Circadian expression and specific localization of synaptotagmin17 in the suprachiasmatic nucleus, the master circadian oscillator in mammals

The Genetic Landscape of Sleep Disorders in Parkinson's Disease

Parknson's disease (PD) is the second most common neurodegenerative disease, affecting 1% of people aged over 60. PD is characterized by a wide range of motor symptoms, however the clinical spectrum of PD covers a wide range of non-motor symptoms, as well. Sleep disorders are among the most common non-motor symptoms of PD, can occur at any stage of the disease and significantly affect quality of life. These include rapid eye movement sleep behavior disorder (RBD), restless legs syndrome (RLS), excessive daytime sleepiness (EDS), insomnia, obstructive sleep apnea (OSA) and circadian rhythm disturbances. One of the main challenges in PD research is identifying individuals during the prodromal phase of the disease. Combining genetic and prodromal data may aid the early identification of individuals susceptible to PD. This review highlights current data regarding the genetic component of sleep disorders in PD patients, focusing on genes that have currently been associated with this PD co-morbidity.

The Genetic Basis of Probable REM Sleep Behavior Disorder in Parkinson's Disease

Patients with Parkinson's Disease (PD) experience REM sleep behavior disorder (RBD) more frequently than healthy controls. RBD is associated with torpid disease evolution. To test the hypothesis that differential genetic signatures might contribute to the torpid disease evolution in PD patients with RBD we compared the rate of genetic mutations in PD patients with or without probable RBD. Patients with a clinical diagnosis of PD in the Parkinson's Progression Markers Initiative (PPMI) database entered the study. We excluded those with missing data, dementia, psychiatric conditions, or a diagnosis change over the first five years from the initial PD diagnosis. Probable RBD (pRBD) was confirmed by a REM Sleep Behavior Disorder Screening Questionnaire score > 5 points. Logistic regression and Machine Learning (ML) algorithms were used to relate Single Nucleotide Polymorphism (SNPs) in PD-related genes with pRBD. We included 330 PD patients fulfilling all inclusion and exclusion criteria. The final logistic multivariate model revealed that the following SNPs increased the risk of pRBD: GBA_N370S_rs76763715 (OR, 95% CI: 3.38, 1.45-7.93), SNCA_A53T_rs104893877 (8.21, 2.26-36.34), ANK2. CAMK2D_rs78738012 (2.12, 1.08-4.10), and ZNF184_rs9468199 (1.89, 1.08-3.33). Conversely, SNP COQ7. SYT17_rs11343 reduced pRBD risk (0.36, 0.15-0.78). The ML algorithms led to similar results. The predictive models were highly specific (95-99%) but lacked sensitivity (9-39%). We found a distinctive genetic signature for pRBD in PD. The high specificity and low sensitivity of the predictive models suggest that genetic mutations are necessary but not sufficient to develop pRBD in PD. Additional investigations are needed.