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Measuring endogenous corticosterone in laboratory mice - a mapping review, meta-analysis, and open source database
Stevie Van der Mierden1, Cathalijn H C Leenaars1, Erin C Boyle1
1Institute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany.
ALTEX
|October 21, 2020
Summary
This study maps corticosterone measurement methods in mice, revealing that sex, time-since-lights-on, and control type significantly impact stress hormone levels. This aids in planning more efficient and valid animal welfare and neuroscience experiments.
Area of Science:
- Animal Welfare Science
- Neuroscience Research
- Laboratory Animal Science
Background:
- Accurate stress assessment in laboratory animals is crucial for animal welfare.
- Corticosterone measurement is a standard method for assessing stress in mice.
- Variations in sample matrices and quantification techniques exist for glucocorticoid measurement.
Purpose of the Study:
- To provide a comprehensive overview of studies measuring endogenous corticosterone in mice.
- To create a searchable database of corticosterone measurement studies up to February 2018.
- To identify factors influencing basal corticosterone concentrations in mice.
Main Methods:
- A mapping review was conducted to collect data from relevant studies.
- A searchable database was created, recording mouse strain, sex, sample matrix, and quantification technique.
- Exploratory meta-regression was performed using 2012 data to analyze predictors of corticosterone levels.
Main Results:
- Seventy-five studies were included in the analysis.
- Sex, time-since-lights-on, and type of control were identified as significant predictors of basal corticosterone concentrations.
- The database provides a comprehensive overview of methodologies used in corticosterone measurement.
Conclusions:
- The developed database can prevent experimental duplication and identify knowledge gaps.
- Standardization or heterogenization of methodologies can be informed by the database.
- Results facilitate the planning of more efficient and valid future experiments and in silico analyses.
