Misfolded α-Synuclein Assessment in the Skin and CSF by RT-QuIC in Isolated REM Sleep Behavior Disorder.

BACKGROUND AND OBJECTIVES: Real-time quaking-induced conversion (RT-QuIC) assay detects misfolded α-synuclein (AS) in the skin and CSF of patients with the synucleinopathies Parkinson disease and dementia with Lewy bodies. Isolated REM sleep behavior disorder (IRBD) constitutes the prodromal stage of these synucleinopathies. We aimed to compare the ability of RT-QuIC to identify AS in the skin and CSF of patients with IRBD. METHODS: This was a cross-sectional study where consecutive patients with polysomnographic-confirmed IRBD and age-matched controls without RBD underwent skin biopsy and lumbar puncture the same day. Three-millimeter skin punch biopsies were obtained bilaterally in the cervical region from dorsal C7 and C8 dermatomes and in distal legs. RT-QuIC assessed AS in these 6 skin sites and the CSF. RESULTS: We recruited 91 patients with IRBD and 41 controls. In the skin, sensitivity to detect AS was 76.9% (95% CI 66.9-85.1), specificity 97.6% (95% CI 87.1-99.9) positive predictive value 98.6% (95% CI 91.0-99.8), negative predictive value 65.6% (95% CI 56.6-73.6), and accuracy 83.3% (95% CI 75.9-89.3). In the CSF, the sensitivity was 75.0% (95% CI 64.6-83.6), the specificity was 97.5% (95% CI 86.8-99.9), the positive predictive value was 98.5% (95% CI 90.5-99.8), the negative predictive value was 63.9% (95% CI 55.2-71.9), and the accuracy was 82.0% (95% CI 74.3-88.3). Results in the skin and CSF samples showed 99.2% agreement. Compared with negative patients, RT-QuIC AS-positive patients had a higher likelihood ratio of prodromal Parkinson disease (p < 0.001) and showed more frequently hyposmia (p < 0.001), dopamine transporter imaging single-photon emission CT deficit (p = 0.002), and orthostatic hypotension (p = 0.014). No severe or moderate adverse effects were reported. There was no difference between the percentage of participants reporting mild adverse events secondary to skin biopsy or lumbar puncture (9.1% vs 17.2%; p = 0.053). One hundred and ten (83%) and 104 (80%) participants, respectively, stated they would accept to undergo skin biopsy and lumbar puncture again for research purposes. DISCUSSION: Our study in IRBD shows that (1) RT-QuIC detects AS in the skin and CSF with similar high sensitivity, specificity, and agreement, (2) AS RT-QuIC positivity is associated with supportive features and biomarkers of synucleinopathy, and (3) skin punch biopsy and lumbar puncture have comparable mild adverse effects, tolerance, and acceptance. RT-QuIC in the skin or CSF might represent a patient selection strategy for future neuroprotective trials targeting AS in IRBD. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that RT-QuIC-detected AS in the skin and CSF distinguishes patients with IRBD from controls.

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering.

BACKGROUND AND OBJECTIVES: Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. METHODS: We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups. RESULTS: We included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters. DISCUSSION: Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features.