Table 5 Effects of genetic risk scores and CSF biomarkers on longitudinal motor severity scores in PD patients.

	Model 1		Model 2		Model 3		Model 4
Predictors	β (SE)	P	β (SE)	P	β (SE)	P	β (SE)	P
GRS-AD	0.63 (0.67)	0.347	0.35 (0.73)	0.635	0.64 (0.64)	0.318	0.21 (0.70)	0.768
GRS-PD	−1.07 (0.62)	0.088	−1.31 (0.65)	0.046	−1.04 (0.59)	0.080	−1.38 (0.63)	0.028
time	2.53 (0.15)	<0.001	2.73 (0.27)	<0.001	2.32 (0.21)	<0.001	2.56 (0.35)	<0.001
GRS-AD*time	0.18 (0.21)	0.374	0.33 (0.38)	0.384	0.03 (0.26)	0.895	0.81 (0.47)	0.087
GRS-PD*time	−0.37 (0.19)	0.048	−0.34 (0.34)	0.320	−0.46 (0.24)	0.059	−0.52 (0.43)	0.232
Time-varying p-tau/Aβ₄₂			−0.02 (0.04)	0.626			−0.01 (0.04)	0.780
Time-varying αSyn			−0.67 (0.58)	0.243			−0.36 (0.58)	0.529
Time-varying DAT-putamen					−6.49 (1.19)	<0.001	−6.84 (1.32)	<0.001

αSyn Alpha-synuclein, AD Alzheimer’s disease, DAT-putamen Putaminal dopamine transporter uptake, GRS Genetic risk score, PD Parkinson’s disease, p-tau/Aβ₄₂ Phosphorylated tau/42-residue amyloid-beta.
Data are presented as the results of linear mixed models for longitudinal motor severity scores, using age, sex, education, and the first four principal components as covariates. Time, GRS-AD, GRS-PD, GRS-AD*time, and GRS-PD*time were used as predictors in model 1. Time-varying CSF biomarkers were included as predictors in model 2. Time-varying DAT-putamen was included as a predictor in model 3. Model 4 included both time-varying CSF biomarkers and time-varying DAT-putamen as predictors.
Significant P values are indicated in bold.

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