Gender-specific aspects of socialisation and risk of cardiovascular disease among community-dwelling older adults: a prospective cohort study using machine learning algorithms and a conventional method

Background Gender influences cardiovascular disease (CVD) through norms, social relations, roles and behaviours. This study identified gender-specific aspects of socialisation associated with CVD. Methods A longitudinal study was conducted, involving 9936 (5,231 women and 4705 men) initially healthy, community-dwelling Australians aged 70 years or more from the ASPirin in Reducing Events in the Elderly (ASPREE) study and ASPREE Longitudinal Study of Older Persons, with a median follow-up time of 6.4 years. Variable categorisation, variable selection (using machine learning (ML) models; Elastic Net and extreme gradient boosting) and Cox-regression were employed separately by binary gender to identity socialisation factors (n=25 considered) associated with CVD. Results Different socialisation factors were identified using the ML models. In the Cox model, for both genders, being married/partnered was associated with a reduced risk of CVD (men: HR 0.76, 95% CI 0.60 to 0.96; women: HR 0.67, 95% CI 0.58 to 0.95). For men, having 3–8 relatives they felt close to and could call on for help (HR 0.76, 95% CI 0.58 to 0.99; reference <3 relatives), having 3–8 relatives they felt at ease talking with about private matters (HR 0.70, 95% CI 0.55 to 0.90; reference <3 relatives) or playing games such as chess or cards (HR 0.82, 95% CI 0.67 to 1.00) was associated with reduced risk of CVD. For women, living with others (HR 0.71, 95% CI 0.55 to 0.91) or having ≥3 friends they felt at ease talking with about private matters (HR 0.74, 95% CI 0.58 to 0.95; reference <3 friends) was associated with a lower risk of CVD. Conclusions This study demonstrates the need to prioritise gender-specific social factors to improve cardiovascular health in older adults. Data may be obtained from a third party and are not publicly available. The ASPREE and ALSOP are not publicly available since they are ongoing. However, they are available to partnering and external researchers for projects of appropriate scientific merit and expressions of interest to analyse data from these datasets are co-ordinated through the ASPREE Access Management Site (AMS) ().