Evidence map›Paper›PMID 40180206›Full record

ArticleJournal of biomedical informatics2025

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.

Manqi Zhou, Alice S Tang, Hao Zhang, Zhenxing Xu, Alison M C Ke, Chang Su, Yu Huang, William G Mantyh, Michael S Jaffee, Katherine P Rankin and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Manqi ZhouDepartment of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
Alice S TangBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, San Francisco and Berkeley, CA 94143, USA.
Hao ZhangDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
Zhenxing XuDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
Alison M C KeDepartment of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
Chang SuDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
Yu HuangBiostatistics and Health Data Science, School of Medicine, Indiana Univeristy, Indianapolis, IN 47374, USA.
William G MantyhDepartment of Neurology, University of Minnesota, Minneapolis, MN 55455, USA.
Michael S JaffeeDepartment of Neurology, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA.
Katherine P RankinBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA.
Steven T DeKoskyDepartment of Neurology, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA.
Jiayu ZhouSchool of Information, University of Michigan, Ann Arbor, MI 48109, USA.
Yi GuoDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, GL 32610, USA.
Jiang BianBiostatistics and Health Data Science, School of Medicine, Indiana Univeristy, Indianapolis, IN 47374, USA.
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Pediatrics, University of California, San Francisco, CA 94143, USA.
Fei WangDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA. Electronic address: few2001@med.cornell.edu.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007618 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ANDERSON, MARK S · 1985 to 2020
$29.6M
An Integrative Multi-Omics Approach to Elucidate Sex-Specific Differences in Alzheimers DiseaseR01AG060393 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SIROTA, MARINA · 2018 to 2022
$4.1M
TREM2 Genotype-Informed Drug Repurposing and Combination Therapy Design for Alzheimer’s DiseaseR01AG076448 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI Feixiong Cheng, Li Gan · 2022 to 2026
$4.0M
Computational Drug Repurposing for AD/ADRD with Integrative Analysis of Real World Data and Biomedical KnowledgeR01AG076234 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Jiang Bian, Fei Wang · 2022 to 2026
$3.8M
Dementia prevalence, APOE, and blood-based biomarkers of AD in Native American communitiesR01AG080806 · NIA · UNIVERSITY OF MINNESOTA · PI William G Mantyh · 2022 to 2026
$3.7M
Eligibility criteria design for Alzheimer's trials with real-world data and explainable AIR01AG080991 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Jiang Bian, Fei Wang · 2023 to 2026
$3.1M
Disparities of Alzheimer's disease progression in Sexual Minority IndividualsR01AG080624 · NIA · UNIVERSITY OF FLORIDA · PI Jiang Bian, Yi Guo · 2023 to 2026
$3.1M
Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior MarkersRF1AG072449 · NIA · MICHIGAN STATE UNIVERSITY · PI DODGE, HIROKO HAYAMA, WANG, FEI · 2021 to 2023
$2.6M
Post-Acute Sequelae of SARS-CoV-2 Infection and Subsequent Disease Progression in Individuals with AD/ADRD: Influence of the Social and Environmental Determinants of HealthRF1AG084178 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI BIAN, JIANG, HU, HUI · 2023 to 2023
$2.6M
Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior MarkersR01AG072449 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HIROKO Hayama DODGE, Fei Wang · 2025 to 2026
$1.4M
Progression Subtyping and Drug Target Identification for Parkinson's Disease with Integrative Machine LearningR01NS140142 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI Chang Su · 2025 to 2026
$1.3M
Leveraging Clinical Data for Phenotyping and Predictive Modelling of Alzheimer’s DiseaseF30AG079504 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TANG, ALICE SUMMER · 2022 to 2025
$187k
NIA NIH HHS F30 AG079504NIA NIH HHS R01 AG060393NIA NIH HHS R01 AG072449NIA NIH HHS R01 AG076234NIA NIH HHS R01 AG076448NIA NIH HHS R01 AG080624NIA NIH HHS R01 AG080806NIA NIH HHS R01 AG080991NIA NIH HHS RF1 AG072449NIA NIH HHS RF1 AG084178NIGMS NIH HHS T32 GM007618NINDS NIH HHS R01 NS140142
6 · The paper itself

Abstract

objectiveIdentification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subphenotypes of Alzheimer's disease progression based on longitudinal real-world patient records.

methodsThe framework, dynaPhenoM, extracts coherent clinical topics across patient visits and employs a time-aware latent class analysis to characterize subphenotypes. We validated dynaPhenoM using three patient databases with a total of 3952 AD patients across the United States, demonstrating its effectiveness in revealing mild cognitive impairment (MCI) progression to AD.

resultsOur study identified five subphenotypes associated with distinct organ systems for disease progression from MCI to AD, including common subtypes across cohorts-respiratory, musculoskeletal, cardiovascular, and endocrine/metabolic-as well as a cohort-specific digestive subtype.

conclusionOur study unravels the complexity and heterogeneity of the progression from MCI to AD. These findings highlight disease progression heterogeneity and can inform both diagnostic and therapeutic strategies, thereby advancing precision medicine for Alzheimer's disease.

Indexed as

Alzheimer DiseaseElectronic Health RecordsMachine LearningAgedAged, 80 and overCognitive DysfunctionDatabases, FactualDisease ProgressionFemaleHumansMalePhenotypeUnited StatesAlzheimer’s diseaseDisease subtypingMachine learning

Identifiers

PMID40180206
PMCPMC12296293

What Socratic holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.