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Alzheimer's Disease: Overview

Alzheimer's Disease: Overview

Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...

Alzheimer's Disease: Treatment

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...

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Updated: Jul 22, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Predicting Alzheimer's Disease with Interpretable Machine Learning.

Maoni Jia¹, Yafei Wu², Chaoyi Xiang¹

¹Center for Aging and Health Research, School of Public Health, Xiamen University, Xiamen, China.

Dementia and Geriatric Cognitive Disorders

|July 23, 2023

Summary

This summary is machine-generated.

Machine learning models effectively predict Alzheimer's disease (AD) risk using blood biomarkers, age, and education. This approach aids in early screening and identifying key factors for targeted prevention strategies.

Keywords:

Alzheimer’s disease Interpretability analysis Machine learning Prediction model

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Area of Science:

Neuroscience
Computational Biology
Gerontology

Background:

Alzheimer's disease (AD) poses a significant global health challenge.
Early prediction and prevention are crucial for managing AD.
Machine learning offers potential for developing predictive models.

Purpose of the Study:

To develop and evaluate machine learning models for predicting Alzheimer's disease (AD) risk.
To identify key predictors for targeted AD prevention strategies.

Main Methods:

Utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Constructed machine learning models using sociodemographic, health, and blood biomarker data.
Employed SHapley Additive exPlanation (SHAP) for predictor identification.

Main Results:

Models incorporating blood biomarkers demonstrated significantly improved AD prediction performance (AUC=0.818 with logistic regression).
Key predictors included ptau protein, plasma neurofilament light, age, blood tau protein, and education level.
Other significant factors identified were taurine, inosine, xanthine, marital status, and L.Glutamine.

Conclusions:

Interpretable machine learning models show promise for identifying individuals at high risk of AD.
The identified key predictors can inform targeted prevention efforts.