Precise Prediction of Total Body Lean and Fat Mass From Anthropometric and Demographic Data: Development and Validation of Neural Network Models

Background: Estimating body composition is relevant in diabetes disease management, such as drug administration and risk assessment of morbidity/mortality. It is unclear how machine learning algorithms could improve easily obtainable body muscle and fat estimates. The objective was to develop and va...

Full description

Saved in:
Bibliographic Details
Published in:Journal of diabetes science and technology 2021-11, Vol.15 (6), p.1337-1343
Main Authors: Cichosz, Simon Lebech, Rasmussen, Nicklas Højgaard, Vestergaard, Peter, Hejlesen, Ole
Format: Article
Language:English
Subjects:
Absorptiometry, Photon
Adipose Tissue - diagnostic imaging
Adult
Anthropometry
Body Composition
Body Mass Index
Child
Cross-Sectional Studies
Demography
Humans
Neural Networks, Computer
Original
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background: Estimating body composition is relevant in diabetes disease management, such as drug administration and risk assessment of morbidity/mortality. It is unclear how machine learning algorithms could improve easily obtainable body muscle and fat estimates. The objective was to develop and validate machine learning algorithms (neural networks) for precise prediction of body composition based on anthropometric and demographic data. Methods: Cross-sectional cohort study of 18 430 adults and children from the US population. Participants were examined with whole-body dual X-ray absorptiometry (DXA) scans, anthropometric assessment, and answered a demographic questionnaire. The primary outcomes were predicted total lean body mass (predLBM), total body fat mass (predFM), and trunk fat mass (predTFM) compared with reference values from DXA scans. Results: Participants were randomly partitioned into 70% training (12 901) data and 30% validation (5529) data. The prediction model for predLBM compared with lean body mass measured by DXA (DXALBM) had a Pearson’s correlation coefficient of R = 0.99 with a standard error of estimate (SEE) = 1.88 kg (P 
ISSN:1932-2968
1932-2968
1932-3107
DOI:10.1177/1932296820971348