Interplay between BMI, neutrophil, triglyceride and uric acid: a case-control study and bidirectional multivariate mendelian randomization analysis.

Abstract

Background: This study aims to explore the interplay between body mass index (BMI), neutrophils, triglyceride levels, and uric acid (UA). Understanding the causal correlation between UA and health indicators, specifically its association with the body's inflammatory conditions, is crucial for preventing and managing various diseases.

Methods: A retrospective analysis was conducted on 4,286 cases utilizing the Spearman correlation method. BMI, neutrophil count, and triglyceride levels were determined as key exposure factors. To further investigate the causal correlation, a two-sample Mendelian randomization(MR) design was utilized, leveraging data from genome-wide association study (GWAS). Within this framework, and multivariable Mendelian randomization(MVMR) was applied to explore the linkage between multiple genetic variants and complex traits.

Results: The study primarily focused on UA, employing genetic variation as a natural tool to assess the causal impact of various factors on UA. Spearman correlation analysis revealed significant association between UA and BMI (ρ = 0.230,p<0.01), neutrophils (ρ = 0.164,p<0.01), and triglyceride levels (ρ = 0.154,p<0.01). Additionally, two-sample MR analysis affirmed a reciprocal causal association between neutrophils and UA (OR = 1.035,95%CI:1.009-1.061,p = 0.008), as well as positive causal connection between UA and both BMI (OR = 1.083,95%CI:1.042-1.126,p<0.001) and triglyceride levels (OR = 1.090,95%CI:1.037-1.146,p<0.001). Neutrophils also demonstrated a positive causal linkage with BMI (OR = 1.034,95%CI:1.009-1.078,p = 0.012) and triglyceride levels (OR = 1.077,95%CI:1.033-1.122,p<0.001), and BMI exhibited a similar causal association with triglyceride levels (OR = 1.300,95%CI:1.212-1.385,p<0.001). These findings shed light on the causal networks connecting UA, BMI, neutrophils, and triglyceride levels. By integrating Spearman correlation analysis with various MR study designs, this study provided a robust framework for identifying key factors influencing hyperuricemia and related health issues, thereby enhancing our understanding of the interplay between inflammatory markers and these health indicators.

Conclusions: Our study presents strong evidence of the complex interconnection between BMI, neutrophils, triglyceride, and UA, revealing complex causal links and highlighting potential inflammatory states as key mediators. The findings may contribute to a better understanding of these factors and potentially lead to improved clinical outcomes and patients' health.