Background: Pragmatic and easy-to-use alternatives to estimating body composition, such as lean body mass and fat mass, could be valuable tools for assessing the risk of diabetes or other metabolic diseases. Previous work has shown how demographic and anthropometric data could be used in a neural network to estimate body composition with high precision. However, there is still a need for a publicly available and user-friendly format before these results can have clinical impact.
Methods: We used data from 18 430 NHANES participants and stepwise linear regression with inclusion of linear, interactions, and quadratic terms to model lean body and fat mass. HTML and Javascript was used to develop a webapp as a frontend of the model.
Results: The models had a correlation cofficent R = 0.99-0.98 (P < .001) withstandard error of estimate [SEE] = 2.07-2.05.
Conclusions: The results indicate that it is possible to develop a "white-box" model with high precision.The proof of concept webapp is available as open source under the MIT license.
Keywords: body composition; diabetes; fat mass; lean body mass; prediction.