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Updated: Jun 5, 2025

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Deep phenotyping obesity using EHR data: Promise, Challenges, and Future Directions.

Xiaoyang Ruan¹, Shuyu Lu¹, Liwei Wang²

¹Department of Health Data Science and AI, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston.

Medrxiv : the Preprint Server for Health Sciences

|December 16, 2024

Summary

This summary is machine-generated.

Electronic health records (EHR) enable deep phenotyping for obesity, identifying distinct patient clusters for personalized medicine. This approach reveals clinically relevant subgroups, paving the way for tailored anti-obesity medication (AOM) strategies.

Keywords:

Anti-obesity medication EHR Obesity Phenotyping Precision medicine

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Area of Science:

Medical informatics
Computational biology
Precision medicine

Background:

Obesity affects a significant portion of the US population, presenting substantial economic and psychosocial challenges.
Varied patient responses to anti-obesity medications (AOMs) necessitate advanced phenotyping for personalized treatment strategies.
Current phenotyping methods often lack the granularity and operational simplicity required for widespread clinical adoption.

Purpose of the Study:

To assess the utility of electronic health records (EHR) as a data source for deep phenotyping patients with obesity.
To explore the feasibility, data requirements, clustering patterns, and challenges of EHR-based obesity phenotyping using a multi-modal longitudinal deep autoencoder.
To identify distinct patient subgroups based on pre-treatment EHR data to inform precision medicine approaches.

Main Methods:

Analysis of 53,688 pre-AOM periods from 32,969 patients, utilizing 92 lab/vital measurements and 79 ICD-derived codes.
Application of a gated recurrent unit with decay (GRU-D) based longitudinal autoencoder to generate patient embeddings.
Utilized Principal Component Analysis (PCA) and Gaussian Mixture Modeling (GMM) for cluster identification.

Main Results:

Identification of at least nine distinct patient clusters, with five demonstrating clear clinical relevance.
Clustering patterns showed stability across multiple training folds, with reproducible clinical significance.
Challenges identified include missing data imputation stability, input feature consistency, and low-dimensional visualization of complex data.

Conclusions:

Longitudinal EHR data serve as a valuable resource for per-visit deep phenotyping in the pre-AOM period.
Identified patient clusters suggest potential implications for tailoring AOM treatment options.
Further validation in larger cohorts is required to confirm reproducibility, clinical relevance, and uncover detailed substructures and treatment responses.