Blood Plasma Biomarkers Improve Prediction Accuracy over and above Genetic Predictors of Alzheimer’s Disease

AbstractBackgroundPrediction models of Alzheimer’s disease using genetic information, such as polygenic risk scores, have been able to reach high levels of prediction accuracy. However, since confirmation of the disease is only possible post‐mortem, these levels of prediction accuracy are typically only achievable in pathologically confirmed cohorts. In living individuals who have been clinically assessed for AD, prediction accuracy by genetics is still good but much lower. Biomarkers can indirectly assess AD pathologies, and so may be able to bridge the prediction accuracy gap. Blood plasma biomarkers may have additional clinical utility as they are cheaper and more accessible compared to traditional CSF or PET methods.MethodWe measured five blood plasma biomarkers known to be linked to AD pathologies (Aβ40, Aβ42, GFAP, NfL, P‐tau181) in a cohort of AD cases (N=1439, mean age 68) and elderly screened controls (N=508, mean age 82). We also gathered information on APOE genotype, age at sample collection, sex, and age at onset and disease duration in cases.ResultLinear regression models showed that all biomarker measurements were associated with age at onset in cases and most were associated with disease duration. Biomarkers were also associated with age at sample collection in both cases and controls, demonstrating their effectiveness for tracking neurological change over time. Using logistic regression, we found prediction accuracies for AD status for each biomarker individually of AUC=0.56‐0.66 and by APOE and PRS AUC=0.73. A model combining all biomarkers had an AUC=0.75. The best prediction accuracy was achieved by combining all biomarkers with genetics and age at sample collection, which reached an AUC=0.81, and explained variance of R2=0.29.ConclusionWe found that blood plasma biomarkers predicted AD status and were associated with disease duration. Furthermore, biomarkers explain some variance not captured by genetic factors and therefore improve accuracy when combined in predictive models. Biomarkers also have the advantage of specificity over clinical assessments, which may confuse dementia subtypes due to phenotype similarity. Therefore, blood plasma biomarkers can be a useful tool for the assessment and prediction of AD on their own or in combination with genetic predictors.