Innovative Framework Developed for Analyzing Metabolic Data to Predict Disease Risks
A new mathematical framework for analyzing large-scale metabolic data enhances disease risk prediction and personalized healthcare by revealing meaningful metabolic patterns and subgroups.
Researchers from the National University of Singapore have introduced a groundbreaking approach to analyze large-scale metabolomic data, significantly advancing personalized healthcare and preventive strategies. Led by Associate Professor Yao Zhigang from the Department of Statistics and Data Science, the team devised a sophisticated mathematical framework that employs low-dimensional manifold fitting within the high-dimensional space of Nuclear Magnetic Resonance (NMR)-based metabolic biomarkers. This technique effectively reduces noise and highlights meaningful patterns associated with metabolic alterations.
The study, conducted in collaboration with Professor Yau Shing-Tung at Tsinghua University, involved clustering 251 metabolic biomarkers measured from over 210,000 UK Biobank participants into seven biologically relevant categories, reflecting the modular organization of human metabolism. The foundational element of this framework is the manifold fitting process, which models individual metabolic profiles within a low-dimensional geometric space. This not only enhances interpretability but also improves the detection of metabolic variations linked to health and disease.
Notably, the framework can distinguish subgroups within populations based on their metabolic states. In three categories, individuals are segmented into subgroups with differing risks for metabolic, cardiovascular, and autoimmune diseases. This capability enables more precise stratification and understanding of disease susceptibility.
Future developments involve integrating genetic data and conducting longitudinal studies to examine the stability and predictive power of metabolic manifolds over time. These efforts aim to uncover genetic influences on metabolic diversity and track metabolic transitions that may signal disease onset.
This innovative approach promises to enhance the accuracy of disease risk prediction and open new avenues for personalized medicine, leveraging advanced metabolic profiling techniques for better health outcomes.
Source: https://medicalxpress.com/news/2025-05-scientists-framework-metabolic-disease.html
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