Predicting Cardiovascular Disease Using the Framingham Heart Study: A Logistic Regression and Risk Stratification Approach
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Background
Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality worldwide. The Framingham Heart Study has provided seminal insights into the development of CVD risk prediction models.
Objective
To develop and evaluate a logistic regression model for predicting incident CVD using traditional risk factors in a subset of the Framingham Heart Study dataset.
Methods
Data from 95 participants were analyzed. Predictors included age, sex, body mass index (BMI), smoking status, systolic and diastolic blood pressure, total cholesterol, glucose, and diabetes status. Logistic regression was used to estimate univariate and multivariable associations with incident CVD. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and calibration using the Hosmer–Lemeshow test.
Results
In univariate analysis, higher BMI (OR = 1.21, 95% CI 1.05–1.40, p = 0.010), systolic BP (OR = 1.02, 95% CI 1.00–1.04, p = 0.042), diastolic BP (OR = 1.06, 95% CI 1.02–1.10, p = 0.006), and categorical BMI (OR = 2.84, 95% CI 1.35–5.96, p = 0.006) were associated with increased odds of CVD. Male sex was associated with lower odds (OR = 0.39, 95% CI 0.16– 0.94, p = 0.037). In multivariable analysis adjusting for BMI, systolic BP, diastolic BP, and sex, higher BMI (AOR = 1.22, 95% CI 1.03–1.45, p = 0.023) and diastolic BP (AOR = 1.09, 95% CI 1.01–1.17, p = 0.026) remained independently associated with higher odds of CVD, whereas male sex was associated with lower odds (AOR = 0.22, 95% CI 0.07–0.65, p = 0.006). Systolic BP was not statistically significant (AOR = 0.98, 95% CI 0.95–1.03, p = 0.439). The multivariable model demonstrated good discrimination (AUC = 0.78, 95% CI 0.68–0.88) and adequate calibration (Hosmer–Lemeshow p = 0.723).
Conclusion
Traditional risk factors, particularly BMI and diastolic blood pressure, were robustly associated with incident CVD even in this small subsample. These results support the reproducibility of classical CVD predictors and the utility of logistic regression modeling in epidemiological research.
