Louis, Erik K. St.

AbstractBackgroundSleep’s crucial role in maintaining brain health is increasingly recognized, particularly due to the rising prevalence of neurodegenerative diseases. It not only supports cognitive function but also aids in clearing brain metabolic waste through cerebrospinal fluid (CSF) dynamics. During sleep, especially in the non‐rapid eye movement (NREM) phase, CSF flow increases, essential for removing neurotoxic substances like amyloid‐beta proteins. This study explores the link between sleep respiratory patterns and CSF flow promotion.MethodElectroencephalography (EEG), and functional magnetic resonance imaging (fMRI) were simultaneously acquired during sleep from Gu, et al. (n = 29) (Gu et al. 2022). Respiration, measured via a respiratory belt, calculated respiratory volume (RV) as the standard deviation within a 5 second sliding window. EEG data processed delta band power (1‐4Hz) averaged across channels. Parcellated fMRI data using the brain template followed by the calculation of the averaged gray matter (GM) blood‐oxygen‐level‐dependent (BOLD) signal using the GM mask. CSF inflow was manually extracted from the fMRI’s bottom slice at the foramen magnum level. Subsequently, all signals were temporally aligned to the peak of the CSF inflow signal.ResultAt the time of CSF inflow (time = 0 sec. in the Figure 1A‐D), we observed an earlier increase in RV and EEG delta power, followed by changes in the GM BOLD signal (Figure 1). EEG and RV changes preceded the BOLD signal changes, which, in turn, preceded changes in CSF flow. Sensitivity to detect RV involvement during MRI varied with the placement of the respiratory belt, being least sensitive on the chest and more on the abdomen.ConclusionThese findings raise an interesting question on the role respiratory patterns during sleep, particularly in the NREM phase. Speculatively, RV change may influence CSF circulation during NREM sleep (Figure 1E). This enhancement in CSF flow is crucial for the clearance of metabolic waste from the brain, potentially reducing the risk of neurodegenerative diseases. Our findings indicate that respiratory volume may serve as a significant marker for CSF circulation during sleep.

Abstract Biomarkers that aid in early detection of neurodegeneration are needed to enable early symptomatic treatment and enable identification of people who may benefit from neuroprotective interventions. Increasing evidence suggests that sleep biomarkers may be useful, given the bi-directional relationship between sleep and neurodegeneration and the prominence of sleep disturbances and altered sleep architectural characteristics in several neurodegenerative disorders. This study aimed to demonstrate that sleep can accurately characterize specific neurodegenerative disorders (NDD). A four-class machine-learning algorithm was trained using age and nine sleep biomarkers from patients with clinically-diagnosed manifest and prodromal NDDs, including Alzheimer’s disease dementia (AD = 27), Lewy body dementia (LBD = 18), and isolated REM sleep behavior disorder (iRBD = 15), as well as a control group (CG = 58). The algorithm was validated in a total of 381 recordings, which included the training data set plus an additional AD = 10, iRBD = 18, Parkinson disease without dementia (PD = 29), mild cognitive impairment (MCI = 78) and CG = 128. Test-retest consistency was then assessed in LBD = 10, AD = 9, and CG = 46. The agreement between the NDD profiles and their respective clinical diagnoses exceeded 75% for the AD, LBD, and CG, and improved when NDD participants classified Likely Normal with NDD indications consistent with their clinical diagnosis were considered. Profiles for iRBD, PD and MCI participants were consistent with the heterogeneity of disease severities, with the majority of overt disagreements explained by normal sleep characterization in 27% of iRBD, 21% of PD, and 26% of MCI participants. For test-retest assignments, the same or similar NDD profiles were obtained for 88% of LBD, 86% in AD, and 98% of CG participants. The potential utility for NDD subtyping based on sleep biomarkers demonstrates promise and requires further prospective development and validation in larger NDD cohorts.

Abstract Obstructive sleep apnoea (OSA) is associated with an increased risk for cognitive impairment and dementia, which likely involves Alzheimer’s disease pathology. Non-rapid eye movement slow-wave activity (SWA) has been implicated in amyloid clearance, but it has not been studied in the context of longitudinal amyloid accumulation in OSA. This longitudinal retrospective study aims to investigate the relationship between polysomnographic and electrophysiological SWA features and amyloid accumulation. From the Mayo Clinic Study of Aging cohort, we identified 71 participants ≥60 years old with OSA (mean baseline age = 72.9 ± 7.5 years, 60.6% male, 93% cognitively unimpaired) who had at least 2 consecutive Amyloid Pittsburgh Compound B (PiB)-PET scans and a polysomnographic study within 5 years of the baseline scan and before the second scan. Annualized PiB-PET accumulation [global ΔPiB(log)/year] was estimated by the difference between the second and first log-transformed global PiB-PET uptake estimations divided by the interval between scans (years). Sixty-four participants were included in SWA analysis. SWA was characterized by the mean relative spectral power density (%) in slow oscillation (SO: 0.5–0.9 Hz) and delta (1–3.9 Hz) frequency bands and by their downslopes (SO-slope and delta-slope, respectively) during the diagnostic portion of polysomnography. We fit linear regression models to test for associations among global ΔPiB(log)/year, SWA features (mean SO% and delta% or mean SO-slope and delta-slope), and OSA severity markers, after adjusting for age at baseline PiB-PET, APOE ɛ4 and baseline amyloid positivity. For 1 SD increase in SO% and SO-slope, global ΔPiB(log)/year increased by 0.0033 (95% CI: 0.0001; 0.0064, P = 0.042) and 0.0069 (95% CI: 0.0009; 0.0129, P = 0.026), which were comparable to 32% and 59% of the effect size associated with baseline amyloid positivity, respectively. Delta-slope was associated with a reduction in global ΔPiB(log)/year by −0.0082 (95% CI: −0.0143; −0.0021, P = 0.009). Sleep apnoea severity was not associated with amyloid accumulation. Regional associations were stronger in the pre-frontal region. Both slow-wave slopes had more significant and widespread regional associations. Annualized PiB-PET accumulation was positively associated with SO and SO-slope, which may reflect altered sleep homeostasis due to increased homeostatic pressure in the setting of unmet sleep needs, increased synaptic strength, and/or hyper-excitability in OSA. Delta-slope was inversely associated with PiB-PET accumulation, suggesting it may represent residual physiological activity. Further investigation of SWA dynamics in the presence of sleep disorders before and after treatment is necessary for understanding the relationship between amyloid accumulation and SWA physiology.

The ability to identify individuals in the prodromal phase of Parkinson’s disease has improved in recent years, raising the question of whether and how those affected should be informed about the risk of future disease. Several studies investigated prognostic counselling for individuals with isolated REM sleep behavior disorder and have shown that most patients want to receive information about prognosis, but autonomy and individual preferences must be respected. However, there are still many unanswered questions about risk disclosure or early diagnosis of PD, including the impact on personal circumstances, cultural preferences and specific challenges associated with different profiles of prodromal symptoms, genetic testing or biomarker assessments. This narrative review aims to summarize the current literature on prognostic counselling and risk disclosure in PD, as well as highlight future perspectives that may emerge with the development of new biomarkers and their anticipated impact on the definition of PD.

Short sleep duration is associated with future cardiovascular disease including hypertension, especially in females, but mechanistic evidence is largely lacking. Experimental sleep restriction has been shown to cause short sleep-mediated tachycardia and reduced heart rate variability, indicative of autonomic cardiac instability, in some but not all studies. Heart rate (HR) response to spontaneous cortical arousals can be regarded as an index of autonomic cardiac control given its characteristic temporal pattern. Whether the HR response to cortical arousals is altered during experimental sleep restriction remains unknown. We hypothesized that sleep restriction would result in augmented HR reactivity to spontaneous arousals, and that this response would be more marked in females. We studied 19 young, healthy subjects (eight females, age: 23.5 ± 5 years, body mass index [BMI]: 24.5 ± 3.6 kg/m2). In a randomized, crossover design, participants were tested during a normal sleep (NS, 9 hr sleep opportunity) or sleep restriction (SR, 4 hr sleep opportunity) study arm. Each 16-day study visit consisted of a 4-day study acclimation phase, 9-day experimental sleep perturbation segment, followed by a 3-day recovery period. An overnight polysomnography (PSG) study was conducted on study days 2, 5 and 11 (at baseline and after 1 and 7 days of restricted versus control sleep). A registered PSG technician provided sleep staging and spontaneous arousal scoring, which was confirmed by an investigator with sleep expertise (IMG). For each identified spontaneous arousal, the HR change was quantified for 30 cardiac cycles post-arousal onset and compared to a pre-arousal baseline (average HR 5 – 10 cardiac cycles prior to arousal). An arousal was considered valid for analysis if at least one minute of stage N2 or rapid eye movement (REM) sleep occurred following sleep stage transition and was at least 30 seconds apart from other sleep disturbance-related events (e.g., limb movement, apneic event, etc.). Given low prevalence of slow wave sleep (SWS) arousals, arousals occurring in that sleep stage were excluded from present analysis. Statistical analysis, including repeated measures ANOVA (RMANOVA, p < 0.05) with condition and time (cardiac cycles) as within-subjects factors and sex as the between-subjects factor, was conducted to compare the beat-to-beat HR response to arousal between study conditions. Additionally, average and peak HR responses to spontaneous arousals were compared via RMANOVA with condition and time (study days) as within-subjects and sex as between-subjects factors. HR response to spontaneous arousal in stage two and REM sleep was similar between NS and SR on study day 5 or day 11 (condition × time: p = 0.943, 0.865). Secondary analysis of average and peak HR responsiveness to spontaneous arousals in stage two and REM sleep between conditions and study days was also not statistically different (condition × time: p = 0.193, 0.185). Last, no significant sex differences were detected across comparisons (condition × time × sex: p > 0.05 for all). These findings suggest that a 9-day sleep restriction paradigm may not alter cardiac autonomic function in otherwise healthy adults. Future work is warranted to determine whether these results extend to individuals with established cardiovascular risk factors or cardiovascular disease. Support: NIH T32-DK007013, R01-114676, and CTSA grant UL1-TR002377. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract Introduction Quantitative rapid eye movement (REM) sleep without atonia (RSWA) recorded during polysomnography (PSG) represents loss of normal REM sleep atonia. RSWA is the neurophysiologic signature of REM sleep behavior disorder (RBD). Visually scored RSWA is a candidate biomarker for synucleinopathies. However, visual RSWA scoring requires expert scoring, limiting widespread application. We comparatively analyzed RSWA in DLB and iRBD using the automated Ferri REM Atonia Index (RAI). Methods We quantitatively analyzed the RAI in 17 DLB and 20 iRBD patients, compared to 20 controls using Hypnolab Software for Sleep Analysis (SWS Soft, Colognola ai Colli, Italy). PSG data were converted to European Data Format (.EDF), and the submentalis surface electromyogram (EMG) signal was notch-filtered at 60 Hertz and rectified. Automatically calculated RAI scores were comparatively analyzed across groups (DLB, iRBD, controls) using Kruskal Wallis H tests for group comparisons, and regression analyses determined relationships between diagnosis, age, and sex. Logistic regression ROC determined optimal RAI for distinguishing iRBD from DLB. When interpreting RAI, lower values represent greater amounts of RSWA, and higher values represent more normal REM atonia. Results Median age was no different between DLB (72.6 years), iRBD (69.4 years), and controls (68.0 years). The DLB group consisted of 16 men and one woman, the iRBD group had 18 men and 2 women, and controls were comprised of 16 men and 4 women. Median RAI was lowest in DLB (0.32), intermediate in iRBD (0.84) and highest in controls (0.96), indicating a gradient in RSWA amounts from DLB patients (highest) to controls (lowest). Regression demonstrated diagnosis independently predicted RAI, controlling for age and sex (adjusted R2=0.64, p=0.0001). The RAI that optimally distinguished iRBD from DLB was 0.48 (specificity 85%, sensitivity 76%, AUC=0.88). Conclusion The RAI demonstrated a clear gradient throughout the Lewy body spectrum; highest (indicating lowest RSWA amounts) in controls, intermediate in iRBD, and lowest (indicating greatest RSWA) in DLB. The RAI is a promising, time efficient and broadly applicable candidate biomarker for DLB and iRBD. Automated quantitative RSWA could be a valuable tool for deep phenotyping throughout the Lewy body disease spectrum. Support (if any) This study was supported by: U01NS100620, R34AG056639, U19AG071754, and P30AG62677.