EXPLORING THE GENETIC BASIS OF ALZHEIMER'S DISEASE: A COMPREHENSIVE STUDY ON THE ROLE OF BIOMARKERS IN EARLY DIAGNOSIS

Abstract

Alzheimer's disease (AD) represents a growing global health crisis, with early diagnosis remaining a significant challenge. This study investigates the complex genetic architecture of AD and evaluates the efficacy of multi-modal biomarker panels for early, pre-symptomatic detection. We conducted a quantitative, problem-based analysis utilizing data from a large, longitudinal cohort (n=2,500) comprising cognitively normal, mild cognitive impairment (MCI), and AD-diagnosed individuals. Genetic screening focused on established risk loci, including APOE ε4, and novel candidates identified through genome-wide association studies (GWAS). Biomarker assessment included cerebrospinal fluid (CSF) levels of amyloid-β42 (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau-181), volumetric magnetic resonance imaging (MRI) of hippocampal atrophy, and fluorodeoxyglucose positron emission tomography (FDG-PET) for metabolic activity. Our results demonstrate that a polygenic risk score (PRS), integrating 32 AD-associated single nucleotide polymorphisms (SNPs), significantly stratified individuals by disease risk (p<0.001). The integration of APOE ε4 status, CSF Aβ42/t-tau ratio, and medial temporal lobe atrophy yielded a diagnostic accuracy of 92% (AUC=0.92) for distinguishing MCI-AD converters from stable MCI. Longitudinal analysis revealed that biomarker abnormalities followed a predictable sequence, with CSF amyloid changes preceding tau pathology and neurodegeneration by over a decade. Key findings indicate that while APOE ε4 is the strongest genetic risk factor, its predictive power is substantially enhanced when combined with core pathological biomarkers. This research underscores the necessity of moving beyond single-modal diagnostics towards integrated genetic-biomarker models. The developed algorithm provides a robust framework for identifying at-risk individuals in the pre-clinical stage, enabling timely intervention and stratification for clinical trials. Future research must validate these panels in more diverse populations to ensure equitable application.