Predicting resting metabolic rate in healthy adults: a comparative analysis using the enable cohort

American Journal of Physiology-Endocrinology and Metabolism 2026 Open access

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Authors: Beate Brandl, Gloria-Maria Keppner, Quirin Manz, C. Schicker, Tobias Fromme, Christina M. Holzapfel, Karin Kleigrewe, Anja Bosy‐Westphal, Manfred James Müller, Dorothee Volkert, Thomas Skurk, Hans Hauner
+2 more Markus List, Martin Klingenspor
DOI: 10.1152/ajpendo.00375.2025
Cited by: 1
References: 54

Abstract

We introduce a novel machine learning framework for predicting resting metabolic rate (RMR), emphasizing the superior performance of Lasso regression. Our analysis incorporates both standard clinical variables and previously underexplored factors such as gut microbiota, fecal short-chain fatty acids (SCFAs), and mean outdoor temperature.

Fields of Study & Subjects

Diet and metabolism studies Gut microbiota and health Adipose Tissue and Metabolism

Publication details

Journal / source
American Journal of Physiology-Endocrinology and Metabolism
Publisher
American Physiological Society
Volume
330
Issue
2
Pages
E247–E256
Publication date
7 January 2026
Language
EN