Associations of Chinese visceral adiposity index and new-onset stroke in middle-aged and older Chinese adults: an observational study

Abstract

Abstract Background Stroke represents the second most prevalent contributor to global mortality. The Chinese Visceral Adiposity Index (CVAI) serves as an established metric for assessing visceral adiposity in the Chinese population, exhibiting prognostic capabilities. This investigation aimed to explore the association of CVAI and new-onset stroke among middle-aged and older Chinese populations. Methods The study employed data from the 2011 and 2018 China Health and Retirement Longitudinal Study (CHARLS) to assess the association of CVAI and the incidence of new-onset stroke. Utilizing a directed acyclic graph (DAG), 10 potential confounders were identified. Moreover, to explore the association between CVAI and new-onset stroke, three multifactor logistic regression models were constructed, accounting for the identified confounders and mitigating their influence on the findings. Results The study comprised 7070 participants, among whom 417 (5.9%) experienced new-onset strokes. After controlling for confounding variables, regression analysis suggested that the new-onset stroke’s highest risk was linked to the fourth quartile (Q4) of the CVAI, with an odds ratio (OR) of 2.33 and a 95% confidence interval (CI) of 1.67–3.28. The decision tree analysis demonstrated a heightened probability of new-onset stroke among hypertensive individuals with a CVAI equal to or greater than 83, coupled with a C-reactive protein level no less than 1.1 mg/l. Age seemed to have a moderating influence on the CVAI and new-onset stroke association, exhibiting a more prominent interaction effect in participants under 60 years. Conclusions In middle-aged and older Chinese populations, a linear relationship was discerned between CVAI and the probability of new-onset stroke. CVAI provides a predictive framework for stroke incidence in this demographic, laying the groundwork for more sophisticated risk prediction models that improve the precision and specificity of stroke risk evaluations.