Ethnicity, subjective wellbeing, and cardiovascular disease: insights from path and machine learning analyses using data from the UK biobank

objectives: (1) to elucidate the effects of ethnicity on subjective wellbeing (SWB) and its interrelation with demographic factors and cardiovascular disease (CVD); and (2) to identify key predictors of SWB and CVD using machine learning. DESIGN: Employing data from 296,767 UK Biobank participants in a cross-sectional design, we conducted path analysis, and machine learning analysis to investigate the impact of ethnicity on CVD and SWB, addressing missing data through sensitivity analyses. Our models used logistic and linear regression, complemented by receiver operating characteristic analysis, to explore direct and indirect effects, and feature importance. RESULTS: Ethnicity was significantly associated to both outcomes directly and acting as a mediating variable when evaluating the association between key demographic variables and the outcomes. Ethnicity influenced CVD and SWB, with non-White groups showing higher CVD odds and lower SWB. Age, BMI, waist circumference, smoking status, depression, and sex were significant predictors of CVD, while factors like handgrip strength and alcohol intake showed protective effects. CONCLUSION: This study underscores the critical need for ethnic-specific health interventions and highlights the complex interplay between demographic factors, CVD, and SWB. Our findings offer a foundation for developing targeted public health strategies and policies aimed at reducing health disparities and improving wellbeing across ethnic groups. Future research should continue to explore these relationships, emphasising the importance of culturally sensitive approaches to health promotion and disease prevention.