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Background <p>Cardiovascular diseases (CVD) remain a&#xa0;major global health burden. Obesity and type&#xa0;2 diabetes mellitus (T2DM) are key modifiable risk factors for coronary heart disease (CHD). The emergence of big data has revolutionized cardiovascular research by enabling deeper risk stratification and detection of complex interactions among clinical, lifestyle, and molecular variables.</p> Objective <p>This article reviews how big data has advanced our understanding of the links between obesity, T2DM, and CHD. It highlights key findings from large cohort studies and international consortia as well as methodological innovations transforming cardiovascular epidemiology.</p> Results <p>The data reveal that obesity and diabetes show significant regional differences in prevalence and incidence and are associated with other risk factor such as hypertension. Large-scale cohorts and consortia have confirmed that diabetes substantially increases CVD and mortality risk two- to fourfold and is linked to an up to 75% higher mortality rate, with earlier onset and poor glycemic control worsening outcomes. Novel approaches, including polygenic risk scores, machine learning, and real-world data integration, have improved prediction and causal inference. The interplay between obesity and diabetes is a&#xa0;major driver of CHD burden.</p> Conclusion <p>Big data has enhanced our understanding of cardiovascular risks associated with obesity and diabetes, improved risk prediction models, and provided a&#xa0;foundation for precision prevention strategies. Continued investment in large cohorts, data harmonization, and digital health tools is essential in order to translate these insights into effective public health strategies and reduce the global CVD burden.</p>

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Big data and cardiovascular risk—insights into obesity, diabetes, and coronary heart disease

  • 期刊论文

  • 发表日期：

    2025年6月24日

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  • Marcus Dörr

摘要

Background

Cardiovascular diseases (CVD) remain a major global health burden. Obesity and type 2 diabetes mellitus (T2DM) are key modifiable risk factors for coronary heart disease (CHD). The emergence of big data has revolutionized cardiovascular research by enabling deeper risk stratification and detection of complex interactions among clinical, lifestyle, and molecular variables.

Objective

This article reviews how big data has advanced our understanding of the links between obesity, T2DM, and CHD. It highlights key findings from large cohort studies and international consortia as well as methodological innovations transforming cardiovascular epidemiology.

Results

The data reveal that obesity and diabetes show significant regional differences in prevalence and incidence and are associated with other risk factor such as hypertension. Large-scale cohorts and consortia have confirmed that diabetes substantially increases CVD and mortality risk two- to fourfold and is linked to an up to 75% higher mortality rate, with earlier onset and poor glycemic control worsening outcomes. Novel approaches, including polygenic risk scores, machine learning, and real-world data integration, have improved prediction and causal inference. The interplay between obesity and diabetes is a major driver of CHD burden.

Conclusion

Big data has enhanced our understanding of cardiovascular risks associated with obesity and diabetes, improved risk prediction models, and provided a foundation for precision prevention strategies. Continued investment in large cohorts, data harmonization, and digital health tools is essential in order to translate these insights into effective public health strategies and reduce the global CVD burden.