Optimal Adiposity Measurement and Risk Stratification in Established Ischaemic Stroke

Background: Prevention strategies post-stroke should target risk factor reduction which includes consideration of weight, diet and lipoprotein profiles. Limited data informs the optimal adiposity measurement post-stroke to target those at highest recurrent risk. This study aims to identify adiposity measurement/s post-stroke that best predict cardiovascular and co-morbid risk. Subjects and Methods: 142 stroke patients (100 males, 42 females; mean age 63 years) participated. Adiposity and metabolic profiles included BMI, waist circumference, waist to height ratio (WHR), triglyceride levels and hypertriglyceridemic waist. The predictive ability of these measures with indices of cardiovascular risk (Cardiovascular Risk Score) and co-morbidity (Charlson’s co-morbidity index) were examined. Results: In hierarchical multiple regression models, age and gender controlled, waist (p = 0.002), triglyceride levels (p = 0.006), BMI and WHR (p = 0.014), uniquely and significantly contributed to the variance in cardiovascular risk, in their models. Only one combination of measures (waist and triglyceride levels) improved the predictive ability of waist in cardiovascular risk stratification (p = 0.001). In men, waist (p = 0.013) and in women triglyceride levels (p = 0.012) performed as the best predictors of cardiovascular risk respectively. No combination of measures was superior to triglyceride levels in women or waist circumference measures in men in predicting cardiovascular risk. With Charlson’s co-morbidity index as the dependent variable, triglyceride levels significantly contributed to variance of the model with age and gender influences controlled (p = 0.047). No combination of measures improved the predictive ability of triglyceride levels for co-morbidity. Conclusion: Waist circumference and triglyceride levels should form a minimum dataset for adiposity when considering cardiovascular and comorbid risk poststroke.