Measuring endogenous corticosterone in laboratory mice - a mapping review, meta-analysis, and open source database
Evaluating stress in laboratory animals is a key principle in animal welfare. Measuring corticosterone is a common method to assess stress in laboratory mice. There are, however, numerous methods to measure glucocorticoids with differences in sample matrix (e.g., plasma, urine) and quantification te...
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Published in: | ALTEX, alternatives to animal experimentation Vol. 38; no. 1; pp. 111 - 122 |
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Main Authors: | , , , , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Germany
Springer Spektrum
01-01-2021
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Subjects: | |
Online Access: | Get full text |
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Summary: | Evaluating stress in laboratory animals is a key principle in animal welfare. Measuring corticosterone is a common method to assess stress in laboratory mice. There are, however, numerous methods to measure glucocorticoids with differences in sample matrix (e.g., plasma, urine) and quantification techniques (e.g., enzyme immunoassay or radioimmunoassay). Here, the authors present a mapping review and a searchable database, giving a complete overview of all studies measuring endogenous corticosterone in mice up to February 2018. For each study, information was recorded regarding mouse strain and sex; corticosterone sample matrix and quantification technique; and whether the study covered the research theme animal welfare, neuroscience, stress, inflammation, or pain (the themes of specific interest in our consortium). Using all database entries for the year 2012, an exploratory meta-regression was performed to determine the effect of predictors on basal corticosterone concentrations. Seventy-five studies were included using the predictors sex, time-since-lights-on, sample matrix, quantification technique, age of the mice, and type of control. Sex, time-since-lights-on, and type of control significantly affected basal corticosterone concentrations. The resulting database can be used, inter alia, for preventing unnecessary duplication of experiments, identifying knowledge gaps, and standardizing or heterogenizing methodologies. These results will help plan more efficient and valid experiments in the future and can answer new questions in silico using meta-analyses. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 ObjectType-Review-3 content type line 23 |
ISSN: | 1868-596X 1868-596X |
DOI: | 10.14573/altex.2004221 |