Sex and age differences in the burden of cardiovascular diseases linked to an unhealthy lifestyle pattern: the ATTICA study (2002-2022)

Background/Introduction The population attributable fraction (PAF) is commonly used to quantify disease burden, which is affected by the strength of the risk factor (usually represented by relative risk), as well as the prevalence of that factor in the population. Differences in risk factor prevalence and/or strength exist in the population, based on non-modifiable characteristics such as age, sex and race, therefore affecting disease burden disparately. Purpose To compare the burden of CVD associated with unhealthy lifestyle patterns between males and females and/or younger and older adults. Methods The study included n=1,988 adults (mean age: 45±14 years, 49.7% male) from the ATTICA prospective cohort study (2002-2022), who were free-of-CVD at baseline. The incidence of CVD was evaluated using WHO-ICD-10 criteria, at all assessments (in 2002, 2006, 2012, 2022). Unhealthy lifestyle patterns were defined as the combination of 6 different CVD risk factor categories: 1. socio-demographic factors (low/middle socio-economic status, urban area of residence), body mass index, clinical factors (hypertension, hypercholesterolemia, diabetes mellitus, chronic kidney disease), lifestyle factors (smoking, unhealthy dietary habits, physical inactivity) and psychological factors (anxiety, depression). PAF and generalized impact fraction (GIF) (percentage of exposure removal: 30%, 50% and 70%) of CVD was computed based on the appropriate equations (please see Table). Results Over the 20-year follow-up period, 718 participants (36%) experienced a combined fatal or non-fatal CVD event (40% in males, 32% in females, p for gender difference <0.001). For an unhealthy lifestyle the PAF for CVD was 82% in the total population. It was similar in males and females (70% and 73% respectively), but it was higher in older participants (87%) compared to younger participants (58%) (median cut-off: 45 years-old). Younger males had higher PAFs compared to younger females (76% vs. 57%), but older females had slightly higher PAFs compared to older males (83% vs. 79%) (Table). Similar results were observed for GIF representing a more realistic scenario of managing risk factors at a level of 30%, 50% or 70% (i.e., percentage of exposure removal) (please see Table). Conclusion The studied factors explained a big percentage of CVD burden; however, variations were observed based on participants' age and sex. It should be highlighted that these well-known CVD risk factors were more explanatory for older participants. More research is warranted to find other factors, such as environmental factors, that should be targeted to effectively manage CVD in younger participants (especially younger females).