Preclinical Alzheimer’s Disease accurate prediction using plasma cell‐free RNA sequences
نویسندگان
چکیده
Background Cerebrospinal fluid (CSF) Alzheimer’s disease (AD) biomarkers are used to clinically diagnose AD with an accuracy of 70-80%. However, they invasive and expensive need be further studied on preclinical-AD. Additionally, a blood-based biomarker can great use. In order obtain biomarker, we have sequenced plasma cell-free RNA (cfRNA) from preclinical-AD individuals develop prediction models for using machine learning (Figure 1). Method Plasma samples were obtained the Knight-Alzheimer-Disease-Research-Center [preclinical-AD (n = 67), clinical-AD 92), controls 48)] (Table 1) in two batches. Sequences processed following standard pipelines normalized DESeq2. Preclinical-AD 47) 26) one sequencing batch train predictive model, then 20) 22) other as testing population. We applied Z-score scaling gene counts Kullback–Leibler (KL) divergence select genes that had similar expression distribution both experiments 2). different KL thresholds predictors Ridge regression find best models. After tested preclinical-AD, individuals. Finally, Parkinson’s 96), dementia Lewy bodies 17), frontotemporal 16) test specificity analyses Result Best sizes include 40, 90, 220 yielding 85.7%, 90.5%, 95.2% respectively. Predicted risk consistently correlates Aβ42 levels. The model included such MT-ATP6, GAB2, or MAPK14, which been associated AD. these neurodegenerative diseases, observed comparison PD, FTD, DLB 3). Conclusion cfRNA is promising tool screen preclinical It has potential minimally since it higher comparable CSF AD-specific.
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ژورنال
عنوان ژورنال: Alzheimers & Dementia
سال: 2023
ISSN: ['1552-5260', '1552-5279']
DOI: https://doi.org/10.1002/alz.060825