The Association between DRD3 Ser9Gly Polymorphism and Depression Severity in Parkinson’s Disease

Abstract

More and more evidence suggests that dopamine receptor D3 gene (DRD3) plays an important role in the clinical manifestations and the treatment of Parkinson’s disease (PD). DRD3 Ser9Gly polymorphism is the most frequently studied variant point. Our aim was to investigate the potential effect of DRD3 Ser9Gly polymorphism on modulating resting-state brain function and associative clinical manifestations in PD patients. We consecutively recruited 61 idiopathic PD patients and 47 healthy controls (HC) who were evaluated by clinical scales, genotyped for variant Ser9Gly in DRD3, and underwent resting-state functional magnetic resonance imaging. Based on DRD3 Ser9Gly polymorphism, PD patients and HCs were divided into four subgroups. Then, two-way analysis of covariance (ANCOVA) was applied to investigate main effects and interactions of PD and DRD3 Ser9Gly polymorphism on the brain function via amplitude of low-frequency fluctuations (ALFF) approach. The association between DRD3 Ser9Gly-modulated significantly different brain regions, and clinical manifestations were detected by Spearman’s correlations. PD patients exhibited decreased ALFF values in the right inferior occipital gyrus, lingual gyrus, and fusiform gyrus. A significant difference in the interaction of “groups × genotypes” was observed in the right medial frontal gyrus. The ALFF value of the cluster showing significant interactions was positively correlated with HAMD-17 scores (r=0.489, p=0.011) and anhedonia scores (r=0.512, p=0.008) in PD patients with the Ser/Gly or Gly/Gly genotypes. Therefore, D3 gene Ser9Gly polymorphism might be associated with the severity of depression characterized by anhedonia in PD patients.

Networks Are Associated With Depression in Patients With Parkinson’s Disease: A Resting-State Imaging Study

“…Our previous study found abnormal ReHo values in both the cingulate cortex and the orbitofrontal area of depressed PD patients ( Wang et al, 2020 ). Zhi et al (2019) found that the right medial frontal gyrus activation could be a biomarker for the occurrence and the severity of depression in PD. The hippocampus is a major culprit of emotional dysregulation, contributing to the pathophysiology of depression in PD ( van Mierlo et al, 2015 ).…”

Alterations of Interhemispheric Functional Connectivity in Parkinson’s Disease With Depression: A Resting-State Functional MRI Study

“…MFG is thought to be involved in cognitive regulation and affective processing (Hänsel and von Känel, 2008), whereas mPFC is widely known to play an important role in cognitive and limbic functions (Liang et al, 2015) and can modulate the expression of reward-seeking behavior through regulating the reward-related neural circuit (Ferenczi et al, 2016;Belujon and Grace, 2017). The abnormal spontaneous neural activity in the MFG has been demonstrated to be an important factor for the development of depressive symptoms in PD patients (Cardoso et al, 2009), whereas the right MFG activation is proposed as a biomarker for the occurrence and the severity of depression in PD patients (Zhi et al, 2019). Previous studies also observed a decreased activation in the mPFC of PDD patients compared with PDND patients (Cardoso et al, 2009).…”

Disrupted Spontaneous Neural Activity in Patients With Thyroid-Associated Ophthalmopathy: A Resting-State fMRI Study Using Amplitude of Low-Frequency Fluctuation

“…In recent years, several amplitude methods have been proposed to characterize the local properties of the rs-fMRI signal (Zang et al, 2007;Zou et al, 2008;Jia et al, 2020), and different metrics have shown their own specific characteristics (Zhao et al, 2018;Yu et al, 2019). Among these indices, ALFF provides direct characterization to the spontaneous brain activity at each voxel (Zang et al, 2007;Lv et al, 2019), and has been regarded as one of the most reliable and reproducible rs-fMRI parameters (Liu et al, 2016;Zhao et al, 2018;Zhi et al, 2019). It is obtained by calculating the square root of the power spectrum at each frequency and then averaging the square root across a certain frequency band (Zang et al, 2007).…”