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

Area of Science:

• Cardiovascular epidemiology and public health.

Background:

• Cardiovascular diseases (CVD) represent a significant global health challenge.
• Obesity and type 2 diabetes mellitus (T2DM) are critical, modifiable risk factors for coronary heart disease (CHD).
• Big data analytics are transforming cardiovascular research through advanced risk stratification and analysis of complex variable interactions.

Purpose of the Study:

• To review the impact of big data on understanding the relationship between obesity, T2DM, and CHD.
• To highlight key findings from large cohort studies and international collaborations.
• To discuss methodological advancements in cardiovascular epidemiology.

Main Methods:

• Analysis of large-scale cohort studies and international consortia data.
• Integration of clinical, lifestyle, and molecular variables.
• Application of novel approaches like polygenic risk scores and machine learning.
• Utilizing real-world data for improved prediction and causal inference.

Main Results:

• Obesity and diabetes exhibit regional variations in prevalence and incidence, often co-occurring with hypertension.
• Diabetes independently increases CVD and mortality risk (2-4 fold), with earlier onset and poor glycemic control exacerbating outcomes.
• The combined effect of obesity and diabetes is a primary contributor to the overall CHD burden.
• Advanced methods have enhanced prediction accuracy and causal inference regarding these risk factors.

Conclusions:

• Big data has significantly improved the understanding of cardiovascular risks linked to obesity and T2DM.
• Enhanced risk prediction models and precision prevention strategies are now feasible.
• Sustained investment in large cohorts, data standardization, and digital health is crucial for effective public health interventions to reduce global CVD burden.