Restless legs syndrome (RLS) is one of the most prevalent sleep disorders, yet its diagnosis continues to rely almost entirely on subjective symptom descriptions. This persistent dependence on phenomenology reflects the absence of reliable biological markers to aid in the process of diagnosis or monitoring. However, there is accumulating molecular evidence that suggests that RLS is associated with systemic biological alterations. These extend beyond the traditional paradigm of iron deficiency. The present scoping review synthesizes the current research on circulating serum biomarkers investigated in RLS outside classical iron indices. A comprehensive search of PubMed, Scopus, and Web of Science databases identified 1050 records, of which 50 studies met eligibility criteria and were included. In the processing of data, clusters emerged into several recurring biological domains, including dysregulated iron regulatory signaling (hepcidin), low-grade immune activation, oxidative stress, and neuroaxonal injury markers. High-throughput omics studies reveal molecular network perturbations involving inflammatory pathways, complement activation, metabolic signaling, and cellular stress responses. Biomarker associations appear stronger when linked to objective motor burden. These findings suggest that RLS may involve multifarious molecular changes detectable in the serum. Consequently, this can support the transition from symptom-based diagnosis toward biomarker-informed stratification, which may enable more precise disease characterization and improved diagnostic accuracy.

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia, and its isolated form is of particular interest, as it is an early phase alpha-synucleinopathy. Machine learning (ML) and deep learning (DL) models offer potential for automated detection, prediction of phenoconversion, and phenotyping. This scoping review identified 75 studies applying ML/DL in RBD and evaluated their methodological and reporting quality using the APPRAISE-AI tool. Most studies (73.3%) focused on RBD detection and mainly used polysomnographic data for this, while 16% addressed prediction of phenoconversion, with imaging data being the most employed modality. Sample sizes were generally small (most studies including only 20-100 individuals). According to APPRAISE-AI scores, 80% of studies had moderate overall methodological and reporting quality. Common deficiencies included lack of transparency in data and code sharing (23.3%), and poor reporting of hyperparameter tuning (17.1%), bias assessment (26.9%), and error analysis (0.66%). Data leakage was observed in 32% of studies. These issues hinder clinical translation and prevent incremental progress between research groups. Without transparent reporting and shared resources, replication and model comparisons become nearly impossible. Future work should adopt open science principles and rigorous validation to advance AI-based tools in sleep medicine.

Orthostatic hypotension (OH) represents a cardinal non-motor manifestation in Parkinson's disease (PD), yet the clinical relevance of its subtypes remains unclear. This study aimed to characterize the clinical and blood pressure (BP) profiles of PD-OH and its subtypes. We enrolled 186 PD patients, including 59 with OH, and collected clinical, postural BP, autonomic, cognitive, emotional, sleep, and REM sleep behavior disorder (RBD) data. Analyses used t-tests, nonparametric tests, and chi-square tests as appropriate. Compared to PD-nOH, PD-OH patients had later onset, higher RBD prevalence, poorer visuospatial function, and better subjective sleep quality. They exhibited lower systolic/diastolic BP (SBP/DBP) and larger orthostatic drops across postures. Within PD-OH, diastolic OH with greater cognitive impairment and specific sleep alterations, while symptomatic with worse motor and autonomic function. RBD was linked to greater autonomic dysfunction; depressive symptoms specifically distinguished PD-OH-RBD, whereas cognitive differences distinguished PD-RBD. Advanced PD-OH showed lower SBP/DBP, especially when seated and immediately after standing. In conclusion, D-OH and symptomatic OH are associated with more severe non-motor symptoms, with cognitive and depressive profiles helping distinguish the interaction of PD, OH, and RBD.

Sleep disturbances significantly impact children with autism spectrum disorder (ASD), yet their heterogeneous manifestations remain poorly understood. This multicenter prospective cohort study employed latent profile analysis to identify distinct sleep phenotypes among 631 children with ASD (aged 3-6 years) and 768 typically developing (TD) controls across three Chinese cities representing Northern, Central, and Western regions. Analysis of Children's Sleep Habits Questionnaire data revealed three distinct sleep phenotypes based on optimal model fit determined by Bayesian information criterion. Compared to TD children who showed generally better sleep patterns with lower sleep onset delay and fewer disturbances overall, the ASD groups exhibited distinctive profiles: Cluster 1 (9.2%) exhibited severe disturbances across multiple domains (sleep anxiety, parasomnias, night wakings and sleep-disordered breathing) and demonstrated the most severe autism symptoms; Cluster 2 (36.0%) presented a mixed profile with comparable bedtime resistance, sleep duration, and daytime sleepiness to TD children but elevated sleep-disordered breathing; and Cluster 3 (54.8%) showed reduced sleep-disordered breathing but elevated night waking and bedtime resistance. One-year follow-up data indicated that Cluster 3, characterized by mild sleep-disordered breathing, showed significant improvements in core symptoms particularly in social cognition, communication, and motivation domains, whereas Clusters 1 and 2 demonstrated modest changes. These findings suggest that early identification of sleep phenotypes may predict treatment response and inform personalized intervention strategies. Our results underscore the importance of incorporating comprehensive sleep assessment and management into ASD care protocols.