期刊
NATURE METHODS
卷 9, 期 1, 页码 57-63出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.1806
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资金
- Biotechnology and Biological Sciences Research Council [BB/G009953/1] Funding Source: Medline
- NHLBI NIH HHS [R01 HL051586] Funding Source: Medline
- NIDDK NIH HHS [R01 DK043051, R37 DK031036, U24 DK059635, R01 DK040936, R01 DK056084, R37 DK043051, R01 DK082659] Funding Source: Medline
- BBSRC [BB/G009953/1] Funding Source: UKRI
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL051586] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [ZIADK075064, R37DK031036, U24DK059635, ZIADK075062, R01DK043051, R37DK043051, R01DK082659, R01DK040936, ZIADK075063, R01DK056084] Funding Source: NIH RePORTER
We present a consolidated view of the complexity and challenges of designing studies for measurement of energy metabolism in mouse models, including a practical guide to the assessment of energy expenditure, energy intake and body composition and statistical analysis thereof. We hope this guide will facilitate comparisons across studies and minimize spurious interpretations of data. We recommend that division of energy expenditure data by either body weight or lean body weight and that presentation of group effects as histograms should be replaced by plotting individual data and analyzing both group and body-composition effects using analysis of covariance (ANCOVA). The epidemic of obesity has generated a large
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