Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Development of a risk prediction model for Alzheimer's disease based on the UK Biobank prospective study.

Journal of Alzheimer's disease : JAD - 1 Nov 2025

Li Huilin, Wu Yiwen, Huang Ting, Sun Yue, Lu Zixuan, Li Musu, Wo Hongmei, Shao Fang, Tang Shaowen, Zhao Yang, Dai Juncheng, Yi Honggang, The Alzheimer's Disease Neuroimaging Initiative

Abstract excerpt

BackgroundEarly prevention and intervention for Alzheimer's disease (AD) are critical due to the absence of effective therapeutic treatment. However, a widely accepted risk prediction model for AD has yet to be established.ObjectiveTo develop a novel risk prediction model for AD by leveraging recent advances in identifying risk factors, focusing on multi-omics data.MethodsGenetic data from the UK Biobank were...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Alzheimer Disease
Share to Decision Support Techniques

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Risk Score

Share to Multiomics

Share to Nomograms

Share to Proportional Hazards Models

Share to ROC Curve

Share to UK Biobank

Source

Identifiers

PMID: 40953107
DOI: 10.1177/13872877251375473

Genes

CAD

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Development of a risk prediction model for Alzheimer's disease based on the UK Biobank prospective study.PMID 40953107Read ↗

Integrative multi-omics approaches identify molecular pathways and improve Alzheimer’s Disease risk predictionSemantic proximity Read ↗

Development and Validation of a Novel Dementia Risk Score in the UK Biobank CohortSemantic proximity Read ↗

Integrative multi-omics approaches identify molecular pathways and improve Alzheimer's disease risk prediction.Semantic proximity Read ↗

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s DiseaseSemantic proximity Read ↗

Improving genetic risk modeling of dementia from real-world data in underrepresented populationsSemantic proximity Read ↗

Improving genetic risk modeling of dementia from real-world data in underrepresented populationsSemantic proximity Read ↗

Toward a Full Understanding of Causal and Modifiable Risk Factors for Alzheimer's Disease by Integrative Phenome-wide Association Studies.Semantic proximity Read ↗

Improving genetic risk modeling of dementia from real-world data in underrepresented populations.Semantic proximity Read ↗

Deep post-GWAS analysis identifies potential risk genes and risk variants for Alzheimer's disease, providing new insights into its disease mechanisms.Semantic proximity Read ↗

Deep Post-GWAS Analysis Identifies Potential Risk Genes and Causal Variants for Alzheimer’s Disease, Providing New Insights Into Its Disease MechanismsSemantic proximity Read ↗

Genetic risk in Alzheimer's disease.Semantic proximity Read ↗

Deep Post-GWAS Analysis Identifies Potential Risk Genes and Causal Variants for Alzheimer’s Disease, Providing New Insights Into Its Disease MechanismsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.