Analysis of energy expenditure and behavioral characteristics in different mouse strains under normal and disease conditions.

BACKGROUND

Metabolic and behavioral traits in mice exhibit substantial variability across strains, ages, and disease states, which can confound preclinical study outcomes. However, systematic characterization of these parameters (e.g., energy metabolism, activity, and feeding behavior) across diverse experimental conditions remains limited.

METHODS

Core metabolic and behavioral parameters were assessed via metabolic cages in five 8-week-old mouse strains (ICR, C57BL/6J, BALB/c, BALB/c-nude, NOD-SCID), six C57BL/6J age groups (3 weeks to 18 months), and four mouse disease models (LPS-induced pneumonia, chronic kidney disease, acute myocardial infarction, and type 1 diabetes). Data were stratified by light/dark cycles and statistically compared.

RESULTS

Herein, metabolic and behavioral phenotypes of mice were systematically characterized across different strains, ages, and four disease models (three organ-specific, one systemic). Distinct strain- and age-related differences in basal metabolism, activity, and energy expenditure were observed. Specifically, ICR mice displayed higher basal aerobic metabolism, whereas C57BL/6J mice exhibited greater locomotor activity; metabolism and energy balance also underwent marked shifts during development, pregnancy, and aging. Furthermore, all disease models presented unique metabolic rearrangements: LPS-induced acute pneumonia reduced aerobic metabolism and locomotor activity; CKD caused hypometabolism, polydipsia, and a substrate shift to carbohydrate oxidation; AMI increased respiratory exchange ratio (RER) and water intake; and T1DM induced polyphagia and polydipsia, with normal metabolism but impaired energy utilization.

CONCLUSIONS

Mouse metabolic and behavioral phenotypes are highly dependent on strain, age, and disease state. These findings highlight the need to standardize experimental conditions (e.g., age, strain, disease state) and account for baseline variability when designing preclinical studies.