کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5504012 | 1535816 | 2016 | 5 صفحه PDF | دانلود رایگان |

- GBA L444P and SNCA-Rep1 variants were independently associated with depression in sporadic PD.
- Multiple correlates collectively and strongly predicted the depressive symptoms in PD.
- Have implication for the further molecular classification and prediction of dPD.
- Provide new clues for the future development of better therapeutic targets for depressed PD patients.
IntroductionPrediction of depression in patients with Parkinson's disease (PD) remains challenging. We investigated whether the common susceptible genetic variants for PD are associated with the risk and improves prediction of development of depression in PD (dPD).Methods1134 individuals with a primary diagnosis of PD were recruited. Demographic information, Unified Parkinson's Disease Rating Scale (UPDRS), and 17-item Hamilton Rating Scale for Depression (HAMD) were obtained. Nine variants located in six susceptible genes for PD were determined in all subjects. Logistic regression analyses were used to identify the study genetic variants that individually and collectively best predicted the presence of depressive disorder (HAMD â¥14).ResultsDepression occurred in 19.8% of patients with PD. The GBA L444P variant was associated with an increased risk of depression (odds ratio [OR] = 2.69, 95% confidence interval [CI] = 1.31-5.53, P = 0.007) and SNCA-Rep1 (CA)12/12 showed a decreased risk for the presence of depression (OR = 0.54, 95% CI = 0.29-0.99, P = 0.049) in the PD population after adjusted for demographic and clinical factors. Stepwise logistic regression model found that female sex, UPDRS part II score, motor fluctuation, GBA L1444P and SNCA Rep-1 variants collectively best predict depression in PD.ConclusionsBesides non PD-specific and PD-specific clinical correlates, we showed that GBA L444P and SNCA Rep-1 were also associated with dPD. Our findings highlight the crucial role of genetic variants for the prediction of dPD in clinical practice and may shed light on the future development of better therapeutic targets for dPD.
Journal: Parkinsonism & Related Disorders - Volume 33, December 2016, Pages 122-126