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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
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Obesity01:24

Obesity

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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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Deep Phenotyping of Obesity: Electronic Health Record-Based Temporal Modeling Study.

Xiaoyang Ruan1, Shuyu Lu1, Liwei Wang2

  • 1Department of Health Data Science and AI, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, United States, 1 713-500-3924.

Journal of Medical Internet Research
|August 20, 2025
PubMed
Summary

Electronic health records (EHR) can be used for obesity deep phenotyping, identifying distinct patient clusters before antiobesity medication (AOM) treatment. This approach aids precision medicine by revealing clinically relevant subgroups for tailored AOM strategies.

Keywords:
EHRanti-obesity medicationobesityphenotypingprecision medicine

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Area of Science:

  • Biomedical Informatics
  • Precision Medicine
  • Obesity Research

Background:

  • Obesity affects 40% of US adults and 15-20% of children, imposing significant burdens.
  • Individual responses to antiobesity medications (AOM) vary widely.
  • Deep phenotyping and precision medicine are crucial for effective obesity treatment.

Purpose of the Study:

  • To evaluate electronic health records (EHR) as a data source for obesity deep phenotyping.
  • To investigate the feasibility, data requirements, and clustering patterns of EHR-based deep phenotyping.
  • To identify challenges in using EHR data for obesity phenotyping.

Main Methods:

  • Analysis of 53,688 pre-AOM periods from 32,969 patients.
  • Utilized 92 lab/vital measurements and 79 ICD-derived CCS codes.
  • Applied a multimodal longitudinal deep autoencoder (GRU-D-AE) and Gaussian Mixture Modeling (GMM) for clustering.

Main Results:

  • Identified at least 9 patient clusters, with 5 showing distinct clinical relevance.
  • Clustering patterns were stable across multiple training folds.
  • Observed overlap with phenotypes from traditional strategies.

Conclusions:

  • Longitudinal EHR data is valuable for deep phenotyping the pre-AOM period.
  • Identified clinically significant clusters with potential implications for AOM selection.
  • Further research is needed to validate findings in larger cohorts and explore AOM response correlations.