The application of big data to cardiovascular disease: paths to precision medicine

Area of Science:

• Cardiovascular Medicine
• Genomics
• Precision Medicine

Background:

• Cardiovascular disease (CVD) phenotyping has advanced with high-resolution omics screening in large trials.
• This approach reveals substantial heterogeneity in genotype, endophenotype, and clinical phenotype within CVDs.
• Conventional reductionist methods have not fully elucidated this complex disease feature.

Purpose of the Study:

• To discuss genomic context and phenotypic heterogeneity in cardiovascular diseases.
• To illustrate the genotypic basis of endophenotypic and clinical diversity in common CVDs.
• To highlight challenges and propose solutions for CVD genotyping and phenotyping.

Main Methods:

• Application of high-resolution omics screening in large-scale observational and clinical trials.
• Examination of commonly encountered cardiovascular diseases.
• Integration of big data and network analysis for interpreting complex datasets.

Main Results:

• Significant genotypic and phenotypic heterogeneity exists across cardiovascular diseases.
• Genomic context plays a crucial role in endophenotypic and clinical diversity.
• Current genotyping and phenotyping methods face challenges in capturing this complexity.

Conclusions:

• Addressing molecular and clinical heterogeneity is essential for advancing precision cardiovascular medicine.
• Novel analytical strategies, including big data integration and network analysis, are needed.
• Broad application of precision cardiovascular medicine requires accommodating, not reducing, patient heterogeneity.