Establishing Comprehensive Transthoracic Echocardiography Reference Ranges for Mouse Models: Insights into the Impact of Anesthesia, Sex, and Age
Oestereicher, M.; Ward, C.; Schneltzer, E.; Marschall, S.; Fuchs, H.; Gailus-Durner, V.; Bou About, G.; Selloum, M.; Meziane, H.; Stewart, M.; Teboul, L. E.; Norris, C.; Pimm, D.; Kan, M.; Lopez Gomez, F.; Wilson, R.; Monroy, M.; Pasha, S.; Zabrodska, E.; Prochazka, J.; Pajuelo Reguera, D.; Nichtova, Z.; Herault, Y.; Wells, S.; Parkinson, H.; Heaney, J.; Sedlacek, R.; Gao, X.; Hrabe de Angelis, M.; Spielmann, N.
Show abstract
Mouse models play a critical role in cardiology research, offering valuable insights into the molecular mechanisms, genetics, and potential treatments for cardiovascular diseases. However, the ability to transfer findings in mice between studies is limited by the absence of standardized protocols and valid reference values for the assessment of normal cardiac function in mice. This study aims to establish comprehensive transthoracic echocardiography (TTE) reference ranges for mice, particularly focusing on C57BL/6N wildtype controls. The study, which includes data from over 15,000 mice through the International Mouse Phenotyping Consortium (IMPC), highlights how variables such as sex, age, body weight, and anesthesia affect TTE parameters. The findings showed that anesthesia is the primary predictor of variability in cardiac function. Isoflurane and tribromoethanol anesthetized mice presented with modified cardiac function compared to conscious mice. Additionally, we observed minimal sex differences in cardiac morphology and function, except for small variations influenced by anesthesia. The effects of aging on cardiac function were modest, characterized by a decrease in heart rate and subtle changes in ventricular dimensions without evidence of pathological remodeling, likely attributable to disease-free cardiovascular aging. Validation of the reference ranges across multiple mouse strains showed that these values provide a reliable baseline for experiments involving cardiac function in mice. The data underscore the importance of using anesthesia-specific reference values when interpreting TTE results, ensuring robust comparisons in genetic and pharmacological studies. These reference ranges serve as quality assurance tools for future cardiac studies in mice, offering insights into typical TTE parameter values, supporting the detection of experimental perturbations, and contributing to more effective translation of findings from mouse to human.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Comparisons of Heart Rate Variability Responses to Head-up Tilt With and Without Abdominal and Lower-Extremity Compression in Healthy Young Individuals: A Randomized Crossover Study 91%
- Machine Learning prediction of cardiac resynchronisation therapy response from combination of clinical and model-driven data 91%
- A distinct pool of Nav1.5 channels at the lateral membrane of murine ventricular cardiomyocytes 90%
Similar papers in this journal
- A G-protein-biased S1P1 agonist, SAR247799, improved LVH and diastolic function in a rat model of metabolic syndrome 93%
- {-}CardiOvascular examination in awake Orangutans (Pongo pygmaeus pygmaeus): Low-stress Echocardiography including Speckle Tracking imaging (the COOLEST method) 93%
- Isoproterenol-induced Cardiac Dysfunction in Male and Female C57Bl/6 Mice 93%
Similar papers in this journal
Similar papers in this journal
- MK2-deficient mice are bradycardic and display delayed hypertrophic remodelling in response to a chronic increase in afterload 94%
- Moderate Endurance Exercise Increases Arrhythmia Susceptibility and modulates Cardiac Structure and Function in a Sexually Dimorphic manner. 94%
- The Tricuspid Valve is Transcriptionally Active During Prolonged Pressure Overload, Right-Sided Heart Failure, and Valve Regurgitation 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.