Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis

Area of Science:

• Cardiology
• Preventive Medicine
• Data Science in Healthcare

Background:

• Cardiovascular disease (CVD) prevention relies on identifying individual cardiovascular risk factor (CVRF) profiles.
• Personalized preventive strategies are crucial for effective CVD risk reduction.
• Longitudinal tracking of CVRFs and risk scores is essential for understanding disease progression.

Purpose of the Study:

• To characterize the evolution of CVRFs and the CVD risk score (SCORE) in a cohort of workers over time.
• To identify distinct patient profiles based on the temporal changes in CVRFs.
• To inform the development of tailored medical approaches for CVD prevention.

Main Methods:

• Utilized data from annual medical examinations between 2009 and 2017.
• Employed descriptive analyses (mean, standard deviation, quartiles, percentages) for quantitative and categorical variables.
• Applied cluster analysis using the Kml3D package in R software to group individuals based on CVRF evolution.

Main Results:

• Cluster analysis divided the cohort into two distinct groups (clusters).
• Cluster 1 consisted of younger workers with favorable CVRF profiles (lower BMI, waist circumference, glucose, SCORE; higher HDL).
• Cluster 2 exhibited opposite characteristics to Cluster 1.
• Over time, Cluster 1 demonstrated superior CVRF control improvement and a lesser increase in SCORE compared to Cluster 2.

Conclusions:

• Distinct CVRF evolution profiles exist within a working population.
• Individuals in Cluster 1 showed better CVRF management and lower SCORE progression.
• Identifying these profiles can guide personalized CVD preventive measures and enhance public health strategies.