Plasma MIA, CRP, and Albumin Predict Cognitive Decline in Parkinson's Disease

Abstract

Objective: Using a multi-cohort, discovery-replication-validation design, we sought new plasma biomarkers that predict which individuals with Parkinson's disease (PD) will experience cognitive decline.

Methods: In 108 discovery cohort PD individuals and 83 replication cohort PD individuals, we measured 940 plasma proteins on an aptamer-based platform. Using proteins associated with subsequent cognitive decline in both cohorts, we trained a logistic regression model to predict which patients with PD showed fast (> = 1 point drop/year on Montreal Cognitive Assessment [MoCA]) versus slow (< 1 point drop/year on MoCA) cognitive decline in the discovery cohort, testing it in the replication cohort. We developed alternate assays for the top 3 proteins and confirmed their ability to predict cognitive decline - defined by change in MoCA or development of incident mild cognitive impairment (MCI) or dementia - in a validation cohort of 118 individuals with PD. We investigated the top plasma biomarker for causal influence by Mendelian randomization (MR).

Results: A model with only 3 proteins (melanoma inhibitory activity protein [MIA], C-reactive protein [CRP], and albumin) separated fast versus slow cognitive decline subgroups with an area under the curve (AUC) of 0.80 in the validation cohort. The individuals with PD in the validation cohort in the top quartile of risk for cognitive decline based on this model were 4.4 times more likely to develop incident MCI or dementia than those in the lowest quartile. Genotypes at MIA single nucleotide polymorphism (SNP) rs2233154 associated with MIA levels and cognitive decline, providing evidence for MIA's causal influence.

Conclusions: An easily obtained plasma-based predictor identifies individuals with PD at risk for cognitive decline. MIA may participate causally in development of cognitive decline. ANN NEUROL 2022;92:255-269.

Fig 1.. Study overview.

An aptamer-based platform was used to quantify the plasma levels of 940 proteins in the Discovery Cohort (left panel) and Replication Cohort (right panel). In each cohort, PD patients were assigned into a fast or a slow cognitive decline group based on change in MoCA score over time. A linear regression model was used to identify proteins differentiating fast versus slow cognitive decline groups in both cohorts, generating the top 9 proteins. Next, these identified proteins were used to train two logistic regression-based models that predict whether individual PD patients subsequently have fast vs. slow cognitive decline. Finally, in an additional validation cohort of 118 PD patients, we measured top biomarker proteins using alternative assays, testing for their performance in separating fast vs. slow cognitive decline subgroups.

Fig 2.. Characterization of cognitive decline subgroups.

Longitudinal cognitive and motor performance in the fast versus slow cognitive decline subgroups was assessed using linear mixed-effect models adjusting for age, sex and disease duration. Subgroups are indicated by color; the band represents the 95% confidence interval. (A-B) In both the Discovery and Replication Cohorts, MoCA scores decrease over time in the fast cognitive decline subgroup, while remaining stable in the slow cognitive decline subgroup. (C-D) In the Discovery Cohort, fast and slow cognitive decline subgroups do not differ in rate of motor change (UPDRS-III score) over time. However, in the Replication Cohort, the fast cognitive decline subgroup also experiences more rapid change in motor symptoms. (E) In the Replication Cohort, the fast cognitive decline subgroup has a faster rate of decline in the DRS score as well. (F) In the Replication Cohort, the fast cognitive decline subgroup has higher rates of incident MCI or dementia over 4 years of follow-up.

Fig 3.. Identification of top biomarkers differentiating fast vs. slow cognitive decline subgroups in both Discovery and Replication Cohorts.

(A-B) Performance characteristics of the logistic regression model for predicting whether an individual PD patient falls in the fast vs slow cognitive decline PD subgroup, trained using the measurements of all 9 proteins (panel A) or only 3 proteins (MIA, CRP, albumin, panel B), together with age, sex and disease duration. The model was trained using Discovery Cohort data (blue curve) and tested in the Replication Cohort (red curve). In the Discovery cohort, area under the receiver operating curve (AUC) was derived by five-fold cross-validation over 50 iterations. In each case, model performance using clinical variables alone is shown in grey. (C) Boxplots (median) showing the distribution of top biomarkers – MIA, CRP/Albumin Ratio levels – in log₁₀ of RFU by PD cognitive decline subgroups. Mann-Whitney test was used to compare biomarker measures between fast vs. slow cognitive decline subgroups. *p<0.05, **p<0.01.

Fig 4.. Validation of top biomarkers using alternative assays in the Validation Cohort.

(A) Comparison of the values for 3 top biomarkers (MIA, CRP, Albumin) obtained on SOMAScan vs. Enzyme-linked immunosorbent (ELISA) or Bromocresol Purple (BCP) assay in 15 duplicate plasma samples. Pearson’s r is shown. (D-E) Boxplots (median) showing the distribution of MIA and CRP/Albumin ratio in the Validation Cohort within fast vs. slow PD cognitive decline subgroups. Mann-Whitney test was used to compare subgroups. (F) Performance characteristics of the logistic-regression model (incorporating MIA, CRP, albumin, age, sex, and disease duration) for predicting fast vs slow cognitive decline subgroup in 118 PD patients from the Validation Cohort. (G) Time to incident MCI or dementia for PD patients in each quartile of risk score generated by the 6-parameter (3 protein, age, sex, disease duration) logistic regression model.

Fig 5.. MIA as a novel blood biomarker for cognitive function decline in PD

(A-B) Cox proportional hazards model adjusted for age, sex and disease duration showing time to incident MCI or dementia for individuals in each quartile of baseline MIA measures in the (A) Discovery Cohort and (B) Validation Cohort, over 5 years of follow-up. (C-D) Boxplot showing the association between genotypes at the MIA locus SNP rs2233154 and MIA expression in the plasma in the (C) Discovery Cohort and (D) Validation Cohort. There were no individuals with the TT genotype in the Validation Cohort. **p<0.01, ****p<0.0001. (E) Effect of rs2233154 genotype on longitudinal MoCA performance assessed using linear mixed-effects models adjusting for age, sex and disease duration. (F) Cox proportional hazards model adjusting for age, sex and disease duration shows a rate of incident MCI or dementia comparing carriers of different rs2233154 genotypes over 5 years of follow-up.