Longitudinal Iron and Dopamine Changes to Understand Genetic Risk Factors in Restless Legs Syndrome
Poster No:
152
Submission Type:
Abstract Submission
Authors:
Rahul Gaurav1, Francois-Xavier LEJEUNE1, Aymeric LANORE1, Anthony RUZE1, Mathieu SANTIN1, Romain VALABREGUE1, Nadya Pyatigorskaya1, Pauline Dodet1, Graziella MANGONE1, Nicolas Villain1, Marie-Odile Habert1, Marie Vidailhet1, Jean-Christophe CORVOL1, Isabelle ARNULF1, Stephane Lehericy1
Institutions:
1Paris Brain Institute (ICM), Paris, France
First Author:
Co-Author(s):
Introduction:
Restless legs syndrome (RLS) is a sensorimotor disorder that can occur with or without Parkinson's disease (PD). The relationship between RLS and PD is still debated. Substantia nigra (SN) demonstrates iron increase in PD and iron deficiency in RLS that can be estimated using quantitative susceptibility mapping (QSM). Genetics also contributes to the risk of developing RLS and PD.
Henceforth, integrating genetics into quantitative and dopamine transporter (DaT) imaging can provide valuable insights. We investigated spatiotemporal iron progression and its association with neuromelanin (NM), dopaminergic dysregulation and genetic risk factors.
Henceforth, integrating genetics into quantitative and dopamine transporter (DaT) imaging can provide valuable insights. We investigated spatiotemporal iron progression and its association with neuromelanin (NM), dopaminergic dysregulation and genetic risk factors.
Methods:
Participants were scanned three times (V1/V2/V3) using a 3.0 Tesla MRI comprising 3D T1 and 2D T1 weighted NM images. T2* multi-echo 3D FLASH were acquired for QSM. R2* and DaT SPECT imaging were also obtained.
We developed a QSM template using a balanced group representation to obtain nucleus accumbens, putamen, caudate, thalamus, pallidum, subthalamic nucleus (STN), whole SN, anterior and posterior territories of the dorsal and ventral SN in QSM and R2*. We segmented the whole SN pars compacta (SNc) using deep learning. We obtained the sensorimotor, limbic and associative regions in SNc. Using DaT, striatal and functional regions in putamen and the caudate were obtained.
We computed QSM mean susceptibility and R2* values. We computed the volume, corrected volume for whole SNc and normalized signal intensity (NSI) for regional SNc in NM. Striatal DaT specific binding ratios (SBR) were also computed.
We used imputed genotypes to calculate polygenic risk scores (PRS) for genetic predisposition to PD (000902, PGS.PD1, 000903, PGS.PD2), iron metabolism disorders (001823, PSG.IRON1, 002031, PSG.IRON2) and RLS (PSG.PRS).
Baseline between-group differences were tested using multivariate linear regression models with age and sex as covariates. Longitudinal analyses were performed on subjects with at least two visits using linear mixed-effects models. Pearson's correlations corrected for multiple tests were performed.
We developed a QSM template using a balanced group representation to obtain nucleus accumbens, putamen, caudate, thalamus, pallidum, subthalamic nucleus (STN), whole SN, anterior and posterior territories of the dorsal and ventral SN in QSM and R2*. We segmented the whole SN pars compacta (SNc) using deep learning. We obtained the sensorimotor, limbic and associative regions in SNc. Using DaT, striatal and functional regions in putamen and the caudate were obtained.
We computed QSM mean susceptibility and R2* values. We computed the volume, corrected volume for whole SNc and normalized signal intensity (NSI) for regional SNc in NM. Striatal DaT specific binding ratios (SBR) were also computed.
We used imputed genotypes to calculate polygenic risk scores (PRS) for genetic predisposition to PD (000902, PGS.PD1, 000903, PGS.PD2), iron metabolism disorders (001823, PSG.IRON1, 002031, PSG.IRON2) and RLS (PSG.PRS).
Baseline between-group differences were tested using multivariate linear regression models with age and sex as covariates. Longitudinal analyses were performed on subjects with at least two visits using linear mixed-effects models. Pearson's correlations corrected for multiple tests were performed.
Results:
Clinical Characteristics: We included 44/32/14 healthy volunteers (HVs), 14/10/6 PD with RLS (PDRLS+), and 94/65/29 without RLS (PDRLS-) at V1/V2/V3 respectively.
Imaging Measures:
Baseline: QSM and R2* showed group differences in posteroventral SN (pvSN) only. QSM increase in pvSN was +17.1% in PDRLS- vs HVs and +21.9% in PDRLS+ vs HVs.
Longitudinal: We observed Group and Visit effects for pvSN for QSM and R2* while Group X Visit interaction effect for QSM. QSM increase in pvSN in PDRLS- was 4.2% (V1 and V2) and 5.7% (V2 and V3) whereas 8.6% in PDRLS+ (V2 and V3). No changes were seen in other basal ganglia regions.
Association Studies:
Iron and NM: For QSM and R2*, in PDRLS-, pvSN iron negatively correlated with volume (corrected and uncorrected) and NSI in all territories. In PDRLS+, pvSN iron negatively correlated with NSI in limbic only.
Iron and DatScan: For QSM and R2*, in PDRLS-, pvSN iron negatively correlated with the DaT-SBR values in all territories while in PDRLS+, pvSN iron positively correlated with DaT-SBR values in all territories with a trend in QSM of limbic putamen.
Iron and PRS: Using QSM, PDRLS- demonstrated positive correlations between PSG.PRS and posterior dorsal iron. A trend in positive correlations were observed between PSG.PRS and iron in STN, whole SN and regional SN iron in ventral, dorsal, anterior ventral, posterior dorsal and anterior dorsal. PDRLS- also demonstrated trend in positive correlations between PSG.IRON1 and pvSN iron.
DaT and PRS: In PDRLS+, we observed positive correlations between PSG.IRON1 and DaT-SBR values in all territories except limbic of caudate and putamen. We did not observe any correlations in PDRLS-.
Imaging Measures:
Baseline: QSM and R2* showed group differences in posteroventral SN (pvSN) only. QSM increase in pvSN was +17.1% in PDRLS- vs HVs and +21.9% in PDRLS+ vs HVs.
Longitudinal: We observed Group and Visit effects for pvSN for QSM and R2* while Group X Visit interaction effect for QSM. QSM increase in pvSN in PDRLS- was 4.2% (V1 and V2) and 5.7% (V2 and V3) whereas 8.6% in PDRLS+ (V2 and V3). No changes were seen in other basal ganglia regions.
Association Studies:
Iron and NM: For QSM and R2*, in PDRLS-, pvSN iron negatively correlated with volume (corrected and uncorrected) and NSI in all territories. In PDRLS+, pvSN iron negatively correlated with NSI in limbic only.
Iron and DatScan: For QSM and R2*, in PDRLS-, pvSN iron negatively correlated with the DaT-SBR values in all territories while in PDRLS+, pvSN iron positively correlated with DaT-SBR values in all territories with a trend in QSM of limbic putamen.
Iron and PRS: Using QSM, PDRLS- demonstrated positive correlations between PSG.PRS and posterior dorsal iron. A trend in positive correlations were observed between PSG.PRS and iron in STN, whole SN and regional SN iron in ventral, dorsal, anterior ventral, posterior dorsal and anterior dorsal. PDRLS- also demonstrated trend in positive correlations between PSG.IRON1 and pvSN iron.
DaT and PRS: In PDRLS+, we observed positive correlations between PSG.IRON1 and DaT-SBR values in all territories except limbic of caudate and putamen. We did not observe any correlations in PDRLS-.
·Figure 1
·Figure 2
Conclusions:
Nigral Iron changes were associated with the dopaminergic dysregulation and genetic predisposition suggesting the role of different pathways in PDRLS+ and PDRLS-. Nonetheless, larger sample size of RLS patients is warranted.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Genetic Association Studies 2
Lifespan Development:
Aging
Modeling and Analysis Methods:
Segmentation and Parcellation
Keywords:
Aging
Basal Ganglia
Brainstem
Movement Disorder
MRI
Neurological
Segmentation
Single Photon Emission Computed Tomography (SPECT)
STRUCTURAL MRI
1|2Indicates the priority used for review
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