Longitudinal plasma proteomics: relation to incident Alzheimer's disease dementia and biomarkers

Abstract

Introduction: We investigated whether longitudinal changes in plasma proteins were associated with baseline cognitive stages related to Alzheimer's disease (AD), their progression, and AD biomarkers. Methods: We analyzed longitudinal proteomics (SomaScan 7K) data (N = 347) from the Indiana AD Research Center using linear mixed-effects models for associations with baseline cognitive stages, AD dementia (ADD) conversion, and AD imaging/plasma biomarkers, followed by machine learning analysis to evaluate predictive performance for incident ADD. Results: Our analysis identified two proteins (ACES and IGFALS) associated with baseline diagnosis stages and six proteins (ACES, C7, ZCD1, IL-17C, CC055, and SO5A1) associated with incident ADD. Longitudinal changes of the identified proteins were also associated with AD imaging/plasma biomarkers. The inclusion of longitudinal protein changes yielded an AUC of 84.8% for predicting incident ADD. Conclusion: Our findings showed molecular signatures for AD progression and the potential of dynamic changes in plasma proteins as biomarkers for predicting incident ADD. Highlights: Changes in plasma ACES and IGFALS linked to baseline AD cognitive stages Changes in ACES, C7, ZCD1, IL-17C, CC055, and SO5A1 associated with incident ADD Changes in those proteins correlated with baseline AD imaging and plasma biomarkers Proteomics model achieved 84.8% AUC-ROC in predicting incident ADD.