EBioMedicine August 2025 PMID 40592257

Identifying common disease trajectories of Alzheimer's disease with electronic health records.

Mingzhou Fu, Sriram Sankararaman, Bogdan Pasaniuc, Keith Vossel, Timothy S Chang

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Mentions

112

Peak: 2 in Jul 2024

across 5 sources since Jul 2024

Extended reach

18,791

potential audience

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Jul 2024 – Aug 2025

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News96 articles
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AI synopsis

What people are saying

These discussions show an overall positive reception, with users emphasizing the study's focus on disease progression patterns and its potential to improve understanding of Alzheimer's development. Words like 'support,' 'highlight,' and 'importance' signal recognition of the study's significance, while references to earlier detection methods demonstrate deep engagement.

AgreementModerate agreement

Most discussions acknowledge the value of the research, with some highlighting that the findings support the importance of sequencing in identifying Alzheimer's risk.

InterestModerate interest

The social discussions reveal a moderate level of interest, with commenters referencing the significance of disease trajectories and potential implications.

EngagementModerate engagement

Comments include specific mentions of the research methods and the importance of early detection, indicating thoughtful engagement.

ImpactModerate impact

Participants generally agree that this research could influence future diagnostics and treatment approaches, suggesting a moderate to high perceived impact.

Abstract in brief

This study analyzed electronic health records of nearly 24,500 patients to identify common multi-step diagnostic trajectories leading to Alzheimer's disease, revealing four major clusters: mental health issues, encephalopathy, mild cognitive impairment, and vascular disease.
The researchers used advanced methods like hazard modeling, dynamic time warping, clustering, and network analysis to understand how sequential diagnoses typically progress, and found that certain patterns consistently appear before AD diagnosis, such as hypertension followed by depressive episodes.
Validation in a large, diverse cohort confirmed that these multistep trajectories are more predictive of AD risk than individual diagnoses alone, emphasizing the importance of studying the sequence of diagnostic events to understand Alzheimer's development better.]

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All mentions

Analyzing Alzheimer's Disease Progression Using Electronic Health Records

This study analyzed electronic health records of nearly 24,500 patients to identify common multi-step diagnostic trajectories leading to Alzheimer's disease, revealing four major clusters: mental health issues…

Sharkkang · Aug 18, 2025 8 views

Alzheimer's Disease Follow Four Distinct Diagnostic Pathways Analysis

A UCLA study analyzing 25,000 patient records reveals Alzheimer's follows four specific diagnostic trajectories, including mental health issues, encephalopathy, cognitive impairment, and vascular disease. These paths…

Science News · Jul 10, 2025 6 views

お茶柱／心理学のべレスク

@otea_stalk · X

Aug 14, 2025 · 21 followers View post →

Walid Alkeridy وليد الكريدي

@WalidAlkeridy · X

Identifying common disease trajectories of Alzheimer’s disease with electronic health records - eBioMedicine https://t.co/OlWkjHfVGD

Jul 15, 2025 · 1 like · 1,297 followers View post →

medRxivpreprint

@medrxivpreprint.bsky.social · Bluesky

Identifying common disease trajectories of Alzheimer's disease with electronic health records https://www.medrxiv.org/content/10.1101/2024.07.26.24311084v1

Jul 27, 2024 View post →

medRxiv

@medrxivpreprint · X

Identifying common disease trajectories of Alzheimer's disease with electronic health records https://t.co/mQUTL1vkrQ #medRxiv

Jul 27, 2024 · 1 like · 17,460 followers View post →