A broader panel of protein markers could lead to better blood tests for Alzheimer's
In Alzheimer's disease, amyloid plaques form in the brain, followed by threadlike structures made of the protein tau. Both changes have toxic effects on the brain's nerve cells years before dementia symptoms appear. Historically, diagnosis has primarily relied on cognitive changes, which manifest at a later stage of the disease. Biomarker-based diagnostics were initially limited to specialized…
Alzheimer's disease is characterized by the formation of amyloid plaques and tau protein threads in the brain, which can cause toxic effects on nerve cells years before dementia symptoms appear. Traditionally, diagnosis has relied on cognitive changes that emerge later in the disease. Biomarker-based diagnostics, such as the p-tau217 blood test, have improved accessibility to earlier detection.
However, these tests still lag behind PET scans in determining the disease stage and predicting progression. A new study published in JAMA Neurology explored whether machine learning applied to blood proteomics data could identify new biomarker profiles to enhance disease assessment. By analyzing data from a novel immunoassay platform measuring over 120 inflammation and neuronal markers in blood samples from two international cohorts, researchers found that adding seven specific proteins to the p-tau217 panel significantly improved its ability to predict advanced tau pathology in individuals with high amyloid plaque levels.
This suggests that a multiprotein-based approach could serve as a viable alternative to tau-PET for staging, treatment selection, and clinical trial screening in Alzheimer's disease.
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