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Related Concept Videos

Obesity01:24

Obesity

374
The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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Polygenic Traits01:18

Polygenic Traits

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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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
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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 medicationEHRObesityPhenotypingPrecision 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.