Modelling and Prediction of Atherogenic Index of Plasma Against Cardiometabolic Risk
Background and aim: Cardiometabolic risk is a constellation of cardiovascular risk factors which include diabetes, hypertension, obesity and dyslipidaemia. Atherogenic Index of Plasma (AIP) is gaining prominence as a screening tool for dyslipidaemia however, these tools are expensive, time consuming, cumbersome and beyond the reach of an average Nigerian. The purpose of this study was to determine the predictors and modelling of AIP against some cardiometabolic parameters among workers in Owerri, Nigeria. Prediction and modelling of AIP will give cost effective options in the assessment of cardiometabolic risk.
Methods: This was designed as a work-site based cross sectional study carried out on three hundred and sixty one (361) transport workers. The respondents were anthropometrically examined. Blood glucose estimation was determined using glucose oxidase/peroxidase method of Trinder. Lipid indices were determined using Freidewald’s method. Data were facilitated using XLSTAT 2016. Principal component analysis and Logistic probit regression models were employed to determine the degree of relationship and superiority.
Results: AIP was shown to be statistically significant and positively correlated with waist circumference (WC), body mass index (BMI) and systolic blood pressure (SBP) based on the Logistic regression analysis with a Goodness of fit of 69.97%. WC is the most powerful anthropometric tool in predicting cardiometabolic syndrome. AIP was shown to be a principal and dominant predictor of cardiometabolic syndrome.
Conclusions: This study has established that AIP correlates statistically and significantly with WC, BMI and SBP. A set of predictive regression models for AIP was developed for WC, BMI and SBP. AIP as a calculated factor can be used in the clinical setting as a cost-effective diagnostic tool in assessing cardiometabolic risk beyond the routinely done lipid profile especially where others have failed and most importantly in resource-poor setting like Nigeria.