ORIGINAL
The relationships among hyperuricemia, body mass index and impaired renal function in type 2 diabetic patients
2018 Volume 65 Issue 3 Pages 281-290
Details
2018 Volume 65 Issue 3 Pages 281-290
Chronic kidney disease (CKD) is a common chronic microvascular complication and the major cause of death in diabetic patients. This study was conceived to explore the possible mechanisms of how hyperuricemia and obesity contribute to renal function impairment in type 2 diabetic (T2DM) patients. A cross-sectional study in 609 participants recruited from a T2DM population in North China was conducted. The multiplicative interaction between body mass index (BMI) and uric acid (UA) level was assessed using an interaction term in a logistic regression analysis. Our results indicate that male T2DM patients having higher BMI (OR 1.711, p = 0.038), blood urine nitrogen (BUN) (OR 1.100, p = 0.034), and 24-hour urinary micro-albumin levels (OR 1.004, p = 0.021) were much more likely to have high UA. Whereas, for female T2DM patients, the OR of BMI, BUN, and triglyceride were 1.169 (p = 0.001), 1.337 (p = 0.000), and 1.359 (p = 0.006), respectively. In this study population, obesity and elevated UA work together to increase the risk of renal injury. In vitro experiments indicate that reactive oxygen species (ROS) production increased with UA treatment in human renal glomerular endothelial cells (HRGECs), while endothelial nitric oxide synthase (eNOS) production level dropped. UA also increased monocyte chemotactic protein-1 (MCP-1) expression and nuclear factor kappa B (NF-κB) activation. Taken together, our results indicate that high concentrations of UA lead to endothelial dysfunction through the activation of the inflammatory response and induction of oxidative stress, even in non-obese T2DM patients.
THE AGING POPULATION, changes in lifestyle and diet habits, as well as the increased use of uretic drugs, has led to an observed worldwide increase in hyperuricemia (HUA) incidence. In China alone, HUA incidence rate has risen from 1.4% in the early 80’s, to close to 10% in the year 2000 [1]. HUA associated with obesity, diabetes mellitus, hyperlipidemia, and insulin resistance (IR), which are key components defining the metabolic syndrome [2]. Notably, obesity is an important risk factor for HUA with a high body mass index (BMI) correlating strongly with an increased incidence of HUA [3]. In males, a 30%, increase in body weight resulted in a corresponding 1 mg/dL increase in serum uric acid (UA) concentration, and for females, a 50% increase in body weight resulted in a 0.8 mg/dL increase in serum UA concentration [4]. In patients with type 2 diabetes (T2DM), BMI variation is now widely accepted as an important risk factor for elevated serum UA [5], piquing the interest in elucidating the relationship behind BMI-related serum UA variations.
Chronic kidney disease (CKD) is a common and important chronic microvascular complication, and also one of the important causes of death in diabetics [6]. Diana et al. [7] suggests that serum UA independently associated with microalbuminuria in diabetic patients and, is also an independent risk factor for diabetic nephropathy [8, 9]. This suggests that serum UA levels play an important role in the progression of renal disease [10]. However, the potential mechanism of how HUA and BMI contribute to impaired renal function in T2DM patients still needs to be elucidated.
Vascular endothelial dysfunction is the main pathogenic factor for CKD. High UA damages glomerular endothelial cells, leading to the activation of the inflammation response in the kidney [11]. Reactive oxygen species (ROS) overproduction by HUA causes endothelial dysfunction, alters vasoreactivity, decreases coagulation, and induces inflammation [12]. Monocyte chemoattractant protein-1 (MCP-1) is usually expressed in leukocytes, endothelial cells, and the vascular smooth muscle cell. MCP-1 induces aggregation of inflammatory cells and have important functions in the pathogenesis of a variety of renal diseases [13]. NF-κB is essential for the interaction among cytokines and adhesion molecules, which initiate the inflammatory response.
In this study, we conducted a cross-sectional study in T2DM patients in the Chinese population, to investigate the relationship between HUA, BMI and renal damage. We also investigated the effects of UA on the expression levels of ROS, eNOS, and MCP-1 in HRGECs, and tested if the NF-κB signaling pathway contributes to endothelial dysfunction.
The study population includes patients admitted to the Tianjin medical university metabolic diseases hospital with the patients’ consents and the approval from the ethical committee. The patients were diagnosed with type 2 diabetes (T2DM) between January 2014 to July 2015. A positive diagnosis of hyperuricemia is defined as UA ≥ 420 (μmol/L) for males and UA ≥ 360 (μmol/L) for female. The exclusion criteria include 1) patients who are pregnant or lactating, 2) patients with type 1 or other special types of diabetes, 3) patients with secondary renal or renal dysfunction caused by primary renal disease or hypertension, 4) patients with urinary tract infection, 5) patients with severe hypertension and 6) patients on anti-hyperuricemic agents within 8 weeks before eligibility confirmation. Among 709 patients with T2DM who were screened, 609 patients met the inclusion criteria and were recruited as subjects of this cross-sectional study.
Other clinical parametersBody mass index (BMI) was calculated using the formula of weight in kilograms divided by the square of height in meters. In all analysis, BMI categories were defined using the WHO criteria: Normal weight = BMI 18.5–24.9; overweight = BMI 25.0–29.9; obese = BMI ≥ 30.0.
Biochemical testsBlood samples were obtained after overnight fasting. Uric acid, glycohemoglobin A1c (HbA1c), blood viscosity, serum lipids, C-reactive protein, 24-hour urinary micro-albumin (UMA/24 hr), BUN, and creatinine clearance rate were measured with standard techniques.
Cell cultureHRGECs were obtained from BeNa Culture Collection. HRGECs were cultured in RPMI 1640 medium with 10% heat-inactivated fetal bovine serum at 37°C in 5% CO2.
Detection of ROSThe intracellular levels of reactive oxygen species (ROS) were measured with the DCFH-DA probe (Beyotime, China). Different concentrations of UA (Sigma Aldrich) was added to the culture media as previously described [14]. After treatment with different concentrations of UA for 24 hrs, cells were washed once with RPMI 1640 medium, followed by three washes with 1× cold PBS (pH 7.4). Cells were then incubated with 10 μM of DCFH-DA at 37°C for 30 min in the dark. Fluorescence was recorded by fluorescence microscopy at an excitation wavelength of 488 nm and an emission wavelength of 535 nm.
RNA extraction and quantitative real-time PCRTotal RNA was extracted from HRGECs using the TRIzol reagent (Invitrogen, USA) following the manufacturer’s protocols and reverse transcribed into complementary DNA (cDNA). Quantitative real-time PCR was performed using an ABI Prism 7500 sequence detector. Primers were designed and synthesized by Shanghai Sangon Biotech (Shanghai, China). The primer sequences for eNOS were 5'-GGAGAAGATGCCAAGGCTGC-3' and 5'-CCAGTGTCCAGACGCACC-3'. The primer sequences for NF-κB were 5'-AAAAACGCATCCCAAGGTGC-3' and 5'-AAGCTCAAGCCACCATACCC-3'. The primer sequences for MCP-1 were 5'-ACCTGGACAAGCAAACCC-3' and 5'-AGGGTGTCTGGGGAAAGCTA-3'. GAPDH was used as an internal reference. The primer sequences for GAPDH were 5'-GCTCGTTCTACGGCACAGG-3' and 5'-GCACGTAGTCTCCACGACAT-3'.
Western blottingHRGECs were lysed in RIPA buffer and stored at –80°C after protein concentration determination using BCA (Beyotime Biotech). SDS-PAGE with a 5% stacking gel and a 12% resolving gel to was used to resolve proteins by molecular weight. Resolved proteins were transferred onto nitrocellulose membranes (Millipore Corp). The primary antibodies used were: anti-NF-κB p65 (Abcam), anti-eNOS (CST), anti-MCP-1 (BD). GAPDH was used as the loading control.
Statistical analysisDescriptive statistics are presented as numbers (proportion) for discrete variables and mean ± SD for continuous normally distributed variables. Data distribution was tested for normality using the Shapiro-Wilk test. Patients were divided into two groups, normal UA group and high UA group based on serum UA concentrations. Comparisons between groups were tested statistically by chi-squared for noncontinuous variables and by student-t test for continuous variables. Comparisons among three BMI groups were tested statistically by one-way ANOVA (analysis of variance). Variables with statistical significance in univariate models and acceptable collinearity were then included in the multivariate analyses. Multivariable logistic regression was also employed to analyze the relationship between UA (normal UA and high UA) and different clinical variables. Statistical tests were two-sided with a 5% significance level. All the data were analyzed using SPSS 18.0 (SPSS Inc, Chicago, IL).
This study includes 301 males and 308 females. Since diagnosis standards for HUA are different between males and females, the clinical characteristics for males and females were analyzed seperately. As shown in Table 1, in male patients, the HUA prevalence is 12.6% and male HUA patients tend to have high BMI (p = 0.031), increased 24-hour urinary micro-albumin (UMA/24 hr) (p = 0.002), high BUN level (p = 0.002), high Cr level (p = 0.000), and decreased creatinine clearance rate (p = 0.003). In female patients, the HUA prevalence is 19.5% and female HUA patients tend to have high BMI (p = 0.000), long waist length (p = 0.000), high waist-hip-ratio (p = 0.000), low HbA1C level (p = 0.015), high VLDL-C level (p = 0.006), high triglyceride level (p = 0.040), increased UMA/24 hr (p = 0.000), high BUN level (p = 0.000), high Cr level (p = 0.000), and decreased creatinine clearance rate (p = 0.000) (Table 2).
| Variable | normal UA (<420) | high UA (≥420) | total | p value# |
|---|---|---|---|---|
| patient number (%) | 263 (87.4) | 38 (12.6) | 301 | — |
| Age, years, mean (SD) | 55.1 (10.5) | 53.1 (14.0) | 54.9 (11.0) | 0.392 |
| Current smokers, n (%) | 186 (70.7) | 31 (81.6) | 217 (72.1) | 0.163 |
| Current drinkers, n (%) | 127 (48.3) | 20 (52.6) | 147 (48.8) | 0.617 |
| Diabetic duration (year), mean (SD) | 7.3 (0.4) | 8.8 (0.8) | 7.5 (0.3) | 0.179 |
| Cornary heart disease, n (%) | 192 (74.7) | 28 (73.7) | 220 (74.6) | 0.892 |
| Diabetic neuropathy, n (%) | 150 (57) | 19 (50.0) | 169 (56.1) | 0.414 |
| BMI (kg/m2), mean (SD) | 26.1 (3.4) | 27.4 (3.7) | 26.3 (3.5) | 0.031* |
| Waist (cm), mean (SD) | 93.5 (9.9) | 96.4 (11.3) | 93.9 (10.1) | 0.136 |
| Waist-hip-rate, mean (SD) | 1.4 (7.5) | 0.9 (0.0) | 1.4 (7.0) | 0.718 |
| HbA1C, mean (SD) | 9.0 (2.3) | 8.4 (2.2) | 8.9 (2.3) | 0.150 |
| Systolic blood pressure (mmHg), mean (SD) | 135.9 (20.5) | 141.6 (20.2) | 136.6 (20.7) | 0.113 |
| Diastolic blood pressure (mmHg), mean (SD) | 82.3 (11.4) | 83.3 (11.7) | 82.4 (11.3) | 0.612 |
| Total cholesterol (mg/dL) | 5.0 (1.2) | 5.0 (1.4) | 2.0 (1.5) | 0.886 |
| HDL cholesterol (mg/dL) | 1.2 (0.3) | 1.1 (0.2) | 1.2 (0.3) | 0.061 |
| LDL cholesterol (mg/dL) | 3.1 (0.9) | 3.2 (1.1) | 3.1 (0.9) | 0.389 |
| VLDL cholesterol (mg/dL) | 0.6 (0.3) | 0.7 (0.3) | 0.6 (0.2) | 0.163 |
| Triglyceride (mmol/L), mean (SD) | 2.0 (0.1) | 2.4 (0.4) | 2.0 (1.5) | 0.075 |
| CRP (mg/L), mean (SD) | 2.7 (1.4) | 3.7 (1.9) | 2.8 (1.7) | 0.139 |
| 24 hours urinary protein (mg) | 80.3 (13.2) | 196.3 (48.3) | 84.4 (13.3) | 0.002** |
| BUN (mmol/L), mean (SD) | 6.2 (3.2) | 9.7 (6.2) | 6.6 (3.8) | 0.002** |
| Cr (μmol/L), mean (SD) | 68.6 (14.2) | 80.4 (35.5) | 72.1 (23.3) | 0.000** |
| Creatinine clearance rate (mL/min), mean (SD) | 117.6 (47.6) | 86.3 (56.2) | 113.7 (49.7) | 0.003** |
# The characteristics of study population with high UA and normal UA were compared using Χ2 tests for categorical variables and Student’s t test for continuous variables.
p for difference between the two groups. * p < 0.05, ** p < 0.01.
BMI, body Mass Index; HbA1C, glycosylated hemoglobin A1C; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; VLDL-C, very low-density lipoprotein cholesterol; CRP, C reactive protein; BUN, blood urea nitrogen; Cr, creatinine.
| Variable | normal UA (<360) | high UA (≥360) | total | p value# |
|---|---|---|---|---|
| patient number (%) | 248 (80.5) | 60 (19.5) | 308 | — |
| Age, years, mean (SD) | 58.3 (10.2) | 60.1 (9.5) | 58.7 (10.1) | 0.220 |
| Current smokers, n (%) | 32 (12.9) | 9 (15) | 41 (13.3) | 0.668 |
| Current drinkers, n (%) | 4 (1.6) | 2 (3.3) | 6 (1.9) | 0.387 |
| Diabetic duration (year), mean (SD) | 9.1 (0.5) | 11.2 (0.7) | 9.5 (0.4) | 0.058 |
| Cornary heart disease, n (%) | 198 (81.1) | 47 (78.3) | 245 (80.6) | 0.621 |
| Diabetic neuropathy, n (%) | 140 (56.5) | 40 (66.7) | 180 (58.4) | 0.150 |
| BMI (kg/m2), mean (SD) | 26.4 (4.0) | 28.6 (4.9) | 26.8 (4.3) | 0.000** |
| Waist (cm), mean (SD) | 91.5 (10.5) | 98.1 (12.0) | 92.7 (11.1) | 0.000** |
| Waist-hip-rate, mean (SD) | 0.91 (0.06) | 0.94 (0.07) | 0.91 (0.06) | 0.026* |
| HbA1C, mean (SD) | 8.8 (1.9) | 8.1 (1.6) | 8.6 (1.8) | 0.015* |
| Systolic blood pressure (mmHg), mean (SD) | 138.8 (20.7) | 142.2 (22.5) | 139.5 (21.0) | 0.276 |
| Diastolic blood pressure (mmHg), mean (SD) | 80.4 (10.3) | 79.8 (11.9) | 80.3 (10.6) | 0.692 |
| Total cholesterol (mg/dL) | 5.4 (1.3) | 5.5 (1.3) | 5.4 (1.3) | 0.466 |
| HDL cholesterol (mg/dL) | 1.5 (0.8) | 1.4 (0.3) | 1.5 (0.6) | 0.327 |
| LDL cholesterol (mg/dL) | 3.4 (1.1) | 3.5 (1.3) | 3.4 (1.1) | 0.482 |
| VLDL cholesterol (mg/dL) | 0.6 (0.1) | 0.7 (0.1) | 0.6 (0.2) | 0.006** |
| Triglyceride (mmol/L), mean (SD) | 1.9 (0.1) | 2.5 (0.3) | 2.0 (0.8) | 0.040* |
| CRP (mg/L), mean (SD) | 3.1 (0.2) | 2.9 (0.3) | 3.1 (0.2) | 0.683 |
| 24 hours urinary protein (mg) | 55.6 (11.0) | 126.5 (26.3) | 69.0 (10.4) | 0.000** |
| BUN (mmol/L), mean (SD) | 5.6 (2.3) | 9.2 (6.1) | 6.3 (3.6) | 0.000** |
| Cr (μmol/L), mean (SD) | 54.7 (14.8) | 69.8 (30.6) | 56.6 (18.2) | 0.000** |
| Creatinine clearance rate (mL/min), mean (SD) | 110.4 (40.9) | 76.0 (43.3) | 104.0 (43.4) | 0.000** |
# The characteristics of study population with high UA and normal UA were compared using Χ2 tests for categorical variables and Student’s t test for continuous variables.
p for difference between the two groups. * p < 0.05, ** p < 0.01.
BMI, body Mass Index; HbA1C, glycosylated hemoglobin A1C; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; VLDL-C, very low-density lipoprotein cholesterol; CRP, C reactive protein; BUN, blood urea nitrogen; Cr, creatinine.
First, we explored the relationship between BMI and UA in our study population. Patients were divided into three BMI groups, normal weight, overweight, and obese. Serum UA increased was accompanied by an elevated BMI in both males and female (Fig. 1A, B). And the percentage of patients with HUA in the obese group was highest compared to the other two BMI groups for both genders (p = 0.005 in male patients and p = 0.003 in female patients, Fig. 1C, D).
The relationship between UA and BMI. A. The distribution of UA in each BMI group in male T2DM patients. B. The distribution of UA in each BMI group in female T2DM patients. C. Percentage of high UA in each BMI group in male T2DM patients. D. Percentage of high UA in each BMI group in female T2DM patients.
Next, we analyzed the influence of BMI and other potential risk factors on HUA by multivariable logistic analyses. The results showed that male T2DM patients having higher BMI (OR 1.711, p = 0.038), BUN (OR 1.100, p = 0.034), and UMA/24 hr (OR 1.004, p = 0.021) were much more likely to have high UA (Table 3). Whereas, for female T2DM patients, the OR of BMI, BUN, and triglyceride were OR 1.169 (p = 0.001), 1.337 (p = 0.000), and 1.359 (p = 0.006), respectively (Table 3). These data indicated that BMI is an independent risk factor for high serum UA in both genders in T2DM patients.
| Variables | OR | 95% CI | p value |
|---|---|---|---|
| In male | |||
| BMI (kg/m2) | 1.711 | 1.030~2.841 | 0.038 |
| BUN (mmol/L) | 1.100 | 1.007~1.200 | 0.034 |
| 24-hour urinary protein (mg) | 1.004 | 1.001~1.008 | 0.021 |
| In female | |||
| BMI (kg/m2) | 1.169 | 1.068~1.280 | 0.001 |
| BUN (mmol/L) | 1.337 | 1.168~1.531 | 0.000 |
| Triglyceride (mmol/L) | 1.359 | 1.091~1.693 | 0.006 |
Then, we analyzed influence of UA condition on renal function in our studied population. The results showed that UMA/24 hr, BUN, and Cr were all increased in high UA group compared to normal UA group (Fig. 2A). Further, using stratified-analysis, in normal serum UA patients, higher UMA/24 hr and serum Cr were observed in overweight and obese groups compare to normal weight group (p = 0.000, Fig. 2B). Whereas, there were no significant differences in these two indexes among the three BMI groups in T2DM patients with high serum UA (p > 0.05, Fig. 2C). There was also no significant differences in BUN levels amongst BMI groups in patients with high or normal serum UA.
The relationship among UA, BMI and renal function impairment. A. The levels of UMA/24 hr, BUN, and Cr in T2DM patients with high serum UA or normal serum UA. *p < 0.05. B. The levels of UMA/24 hr, BUN, and Cr in each BMI group in T2DM patients with normal serum UA. C. The levels of UMA/24 hr, BUN, and Cr and BMI in T2DM patients with high serum UA. *p < 0.05.
Taken together, our cross-sectional study shows that in patients with high serum UA, renal function impairment does not seem to have any relationship with BMI levels. However, renal function might still be impaired in patients with abnormal BMI even if their serum UA levels were normal.
High UA induced ROS production in human renal glomerular endothelial cells (HRGECs)Uric acid can scavenge free radicals and singlet oxygen. However, high concentrations of UA results in ROS overproduction and contribute to vascular endothelial dysfunction [15]. Utilizing the DCFH-DA probe to detect intracellular reactive oxygen species (ROS) levels, it was observed that ROS levels increased with UA treatment in a dose-dependent manner (Fig. 3). These results indicate that UA induces ROS production in HRGECs.
High uric acid levels increase ROS production in HRGECs. A. Representative images showing intracellular ROS production measured with DCFH-DA (a fluorescent probe) after treatment of HRGECs with different concentrations of UA for 24 h. B. Histogram representing the number of ROS-positive cells. The results shown are representative of at least two independent experiments. Bar = mean; error bar = SD (**p < 0.01).
Many studies have demonstrated that HUA may be a risk factor for endothelial dysfunction in which the inflammatory response and oxidative stress play a vital role [16]. We next examined the molecular mechanisms behind UA-induced impaired renal function. At both mRNA and protein levels, eNOS expression decreased with UA treatment in a dose-dependent manner, whereas expression of NF-κB and MCP-1 were enhanced with UA treatment (Fig. 4). These results indicate that high levels of UA resulted in endothelial dysfunction in HRGECs.
Effect of UA treatment on the expression levels of eNOS, NF-κB and MCP-1. A. qRT-PCR analysis of mRNA levels of eNOS, NF-κB and MCP-1 in HRGECs after treatment with different concentrations of UA for 24 h. B. Western blot analysis of protein levels of eNOS, NF-κB and MCP-1 in HRGECs after treatment with different concentrations of UA for 24 h. The histograms show the relative intensities of eNOS (C), NF-κB (D), and MCP-1 (E) normalized to β-actin levels. The results shown are the representative of at least two independent experiments. Bar = mean; error bars = SD (**p < 0.01).
Serum uric acid (SUA) level positively associated with T2DM development [17], and elevated SUA is an independent predictor of mortality in T2DM patients. A 0.1 mmol/L increase in SUA leads to a 9% increase in the risk of mortality in T2DM [18]. To date, there are limited reports investigating HUA prevalence and its associated risk factors in Chinese T2DM patients with obesity. In the current study, we demonstrated that the prevalence rates of HUA were 19.5% in women and 12.6% in men amongst North Chinese T2DM patients. Higher HUA prevalence was observed in both the overweight and obese groups in women, while in men, only the obese group had higher HUA prevalence. Menopausal women have higher SUA levels than premenopausal women since estrogen reduces SUA levels [19]. Our study showed that HUA prevalence was 26.6% in obese women and 22.6% in obese men, which is similar to that of a previous study conducted in South Chinese T2DM patients with central obesity [20]. Also, our results indicate that BMI is an independent risk factor for SUA in T2DM patients in North China for both men and women, which is also consistent with previous reports [21]. Therefore, HUA is an important public health problem in Chinese T2DM patients, especially in patients with higher BMI.
Further, our results demonstrated that T2DM patients have a higher risk of developing CKD when they present with a higher BMI, even if serum UA levels are normal. Zhang J et al. previously showed that for metabolically healthy individuals, the risk of CKD increases with a higher BMI, and obese people tend to present with a higher risk of developing CKD [22]. Therefore, it is recommended that T2DM patients pay particular attention to the maintenance of a healthy body weight as a BMI in the normal range will lower the risk of renal function impairment.
Hyperuricemia is not only a causative factor for gout [23], but is also a risk factor for CKD [24]. The cytoplasmic NLR Family Pyrin Domain Containing 3 (NLRP3) protein participates in the inflammatory response in both humans and rodents [25]. Previous studies have shown that soluble UA activates the NLRP3/ASC inflammasome in macrophages, and hyperuricemia contributes to the progression of diabetic nephropathy [26]. Also, intracellular lysosomes can take up crystallized UA, and subsequent lysosomal breakdown of UA coupled with mitochondrial ROS production contributes to the activation of the NLRP3/ASC inflammasome [27]. Oxidative stress also plays an important role in hyperuricemia-induced endothelial dysfunction [28, 29]. Some studies indicate that hyperuricemia causes oxidative stress, activates the inflammatory response and decreases eNOS synthesis, resulting in damage to the endothelial cells [30]. Our results indicate that UA induces ROS generation. We also found UA downregulated the production of eNOS in HRGECs. A recent study concluded that mitochondrial abnormalities are a consequence of a chronic inflammation but not a direct effect of UA [31]. Therefore, oxidative stress might arise from the activation of the NLRP3/ASC pathway. Moving forward, further studies to elucidate the detailed molecular mechanisms mediated by UA in CKD progress will be an exciting avenue to pursue.
It is widely known that NF-κB is an important transcription factor with roles in immune response, proliferation, apoptosis, and the inflammatory process. In this study, MCP-1 expression increased with higher levels of UA treatment. Moreover, soluble UA stimulates the NLRP3 inflammasomes to produce IL-1, resulting in the activation of NF-κB in human renal proximal tubular cells [26]. Our results also showed that in HRGECs, UA treatment induced the overproduction of NF-κB. These results demonstrate that UA may activate NF-κB in HRGECs and further promote the of production and release of pro-inflammatory cytokines, contributing to an amplification of the inflammatory response, leading to endothelial dysfunction.
In summary, based on our cross-sectional investigation, in T2DM patients with high serum UA, renal function impairment does not seem to have any relationship with BMI levels. However, renal function might still be impaired in patients with abnormal BMI even if their serum UA levels were normal. Furthermore, our in vitro experiments suggest that high concentrations of uric acid have a pro-oxidative effect which triggers endothelial dysfunction.
This study is funded by the China Postdoctoral Science Foundation (No. 2015M581309 to Bei SUN) and the National Natural Science Foundation of China (No. 81502828 to Xinyue ZHI).
None of the authors have any potential conflicts of interest associated with this research.
