Mayà, Gerard

Abstract Background Sleep disturbances are common in Alzheimer's disease and have been reported in its preclinical stage. A detailed characterization of sleep disruption across the Alzheimer’s disease continuum is required to clarify its role as a potential therapeutic target and to evaluate the utility of sleep-related biomarkers. Methods Sleep architecture was characterized through detailed spectral and temporal analysis of the slow-wave activity using polysomnography across the Alzheimer’s disease continuum . Participants included: cognitively unimpaired individuals without abnormal Alzheimer’s disease biomarkers (n = 30), cognitively unimpaired individuals with abnormal Alzheimer’s disease biomarkers (n = 45), and individuals with mild cognitive impairment or mild dementia due to Alzheimer’s disease (n = 41). Associations between slow-wave features and longitudinal cognitive performance were tested in cognitively unimpaired participants. Sex differences were also explored. Results A stepwise increased wake after sleep onset and latency to rapid eye movement sleep was observed, alongside reduced spectral power of slow-wave activity and a progressive flattening of slow-waves across the Alzheimer’s disease continuum in both sexes. In cognitively unimpaired individuals, higher slow-wave slope and greater peak-to-peak amplitude were associated with better cognitive trajectories over time. Conclusions Sleep macro- and microarchitecture are altered early and progressively deteriorate across the Alzheimer’s disease continuum . Slow-wave features relate to cognitive trajectories in preclinical Alzheimer’s disease, with exploratory analyses suggesting stronger associations in men. These findings support slow-wave morphology as a candidate non-invasive marker for future sleep-based intervention studies.

Abstract Isolated/idiopathic rapid-eye-movement (REM)-Sleep Behavior Disorder (iRBD) is characterized by dream enactment behaviors associated with loss of REM atonia. iRBD is in most cases a prodromal synucleinopathy, and emerging evidence suggests associations between RBD and other neurological and psychiatric conditions. In this study, we performed pathway-based polygenic risk score (PRS) and rare variant burden analyses to examine these potential associations. Pathway-specific PRS were constructed from genome-wide association study summary statistics of five neurodegenerative and seven psychiatric traits across 10 biologically relevant pathway categories, including a total of 279 pathways, in 1,573 iRBD cases and 16,022 controls from the International RBD Study Group and UK Biobank. Rare variant burden tests were performed in 1,264 iRBD cases and 2,581 controls. We identified multiple potential pathways indicating shared polygenic risk between RBD and both neurodegenerative and psychiatric disorders. Lewy body diseases and post-traumatic stress disorder had the most shared polygenic risk pathways in neurological and psychiatric disorders, respectively. Two pathways, the serotonin transport pathway and the chaperone-mediated autophagy pathway, showed the strongest association with iRBD, and gene-based rare variants analyses revealed five genes associated with iRBD: GBA1 , PLEKHM1 , LRP2 , P2RX1 , and HAP1. Subsequent analysis of these genes in Parkinson’s disease and dementia with Lewy bodies replicated several associations. Together, these findings provide novel insights into the shared genetic architecture underlying iRBD, neurodegenerative disorders, and psychiatric traits, with implications for early identification and mechanistic understanding.

Abstract INTRODUCTION Sleep disruption, particularly loss of slow‐wave sleep (SWS), is common in Alzheimer's disease (AD), but its neurobiological underpinnings remain unclear. We investigated whether locus coeruleus (LC) integrity relates to SWS across the AD continuum and whether sex and perivascular spaces (PVSs) modify these associations. METHODS In a cohort (11 controls, 30 mild cognitive impairment, 17 AD dementia) we combined overnight polysomnography with LC‐sensitive magnetic resonance imaging of the LC, basal ganglia and centrum semiovale PVS ratings, and cerebrospinal fluid noradrenaline. Multivariable linear regression adjusted for demographics, disease stage, and medication use. RESULTS Higher LC integrity was associated with greater slow‐wave activity and slow oscillation power, with stronger effects in females. Basal ganglia PVS burden was related to lower SWS spectral power, whereas noradrenaline levels were not associated with sleep. DISCUSSION LC integrity, sex, and PVS burden show associations with SWS alterations in aging and AD, supporting restorative sleep as a potential therapeutic target.

Objective To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). Method s In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography‐confirmed RBD who eventually phenoconverted to overt alpha‐synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non‐phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. Results Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD‐converters from DLB‐converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z ‐score cutoff of −1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of −1.0 at the most affected hemisphere putamen/caudate ratio characterized PD‐converters. Interpretation Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease‐modifying trials. ANN NEUROL 2024;95:1178–1192