# Prevalence of Hyperuricemia and Gout in Mainland China from 2000 to 2014: A Systematic Review and Meta-Analysis

Authors: Rui Liu; Cheng Han; Di Wu; Xinghai Xia; Jianqiu Gu; Haixia Guan; Zhongyan Shan; Weiping Teng
Journal: BioMed Research International
 (2015)

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## Abstract

We systematically identified the prevalence of hyperuricemia and gout in mainland China and provided informative data that can be used to create appropriate local public health policies. Relevant articles from 2000 to 2014 were identified by searching 5 electronic databases: PubMed, Google Scholar, Chinese Wanfang, CNKI, and Chongqing VIP. All of the calculations were performed using the Stata 11.0 and SPSS 20.0 software. The eligible articles (n=36; 3 in English and 33 in Chinese) included 44 studies (38 regarding hyperuricemia and 6 regarding gout). The pooled prevalence of hyperuricemia and gout was 13.3% (95% CI: 11.9%, 14.6%) and 1.1% (95% CI: 0.7%, 1.5%), respectively. Although publication bias was observed, the results did not change after a trim and fill test, indicating that that impact of this bias was likely insignificant. The prevalence of hyperuricemia and gout was high in mainland China. The subgroup analysis suggested that the geographical region, whether the residents dwell in urban or rural and coastal or inland areas, the economic level, and sex may be associated with prevalence.


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## Body

## 1. Introduction

Serum uric acid is the final enzymatic product of purine metabolism [1, 2]. Abnormalities in serum uric acid metabolism may cause hyperuricemia and gout. Hyperuricemia is the result of interactions among multiple factors, including sex, age, genetics, lifestyle, and environment [3]. Several studies have suggested that hyperuricemia is associated with many diseases, including diabetes mellitus [4], hypertension [5, 6], stroke [2, 7], dyslipidemia [8], chronic kidney disease [9], cardiovascular events, and heart failure [10–12]. Hyperuricemia is considered to be a precursor of gout as the deposition of urate crystals in the joints results in an acute inflammatory response. Deposition in the soft tissue can lead to tophi [13–15]. Gout is also a serious health issue and is an independent risk factor for heart failure and metabolic syndrome [16, 17]. In recent years, an increasing trend in the prevalence of hyperuricemia and gout has been observed in epidemiological studies [13, 18–21], and both diseases have become public health problems that need to be solved quickly.Due to rapid economic development, the lifestyle of the Chinese has changed greatly, a huge transition from a dietary pattern traditionally based on carbohydrates and vegetables to a pattern that relies on meat, dairy products, and other purine-rich foods that are closely related to hyperuricemia and gout [22, 23].As a large developing country, China has marked regional differences and varied populations. To date, most investigations have been limited to certain areas or have focused on specific occupations. Therefore, a comprehensive study on the epidemiology of hyperuricemia and gout in the entire mainland China is needed. As most of the published data are in Chinese, we present our study in the widely read English medium. Obtaining an accurate prevalence of hyperuricemia and gout is important to help us formulate appropriate local public health policies. In addition, such a study will benefit the people through health education by increasing awareness of hyperuricemia and gout and also the importance of improving lifestyle and maintaining a healthy diet.Due to varied geographic locations that include diverse populations and different socioeconomic conditions, a unified epidemiological investigation about the prevalence of hyperuricemia and gout remains difficult. We conducted a meta-analysis regarding the prevalence of both diseases in mainland China between January 2000 and December 2014 to determine the epidemiology and to review the results from previous studies.


## 2. Methods

### 2.1. Search Strategy

We manually searched all of the literatures regarding population-based research on the prevalence of hyperuricemia and gout from 2000 to 2014 using the PubMed, Google Scholar, CNKI (Chinese National Knowledge Infrastructure), Chinese Wangfang, and Chongqing VIP electronic databases. The keywords for search were “uric acid,” “HUA,” “HU,” “hyperuricemia,” “gout,” “prevalence(s),” “incidence(s),” and “epidemiology.” To find additional studies, the reference lists of the identified studies were also examined.


### 2.2. Inclusion and Exclusion Criteria

Papers were included if they met all of the following criteria: (1) all study participants living in mainland China; (2) study data being general population-based rather than hospital-based; (3) prevalence rate being also analyzed by according to sex; (4) accurate diagnostic criteria and clear study date; and (5) the most detailed study of duplicate studies on the same population.Studies were excluded if they (1) were not original research, such as a review or case report, (2) included participants with concomitant diseases or had medication history known to affect uric acid metabolism, or (3) focused on specific population groups, such as teenagers, elderly people, or single gender, or a certain occupation.


### 2.3. Definition of Hyperuricemia and Gout

The diagnostic criteria for hyperuricemia varied among the studies; we have listed each criterion in Table1. The diagnostic criteria for gout were listed in Table 2 [24, 25].Table 1
Characteristics of studies on the prevalence of hyperuricemia and gout.
First author
Publication year
Area
Diagnostic criterion (μmol/L)  (Men/Women)
Rural/urban
Inland/coastal
Study year
Sample size
Case
Prevalence (%)
Prevalence of hyperuricemia
Shi [29]
2013
Shijingshan, Beijing
≥420/≥350
Urban
Inland
2012
3961
438
11.06
Ma [30]
2014
Xichengqu, Beijing
≥417/≥357
Urban
Inland
2012
834
100
11.99
Li [31]
2013
Bortala, Xinjiang
>420/>350
Rural
Inland
2009
2046
261
12.76
Zheng [32]
2010
Wenzhou, Zhejiang
≥417/≥357
Urban
Inland
2008
1520
114
7.50
Sun [33]
2008
Dalian, Liaoning
≥420/≥350
Rural
Coastal
2007
1024
100
9.77
Hou [34]
2010
Dalian, Liaoning
>420/>350
Rural
Coastal
2007
1021
97
9.50
Wang [35]
2010
Baoshan, Yunnan
>420/>350
Urban
Coastal
2009
1501
210
13.99
Yu [36]
2010
Foshan, Guangdong
≥417/≥357
Urban
Coastal
2008
7403
1117
15.09
Wu [37]
2008
Guangzhou, Guangdong
≥417/≥357
Urban
Inland
2007
2788
578
20.73
Zou [38]
2011
Guilin, Guangxi
≥420/≥360
Urban
Inland
2009
6273
1477
23.55
Wang [39]
2008
Zhoushan, Zhejiang
>420/>360
Rural
Inland
2007
1438
158
10.99
Meng [40]
2012
Gaoyou, Jiangsu
≥420/≥360
Rural
Inland
2010
4504
538
11.94
Shen [41]
2014
Wuxi, Jiangsu
≥417/≥357
Urban
Inland
2009
3723
754
20.25
Song [42]
2014
Nanchang, Jiangxi
>420/>350
Urban
Inland
2011
3795
795
20.95
Shao [43]
2003
Nanjing, Jiangsu
≥417/≥357
Urban
Inland
2003
7778
1038
13.35
Zhou [44]
2013
Ningbo, Zhejiang
>420/>370
Urban
Coastal
2008
2110
190
9.00
Huang [45]
2013
Ningbo, Zhejiang
>420/>360
Urban
Coastal
2012
1754
195
11.12
Xin [46]
2013
Qingdao, Shandong
>420/>350
Urban
Coastal
2011
5165
748
14.48
Tian [47]
2008
Qingdao, Shandong
>420/>350
Urban
Coastal
2006
2363
471
19.93
Tian [47]
2008
Qingdao, Shandong
>420/>350
Rural
Coastal
2006
2467
405
16.42
Dong [48]
2004
Qingdao, Shandong
>420/>350
Urban
Coastal
2002
2190
402
18.36
Zhang [49]
2006
Haiyang, Shandong
>416.36/>356.88
Rural
Coastal
2004
5372
649
12.08
Wang [50]
2010
Shenyang, Liaoning
>420/>350
Urban
Inland
2009
675
78
11.56
Chen [51]
2008
Chengdu, Sichuan
≥428
Urban
Inland
2006
2566
400
15.59
Guo [52]
2012
Taiyuan, Shanxi
≥420
Urban
Inland
2010
4228
371
8.77
Wang [53]
2010
Wenzhou, Zhejiang
>420/>350
Urban
Coastal
2008
3478
260
7.48
Shao [54]
2011
Wenzhou, Zhejiang
>420/>350
Urban
Coastal
2008
3480
260
7.47
Pan [55]
2014
Changzhou, Jiangsu
>420/>380
Rural
Inland
2008
3122
573
18.35
Duan [56]
2013
Korla, Xinjiang
>417/>357
Urban
Inland
2009
2046
261
12.76
Zhang [57]
2014
Xingtai, Hebei
>420/>350
Rural
Inland
2013
2109
177
8.39
Mou [58]
2013
Yantai, Shandong
≥380
Urban
Coastal
2012
635
66
10.39
Li [59]
2010
Yan’an, Shaanxi
>417/>357
Urban
Inland
2008
1290
71
5.50
Chen [60]
2009
Dali, Yunnan
>420/>350
Urban
Inland
2006
7505
923
12.30
Jin [61]
2009
Zhuhai, Guangdong
>420/>360
Rural
Coastal
2007
1112
164
14.75
Cai [62]
2009
Hangzhou, Zhejiang
>420/>360
Urban
Inland
2008
4155
702
16.90
You [63]
2014
Mongolian
≥416/≥357
Urban
Inland
2009
630
120
19.05
You [63]
2014
Mongolian
≥416/≥357
Rural
Coastal
2009
179
23
12.85
Zhang [64]
2011
Tianjin
>420/>360
Urban
Coastal
2009
17762
2160
12.16
Prevalence of gout
Yu [36]
2010
Foshan, Guangdong
—
Urban
Coastal
2008
7403
77
1.04
Wu [37]
2008
Guangzhou, Guangdong
—
Urban
Inland
2007
2788
40
1.43
Song [42]
2014
Nanchang, Jiangxi
—
Urban
Inland
2011
3795
58
1.53
Shao [43]
2003
Nanjing, Jiangsu
—
Urban
Inland
2003
7778
105
1.35
Zhang [49]
2006
Haiyang, Shandong
—
Rural
Coastal
2004
5372
23
0.43
Zhang [57]
2014
Xingtai, Hebei
—
Rural
Inland
2013
2109
26
1.23Table 2
Gout classification criteria.
Yu et al. [36]
Wu et al., Song et al., Shao et al., Zhang et al., and Zhang et al. [37, 42, 43, 49, 57]
Classification criteria for gout [25](1) More than one attack of acute arthritis(2) Maximum inflammation developed within 1 day(3) Oligoarthritis attack(4) Redness observed over joints(5) First MTP joint painful or swollen(6) Unilateral first MTP joint attack(7) Unilateral tarsal joint attack(8) Tophus (suspected or proven)(9) Hyperuricemia (more than 2 S.D. greater than the normal population average)(10) Asymmetric swelling within a joint on X-ray(11) Complete termination of an attackCase definition: ≥6 of 11 clinical criteria
ARA preliminary classification criteria for acute gout 1977 [24] (1) More than one attack of acute arthritis(2) Maximum inflammation developed within 1 day(3) Oligoarthritis attack(4) Redness observed over joints(5) First MTP joint painful or swollen(6) Unilateral first MTP joint attack(7) Unilateral tarsal joint attack(8) Tophus (suspected or proven)(9) Hyperuricemia (more than 2 S.D. greater than the normal population average)(10) Asymmetric swelling within a joint on X-ray(11) Subcortical cysts without erosions on X-ray(12) Complete termination of an attackCase definition: ≥6 of 12 clinical criteria required or presence of MSU crystals in SF or in tophus.


### 2.4. Data Extraction

Two reviewers searched the literature independently. Any disagreement on data extraction between the two reviewers was mediated by discussion [26]. Figure 1 shows the literature-search process. We recorded the characteristics of all the included papers in Table 1, including the title, author’s name, publication date, study year, study population, geographic area, rural/urban, inland/coastal, sample size, case, prevalence, and diagnostic criterion.Figure 1
Flow diagram for the literature-search process.


### 2.5. Statistical Analysis

Pooled prevalence and 95% confidence intervals (CIs) were calculated to estimate the prevalence of hyperuricemia and gout in mainland China. We adopted the Chi-squared-basedQ test and the I2 test to evaluate the heterogeneity of the studies; 25%, 50%, and 75% were considered low, moderate, and high levels, respectively [27, 28]. If the level of heterogeneity was moderate or high, we used a random-effects meta-analysis model instead of a fixed-effects model. To perform a secondary analysis and to address heterogeneity, a subgroup analysis was required. Egger’s test was used to estimate publication bias. A P value less than 0.05 was considered statistically significant. Meta-analysis was calculated using Stata Version 11.0 (Stata Corp LP, College Station, TX, USA). Significant differences in prevalence among the groups were examined through the Chi-square test using SPSS Version 20.0 (SPSS Software, Chicago, IL, USA). All figures were generated using Stata 11.0 (Stata Corp LP, College Station, TX, USA) or Microsoft PowerPoint (Microsoft, Redmond, USA).


## 2.1. Search Strategy

We manually searched all of the literatures regarding population-based research on the prevalence of hyperuricemia and gout from 2000 to 2014 using the PubMed, Google Scholar, CNKI (Chinese National Knowledge Infrastructure), Chinese Wangfang, and Chongqing VIP electronic databases. The keywords for search were “uric acid,” “HUA,” “HU,” “hyperuricemia,” “gout,” “prevalence(s),” “incidence(s),” and “epidemiology.” To find additional studies, the reference lists of the identified studies were also examined.


## 2.2. Inclusion and Exclusion Criteria

Papers were included if they met all of the following criteria: (1) all study participants living in mainland China; (2) study data being general population-based rather than hospital-based; (3) prevalence rate being also analyzed by according to sex; (4) accurate diagnostic criteria and clear study date; and (5) the most detailed study of duplicate studies on the same population.Studies were excluded if they (1) were not original research, such as a review or case report, (2) included participants with concomitant diseases or had medication history known to affect uric acid metabolism, or (3) focused on specific population groups, such as teenagers, elderly people, or single gender, or a certain occupation.


## 2.3. Definition of Hyperuricemia and Gout

The diagnostic criteria for hyperuricemia varied among the studies; we have listed each criterion in Table1. The diagnostic criteria for gout were listed in Table 2 [24, 25].Table 1
Characteristics of studies on the prevalence of hyperuricemia and gout.
First author
Publication year
Area
Diagnostic criterion (μmol/L)  (Men/Women)
Rural/urban
Inland/coastal
Study year
Sample size
Case
Prevalence (%)
Prevalence of hyperuricemia
Shi [29]
2013
Shijingshan, Beijing
≥420/≥350
Urban
Inland
2012
3961
438
11.06
Ma [30]
2014
Xichengqu, Beijing
≥417/≥357
Urban
Inland
2012
834
100
11.99
Li [31]
2013
Bortala, Xinjiang
>420/>350
Rural
Inland
2009
2046
261
12.76
Zheng [32]
2010
Wenzhou, Zhejiang
≥417/≥357
Urban
Inland
2008
1520
114
7.50
Sun [33]
2008
Dalian, Liaoning
≥420/≥350
Rural
Coastal
2007
1024
100
9.77
Hou [34]
2010
Dalian, Liaoning
>420/>350
Rural
Coastal
2007
1021
97
9.50
Wang [35]
2010
Baoshan, Yunnan
>420/>350
Urban
Coastal
2009
1501
210
13.99
Yu [36]
2010
Foshan, Guangdong
≥417/≥357
Urban
Coastal
2008
7403
1117
15.09
Wu [37]
2008
Guangzhou, Guangdong
≥417/≥357
Urban
Inland
2007
2788
578
20.73
Zou [38]
2011
Guilin, Guangxi
≥420/≥360
Urban
Inland
2009
6273
1477
23.55
Wang [39]
2008
Zhoushan, Zhejiang
>420/>360
Rural
Inland
2007
1438
158
10.99
Meng [40]
2012
Gaoyou, Jiangsu
≥420/≥360
Rural
Inland
2010
4504
538
11.94
Shen [41]
2014
Wuxi, Jiangsu
≥417/≥357
Urban
Inland
2009
3723
754
20.25
Song [42]
2014
Nanchang, Jiangxi
>420/>350
Urban
Inland
2011
3795
795
20.95
Shao [43]
2003
Nanjing, Jiangsu
≥417/≥357
Urban
Inland
2003
7778
1038
13.35
Zhou [44]
2013
Ningbo, Zhejiang
>420/>370
Urban
Coastal
2008
2110
190
9.00
Huang [45]
2013
Ningbo, Zhejiang
>420/>360
Urban
Coastal
2012
1754
195
11.12
Xin [46]
2013
Qingdao, Shandong
>420/>350
Urban
Coastal
2011
5165
748
14.48
Tian [47]
2008
Qingdao, Shandong
>420/>350
Urban
Coastal
2006
2363
471
19.93
Tian [47]
2008
Qingdao, Shandong
>420/>350
Rural
Coastal
2006
2467
405
16.42
Dong [48]
2004
Qingdao, Shandong
>420/>350
Urban
Coastal
2002
2190
402
18.36
Zhang [49]
2006
Haiyang, Shandong
>416.36/>356.88
Rural
Coastal
2004
5372
649
12.08
Wang [50]
2010
Shenyang, Liaoning
>420/>350
Urban
Inland
2009
675
78
11.56
Chen [51]
2008
Chengdu, Sichuan
≥428
Urban
Inland
2006
2566
400
15.59
Guo [52]
2012
Taiyuan, Shanxi
≥420
Urban
Inland
2010
4228
371
8.77
Wang [53]
2010
Wenzhou, Zhejiang
>420/>350
Urban
Coastal
2008
3478
260
7.48
Shao [54]
2011
Wenzhou, Zhejiang
>420/>350
Urban
Coastal
2008
3480
260
7.47
Pan [55]
2014
Changzhou, Jiangsu
>420/>380
Rural
Inland
2008
3122
573
18.35
Duan [56]
2013
Korla, Xinjiang
>417/>357
Urban
Inland
2009
2046
261
12.76
Zhang [57]
2014
Xingtai, Hebei
>420/>350
Rural
Inland
2013
2109
177
8.39
Mou [58]
2013
Yantai, Shandong
≥380
Urban
Coastal
2012
635
66
10.39
Li [59]
2010
Yan’an, Shaanxi
>417/>357
Urban
Inland
2008
1290
71
5.50
Chen [60]
2009
Dali, Yunnan
>420/>350
Urban
Inland
2006
7505
923
12.30
Jin [61]
2009
Zhuhai, Guangdong
>420/>360
Rural
Coastal
2007
1112
164
14.75
Cai [62]
2009
Hangzhou, Zhejiang
>420/>360
Urban
Inland
2008
4155
702
16.90
You [63]
2014
Mongolian
≥416/≥357
Urban
Inland
2009
630
120
19.05
You [63]
2014
Mongolian
≥416/≥357
Rural
Coastal
2009
179
23
12.85
Zhang [64]
2011
Tianjin
>420/>360
Urban
Coastal
2009
17762
2160
12.16
Prevalence of gout
Yu [36]
2010
Foshan, Guangdong
—
Urban
Coastal
2008
7403
77
1.04
Wu [37]
2008
Guangzhou, Guangdong
—
Urban
Inland
2007
2788
40
1.43
Song [42]
2014
Nanchang, Jiangxi
—
Urban
Inland
2011
3795
58
1.53
Shao [43]
2003
Nanjing, Jiangsu
—
Urban
Inland
2003
7778
105
1.35
Zhang [49]
2006
Haiyang, Shandong
—
Rural
Coastal
2004
5372
23
0.43
Zhang [57]
2014
Xingtai, Hebei
—
Rural
Inland
2013
2109
26
1.23Table 2
Gout classification criteria.
Yu et al. [36]
Wu et al., Song et al., Shao et al., Zhang et al., and Zhang et al. [37, 42, 43, 49, 57]
Classification criteria for gout [25](1) More than one attack of acute arthritis(2) Maximum inflammation developed within 1 day(3) Oligoarthritis attack(4) Redness observed over joints(5) First MTP joint painful or swollen(6) Unilateral first MTP joint attack(7) Unilateral tarsal joint attack(8) Tophus (suspected or proven)(9) Hyperuricemia (more than 2 S.D. greater than the normal population average)(10) Asymmetric swelling within a joint on X-ray(11) Complete termination of an attackCase definition: ≥6 of 11 clinical criteria
ARA preliminary classification criteria for acute gout 1977 [24] (1) More than one attack of acute arthritis(2) Maximum inflammation developed within 1 day(3) Oligoarthritis attack(4) Redness observed over joints(5) First MTP joint painful or swollen(6) Unilateral first MTP joint attack(7) Unilateral tarsal joint attack(8) Tophus (suspected or proven)(9) Hyperuricemia (more than 2 S.D. greater than the normal population average)(10) Asymmetric swelling within a joint on X-ray(11) Subcortical cysts without erosions on X-ray(12) Complete termination of an attackCase definition: ≥6 of 12 clinical criteria required or presence of MSU crystals in SF or in tophus.


## 2.4. Data Extraction

Two reviewers searched the literature independently. Any disagreement on data extraction between the two reviewers was mediated by discussion [26]. Figure 1 shows the literature-search process. We recorded the characteristics of all the included papers in Table 1, including the title, author’s name, publication date, study year, study population, geographic area, rural/urban, inland/coastal, sample size, case, prevalence, and diagnostic criterion.Figure 1
Flow diagram for the literature-search process.


## 2.5. Statistical Analysis

Pooled prevalence and 95% confidence intervals (CIs) were calculated to estimate the prevalence of hyperuricemia and gout in mainland China. We adopted the Chi-squared-basedQ test and the I2 test to evaluate the heterogeneity of the studies; 25%, 50%, and 75% were considered low, moderate, and high levels, respectively [27, 28]. If the level of heterogeneity was moderate or high, we used a random-effects meta-analysis model instead of a fixed-effects model. To perform a secondary analysis and to address heterogeneity, a subgroup analysis was required. Egger’s test was used to estimate publication bias. A P value less than 0.05 was considered statistically significant. Meta-analysis was calculated using Stata Version 11.0 (Stata Corp LP, College Station, TX, USA). Significant differences in prevalence among the groups were examined through the Chi-square test using SPSS Version 20.0 (SPSS Software, Chicago, IL, USA). All figures were generated using Stata 11.0 (Stata Corp LP, College Station, TX, USA) or Microsoft PowerPoint (Microsoft, Redmond, USA).


## 3. Results

### 3.1. Characteristics of Included Studies

A total of 604 articles were identified (Figure1). After screening for population base, study type, relevancy, and duplicates, 36 literary papers (3 in English and 33 in Chinese) containing 44 studies (38 regarding hyperuricemia and 6 regarding gout) met our inclusion criteria. A detailed description of these studies is provided in Table 1.


### 3.2. Pooled Prevalence of Hyperuricemia and Gout

As shown in Figure2, the pooled prevalence of hyperuricemia was 13.3% (95% CI: 11.9%, 14.6%), with the prevalence ranging from 5.5% to 23.6%. As shown in Figure 3, the pooled prevalence of gout was 1.1% (95% CI: 0.7%, 1.5%), with a range of 0.4–1.5%.Figure 2
Forest plot of the pooled prevalence of hyperuricemia in mainland China.Figure 3
Forest plot of the pooled prevalence of gout in mainland China.Figures4 and 5 showed the individual prevalence of hyperuricemia and gout, respectively, in different provinces, municipalities, and autonomous regions.Figure 4
Regional distribution of pooled prevalence of hyperuricemia in mainland China.Figure 5
Regional distribution of pooled prevalence of gout in mainland China.


### 3.3. Subgroup Analysis

The prevalence of hyperuricemia in mainland China was analyzed in subgroups, which were separated based on the following categories: rural or urban, coast or inland, location (north, south, northwest, northeast, and southwest China), economic level, and sex. As shown in Table3, location in an urban area (χ2=25.53, P<0.001), inland area (χ2=117.95, P<0.001), or south China (χ2=507.39, P<0.001) and a high economic level (χ2=8.40, P=0.004) might indicate a high prevalence of hyperuricemia. Notably, sex may also be closely associated with hyperuricemia prevalence, as the prevalence among men and women was 19.4% (95% CI: 17.6%, 21.1%) and 7.9% (95% CI: 6.6%, 9.3%), respectively.Table 3
Stratified prevalence of hyperuricemia in mainland China.
Subgroups
Prevalence (%) (95% CI)
Number of studies
Heterogeneity
Case/total
I
2%
P value
Area
Urban
13.7  (12.0, 15.4)
27
98.4
<0.001
14322/101787
Rural
12.3  (10.5, 14.1)
11
94.3
<0.001
3154/24581
Coastal/inland
Inland
13.8  (11.8, 15.7)
23
98.3
<0.001
10160/68666
Coast
12.5  (10.8, 14.2)
15
97.3
<0.001
7316/57702
Location
North China
13.2  (11.5, 14.8)
13
96.3
<0.001
6162/48261
East China
12.9  (10.2, 15.6)
12
98.6
<0.001
5577/40857
Northwest
10.3  (5.4, 15.3)
3
97.4
<0.001
593/5382
Northeast
10.1  (8.9, 11.2)
3
0.0
0.376
275/2720
Southwest
13.9  (11.7, 16.1)
3
88.6
<0.001
1533/11572
South China
18.6  (13.8, 23.3)
4
98.3
<0.006
3336/17576
Economic level
High
13.8  (12.0, 15.6)
20
98.0
<0.001
8094/59811
Low
12.6  (10.6, 14.7)
18
98.1
<0.001
9382/66557
Sex
Male
19.4  (17.6, 21.1)
38
96.7
<0.001
11644/60768
Female
7.9  (6.6, 9.3)
38
97.9
<0.001
5859/65654
Total
13.3  (11.9, 14.6)
38
98.0
<0.001
17476/126368For gout, the prevalence among the subgroups was very different (Table4). Urban residents had a much higher prevalence of gout (1.2%, 95% CI: 0.7%, 1.8%) compared with rural residents (0.9%, 95% CI: 0.2%, 1.6%; χ2=19.96, P<0.001). Inland area residents had a higher prevalence of gout (1.4%, 95% CI: 0.8%, 1.9%) than coastal area residents (0.8%, 95% CI: 0.2%, 1.4%; χ2=23.88, P<0.001). An increasing prevalence of gout was seen over the years; 0.9% (95% CI: 0.0%, 1.8%) of subjects investigated from 2000 to 2005 were diagnosed with gout, and this number increased to 1.4% (95% CI: 0.5%, 2.2%) after 2010 (χ2=7.47, P=0.024). Regarding sex, the prevalence rate was 1.5% (95% CI: 0.8%, 2.1%) in men and 0.9% (95% CI: 0.0%, 1.0%) in women.Table 4
Prevalence of gout in mainland China by different stratification factors.
Subgroups
Prevalence (%)  (95% CI)
Number of studies
Heterogeneity
Case/total
I
2%
P value
Area
Urban
1.2 (0.7, 1.8)
4
0.0
0.830
280/21764
Rural
0.9 (0.2, 1.6)
2
14.0
0.313
49/7481
Coastal/inland
Inland
1.4 (0.8, 1.9)
4
0.0
0.989
229/16470
Coastal
0.8 (0.2, 1.4)
2
0.4
0.316
100/12775
Study year
2000–2005
0.9 (0.0, 1.8)
2
59.1
0.118
128/13150
2006–2010
1.1 (0.4, 1.8)
2
0.0
0.655
117/10191
2011–2014
1.4 (0.5, 2.2)
2
0.0
0.737
84/5904
Sex
Male
1.5 (0.8, 2.1)
6
1.9
0.404
226/14060
Female
0.9 (0.0, 1)
6
0.0
0.924
78/15185
Total
1.1 (0.7, 1.5)
6
0.0
0.644
329/29245


### 3.4. Analysis of Heterogeneity and Publication Bias

A significant overall heterogeneity was noted in the study on hyperuricemia (P<0.001, I2=98%); however, the heterogeneity decreased in the subgroup analysis. We observed publication bias in both studies according to Egger’s test. Then we performed a trim and fill method to address the problem of publication bias. However, it became unchanged after we applied the trim and fill method [65].


## 3.1. Characteristics of Included Studies

A total of 604 articles were identified (Figure1). After screening for population base, study type, relevancy, and duplicates, 36 literary papers (3 in English and 33 in Chinese) containing 44 studies (38 regarding hyperuricemia and 6 regarding gout) met our inclusion criteria. A detailed description of these studies is provided in Table 1.


## 3.2. Pooled Prevalence of Hyperuricemia and Gout

As shown in Figure2, the pooled prevalence of hyperuricemia was 13.3% (95% CI: 11.9%, 14.6%), with the prevalence ranging from 5.5% to 23.6%. As shown in Figure 3, the pooled prevalence of gout was 1.1% (95% CI: 0.7%, 1.5%), with a range of 0.4–1.5%.Figure 2
Forest plot of the pooled prevalence of hyperuricemia in mainland China.Figure 3
Forest plot of the pooled prevalence of gout in mainland China.Figures4 and 5 showed the individual prevalence of hyperuricemia and gout, respectively, in different provinces, municipalities, and autonomous regions.Figure 4
Regional distribution of pooled prevalence of hyperuricemia in mainland China.Figure 5
Regional distribution of pooled prevalence of gout in mainland China.


## 3.3. Subgroup Analysis

The prevalence of hyperuricemia in mainland China was analyzed in subgroups, which were separated based on the following categories: rural or urban, coast or inland, location (north, south, northwest, northeast, and southwest China), economic level, and sex. As shown in Table3, location in an urban area (χ2=25.53, P<0.001), inland area (χ2=117.95, P<0.001), or south China (χ2=507.39, P<0.001) and a high economic level (χ2=8.40, P=0.004) might indicate a high prevalence of hyperuricemia. Notably, sex may also be closely associated with hyperuricemia prevalence, as the prevalence among men and women was 19.4% (95% CI: 17.6%, 21.1%) and 7.9% (95% CI: 6.6%, 9.3%), respectively.Table 3
Stratified prevalence of hyperuricemia in mainland China.
Subgroups
Prevalence (%) (95% CI)
Number of studies
Heterogeneity
Case/total
I
2%
P value
Area
Urban
13.7  (12.0, 15.4)
27
98.4
<0.001
14322/101787
Rural
12.3  (10.5, 14.1)
11
94.3
<0.001
3154/24581
Coastal/inland
Inland
13.8  (11.8, 15.7)
23
98.3
<0.001
10160/68666
Coast
12.5  (10.8, 14.2)
15
97.3
<0.001
7316/57702
Location
North China
13.2  (11.5, 14.8)
13
96.3
<0.001
6162/48261
East China
12.9  (10.2, 15.6)
12
98.6
<0.001
5577/40857
Northwest
10.3  (5.4, 15.3)
3
97.4
<0.001
593/5382
Northeast
10.1  (8.9, 11.2)
3
0.0
0.376
275/2720
Southwest
13.9  (11.7, 16.1)
3
88.6
<0.001
1533/11572
South China
18.6  (13.8, 23.3)
4
98.3
<0.006
3336/17576
Economic level
High
13.8  (12.0, 15.6)
20
98.0
<0.001
8094/59811
Low
12.6  (10.6, 14.7)
18
98.1
<0.001
9382/66557
Sex
Male
19.4  (17.6, 21.1)
38
96.7
<0.001
11644/60768
Female
7.9  (6.6, 9.3)
38
97.9
<0.001
5859/65654
Total
13.3  (11.9, 14.6)
38
98.0
<0.001
17476/126368For gout, the prevalence among the subgroups was very different (Table4). Urban residents had a much higher prevalence of gout (1.2%, 95% CI: 0.7%, 1.8%) compared with rural residents (0.9%, 95% CI: 0.2%, 1.6%; χ2=19.96, P<0.001). Inland area residents had a higher prevalence of gout (1.4%, 95% CI: 0.8%, 1.9%) than coastal area residents (0.8%, 95% CI: 0.2%, 1.4%; χ2=23.88, P<0.001). An increasing prevalence of gout was seen over the years; 0.9% (95% CI: 0.0%, 1.8%) of subjects investigated from 2000 to 2005 were diagnosed with gout, and this number increased to 1.4% (95% CI: 0.5%, 2.2%) after 2010 (χ2=7.47, P=0.024). Regarding sex, the prevalence rate was 1.5% (95% CI: 0.8%, 2.1%) in men and 0.9% (95% CI: 0.0%, 1.0%) in women.Table 4
Prevalence of gout in mainland China by different stratification factors.
Subgroups
Prevalence (%)  (95% CI)
Number of studies
Heterogeneity
Case/total
I
2%
P value
Area
Urban
1.2 (0.7, 1.8)
4
0.0
0.830
280/21764
Rural
0.9 (0.2, 1.6)
2
14.0
0.313
49/7481
Coastal/inland
Inland
1.4 (0.8, 1.9)
4
0.0
0.989
229/16470
Coastal
0.8 (0.2, 1.4)
2
0.4
0.316
100/12775
Study year
2000–2005
0.9 (0.0, 1.8)
2
59.1
0.118
128/13150
2006–2010
1.1 (0.4, 1.8)
2
0.0
0.655
117/10191
2011–2014
1.4 (0.5, 2.2)
2
0.0
0.737
84/5904
Sex
Male
1.5 (0.8, 2.1)
6
1.9
0.404
226/14060
Female
0.9 (0.0, 1)
6
0.0
0.924
78/15185
Total
1.1 (0.7, 1.5)
6
0.0
0.644
329/29245


## 3.4. Analysis of Heterogeneity and Publication Bias

A significant overall heterogeneity was noted in the study on hyperuricemia (P<0.001, I2=98%); however, the heterogeneity decreased in the subgroup analysis. We observed publication bias in both studies according to Egger’s test. Then we performed a trim and fill method to address the problem of publication bias. However, it became unchanged after we applied the trim and fill method [65].


## 4. Discussion

We analyzed 44 epidemiological surveys covering 16 provinces, municipalities, and autonomous regions in mainland China. An important strength of our study is that it is a cross-sectional study. We systematically analyzed the prevalence of hyperuricemia and gout in mainland China. To our knowledge, this is the first study of this kind to focus on mainland China and cover the years from 2000 to 2014.In our meta-analysis, the prevalence of hyperuricemia in mainland China was 13.3% (19.4% in men and 7.9% in women), which was in accordance with the worldwide prevalence rate reported to be ranging from 2.6% to 36% in different populations [66]. Our result was lower than that observed in several developed countries, such as the United States (21.2% in men and 21.6% in women) [21] and Japan (25.8% overall, 34.5% in men and 11.6% in women) [67]. As expected, the prevalence is close to that in most developing countries; for example, it is 10.6% in Thailand (18.4% in men and 7.8% in women) [68] and 12.1% in Turkey (19.0% in men and 5.8% in women) [69]. Chuang et al. performed the Nutrition and Health Survey in Taiwan (NAHSIT) study from 2005 to 2008, which focused on a Chinese population, but the results of their study differed significantly from those of our study. In their reports the prevalence of hyperuricemia was 21.6% in men and 9.6% in women [70], which was higher than ours and similar to that in developed countries. Our research was performed on mainland China, whereas Chuang’s study was conducted in Taiwan, an economically-developed region in China. We believe that our results are more representative of the Chinese population living in the mainland.As China is geographically vast, the prevalence of hyperuricemia varies significantly in different geographic regions. The prevalence in south China was 18.6%, which is much higher than the pooled prevalence, followed by southwest China (13.9%), north China (13.2%), east China (12.9%), northwest China (10.3%), and northeast China (10.1%). Such differences might be related to variability in lifestyle and economic development. As a previous study described, rapidly increasing economic development has led to unhealthy lifestyles [71]. Residents in south China, which is an economically developed region, consume more meat, seafood, and alcohol than residents elsewhere; therefore, the prevalence of hyperuricemia was higher in south China than in other regions. Also, hyperuricemia was more common in urban residents than in rural residents, and the inland prevalence of hyperuricemia was much higher than in coastal areas.From our study, the pooled prevalence of gout was 1.1%, which is similar to that in Italy (0.9% in 2009) [19], France (0.9% in 2013) [72], the United Kingdom, and Germany (1.4% in 2000–2005) [17]. In addition, the prevalence of gout in our country was much higher than that in Turkey (0.31% in 2001-2002) [73], Mexico (0.3% in 2011) [74], Greece (0.47% in 2003) [75], and the Czech Republic (0.3% in 2002-2003) [76] but is markedly lower than that in New Zealand (2.69% in 2008-2009) [77], the USA (3.9% in 2007-2008) [21], and Australia (9.7% in 2002) [78].Another main finding in our study was that the prevalence of gout in men (1.5%) was remarkably higher than in women (0.9%). This difference in sex was consistent with previous studies in other populations. Soriano et al. investigated the current epidemiology of gout in the general United Kingdom population and suggested that the incidence of gout was 4.42 per 1,000 persons per year in men and 1.32 per 1,000 persons per year in women [13]. Zhu et al. reported that the prevalence in the US was 5.9% in men, which was much higher than the 2.0% observed for women [21]. In accordance with these researches, prevalence of gout in Taiwan was 9.2% for men and 2.3% for women [70]. Sex hormones may explain the difference between the sexes. Ghei et al. suggested that the serum uric acid levels were higher in men than in women and that this difference is under the influence of sex hormones. Uric acid levels in women tend to increase after menopause [69, 79].Moreover, in line with previous results, a rise in the prevalence of gout was observed in the current study. The prevalence was 0.9% in 2000–2005, 1.1% in 2006–2010, and 1.4% in 2011–2014. The US National Health and Nutrition Examination Survey (NHANES) study conducted in 2007-2008 demonstrated that the prevalence of gout was 3.9%, though it was only 2.7% in 1988–1994 [21]. The NAHSIT studies, carried out during 1993–1996 and 2005–2008, showed that the prevalence of gout increased from 4.7% to 8.2% in men and 2.2% to 2.3% in women [70]. To help reduce the increasing burden of these diseases, prospective data on modifiable risk factors in lifestyle and diet for these conditions should be considered including, but not limited to, weight control, regular exercise, restricted intake of meat and purine-rich foods, and avoidance of heavy drinking. Vitamin C supplementation may also be considered a long-term preventive measure as it can lower the risk of gout through lowering serum urate levels [80, 81].Noteworthy, there is a lack of unified diagnostic criteria for gout, and several sets of criteria exist, such as the Rome criteria, the New York criteria, and the American Rheumatology Association (ARA) criteria [24]. The gold standard to diagnose gout is the presence of monosodium urate monohydrate (MSU) crystals in the synovial fluid (SF) at the time the patient experiences a gout attack [82]. The sets of criteria that include MSU crystals in SF have high specificity, and the exclusion of MSU crystal examination has led to a dramatic reduction in sensitivity [83]. However, MSU crystal examination is not always feasible in clinical practice. In 2015, Taylor et al. performed the Study for Updated Gout Classification Criteria (SUGAR) and determined ten parameters for accurately distinguishing gout from nongout [84]. In the same year, the American College of Rheumatology developed a new classification criteria for gout [85]. All the studies included in our analysis were performed from 2000 to 2014; therefore they were unable to adopt the new classification criteria. The diagnostic criteria used in this study could lead to a possible high sensitivity but low specificity. Because of this, the prevalence of gout in our analysis may be slightly higher than the actual rate, but it represents the general prevalence of gout and its geographical distribution in China.Our meta-analysis has several other limitations. First, the pooled data covered only part of mainland China, especially for gout; however, our data did cover 16 provinces, municipalities, and autonomous regions. To our knowledge, it is the most encompassing cross-sectional study on hyperuricemia and gout prevalence in China. Second, the primary studies on hyperuricemia used different assays to assess serum uric acid levels with different reference intervals. Third, there were variations in the quality of the selected articles; hence heterogeneity may be influenced by uncertain data. Fourth, as much concern is given to this topic by Chinese doctors, the majority of the studies included were published in Chinese. However, this limitation was overcome by the current authors who are proficient in Chinese for interpretation and extraction of data. Also, sample size of included papers was too small in our subgroup analysis for the prevalence of gout, so there was no statistical power to explore the association between gout prevalence and geographic regions. Our work underlines the need for additional population-based investigations in the areas absent from our analysis. This is the first study to assess the nationwide epidemiology of hyperuricemia and gout in mainland China.In conclusion, as previous studies were limited to specific regions, our study on the epidemiology of hyperuricemia and gout is of value to public health policies. Based on previous studies, we show that the prevalence of these diseases is high and that the rate of gout is rising. Consequently, large well-designed multicenter investigations are required in the future to provide information regarding the outcomes and prognosis of these chronic diseases in the entire population. Furthermore, effective measures should be adopted to prevent the increase in incidence of these diseases.



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*Source: 762820-2015-11-10.xml*