Nocturnal Hypoxia and Sleep Fragmentation May Drive Neurodegenerative Processes: The Compared Effects of Obstructive Sleep Apnea Syndrome and Periodic Limb Movement Disorder on Alzheimer’s Disease Biomarkers

Abstract

Background:

Sleep disorders may cause dysregulation in cerebral glucose metabolism and synaptic functions, as well as alterations in cerebrospinal fluid (CSF) biomarker levels.

Objective:

This study aimed at measuring sleep, CSF Alzheimer’s disease (AD) biomarkers, and cerebral glucose consumption in patients with obstructive sleep apnea syndrome (OSAS) and patients with periodic limb movement disorder (PLMD), compared to controls.

Methods:

OSAS and PLMD patients underwent ¹⁸F-fluoro-2-deoxy-D-glucose positron emission tomography (¹⁸F-FDG PET), polysomnographic monitoring, and lumbar puncture to quantify CSF levels of amyloid-β₄₂ (Aβ₄₂), total tau, and phosphorylated tau. All patients were compared to controls, who were not affected by sleep or neurodegenerative disorders.

Results:

Twenty OSAS patients, 12 PLMD patients, and 15 controls were included. Sleep quality and sleep structure were altered in both OSAS and PLMD patients when compared to controls. OSAS and PLMD patients showed lower CSF Aβ₄₂ levels than controls. OSAS patients showed a significant increase in glucose uptake in a wide cluster of temporal-frontal areas and cerebellum, as well as a reduced glucose consumption in temporal-parietal regions compared to controls. PLMD patients showed increased brain glucose consumption in the left parahippocampal gyrus and left caudate than controls.

Conclusion:

Sleep dysregulation and nocturnal hypoxia present in OSAS patients, more than sleep fragmentation in PLMD patients, were associated with the alteration in CSF and ¹⁸F-FDG PET AD biomarkers, namely reduction of CSF Aβ₄₂ levels and cerebral glucose metabolism dysregulation mainly in temporal areas, thus highlighting the possible role of sleep disorders in driving neurodegenerative processes typical of AD pathology.

Keywords

Alzheimer’s disease amyloid-β brain glucose consumption cerebrospinal fluid positron emission tomography sleep

INTRODUCTION

In the last decade, increasing evidence supports the hypothesis that sleep disorders represent a risk factor for neurodegenerative diseases, such as Alzheimer’s disease (AD) [1 –4]. Among sleep disorders, obstructive sleep apnea syndrome (OSAS) and insomnia have been investigated as risk factors for dementia and AD [1 , 5–10]. These two conditions are the most common sleep disorders in the adult and older population. However, periodic limb movement disorder (PLMD), a sleep disorder affecting the adult and elderly population, has been currently receiving increased attention following its recent improved definition. Similarly to what happens in insomnia and OSAS, PLMD can disrupt sleep causing sleep fragmentation [11]. Moreover, PLMD is associated with daytime napping and excessive daytime sleepiness, as OSAS [12, 13]. The importance of diagnosing and treating sleep disorders in the adult and older subjects also increased following the documentation of AD biomarker pathological changes in patients affected by OSAS. Accordingly, OSAS has been associated with alteration in cerebrospinal fluid (CSF) and ¹⁸F-fluoro-2-deoxy-D-glucose positron emission tomography (¹⁸F-FDG PET) AD biomarkers [6 , 14–20]. In particular, OSAS patients may show pathological modifications of CSF amyloid-β₄₂ (Aβ₄₂) and dysregulation in cerebral glucose metabolism at ¹⁸F-FDG PET [21 –24]. Considering the importance of these biomarkers in supporting the diagnosis of AD from the early stages of the disease, it has been suggested that sleep impairment can trigger AD neurodegeneration [25]. From the other point of view, AD has been associated with sleep disorders that can be present from the early stage of the neuropathological process. In support of this evidence, monoaminergic and orexinergic dysregulation have been documented in patients with AD, thus reinforcing the association between sleep dysregulation and AD pathology [26 –28]. Therefore, impairment in nocturnal sleep quality and continuity may cause dysregulation of cerebral glucose consumption and alterations in CSF biomarker levels. However, not only sleep impairment but also intermittent hypoxia can trigger AD neurodegeneration, as shown by animal model studies [29 –31]. On these bases, although OSAS and PLMD are associated with sleep impairment and daytime sleepiness, they may differently act on brain health. Accordingly, OSAS may cause a double-hit effect on brain function due to the combined effect of sleep fragmentation and nocturnal hypoxia, whereas PLMD may alter brain health only by causing sleep impairment. Therefore, to better understand the different roles of these sleep disorders in triggering AD neuropathological processes, and hypothesizing that the combination of sleep fragmentation and intermittent hypoxia, which are the main features of OSAS, may have a stronger pathological effect on the AD biomarkers than the sleep fragmentation alone, present in PLMD, the present study aimed at evaluating CSF and ¹⁸F-FDG PET AD biomarkers in patients with PLMD compared to patients with OSAS and a control group (CG).

METHODS

Participants and study procedures

For the present study, adult patients with OSAS and patients with PLMD, as well as controls were consecutively recruited at the Sleep Medicine Centre of the Neurology Unit in the University Hospital of Rome “Tor Vergata”. All the participants underwent a standard sleep medicine visit, physical and neurological examinations, polysomnographic recording (PSG), CSF AD biomarker analysis, and ¹⁸F-FDG PET. OSAS and PLMD were classified accordingly to the International Classification of Sleep Disorders-3rd Edition. The CG included inpatients at the same medical center undergoing clinical neurologic examination and ¹⁸F-FDG PET for suspected neurological disorders, which were ruled out after diagnostic investigations.

The inclusion criteria for patients and controls were: no neurological or psychiatric diseases, no dementia (Clinical Dementia Rating [CDR] > 1, Mini-Mental State Examination > 24), and no use of CNS-active drugs. Participants with systemic and/or neurologic infectious, inflammatory, or autoimmune diseases, diabetes, and history of alcohol or other substance abuse were excluded from the present study. Moreover, magnetic resonance imaging was used to exclude signs of brain atrophy, particularly in the hippocampus, and white matter abnormalities due to stroke or asymptomatic cerebral vessel disease. Patients with moderate-severe OSAS (apnea-hypopnea index (AHI) ≥15/h), and PLMD (periodic limb movement index (PLMI)≥15/h) were included. Notably, PLMD patients showing an AHI≥15/h were excluded, and a further specific inclusion criterion for those patients was the beneficial effect of dopamine-agonists on sleep and wake symptoms determined at the follow-up visit.

Written informed consent was obtained from all participants in the study, and the study protocol was approved by the Ethical Committee of the University Hospital of Rome “Tor Vergata” [32].

Polysomnographic recordings

At first, all patients underwent PSG recording to evaluate nocturnal sleep (SOMNOscreen, SOMNOmedics GmbH, Randersacker, Germany). The following standard parameters were computed: time in bed (TIB, the total elapsed time between the ‘Lights Out’ and ‘Got Up’ times), sleep onset latency (SL, the time-interval between the lights off and the first sleep epoch), total sleep time (TST, the actual sleep time without SL and awakenings), sleep efficiency (SE, the ratio between TST and time in bed), rapid eye movement (REM) sleep latency (REML, the time interval between sleep onset and the first epoch of REM sleep), stage 1 of non-REM sleep (N1), stage 2 of non-REM sleep (N2), stage 3 of non-REM sleep (N3), REM sleep (REM), and wakefulness after sleep onset (WASO). Periodic limb movements (PLMS) were scored according to the proposed current guidelines. Apnea was defined as a reduction of > 90% of respiratory airflow for 10 or more seconds, while hypopnea has been determined as the reduction of > 30% of respiratory airflow for 10 or more seconds associated with an oxygen desaturation of≥3%. The severity of OSAS is determined by AHI (the sum of all apneas and hypopneas per hour of sleep) and the severity of PLMD was defined by the PLMIndex (the sum of all PLMS). The following oxygen saturation (SaO2) parameters were evaluated: mean SaO2, nadir SaO2, time spent with SaO2 < 90% (T < 90), and ODI (number of oxygen desaturations≥3% per hour). PSG recordings were scored based on the international standard criteria of the American Academy of Sleep Medicine [33].

CSF biomarker analysis

All CSF samples were obtained the day after the PSG recording by lumbar puncture (LP) performed in the decubitus position between 8:00 and 9:30 AM, within 1 to 2 h after morning awakening, using an atraumatic needle. CSF samples were collected in polypropylene tubes using standard sterile techniques. The first 4 mL CSF sample was used for routine biochemistry analysis, including the total cell count. A second 4 mL CSF sample was centrifuged to eliminate cells and cellular debris and immediately frozen at –80°C until the analysis to assess t-tau, p-tau, and Aβ₄₂ levels could be performed. The CSF Aβ₄₂, t-tau, and p-tau levels were determined according to previously published standard procedures, using commercially available sandwich enzyme-linked immunosorbent assays (Innotest β-Amyloid 1–42, Innotest h-T-tau, Innotest Phospho-T-tau 181; Innogenetics, Ghent, Belgium) [15 , 34–36]. The cut-off values for the CSF biomarkers positive for AD pathology were set in-house [37, 38], and our laboratory results are in line with those of the external quality control program for the CSF biomarkers, promoted by the AD Association [39].

PET/CT scanning protocol

The PET/CT protocol study was conducted at the Nuclear Medicine facility of the University Hospital of Rome “Tor Vergata”. The system used was General Electric VCT PET/CT scanner. All subjects were intravenously injected with ¹⁸F-FDG (dose range 185–295 megabequerels) and hydrated with 500 mL of saline (0.9 % sodium chloride). PET/CT acquisition started 30±5 min after ¹⁸F-FDG injection and lasted 10 min for all participants. The reconstruction parameters were as follows: ordered subset expectation maximization, 4 subsets and 12 iterations; matrix 256×256; full width at half maximum (FWHM): 5 mm [40].

Data and statistical analysis

PSG and CSF data analysis

A commercial statistical software was used for statistical analysis (SPSS version 25, IBM Corporation, Armonk, NY, USA) [41]. The Kolmogorov-Smirnov test was used to check for the normal distribution of PSG and CSF data. All demographic, PSG and CSF data were then compared between the three groups (OSAS versus PLMD versus CG) using the Kruskal-Wallis test. Statistical significance was set at p≤0.05.

¹⁸F-FDG PET analysis

Statistical parametric mapping 12 (SPM12) implemented in MATLAB 2018a was used to analyze PET scans in this study (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/), as previously reported [42]. PET data were converted from DICOM to NIfTI format using MRIcro software available at https://www.nitrc.org/projects/mricron and then subjected to a normalization process. A bias regularization was applied (0.0001) to limit biases due to smooth, spatially varying artefacts that modulate the image’s intensity and impede the automated processing of the images. To prevent the algorithm from trying to model the intensity variation due to different tissue types, the FWHM of the Gaussian smoothness of bias was set at a 60 mm cut-off. A tissue probability map implemented in SPM12 was used (TPM.nii). To achieve approximate alignment to the ICBM space template—European brains [43, 44], a mutual information affine registration was used with the tissue probability maps [45].

Warping regularization was set with the following 1×5 arrays (0, 0.001, 0.5, 0.05, 0.2). To cope with functional anatomical variability that is not compensated by spatial normalization and improves the signal-to-noise ratio, smoothness was set at 5 mm, and the sampling distance, which encodes the approximate distance between sampled points when estimating the model parameters, was set at 3.

To blur the individual variations (especially gyral variations) and to increase the signal-to-noise ratio, an 8 mm isotropic Gaussian filter was applied. Before regression analysis was applied, it was necessary to define the parameters and post-processing tools; global normalization (which escalates images to a global value) = 50 (using proportional scaling); the masking threshold (to help identify voxels with an acceptable signal in them) was set to 0.8; a transformation tool of statistical parametric maps into normal distribution was used; the correction of SPM coordinates to match the Talairach coordinates, subroutine implemented by Matthew Brett (http://www.mrc-cbu.cam.ac.uk/Imaging) was made. Brodmann areas (BA) were identified at a range from 0 to 3 mm from the corrected Talairach coordinates of the SPM output isocentre using a Talairach client available at http://www.talairach.org/index.html. As proposed by Bennett et al. [46], SPM t-maps were corrected for multiple comparisons using the false discovery rate (p≤0.05) and corrected for multiple comparisons at the cluster level (p≤0.001). The level of significance was set at 100 (5×5×5 voxels, i.e. 11×11×11 mm) contiguous voxels [46]. The following voxel-based comparisons were assessed: 1) CG versus OSAS patients and vice versa; 2) CG versus PLMD patients and vice versa; 3) OSAS patients versus PLMD patients and vice versa. All comparisons were performed using the “two-sample t-test” design model available in SPM12 [40]. Sex, age and CSF levels were used as covariates in the analyses of the CG, OSAS, and PLMD patients.

RESULTS

Twenty patients affected by OSAS (AHI: 33.7±19.01), 12 patients with PLMD (PLM Index: 27.11±9.59) were recruited for this study, and 15 subjects were included in the CG. The OSAS patients had a mean age of 58.75 (SD = 3.53), which was significantly lower than the mean age of PLMD patients and controls. Moreover, PLMD patients were older than controls. Demographic information and CSF data of the study groups are reported in Table 1.

Table 1

Demographic and CSF data of OSAS, PLMD and control group

	Differences between groups			Post hoc comparisons
	OSAS (n = 20)	PLMD (n = 12)	Controls (n = 15)	p	OSAS versus Controls	PLMD versus Controls	OSAS versus PLMD
Mean age, y	58.75±3.53	72.23±5.29	63.8±8.46	< 0.001	< 0.001	< 0.01	0.02
Gender, n (%)
Male	14 (70.0%)	7 (58.33%)	8 (53.33%)	0.58
Female	6 (30.0%)	5 (41.67%)	7 (46.67%)
Education, y	8.94±3.64	7.85±4.41	8.09±3.75	0.70
Amyloid-β 42, pg/mL	622.05±270.43	717.23±288.46	889.4±323.16	0.04	< 0.01	0.04	0.35
Total tau, pg/mL	205.35±98.59	288. 45±255. 89	176.2±89.27	0.16
Phospho-tau, pg/mL	39.1±14.88	41.37±40.07	38.3±6.84	0.94
APOE4 + positivity, n.	8/20	4/12	6/15

Continuous data are presented as Mean±SD.

Sleep data

PSG results are presented in Table 2. Both OSAS and PLMD patients had lower SE and higher TIB and REML than controls. PLMD patients showed a higher TST than controls. OSAS patients showed a significant lower REM duration than both PLMD patients and controls. Both OSAS and PLMD patients had a higher WASO than controls, and patients with OSAS also showed a higher WASO than those with PLMD. Regarding sleep structure, both OSAS and PLMD patients showed a higher duration of N1 stage and a lower duration of N3 stage. Moreover, OSAS patients showed a higher duration of N2 stage when compared to controls. No significant differences were found between OSAS and PLMD patients in TST, SE, REML, duration of N1, N2, and N3 stages.

Table 2

PSG data of OSAS, PLMD patients and controls

	Differences between groups			Post hoc comparisons
OSAS (n = 20)	PLMD (n = 12)	Controls (n = 15)	p	OSAS versus Controls	PLMD versus Controls	OSAS versus PLMD
Total Time in Bed	481.75±58.58	475.09±31.67	338.08±145.51	< 0.001	< 0.01	< 0.01	0.72
Total sleep time (min)	335.2±84.36	369.0±33.82	298.25±67.37	0.039	0.17	< 0.01	0.19
Sleep efficiency (%)	69.58±14.4	77.67±10.68	88.22±4.63	< 0.001	< 0.001	< 0.001	0.10
WASO (min)	146.05±25.28	106.09±30.12	39.83±21.86	< 0.001	< 0.001	< 0.001	< 0.01
REM sleep latency (min)	175.37±97.22	207.11±62.86	87.13±26.51	< 0.001	< 0.001	< 0.01	0.32
REM (min)	40.5±20.69	66.83±17.44	75.7±14.19	< 0.001	< 0.001	0.16	< 0.001
N1 (min)	58.42±44.16	67.27±16.6	15.12±4.97	< 0.001	< 0.001	< 0.001	0.51
N2 (min)	180.3±58.43	151.01±50.44	129.11±6.73	0.008	< 0.01	0.11	0.16
N3 (min)	55.97±35.96	54.09±14.8	96.16±40.90	0.002	< 0.01	< 0.01	0.86

Data are presented as Mean±SD. WASO, wake after sleep onset; REM, Rapid Eye Movement, N1, Non-REM stage 1; N2, Non-REM stage 2; N3, Non-REM stage 3; OSAS, obstructive sleep apnea syndrome; PLMD, periodic limb movement disorder.

AD biomarker data

OSAS patients and PLMD patients showed lower CSF Aβ₄₂ levels than controls. No differences were found in CSF levels of t-tau and p-tau between groups.

Considering ¹⁸F-FDG PET analysis, patients with OSAS showed a significant increase in brain glucose consumption in the left insula, frontal, and cingulate lobes, in the left parahippocampal gyrus, both right and left cerebellum, in the right temporal lobe compared to controls. OSAS patients also showed a significant reduction in brain glucose consumption in a wide cluster that includes the right temporal and limbic lobe, right and left parietal lobe, and left cerebellum (Table 3, Fig. 1). Furthermore, PLMD patients showed a significant increase in brain glucose consumption in the left parahippocampal gyrus and left caudate when compared to controls. No significant areas of decreased glucose consumption have been detected in PLMD when compared to the CG (Table 4, Fig. 2). When comparing OSAS to PLMD patients, patients with OSAS showed a reduction in brain glucose consumption in the right temporal lobe. No other significant differences were found when subtracting OSAS to PLMD patients (Table 5, Fig. 3).

Table 3

Numerical results of SPM comparisons in ¹⁸F-FDG uptake between OSAS and CG

Analysis	Cluster level					Voxel level
	Cluster p(FWE-corr)	Cluster p(FDR-corr)	Cluster extent	Cortical Region	Z score of maximum	Talairach coordinates	Cortical region	BA
OSAS - CG	0.000	0.001	1173		5.09	–40, –28, –4	Left insula	22
					4.78	–40, –20, –12	Left parahippocampal gyrus	36
					4.50	–32, –2, –16	Left parahippocampal gyrus	Amygdala
	0.000	0.001	1078		5.37	28, –46, –40	Right cerebellum	Cerebellar tonsil
					3.92	20, –64, –32	Right cerebellum	Cerebellar tonsil
					3.68	26, –68, –28	Right cerebellum	Pyramis
	0.002	0.002	814		5.28	–24, 40, 6	Left medial frontal gyrus	32
					4.43	–8, 32, –8	Left anterior cingulate	32
	0.001	0.002	872		4.64	36, –4, –16	Right insula	13
					4.56	44, –18, –12	Right temporal lobe	21
					4.53	38, 4, –14	Right temporal lobe	38
	0.002	0.002	787		4.73	–28, –42, –40	Left cerebellum	Cerebellar tonsil
					4.57	–20, –56, –36	Left cerebellum	Cerebellar tonsil
CG - OSAS	0.000	0.000	4996		5.71	6, –60, 22	Right precuneus	31
					5.46	6, –44, 30	Right limbic lobe	31
					4.28	6, –76, 48	Right precuneus	7
	0.000	0.000	3774		5.19	50, –62, 32	Right parietal lobe	39
					5.15	48, –62, 48	Right parietal lobe	40
					5.06	56, –52, 18	Right temporal lobe	40

In the ‘cluster level’ section on left, the number of voxels, the corrected p value of significance and the cortical region where the voxel is found, are all reported for each significant cluster. In the ‘voxel level’ section, all of the coordinates of the correlation sites (with the Z-score of the maximum correlation point), the corresponding cortical region and BA are reported for each significant cluster. L, left; R, right; BA, Brodmann’s area. In the case that the maximum correlation is achieved outside the grey matter, the nearest grey matter (within a range of 5 mm) is indicated with the corresponding BA.

Table 4

Numerical results of SPM comparisons in ¹⁸F-FDG uptake between PLMD and CG

Analysis	Cluster level				Voxel level
	Cluster p(FWE-corr)	Cluster p(FDR-corr)	Cluster extent	Z score of maximum	Talairach coordinates	Cortical region	BA
PLMD - CG	0.044	0.112	331	4.19	–26, –12, –10	Left parahippocampal gyrus	Hippocampus
				3.70	–36, –20, –10	Left parahippocampal gyrus	Hippocampus
				3.69	–32, –26, –2	Left caudate	Caudate tail

Table 5

Numerical results of SPM comparisons in ¹⁸F-FDG uptake between OSAS and PLMD

Analysis	Cluster level				Voxel level
Cluster p(FWE-corr)	Cluster p(FDR-corr)	Cluster extent	Z score of maximum	Talairach coordinates	Cortical region	BA
PLMD - OSAS	0.031	0.092	521	4.18	60, –60, 0	Right middle temporal gyrus	21
				3.94	54, –54, –12	Right inferior temporal gyrus	20

Fig. 1

Axial view showing a superimposition with MRI of the results of SPM comparisons between ¹⁸F-FDG uptake in OSAS patients as compared to control group (data presented in Table 3). OSAS patients show a significant increase in brain glucose consumption in the left insula, frontal, and cingulate lobes, in the left parahippocampal gyrus, both right and left cerebellum, in right temporal lobe (a). On the other hand, OSAS patients show a significant reduction of brain glucose consumption in a wide cluster that includes right temporal and limbic lobe, right and left parietal lobe and left cerebellum (b).

Fig. 2

Axial view showing a superimposition with MRI of the results of SPM comparisons between 18 F-FDG uptake in PLMD patients as compared to control group (data presented in Table 4). PLMD patients show an increase in brain glucose consumption in the left parahippocampal gyrus and left caudate. No significant areas of decreased glucose consumption have been detected in PLMD as compared to control group.

Fig. 3

Axial view showing a superimposition with MRI of the results of SPM comparisons between ¹⁸F-FDG uptake in OSAS patients as compared to PLMD (data presented in Table 5). OSAS patients show a reduction in brain glucose consumption in the right temporal lobe. We did not find significant differences when subtracting OSAS to PLMD patients.

DISCUSSION

Sleep disorders have been recognized as a risk factor for AD, since sleep impairment may reduce the beneficial effect of the glymphatic system active during nocturnal sleep. Based on this evidence, recent studies evaluated the impact of OSAS on the AD biomarkers and documented that this sleep disorder may significantly alter CSF and ¹⁸F-FDG PET biomarkers. OSAS is featured by sleep fragmentation and intermittent hypoxia and both mechanisms have been hypothesized to trigger neuropathological processes typical of AD. To better understand the role of sleep fragmentation on AD biomarkers, this study measured and compared the effects of two different sleep disorders, OSAS and PLMD, commonly diagnosed in adults and elderly, on CSF Aβ₄₂ and tau proteins levels and brain glucose metabolism measured by ¹⁸F-FDG PET.

The findings of the present study confirmed, as expected, that OSAS and PLMD cause a similar alteration in sleep quality and sleep structure. Notwithstanding, the main finding regards the documentation of a more prominent alteration in CSF Aβ₄₂ levels and cerebral glucose metabolism in OSAS patients possibly due to the presence of a double-hit effect on the brain, mediated by both sleep fragmentation and intermittent hypoxia, while the PLMD patients showed exclusively the sleep fragmentation effect.

Regarding sleep structure data, patients with PLMD, as well as patients with OSAS, showed lower TST, SE, N3, and REM and higher REML, N1, N2, and WASO when compared to the CG. However, OSAS patients showed lower REM sleep and higher WASO than PLMD patients. This impairment in sleep architecture can be responsible for the significant differences found in CSF Aβ₄₂ levels and ¹⁸F-FDG cerebral uptake in PLMD and OSAS patients when separately compared to the CG. Significantly, both OSAS and PLMD patients presented lower CSF Aβ₄₂ levels when compared to the CG. This significant difference may be attributed to the alteration in sleep quality and continuity and thus to the hypothesized dysregulation of the glymphatic system that is unable to restore brain dynamics and clear the brain from Aβ₄₂ accumulating during wakefulness [25]. Consistently, previous studies documented that OSAS is associated with low CSF Aβ₄₂ levels and high brain Aβ plaque accumulation and deposition [15 , 48], and these pathological modifications concur with the increased risk of neurodegeneration in OSAS patients when assessed longitudinally [20 , 50]. Moreover, OSAS is also a common comorbidity in patients with AD and its diagnosis is more frequent in these patients when compared to the older adult population not affected by AD [51 –54]. Conversely, less is known about the presence of PLMD in patients with AD, and therefore the more evident reduction in CSF Aβ₄₂ levels in OSAS patients may be due to the possible underlined AD pathology affecting patients with OSAS more than patients with PLMD. Further longitudinal studies comparing patients with PLMD with those with OSAS should be planned in order to understand and compare the effective risk of developing AD in both sleep disorders. Regarding ¹⁸F-FDG PET data, patients with OSAS presented a more diffuse alteration of cerebral glucose metabolism than those with PLMD and controls. Several networks were impaired in patients with OSAS, confirming the detrimental effect of this sleep disorder on brain function. On the one hand, as previously shown, reduced glucose consumption was evident in critical brain areas for cognitive and behavioral processes and postural and vestibular functions (see Fig. 1) and that may explain memory, attention, behavior impairment, and postural-vestibular reflex control alteration in OSAS patients, respectively [47 , 56]. On the other hand, a pattern of higher glucose consumption was also evident in regions owing to cognitive processes usually altered at the beginning of AD pathology and possibly in a hyper-functional compensatory state, usually evident in the early stages of the disease [57]. When comparing PLMD patients to the CG, a slightly significant difference in cerebral ¹⁸F-FDG uptake was found exclusively in caudate and hippocampus. This finding can also be explained by greater compensatory glucose metabolism in central cognitive areas, thus reflecting neuronal hyperactivity processes activated as an effect of sleep impairment. Finally, when comparing OSAS to PLMD patients, a reduced glucose consumption in temporal regions was evident in patients with OSAS. However, this finding should be interpreted with caution, although it may reflect the increased susceptibility of OSAS patients’ brains to AD processes that usually start in the temporal cortex, which is early affected by AD neurodegenerative processes.

The present study has some limitations that need to be addressed. First, the limited sample size, especially the small sample of patients with PLMD included in this study. Further studies with a larger sample of patients are needed to further support these findings. Second, the cross-sectional design of the study did not allow the assessment of the cognitive trajectories of patients with these sleep disorders. Third, a healthier control group should be considered in future studies given that the CG of the present study included subjects undergoing PSG, LP, and ¹⁸F- FDG PET/CT for suspected neurological disorders, which were ruled out after the diagnostic investigations.

In conclusion, the current findings suggest that sleep dysregulation present in both patients with OSAS and PLMD is associated with the alteration of biomarkers consistent with a neurodegenerative process, such as the significant changes in CSF and ¹⁸F-FDG PET biomarkers. However, brain Aβ₄₂ dynamics and cerebral glucose metabolism were more altered in the OSAS group possibly owing to both sleep fragmentation and intermittent hypoxia, while the PLMD group presented exclusively nocturnal sleep impairment. Although this preliminary evidence exhorts future studies to better understand the stronger risk of developing AD due to OSAS more than any other sleep disorder, it also highlights the significant role of OSAS in triggering neurodegenerative processes. Moreover, the alteration in AD biomarkers documented in patients with PLMD, although less evident, emphasizes the critical role of sleep in ensuring brain health and functioning and further supports the need of treating sleep disorders and improving sleep to prevent neurodegenerative processes in the adult and elderly population. Finally, this study adds to the growing necessity of performing a sleep medicine interview and planning a sleep study in adults and older subjects since sleep problems represent a risk for AD pathology. Physicians and researchers are then encouraged to target sleep enhancement in patients with early signs and symptoms of AD pathology since sleep impairment may impact brain health and trigger neurodegenerative processes. However, PSG recording, better identifying sleep disorders, may be more critical than the only clinical recognition of sleep impairment at the sleep medicine interview, since sleep disorders can differently impact brain functioning and drive AD biomarker pathological changes.

DISCLOSURE STATEMENT

Authors’ disclosures available online (https://www.j-alz.com/manuscript-disclosures/21-5734r1).

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Nocturnal Hypoxia and Sleep Fragmentation May Drive Neurodegenerative Processes: The Compared Effects of Obstructive Sleep Apnea Syndrome and Periodic Limb Movement Disorder on Alzheimer’s Disease Biomarkers

Abstract

Background:

Objective:

Methods:

Results:

Conclusion:

Keywords

INTRODUCTION

METHODS

Participants and study procedures

Polysomnographic recordings

CSF biomarker analysis

PET/CT scanning protocol

Data and statistical analysis

PSG and CSF data analysis

18F-FDG PET analysis

RESULTS

Sleep data

AD biomarker data

DISCUSSION

DISCLOSURE STATEMENT

References

¹⁸F-FDG PET analysis