Possible predictors of phenoconversion in isolated REM sleep behaviour disorder: a systematic review and meta-analysis. Issue 4 (22nd December 2021)

• Record Type: Journal Article
• Authors: Wang, Chunyi; Chen, Fangzheng; Li, Yuanyuan; Liu, Jun
• Is Part Of: Journal of neurology, neurosurgery and psychiatry. Volume 93:Issue 4(2022)
• Page Start: 395
• Page End: 403
• Publication Date: 2021-12-22
• Subjects: sleep disorders -- parkinson's disease -- meta-analysis; Neurology -- Periodicals; Nervous system -- Surgery -- Periodicals; Psychiatry -- Periodicals; 616.8
• DOI: 10.1136/jnnp-2021-328062
• Languages: English
• ISSNs: 0022-3050

Abstract

Abstract : Background: A number of promising biomarkers for predicting imminent α-synucleinopathies have been suggested in isolated rapid eye movement sleep behaviour disorder (iRBD). However, existing evidence is conflicting without quantitative evaluation. Methods: PubMed, Web of Science and ClinicalTrials.gov were searched through June 2021 to identify possible predictors of phenoconversion from iRBD to Parkinson's disease (PD). The pooled HRs and standardised mean differences (SMDs) with 95% CIs were calculated using fixed-effects or random-effects model. Results: A total of 123 studies were included in the meta-analysis. Significant motor dysfunction (HR 1.83, 95% CI 1.33 to 2.51, I 2 =86.8%, p<0.001), constipation (HR 1.52, 95% CI 1.26 to 1.84, I 2 =8.3%, p=0.365), orthostatic hypotension (HR 1.93, 95% CI 1.05 to 3.53, I 2 =54.9%, p=0.084), hyposmia (HR 2.78, 95% CI 1.83 to 4.23, I 2 =23.9%, p=0.255), mild cognitive impairment (HR 2.27, 95% CI 1.58 to 3.27, I 2 =0%, p=0.681) and abnormal colour vision (SMD −0.34, 95% CI −0.63 to −0.05, I 2 =45.6%, p=0.087) correlated with susceptibility to PD. The process can also be traced by putaminal dopamine transporter imaging (HR 2.60, 95% CI 1.94 to 3.48, I 2 =0%, p=0.781) and tonic electromyographic activity (HR 1.50, 95% CI 1.04 to 2.15, I 2 =70%, p=0.018). Conclusions: The predictive value of each biomarker was initially highlighted with comprehensive evaluation. Combining specific predictors with high sensitivity is promising for detecting phenoconversion in the prodromal stage. Large-scale and multicentre studies are pivotal to extend our findings.