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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Integrative Multi-Omics and Routine Blood Analysis Using Deep Learning: Cost-Effective Early Prediction of Chronic

Zhibin Dong^1,2, Pei Li¹, Yi Jiang³

¹Medical Innovation Research Division, Chinese PLA General Hospital, Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Medical Engineering Laboratory of Chinese PLA General Hospital, Beijing, 100038, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|April 2, 2025

Summary

This summary is machine-generated.

Early prediction of chronic noncommunicable diseases (NCDs) is now possible using multi-omics data and a deep learning model. This approach analyzes routine blood tests to identify disease risks years in advance, aiding personalized medicine.

Keywords:

multi‐omics pathway discovery potential risk classification

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Published on: September 20, 2024

Area of Science:

Biomedical Informatics
Genomics
Preventive Medicine

Background:

Chronic noncommunicable diseases (NCDs) pose a global health challenge due to their gradual onset and difficulty in early prediction.
Current screening methods often lack the sensitivity for early detection of complex disease trajectories.

Purpose of the Study:

To explore disease interconnectedness using multi-omics data.
To develop and validate a deep learning model for preclinical risk assessment of NCDs.
To create an accessible NCDs risk prediction system based on routine blood tests.

Main Methods:

Collected multi-omics data from 160 sub-healthy individuals.
Developed and applied a deep learning model, Omicsformer, for analyzing routine blood samples.
Validated the model's predictive capabilities using 20-year clinical patient data.

Main Results:

Omicsformer successfully identified potential risks for nine diseases, including cancer, cardiovascular, and psychiatric conditions.
Risk trajectory analysis confirmed the model's validity in preclinical risk assessment.
The study revealed trends of increased disease risk correlating with disease onset.

Conclusions:

Multi-omics analysis is crucial for predicting chronic disease risk.
A predictive model based on routine blood tests can advance personalized medicine.
This approach has the potential to reduce community-level disease screening costs.