Advancements in APOE and dementia research: Highlights from the 2023 AAIC Advancements: APOE conference.

INTRODUCTION: The apolipoprotein E gene (APOE) is an established central player in the pathogenesis of Alzheimer's disease (AD), with distinct apoE isoforms exerting diverse effects. apoE influences not only amyloid-beta and tau pathologies but also lipid and energy metabolism, neuroinflammation, cerebral vascular health, and sex-dependent disease manifestations. Furthermore, ancestral background may significantly impact the link between APOE and AD, underscoring the need for more inclusive research. METHODS: In 2023, the Alzheimer's Association convened multidisciplinary researchers at the "AAIC Advancements: APOE" conference to discuss various topics, including apoE isoforms and their roles in AD pathogenesis, progress in apoE-targeted therapeutic strategies, updates on disease models and interventions that modulate apoE expression and function. RESULTS: This manuscript presents highlights from the conference and provides an overview of opportunities for further research in the field. DISCUSSION: Understanding apoE's multifaceted roles in AD pathogenesis will help develop targeted interventions for AD and advance the field of AD precision medicine. HIGHLIGHTS: APOE is a central player in the pathogenesis of Alzheimer's disease. APOE exerts a numerous effects throughout the brain on amyloid-beta, tau, and other pathways. The AAIC Advancements: APOE conference encouraged discussions and collaborations on understanding the role of APOE.

Discovery of novel CSF biomarkers to predict progression in dementia using machine learning.

Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques with the aim to identify cerebrospinal fluid (CSF) biomarkers that predict the rate of cognitive decline within dementia patients. First, longitudinal mini-mental state examination scores (MMSE) of 210 dementia patients were used to create fast and slow progression groups. Second, we trained random forest classifiers on CSF proteomic profiles and obtained a well-performing prediction model for the progression group (ROC-AUC = 0.82). As a third step, Shapley values and Gini feature importance measures were used to interpret the model performance and identify top biomarker candidates for predicting the rate of cognitive decline. Finally, we explored the potential for each of the 20 top candidates in internal sensitivity analyses. TNFRSF4 and TGF [Formula: see text]-1 emerged as the top markers, being lower in fast-progressing patients compared to slow-progressing patients. Proteins of which a low concentration was associated with fast progression were enriched for cell signalling and immune response pathways. None of our top markers stood out as strong individual predictors of subsequent cognitive decline. This could be explained by small effect sizes per protein and biological heterogeneity among dementia patients. Taken together, this study presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia.