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. 2015 Nov 10;2015:762820. doi: 10.1155/2015/762820

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

Rui Liu 1, Cheng Han 1, Di Wu 1, Xinghai Xia 1, Jianqiu Gu 1, Haixia Guan 1,*, Zhongyan Shan 1, Weiping Teng 1,*
PMCID: PMC4657091  PMID: 26640795

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.

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 Table 1. 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 1.23
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Table 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 attack
Case 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 attack
Case definition: ≥6 of 12 clinical criteria required or presence of MSU crystals in SF or in tophus.
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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.

Figure 1

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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-based Q test and the I 2 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 (Figure 1). 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 Figure 2, 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.

Figure 2

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Forest plot of the pooled prevalence of hyperuricemia in mainland China.

Figure 3.

Figure 3

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Forest plot of the pooled prevalence of gout in mainland China.

Figures 4 and 5 showed the individual prevalence of hyperuricemia and gout, respectively, in different provinces, municipalities, and autonomous regions.

Figure 4.

Figure 4

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Regional distribution of pooled prevalence of hyperuricemia in mainland China.

Figure 5.

Figure 5

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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 Table 3, location in an urban area (χ 2 = 25.53, P < 0.001), inland area (χ 2 = 117.95, P < 0.001), or south China (χ 2 = 507.39, P < 0.001) and a high economic level (χ 2 = 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 17476/126368
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For gout, the prevalence among the subgroups was very different (Table 4). 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%; χ 2 = 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%; χ 2 = 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 (χ 2 = 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
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3.4. Analysis of Heterogeneity and Publication Bias

A significant overall heterogeneity was noted in the study on hyperuricemia (P < 0.001, I 2 = 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.

Acknowledgments

The authors thank all the authors and participants in the studies mentioned in our research.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors' Contribution

Rui Liu and Cheng Han carried out the study. Rui Liu, Cheng Han and Di Wu wrote the main paper. Xinghai Xia and Jianqiu Gu prepared the figures and tables. Haixia Guan and Weiping Teng designed and funded the study. Zhongyan Shan revised the paper. All authors reviewed the paper. Rui Liu and Cheng Han contributed equally to this work.

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