The association between lean-to-fat mass ratio and cardiometabolic abnormalities: an analytical crosssectional study

Abstract

Background: Cardiovascular diseases (CVDs) are a global health threat, significantly impacting Latin America. Cardiometabolic abnormalities (CAs), encompassing lipid profile, fasting plasma glucose, and blood pressure, contribute to CVD prevalence. Despite high CA incidence, research in Latin America has primarily focused on traditional adiposity indices, overlooking the intricate relationship between fat and lean body components. The study aimed to analyze the association between the lean-to-fat mass ratio (LFMR) and CAs in the adult Peruvian population. Methods: This was an analytical cross-sectional study using secondary data from the PERU MIGRANT study (2007, 989 participants). The main outcome variable was CA defined as having > 2 out of six metabolic components (high triglycerides, impaired fasting glucose, high blood pressure, low high-density lipoprotein (HDL)-cholesterol, insulin resistance, and high C-reactive protein). The main exposure variable LFMR was divided into tertiles. A generalized linear model was used with log link and robust variance Poisson family to calculate crude (cPR) and adjusted prevalence ratios (aPRs) and 95% confidence intervals (95% CIs). Results: A total of 959 adults aged 30 years or older were included in the analysis (53% females). The prevalence of CA was 50.9%. Females aged 30 - 44 years old showed statistically significant inverse associations for the middle (aPR: 0.57, 95% CI: 0.42 - 0.78) and highest (aPR: 0.22, 95% CI: 0.14 - 0.35) LFMR categories. Similar trends were seen for females aged 45 - 59 years and > 60 years, and males aged 30 - 44 years, while for males aged 45 - 59 years, only the middle LFMR category was associated. No statistically significant association between LFMR and CA was found among old males. Conclusions: LFMR was negatively associated with CA, among the Peruvian adult population. These findings underscore the relevance of LFMR in understanding cardiometabolic health disparities.