Correlations Between Novel Adiposity Indices and Electrocardiographic Evidence of Left Ventricular Hypertrophy in Individuals with Arterial Hypertension.

Abstract

Background/Objectives: Obesity is a key driver of cardiovascular disease (CVD), with central adiposity directly involved in adverse cardiac remodeling. Body mass index (BMI) is limited in capturing fat distribution and associated cardiovascular risk. Novel anthropometric indices, including A Body Shape Index (ABSI) and Body Roundness Index (BRI), may offer greater clinical value, but their relationship with electrocardiographic markers of left ventricular hypertrophy (LVH) remains underexplored. This study aims to assess the correlation between novel adiposity indices (ABSI and BRI) and electrocardiographic evidence of LVH, as measured by the Sokolow-Lyon Index (SLI), in individuals with arterial hypertension. Methods: 274 hypertensive patients were recruited, and BMI, ABSI, and BRI were calculated. LVH was assessed via SLI on 12-lead ECG. Participants were stratified by the SLI (≤35 mm vs. >35 mm) for statistical analyses. Results: Patients with a lower SLI showed significantly higher values of ABSI and BRI compared to those in higher SLI group, without differences in BMI. In the entire population, SLI was significantly and inversely correlated with both ABSI (r = -0.296, p < 0.001) and BRI (r = -0.238, p < 0.01), but not with BMI. Multivariate regression analysis confirmed ABSI (p = 0.013) and BRI (p = 0.038) as independent predictors of SLI, even after adjusting for age, blood pressure, renal function, and metabolic parameters. Conclusions: ABSI and BRI are inversely and independently associated with ECG-derived SLI in hypertensive individuals, suggesting that central adiposity may attenuate ECG voltages and obscure LVH detection. Incorporating novel adiposity indices into ECG interpretation may enhance diagnostic accuracy and risk stratification in obese and hypertensive populations. Longitudinal studies are needed to validate these findings and refine clinical algorithms.