Fusion of MRI and Transcriptomic Signatures for Accurate Early Diagnosis of Alzheimer's Disease
Keywords:
Alzheimer Disease; MRI biomarkers; transcriptomics; multi-modal fusion, early diagnosis, neuroinflammation.Abstract
Alzheimer Disease (AD) is a universal health issue whose proper therapeutic management has much reliance on its early and precise diagnosis. The existing methods of diagnosis are sophisticated but they tend to reveal the pathology when the neurons have been damaged to large extents. The research problem of this study is the investigation of the synergistic capability of structural MRI biomarkers and blood-based transcriptomic signatures in order to develop a multi-modal diagnostic model of early-stage AD detection. We examined cross-sectional data of 847 subjects (283 healthy controls, 284 mild cognitive impairment [MCI], 280 AD dementia) of the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The combination of volumetric MRI measurements (hippocampal volume, entorhinal cortex thickness, ventricular volume) with blood-based transcriptomic signatures of neuroinflammatory (TSPO, GFAP, TREM2), oxidative stress (SOD1, CAT, GPX1), and tau-related (MAPT, EIF2AK2) gene expression was used. The implementation and validation of multi-modal machine learning models (Random Forest, Support Vector Machine, XGBoost) were carried out with the help of 5-fold cross-validation. Integrated MRI-transcriptomic model showed excellent diagnostic outcomes (AUC = 0.94, 95% CI: 0.91-0.96) than MRI-only (AUC = 0.86) and transcriptomic-only (AUC = 0.82) models. The strongest predictors were hippocampal volume (importance: 0.24) and GFAP expression (importance: 0.18). The model reached 89 and 91 sensitivity and specificity in differentiating MCI and controls respectively and had especially high rates in early-stage diagnosis. Combining neuroimaging biomarkers with transcriptomic biomarkers gives complementary data which dramatically increases the diagnosis accuracy of early AD patients. This multi-modern model provides an encouraging prospect of achieving precision medicine in AD management.
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