Method and device for comprehensively analyzing immune metabolism and ad disease-related genes through multiple omics

By integrating multi-omics analysis methods, combined with single-cell sequencing and genome-wide association studies, we identified Alzheimer's disease-related immune cells and metabolites, solving the problem of lack of multi-dimensional data integration in existing gene screening methods, and achieving highly accurate prediction of AD risk and a comprehensive explanation of the biological mechanisms.

CN121171327BActive Publication Date: 2026-07-07WUHAN UNIV
2 Cites 0 Cited by

Patent Information

Application Number
CN202511218559.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-28
Publication Date
2026-07-07
Estimated Expiration
2045-08-28

AI Technical Summary

Technical Problem

Existing gene screening methods rely on single-omics data and lack multi-dimensional data integration, making it difficult to analyze the relationship between gene function and metabolic pathways, resulting in the omission of key genes related to immune metabolism.

Method used

Using a comprehensive multi-omics analysis approach, combining single-cell sequencing data and genome-wide association studies, we identified causal relationships among immune cell characteristics and metabolite factors through Mendelian randomized causal inference, and performed classification and enrichment analysis to identify metabolic pathways and related genes affecting Alzheimer's disease.

Benefits of technology

It improves the accuracy of predicting Alzheimer's disease risk, provides a more comprehensive biological interpretation by integrating data from multiple biological levels, identifies AD-related immune cells and metabolites, and delves into the role of differentially expressed genes in the AD patient population.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The application discloses a method and device for comprehensively analyzing immune metabolism and AD disease-related genes by multiple omics. The method comprises the following steps: obtaining single-cell sequencing data of AD patients; obtaining GWAS data of immune cells and metabolites of AD patients; screening independent single nucleotide polymorphisms (SNPs); obtaining causally related immune cell characteristics and metabolite factor characteristics through Mendelian randomization causal inference; analyzing the causally related immune cells and metabolites to identify immune cells and metabolites that have an important impact on the occurrence of AD, and then obtaining metabolic pathways that affect AD; obtaining gene expression data of expression differences of metabolic pathways in different cell populations; performing differential gene analysis to obtain gene analysis results; and screening genes related to AD disease. The method disclosed by the application uses advanced machine learning technology to identify key biomarkers related to Alzheimer's disease and accurately predict the risk of individuals in disease development.
Need to check novelty before this filing date? Find Prior Art