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Calopy — an advanced framework for the integration and analysis of indirect calorimetry data

Nature Metabolism volume 7, pages 1093–1095 (2025)Cite this article

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Here we introduce Calopy, an innovative software suite for the intuitive and comprehensive analysis of indirect calorimetry data. Calopy is an open-source, web-based Shiny for Python application that is accessible online or locally; it is platform-independent and available via any web browser at https://www.calopy.app.

Indirect calorimetry is a widely used technique for measuring energy metabolism in both humans and animals1. At its core, indirect calorimetry tracks oxygen consumption \((\dot{V}{\mathrm{O}}_{2})\) and carbon dioxide production \((\dot{V}{\mathrm{CO}}_{2})\) to analyse energy metabolism through parameters such as energy expenditure and resting metabolic rate (RMR)2 or respiratory exchange ratio (RER), a key indicator of substrate utilization3. In comprehensive indirect calorimetry systems for animals, additional relevant metabolic parameters such as locomotor activity, food and water intake, and body temperature can also be measured at high temporal resolution, along with environmental factors such as cage temperature, lighting and humidity. Continuous or categorical phenotypic variables, such as body weight, genotype and treatments, are also recorded. Noteworthy indirect calorimetry data may further be affected through factors that entail mutual dependencies such as circadian rhythms, food intake, environmental conditions and data noise, which adds further complexity4. Together, these data form a complex set of time-resolved, continuous and categorical variables, which require sophisticated statistics and methods for their analysis.

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Fig. 1: Indirect calorimetry data analysis with Calopy.

Code availability

All source code is available from the Gitlab repository: https://gitlab.com/computational-discovery-research/calopy

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Acknowledgements

We express our sincere appreciation to the members of the Helmholtz Diabetes Center for valuable feedback and comments, in particular N. Dragano, P. Pfluger and C. Mencias Castillejo. Special thanks are extended to S. Haffner for help with prototype coding, J. Hasenauer for valuable input on method development, and G. Miller and M.Jimenez Munoz from the statistics consulting core facility. Support from the German Center for Diabetes Research (DZD-Grant 2020/2021: ‘Cell free DNA as a biomarker of type 2 diabetes and diabetic complications’) and Deutsche Forschungsgemeinschaft DFG Project ‘BATenergy’ (TRR333/1, project 450149205) is gratefully acknowledged. This work is partially based on work from COST Action CA20135 ‘Improving biomedical research by automated behavior monitoring in the animal home-cage’, supported by COST (European Cooperation in Science and Technology).

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Author notes

  1. Unaffiliated: Matthias Grosholz.

Authors and Affiliations

  1. Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    Stefan Loipfinger

  2. Computational Discovery Research Group, Institute for Diabetes and Obesity, Helmholtz Munich, Neuherberg, Germany

    Stefan Loipfinger, Santhosh Kumar, Helin Erbilir & Dominik Lutter

  3. Metabolic Physiology, Institute for Diabetes and Cancer, Helmholtz Diabetes Center, Helmholtz Munich, Neuherberg, Germany

    Kenneth Allen Dyar

  4. German Center for Diabetes Research (DZD), Neuherberg, Germany

    Kenneth Allen Dyar, Timo Dirk Müller & Dominik Lutter

  5. Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany

    Timo Dirk Müller

  6. Walther Straub Institute of Pharmacology and Toxicology, Ludwig Maximilian University, Munich, Germany

    Timo Dirk Müller

  7. Life and Medical Sciences (LIMES) Institute, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

    Stephan Grein

  8. Bonn Center for Mathematical Life Sciences, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

    Stephan Grein

  9. Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Jan Rozman

  10. Chair for Molecular Nutritional Medicine, TUM School of Life Sciences, Research Department of Molecular Life Sciences, Technical University of Munich, Freising, Germany

    Martin Klingenspor

  11. Else Kröner-Fresenius Center for Nutritional Medicine, Technical University of Munich, Freising, Germany

    Martin Klingenspor

  12. Institute of Pharmacology, University of Marburg, Marburg, Germany

    Carola Meyer

Authors
  1. Stefan Loipfinger
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  2. Matthias Grosholz
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  7. Stephan Grein
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  8. Jan Rozman
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  9. Martin Klingenspor
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  10. Carola Meyer
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  11. Dominik Lutter
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Contributions

S.L., M.G., S.K., H.E. and D.L. wrote the code and designed the software. K.A.D., J.R., C.M. and T.D.M. contributed data and helped design the application. J.R., M.K., S.G. and C.M. helped design the application. D.L. wrote the manuscript and conducted the whole project.

Corresponding author

Correspondence to Dominik Lutter.

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Competing interests

T.D.M. holds stocks from Eli Lilly and receives research funding from Novo Nordisk. T.D.M. further received speaking fees within the past 3 years from Merck, AstraZeneca, Boehringer Ingelheim, Eli Lilly and Novo Nordisk. All remaining authors declare no competing interests.

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Loipfinger, S., Grosholz, M., Kumar, S. et al. Calopy — an advanced framework for the integration and analysis of indirect calorimetry data. Nat Metab 7, 1093–1095 (2025). https://doi.org/10.1038/s42255-025-01316-8

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