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.