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Related Concept Videos

Obesity01:24

Obesity

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 adipocytes...
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Pharmacokinetics in Obese Patients: Drug Absorption and Distribution

Obesity significantly alters the pharmacokinetic processes of drug absorption and distribution, presenting unique challenges in medical treatment. The increased fat tissue and decreased lean muscle in obese individuals can significantly affect how drugs are absorbed into the body and distributed across different tissues. This alteration can lead to variances in the effectiveness and safety of medications, necessitating adjustments in dosing or drug selection for obese patients.One notable...
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Cholesterol: Significance and Regulation

Although not a source of energy, cholesterol plays a significant role as a foundational structure for bile salts, steroid hormones, and vitamin D, as well as being a crucial component of plasma membranes. Approximately 15% of blood cholesterol is derived from our diet, with the remainder synthesized from acetyl CoA by the liver and intestines. Cholesterol is eliminated from the body through its conversion into bile salts, which are eventually discarded in the feces.
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Regulation of Metabolism01:19

Regulation of Metabolism

Cellular needs and conditions vary from cell to cell and change within individual cells over time. For example, the required enzymes and energetic demands of stomach cells are different from those of fat storage cells, skin cells, blood cells, and nerve cells. Furthermore, a digestive cell works much harder to process and break down nutrients during the time that closely follows a meal compared with many hours after a meal. As these cellular demands and conditions vary, so do the amounts and...

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Correction: Pramesthi et al. Evaluating the Impact of Indonesia's National School Feeding Program (ProGAS) on Children's Nutrition and Learning Environment: A Mixed-Methods Approach. <i>Nutrients</i> 2025, <i>17</i>, 3575.

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Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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Muscle Mass Moderates Metabolic Syndrome Risk Associated with Adiposity: A SHAP-Based Machine Learning Study.

Rodrigo Yáñez-Sepúlveda1,2, Boryi A Becerra-Patiño3, Santiago Ramos Bermúdez4

  • 1Faculty Education and Humanities, Universidad Andres Bello, Viña del Mar 2520000, Chile.

Nutrients
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Muscle mass significantly reduces obesity risk by acting as a metabolic mediator. Machine learning models, particularly neural networks, effectively predict obesity risk from body composition data in adults.

Keywords:
algorithmsartificial intelligencehealthobesity

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Area of Science:

  • Physiology
  • Data Science

Background:

  • Muscle mass and visceral fat impact metabolic health.
  • Limited research exists on machine learning (ML) for muscle mass and adiposity risk.

Purpose of the Study:

  • Identify obesity predictors using ML on adult body composition data.
  • Analyze the relationship between muscle mass and obesity risk.

Main Methods:

  • Cross-sectional analysis of 13,663 adults (6877 men, 6786 women).
  • Body composition analysis using 8-point multifrequency BIA (Inbody® Model 770).
  • Logistic models and probability heatmaps visualized ML algorithm performance.

Main Results:

  • Multilayer perceptron (MLP) showed superior predictive performance (AUC-ROC 0.981 men, 0.993 women).
  • High accuracy (>95%) observed, particularly in the female cohort.
  • Muscle mass demonstrated a significant role in modulating visceral adiposity risk.

Conclusions:

  • Muscle mass acts as a key metabolic mediator, reducing visceral adiposity risk.
  • ML algorithms, especially neural networks, are effective for analyzing visceral fat-related risks.