Study population
Ningxia Hui autonomous region (NHAR) has a population of around 7.4 million and is the smallest and less-developed provincial autonomous region in Northwest China. From January 2020 to December 2021, a cross-sectional survey was conducted in NHAR to investigate the prevalence and risk factors of cardiovascular disease including coronary heart disease, obesity,hypertension, dyslipidemia, diabetes mellitus, and hyperuricemia. The present study used the samples in this survey. Briefly, a four-staged, stratified cluster sampling method was used to select the regionally representative sample of the general population aged 18 and over. In the first stage, nine counties of the NHAR were selected according to administrative and economic levels. In the second stage, two towns in each county were selected using Simple Random Sampling (SRS) according to the list of towns provided by local Centre for Disease Control and Prevention. In the third stage, three communities or villages in each selected towns were selected using SRS. The final stage of sampling was stratified by gender and age distribution on the basis of China census data from 2010.
A total of 10,803 permanent residents were recruited in the survey, of whom 207 were excluded from the present study because of self-reported diabetes (n=579) and end-stage kidney disease (n=7). Finally, 10 217 individuals were included in the current analyses.
Data collection and anthropometry
All participants attended to the community or village health center in the morning after overnight fasting for at least 8 hours. Anthropometry data such as height, weight and waist were measured according to standard procedures. After 5 min resting, sitting blood pressure was measured in right arm by an electronic blood pressure monitor (OMRON, HBP-1120U). Blood pressure was measured three times and the measurements were averaged. Information on personal characteristics, socioeconomic status, lifestyle factors and health status of the participants were collected using a computer-aided one-on-one questionnaire by well-trained interviewers.
Peripheral venous blood samples were then collected from each participant in a heparin sodium anticoagulant tube. Then, these tubes were centrifuged at 1500 rpm for 10min and the supernatants was collected in cryogenic vials and stored at - 80 ℃. Supernatants were sent for measurement of SUA, FPG, triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and serum creatinine (SCr) at the Beijing CIC Medical Laboratory. SUC, FPG, TG, TC, LDL-C, HDL-C and SCr were analyzed with a Beckman Coulter AU 5800 device (Beckman Coulter, Inc., California, USA) with commercially available reagents (BiosinoBiotechnolgy and Science Inc., Beijing, China). All experiments were performed in accordance with relevant guidelines and regulations.
The glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration study equation (20):
Where SCr is expressed in mg/dl and age in years; k: 0.7 for female and 0.9 for male; α: 0.329 for female and 0.411 for male; min indicates the minimum of SCr/k or 1, and max indicates the maximum of SCr/k or 1.
The estimated triglyceride-glucose index (TyG) was calculated using the following equation (21):
FPG and TG are expressed in mg/dL.
Diagnostic criteria
Diabetes was defined as FPG≥7.0mmol/L. Pre-diabetes was determined among the participants who had FPG≥6.1mmol/Land <7.0mmol/L. Non-diabetic healthy individuals (nor-moglycemia) was identified based on FPG<6.1mmol/L and absence of the criteria that relates to pre-diabetes and diabetes. All participants were divided into Q1(SUA<270µmol/L), Q2 (270µmol/L≤SUA≤318µmol/L), Q3(319µmol/L≤SUA≤360µmol/L), Q4(361µmol/L≤SUA≤410µmol/L) and Q5 (SUA>410µmol/L) groups. HUA was defined as SUA >420 µmol/L for male and >360 µmol/L for female. The body mass index (BMI) was calculated as weight in kilograms divided into height in meters squared (kg/m2). End-stage kidney disease was defined as an eGFR< 15mL/min/1.73 m2.
Statistics
All statistical analyses were completed using R software program version 3.2.2 (http://www.Rproject.org). Descriptive statistics were calculated for all clinical and metabolic characteristics. The category variables were presented in percentages and continuous variables were described as Means±SD for normally distributed data or median (IQR) for skewed data. One-way analysis of variance or the Kruskal–Wallis rank-sum test was used for continuous variables and the chi-square test for categorical variables to compare the baseline characteristics of patients with different groups.
Nonlinear relationships between SUA and FPG were explored using smoothing splines generated in generalized additive models (GAM) by the R package mgcv. Based on the cut-off values indicated in the splines, the participants were stratified into two groups. Within each group, multi variable linear regression analyses were conducted to quantify the relationships of SUA and FPG after controlling for potential covariates. Model 1 was an unadjusted model. Model 2 was adjusted for age. Model 3 was adjusted for age, BMI, blood pressure, TC, TG, LDL-C, HDL-C and eGFR. Model 4 was further adjusted for variables used in model 1, 2 and 3 and smoking and drinking.