Abstract
Background
To study the relationship between hyperuricemia and cardiovascular diseases (CVDs) risk factors in a Chinese population.
Material/Methods
Data analyzed in this study were from the Chinese Hyperuricemia and Gout Database. Indicators of serum uric acid (SUA) level, height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking status, alcohol consumption, blood glucose, cholesterol, and triglycerides were measured. T test, one-way analysis of variance, Pearson’s correlation, multivariate linear regression, and multivariate logistic regression were used.
Results
Compared with normouricemic men, hyperuricemic men had greater height (P<0.01), weight (P<0.001), body mass index (BMI) (P<0.001), SBP (P<0.01), DBP (P<0.001), cholesterol (P<0.01), and triglyceride (P<0.001). Compared with normouricemic women, hyperuricemic women were older (P<0.01) and had greater weight (P<0.05), BMI (P<0.01), SBP (P<0.01), DBP (P<0.05), glucose (P<0.05), and triglyceride (P<0.001). In men, an increase of 1 mg/dL in SUA was associated with a 0.279 kg/m2 increase in BMI (P<0.001), a 2.438 mg/dL increase in cholesterol (P<0.05), a 10.358 mg/dL increase in triglyceride (P<0.001), and a 3.1 mg/dL decrease in glucose (P<0.01). In women, an increase of 1 mg/dL SUA was associated with a 0.168 kg/m2 increase in BMI (P<0.01) and a 3.708 mg/dL increase in triglyceride (P<0.01). After adjustment, SUA was strongly associated with obesity and hyperlipidemia in both sexes.
Conclusions
Elevated serum uric acid concentration was strongly associated with obesity and hyperlipidemia in both men and women. These results indicated that, among hyperuricemia patients, we should pay more attention to the possibility of cardiovascular complications. These results might provide a novel target or a possible new treatment for cardiovascular diseases by lowering the level of serum uric acid.
MeSH Keywords: Cardiovascular Diseases, Hyperuricemia, Risk Factors
Background
Cardiovascular diseases (CVDs) are a set of multiple disorders of the heart and blood vessels, including coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis, and pulmonary embolism [1]. According to WHO data [2], approximately 17.3 million people world-wide died from CVDs in 2008, over 80% of which lived in low- and middle-income countries. In 2012, cardiovascular diseases were the leading cause of non-communicable disease deaths (17.5 million deaths), and it has been predicted that there will be more than 23 million people world-wide dying annually from CVDs by 2030 [3]. There are various risk factors involved for CVDs, including heredity/family history, sex, race/ethnicity, age, hypertension, hypercholesterolemia, diabetes mellitus, obesity, smoking/tobacco, stress/depression, and risk behaviors [4–8]. Genetic risk factors such as carotid intima-media thickness are related to cardiovascular morbidity and mortality [4,9], and socioeconomic factors and social environment also affect the deterioration and prognosis of CVDs [6,7].
Hyperuricemia has been viewed as being connected with CVDs risk factors since the last century [10]. Hyperuricemia was observed with an increased morbidity and mortality of CVDs such as hypertension, coronary heart disease (CHD), and myocardial infarction (MI) [11–13]. Many CVDs risk factors were thought to be associated with increased serum uric acid (SUA), such as: indicators of obesity, including body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR); indicators of hyperlipidemia including cholesterol, triglyceride, low-density lipoprotein (LDL), and high-density lipoprotein (HDL); and indicators of hypertension, including systolic blood pressure (SBP), and blood glucose and insulin level [13–21]. These results indicate that SUA-lowing treatment may be useful in offering a possible novel target for controlling CVDs [20,22–24].
However, the relationship between hyperuricemia and CVDs risk factors is controversial and conflicting. First, the debate focuses on whether hyperuricemia is an independent risk factor for CVDs or is only associated with CVDs because of confounding factors. Some studies reported that increased SUA was an independent risk factor contributing to CVDs [13,19,20], but other studies found that SUA was not a truly independent risk factor for CVDs. Increased SUA appeared to be an integral part of the cluster of risk factors associated with CVDs, including obesity, raised serum triglycerides, and cholesterol [17,18]. Second, the debate focuses on the sex difference in this relationship. Some studies found that the significant association between hyperuricemia and CVDs only existed in women but not in men [13,18]. However, other studies indicated that the positive association was observed in men [11,17]. Also, there were studies demonstrating that the relationship was seen in both sexes [21]. Third, the prevalence of hyperuricemia was different in different racial populations [14,25–27].
However, there are few studies on this relationship in the Chinese population. We designed the present study to investigate the relationship between hyperuricemia and CVDs risk factors in Chinese men and women. Indicators of CVDs were evaluated and the association with SUA levels was analyzed.
Material and Methods
Participants
The data analyzed in the present study were based on Chinese Hyperuricemia and Gout Database, provided by the Chinese National Scientific Data Sharing Platform for Population and Health. The participants were composed of health checkup residents in the Beijing Xiehe Hospital and part of the community population in Beijing, China. There were 940 participants in total, including 599 men, 288 women, and other 53 participants without sex information, ranging from 18 to 90 years old. Those cases without sex information were excluded from further analysis.
Measures
Indicators of SUA level, height, weight, SBP, DBP, fatty liver, smoking status, alcohol consumption, blood glucose, cholesterol, and triglycerides were measured in the participants. SUA was measured to the nearest 0.1 mg/dL. Hyperuricemia was defined as SUA ≥7.0 mg/dL for men and SUA ≥6.0 mg/dL for women [28,29]. Complications were analyzed by the diagnostic history, including CHD, hypertension, stroke, hyperlipidemia, DM, and gout. Fatty liver was observed by B-mode ultrasonography. Smoking status was divided into 3 groups: current smoker, non-smoker, and former smoker. Alcohol consumption was classified into current drinker, non-drinker, and former drinker. Among current drinkers, the frequency of alcohol consumption was recorded as frequency/week, and the alcoholic beverage classifications (spirits, beer, and wine) were also recorded. Physical activities were recorded by frequency per week, light activity defined as less than 2 times/week, mediate activity defined as 3–5 times/week, and heavy activity defined as more than 6 times/week [30].
Height was measured to the nearest 1 cm, weight was measured to the nearest 0.1 kg, and BMI was calculated as body weight/height2 (kg/m2). Obesity was defined as BMI ≥30.0 kg/m2, overweight was defined as 25.0 kg/m2 ≤BMI ≤29.9 kg/m2, normal was defined as 18.5 kg/m2 ≤BMI ≤24.9 kg/m2, and underweight was defined as BMI ≤18.4 kg/m2 [30]. Both SBP and DBP were measured to the nearest 1 mmHg. Hypertension was defined as having blood pressure ≥140/90 mm Hg, or currently undergoing anti-hypertensive pharmacologic treatment [31]. Fasting plasma glucose was measured to the nearest 1 mg/dL. Diabetes mellitus (DM) was defined as fasting plasma glucose (FPG) ≥126 mg/dL or currently undergoing pharmacologic treatment, impaired fasting glucose (IFG) was defined as 110 mg/dL ≤FPG <126 mg/dL, and normal state was defined as FPG <110 mg/dL [32]. Cholesterol and triglyceride were measured to the nearest 1 mg/dL. Hyperlipidemia was defined as serum triglyceride level ≥150 mg/dL or total cholesterol level ≥200 mg/dL [33].
Statistical analyses
Continuous variables are provided as mean with standard deviation (SD). Categorical variables were classified into groups as described above. The t test was used to detect the difference between men and women, or to compare patients with hyperuricemia and participants with normouricemia. One-way analysis of variance (ANOVA) was used when there were more than 2 groups. Pearson’s correlation coefficients were calculated to detect the correlation between the level of SUA and other CVDs risk factors in both men and women. Two multivariate linear regression models were applied to determine the different effects of different covariates contributed to the variation of the SUA. The first model was a stepwise section procedure and the second model was an enter section procedure. Also, for those categorical variables, multivariate logistic regression models (adjusted by age and other confounding factors) were used to determine the relationship between hyperuricemia and CVDs risk factors. P<0.05 was regarded as statistical significance.
Results
We calculated the mean value of each indicator in Chinese men and women, as shown in Table 1. By comparing the indicators between men and women, we found that, except for age and cholesterol, other indicators, such as SUA, height, weight, BMI, SBP, DBP, the level of serum glucose, and triglyceride, were significantly different for men and women. These results indicated that the study should be implemented for each sex separately, instead of a mixed-sex study. In Table 2, the portions of different status were also calculated in both sexes. The comparison between the 2 sexes was similar with the results of Table 1, except for the DM, IFG, and normal state divided by the level of glucose. However, we noted that in Chinese women, the numbers of current and former smokers were too small to be included as accurate factors.
Table 1.
The comparison of cardiovascular disease risk factors between men and women.
| Unit | Men | Women | P value | |||
|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | |||
| Number | 599 | 288 | ||||
| SUA | mg/dL | 5.60 | 1.20 | 4.21 | 1.08 | *** |
| Age | years | 46.61 | 16.17 | 48.28 | 17.49 | P=0.175 |
| Height | cm | 170.60 | 6.05 | 158.90 | 6.46 | *** |
| Weight | kg/m2 | 72.35 | 10.56 | 58.36 | 8.81 | *** |
| BMI | mmHg | 24.83 | 3.10 | 23.22 | 3.44 | *** |
| SBP | mmHg | 123.63 | 15.78 | 115.76 | 15.08 | *** |
| DBP | mmHg | 75.69 | 9.49 | 69.57 | 9.13 | *** |
| Serum glucose | mg/dL | 102.44 | 26.28 | 97.04 | 13.88 | *** |
| Cholesterol | mg/dL | 181.19 | 32.70 | 184.47 | 37.83 | P=0.211 |
| Triglyceride | mg/dL | 139.55 | 85.51 | 118.61 | 78.12 | *** |
SD – standard deviation; SUA – serum uric acid; BMI – body mass index; SBP – systolic blood pressure; DBP – diastolic blood pressure; P value was compared between men and women;
means P<0.001.
Table 2.
The portions of different status of cardiovascular disease risk factors between men and women.
| Men | Women | P value | ||||
|---|---|---|---|---|---|---|
| Number | Percentage | Number | Percentage | |||
| Smoking status | ||||||
| Current smoker | 116 | 32.13% | 6 | 2.94% | ||
| Non-smoker | 198 | 54.85% | 194 | 95.10% | ||
| Former smoker | 47 | 13.02% | 4 | 1.96% | *** | |
| Alcohol consumption | ||||||
| Current drinker | 217 | 60.11% | 48 | 23.53% | ||
| Non-drinker | 117 | 32.41% | 154 | 75.49% | ||
| Former drinker | 27 | 7.48% | 2 | 0.98% | *** | |
| Physical activities | ||||||
| Light activity | ≤2 times/week | 91 | 25.35% | 76 | 37.25% | |
| Mediate activity | 3–5 times/week | 136 | 37.88% | 49 | 24.02% | |
| Heavy activity | ≥6 times/week | 132 | 36.77% | 79 | 38.73% | ** |
| Body mass index | ||||||
| Obesity | BMI ≥30.0 kg/m2 | 25 | 4.48% | 10 | 3.51% | |
| Overweight | 25.0 kg/m2 ≤BMI ≤29.9 kg/m2 | 244 | 43.73% | 73 | 25.61% | |
| Normal state | 18.5 kg/m2 ≤BMI ≤24.9 kg/m2 | 279 | 50.00% | 184 | 64.56% | |
| Underweight | BMI ≤18.4 kg/m2 | 10 | 1.79% | 18 | 6.32% | *** |
| Blood pressure | ||||||
| Hypertension | BP ≥140/90 mm Hg | 80 | 14.41% | 19 | 6.71% | |
| Normal state | BP <140/90 mm Hg | 475 | 85.59% | 264 | 93.29% | *** |
| Serum glucose | ||||||
| DM | FPG ≥126 mg/dL | 41 | 7.11% | 11 | 3.83% | |
| IFG | 110 mg/dL ≤FPG <126 mg/dL | 52 | 9.01% | 20 | 6.97% | |
| Normal state | FPG <110 mg/dL | 484 | 83.88% | 256 | 89.20% | P=0.082 |
| Cholesterol | ||||||
| Hyperlipidemia | Chol ≥200 mg/dL | 167 | 28.99% | 99 | 34.49% | |
| Normal state | Chol <200 mg/dL | 409 | 71.01% | 188 | 65.51% | P=0.101 |
| Triglyceride | ||||||
| Hyperlipidemia | TG ≥150 mg/dL | 187 | 32.47% | 73 | 25.52% | |
| Normal state | TG <150 mg/dL | 389 | 67.53% | 213 | 74.48% | * |
SD – standard deviation; SUA – serum uric acid; BMI – body mass index; SBP – systolic blood pressure; DBP – diastolic blood pressure; DM – diabetes mellitus; FPG – fasting plasma glucose; IFG – impaired fasting glucose; Chol – cholesterol; TG – triglyceride; P value was compared between men and women;
means P<0.05;
means P<0.01;
means P<0.001.
In Table 3, for both sexes, we compared the mean value of each indicator between patients with hyperuricemia and participants with normouricemia. In men, the age of hyperuricemic patients was not significantly different from that of normal participants (P=0.644). However, the height, weight, and BMI of patients with hyperuricemia were significantly higher than those of participants with normouricemia. Also, both SBP and DBP of patients with hyperuricemia were significantly higher than those of normal participants. In addition, the level of cholesterol and triglyceride were also higher for patients. In women, hyperuricemic patients were older, with higher value of weight, BMI, SBP, DBP, glucose, and triglyceride.
Table 3.
The comparison of cardiovascular disease risk factors between patients with hyperuricemia and participants with normouricemia.
| Unit | Men | Women | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Hyperuricemia | Normouricemia | P value | Hyperuricemia | Normouricemia | P value | ||||||
| Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||||
| Number | 66 | 512 | 20 | 266 | |||||||
| SUA | mg/dL | 7.75 | 0.69 | 5.33 | 0.95 | *** | 6.55 | 0.45 | 4.04 | 0.89 | *** |
| Age | years | 46.14 | 14.95 | 47.05 | 16.45 | P=0.644 | 60.25 | 14.23 | 47.46 | 17.42 | ** |
| Height | cm | 172.29 | 4.93 | 170.37 | 6.18 | ** | 158.37 | 6.54 | 158.94 | 6.49 | P=0.715 |
| Weight | kg | 78.57 | 10.21 | 71.52 | 10.38 | *** | 62.71 | 8.79 | 58.24 | 8.78 | * |
| BMI | kg/m2 | 26.45 | 3.04 | 24.61 | 3.05 | *** | 25.00 | 3.05 | 23.08 | 3.49 | ** |
| SBP | mmHg | 129.12 | 14.89 | 123.03 | 15.87 | ** | 127.68 | 16.38 | 114.93 | 14.66 | ** |
| DBP | mmHg | 80.22 | 9.32 | 75.18 | 9.37 | *** | 72.84 | 10.22 | 69.36 | 9.05 | * |
| Serum glucose | mg/dL | 101.56 | 13.79 | 102.56 | 27.49 | P=0.634 | 106.35 | 19.65 | 96.33 | 13.16 | * |
| Cholesterol | mg/dL | 195.69 | 36.20 | 179.75 | 30.86 | ** | 200.30 | 37.87 | 183.22 | 37.69 | P=0.065 |
| Triglyceride | mg/dL | 201.74 | 103.16 | 131.83 | 79.79 | *** | 189.70 | 98.35 | 113.68 | 73.75 | ** |
SD – standard deviation; SUA – serum uric acid; BMI – body mass index; SBP – systolic blood pressure; DBP – diastolic blood pressure; P value was compared between patients with hyperuricemia and participants with normouricemia;
means P<0.05;
means P<0.01;
means P<0.001.
The sex-specific Pearson’s correlation coefficients of SUA with those components of CVDs risk factors are shown in Table 4. In both men and women, we observed that weight, BMI, and the level of triglyceride showed the strongest positive correlation. In men, the positive correlation coefficients were age, height, weight, BMI, SBP, DBP, cholesterol, and triglyceride. In women, the positive correlation coefficients were age, weight, BMI, SBP, DBP, glucose, cholesterol, and triglyceride. Using the stepwise section procedure of multivariate linear regression models, we observed that for men, age, BMI, DBP, glucose, cholesterol, and triglyceride were the major determinants for the variation of the level of SUA (Table 5), but for women, the major determinants were only BMI and triglyceride.
Table 4.
Pearson’s correlation coefficients of serum uric acid with those components of cardiovascular disease risk factors.
| Men | Women | |||
|---|---|---|---|---|
| r | P value | r | P value | |
| Serum uric acid | 1.000 | 1.000 | ||
| Age | −0.083 | * | 0.300 | *** |
| Height | 0.143 | ** | −0.062 | P=0.301 |
| Weight | 0.276 | *** | 0.347 | *** |
| BMI | 0.244 | *** | 0.385 | *** |
| SBP | 0.161 | *** | 0.292 | *** |
| DBP | 0.219 | *** | 0.203 | ** |
| Serum glucose | −0.070 | P=0.092 | 0.215 | *** |
| Cholesterol | 0.150 | *** | 0.295 | *** |
| Triglyceride | 0.268 | *** | 0.432 | *** |
SUA – serum uric acid; BMI – body mass index; SBP – systolic blood pressure; DBP – diastolic blood pressure;
means P<0.05;
means P<0.01;
means P<0.001
Table 5.
The major determinants of the level of serum uric acid by stepwise section procedure of multivariate linear regression models.
| Men | Women | |||
|---|---|---|---|---|
| R2=0.130 | R2=0.219 | |||
| beta | P value | beta | P value | |
| Age | −0.008 | * | – | – |
| BMI | 0.053 | ** | 0.072 | *** |
| SBP | – | – | – | – |
| DBP | 0.014 | * | – | – |
| Serum glucose | −0.008 | ** | – | – |
| Cholesterol | 0.003 | * | – | – |
| Triglyceride | 0.002 | *** | 0.004 | *** |
BMI – body mass index; SBP – systolic blood pressure; DBP – diastolic blood pressure;
means P<0.05;
means P<0.01;
means P<0.001.
Analyzed by multivariate linear regression models, Table 6 shows the relationship between SUA concentration and each CVDs risk factor by adjusting for other potential confounding factors, including age, BMI, SBP, DBP, the level of glucose, cholesterol, and triglyceride. In men, after adjustment, SUA concentration showed significant positive associations with BMI, cholesterol, and triglyceride, and an inverse association with glucose. The results indicate that, after adjustment, an increase of 1 mg/dL in SUA concentration was associated with a 0.279 kg/m2 increase in BMI (P<0.001), a 2.438 mg/dL increase in cholesterol (P<0.05), a 10.358 mg/dL increase in triglyceride (P<0.001), and a 3.1 mg/dL decrease in glucose (P<0.01). In women, after adjustment, only BMI and triglyceride showed significant associations with the level of SUA. The results indicated that, after adjustment, an increase of 1 mg/dL the level of SUA was associated with a 0.168 kg/m2 increase in BMI (P<0.01) and a 3.708 mg/dL increase in triglyceride (P<0.01).
Table 6.
The relationship between serum uric acid concentration and each cardiovascular disease risk factor by adjusting for other potential confounding factors.
| SUA | BMI | SBP | DBP | glucose | CHOL | TG | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| beta | P value | beta | P value | beta | P value | beta | P value | beta | P value | beta | P value | beta | P value | |
| Men | ||||||||||||||
| R2 | R2=0.135 | R2=0.286 | R2=0.454 | R2=0.44 | R2=0.122 | R2=0.165 | R2=0.264 | |||||||
| SUA | – | – | 0.279 | *** | 0.751 | 0.106 | 0.336 | 0.238 | −3.1 | ** | 2.438 | * | 10.358 | *** |
| Age | −0.01 | ** | −0.002 | ** | 0.281 | *** | −0.072 | ** | 0.26 | ** | 0.314 | *** | 0.296 | 0.191 |
| BMI | 0.049 | ** | – | – | 0.551 | ** | 0.617 | *** | 0.15 | 0.727 | 0.531 | 0.273 | 6.336 | *** |
| SBP | 0.007 | 0.106 | 0.028 | ** | – | – | 0.313 | *** | 0.229 | *** | −0.129 | 0.237 | −0.158 | 0.572 |
| DBP | 0.008 | 0.238 | 0.083 | 0 | 0.834 | 0 | – | – | −0.103 | 0.51 | 0.336 | 0.058 | 1.391 | ** |
| Serum glucose | −0.006 | ** | 0.002 | 0.727 | 0.048 | * | −0.008 | 0.51 | – | – | 0.117 | * | 0.38 | ** |
| Cholesterol | 0.004 | * | 0.004 | 0.273 | −0.021 | 0.237 | 0.021 | 0.058 | 0.091 | * | – | – | 0.554 | *** |
| Triglyceride | 0.002 | *** | 0.008 | *** | −0.004 | 0.572 | 0.013 | ** | 0.045 | ** | 0.084 | *** | – | – |
| Women | ||||||||||||||
| R2 | R2=0.234 | R2=0.421 | R2=0.596 | R2=0.51 | R2=0.278 | R2=0.304 | R2=0.443 | |||||||
| SUA | – | – | 0.168 | ** | 0.623 | 0.201 | 0.417 | 0.269 | 0.775 | 0.99 | 2.055 | 0.077 | 3.708 | ** |
| Age | 0.004 | 0.952 | 0.012 | *** | 0.043 | *** | 0.03 | ** | 0.055 | ** | 0.143 | *** | 0.271 | 0.712 |
| BMI | 0.021 | ** | – | – | 0.222 | 0.678 | 0.143 | *** | 0.274 | 0.461 | 0.732 | 0.416 | 1.32 | ** |
| SBP | 0.006 | 0.201 | 0.017 | 0.678 | – | – | 0.032 | *** | 0.073 | *** | 0.2 | 0.206 | 0.366 | 0.053 |
| DBP | 0.009 | 0.269 | 0.024 | *** | 0.073 | *** | – | – | 0.112 | 0.055 | 0.298 | 0.071 | 0.551 | 0.598 |
| Serum glucose | 0.005 | 0.99 | 0.013 | 0.461 | 0.048 | *** | 0.033 | 0.055 | – | – | 0.162 | 0.766 | 0.292 | ** |
| Cholesterol | 0.002 | 0.077 | 0.005 | 0.416 | 0.018 | 0.206 | 0.012 | 0.071 | 0.023 | 0.766 | – | – | 0.106 | *** |
| Triglyceride | 0.001 | ** | 0.003 | ** | 0.01 | 0.053 | 0.007 | 0.598 | 0.012 | ** | 0.032 | *** | – | – |
SUA – serum uric acid; BMI – body mass index; SBP – systolic blood pressure; DBP – diastolic blood pressure; Chol – cholesterol; TG – triglyceride;
means P<0.05;
means P<0.01;
means P<0.001.
Analyzed by multivariate logistic regression models, Table 7 showed the odds ratio for hyperuricemia according to different status of smoking, drinking, physical activities and so on. In men, before adjustment or after age adjusted, drinking, overweight/obesity, hypertension, and hyperlipidemia all played positive roles in increasing the odds ratio of hyperuricemia. After adjustment for other potential confounding factors, drinking, overweight, and high level of triglyceride played positive roles in increasing the odds ratio of hyperuricemia. In women, before adjustment, heavy activities, overweight/obesity, hypertension, IFG/DM, and hyperlipidemia all played positive roles in increasing the odds ratio of hyperuricemia. After adjustment for age, only overweight/obesity and hyperlipidemia played positive roles in increasing the odds ratio of hyperuricemia.
Table 7.
The odds ratio for hyperuriceima according to different status of men and women.
| OR | Age-adjusted OR | Full-adjusted OR | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | P value | OR | 95% CI | P value | OR | 95% CI | P value | ||||
| Men | ||||||||||||
| Smoking status | ||||||||||||
| Non-smoker | ||||||||||||
| Current smoker | 1.087 | 0.901 | 1.312 | 0.382 | 1.07 | 0.88 | 1.301 | 0.496 | 0.931 | 0.733 | 1.182 | 0.556 |
| Former smoker | 1.013 | 0.781 | 1.313 | 0.924 | 1.04 | 0.804 | 1.346 | 0.764 | 0.899 | 0.658 | 1.23 | 0.506 |
| Alcohol consumption | ||||||||||||
| Non-drinker | ||||||||||||
| Current drinker | 1.382 | 1.13 | 1.69 | ** | 1.38 | 1.121 | 1.7 | ** | 1.416 | 1.115 | 1.797 | ** |
| Former drinker | 1.242 | 0.869 | 1.774 | 0.234 | 1.196 | 0.827 | 1.73 | 0.341 | 1.432 | 0.93 | 2.205 | 0.103 |
| Physical activities | ||||||||||||
| Light activity ≤2 times/week | ||||||||||||
| Mediate activity 3–5 times/week | 0.901 | 0.726 | 1.118 | 0.344 | 0.877 | 0.698 | 1.101 | 0.259 | 0.733 | 0.557 | 0.963 | * |
| Heavy activity ≥6 times/week | 0.874 | 0.703 | 1.087 | 0.225 | 0.905 | 0.721 | 1.135 | 0.386 | 0.863 | 0.664 | 1.12 | 0.267 |
| Body mass index | ||||||||||||
| Normal state 18.5 kg/m2 ≤BMI ≤24.9 kg/m2 | ||||||||||||
| Underweight BMI ≤18.4 kg/m2 | 0.354 | 0.195 | 0.643 | ** | 0.352 | 0.178 | 0.698 | ** | 0.113 | 0.019 | 0.667 | * |
| Overweight 25.0 kg/m2 ≤BMI ≤29.9 kg/m2 | 1.349 | 1.153 | 1.578 | *** | 1.389 | 1.179 | 1.638 | *** | 1.266 | 1.009 | 1.588 | * |
| Obestiy BMI ≥30.0 kg/m2 | 1.459 | 1.033 | 2.06 | * | 1.514 | 1.076 | 2.13 | * | 1.437 | 0.907 | 2.279 | 0.123 |
| Blood pressure | ||||||||||||
| Normal state BP <140/90 mm Hg | ||||||||||||
| Hypertension BP ≥140/90 mm Hg | 1.286 | 1.055 | 1.569 | ** | 1.508 | 1.209 | 1.881 | *** | 1.244 | 0.883 | 1.752 | 0.212 |
| Serum glucose | ||||||||||||
| Normal state FPG <110 mg/dL | ||||||||||||
| IFG 110 mg/dL ≤FPG <126 mg/dL | 1.249 | 0.985 | 1.583 | 0.067 | 1.308 | 1.034 | 1.655 | * | 1.004 | 0.711 | 1.418 | 0.981 |
| DM FPG ≥126 mg/dL | 0.951 | 0.723 | 1.251 | 0.719 | 1.004 | 0.765 | 1.318 | 0.978 | 0.932 | 0.645 | 1.346 | 0.706 |
| Cholesterol | ||||||||||||
| Normal state Chol <200 mg/dL | ||||||||||||
| Hyperlipidemia Chol ≥200 mg/dL | 1.263 | 1.081 | 1.476 | ** | 1.281 | 1.094 | 1.501 | ** | 1.07 | 0.862 | 1.328 | 0.541 |
| Triglyceride | ||||||||||||
| Normal state TG <150 mg/dL | ||||||||||||
| Hyperlipidemia TG ≥150 mg/dL | 1.639 | 1.39 | 1.932 | *** | 1.67 | 1.409 | 1.98 | *** | 1.396 | 1.101 | 1.771 | ** |
| Women | ||||||||||||
| Alcohol consumption | ||||||||||||
| Non-drinker | ||||||||||||
| Current drinker | 0.868 | 0.637 | 1.185 | 0.373 | 1.016 | 0.73 | 1.413 | 0.927 | 0.917 | 0.622 | 1.353 | 0.662 |
| Former drinker | 1.739 | 0.531 | 5.699 | 0.361 | 1.699 | 0.538 | 5.363 | 0.366 | 0 | 0 | .b | 0.989 |
| Physical activities | ||||||||||||
| Light activity ≤2 times/week | ||||||||||||
| Mediate activity 3–5 times/week | 1.202 | 0.845 | 1.71 | 0.306 | 1.379 | 0.936 | 2.031 | 0.104 | 1.243 | 0.787 | 1.963 | 0.351 |
| Heavy activity ≥6 times/week | 1.516 | 1.113 | 2.065 | ** | 1.33 | 0.957 | 1.85 | 0.089 | 1.397 | 0.955 | 2.042 | 0.085 |
| Body mass index | ||||||||||||
| Normal state 18.5 kg/m2 ≤BMI ≤24.9 kg/m2 | ||||||||||||
| Underweight BMI ≤18.4 kg/m2 | 0.786 | 0.461 | 1.342 | 0.378 | 0.938 | 0.561 | 1.57 | 0.809 | 1.038 | 0.577 | 1.867 | 0.901 |
| Overweight 25.0 kg/m2 ≤BMI ≤29.9 kg/m2 | 1.842 | 1.399 | 2.424 | *** | 1.509 | 1.121 | 2.033 | ** | 1.225 | 0.805 | 1.863 | 0.344 |
| Obestiy BMI ≥30.0 kg/m2 | 2.588 | 1.443 | 4.642 | ** | 1.84 | 1.115 | 3.035 | * | 1.824 | 0.864 | 3.849 | 0.115 |
| Blood pressure | ||||||||||||
| Normal state BP <140/90 mm Hg | ||||||||||||
| Hypertension BP ≥140/90 mm Hg | 0.527 | 0.349 | 0.798 | ** | 1.589 | 0.97 | 2.603 | 0.066 | 1.135 | 0.403 | 3.2 | 0.811 |
| Serum glucose | ||||||||||||
| Normal state FPG <110 mg/dL | ||||||||||||
| IFG 110 mg/dL ≤FPG <126 mg/dL | 1.624 | 1.086 | 2.426 | * | 1.235 | 0.787 | 1.938 | 0.359 | 0.942 | 0.47 | 1.89 | 0.867 |
| DM FPG ≥126 mg/dL | 1.83 | 1.085 | 3.085 | * | 1.396 | 0.752 | 2.592 | 0.291 | 1.392 | 0.546 | 3.546 | 0.488 |
| Cholesterol | ||||||||||||
| Normal state Chol <200 mg/dL | ||||||||||||
| Hyperlipidemia Chol ≥200 mg/dL | 1.91 | 1.485 | 2.458 | *** | 1.595 | 1.212 | 2.099 | ** | 1.466 | 0.993 | 2.164 | 0.054 |
| Triglyceride | ||||||||||||
| Normal state TG <150 mg/dL | ||||||||||||
| Hyperlipidemia TG ≥150 mg/dL | 2.278 | 1.719 | 3.018 | *** | 1.849 | 1.356 | 2.523 | *** | 1.47 | 0.899 | 2.403 | 0.125 |
OR – odds ratio; CI – confidence interval; SUA – serum uric acid; BMI – body mass index; SBP – systolic blood pressure; DBP – diastolic blood pressure; DM – diabetes mellitus; FPG – fasting plasma glucose; IFG – impaired fasting glucose; Chol – cholesterol; TG – triglyceride;
means P<0.05;
means P<0.01;
means P<0.001.
Discussion
According to our results, there was a significant relationship between hyperuricemia and CVDs risk factors in both Chinese men and women. The participants with higher levels of serum uric acid tended to sustain more risk factors in cardiovascular diseases, and those patients with higher CVDs risk factors were easier to diagnose with hyperuricemia.
Compared with normouricemic men, hyperuricemic men had greater height, weight, BMI, SBP, DBP, cholesterol, and triglyceride. Compared with normouricemic women, hyperuricemic women were older and had higher weight, BMI, SBP, DBP, glucose, and triglyceride. In men, the associated CVDs risk factors included age, alcohol consumption, BMI, DBP, glucose, cholesterol, and triglyceride. After adjustment, SUA was strongly associated with alcohol consumption, obesity, and hyperlipidemia. In women, the strong determinants were obesity and hyperlipidemia.
Unfortunately, the mechanism to account for this association is still unclear. One possible reason is about the impaired kidney function, which was the main cause of hyperuricemia. Patients’ SUA levels increased mainly as a consequence of impaired renal excretion. In conditions of local ischemia, an increased production of uric acid occurred in parallel with that of reactive oxygen species (ROS). The pro-oxidant and pro-inflammatory effects of ROS accumulation might further affect those CVDs indicators [20]. The second possible reason was the damage to endothelial cells (ECs) and vascular smooth muscle cells (VSMCs) caused by hyperuricemia. In vitro and in vivo research suggests that uric acid might contribute to endothelial dysfunction by inducing anti-proliferative effects on endothelium and impairing nitric oxide production. Pro-inflammatory and proliferative effects of soluble uric acid have been described in VSMCs. In animal models of mild hyperuricemia, hypertension developed in association with intrarenal vascular disease [31].
However, according many studies, the association between hyperuricemia and CVDs risk factors is conflicting and complicated. Some studies [17,25] reported that SUA was not a truly independent risk factor for CVD, but was secondary to its association with the insulin resistance syndrome (IRS). Also, there is research [18] showing that after additional adjustment for CVDs risk factors, uric acid level was no longer associated with CHD, death from CVDs, or death from all causes. However, according to our results, after adjustment for other potential risk factors of CVD, there was still a strong and significant connection between the level of SUA and obesity, as well as hyperlipidemia, in both men and women. Our results were similar to and consistent with some additional studies. In adolescents with new-onset essential hypertension, the prevalence of elevated SUA was more than 90%, and a preliminary clinical trial evidence suggested that agents that lower SUA may also lower BP [19]. For each increase of 1 mg/dL in uric acid level, the pooled multivariate risk ratio for CHD mortality was 1.12 [13]. In untreated subjects with essential hypertension, raised uric acid was a powerful risk marker for subsequent CVDs and all-cause mortality [21]. Also, some studies noted that hypertriglyceridemia was related to hyperuricemia independent of obesity and central body fat distribution [16]. Children and young adults with hyperuricemia had significantly higher plasma glucose, insulin levels, cholesterol, triglyceride, very low-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and total protein levels than subjects without hyperuricemia; high-density lipoprotein cholesterol level was significantly lower in subjects with hyperuricemia than in those without it [14].
Besides the conflict on the relation itself, there were also debates on different sex patterns of this relationship. According to Kim’s study [13], there was no significant association between hyperuricemia and CHD incidence/mortality in men, but an increased risk for CHD mortality was found in women. Culleton [18] reported that in men, after adjustment for age, elevated SUA level was not associated with increased risk for an adverse outcome. In women, after adjustment for age, uric acid level was predictive of CHD, and death from CVDs. Liese found [11] a strong positive association of elevated SUA with all-cause mortality of CVDs in men. According to Wannamethee’s study [17], when the association between SUA and risk of CHD was examined by the presence and grade of pre-existing CHD, a positive association was seen only in men with previous definite MI, even after full adjustment. Verdecchia [21] found that the relationship between uric acid and CVDs event rate was J-shaped in both sexes. According to our study results, the relationship between SUA and CVDs risk factors exist in both sexes, but some details were different. In men, there were many related CVDs risk factors, while in women only BMI and triglyceride were related. In both sexes, obesity and hyperlipidemia showed the strongest association with hyperuricemia.
Considering all these differences in various studies, we suggest there might be several explanations. First, the definition of hyperuricemia was not exactly the same among various studies. In some studies, the definition of hyperuricemia was described as SUA >7.7 mg/dL for men and SUA >6.6 mg/dL for women [33]. In other studies, including the present one, hyperuricemia was defined as SUA ≥7.0 mg/dL for men and SUA ≥6.0 mg/dL for women [28,29]. Actually, the definition of hyperuricemia is currently arbitrary and varies from 5.6 to 7.7 mg/dL in men and from 4.7 to 7.0 mg/dL in women [13]. Second, the studied population was unique in each study. For example, black men might have lower SUA levels and a lower prevalence of hyperuricemia when compared with white men [25]. Third, since genes and environment can affect obesity and cardiovascular diseases, diet, genetics, and environmental factors of each population might explain the differences found in this association [34–36].
Our study has certain strengths. First, we studied the relationship between hyperuricemia and CVDs risk factors in a Chinese population, which has rarely been studied. Second, we detected and calculated many CVDs risk factors, including: height, weight, and BMI, which reflect obesity; SBP and DBP, which reflect hypertension; the level of glucose, which reflects DM; and the level of cholesterol and triglyceride, which reflect CHD and MI. Third, to better study the relationship between SUA and each factor, we ran the adjustment to exclude the effect of other confounding factors. Fourth, we studied the relationship in both sexes and compared the differences between men and women. However, our study also has some limitations. First, it was a cross-sectional study without any longitudinal observations. Second, the simple number of hyperuricemic women was too small, which might make the results disputable when we divided women into 2 groups: hyperuricemic and normouricemic. Third, the population in our study was only Chinese, which limits generalization of our results to other populations.
Conclusions
We found that elevated serum uric acid concentration was strongly associated with obesity and hyperlipidemia in both men and women, indicating that, among hyperuricemic patients, we should pay more attention to the possibility of cardiovascular complications. These results might provide a novel target or a new treatment for cardiovascular diseases by lowering the level of serum uric acid.
Acknowledgment
The Chinese Hyperuricemia and Gout Database was provided by the Chinese National Scientific Data Sharing Platform for Population and Health.
Footnotes
Ethical standards
The data used in this study were based on Chinese Hyperuricemia and Gout Database, provided by the Chinese National Scientific Data Sharing Platform for Population and Health. Protocols used in this database were approved by the Ethics Committee of the Chinese National Scientific Data Sharing Platform for Population and Health.
Conflict of interest
The authors declare that they have no conflicts of interest.
Source of support: Departmental sources
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