New Study Maps Four Key Pathways to Alzheimer’s Disease whitehoune - stock.adobe.com
Medicine

New Study Maps Four Key Pathways to Alzheimer’s Disease

UCLA Health researchers identify sequential diagnostic patterns that could improve early detection and prevention strategies

MBT Desk

UCLA Health researchers have identified four distinct pathways that lead to Alzheimer's disease by analyzing electronic health records, offering new insights into how the condition develops over time rather than from isolated risk factors.

The study[1], published in the journal eBioMedicine, examined longitudinal health data from nearly 25,000 patients in the University of California Health Data Warehouse and validated findings in the nationally diverse All of Us Research Program. Unlike previous research that focused on individual risk factors, the UCLA analysis mapped sequential diagnostic patterns that revealed how conditions progress step-by-step toward Alzheimer's disease.

We found that multi-step trajectories can indicate greater risk factors for Alzheimer's disease than single conditions. Understanding these pathways could fundamentally change how we approach early detection and prevention.
Mingzhou Fu,medical informatics pre-doctoral student, UCLA.

The research identified four major trajectory clusters:

Each pathway showed distinct demographic and clinical characteristics, suggesting that different populations may be vulnerable to different progression routes.

The study found that approximately 26% of diagnostic progressions showed consistent directional ordering. For example, hypertension often preceded depressive episodes, which then increased Alzheimer's risk.

Recognizing these sequential patterns rather than focusing on diagnoses in isolation  may help clinicians improve Alzheimer’s disease diagnosis.
Dr. Timothy Chang, assistant professor in Neurology,UCLA Health.

When validated in an independent population, these multi-step trajectories predicted Alzheimer's disease risk more accurately than single diagnoses alone. This finding suggests that healthcare providers could use trajectory patterns for:

  • Enhanced risk stratification: Identifying high-risk patients earlier in disease progression

  • Targeted interventions: Interrupting harmful sequences before they advance

  • Personalized prevention: Tailoring strategies based on individual pathway patterns

The validation in the All of Us Research Program — a diverse, nationally representative cohort — confirmed that these trajectory patterns apply across different populations and demographics.

 Research Methodology

The team analyzed 5,762 patients who contributed 6,794 unique Alzheimer's progression trajectories. Using advanced computational methods including dynamic time warping, machine learning clustering, and network analysis, researchers mapped the temporal relationships between diagnoses leading to Alzheimer's disease.

References

1) https://www.sciencedirect.com/science/article/pii/S2352396425002750?via%3Dihub#coi0010

(Newswise/DJR)

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