Several studies have confirmed the α-synuclein real-time quaking-induced conversion (RT-QuIC) assay to have high sensitivity and specificity for Parkinson’s disease. However, whether the assay can be used as a robust, quantitative measure to monitor disease progression, stratify different synucleinopathies and predict disease conversion in patients with idiopathic REM sleep behaviour disorder remains undetermined. The aim of this study was to assess the diagnostic value of CSF α-synuclein RT-QuIC quantitative parameters in regard to disease progression, stratification and conversion in synucleinopathies. We performed α-synuclein RT-QuIC in the CSF samples from 74 Parkinson’s disease, 24 multiple system atrophy and 45 idiopathic REM sleep behaviour disorder patients alongside 55 healthy controls, analysing quantitative assay parameters in relation to clinical data. α-Synuclein RT-QuIC showed 89% sensitivity and 96% specificity for Parkinson’s disease. There was no correlation between RT-QuIC quantitative parameters and Parkinson’s disease clinical scores (e.g. Unified Parkinson’s Disease Rating Scale motor), but RT-QuIC positivity and some quantitative parameters (e.g. V_max) differed across the different phenotype clusters. RT-QuIC parameters also added value alongside standard clinical data in diagnosing Parkinson’s disease. The sensitivity in multiple system atrophy was 75%, and CSF samples showed longer T₅₀ and lower V_max compared to Parkinson’s disease. All RT-QuIC parameters correlated with worse clinical progression of multiple system atrophy (e.g. change in Unified Multiple System Atrophy Rating Scale). The overall sensitivity in idiopathic REM sleep behaviour disorder was 64%. In three of the four longitudinally followed idiopathic REM sleep behaviour disorder cohorts, we found around 90% sensitivity, but in one sample (DeNoPa) diagnosing idiopathic REM sleep behaviour disorder earlier from the community cases, this was much lower at 39%. During follow-up, 14 of 45 (31%) idiopathic REM sleep behaviour disorder patients converted to synucleinopathy with 9/14 (64%) of convertors showing baseline RT-QuIC positivity. In summary, our results showed that α-synuclein RT-QuIC adds value in diagnosing Parkinson’s disease and may provide a way to distinguish variations within Parkinson’s disease phenotype. However, the quantitative parameters did not correlate with disease severity in Parkinson’s disease. The assay distinguished multiple system atrophy patients from Parkinson’s disease patients and in contrast to Parkinson’s disease, the quantitative parameters correlated with disease progression of multiple system atrophy. Our results also provided further evidence for α-synuclein RT-QuIC having potential as an early biomarker detecting synucleinopathy in idiopathic REM sleep behaviour disorder patients prior to conversion. Further analysis of longitudinally followed idiopathic REM sleep behaviour disorder patients is needed to better understand the relationship between α-synuclein RT-QuIC signature and the progression from prodromal to different synucleinopathies.

To compare the diagnostic accuracy of the immunofluorescence (IF) technique and aSyn-seed amplification assay (aSyn-SAA) of skin and cerebrospinal fluid (CSF) in disclosing pathological α-syn in idiopathic idiopathic REM sleep behavior disorder (iRBD) as early phase of a synucleinopathy. We prospectively recruited 41 patients with iRBD and 40 matched clinical controls including RBD associated with type 1 Narcolepsy (RBD-NT1, 21 patients), iatrogenic causes (2 pt) or OSAS (6 pt) and 11 patients with peripheral neuropathies. IF from samples taken by skin biopsy and aSyn-SAA from skin and CSF samples were analysed blinded to the clinical diagnosis. IF showed a good diagnostic accuracy (89%) that was lower in the case of skin and CSF-based aSyn-SAA (70% and 69%, respectively) because of a lower sensitivity and specificity. However, IF showed a significant agreement with CSF aSyn-SAA. In conclusion, our data may favor the use of skin biopsy and aSyn-SAA as diagnostic tools for a synucleinopathy in iRBD.

Narcolepsy type 1 (NT1) is still largely underdiagnosed or diagnosed too late in children. Difficulties in obtaining rapid and reliable diagnostic evaluations of the condition in clinical practice partially explain this problem. Predictors of NT1 include cataplexy and sleep-onset REM periods (SOREMPs), documented during nocturnal polysomnography (N-PSG) or through the multiple sleep latency test (MSLT), although low CSF hypocretin-1 (CSF hcrt-1) is the definitive biological disease marker. Obtaining reliable MSLT results is not always feasible in children; therefore, this study aimed to validate daytime continuous polysomnography (D-PSG) as an alternative diagnostic tool. Two hundred consecutive patients aged younger than 18 years (112 with NT1; 25 with other hypersomnias, including narcolepsy type 2 and idiopathic hypersomnia; and 63 with subjective excessive daytime sleepiness) were randomly split into 2 groups: group 1 (n = 133) for the identification of diagnostic markers and group 2 (n = 67) for the validation of the detected markers. The D-PSG data collected included the number of spontaneous naps, total sleep time, and the number of daytime SOREMPs (d-SOREMP). D-PSG data were tested against CSF hcrt-1 deficiency (NT1 diagnosis) as the gold standard using receiver operating characteristic (ROC) curve analysis in group 1. ROC diagnostic performances of single and combined D-PSG parameters were tested in group 1 and validated in group 2. In group 1, the areas under the ROC curve (AUCs) were 0.91 (95% CI 0.86-0.96) for d-SOREMPs, 0.81 (95% CI 0.74-0.89) for the number of spontaneous naps, and 0.70 (95% CI 0.60-0.79) for total sleep time. A d-SOREMP count ≥1 (sensitivity of 95% and specificity of 72%), coupled with a diurnal total sleep time above 60 minutes (sensitivity of 89% and specificity of 91%), identified NT1 in group 1 with high reliability (area under the ROC curve of 0.93, 95% CI 0.88-0.97). These results were confirmed in the validation group with an AUC of 0.88 (95% CI 0.79-0.97). D-PSG recording is an easily performed, cost-effective, and reliable tool for identifying NT1 in children. Further studies should confirm its validity with home D-PSG monitoring. These alternative procedures could be used to confirm NT1 diagnosis and curtail diagnostic delay.

Study Objectives: The role of actigraphy in central disorders of hypersomnolence (CDH) is expanding but evidence of reliability with polysomnography (PSG) is scarce and provided only during nighttime. We explored the agreement between actigraphy and continuous 24-hour PSG at CDH diagnosis. Methods: Forty-four consecutive drug-naïve patients (28 narcolepsy and 16 idiopathic hypersomnia [IH]) underwent actigraphy during 24 hours of free-running PSG, during multiple sleep latency test (MSLT) and 13 of them also during maintenance of wakefulness test (MWT). Daytime and nighttime sleep features and MSLT and MWT mean sleep latencies (mSL) were estimated with the actigraphic algorithms by Cole-Kripke (CK), Sadeh (Sad), and University of California San Diego (UCSD). Agreement to corresponding PSG measures was assessed with Bland–Altman plots. Results: Nighttime-total sleep time (TST) in narcolepsy was significantly underestimated with CK (bias 27.8 minutes, 95% CI 13.7 to 41.9) and Sad (bias 56.7 minutes, 95% CI 38.8/74.5). Daytime-TST was overestimated in IH and narcolepsy with all algorithms (CK: bias −42.2, 95% CI −67/−17.4; Sad: bias −30.2 minutes, 95% CI −52.7/−7.7; UCSD bias −86.9 minutes, 95% CI −118.2/−55.6). 24-hour-TST was overestimated by CK and UCSD in IH (CK: bias −58.5 minutes, 95% CI −105.5/−11.5; UCSD: bias −118.8 minutes, 95% CI −172.5/−65), and by UCSD in narcolepsy (bias −68.8 minutes, 95% CI −109.3/−38.2). In the entire cohort, actigraphy overestimated MSLT mSL but not MWT mSL. Conclusions: Conventional actigraphic algorithms overestimate 24-hour TST in IH and underestimate nighttime TST in narcolepsy. These discrepancies call for the cautious application of actigraphy in the diagnostic process of CDH and the development of new quantitative signal analysis approaches.