Latest in Sleep Medicine News

Bruxism is an oromandibular condition characterized by 2 distinct circadian manifestations, sleep bruxism and awake bruxism. Bruxism may be protective for obstructive sleep apnea. Bruxism can have significant implications for restorative dentistry, in terms of failure of natural teeth and dental restorations. Bruxism may also have specific implications in various dental specialties such as prosthodontics, endodontics, orofacial pain, and implantology, amongst others. Thus, bruxism may necessitate recognition and management through an interdisciplinary approach.

This study investigated relationships between sleep quality, sleep apnoea and depression during pregnancy, focusing on their associations and potential bidirectional effects.

The neurodegenerative process responsible for Parkinson's disease (PD) begins years before the level of dopamine denervation of the basal ganglia leads to the characteristic clinical phenotype of the disease. During the past 20years, numerous symptoms that may occur during the prodromal stage of the disease have been identified: subtle motor symptoms, psychocognitive disorders, sleep disorders, sensorial dysfunction, and dysautonomia. Among them, rapid eye movement sleep behaviour disorder (RBD) is one of the most specific. The follow-up of cohorts of subjects affected by this disorder has provided valuable information about the prodromal stage, including evidence of various biological or imaging biomarkers associated with the pre-clinical stage of the disease. From all the knowledge acquired about this stage of the disease, criteria for diagnosing prodromal PD have been proposed and have progressively improved in sensitivity and specificity. The strong focus on the RBD-associated prodromal stage has, however, tended to conceal other, less florid forms of prodromal PD, such as those beginning with mild cognitive impairment or mild motor symptoms, which affected subjects are less likely to notice. Here, we review the various symptoms observed in the prodromal stage of PD, progress on identifying relevant imaging and biological biomarkers, and recent insights into the pathogenesis of a disease having such a wide spectrum of presentation and progression. Advances in knowledge about prodromal PD will lead to earlier diagnosis and better identification of prognostic factors, and, subsequently, to the capacity to initiate personalized treatment and potentially slow down the degenerative process.

Presymptomatic neurological diseases are marked by early pathological changes that occur before overt clinical symptoms. These stages, which include prodromes such as REM sleep behavior disorder in Parkinson's or mild cognitive impairment in Alzheimer's, offer critical opportunities for early intervention. However, their detection remains challenging due to the subtlety of changes and the overlap with normal interindividual variability. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), offers new tools to uncover hidden signatures in complex biomedical data. First, we explore how supervised ML models can detect known prodromal patterns across diverse modalities, including EEG, cognitive scores, and structural imaging. Depending on the input, various model types - such as tree-based algorithms for structured data and convolutional or transformer networks for images and signals - can extract predictive features of early neurodegeneration. These approaches have demonstrated success in identifying at-risk individuals before clinical thresholds are reached. Yet, detecting only known patterns limits the scope of early intervention. Many individuals who will go on to develop neurological disease may not yet exhibit any recognized prodromal syndrome. Bridging this gap requires moving beyond predefined labels toward models capable of identifying subtle, unknown anomalies in individuals still considered healthy. Second, we address the detection of latent anomalies among individuals not yet considered at risk without identifiable known prodromal patterns. By mining clinical records, free-text medical notes, and population-level health databases (e.g., UK Biobank, EDS-AP-HP), and by analyzing sensor data from smartphones or wearables, AI can flag deviations from healthy patterns long before symptom onset or formal diagnosis. This approach holds promise for scalable, low-burden, ecological screening. Finally, we introduce the concept of pseudo-healthy twins - synthetic, personalized baselines generated from structural data such as MRI, to improve anomaly detection. These models predict a patient's expected healthy signal in another modality, such as PET, enabling the subtraction of normal anatomical and physiological variability to isolate disease-specific effects. Generative models like GANs and VAEs have shown promise in producing these cross-modal references, enhancing early anomaly detection in diseases like Alzheimer's and multiple sclerosis. Together, these approaches show how AI can bridge the gap between normal variation and early pathology, enabling more sensitive, personalized, and population-scalable detection of presymptomatic neurological disease.

To investigate the relationship between a quality of life (QOL) score and clinical parameters in patients with hypertrophic cardiomyopathy (HCM).

Lifestyle, including physical exercise, diet, intellectual and social engagement, and sleep, represents a powerful nonpharmacological alternative to improve cognitive performance or mitigate cognitive decline associated with ageing or neuropathological conditions. Among brain cells, astrocytes, the most abundant glial cell, are involved in virtually all brain functions, from neurogenesis and synaptogenesis to brain homeostasis, defence, and cognition. Recent studies demonstrate that astrocytes are very sensitive to lifestyle changes, undergoing morphological, molecular, and functional remodelling. These adaptations include enhanced astrocyte complexity, increased synaptic coverage, modulation of inflammatory pathways, and changes in gene expression. Such modifications, accompanied by cognitive improvements, are observed in models of Alzheimer's and Parkinson's, depression, and cerebral ischaemia, as well as in sleep quality and dietary patterns-making astrocytes key elements in lifestyle-induced neural plasticity driving brain health and cognitive improvements. Here, we discuss astroglial roles in translating lifestyle benefits into cognitive improvements; such mechanisms may represent a therapeutic target to prevent or even mitigate cognitive dysfunction associated with ageing or neurological conditions, ultimately contributing to quality of life.

Periodic limb movements during sleep (PLMS) and obstructive sleep apnea syndrome (OSAS) are common sleep disorders that affect sleep quality. Their combined impact on sleep architecture and overall sleep disruption remains uncertain.

Based on the bidirectional relationship between sleep and macrophages in healthy and diseased states, this systematic review and meta-analysis investigated the effect of disrupted sleep on macrophage polarization.

The support of family caregivers is vital in home-based palliative care, ensuring quality care for patients with palliative care needs. In their commitment to prioritising the well-being of their loved ones, caregivers often neglect their own physical and emotional health, leading to varying degrees of caregiving burden. This study aims to estimate the prevalence of caregiver burden and identify the key factors associated with it among family caregivers of patients receiving services from a palliative care institution in Thiruvananthapuram district, Kerala.