Journal Article
Research Support, Non-U.S. Gov't
Add like
Add dislike
Add to saved papers

Association between common genetic risk variants and depression in Parkinson's disease: A dPD study in Chinese.

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.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

Related Resources

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app