Research on Alzheimer's disease (AD) has increasingly focused on identifying mechanisms and biomarkers that can aid in early diagnosis and understanding disease progression. A significant study revealed that myeloid innate immune memory contributes to systemic dysfunction following brain injury, suggesting that immune responses may play a role in AD pathology (ref: Simats doi.org/10.1016/j.cell.2024.06.028/). Another study developed an AI-based model for differential diagnosis of dementia, achieving a mean AUROC of 0.78 for mixed dementia cases, indicating that AI can enhance diagnostic accuracy compared to neurologist assessments alone (ref: Xue doi.org/10.1038/s41591-024-03118-z/). Furthermore, the role of glucose metabolism was highlighted, showing that elevated plasma glucose levels were associated with increased tau load over a 14-year period, while insulin levels did not correlate with amyloid-β or tau (ref: van Gils doi.org/10.2337/dc24-0162/). These findings underscore the complexity of AD, where both metabolic and immune factors intertwine with genetic predispositions, as seen in individuals with Down syndrome who have a high lifetime risk of developing AD due to genetic factors (ref: Fortea doi.org/10.1038/s41591-024-03159-4/). The Dominantly Inherited Alzheimer Network study also provided insights into the relationship between γ-secretase activity and clinical features, revealing that lower γ-secretase activity correlates with faster cognitive decline and increased amyloid deposition (ref: Schultz doi.org/10.1016/S1474-4422(24)00236-9/). Overall, these studies collectively emphasize the need for multifaceted approaches in understanding AD, integrating genetic, metabolic, and immune perspectives.