Clinical value of cognitive and motor function in predicting phenoconversion in patients with isolated rapid eye movement sleep behavior disorder
10.3760/cma.j.cn113694-20231012-00102
- VernacularTitle:认知和运动功能预测孤立性快速眼球运动睡眠期行为障碍表型转化的临床价值
- Author:
Xuan ZHANG
1
;
Yaqin HUANG
;
Li MA
;
Danqi LIANG
;
Yahui WAN
;
Kaili ZHOU
;
Rong XUE
Author Information
1. 天津医科大学总医院空港医院神经内科，天津300308
- Keywords:
REM sleep behavior disorder;
Cognition disorders;
Movement disorders;
Phenotype;
Biological markers
- From:
Chinese Journal of Neurology
2024;57(7):746-754
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the clinical value of cognitive and motor function in predicting conversion to neurodegenerative disorders in patients with isolated rapid eye movement sleep behavior disorder (iRBD).Methods:Forty-seven patients with iRBD were collected from the Department of Neurology of Tianjin Medical University General Hospital and Tianjin Medical University General Hospital Airport Site during October 2018 and June 2022. All participants received comprehensive evaluations of cognitive and motor function at baseline. The visuospatial function was evaluated by Rey-Osterrieth Complex Figure Test (ROCF)-copy, the memory function was evaluated by Auditory Verbal Learning Test and ROCF-recall, the attention-executive function was evaluated by Trail Making Test (TMT) and Stroop Color-Word Test, and the language function was evaluated by Boston Naming Test. The motor function was evaluated by Unified Parkinson′s Disease Rating Scale-Ⅲ, Alternate-tap Test (ATT), and 3-meter Timed Up and Go Test. The iRBD patients with phenoconversion were identified during follow-up. Receiver operating characteristic curve and generalized linear model Logistic regression were applied to identify the optimal combination of cognitive and motor tests in distinguishing the converters from non-converters in patients with iRBD. Multivariate Cox regression analyses were applied to evaluate the independent risk factors in predicting conversion to neurodegenerative diseases in patients with iRBD.Results:The median follow-up duration was 3 years. Forty-five iRBD patients were included in the analysis eventually, as 2 dropped out at follow-up. Twenty-one iRBD patients developed neurodegenerative disorders, with 14 presenting motor phenotype and 7 cognitive phenotype. Baseline ROCF-copy, TMT-A and ATT were best combination in identifying iRBD patients with phenoconversion [sensitivity: 90.0%, specificity: 87.5%, area under curve (AUC): 0.931, P<0.001]. Baseline TMT-A and ATT were best combination in identifying iRBD patients with motor phenotype conversion (sensitivity: 100.0%, specificity: 66.7%, AUC: 0.872, P<0.001); Baseline TMT-A performed best in identifying iRBD patients with cognitive phenotype conversion (sensitivity: 83.3%, specificity: 91.7%, AUC: 0.917, P<0.001). Multivariate Cox regression analysis showed that individuals with poorer performance of TMT-A (cut-off value: 63.0 s) and ATT (cut-off value: 205.5 taps/min) than the cut-off values at baseline had higher risks for developing to neurodegenerative disorders, with HR values of 5.455 (95% CI 1.243-23.941, P=0.025) and 11.279 (95% CI 1.485-85.646, P=0.019), respectively. Conclusions:In iRBD, ROCF-copy, TMT-A and ATT served as optimum combination in predicting phenoconversion, whereas TMT-A and ATT served as optimum combination in predicting motor phenotype, and TMT-A performed best in predicting cognitive phenotype. The performance in TMT-A and ATT in iRBD could predict the risk of developing to neurodegenerative disorders independently.
