Development and Implementation of an Integrated Preclinical Atherosclerosis Database

Area of Science:

• Cardiovascular Research
• Translational Medicine
• Bioinformatics

Background:

• Preclinical animal models are crucial for understanding human disease mechanisms, yet translation to clinical practice remains limited.
• Thousands of publications support causative inferences from animal studies, but a lack of integration hinders progress.
• An integrated approach to preclinical data offers potential for improved translational success in medicine.

Purpose of the Study:

• To develop a comprehensive database of preclinical atherosclerosis research using common mouse models.
• To facilitate integrated analyses of experimental conditions, interventions, and outcomes from published studies.
• To provide a resource for interrogating the global impact of atherosclerosis research and improving translational outcomes.

Main Methods:

• Extracted data from mouse models of atherosclerosis (ApoE-knockout and LDLR-knockout) published in key journals.
• Collected predefined variables including animal sex, diet, intervention, and plaque pathologies (size, inflammation, lipid content).
• Developed procedures for data standardization, intervention attribution, and transformation for transcriptomics software.

Main Results:

• Created a preclinical atherosclerosis database integrating hundreds of genes tested in vivo.
• The database includes detailed information on experimental conditions and outcomes from published atherosclerosis research.
• Standardized data extraction and transformation methods are provided for community use.

Conclusions:

• The database serves as a resource for interrogating specific data subsets related to plaque pathologies, cell types, or sex.
• Provided methods and software can expand the dataset and be applied to other human diseases studied in preclinical models.
• This integrated approach aims to enhance the translation of preclinical findings into clinical advancements.