A Cluster‐Based Machine Learning Approach for Evaluating Factors Associated with Tau Protein in Plasma

Abstract

AbstractBackgroundPlasma total tau (t‐tau) has shown acceptable diagnostic accuracy for Alzheimer’s disease (AD), but other factors than levels of t‐tau in the brain may influence peripheral levels. While little is known about peripheral factors that may influence plasma levels of t‐tau, such factors may be similar to those associated with AD, such as cardiovascular risk factors, which are often correlated. We have developed a machine learning pipeline to identify individual and clusters of correlated variables that might explain a proportion of the variance of plasma t‐tau levels in a well‐phenotyped population‐based cohort.MethodWe studied 992 dementia‐free older adults (mean age 76 years, 44% men) in the Age, Gene/Environment Susceptibility‐Reykjavik Study. We used agglomerative hierarchical clustering along with principal component (PC) analysis to group factors with moderate‐to‐high similarity from 290 potential predictors including socio‐demographic, anthropometric, laboratory, cardiometabolic and lifestyle factors, diseases and medications. We then calculated the PCs of the clusters and examined their relationship to plasma t‐tau. We trained two nonlinear models using t‐tau as the dependent outcome variable ‐ the extreme gradient boosting model (GBM) and support vector machine (SVM) ̶ on 80% of data using a ten‐fold cross‐validation approach; the models were evaluated on the remaining 20% of data. Model performance was assessed using the root mean square error (RMSE) and correlation. SHapley Additive exPlanations (SHAP) was used to rank and interpret the contribution of the clusters to the best‐performed model.ResultWe identified 25 distinct clusters. The best performing model was a SVM model including age, sex and the first PC from all 25 clusters(Figure 1). Based on the SHAP values in this model, we found the top 10 ranked clusters associated with p‐tau(Figure 2): platelets(5 variables), body size(33), physical activity‐walk(6), and tobacco(9) were negatively associated with t‐tau level, while body fat(8), sex, cognitive function(27), inflammation(7), blood pressure(6), and red blood cells(11) were positively associated with t‐tau.ConclusionOur novel approach suggests additional factors explain some of the variance in peripheral tau levels in older adults. Additional research is needed to confirm these findings and investigate whether these factors modulate or mediate tau‐related pathology.