Association between the Triglyceride Glucose Index and Hyperuricemia in Patients with Primary Hypertension: A Cross-Sectional Study

Liu, Shanshan; Zhou, Zhixian; Wu, Miao; Zhang, Hao; Xiao, Yao

doi:https://doi.org/10.1155/2023/5582306

International Journal of Endocrinology

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Abstract Introduction Methods Results Discussion Conclusion Data Availability Ethical Approval Consent Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 5582306 | https://doi.org/10.1155/2023/5582306

Association between the Triglyceride Glucose Index and Hyperuricemia in Patients with Primary Hypertension: A Cross-Sectional Study

Shanshan Liu,¹Zhixian Zhou,²Miao Wu,²Hao Zhang,²and Yao Xiao²

Academic Editor: Flavia Prodam

Received22 Apr 2023

Revised23 May 2023

Accepted07 Jun 2023

Published15 Jun 2023

Abstract

Objective. The aim of this study was to investigate the association between the triglyceride glucose (TyG) index and hyperuricemia (HUA) in patients with grades 1–3 hypertension. Study Design. This is a cross-sectional study. A total of 1,707 patients from the cardiovascular department of Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine were studied. In this study, 899 patients with grades 1-2 hypertension were included, of which 151 had HUA; additionally, 808 patients with grade 3 hypertension were included, of which 162 patients had HUA. This study obtained all patient data from the electronic medical record system of the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine. The TyG index was calculated as Ln (triglycerides × fasting glucose/2). Hyperuricemia was defined as uric acid ≥420 μmol/L (7 mg/dL). Multivariate logistic regression, penalized spline regression, and generalized additive models were used to evaluate the association between the TyG index and HUA. Stratified analyses were performed to assess the association in populations with different grades of hypertension. Results. The average TyG index was 8.71 ± 0.58. After adjusting for correlated variables, the logistic regression analysis revealed a positive correlation between the TyG index and HUA (OR = 1.83; 95% CI: 1.40–2.39). Smooth curve fitting showed that this correlation was linear in the whole range of the TyG index. In the subgroup analysis, the TyG index more strongly associated with HUA in the grades 1-2 hypertension group (OR = 2.22; 95% CI: 1.44–3.42) compared to that in the grade 3 hypertension group (OR = 1.58; 95% CI: 1.11–2.24; for interaction = 0.03). In addition, this association was consistent in all models. Conclusion. The TyG index was positively associated with HUA in patients with hypertension, and the association was more strongly confirmed in those with grades 1-2 hypertension rather than in those with grade 3 hypertension.

1. Introduction

Hyperuricemia (HUA) is a metabolic syndrome caused by a purine metabolism disorder. According to the Chinese guidelines [1], HUA can be diagnosed regardless of sex when serum uric acid (SUA) is ≥420 μmmol/L (7 mg/dL). Hyperuricemia can directly cause gout and uric acid nephropathy through sodium urate crystals’ deposition. Meanwhile, HUA is an independent risk factor for chronic kidney disease, cardiovascular and cerebrovascular diseases, diabetes, and other cardiovascular events [2–4]. Regarding its pathogenic mechanism, a series of studies have shown that HUA is closely related to insulin resistance (IR) [5–7].

Insulin resistance is a clinical and biochemical disorder that can lead to impaired glucose tolerance and further cause diabetes. Previous studies have proved that IR is associated with obesity, cardiovascular diseases, hypertension, and other diseases of impaired insulin sensitivity and metabolic syndrome [8]. The triglyceride glucose (TyG) index is a cheap, simple, and reliable substitute for IR compared to the homeostasis model (HOMA-IR) index [9]. Thus, the TyG index can be used as an early diagnostic indicator of IR and is helpful in identifying the population at a risk of cerebrocardiovascular disease [10–13].

Several previous studies have proved that the TyG index is associated with HUA [14–16]. However, only a few studies have analyzed the association between the TyG index and HUA in patients with hypertension [16, 17]. Previous studies have focused on all populations with hypertension, with few studies investigating different hypertension grades. Therefore, our study concentrated on the association between the TyG index and HUA in the specific population.

2. Methods

2.1. Participants

Our study included a total of 5,153 in-patients with primary hypertension from January, 2020, to December, 2021, who attended the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine. All participants were adults (aged 18 years or over) and hospitalized in the Department of Cardiology. Primary hypertension was diagnosed by referring to the 2018 ESC/ESH [18] guidelines for the management of arterial hypertension, which defines hypertension as office systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, when blood pressure value are measured in a resting and sitting position. Final blood pressure values were obtained by averaging three consecutive measurements, following a protocol that required at least 5 minutes of seated rest and two repeated measurements with 5-minute intervals. In-patients who were taking antihypertensive medications or self-reported hypertensive diagnosis were also included. The exclusion criteria were as follows: (1) those with incomplete SUA, triglyceride (TG), and glucose data; (2) those with secondary hypertension, diabetes, acute myocardial infarction, renal insufficiency (estimated glomerular filtration rate (eGFR) <60 mL/min per 1.73 m²), or malignant tumor; (3) those taking lipid-lowering drugs in the past 1 month; and (4) those with a history of using diuretics and other drugs that may affect the metabolism of uric acid within 2 months of inclusion. Finally, a total of 1,707 consecutive patients with grades 1–3 primary hypertension were included in our study, of which 899 had grades 1-2 hypertension and 808 patients had grade 3 hypertension. Figure 1 describes the initial sample population and exclusion criteria. Ethics approval was obtained from the Ethics Committee of the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine (no.: JZFYLL20220727034). The data were anonymous; thus, the requirement for informed consent was waived.

2.2. Data Collection and Definition

Clinical data, such as sex, age, medical history, as well as drug and blood test data, were collected from the electronic medical record system. To obtain blood samples, patients fasted overnight for 10–12 h, and then 4-5 mL venous blood was drawn the next morning. All samples were tested in the Department of Laboratory Medicine, Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine. The following lab methods were used: fast blood glucose was assessed using the hexokinase method; SUA and blood urea nitrogen (BUN) were measured using the enzyme method; blood creatinine (Scr) was measured using the picric acid method; TG and total cholesterol (TC) were tested using the oxidase method; biochemical indicators, such as lipoprotein a (LPa), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), d-dimer, and high-sensitivity C-reactive protein (hs-CRP), were measured using immunoturbidimetry; the international normalized ratio (INR) was calculated using its formula; serum albumin (ALB) was analyzed using the bromide green method; alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were assessed using the enzymatic rate method; and all of the previous biomarkers were analyzed using an automatic biochemical analyzer (Siemens advia2400). The TyG index = Ln (TG [mg/dL] × fasting glucose [mg/dL]/2) [19]. The modified modification of diet in renal disease (MDRD) equation was used to estimate eGFR [20]. Hyperuricemia was diagnosed as SUA ≥ 420 μmol/L in men and women using the 2019 Chinese guidelines for the diagnosis and treatment of HUA and gout [1].

2.3. Statistical Analysis

Continuous variables were presented as the mean ± standard deviation or as the median (interquartile range), and categorical variables were presented as absolute values and/or frequency (%). The baseline characteristics among the groups organized according to grades of hypertension (grades 1-2 or grade 3) were compared using chi-square tests (categorical variables), one-way analysis of variance (normal distribution), and the Kruskal–Wallis (skewed distribution) tests, respectively.

Multivariate logistic regression analyses were performed to assess the independent association between the TyG index and HUA after adjusting for correlated variables in the three models. The variables were selected based on the following: clinical importance, statistical significance in the univariable analyses, and an estimated variable change of at least 10% of the potential confounding effects. The restricted cubic spline model (a fitted smooth curve) was used to determine the dose-response relationship of the TyG index with SUA and HUA.

Subgroup analyses were stratified by relevant effect covariates as follows: age (<65 years vs. ≥65 years), hypertension grades (1-2 vs.3), sex (male vs. female), TyG index (tertile 1, 7.1–8.4; tertile 2, 8.4–8.9; and tertile 3, 8.9–11.8), and TG (<1.7 mmol/L vs. ≥1.7 mmol/L). In the subgroup analyses of hypertension grades, we used the generalized additive model to analyze the dose-response association among the TyG index, SUA, and HUA. In the three models, pertinent covariables included age, sex, ALB, ALT, AST, Scr, BUN, d-dimer, INR, eGFR, hypertension grade, LDL-C, HDL-C, and LPa. All analyses were performed using R Statistical Software (https://www.R-project.org, The R Foundation) and Free Statistics software versions 1.7. A two-tailed value <0.05 was considered to be statistically significant for all analyses.

3. Results

3.1. Baseline Characteristics

Of the 1,707 patients, the mean age was 62.97 ± 12.87 years and 786 patients were men. In the grades’ 1-2 hypertension group, 403 (44.7%) patients were men. In the grade 3 hypertension group, 384 (47.5%) patients were men. The average TyG index was 8.71 ± 0.58, and there was no significant difference between the two groups. Of the 1,707 patients, 313 (18.3%) had HUA, including 151 (16.8%) with grades 1-2 hypertension and 162 (20%) with grade 3 hypertension. Missing data for ALB (0.1%), d-dimer (4.0%), and INR (4.0%) were imputed using the mean value. In the grades’ 1-2 hypertension group, the levels of SUA, glucose, and hs-CRP were lower than those in the grade 3 hypertension group (all ). There was no significant difference in the age and sex of the participants between the groups (, Table 1). In addition, there was no significant difference in the value of ALB, LDL-C, HDL-C, AST, Scr, BUN, TC, INR, eGFR, ALT, homocysteine (HCY), LPa, d-dimer, and TG between the two groups (, Table 1).

3.2. The Positive Association of the TyG Index with HUA

As shown in Table 2, the univariate analysis revealed that the TyG index, sex, age, ALB, HDL-C, AST, Scr, BUN, glucose, eGFR, ALT, LPa, and TG were associated with HUA in patients with primary hypertension (all ). Furthermore, the multivariate analysis also revealed that the TyG index was positively associated with SUA and HUA after adjusting for potential confounding factors, as demonstrated in Tables 2 and 3.

Figure 2 shows the dose-response relationship among the TyG index, SUA, and HUA levels in patients with primary hypertension. We found a linear relationship between the TyG index and HUA in patients with primary hypertension and an increasing trend in the incidence of HUA with the increasing TyG index. When the TyG index was assessed as tertiles, the incidence risks of HUA in the 2^nd and 3^rd tertiles were 1.45 (95% CI: 0.98–2.15) and 2.40 (95% CI: 1.60–3.60) times greater than that in the lowest tertile ( for trend <0.001), as shown in Table 4.

(a)

(b)

Figure 2

Dose-response association between the TyG index and the risk of HUA. (a) TyG index and SUA and (b) TyG index and HUA. All adjusted for age, sex, ALB, ALT, AST, Scr, BUN, d-dimer, INR, eGFR, hypertension grades, LDL-C, HDL-C, and LPa. In this figure, we can find the association displayed a linear pattern in the whole range of TyG. TyG, triglyceride-glucose index; HUA, hyperuricemia; SUA, serum uric acid; ALB, serum albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; Scr, blood creatinine; BUN, blood urea nitrogen; INR, international normalized ratio; eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; and LPa, lipoprotein a.

3.3. Subgroup Analysis

To further reveal the association between the TyG index and HUA in different subgroups, we conducted stratified analyses (Figure 3). Some subgroups had no significant interactions: age (<65 vs. ≥65 years), TG (<1.7 vs. ≥1.7 mmol/L), and TC (<5.7 vs. ≥5.7 mmol/L). However, we found significant interactions in two subgroups: sex (male vs. female) and hypertension grade (grades 1-2 vs. grade 3). Women had a greater risk (OR = 3.2, 95% CI: 1.77–5.85) of developing HUA than men (OR = 1.49, 95% CI: 1.1–2.01; P for interaction = 0.03). Similarly, the association was strongly confirmed in the grades’ 1-2 hypertension group compared to the grade 3 hypertension group (grades 1-2 hypertension: OR = 2.22, 95% CI: 1.44–3.42; grade 3 hypertension: OR = 1.58, 95% CI: 1.11–2.24; for interaction = 0.03). After the TyG index was divided into tertiles, the risks of HUA in the 2^nd and 3^rd tertiles were 2.57 (95% CI: 1.40–4.71) and 3.02 (95% CI: 1.57–5.82) times higher than that in the lowest tertile in the grades’ 1-2 hypertension group ( for trend = 0.002). The risks of HUA in the 3^rd tertiles were 1.99 (95%CI: 1.17–3.39) times higher than that in the lowest tertile in grade 3 hypertension group ( for trend = 0.004). There was no significant difference between the 2^nd tertiles and the 1^st tertiles. In the 1^st–3^rd tertiles, the TyG index had a strong significant association with HUA in the grades’ 1-2 hypertension group compared to that in the grade 3 hypertension group ( for interaction <0.05), as shown in Table 5. The results of the analysis of the association between the TyG index and SUA were consistent and are presented in Table 6.

Figure 3

Subgroup analysis of the association between the TyG index and HUA. Adjusted for age, sex, ALB, ALT, AST, Scr, BUN, d-dimer, INR, eGFR, hypertension, LDL-C, HDL-C, and LPa. TyG, triglyceride-glucose index; HUA, hyperuricemia; ALB, serum albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; Scr, blood creatinine; BUN, blood urea nitrogen; INR, international normalized ratio; eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; and LPa, lipoprotein a.

4. Discussion

We analyzed the correlation between the TyG index and HUA in patients with grades 1-3 hypertension. The results showed that the TyG index was positively associated with SUA and HUA. The restricted cubic spline indicated that the association was linear across the TyG range. The risk of HUA gradually increased with an increase in the TyG index. Moreover, the subgroup analyses suggested that the positive association seemed to be strong among grades 1-2 hypertension and women ( for interaction = 0.03).

In recent years, there are some studies that have drawn conclusions on the association among the TyG index, SUA, and HUA. Kahaer et al. [21] found that the TyG index had a significant association with HUA and could be used as a risk screening indicator of HUA in the Xinjiang population in China. Meanwhile, da Silva et al. [11] conducted a linear relationship analysis between the TyG index and HUA in the general Chinese population by using the TyG index values to predict HUA. The results are helpful in providing a simple and cost-effective method for the prevention and control of HUA. Furthermore, Zhou et al. [13] included 13,060 hypertensive patients in a cross-sectional study to analyze the association between the TyG index and HUA. The research divided the TyG index into four quartiles and found that the risk of HUA in the highest tertile was 2.79 times higher than that of the lowest tertile, again confirming a strong linear relationship between the TyG index and HUA in a hypertensive population. Our study revealed that when the TyG index was divided into three tertiles, and the risk of HUA was similar to the previous results. We further conducted subgroup analyses for grades 1-2 hypertension and grade 3 hypertension after performing a stratified analysis of the TyG index. The results showed that the HUA risk odds ratio (OR) and regression coefficient of grades 1-2 hypertension were higher than those of grade 3 hypertension ( value for interaction <0.05). The difference was consistent in all models, indicating that the difference was steady for these risk factors; thus, the study concluded that the TyG index was positively associated with HUA, and the association was more strongly confirmed in those with grades 1-2 hypertension rather than in those with grade 3 hypertension. However, the mechanisms underlying these differences are not yet fully understood.

Previous studies have confirmed that the TyG index identified HUA differently in women and men [17]. Similarly, our study found that the OR and regression coefficient values of the TyG index were higher in women, suggesting that the TyG index had a strong association with HUA in female populations with hypertension. This may be explained by estrogen being related to complex endocrine factors as well as being a uric acid producing agent. Estrogen also accounts for some differences in lipid metabolism between women and men.

Although our study and previous research have confirmed the association between the TyG index and HUA, the specific mechanisms remain unclear. The most commonly recognized mechanism is related to IR. Previous epidemiological studies have revealed a significant relationship between IR and SUA [22, 23]. After IR, compensatory hyperinsulinemia occurs, leading to decreased uric acid excretion through renal tubular sodium reabsorption, which further causes HUA. In addition, one study proved that early β-cell dysfunction was mainly associated with elevated uric acid levels [24]. Insulin resistance also causes HUA through several other pathways, including by inducing systemic inflammation, causing kidney damage, decreasing renal uric acid excretion, and affecting lipid metabolism [25]. Conversely, HUA can lead to IR and inflammation by affecting adipocytes and reducing mitochondrial oxidative stress and nitric oxide bioavailability [26]. HUA and IR are both important risk factors for hypertension. The specific mechanisms are as follows: (1) human studies have shown that HUA may be related to impaired endothelial function [27, 28], (2) animal studies shown that HUA can activate the renin-angiotensin system, which leads to increased systemic BP and vascular resistance [29], and (3) activating the immune system and its related hemodynamic effects [30]. These changes can aggravate systemic hypertension and lead to end-organ damage. Wang et al. [31] conducted a prospective cohort study included 21,999 subjects without hypertension or gout at the baseline. The results showed that the elevated SUA is associated with an increased risk of hypertension, and IR may play an intermediary role in the relationship between SUA and hypertension.

Studies have shown that the TyG index is a more sensitive and specific indicator for IR detection compared to other IR indicators [32]. In addition, some researchers found an association between the TyG index and HUA in populations from different regions.

Our study aimed to explore the association of the TyG index and HUA in hospitalized patients with grades 1–3 hypertension. One main strength of our research was that we conducted stratified analyses in populations with grades 1-2 hypertension and grade 3 hypertension. These results are helpful to show the association between the TyG index and HUA in these specified populations.

Our study had some limitations. First, this was a cross-sectional study, and therefore, the association between the TyG index and HUA in patients with grades 1–3 hypertension is insufficient to draw causal conclusions. In the future, prospective randomized studies could help obtain more evidence to support the associations found in our study. Second, we included only a subset of hospitalized patients in Jiangxi, China, which was not representative of the entire population. In addition, most in-patients had grade 2 and grade 3 hypertension and fewer in-patients had grade 1 hypertension. Therefore, we combined the grade 1 and grade 2 hypertension cases for further analysis. The target population of our study was hospitalized hypertensive patients, most of whom had grades 2-3 hypertension; thus, our study is consistent with clinical reality. Third, data derived from the electronic medical record system lacked information on waist circumference (WC), height, weight, alcohol consumption, and smoking history. These unrecorded risk factors for HUA could be residual confounding variables, potentially causing further bias in our results. However, most studies found that the positive association between the TyG index and HUA was still significant after adjusting for these risk factors [16, 17, 21], and therefore, we consider our conclusions to be reliable.

5. Conclusion

In conclusion, our study confirmed the positive association between the TyG index and the risk of HUA in hospitalized patients with hypertension. The association was more strongly confirmed in women than in men and in grades 1-2 hypertension than in grade 3 hypertension.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Ethical Approval

Ethical approval was obtained from the Ethics Committee of the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine (no.: JZFYLL20220727034). The data were anonymized, and the requirement for informed consent was waived.

All subjects signed the informed consent form.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors would like to thank all participants of the study.

References

Chinese Medical Association Endocrinology Branch, “Guideline for the diagnosis and management of hyperuricemia and gout in China (2019),” Chin J Endocrinol Metab, vol. 36, 2020.
View at: Google Scholar
A. Srivastava, A. D. Kaze, C. J. McMullan, T. Isakova, and S. S. Waikar, “Uric acid and the risks of kidney failure and death in individuals with CKD,” American Journal of Kidney Diseases, vol. 71, no. 3, pp. 362–370, 2018.
View at: Publisher Site | Google Scholar
H. Wang, H. Zhang, L. Sun, and W. Guo, “Roles of hyperuricemia in metabolic syndrome and cardiac-kidney-vascular system diseases,” Am J Transl Res, vol. 10, no. 9, pp. 2749–2763, 2018.
View at: Google Scholar
N. Katsiki, G. D. Dimitriadis, and D. P. Mikhailidis, “Serum uric acid and diabetes: from pathophysiology to cardiovascular disease,” Current Pharmaceutical Design, vol. 27, no. 16, pp. 1941–1951, 2021.
View at: Publisher Site | Google Scholar
E. Abreu, M. J. Fonseca, and A. C. Santos, “Association between hyperuricemia and insulin resistance,” Acta Med Port, vol. 24, no. 2, pp. 565–574, 2011.
View at: Google Scholar
Z. Jiao, H. Gao, H. Li et al., “Pre-diabetes mellitus influenced by hyperuricemia,” Zhonghua Liu Xing Bing Xue Za Zhi Zhonghua Liuxingbingxue Zazhi, vol. 34, pp. 725–727, 2013.
View at: Google Scholar
B. M. Galindo-Yllu, R. Rojas-Humpire, C. J. Toro-Huamanchumo, R. Gutierrez-Ajalcriña, and A. N. Soriano, “Serum uric acid is associated with metabolic syndrome and insulin resistance among health personnel from Peru,” Journal of Nutrition and Metabolism, vol. 2021, Article ID 9933319, 7 pages, 2021.
View at: Publisher Site | Google Scholar
V. Ormazabal, S. Nair, O. Elfeky, C. Aguayo, C. Salomon, and F. A. Zuñiga, “Association between insulin resistance and the development of cardiovascular disease,” Cardiovascular Diabetology, vol. 17, no. 1, p. 122, 2018.
View at: Publisher Site | Google Scholar
F. Guerrero-Romero, R. Villalobos-Molina, J. R. Jiménez-Flores et al., “Fasting triglycerides and glucose index as a diagnostic test for insulin resistance in young adults,” Archives of Medical Research, vol. 47, no. 5, pp. 382–387, 2016.
View at: Publisher Site | Google Scholar
K. Park, C. W. Ahn, S. B. Lee et al., “Elevated TyG index predicts progression of coronary artery calcification,” Diabetes Care, vol. 42, no. 8, pp. 1569–1573, 2019.
View at: Publisher Site | Google Scholar
A. da Silva, A. P. S. Caldas, H. H. M. Hermsdorff et al., “Triglyceride-glucose index is associated with symptomatic coronary artery disease in patients in secondary care,” Cardiovascular Diabetology, vol. 18, no. 1, p. 89, 2019.
View at: Publisher Site | Google Scholar
A. Wang, G. Wang, Q. Liu et al., “Triglyceride-glucose index and the risk of stroke and its subtypes in the general population: an 11-year follow-up,” Cardiovascular Diabetology, vol. 20, no. 1, p. 46, 2021.
View at: Publisher Site | Google Scholar
Y. Zhou, Y. Pan, H. Yan et al., “Triglyceride glucose index and prognosis of patients with ischemic stroke,” Frontiers in Neurology, vol. 11, p. 456, 2020.
View at: Publisher Site | Google Scholar
W. Shi, L. Xing, L. Jing, Y. Tian, and S. Liu, “Usefulness of triglyceride-glucose index for estimating hyperuricemia risk: insights from a general population,” Postgraduate Medicine, vol. 131, no. 5, pp. 348–356, 2019.
View at: Publisher Site | Google Scholar
L. Liu, R. Xia, X. Song et al., “Association between the triglyceride-glucose index and diabetic nephropathy in patients with type 2 diabetes: a cross-sectional study,” J Diabetes Investig, vol. 12, no. 4, pp. 557–565, 2021.
View at: Publisher Site | Google Scholar
C. Yu, T. Wang, W. Zhou et al., “Positive association between the triglyceride-glucose index and hyperuricemia in Chinese adults with hypertension: an insight from the China H-type hypertension registry study,” International Journal of Endocrinology, vol. 2022, Article ID 4272715, 8 pages, 2022.
View at: Publisher Site | Google Scholar
J. Dong, H. Yang, Y. Zhang, and Q. Hu, “Triglyceride-glucose index is a predictive index of hyperuricemia events in elderly patients with hypertension: a cross-sectional study,” Clinical and Experimental Hypertension, vol. 44, no. 1, pp. 34–39, 2022.
View at: Publisher Site | Google Scholar
European Society of Cardiology (ESC) and the European Society, B. Williams, G. Mancia et al., “2018 ESC/ESH Guidelines for the management of arterial hypertension,” Rev Espanola Cardiol Engl Ed, vol. 72, p. 160, 2019.
View at: Publisher Site | Google Scholar
M. Li, A. Zhan, X. Huang et al., “Positive association between triglyceride glucose index and arterial stiffness in hypertensive patients: the China H-type Hypertension Registry Study,” Cardiovascular Diabetology, vol. 19, no. 1, p. 139, 2020.
View at: Publisher Site | Google Scholar
Y.-C. Ma, L. Zuo, J.-H. Chen et al., “Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease,” Journal of the American Society of Nephrology, vol. 17, no. 10, pp. 2937–2944, 2006.
View at: Publisher Site | Google Scholar
M. Kahaer, B. Zhang, W. Chen et al., “Triglyceride glucose index is more closely related to hyperuricemia than obesity indices in the medical checkup population in Xinjiang, China,” Frontiers in Endocrinology, vol. 13, Article ID 861760, 2022.
View at: Publisher Site | Google Scholar
S.-I. Adachi, F. Yoshizawa, and K. Yagasaki, “Hyperuricemia in type 2 diabetic model KK-Ay/Ta mice: a potent animal model with positive correlation between insulin resistance and plasma high uric acid levels,” BMC Research Notes, vol. 10, no. 1, p. 577, 2017.
View at: Publisher Site | Google Scholar
J. Zong, Y. Sun, Y. Zhang et al., “Correlation between serum uric acid level and central body fat distribution in patients with type 2 diabetes,” Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, vol. 13, pp. 2521–2531, 2020.
View at: Publisher Site | Google Scholar
L. E. Simental-Mendía, E. Simental-Mendía, M. Rodríguez-Morán, and F. Guerrero-Romero, “Hyperuricemia is associated with the increase of insulin release in non-obese subjects with normal glucose tolerance,” Endocrine Research, vol. 42, pp. 1–5, 2017.
View at: Publisher Site | Google Scholar
E. P. de Oliveira and R. C. Burini, “High plasma uric acid concentration: causes and consequences,” Diabetology & Metabolic Syndrome, vol. 4, no. 1, p. 12, 2012.
View at: Publisher Site | Google Scholar
W. Baldwin, S. McRae, G. Marek et al., “Hyperuricemia as a mediator of the proinflammatory endocrine imbalance in the adipose tissue in a murine model of the metabolic syndrome,” Diabetes, vol. 60, no. 4, pp. 1258–1269, 2011.
View at: Publisher Site | Google Scholar
T. de A Coutinho, S. T. Turner, and I. J. Kullo, “Serum uric acid is associated with microvascular function in hypertensive individuals,” Journal of Human Hypertension, vol. 21, no. 8, pp. 610–615, 2007.
View at: Publisher Site | Google Scholar
D. I. Feig, B. Soletsky, and R. J. Johnson, “Effect of allopurinol on blood pressure of adolescents with newly diagnosed essential hypertension: a randomized trial,” JAMA, vol. 300, no. 8, pp. 924–932, 2008.
View at: Publisher Site | Google Scholar
C. J. McMullan, L. Borgi, N. Fisher, G. Curhan, and J. Forman, “Effect of uric acid lowering on renin-angiotensin-system activation and ambulatory BP: a randomized controlled trial,” Clinical Journal of the American Society of Nephrology, vol. 12, no. 5, pp. 807–816, 2017.
View at: Publisher Site | Google Scholar
C. Ponticelli, M. A. Podestà, and G. Moroni, “Hyperuricemia as a trigger of immune response in hypertension and chronic kidney disease,” Kidney International, vol. 98, no. 5, pp. 1149–1159, 2020.
View at: Publisher Site | Google Scholar
R. Wang, Z. Bai, D. Zhang et al., “Mediating effects of insulin resistance on the development of hypertension associated with elevated serum uric acid: a prospective cohort study,” Journal of Human Hypertension, vol. 36, no. 8, pp. 760–766, 2022.
View at: Publisher Site | Google Scholar
F. Guerrero-Romero, L. E. Simental-Mendia, M. Gonzalez-Ortiz et al., “The product of triglycerides and glucose, a simple measure of insulin sensitivity Comparison with the euglycemic-hyperinsulinemic clamp,” Journal of Clinical Endocrinology and Metabolism, vol. 95, no. 7, pp. 3347–3351, 2010.
View at: Publisher Site | Google Scholar

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Copyright © 2023 Shanshan Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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