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Updated: May 1, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
Published on: September 20, 2022
From visualization to prediction: Dissecting metabolic heterogeneity with DDRTree.
1Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.
The discriminative dimensionality reduction tree (DDRTree) framework helps identify distinct patient subtypes in type 2 diabetes and obesity. This approach reveals metabolic heterogeneity for better risk profiling.
Area of Science:
- Metabolic heterogeneity
- Biostatistics
- Computational biology
Background:
- Metabolic diseases like type 2 diabetes and obesity exhibit significant patient heterogeneity.
- Identifying distinct subtypes is crucial for targeted treatments and risk stratification.
Purpose of the Study:
- To apply the discriminative dimensionality reduction tree (DDRTree) framework to metabolic data.
- To delineate clinically relevant subtypes in type 2 diabetes and obesity.
Main Methods:
- Utilized the discriminative dimensionality reduction tree (DDRTree) framework.
- Applied the framework to analyze metabolic data from patients with type 2 diabetes and obesity.
Main Results:
- Successfully identified distinct patient subtypes within type 2 diabetes and obesity cohorts.
- These subtypes exhibited varied metabolic profiles and associated risk factors.
Conclusions:
- The DDRTree framework is effective for unraveling metabolic heterogeneity.
- Delineated subtypes offer potential for personalized medicine approaches in managing type 2 diabetes and obesity.
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