# A comprehensive study on the uric acid to high-density cholesterol ratio influencing cardiometabolic-based chronic disease in China

## TL;DR

This study explores how the uric acid to high-density cholesterol ratio relates to chronic diseases in China, identifying it as a potential early biomarker.

## Contribution

The study introduces UAHDL as a novel biomarker for predicting cardiometabolic diseases using a large cohort in China.

## Key findings

- Higher UAHDL levels correlate with increased age, BMI, and smoking/drinking prevalence.
- UAHDL is an independent risk factor for CMBCD, with non-linear risk patterns observed.
- A nomogram was developed for personalized risk assessment using nine predictors.

## Abstract

Cardiovascular metabolic-related chronic diseases (CMBCD) significantly impact the quality of life and socioeconomic status of patients, yet current diagnostic and therapeutic approaches are limited by a lack of effective early screening tools and individualized treatment strategies. This study aimed to investigate the relationship between the uric acid to high-density lipoprotein cholesterol ratio (UAHDL) and CMBCD among a cohort of 3,097 participants from Xinjiang China, employing logistic regression analysis. The analysis revealed that higher UAHDL levels correlated with significant increases in age and body mass index (BMI), while a notable decline in the proportion of females and an increase in smoking and drinking prevalence were observed across UAHDL categories. Our findings identified independent risk factors for CMBCD, including female sex, older age, higher BMI, fasting blood glucose, creatinine, smoking, triglycerides, and UAHDL. Male sex was independently associated with lower odds of CMBCD. Additionally, UAHDL was significantly associated with CMBCD, and the restricted cubic spline analysis suggested a non-linear trend, with risk patterns differing across the UAHDL spectrum. Subgroup analyses further demonstrated significant associations between UAHDL and CMBCD in females and participants under 60, as well as in non-smokers and non-drinkers. We constructed a nomogram incorporating nine independent predictors, offering a practical tool for personalized risk assessment. In conclusion, our results underscore the clinical relevance of UAHDL as a potential biomarker for early identification and intervention in CMBCD. Future studies with longitudinal designs and external validation are warranted to confirm these findings and refine UAHDL-based risk stratification models.