Abstract
Obesity caused by caloric overload has assumed epidemic proportions. Obesity is frequently associated with metabolic dysfunctions, such as type 2 diabetes, non-alcoholic steatohepatitis (NASH), cardiovascular diseases, and cancer. Metabolic phenotyping is a set of techniques for studying metabolic dysfunction and behavior information including energy expenditure, body weight gain, glucose homeostasis, and lipid profile. Among different metabolic phenotyping methods, indirect calorimetry is an indispensable tool for quantifying the energy balance/imbalance in various mouse models, which enables researchers to probe the development of disease and to evaluate the therapeutic benefit from different interventions. In this chapter, we will describe the procedures of metabolic phenotyping using indirect calorimetry in db/db mouse, a metabolic disorder mouse model which develops NASH.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Keith SW, Redden DT, Katzmarzyk PT et al (2006) Putative contributors to the secular increase in obesity: exploring the roads less traveled. Int J Obes 30:1585–1594
Dee A, Kearns K, O’Neill C et al (2014) The direct and indirect costs of both overweight and obesity: a systematic review. BMC Res Notes 7:242
Popkin BM, Kim S, Rusev ER et al (2006) Measuring the full economic costs of diet, physical activity and obesity-related chronic diseases. Obes Rev 7:271–293
Bhatt HB, Smith RJ (2015) Fatty liver disease in diabetes mellitus. Hepatobil Surg Nutr 4:101–108
Youssef W, McCullough AJ (2002) Diabetes mellitus, obesity, and hepatic steatosis. Semin Gastrointest Dis 13:17–30
Fontaine E, Muller MJ (2011) Adaptive alterations in metabolism: practical consequences on energy requirements in the severely ill patient. Curr Opin Clin Nutr Metab Care 14:171–175
Lodwig TH, Smeaton WA (1974) The ice calorimeter of Lavoisier and Laplace and some of its critics. Ann Sci 31:1–18
Chen S, Wohlers E, Ruud E et al (2018) Improving temporal accuracy of human metabolic chambers for dynamic metabolic studies. PLoS One 13:e0193467
Even PC, Nadkarni NA (2012) Indirect calorimetry in laboratory mice and rats: principles, practical considerations, interpretation and perspectives. Am J Physiol Regul Integr Comp Physiol 303:R459–R476
Pendergast DR, Leddy JJ, Venkatraman JT (2000) A perspective on fat intake in athletes. J Am Coll Nutr 19:345–350
Marvyn PM, Bradley RM, Mardian EB et al (2016) Data on oxygen consumption rate, respiratory exchange ratio, and movement in C57BL/6J female mice on the third day of consuming a high-fat diet. Data Brief 7:472–475
Storlien L, Oakes ND, Kelley DE (2004) Metabolic flexibility. Proc Nutr Soc 63:363–368
Hesselink MKC, Schrauwen-Hinderling V, Schrauwen P (2016) Skeletal muscle mitochondria as a target to prevent or treat type 2 diabetes mellitus. Nat Rev Endocrinol 12:633–645
Vallerie SN, Bornfeldt KE (2015) Metabolic flexibility and dysfunction in cardiovascular cells. Arterioscler Thromb Vasc Biol 35:e37–e42
Corpeleijn E, Saris WH, Blaak EE (2009) Metabolic flexibility in the development of insulin resistance and type 2 diabetes: effects of lifestyle. Obes Rev 10:178–193
Corpeleijn E, Mensink M, Kooi ME et al (2008) Impaired skeletal muscle substrate oxidation in glucose-intolerant men improves after weight loss. Obesity 16:1025–1032
Phielix E, Meex R, Ouwens DM et al (2012) High oxidative capacity due to chronic exercise training attenuates lipid-induced insulin resistance. Diabetes 61:2472–2478
Dube JJ, Coen PM, DiStefano G et al (2014) Effects of acute lipid overload on skeletal muscle insulin resistance, metabolic flexibility, and mitochondrial performance. Am J Physiol Endocrinol Metab 307:E1117–E1124
Weir JBD (1949) New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 109:1–9
Trak-Smayra V, Paradis V, Massart J et al (2011) Pathology of the liver in obese and diabetic ob/ob and db/db mice fed a standard or high-calorie diet. Int J Exp Pathol 92:413–421
Mina AI, LeClair RA, LeClair KB et al (2018) CalR: a web-based analysis tool for indirect calorimetry experiments. Cell Metab 28:656–666
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Ni, B., Chen, S., Farrar, J.S., Celi, F.S. (2022). Metabolic Phenotyping in Mice with NASH Using Indirect Calorimetry. In: Sarkar, D. (eds) Non-Alcoholic Steatohepatitis. Methods in Molecular Biology, vol 2455. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2128-8_17
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2128-8_17
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2127-1
Online ISBN: 978-1-0716-2128-8
eBook Packages: Springer Protocols