and older participants who tested positive for amyloid plaque (amyloid+) had the lowest circadian amplitudes.
Figure 5. Cosinor fit for group-averaged Aβ42 (A) and Aβ40 (B) levels over time showing that the cerebrospinal fluid Aβ circadian amplitude decreases with age. The Aβ42 circadian amplitude decreased with age and amyloidosis. The Aβ40 circadian amplitude was lower in the older groups. Amyloid− indicates participants who tested negative for amyloid plaque; amyloid+, participants who tested positive for amyloid plaque; YNC, younger normal controls.
Figure 6. Circadian rhythms of mean total sleep times (minutes of sleep per hour) and Aβ circadian patterns in a subset of 12 younger normal control participants. The cerebrospinal fluid Aβ circadian pattern follows sleep after a 6-hour delay. A delay of 6 hours was observed between the maximum sleep times and the minimum Aβ levels.
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Huang Y, Potter R, Sigurdson W, et al. Effects of Age and Amyloid Deposition on Aβ Dynamics in the Human Central Nervous System. Arch Neurol. 2012;69(1):51–58. doi:10.1001/archneurol.2011.235
Author Affiliations: Departments of Neurology (Drs Huang, Ju, Kasten, Morris, Duntley, and Bateman and Mss Potter, Sigurdson, and Santacruz), Internal Medicine (Ms Shih), Pathology and Immunology (Dr Morris), and Radiology (Dr Mintun), Charles F. and Joanne Knight Alzheimer's Disease Research Center (Drs Morris and Bateman), and Hope Center for Neurological Disorders (Dr Bateman), Washington University School of Medicine, St Louis, Missouri. Dr Mintun is now with Avid Radiopharmaceuticals, Philadelphia, Pennsylvania.
Background The amyloid hypothesis predicts that increased production or decreased clearance of β-amyloid (Aβ) leads to amyloidosis, which ultimately culminates in Alzheimer disease (AD).
Objective To investigate whether dynamic changes in Aβ levels in the human central nervous system may be altered by aging or by the pathology of AD and thus contribute to the risk of AD.
Design Repeated-measures case-control study.
Setting Washington University School of Medicine in St Louis, Missouri.
Participants Participants with amyloid deposition, participants without amyloid deposition, and younger normal control participants.
Main Outcome Measures In this study, hourly cerebrospinal fluid (CSF) Aβ concentrations were compared with age, status of amyloid deposition, electroencephalography, and video recording data.
Results Linear increases were observed over time in the Aβ levels in CSF samples obtained from the younger normal control participants and the older participants without amyloid deposition, but not from the older participants with amyloid deposition. Significant circadian patterns were observed in the Aβ levels in CSF samples obtained from the younger control participants; however, circadian amplitudes decreased in both older participants without amyloid deposition and older participants with amyloid deposition. Aβ diurnal concentrations were correlated with the amount of sleep but not with the various activities that the participants participated in while awake.
Conclusions A reduction in the linear increase in the Aβ levels in CSF samples that is associated with amyloid deposition and a decreased CSF Aβ diurnal pattern associated with increasing age disrupt the normal physiology of Aβ dynamics and may contribute to AD.
The Aβ peptide has been implicated as a critical initiator of Alzheimer disease (AD).1 Pathologic studies of the brain tissue of patients with AD demonstrate that extensive amyloid plaques associated with the disrupted neuropil are deposited throughout the cortex. Aβ peptides are the primary component of amyloid plaques, which account for a 1000-fold increase of Aβ in the AD brain. Increased brain Aβ in AD has been postulated to be caused by increased Aβ production or decreased clearance.2,3 Thus, the study of Aβ levels in individuals with AD is likely to lead to a better understanding of AD pathophysiological changes as well as normal Aβ physiology.
Aβ in the brain is produced predominantly by neurons, by the cleavage of the amyloid precursor protein by β- and γ-secretases. Aβ travels by diffusion and bulk flow to the cerebrospinal fluid (CSF) via interstitial fluid drainage pathways. The Aβ level in CSF can be sampled as a biomarker of amyloidosis and can be used to diagnose and predict AD with 70% to 95% accuracy.4,5 Compared with controls, concentrations of CSF Aβ42 are consistently decreased by approximately half in patients with AD.6 Repeat measurements of CSF over months to years demonstrate stable CSF Aβ42 concentrations in AD,7,8 but less stable levels of Aβ40.9 However, in younger healthy participants, hourly measurements of CSF Aβ levels demonstrated highly dynamic and variable Aβ40 and Aβ42 concentrations.10 Consistent with these dynamic CSF Aβ changes, microdialysis measurements of human brain tissue also demonstrated highly dynamic Aβ changes.11
One hypothesis suggests that neuronal activity is responsible for the dynamics of Aβ.12,13 Modulation of neuronal activity by electrical, pharmacological, and behavioral interventions has direct effects on the concentrations of Aβ in the central nervous system (CNS).13,14 For example, increased stress and decreased sleep have both been demonstrated to increase Aβ concentrations in animal models.15,16 Furthermore, a recent study17 demonstrated the effect of sleep on several synaptic markers, suggesting that sleep may modulate the metabolism of a number of CNS proteins.
Circadian rhythms have been described for a variety of biochemical, physiological, and behavioral processes that occur over a 24-hour cycle.18 Examples of circadian rhythms include body temperature, circulating levels of hormones such as cortisol, and blood levels of ions such as sodium. The level of Aβ in CSF demonstrates a circadian pattern in healthy younger participants10; however, the dynamics of Aβ in aging and AD are less well understood. Although prior reports indicate that the level of Aβ42 in CSF is stable in individuals with AD, little is known about the effects of age and amyloid deposition on Aβ dynamics in the human CNS. Because age is the largest risk factor for AD, understanding changes in the dynamics of Aβ with aging may inform about the pathophysiological processes that lead to amyloidosis and ultimately AD. Furthermore, the effects of amyloidosis may reveal changes in CNS Aβ dynamics that are associated with the pathology of AD.
In our study, we investigated CSF Aβ dynamics and how they are affected by aging and amyloidosis. Samples of CSF were collected hourly from each participant, and electroencephalography (EEG) and video recordings were obtained continuously from a subset of study patients as diagrammed in Figure 1. The Aβ dynamics in CSF samples were modeled and compared by age, amyloid deposition, and sleep and awake behaviors. Our study confirms CSF Aβ dynamics over time; describes associations between Aβ dynamics and sleep, age, and amyloid deposition; and provides insight into normal CNS Aβ changes over time and the effects of age and amyloid deposition.
Our study was a repeated-measures case-control study conducted at the Washington University School of Medicine in St Louis, Missouri. Three groups of volunteers were enrolled: (1) a case group of participants who tested positive for amyloid plaque by use of carbon 11–labeled Pittsburgh Compound B–positron emission tomography ([11C]PiB-PET) (the amyloid+ group)19; (2) an age-matched control group of participants who tested negative for amyloid plaque (the amyloid− group); and (3) a younger normal control (YNC) group. [11C]PiB binds to amyloid plaques in the brain, and the binding potentials of this compound for the prefrontal cortex, precuneus, lateral temporal cortex, and gyrus rectus were averaged to yield the mean cortical binding potential for each participant.20 A mean cortical binding potential of 0.2 or greater was considered to be amyloid plaque positive, and a mean cortical binding potential of less than 0.2 was considered to be amyloid plaque negative.
Participants in the amyloid+ and amyloid− groups were older than 60 years of age and were enrolled in the Washington University Alzheimer Disease Research Center. Younger controls were between the ages of 18 and 60 years. All participants were in good general physical health and had no other clinical neurological diseases. Volunteers with active infections, with bleeding disorders, or treated with anticoagulants were excluded from our study. All human study protocols were approved by the Washington University Human Studies Committee and the General Clinical Research Center Advisory Committee. Informed consent was obtained from all participants.
A total of 46 participants were analyzed in our study, among whom 53% were women and 47% were men. Of these 46 participants, 36 (78%) were white and 10 (22%) were African American. The proportion of African Americans was significantly higher in the YNC group compared with the other 2 groups (72% of participants were African American in the YNC group, 7% were African American in the amyloid− group, and 9% were African American in the amyloid+ group; P < .05). However, no statistical difference in Aβ dynamics was found between races within the YNC group. The mean (SD) age was 35.5 (10.7) years for the 20 participants in the YNC group, 71.0 (6.0) years for the 15 participants in the amyloid− group, and 76.7 (7.7) years for the 11 participants in the amyloid+ group. There was no statistical difference in age between the amyloid+ and amyloid− groups (P = .77). APOE genotypes were available for 28 participants, among whom 11 (39%) were found to have E3/E3 alleles, 10 (36%) were found to have E3/E4 alleles, 4 (14%) were found to have E4/E4 alleles, 2 (7%) were found to have E2/E3 alleles, and 1 (4%) was found to have E2/E4 alleles. The prevalence of 1 or more E4 alleles was 67% in the amyloid+ and YNC groups, and 30% in the amyloid− group. Of the 15 participants in the amyloid− group, 14 were not clinically demented, with a Clinical Dementia Rating (CDR) score of 0, and 1 participant had a CDR score of 0.5. Of the 11 participants in the amyloid+ group, 6 were clinically demented (5 had a CDR score of 0.5, and 1 had a CDR score of 1), and 5 were not (with a CDR score of 0).
An intrathecal lumbar catheter was placed in all participants between 7:30 AM and 9:00 AM, and the collection of samples was started between 8:00 AM and 9:30 AM. For 36 hours, 6 mL of CSF were obtained every hour. Aliquots of CSF were frozen at −80°C immediately after being collected in 1-mL polypropylene tubes. Participants were encouraged to stay in bed and were allowed to choose when to sleep, read, watch television, or talk throughout the study period. Participants had meals served at 9:00 AM, 1:00 PM, and 6:00 PM.
From each hour of collection, 1 mL of CSF was thawed, and Aβ40 and Aβ42 were measured by use of an enzyme-linked immunosorbent assay (ELISA).10 In brief, 2G3 (anti-Aβ40) and 21F12 (anti-Aβ42) antibodies were used as the capture antibodies, and biotinylated 3D6 antibody (anti–Aβ1-5) was used as the detection antibody. Each sample was assessed in duplicate. All samples from each participant were measured together on the same ELISA plate to avoid interplate variation. To measure the effect of ELISA variability, we ran separate ELISA plates for Aβ40 and Aβ42 with a single CSF sample for both assays. The mean percentage of the intrasample coefficient of variation for duplicates was 10.4% for Aβ40 and 5.9% for Aβ42. Similarly, total protein levels for each sample were measured using a bicinchoninic acid assay. The mean percentage of the intrasample coefficient of variation for duplicates was 2% for total protein.
Electroencephalographic data were collected for the YNC group. Ambulatory equipment (TrackIt; Lifelines Ltd, Southampton, England) was used to record signals from 6 EEG electrodes (F3, F4, C3, C4, O1, and O2), a chin electromyogram, and left and right electro-oculograms. This information was then imported and scored using Polysmith 6.0 (Nihon Kohden, Tokyo, Japan) using standard sleep scoring criteria.21 The sleep stage (awake, rapid-eye movement sleep, N1, N2, or N3) was scored for each 30-second epoch. Total sleep time (including the sleep stages of rapid-eye movement sleep, N1, N2, and N3) was binned every hour and expressed as minutes of sleep per hour.
A participant's activity was video recorded using a Logitech Quickcam (Fremont, California) installed on a laptop computer for the duration of our study. Recording began shortly after the intrathecal catheter was inserted and continued for a 36- to 48-hour period. Videos were reviewed, and a participant's activity was coded in 30-second intervals using Microsoft Excel 2007 (Microsoft Corp, Redmond, Washington). Activities were rated as sleeping, talking, eating, reading, watching television, defecation and/or urination, writing, computer use, sample collection, and catheter manipulation (eTable). The position of the participant was coded as upright (>60°), partially upright (15°-60°), or flat (<15°). After the videos were reviewed, they were quality-checked for accurate recording of the sample collection using patient charts. All video coders reviewed the same “test video,” and Cohen κ coefficients were used to measure interrater agreement of video coding. The weighted κ coefficients among the 3 different coders ranged from 0.77 to 0.79.
All analyses were performed using SAS version 9.2 (SAS Institute, Cary, North Carolina). Graphs were plotted in GraphPad Prism version 4.03 for Windows (GraphPad Software, San Diego, California). Linear changes and circadian rhythms of Aβ fluctuation over time were explored in our study. Both individual time-course data for each patient and group-averaged data were used in circadian pattern recognition. Group-averaged data were calculated as follows: for each patient, (1) the mean Aβ40 and Aβ42 levels over a 36-hour period were calculated; (2) the mean-adjusted Aβ40 and Aβ42 levels were estimated for each time point and expressed as a percentage of the mean; and (3) hourly serial values of mean-adjusted Aβ40 and Aβ42 levels were grouped based on their amyloid plaque status, and an hourly average was calculated for each group. Mean levels of Aβ40 and Aβ42 and the Aβ42-to-Aβ40 ratios over the 36-hour study period for each participant were compared between the YNC group, the amyloid− group, and the amyloid+ group using analysis of variance.
Single cosinor analysis was used to analyze the patterns of the Aβ40 and Aβ42 levels in each participant over the 36-hour period. A cosine transformation was applied to the time variable using 24 hours as the default circadian cycle, and the SAS PROC NLIN procedure (SAS Institute, Cary, North Carolina) was used to estimate the parameters of the circadian patterns for Aβ fluctuations. The mesor (midline of the Aβ oscillation), amplitude (distance between the peak and mesor), and acrophase (the time corresponding to the peak of the curve) were calculated for each patient and averaged within each group. Analysis of variance was used to assess the differences in mesor and amplitude among different groups. Similarly, group-averaged data were used to estimate the parameters of circadian rhythms in the 3 study groups.
To compare the effects of age and amyloid deposition on hourly fluctuations, the YNC group was compared with the older amyloid− and amyloid+ groups, as determined by [11C]PiB-PET.20 Variability of Aβ in each patient was calculated as the standard deviation of serial Aβ measurements over time, and the 36-hour mean Aβ concentration was averaged by group (the YNC, amyloid−, and amyloid+ groups), as shown in Tables 1 and 2.
As expected, the CSF Aβ42 concentration was lower in the amyloid+ group than in the amyloid− group (63% lower; P < .01) (Table 1). In addition, there was also a 3-fold decrease in the variability (standard deviation) of Aβ42 hourly changes in the amyloid+ group compared with either the amyloid− group or the YNC group (P < .01). There was a nonsignificant trend toward decreased variability in the cognitively impaired amyloid+ group compared with the cognitively normal amyloid+ group: a mean Aβ40 standard deviation of 260pM for the 5 participants in the cognitively impaired amyloid+ group vs 562pM for the 6 participants in the cognitively normal amyloid+ group (P = .11), and a mean Aβ42 standard deviation of 9.7pM for the 5 participants in the cognitively impaired amyloid+ group vs 19.2pM for the 6 participants in the cognitively normal amyloid+ group (P = .30). However, there was no difference in variability between the amyloid− and YNC groups, which indicates that the reduction in Aβ42 and Aβ42 variability was due to amyloid status, not age.
No difference was found in mean Aβ40 level or mean Aβ40 variability (P > .05; Table 2) between amyloid− and amyloid+ groups; however, there was lower variability in the amyloid+ group than in the YNC group (P < .05). Individual plots of Aβ concentration highlight the significant decrease in hourly variability between the amyloid+, amyloid−, and YNC groups (eFigure 1).
We explored CSF Aβ dynamics with respect to linear change over time and circadian rhythm. In the younger control group, a linear increase in the mean Aβ concentration was observed over time (Figure 2A). Circadian patterns remained after the linear trend was removed (Figure 2B).
To compare the associations between the linear increase in Aβ concentrations (the so-called Aβ linear rise), age, and amyloid deposition, calculations of the Aβ linear rise were expressed as the percent change over 24 hours for participants in the YNC, amyloid−, and amyloid+ groups. The mean percent change of the linear increase of Aβ42 concentrations over 24 hours was 17% for the YNC group, 24% for the amyloid− group, and 7% for the amyloid+ group (Table 3). The amyloid+ group demonstrated a 66% lower Aβ42 linear rise compared with the combined results of the amyloid− and YNC groups (P < .05). Furthermore, with increased amyloid deposition, as measured by the mean cortical binding potential of [11C]PiB, there was less of a linear rise in Aβ42 level over 24 hours (Figure 3). Most participants with amyloid deposition (those in the amyloid+ group) had no significant Aβ42 linear rise. Conversely, most participants without amyloid deposition (those in the amyloid− group) or those younger participants unlikely to have amyloid deposition by virtue of their age (those in the YNC group) had a significant Aβ42 linear rise.
The mean percent change in the Aβ40 linear rise per 24 hours was 19% for the YNC group, 24% for the amyloid− group, and 15% for the amyloid+ group (Table 4). Although the amyloid+ group had a lower mean Aβ40 linear rise, the trend was not statistically different from the amyloid− and YNC groups (P = .20).
Cosinor analysis was used to assess the circadian patterns of Aβ dynamics in each individual participant. The mesor (midline of cosinor fit), amplitude (difference between mesor and peak of the cosinor fit), and amplitude-to-mesor ratio for each cosinor analysis were compared across groups with the YNC group as the reference group. Aβ42 circadian amplitudes decreased by 53% in the older amyloid− group and by 81% in the older amyloid+ group compared with the YNC group (P < .01; Table 3). In addition, Aβ42 circadian amplitude–to–mesor ratios decreased by 45% in the older amyloid− group and by 53% in the older amyloid+ group compared with the YNC group (P < .01; Table 3). The Aβ40 cosinor amplitude and the amplitude-to-mesor ratio were 40% lower in the older amyloid− group compared with the YNC group (P < .05; Table 4). Thus, the largest decrease in circadian pattern was due to age, with amyloid deposition having a lesser effect.
To further explore the relationship between age and Aβ42 circadian rhythms, we plotted each participant's Aβ42 circadian amplitude vs age (Figure 4). The amplitude of the Aβ42 circadian pattern was inversely correlated with age when the YNC and amyloid− groups were included (r = −0.49, P < .01) and when all 3 groups were included in the analysis (r = −0.61, P < .01). After controlling for age, there was no significant difference in amplitudes between the amyloid+, amyloid−, and YNC groups (P = .27). There was a nonsignificant trend toward a decreasing circadian pattern in the cognitively impaired amyloid+ group compared with the cognitively normal amyloid+ group: a mean Aβ40 circadian amplitude of 126pM for the 5 participants in the cognitively impaired amyloid+ group vs 309pM for the 6 participants in the cognitively normal amyloid+ group (P = .19), and a mean Aβ42 circadian amplitude of 3.2pM for the 5 participants in the cognitively impaired amyloid+ group vs 8.8pM for the 6 participants in the cognitively normal amyloid+ group (P = .08).
Cosinor analyses were also conducted on the group-averaged data for both Aβ40 and Aβ42 levels in the 3 groups (Figure 5). The range in mean Aβ levels over time before cosinor transformation was approximately 40% of the mean (Figure 5). Aβ40 circadian patterns were found by use of a cosinor curve fit (P < .01) in all 3 groups; however, an Aβ42 cosinor pattern was only found in the YNC group. Similar to individual cosinor analysis, the YNC group had higher circadian amplitudes in both Aβ40 and Aβ42. The peak CSF Aβ40 levels (acrophase) occurred at approximately 10 PM, and the lowest levels occurred at approximately 10 AM, for the YNC, amyloid−, and amyloid+ groups. Similarly, CSF Aβ42 amplitudes reached a maximum at 10 PM and a minimum at 10 AM for the YNC group.
Previous animal studies have demonstrated a direct relationship between wakefulness and increases in Aβ level.16 Herein, we assessed the correlation between Aβ levels and total sleep time in a subset of 12 participants who had EEG recordings in the YNC group (Figure 6). As expected, a circadian pattern was identified in the mean total sleep time (P < .01). Interestingly, the peak of wakefulness occurred at 4 PM, whereas the CSF Aβ peak occurred 6 hours later at 10 PM. The peak sleep time occurred at 4 AM, whereas the lowest CSF Aβ level occurred 6 hours later at 10 AM. A 6-hour delay from sleep to change in Aβ is expected because there is a 6-hour lag from the time of labeling to the time of detection of labeled Aβ in the lumbar CSF.22 Thus, both CSF Aβ40 and Aβ42 levels were inversely correlated with sleep after a 6-hour delay.
Because individual behaviors may influence neuronal activity and therefore influence Aβ production, we assessed correlations between individual activities and hourly CSF Aβ levels. Video-rated activities included catheter manipulation, CSF sample collection, computer use, defecation and/or urination, eating, reading, sleeping, talking, watching television, and writing (eTable). A subset of 9 younger participants who were monitored with video analysis were included in the analysis. We assessed correlations between each activity and CSF Aβ40 and Aβ42 levels after a 6-hour delay due to an expected 6-hour lag time. However, there were no significant correlations between individual behaviors and CSF Aβ levels (IrI < 0.1). Correlations remained low when assessed with no delay between activity and Aβ levels.
For 30 participants, there was no hourly correlation between total protein and Aβ40 (r = −0.56 to 0.54; mean [SD] correlation, 0.06 [0.28]). Furthermore, there was no correlation between total protein and Aβ40 among the YNC, amyloid−, and amyloid+ groups.
The mean levels of Aβ40 and total protein for each person over time were also calculated, and no correlation was found between them (r = 0.15, P = .44). As expected, mean total protein levels were significantly lower in young controls compared with the older participants (with a mean of 540.6 μg/mL for participants in the YNC group, 696.4 μg/mL for participants in the amyloid− group, and 656.1 μg/mL for participants in the amyloid+ group; P = .014). Total protein levels were averaged by group, and a cosinor fit was applied. There was no significant circadian pattern in total protein for any of the 3 groups (P > .05) (eFigure 2). Thus, CSF Aβ dynamics appear to be independent of CSF total protein changes.
The relationship between soluble Aβ concentrations in the human CNS and sleep-wake cycles reveals novel insights into the normal physiology of the brain and Aβ metabolism. In healthy participants, the peak-to-peak magnitude of the circadian pattern was 30%, which is significant compared with other circadian biological rhythms such as temperature, with a peak-to-peak magnitude of approximately 2%. We report that normal circadian patterns of CSF Aβ were disrupted by increasing age, the largest risk factor for AD, and hourly CSF Aβ dynamics and the linear increase in Aβ concentrations were attenuated with amyloid deposition. This “flatline” CSF Aβ42 level was a characteristic finding with amyloid deposition but was not found in older non–amyloid-burdened individuals. These findings suggest that the normal physiologic patterns of CSF Aβ are dynamic and circadian, whereas amyloid deposition and aging diminish normal CSF Aβ dynamics.
The CSF Aβ circadian pattern was strongly correlated with sleep after a 6-hour delay. For the group-averaged data (Figure 2), the peak of wakefulness at 4 PM occurred 6 hours prior to the maximum levels of Aβ at 10 PM. These findings were consistent with a 6-hour delay observed from the time of labeling Aβ in the human CNS to the appearance of labeled Aβ in the lumbar CSF.22 Taken together, these findings suggest that wakefulness precedes and causes increased levels of CSF Aβ, whereas sleep causes decreased levels of CSF Aβ, generalizing findings from murine models16 to humans.
Circadian amplitudes were approximately 2 times higher for both Aβ40 and Aβ42 in the YNC group compared with the older amyloid+ and amyloid− groups. Furthermore, there was a significant inverse correlation between circadian amplitude and age. However, circadian amplitudes were not significantly different between those with and without amyloid deposition, suggesting that increased age primarily affects Aβ circadian rhythms. Possible causes for the loss of correlation between sleep and CSF Aβ include impaired Aβ transport or clearance mechanisms from the brain to the CSF,3 or it may be due to the fact that Aβ production was no longer being modulated by sleep.
In our study, we observed a linear increase in CSF Aβ levels over time.
The Aβ linear rise has been observed by multiple pharmaceutical groups and several academic groups that perform serial CSF collection studies. The Aβ linear rise was not different between the younger and older amyloid− groups, but was significantly decreased in the amyloid+ group. This finding indicates that amyloidosis or associated disease processes attenuate the rise in Aβ levels and that this attenuation is independent of age. We postulate that this steady increase in Aβ levels may be caused by interrupted sleep and cumulatively increased stress in our study participants, who demonstrated interrupted sleep in EEG recordings. Animal studies indicate that both stress15 and sleep deprivation16 can increase Aβ levels in the CNS. Alternatively, the CSF Aβ rise may be caused by changes in CSF flow pathways as a result of CSF sampling, and these changes increase lumbar CSF Aβ levels to concentrations approximating brain or subarachnoid CSF levels. Amyloidosis may be associated with or may cause impairments in the clearance of Aβ to the CSF, thus blocking the normal Aβ linear rise.
The loss of dynamic patterns was more pronounced in Aβ42 than in Aβ40. More selective loss of dynamics in Aβ42 may be due to its greater propensity to aggregate and deposit in amyloid plaques. Studies of Aβ generation indicate brain Aβ is dynamic over minutes to hours11,14 and is circadian in animal models.16 Studies of single measures of CSF Aβ42 demonstrate low levels of CSF Aβ42 in the presence of amyloid deposition.23 In our study, we found decreased CSF Aβ42 dynamics in the presence of amyloid deposition. Taken together, these results suggest that the dynamic changes in Aβ42 concentrations in the brain may be buffered by amyloid plaques that serve as a pool of Aβ42 species to both decrease CSF Aβ42 levels and buffer dynamic changes in CSF Aβ42 concentrations.23
The level of CSF Aβ has been successfully used as a diagnostic,6 prognostic,5 and therapeutic biomarker.24 Our results are consistent with reports of decreased and stable levels of Aβ42 in AD,8 with higher variability in CSF Aβ40,9 and highly variable and dynamic Aβ changes in YNCs.10 The range of mean Aβ levels over time before cosinor transformation was approximately 20% to 40% of the mean (Figure 5), indicating that sampling time can significantly affect test results in both younger controls and older participants. Therefore, sampling at consistent times is helpful in making comparisons of CSF Aβ levels between patients and groups, especially for Aβ measurements in controls.
These findings provide insight into the normal dynamic changes of the Aβ protein in the human CNS, as well as the effects of aging and amyloidosis as they relate to AD. Further research into the mechanisms that contribute to the age- and amyloid-related changes in Aβ dynamics may offer novel therapeutic approaches for AD.
Correspondence: Randall J. Bateman, MD, Department of Neurology, Washington University School of Medicine, 660 S Euclid, PO Box 8111, St Louis, MO 63110 (email@example.com).
Accepted for Publication: June 24, 2011.
Published Online: September 12, 2011. doi:10.1001/archneurol.2011.235
Author Contributions:Study concept and design: Shih and Bateman. Acquisition of data: Sigurdson, Shih, Kasten, Morris, Duntley, and Bateman. Analysis and interpretation of data: Huang, Potter, Santacruz, Ju, Kasten, Mintun, Duntley, and Bateman. Drafting of the manuscript: Huang, Potter, Kasten, and Bateman. Critical revision of the manuscript for important intellectual content: Potter, Sigurdson, Santacruz, Shih, Ju, Kasten, Morris, Mintun, Duntley, and Bateman. Statistical analysis: Huang, Potter, and Bateman. Obtained funding: Morris and Bateman. Administrative, technical, and material support: Potter, Sigurdson, Santacruz, Kasten, Morris, Mintun, Duntley, and Bateman. Study supervision: Duntley and Bateman.
Financial Disclosure: Eli Lilly provided antibodies for this research study.
Funding/Support: This work was supported by grants from the US National Institutes of Health (K08 AG027091-01, K23 AG 03094601, R-01-NS065667, P50 AG05681-22, and P01 AG03991-22), Washington University Clinical & Translational Science Award UL1 RR024992, grants from an anonymous foundation, a gift from Betty and Steve Schmid, The Knight Initiative for Alzheimer Research, The James and Elizabeth McDonnell Fund for Alzheimer Research, and a research grant from Eli Lilly for the purchase of antibodies.
Additional Contributions: We thank the Clinical Core of the The Charles F. and Joanne Knight Alzheimer's Disease Research Center for characterization of the older participants, and we thank the participants for their time and effort.
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