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Objective:To evaluate the sleep disorders in patients with Parkinson′s disease (PD) with different onset sides, and to analyze the correlation between PD kinesia-onset side and sleep disorders.Methods:A total of 658 patients with primary PD admitted to the Special Outpatient Department of Parkinson′s disease in Fujian Medical University Union Hospital from January 2015 to March 2021 were collected. According to the onset side of motor symptoms, they were divided into the left group (313 cases) and the right group (345 cases). The medical history collection and physical examination were conducted to evaluate the motor symptoms, non-motor symptoms [Non-Motor Symptom Scale (NMSS)], depression state and cognitive function of the patients. Parkinson′s Disease Sleep Sclale-2 (PDSS-2) and the Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire (RBDSQ) were used to evaluate and analyze their sleep status, and comparisons were made between groups. Binary multivariate Logistic regression analysis was used to access the risk factors associated with sleep disorders in Parkinson′s disease.Results:The scores of daytime fatigue [2.00(0, 4.00)] and unexplained limb pain [4.00(0, 4.00)] in NMSS assessment of PD patients in the left onset group were significantly higher than those in the right onset group [1.00(0, 3.00), Z=-2.545, P=0.001; 2.00(0, 4.00), Z=-2.797, P=0.005]. There was no significant difference in the total score of PDSS-2 between the two groups, but there were significant differences in limb restlessness, periodic limb activity, muscle spasm and early drowsiness between the two groups. In the evaluation of rapid eye movement sleep behavior disorder (RBD), the total score of RBDSQ in the left onset group [2.00(0, 4.00)] was significantly higher than that in the right onset group [1.00(0, 3.00), Z=-4.363, P<0.001]. The incidence of dream content, nocturnal behavior, nocturnal exercise, self-injury and bed partner in dream, abnormal behavior at night, nighttime awakening, dream memory and sleep disorder in the left onset group was also higher than that in the right onset group. In addition, binary multivariate Logistic regression showed that PD-related sleep disorders were associated with onset of advanced age ( OR=1.037, 95% CI 1.018-1.057, P<0.001), course of disease ( OR=1.014, 95% CI 1.010-1.018, P<0.001) and onset of abnormal postural gait ( OR=1.505,95% CI 1.058-2.141, P=0.023). RBD in patients with PD was associated with left onset ( OR=2.215,95% CI 1.395-3.515, P=0.001), advanced age onset ( OR=1.045,95% CI 1.019-1.072, P=0.001) and course of disease ( OR=1.014,95% CI 1.009-1.019, P<0.001). Conclusions:PD patients with left onset are more likely to have sleep disorders such as limb restlessness, periodic limb activity, muscle spasm and early drowsiness. At the same time, the incidence and severity of RBD in patients with left onset of PD are significantly higher than those of patients with right onset of PD. The onset side of motor symptoms of PD is an important factor affecting sleep disorders, and the onset of left side may be a risk factor for PD patients with RBD.
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Many pathogenic genes have been identified in early-onset Parkinson′s disease, but the early-onset Parkinson′s disease with 22q11.2 deletion has not been reported in Chinese. A case of early-onset Parkinson′s disease with 22q11.2 deletion was confirmed by whole-exome sequencing-based copy number variation detection in Fujian Medical University Union Hospital. This article reports its clinical characteristics and discusses its pathogenesis, diagnosis and treatment management.
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At present the parkinsonian rigidity assessment depends on subjective judgment of neurologists according to their experience. This study presents a parkinsonian rigidity quantification system based on the electromechanical driving device and mechanical impedance measurement method. The quantification system applies the electromechanical driving device to perform the rigidity clinical assessment tasks (flexion-extension movements) in Parkinson's disease (PD) patients, which captures their motion and biomechanical information synchronously. Qualified rigidity features were obtained through statistical analysis method such as least-squares parameter estimation. By comparing the judgments from both the parkinsonian rigidity quantification system and neurologists, correlation analysis was performed to find the optimal quantitative feature. Clinical experiments showed that the mechanical impedance has the best correlation (Pearson correlation coefficient = 0.872, < 0.001) with the clinical unified Parkinson's disease rating scale (UPDRS) rigidity score. Results confirmed that this measurement system is capable of quantifying parkinsonian rigidity with advantages of simple operation and effective assessment. In addition, the mechanical impedance can be adopted to help doctors to diagnose and monitor parkinsonian rigidity objectively and accurately.
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Objective To evaluate the efficacy of gait training and assessment system of Lokomat automatic robot (Lokomat robot) in patients with Parkinson's disease (PD).Methods Based on Hoehn-Yahr scale, 30 PD patients ranging from stage 2.5 to 3 were included and randomly assigned to Lokomat robot group ( n＝15) and control group ( n＝15).Lokomat robot system was employed in the training session of the Lokomat robot group, whereas patients in the control group were trained under auditory and visual guidance.Each training session lasted for 20 minutes, and repeated three days per week.Three motor assessments were performed before and after the four weeks training , including timed up and go test (TUGT), Unified Parkinson's Disease Rating Scale Ⅲ(UPDRS-Ⅲ) and three-dimensional gait analysis. Repeated measure analysis was performed under general linear mode , using SPSS 20.0.Results Gender, age, height and age of onset were matched in the Lokomat robot and the control groups .Scores of UPDRS-Ⅲ(Lokomat robot group , 23.46 ±2.72 vs 15.87 ±2.07; control group, 23.73 ±1.98 vs 18.07 ±0.80) and results of TUGT (Lokomat robot group, (15.42 ±5.59) vs (10.06 ±4.88) min; control group, (15.75 ± 4.67) vs (12.98 ±3.24) min) showed statistically significant differences before and after the gait training (UPDRS-Ⅲ, F＝258.598, P＝0.000; TUGT, F＝64.998, P＝0.000), and between the two groups (UPDRS-Ⅲ, F＝5.492, P＝0.026; TUGT, F＝6.522, P＝0.016).The step length (Lokomat robot group, (40.00 ±7.05) vs (52.70 ±7.62) cm; control group, (39.16 ±4.52) vs (46.72 ±7.29) cm), stride length (Lokomat robot group, (76.03 ±12.50) vs (90.60 ±12.46) cm; control group, (77.25 ± 8.07 ) vs (88.21 ±8.17) cm), walking pace ( Lokomat robot group, (67.16 ±12.79) vs (83.72 ± 10.96) m/min; control group, (65.35 ±11.56) vs (77.18 ±10.60) m/min), and total supporting phase (Lokomat robot group, 62.31% ±3.32% vs 56.05% ±3.98%; control group, 62.52% ±3.73% vs 57.96%±3.51%) showed significant improvement after training ( step length, F＝90.866, P＝0.000;stride length, F＝218.152, P＝0.000; walking pace, F＝172.236, P＝0.000; total supporting phase , F＝197.945, P＝0.000).Meanwhile, these improvements were more significant in the Lokomat robot group than the control group ( step length, F＝5.853, P＝0.022; stride length, F＝4.346, P＝0.046;walking pace, F＝4.904, P＝0.035; total supporting phase, F＝4.845, P＝0.036).No significant difference in step frequency was found before and after gait training.Conclusion Both gait trainings improved walking ability in PD patients , and Lokomat robot system guided training showed more obvious improvement than the traditional training under hearing and visual cue.