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Updated: Sep 18, 2025

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Machine learning-based equations for improved body composition estimation in Indian adults.

Nick Birk1, Bharati Kulkarni2, Santhi Bhogadi3

  • 1Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.

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|June 23, 2025
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Summary
This summary is machine-generated.

New equations combining bioelectrical impedance analysis (BIA) with anthropometric measures improve body composition accuracy in Indian adults. These novel methods offer more precise body fat and lean mass estimations than existing BIA algorithms.

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Area of Science:

  • Anthropometry
  • Body Composition Analysis
  • Bioelectrical Impedance Analysis (BIA)

Background:

  • Bioelectrical impedance analysis (BIA) is a cost-effective method for assessing body composition in large studies.
  • Existing BIA equations may lack generalizability across diverse populations, including South Asians.
  • Dual-energy X-ray absorptiometry (DXA) is a gold standard but is less accessible for large-scale studies.

Purpose of the Study:

  • To develop and validate novel machine learning-based equations for predicting DXA-measured body composition parameters in Indian adults.
  • To enhance the accuracy of BIA measurements by integrating simple anthropometric data.
  • To improve the generalizability of body composition assessments in South Asian populations.

Main Methods:

  • Combined BIA (TANITA BC-418) with skinfold thickness, body circumferences, and grip strength measurements.
  • Utilized machine learning techniques on a cohort of 2,632 Indian adults (1615 males, 1422 females).
  • Split data into training (80%) and testing (20%) sets to develop and validate equations for six body composition parameters.

Main Results:

  • Novel equations significantly outperformed existing BIA estimation algorithms and traditional equations (e.g., Durnin-Womersley).
  • Mean absolute errors for total body fat mass were substantially reduced: 0.935 kg (males) and 0.976 kg (females) with new equations.
  • The developed equations demonstrated improved validity for predicting total body fat mass, lean mass, and various fat/lean mass percentages.

Conclusions:

  • Supplementing BIA devices with anthropometric measures significantly enhances body composition assessment validity in South Asians.
  • The developed machine learning approach offers a more accurate and accessible method for body composition analysis in specific populations.
  • This methodology can be extended to other BIA devices and populations to improve the performance and applicability of BIA technology.