Association between serum uric acid and fasting plasma glucose level in non-diabetic, pre-diabetic and diabetic adults: a population-based study in China

DOI: https://doi.org/10.21203/rs.3.rs-2263784/v1

Abstract

Objective: This study aimed to examine the relationship between serum uric acid (SUA) and fasting blood glucose (FBG) levels in a general population from Northwest China.

Methods: The current study used data from a cross-sectional survey conducted in Ningxia Hui Autonomous Region, which investigated the prevalence and risk factors of cardiovascular disease. All subjects underwent SUA and FPG test. Generalized additive model and two-piece wise linear regression models were applied to explore the relationships between SUA and FPG level. Triglyceride-glucose (TyG) was further taken as the index of insulin resistance, and its mediating effects on the association between SUA and FPG level was analyzed.

Results: A total of 10 217 individuals aged 18 and over were included in the current analysis. Generalized additive models verified the inverted U-shaped association between SUA and FPG level and the inflection points of FPG levels in the curves were 6.5mmol/L in male and 8.8mmol/L in female respectively. TyG is an intermediate variable in the relationship between SUA and elevated FPG level, the mediating effect of which are 12.82% (P<0.001) and 34.02% (P<0.001) in male and female respectively.

Conclusions: Inverted U-shaped associations between FPG and SUA levels were observed in both genders. The threshold of FPG level was generally lower in male than in female. The association is partly mediated through insulin insistence.

Introduction

Diabetes is a metabolic disorder characterized by hyperglycemia (1). Over the past decades, the disease burden of diabetes has increased  across the world (2, 3). Despite improved understanding of the pathophysiology of the disease, identifying which diabetes patients are most likely to develop life-threatening complications is an ongoing challenge (4).

Serum uric acid (SUA) is emerging as a potential marker of diabetes risk in this context (5-7). Uric acid is final product of purine nucleotides metabolism that is mainly filtered by glomerulus and reabsorbed by the proximal tubule (8). Greater serum concentrations of insulin cause higher renal reabsorption of uric acid, increasing concentrations of SUA (9). Furthermore, there is mounting observational evidence that an elevated SUA level precedes the development of insulin resistance and diabetes (10, 11).

However, those above-mentioned findings conflict with studies showing that diabetes is inversely associated with SUA level (12, 13) and protective against complications of hyperuricemia (14). In some cases, there was no significant association between SUA and glycemic state (15). Moreover, an inverted U-shaped relationship between SUA and FPG level was found and the association dynamically changed according to the levels of glucose tolerance in many studies (16-18). The relationship between uric acid level and diabetes is still inconclusive, which might be attributable to the heterogeneity in ethnicities and glycemic status of the participants.

Previously, most studies were conducted in the population selected in hospital, whom with existing co-morbid conditions such as old age, diabetes, or at high risk for kidney or cardiovascular disease (11, 12, 16). There are few studies that have evaluated the relationship between SUA level and FPG across the full spectrum of glucose tolerance in a natural population. It is important to know the actual trend of SUA in healthy, pre-diabetic and diabetic individuals, as hyperuricemia is increasingly found to be associated with a number of modifiable risk factors contributing to cardiovascular diseases (19). 

Based on a large sample of natural population with a full spectrum of glucose tolerance in Ningxia, China, we aimed to assess the relationship between SUA and FPG levels in non-diabetic healthy, pre-diabetic and diabetic individuals.

Methods

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. 

Results

Characteristics of the study population 

The clinical and metabolic characteristics of the study population are shown in Table 1. Of the 10,207 participants in this study, 4,611(45.10%) were male and 5,613 (54.90%) were female. The median age was 44 years. In addition, the prevalence of pre-diabetes and diabetes were higher in male than in female (P<0.001). Compared with non-diabetes participants, pre-diabetes and diabetes participants were older, more obese, and more likely to have higher proportion of drinking, blood pressure, TG, TC, LDL-C and SUA, while HDL-C and eGFR were lower (P<0.05).

Prevalence of hyperuricemia by gender and glycemic statues

The prevalence of hyperuricemia differed between male and female according to their glycemic statues (Figure.1). Male participants had a higher prevalence of hyperuricemia than female in no-diabetes and pre-diabetes groups (P<0.05). Moreover, the prevalence was higher in pre-diabetes group (28.08%) compared to pre-diabetes (24.90%) and diabetes (21.17%) groups in male (P<0.05). An increasing trend of the prevalence of hyperuricemia was observed across no-diabetes, pre-diabetes and diabetes groups in female (P<0.05).

Nonlinear relationship between serum uric acid and fasting plasma glucose level

The associations between FPG and SUA levels were explored with unadjusted GAM after stratifying participants by gender (Figure.2). Smoothing splines suggested an inverted U-shaped relationship between FPG and SUA levels. The SUA levels decreased with increasing FPG levels before the inflection points and then increased. The inflection points for FPG differed in two genders, with a threshold of 6.5mmol/L in male and 8.8mmol/L in female. 

Linear regression analyses between serum uric acid and fasting plasma glucose level by gender

In order to further verify the relationships between SUA and FPG level, stratified linear regression analyses were performed by gender and pre-specified subgroups. As shown in Table 2, positive correlations were observed between SUA and FPG level in participants whose FPG level did not exceed the above mentioned threshold in both male and female (β=14.53,19.86,7.79,7.29 in male, β=14.24,15.50,7.95,7.96 in female, P<0.05 for all models). Meanwhile, a negative correlation was observed in male participants whose FPG level were greater than the threshold in all models (β=-5.71, -5.87, -6.54, -6.69, P<0.05 for all models).

Mediating effects of insulin resistance on the association between serum uric acid and fasting plasma glucose level

TyG was further taken as the index of insulin resistance, and its mediating effects on the association between SUA and FPG level is shown in Table 3. After adjusting for TyG, the association between FPG and SUA is moderately attenuated in participants whose FPG levels under the above mentioned threshold. TyG is an intermediate variable in the relationship between SUA and elevated FPG level, the mediating effect of which are 12.82% (P<0.001) and 34.02% (P<0.001) in male and female respectively.

Discussion

There has been much debate over the relationship between SUA levels and diabetes mellitus. In this study of a large population with carefully adjusting for confounders, inverted U-shaped associations between FPG and SUA levels were observed in both genders. The threshold of FPG level was generally lower in male than in female. This association was consistent with many previous studies that SUA is significantly elevated with increasing FPG level in non-diabetic stage, and reduced after the onset of diabetes (11, 13, 18, 22), though the threshold varied. 

It has been demonstrated that insulin might play an important role in the regulation of uric acid (23-25). Consistent with previous studies, mediating effects of insulin resistance on the association between SUA and FPG level were observed in the present study. Greater serum concentrations of insulin represent a compensatory mechanism to overcome insulin resistance (24) and hyper insulinemia might be the driver of the ascending segment of the splines. Insulincan promote uric acid synthesis by enhancing the xanthine dehydrogenase and purine nucleoside phosphorylase activities (25). Moreover, the reabsorption of renal uric acid might be enhanced by high serum insulin levels through elevated expression of the urate transporter-1 (26).

However, pronounced hyperglycemia offset the effect of insulin on SUA levels when hyperglycemia develops and insulin secretion declines with the ß-cell function altered (27), as shown in the descending segment of the splines. Competition for reabsorption between glucose and uric acid was the possible mechanism. Plasma glucose is freely filtered at the glomerulus, with almost all of it reabsorbed in the proximal tubule in normal situations (28). Increased levels of glucose in the urine could competitively inhibit renal uric acid reabsorption by regulating of sodium dependent anion transporters, such as Sodium-glucose co-transporter 2 (SGLT2) (29) and Sodium-glucose co-transporter 1(SGLT1) (30). Combined with the results of this study, we deduced that the interaction between uric acid and glucose is dominated by insulin resistance when FPG level under the threshold and dominated by osmotic diuresis when FPG level over the threshold.

The relationship among SUA, FPG and serum lipid are also deserve to be mentioned. In line with previous studies (31, 32), increasing trend at the mean level of TG, TC, and LDL-C were observed across the glycemic statues and SUA quartiles, while HDL-C decreased. Several putative mechanisms may be proposed for the observed results. The increase of TG caused by high-fat diet, lower physical activity, etc., which would induce ectopic fat and then insulin resistance (33). Changes in glycolysis process under insulin resistance could lead to increased production of SUA, which caused the reduction of lipoprotein lipase activity and TG decomposition, therefore the more serious insulin resistance (5, 34). At the initial stage of insulin resistance, the ß-cell could make up for the relative deficiency of insulin by increasing insulin secretion. However, insulin secretion declines with the ß-cell function altered and hyper glycemia aggravates the ß-cell dysfunction (35). Insufficient insulin level in pre-diabetes and diabetes weakened the fat decomposition and free fatty acids are released in large quantities, which provide rich raw materials for the liver to synthesize TG, and hyperglycemia may also accelerate the dissociation. Elevated TG may increase the activity of cholesterol ester transfer protein (CEPT), which decreased HDL-C and increased LDL-C. 

Furthermore, our results showed an apparent gender difference in the nonlinear relationships between SUA and FPG levels, which were in line with previous studies. For example, ZhuYuanyue et al. found that the inflection point values of FBP were 6.5mmol/L in male and 8.0mmol/L in female (17). Likewise, Whitehead et al. demonstrated that the threshold of the inverted U-shapes were higher in female (9.0mmol/L) than in male (7.0mmol/L) (18). In addition, the prevalence of hyperuricemia differed in male and female, which stably rose from no-diabetic to diabetic condition in male, while the highest prevalence was observed among pre-diabetic male rather than diabetic male. A possible explanation for the difference in inflection points is that hyperuricemia in female is relatively mild, which is not enough to inhibit the reabsorption of serum glucose until it reaches a higher level (36).Therefore, the prevalence of hyperuricemia decreased earlier with FPG level in male than in female. However, further studies with a longitudinal design are essential to examine the underlying mechanisms.

The main strengths of the current study included a population-based study design with random sampling, a reasonably large sample size and age distribution. Moreover, the inclusion of individuals with different glycemic status and the availability of data on potential confounders for multi variable adjusted, which enabled the more comprehensive description of the association between SUA and FPG levels. The major limitation of the study was the cross-sectional design, which could not provide any information about cause and effect. Prospective studies are required for further investigation of these findings. In addition, we did not collect information on medical histories and therefore could not eliminate the influence of related drugs or impaired renal function on the association. 

Conclusions

In summary, inverted U-shaped association between SUA and FPG levels were observed in this general population in Northwest China. The threshold of FPG level was generally lower in male than in female. The association is partly mediated through serum insulin levels. Further studies with longitudinal designs would better reveal the underlying mechanisms for the relationship.

Abbreviations

NHAR: Ningxia Hui Autonomous Region; WC: waist circumference; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; FPG: fasting plasma glucose; TG: triglycerides; TC: total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; SUA: serum uric acid; TyG: triglyceride-glucose index; eGFR: glomerular filtration rate.

Declarations

Acknowledgements

The authors would like to thank the Municipal Health Bureau of the Ningxia Hui Autonomous Region for providing support for field work. The authors also thank the Beijing CIC Medical Laboratory for providing laboratory tests for the present study.

Authors’ contributions

Yining Liu: Investigation, Writing original draft, Formal analysis. Peifeng Liang: Investigation, Formal analysis. Chen Chen: Investigation, Formal analysis. Hongjuan Shi: Investigation. Hong Luan: Investigation. Chao Shi: Conceptualization, Methodology, Investigation, Writing original draft, Formal analysis, Supervision.

Funding

This work was supported by the Key R&D Program of Ningxia Hui Autonomous Region (No. 2020BEB04032), the Natural Science Foundation of China (No. 82160644).

Availability of data and materials

The datasets used and analyzed during the present study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Research protocols of the present study were approved by the Institutional Review Board of the People's Hospital of Ningxia Hui Autonomous. All participants provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Tables

Table 1. Clinical and metabolic characteristics of the study population by categories of fasting plasma glucose level

Variables

All

 

Non-diabetes

 

Pre-diabetes

 

Diabetes

value

 (n=10 217)

 

 (n=8 654)

 

 (n=1 034)

 

 (n=529)

Age, Median (quartile)

44 (32;58)

 

43 (31;56)

 

51 (40;65)

 

54 (41;68)

<0.001 a

Gender

 

 

 

 

 

 

 

<0.001 b

     Male, n (%)  

4 611 (45.10)

 

3 817 (44.09)

 

520 (50.29)

 

274 (51.50)

 

     Female, n (%)  

5 613 (54.90)

 

4 841 (55.91)

 

514 (49.71)

 

258 (48.50)

 

Smoking, n (%)

2 397 (23.46)

 

1 996 (23.06)

 

270 (26.11)

 

131 (24.76)

0.07 b

Drinking, n (%)

2 177 (21.31)

 

1 815 (20.97)

 

252 (24.37)

 

110 (20.79)

<0.05 c

WC (cm), Mean±SD

83.06±11.66

 

82.17±11.47

 

87.12±11.39

 

89.58±11.59

<0.001c

BMI (kg/m2)

24.99±4.81

 

24.75±4.89

 

26.04±3.88

 

26.91±4.46

<0.001 c

SBP (mm Hg), Mean±SD

129.44±19.76

 

127.90±19.26

 

137.02±19.78

 

139.76±21.25

<0.001 c

DBP (mm Hg), Mean±SD

80.75±11.17

 

80.06±10.99

 

84.01±11.15

 

85.61±11.76

<0.001 c

FPG (mmol/L), Mean±SD

5.56±1.15

 

5.25±0.49

 

6.44±0.24

 

8.99±2.54

<0.001 c

TG (mmol/L), Mean±SD

1.48±1.31

 

1.34±0.92

 

1.87±1.46

 

2.94±3.53

<0.001 c

TC (mmol/L), Mean±SD

4.26±0.96

 

4.21±0.93

 

4.44±1.01

 

4.64±1.19

<0.001 c

HDL-C (mmol/L), Mean±SD

1.26±0.27

 

1.28±0.27

 

1.21±0.27

 

1.18±0.25

<0.001 c

LDL-C (mmol/L), Mean±SD

2.58±0.77

 

2.55±0.75

 

2.73±0.80

 

2.80±0.85

<0.001 c

SUA (µmol/L), Mean±SD

324.50±90.46

 

321.02±89.83

 

343.61±91.92

 

344.05±90.89

<0.001 c

TyG

6.96±0.68

 

6.85±0.61

 

7.36±0.63

 

7.97±0.82

<0.001 c

eGFR (ml/min/1.73 m2

107.60±18.39

 

108.29±18.23

 

104.02±18.21

 

103.27±19.96

<0.001 c

Abbreviations: WC, waist circumference; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglycerides; TC, total cholesterol;  HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SUA, serum uric acid; TyG, triglyceride-glucose index; eGFR,     glomerular filtration rate.

P value was obtained from Kruskal–Wallis rank-sum test.

P values were obtained from chi-square test.

P values were obtained from one-way ANOVA.

 

Table 2.  Linear regression analyses between serum uric acid and fasting plasma glucose level by gender

Fasting plasma glucose

Model 1 a

 

Model 2 b

 

Model 3 c

 

Model 4 d

 β (95%CI)

value

 

 β (95%CI)

value

 

 β (95%CI)

value

 

 β (95%CI)

value

Male

 

 

 

 

 

 

 

 

 

 

 

 FPG<6.5mmol/L

14.53 (9.57, 19.47)

<0.001

 

19.86(15.08, 4.65)

<0.001

 

 7.79 (3.28, 12.29)

0.001

 

 7.29 (2.77, 11.80)

<0.005

 FPG≥6.5mmol/L

-5.71(-9.29, -2.13)

<0.005

 

-5.87 (-9.34, -2.40)

0.001

 

 -6.54(-9.75, -3.33)

<0.001

 

-6.69 (-9.91, -3.47)

<0.001

Female

 

 

 

 

 

 

 

 

 

 

 

FPG<8.8mmol/L

15.24(12.52, 17.96)

<0.001

 

15.50(12.71, 8.29)

<0.001

 

7.95 (5.36, 1.32)

<0.001

 

7.96 (5.37, 10.55)

<0.001

FPG≥8.8mmol/L

-6.49 (-13.67, 0.69)

0.076

 

-6.21 (-13.39, 0.96)

0.089

 

-5.28 (-11.93, 1.36)

0.117

 

 -5.76(-12.44, .92)

0.090

Abbreviations: WC, waist circumference; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglycerides; TC, total cholesterol;  HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SUA, serum uric acid; TyG, triglyceride-glucose index; eGFR,     glomerular filtration rate.

a Model was unadjusted.

b Model was adjusted for age.

c Model was unadjusted for age, BMI, blood pressure, TC, LDL-C, HDL-C, TG, eGFR.

d     Model was unadjusted for age, BMI, blood pressure, TC, LDL-C, HDL-C, TG, eGFR, smoking and drinking.

 

Table 3  Mediating effects of insulin resistance on the association between serum uric acid and fasting plasma glucose level

Fasting plasma glucose level

Total effect a

valuec

Direct effect b

valuec

Proportion of mediating effects (%)

Male

 

 

 

 

 

FPG<6.5mmol/L

 7.29 (2.77, 11.80)

<0.001

6.35 (1.39, 11.32)

<0.001

12.82

Female

 

 

 

 

 

FPG<8.8mmol/L

7.96 (5.37, 10.55)

<0.001

5.25 (2.41, 8.10)

<0.001

34.02

Abbreviations: WC, waist circumference; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglycerides; TC, total cholesterol;  HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SUA, serum uric acid; TyG, triglyceride-glucose index; eGFR,     glomerular filtration rate.

a Model was adjusted for age, BMI, blood pressure, TC, LDL-C, HDL-C, TG, eGFR, smoking and drinking.

b Model was adjusted for age, BMI, blood pressure, TC, LDL-C, HDL-C, TG, eGFR, smoking, drinking and TyG.

P values were derived from Sobel test.