Is There a Nonlinear Relationship between Serum Uric Acid and Lipids in a Hypertensive Population with eGFR ≥30 ml/min/1.73 m2? Findings from the China Hypertension Registry Study

Background Evidence regarding the nonlinear relationship between serum uric acid (SUA) and blood lipids in Chinese population with hypertension is limited. Therefore, the present study aimed to investigate whether there is a nonlinear association between SUA and lipids in Chinese hypertensive population with an estimated glomerular filtration rate (eGFR) ≥30 ml/min/1.73 m2. Methods A total of 13,355 hypertensive participants with eGFR ≥30 ml/min/1.73 m2 were selected from the Chinese Hypertension Registry Study. Multivariate linear regression was used to examine the linear relationship between SUA and lipids. Smooth curve fitting (penalized spline method) and threshold saturation effects were used to analyze the nonlinear association between SUA and lipids. Results In the fully adjusted model, the results showed a positive correlation between SUA and TG (β = 0.15; 95% CI: 0.14, 0.16) and LDL-C (β = 0.06; 95% CI: 0.05, 0.07), respectively. However, the relationship between SUA and HDL-C was nonlinear. The inflection point of SUA was 7.24 mg/dL. On the left side of the inflection point (<7.24 mg/dL), SUA was negatively associated with HDL-C (β = −0.02; 95% CI −0.02, −0.01). On the right side of the inflection point (≥7.24 mg/dL), SUA was not related to HDL-C (β = 0.01; 95% CI −0.01, 0.02). Conclusion After adjusting for all covariates, SUA was positively associated with TG and LDL-C. The relationship between SUA and HDL-C was nonlinear. The negative correlation between SUA and HDL-C only existed when the SUA was less than 7.24 mg/dL in a hypertensive population with eGFR ≥30 ml/min/1.73 m2.


Introduction
Coronary heart disease (CHD) is the leading cause of death in the global population, of which dyslipidemia was reported as the most important risk factor for CHD [1,2]. Besides, serum uric acid (SUA) is the end product of purine metabolism, and elevated levels of SUA are generally considered as risk factors for CHD [3,4]. Blood lipids and SUA are closely related, but this relationship has been controversial. Several recent studies showed that elevated SUA was positively associated with triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) [5][6][7]. Other studies reported that SUA was negatively associated with HDL-C, while SUA was not related with TG and low-density lipoprotein cholesterol (LDL-C) [8,9]. In addition, some studies reported a nonlinear relationship between SUA level and the occurrence of CHD events [10,11], in which blood lipids are the closest risk factor for CHD [12], and it is unclear whether there also is a nonlinear relationship between SUA and blood lipids.
Epidemiological survey showed that 23.2% of Chinese adults had hypertension, which caused a great social burden [13]. It should be noted that patients with hypertension often have dyslipidemia and hyperuricemia [14,15]. However, to our knowledge, few studies examined the relationship between SUA and blood lipids in patients with hypertension. In addition, SUA cannot be effectively excreted through the kidney when the estimated glomerular filtration rate (eGFR) is <30 ml/min/1.73 m 2 , resulting in abnormal elevation of SUA levels [16]. In the present study, we tried to examine the relationship between SUA and blood lipids (including TG, LDL-C, and HDL-C) in a hypertensive population with eGFR ≥30 ml/min/1.73 m 2 .

Study Design and Participants.
e study data were drawn from the China Hypertension Registry study. e primary objective of this study was to establish a Chinese population-based cohort of hypertensive patients to collect baseline data and investigate the current situation of hypertensive patients in China. A total of 14,268 study participants were recruited from March 2018 to August 2018 in Wuyuan, Jiangxi Province, China. Details of this study are shown in detail in the previously published literature [17].
e study protocol was approved by the Ethics Committee of the Anhui Medical University Biomedical Institute. All study participants completed written informed consent.

Laboratory Assay.
Venous blood samples were collected from all study participants in fasting by trained study staff. Blood lipids, SUA, fasting blood glucose (FBG), homocysteine (Hcy), liver function, and kidney function were measured with an automatic clinical analyzer (Beckman Coulter, USA) in Biaojia Biotechnology Laboratory, Shenzhen, China.

Other Variables.
Body mass index (BMI, kg/m 2 ) was calculated by dividing weight by the square of height. Blood pressure (BP, mmHg) was measured using an electronic sphygmomanometer. Information on age, sex, smoking, alcohol use, history of disease, and history of taking medicine was obtained through standard questionnaires.

Statistical Analysis.
In order to reduce the impact of the maximum and minimum values on the statistical results, we removed the 2.5% interval on both sides of the SUA value. e baseline characteristics of the study participants were described according to the tertiles of SUA values. To examine the correlation between SUA and lipids, our statistical analyses consisted of 3 steps.
Step 1: Multivariate linear regression was used to analyze the linear relationship between uric acid and blood lipids (including TG, LDL-C, and HDL-C). Crude model (unadjusted), Model 1 (minor adjusted), and Model 2 (fully adjusted) were constructed to explore the relationship between SUA and lipids under the adjustment of different variables. e P values for trends were used to determine whether there was a dose-dependent relationship between SUA and lipids.
Step 2: Smooth curve fitting (penalized spline method) was used to address for nonlinear association between SUA and lipids (including TG, LDL-C and HDL-C). If nonlinearity was detected, the threshold effect analysis was specifically used to accurately describe nonlinear relationships. e inflection point is calculated by recursive algorithm, and then, a twopiecewise Cox proportional hazards model was constructed on both sides of the inflection point. e threshold level was determined by choosing the inflection point which provided the maximum model likelihood, using the trial and error method, along with a log likelihood ratio test comparing the one-line Cox proportional hazards model with a two-piecewise Cox proportional hazards model to examine the statistical significance [18].
Step 3: Subgroup analysis was used to assess whether the relationship was stable in different variable stratifications. All statistical analyses were performed using the statistical package R (http://www.R-project.org, e R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). Statistical significance was defined as two-tailed P < 0.05.

Analyses of Nonlinear Relationship between SUA and HDL-C.
In the present study, the nonlinear relationships between SUA and TG, LDL-C, and HDL-C was shown by  smooth curve fitting (Figures 1-3). Figure 3 shows a nonlinear relationship between SUA and HDL-C after adjusting all covariates. Furthermore, we used a two-piecewise linear regression model to fit the nonlinear relationship between SUA and HDL-C (Table 3). e P value for the log likelihood ratio test is <0.05, indicating that the relationship between SUA and HDL-C was nonlinear. e inflection point of SUA was 7.24 mg/dL. On the left of the inflection point, the effect size and 95% CI were −0.02 and −0.02 to −0.01. However, on the right side of the inflection point, the association between SUA and HDL-C was not significant (β � −0.01, 95% CI: −0.01, 0.02). Table S2 shows that the relationship between SUA and TG has significant differences under age, BMI, diabetes, and eGFR stratification. Table S3 shows that the relationship between SUA and LDL-C has significant differences under BMI, diabetes, and smoking stratification. Table S4 shows that the relationship between SUA and HDL-C has significant differences under age and eGFR stratification (P for interaction <0.05).

Discussion
In the present study, our results suggested a positive association between SUA and TG and LDL-C, respectively. However, there was a nonlinear relationship between SUA and HDL-C. e negative relationship between SUA and HDL-C only existed when SUA was less than 7.24 mg/dL in hypertensive population with eGFR ≥30 ml/min/1.73 m 2 . e β (95% CI) values were −0.02 (−0.02, −0.01) on the left side of the inflection point.
Previous studies have examined the relationship between SUA and lipids, but these studies have inconsistent conclusions. Li et al. [8] included 1,026 diabetic patients for analysis (mean age 65.57 ± 11.70 years) and found a negative correlation between SUA and HDL-C, but the relationship between SUA and TG and LDL-C was not significant. Kim et al. [9] analyzed data from 504 diabetic patients (mean age: 57.3 ± 13.9 years) and found a negative association between SUA and HDL-C, but SUA was not related to LDL-C. Tian et al. [19] included 15,401 middle-aged and elderly patients for analysis and found a positive correlation between SUA and TG but a negative correlation between SUA and HDL-C. Besides, to our knowledge, few studies have directly explored the specific relationship between SUA and lipids in Chinese hypertensive population. Hence, the exact relationship between SUA and lipids in hypertensive patients remains unclear indeed. Of note, several studies have reported a nonlinear relationship between SUA and CVD [10,11]. Because lipids and CVD are closely related, there may also be a nonlinear relationship between SUA and lipids. To test this hypothesis, we used sufficient statistical methods to reexamine the relationship between SUA and lipids and to explore the possibility of nonlinearity.
In the present study, we found a positive correlation between SUA and TG. Consistent with our study, some previous studies have also found this relationship [5,6,19].
Several potential mechanisms can explain this relationship. e high levels of SUA may limit the catabolism of triacylglycerol by reducing the activity of lipoprotein lipase, thus leading to increased TG levels [20]. In addition, higher SUA levels can damage pancreatic β cells and reduce insulin receptor sensitivity, which can lead to reduced lipolysis and consequently increased TG levels [21]. Meanwhile, we also found a positive correlation between SUA and LDL-C. is relationship has been found in the normal population [5].
is may be because SUA as an important antioxidant in serum effectively prevented the oxidation of LDL-C, thus leading to the elevation of LDL-C level [22]. Of note, first, we found a nonlinear relationship between SUA and HDL-C in a hypertensive population. In the SUA <7.24 mg/dL group, there was a negative association between SUA and HDL-C. is relationship may be caused by adiponectin. Adiponectin can increase the level of HDL-C by accelerating the oxidation of free fatty acids [23]. However, higher SUA leads to a decrease in adiponectin levels, which in turn reduces HDL-C levels. In the SUA ≥7.24 mg/dL group, SUA was positively associated with HDL-C, but this relationship was not statistically significant. In our study, higher SUA levels were associated with alcohol use (Table S1). Appropriate alcohol intake can lead to an increase in HDL-C levels [24]. erefore, this relationship may be related to alcohol use. Although we found a nonlinear relationship between uric acid and HDL-C in hypertensive status, further basic experiments are needed to fully elucidate the specific biological mechanisms underlying this association.
ere are some strengths in our study. First, we used adequate statistical analysis to examine the linear and nonlinear relationship between SUA and lipids. Second, considering that patients with eGFR <30 ml/min/1.73 m 2 had abnormally high SUA levels, we excluded those with eGFR <30 ml/min/1.73 m 2 in order to make our results more rigorous. Finally, compared with similar studies, we included a larger population and adjusted more confounding factors in the present study.
Some limitations should be noted. First, as a crosssectional study, our results failed to provide causality regarding the relationship between SUA and lipids (including TG, LDL-C, and HDL-C). Second, we only quantitatively analyzed the relationship between SUA and lipids and did not collect information on the particle size, composition, function, and subclasses of TG, LDL-C, and HDL-C, so we could not conduct a more in-depth discussion. ird, we exclude study participants with eGFR <30 ml/min/1.73 m 2 . Hence, the study results may not be suitable for population with eGFR <30 ml/min/1.73 m 2 . Finally, we found a nonlinear relationship between SUA and HDL-C, but more basic research is needed to explain the mechanism of this finding.

Conclusion
In the present study, we found that SUA was positively associated with TG and LDL-C in hypertensive population with eGFR ≥30 ml/min/1.73 m 2 . e relationship between SUA and HDL-C was nonlinear. e negative correlation between SUA and HDL-C only existed when the SUA was less than 7.24 mg/dL in hypertensive population with eGFR ≥30 ml/min/1.73 m 2 .
is finding suggests that clinicians  International Journal of Endocrinology must control the SUA level of hypertensive patients in the normal range. When the SUA level is within the normal range, SUA-lowering therapy can increase HDL-C levels in hypertensive patients, thereby obtaining greater clinical benefit.
Data Availability e datasets generated and analyzed during the current study are not publicly available because this study is still ongoing and the follow-up is not finished, but are available from the corresponding author on reasonable request.

Conflicts of Interest
e authors declare no conflicts of interest in this work.