A Critical Analysis of Biomarkers and Diagnostics Strategies for Alzheimer's Syndrome
DOI:
https://doi.org/10.56294/hl2025773Keywords:
Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI), Amyloid-β (Aβ), statistical methods, biomarker analysis, neuroimaging, predictive modelingAbstract
These analyses assess the early recognition and prediction of Alzheimer's disease (AD) by investigative biomarkers in the prodromal and transitional stages. The traditional diagnostic technique often fails to detect subtle cognitive modifications in the early stages, which leads to delayed interference and behavior. Data were gathered from 350 participants diagnosed with mild cognitive impairment (MCI), including cerebrospinal fluid (CSF) biomarker levels, neuroimaging scans, and clinical assessment. The research employs three Statistical analyses, using IBM SPSS Statistics version 30 including analyze the predictive validity of these biomarkers: regression analysis, and survival analysis. Regression analysis exposed a strong relationship between tau protein levels and the expansion from MCI to AD, with a coefficient of determination, representative of the power of CSF biomarkers in predicting AD onset. Survival analysis using Kaplan-Meier curves showed that participants with elevated amyloid plaque accumulation had a considerably higher risk of transitioning to AD. The researches underscore the limitations of the traditional diagnostic approach and emphasizes the importance of integrating other statistical method to improve early diagnosis. These findings suggest that refined statistical frameworks are crucial for enhancing the accuracy of AD diagnosis and advancing targeted treatment strategy.
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Copyright (c) 2025 Sushmita Sil , Abhijit Debnath , Prakash S. Surjya, Sahil Suri, Kamineni Sairam , Bhavuk Samrat (Author)
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