Predictive biomarkers for cognitive decline in patients with idiopathic REM sleep behavior disorder

Abstract

AbstractBackgroundIdiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is regarded as an early stage of the synucleinopathy, which may precede the onset of cognitive impairment or parkinsonism. Biomarkers for cognitive decline in patients with iRBD would be helpful to early intervention. Our aim was to identify biomarkers for predicting the cognitive deterioration risk in patients with iRBD.MethodWe involved patients with polysomnographically confirmed iRBD who has at least 2 visits from the Parkinson’s Progression Markers Initiative (PPMI) database. Progression of cognitive state to MCI or dementia during follow‐up was defined as the endpoint. Baseline clinical features, biofluid and genetic biomarkers were compared between iRBD with or without progression of cognitive decline. Predictive biomarkers were identified with Kaplan‐Meier and Cox proportional hazards analysis, adjusting for age, sex, and years of education.ResultA total of 38 patients with iRBD were enrolled (average age: 69.53 ± 5.54, male: 84.21%) . During follow‐up (median 5.45, IQR 4.08‐7.1 years), Progression of cognitive decline occurred in 57.89% (22/38) patients with iRBD. Baseline clinical features including hallucinations and psychosis (HR = 13.783, p = 0.022) and higher confidence abnormalities consistent with a neurodegenerative parkinsonian syndrome (HR = 2.258, p = 0.017) increased the risk. Patients with a lower loge‐transformed the ratio of Aβ 1‐42 in cerebrospinal fluid (CSF) to ΝfL in serum (Aβ/NfL, HR = 0.375, p = 0.044) and t‐tau in CSF to ΝfL in serum (t‐tau/NfL, HR = 0.277, p = 0.044) had a higher risk of cognitive decline. Genetic biomarkers consisting of GBA rs75548401(HR = 13.783, p = 0.022), STK39 rs1955337 (HR = 11.882, p<0.001), and COMT rs4680 (HR = 2.376, p = 0.021) variants increased the risk, while MCCC1 rs12637471 (HR = 0.408, p = 0.024), COMT rs165656 (HR = 0.451, p = 0.033), COMT rs4633 (HR = 0.421, p = 0.021), and COMT rs165599 (HR = 0.427, p = 0.023) variants decreased the risk. A model combining age and variables with p<0.05 exhibited above presented the best predictive performance, with an AUC of 0.963 (95% CI 0.896–1.000, p<0.001).ConclusionOur findings provide potential biomarkers to evaluate the risk of progression to MCI or dementia in patients with iRBD. Combination of clinical features, biofluid and genetic biomarkers performs optimal predicting accuracy.