Evidence map›Paper›PMID 42811779›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2026

Exposome-wide association study and predictive risk score for incident Alzheimer's disease and related dementias.

Chenshuang Li, Chenxu Zhao, Yiqun Lan, Randall J Ellis, Shakson Isaac, Sai Zhang, Gary W Miller, Yi Sun, Shalini Jain, Hariom Yadav and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Chenshuang LiDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Chenxu ZhaoCenter for Smart and Healthy Buildings, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yiqun LanCenter for Smart and Healthy Buildings, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Randall J EllisDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
Shakson IsaacDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
Sai ZhangDepartment of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, Connecticut, USA.
Gary W MillerDepartment of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA.
Yi SunDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Shalini JainUSF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, Florida, USA.
Hariom YadavUSF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, Florida, USA.
Peng GaoDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-4311-584X
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-8756-8525

Funding

NEXUS: Network for Exposomics in the U.S.U24ES036819 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Rima Habre, GARY W MILLER · 2024 to 2026
$4.6M
Data science tools to identify robust exposure-phenotype associations for precision medicineR01ES032470 · NIEHS · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR, PATEL, CHIRAG J. · 2021 to 2025
$3.5M
The confluence of extreme heat and cold on aging and longevityR01AG074372 · NIA · HARVARD MEDICAL SCHOOL · PI Francesca Dominici, Hossein Estiri · 2025 to 2026
$1.5M
Harvard Data Science Initiative Faculty Special Projects FundNIA NIH HHS R01AG074372NIEHS NIH HHS R01ES032470NIEHS NIH HHS U24ES036819
6 · The paper itself

Abstract

introductionEnvironmental exposures contribute substantially to Alzheimer's disease and related dementias (ADRD), yet comprehensive exposome-wide investigations systematically evaluating multi-domain environmental factors remain limited and lacking utility for precision medicine.

methodsWe conducted an exposome-wide association study (ExWAS) among 499,992 UK Biobank participants, examining 193 exposures across five domains (social, physical, chemical, lifestyle, and ecosystems). Cox proportional hazard models with Bonferroni correction identified ADRD-associated exposures. We used the XGBoost classifier to build a predictive model and further developed an exposomic risk score (ERS) for risk stratification.

resultsDuring a 15.3-year median follow-up, 8881 participants (1.77%) developed ADRD, including 4000 with Alzheimer's disease. ExWAS identified 76 exposures after Bonferroni correction. The prediction model achieved area under the curve (AUC) = 0.718, outperforming demographics+apolipoprotein E (APOE) status alone (AUC = 0.678). The ERS stratified ADRD risk (highest vs lowest quartile hazard ratio [HR] = 2.97, 95% confidence interval [CI]: 2.59-3.41). DISCUSSION: The ERS has the potential to contribute to clinically relevant prevention strategies for ADRD.

Indexed as

Alzheimer DiseaseDementiaEnvironmental ExposureExposomeAgedFemaleHumansIncidenceMaleMiddle AgedProportional Hazards ModelsRisk AssessmentRisk FactorsUK BiobankUnited KingdomAlzheimer's diseasedementiaexposomeExWASmachine learningmodifiable risk factorsrisk predictionUK Biobank

Identifiers

PMID42811779
PMCPMC13624529

What Socratic holds

Textmetadata
Read underepoch 390

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.