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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
Published on: January 7, 2014
Association between common genetic risk variants and depression in Parkinson's disease: A dPD study in Chinese
Xiaojuan Dan1, Chaodong Wang1, Jinglin Zhang2
1Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China; Department of Neurobiology, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, China; Key Laboratory on Neurodegenerative Disorders of Ministry of Education, Beijing, China; Key Laboratory on Parkinson's Disease of Beijing, Beijing, China.
Introduction:
Prediction 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).
Methods:
1134 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 ≥14).
Results:
Depression 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.
Conclusions:
Besides 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.
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