Parental Age of Onset of Cardiovascular Disease as a Predictor for Offspring Age of Onset of Cardiovascular Disease

Objective The risk for cardiovascular disease (CVD) is higher for individuals with a first-degree relative who developed premature CVD (with a threshold at age 55 years for a male or 65 years for a female). However, little is known about the effect that each unit increase or decrease of maternal or paternal age of onset of CVD has on offspring age of onset of CVD. We hypothesized that there is an association between maternal and paternal age of onset of CVD and offspring age of onset of CVD. Methods We used the Framingham Heart Study database and performed conditional imputation for CVD-censored parental age (i.e. parents that didn't experience onset of CVD) and Cox proportional regression analysis, with offspring's age of onset of CVD as the dependent variable and parental age of onset of CVD as the primary predictor. Modifiable risk factors in offspring, such as cigarette smoking, body mass index (BMI), diabetes mellitus, systolic blood pressure (SBP), high-density lipoprotein (HDL) level, and low-density lipoprotein (LDL) level, were controlled for. Separate analyses were performed for the association between maternal age of onset of CVD and offspring age of onset of CVD and the association between paternal age of onset of CVD and offspring age of onset of CVD. Results Parental age of onset of CVD was predictive of offspring age of onset of CVD for maternal age of onset of CVD (P < .0001; N = 1401) and for paternal age of onset of CVD (P = 0.0134; N = 1221). A negative estimate of the coefficient of interest signifies that late onset of cardiovascular events in parents is protective of onset of CVD in offspring. Cigarette smoking and HDL level were important associated confounders. Conclusions Offspring age of onset of cardiovascular disease is significantly associated with both maternal and paternal age of onset CVD. The incorporation of the parameters, maternal or paternal age of onset of CVD, into risk estimate calculators may improve accuracy of identification of high-risk patients in clinical settings.