ADNC-RS, a clinical-genetic risk score, predicts Alzheimer's pathology in autopsy-confirmed Parkinson's disease and Dementia with Lewy bodies

Abstract

Growing evidence suggests overlap between Alzheimer's disease (AD) and Parkinson's disease (PD) pathophysiology in a subset of patients. Indeed, 50-80% of autopsy cases with a primary clinicopathological diagnosis of Lewy body disease (LBD)-most commonly manifesting during life as PD-have concomitant amyloid-beta and tau pathology, the defining pathologies of AD. Here we evaluated common genetic variants in genome-wide association with AD as predictors of concomitant AD pathology in the brains of people with a primary clinicopathological diagnosis of PD or Dementia with Lewy Bodies (DLB), diseases both characterized by neuronal Lewy bodies. In the first stage of our study, 127 consecutive autopsy-confirmed cases of PD or DLB from a single center were assessed for AD neuropathological change (ADNC), and these same cases were genotyped at 20 single nucleotide polymorphisms (SNPs) found by genome-wide association study to associate with risk for AD. In these 127 training set individuals, we developed a logistic regression model predicting the presence of ADNC, using backward stepwise regression for model selection and tenfold cross-validation to estimate performance. The best-fit model generated a risk score for ADNC (ADNC-RS) based on age at disease onset and genotype at three SNPs (APOE, BIN1, and SORL1 loci), with an area under the receiver operating curve (AUC) of 0.751 in our training set. In the replication stage of our study, we assessed model performance in a separate test set of the next 81 individuals genotyped in our center. In the test set, the AUC was 0.781, and individuals with ADNC-RS in the top quintile had four-fold increased likelihood of having AD pathology at autopsy compared with those in each of the lowest two quintiles. Finally, in the validation stage of our study, we applied our ADNC-RS model to 70 LBD individuals from 20 Alzheimer's Disease Research Centers (ADRC) whose autopsy and genetic data were available in the National Alzheimer's Coordinating Center (NACC) database. In this validation set, the AUC was 0.754. Thus, in patients with autopsy-confirmed PD or DLB, a simple model incorporating three AD-risk SNPs and age at disease onset substantially enriches for concomitant AD pathology at autopsy, with implications for identifying LBD patients in which targeting amyloid-beta or tau is a therapeutic strategy.

Keywords: Alzheimer’s disease; Dementia; Genetics; Parkinson’s disease.

Fig 1

Alzheimer’s Disease Neuropathological Change (ADNC) scores in N = 208 cases from Penn with a primary clinicopathological diagnosis of PD or DLB. a Number of subjects and % of whole cohort at each level of ADNC. b Representative immunohistochemical sections (160X) demonstrating Lewy body aSyn pathology alone in anterior cingulate (left panel, in red), concomitant Aβ (brown) and aSyn (red) pathology in anterior cingulate (middle panel), and Aβ (red) and tau NFT (brown) pathology in middle frontal cortex (right panel). aSyn pathology was detected with the MJFR13 antibody against phosphorylated aSyn. Aβ pathology was detected with the NAB228 antibody, and tau NFT’s were detected with the 17028 rabbit polyclonal anti-tau antibody.

Fig 2

Backward stepwise logistic regression model selection for predicting concomitant Alzheimer’s Disease (AD) pathology in N = 127 cases (Training set) with a clinicopathologic diagnosis of PD or DLB from Penn. Concomitant AD pathology is defined as an AD Neuropathological Change (ADNC) score of Intermediate or High. a Akaike information criterion (AIC, left axis) at each step during model selection and the corresponding area under the receiver operating characteristics curve (AUC, right axis), estimated by ten-fold cross-validation, within the Training set are shown. Initial model included all AD risk SNPs, sex, and age at disease onset as predictors; sequential elimination of predictors and effect on AIC and AUC are shown from left to right. As the Training set cases showed no genetic variability at the TREM2 locus, this locus was not included in the model. b Coefficients (β), standard error (SE), and p-values for the four predictors included in the best model (lowest AIC) for predicting concomitant AD in LBD cases.

Fig 3

Performance characteristics of the best model for predicting concomitant Alzheimer’s disease (AD) pathology among Penn cases with a clinicopathological diagnosis of PD or DLB. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) of the final model (with age at onset, number of APOE4 alleles, BIN1 genotype, and SORL1 genotype as predictors) in the Training (a) and Test (b) cohorts are shown. c The Alzheimer’s Disease Neuropathological Change Risk Score (ADNC-RS) calculated from the best logistic regression model is shown for both the Training set and Test set cohorts. Individuals positive for ADNC showed higher average ADNC-RS. d The probability of concomitant AD pathology was calculated from the ADNC Risk Score for each case. Values above 0.5 have a high probability of concomitant AD pathology, while values below 0.5 have a low probability of concomitant AD pathology. The prevalence of concomitant AD pathology at each quintile of ADNC Risk Score in the Training (e) and Test (f) cohorts demonstrates fourfold enrichment for the presence of ADNC for individuals in the top quintile vs. individuals in the first two quintiles of risk. *p<0.05.

Fig 4

Performance characteristics of the best model for predicting concomitant Alzheimer’s disease (AD) pathology among non-Penn, NACC cases with neuropathological evidence of Lewy bodies and presumed clinical diagnosis of LBD. a Receiver operating characteristic (ROC) curve and area under the curve (AUC) of the final model (developed in the Penn-based Training set, with age at disease onset, number of APOE4 alleles, BIN1 genotype, and SORL1 genotype as predictors) are shown. b The Alzheimer’s Disease Neuropathological Change Risk Score (ADNC-RS) calculated from the final model is shown for LBD cases from the NACC. Individuals positive for ADNC showed higher average ADNC-RS. c Despite the NACC database’s enrichment for ADNC-positive individuals compared to the Penn-based cases (Training and Test sets combined), the ADNC-RS correlated with prevalence of ADNC in both groups.