Clinical Rheumatology

, Volume 32, Issue 11, pp 1641–1648 | Cite as

A meta-analysis of alcohol consumption and the risk of gout

  • Meiyun Wang
  • Xiubo Jiang
  • Wenlong Wu
  • Dongfeng Zhang
Original Article

Abstract

Alcohol consumption had been linked to the risk of gout theoretically, but the results from observational studies were conflicting. Hence, a meta-analysis was conducted to assess the effect of alcohol consumption on the risk of gout. A comprehensive search was performed to identify all eligible studies on the association of alcohol consumption with gout risk. Pooled relative risks (RRs) with 95 % confidence intervals (CIs) from fixed and random effects models were calculated. A total of 12 articles with 17 studies involving 42,924 cases met the inclusion criteria. The pooled RR for highest vs. non/occasional alcohol drinking in every study was 1.98 (95 % CI, 1.52–2.58). The RRs for light (≤1 drink/day), moderate (>1 to <3 drinks/day), and heavy drinking (≥3 drinks/day) vs. non/occasional alcohol drinking were 1.16 (95 % CI, 1.07–1.25), 1.58 (95 % CI, 1.50–1.66), and 2.64 (95 % CI, 2.26–3.09), respectively. The results suggested that alcohol consumption might be associated with increased risk of gout.

Keywords

Alcohol consumption Gout Meta-analysis Risk factor 

Introduction

Gout is the most prevalent and extremely painful inflammatory arthritis. It affects at least 1 % of the population in Western countries [1, 2, 3]. As the economy develops, the prevalence of gout is increasing year by year all around the world [4, 5, 6, 7]. Besides, evidence suggested that gout is strongly associated with metabolic syndrome including hypertension, insulin resistance, obesity, renal dysfunction, hyperlipidemia, etc. [8, 9, 10]. These comorbidities could lead to the impairment of the patients' health [11]. Therefore, it is very important to explore the factors associated with gout. A number of epidemiological studies have shown that lifestyle-related factors such as alcohol, obesity, meat, and seafood play an important role in the development of gout [3, 12].

Excessive alcohol consumption is widely believed to be harmful on health [13]. Besides, alcohol consumption had been linked to the risk of gout theoretically. However, studies evaluating alcohol consumption and gout risk have yielded inconsistent results. A prospective study by Choi et al. [14] reported that only heavy alcohol consumption rather than light or moderate was the risk factor for gout. In contrast, a nested case–control study by Soriano et al. [15] showed that even light-to-moderate alcohol consumption could increase the risk of gout. Other studies about the association between alcohol consumption and the risk of gout were also with equivocal results [16, 17, 18, 19, 20]. Therefore, we conducted a meta-analysis to (1) assess the gout risk for highest vs. non/occasional alcohol drinking in every study, (2) evaluate different amounts of alcohol consumption vs. non/occasional alcohol drinking with gout risk, and (3) assess the heterogeneity among studies and publication bias.

Methods

Search strategy

The available studies published in English or Chinese (up to January 2013) were identified by extended computer-based searches from the following databases: (1) PubMed, (2) Web of Science (ISI), (3) Google scholar, and (4) Wanfang Med Online. Search terms included “beverages,” “alcohol consumption,” “ethanol,” “risk factors,” and “gout.” Moreover, we reviewed the reference lists from retrieved articles to identify additional studies not captured by our databases. Abstract and unpublished studies were not included. The detailed steps of the literature search are shown in Fig. 1.
Fig. 1

The detailed steps of our literature search

Inclusion criteria

The inclusion criteria were as follows: (1) case–control or cohort study published as an original study to evaluate the association between alcohol consumption and risk of gout, (2) multivariate-adjusted relative risk (RR) with 95 % confidence interval (CI) was provided, and (3) non/occasional drinking as the reference category. If one data from the same population had been published more than once, the most recent and complete studies were chosen.

Two investigators searched articles and reviewed all retrieved studies independently. If the two investigators disagreed about the eligibility of an article, it was resolved by consensus with a third reviewer.

Data extraction

Data were independently extracted by two investigators who reached a consensus on all of the items. Information extracted from each study were as follows: publication year, name of first author, country, number in case (exposed) and control (unexposed) groups, mean age, variables adjusted for in the analysis, amount of alcohol, diagnostic criteria of gout, duration of follow-up for cohort studies, and multivariate-adjusted RRs or ORs (we presented all results as ORs for simplicity) with their 95 % CIs.

Statistical analysis

For alcohol consumption, the measurement of alcohol intake varied among studies. Gram/day was chosen as the standard measure. The equivalence was as follows: one drink, 1 ml and 1 oz as 12.5, 0.8, and 28.35 g of ethanol [21]. The median or mean amount of alcohol consumption for each category was assigned to the corresponding RR for every study. If the upper boundary of the highest category was not provided, 1.2-fold of its lower bound was used [22]. Non/occasional drinkers were regarded as the reference group. The daily amount of alcohol consumption was assigned to three levels: light (≤1 drink, i.e., ≤12.5 g), moderate (>1 to <3 drinks/day, i.e., 12.6–37.4 g), and heavy (≥3 drinks, i.e., ≥37.5 g) [23]. When more than one exposure amount fell into the same level (light, moderate, or heavy) in a study, all the corresponding estimates that fell into the same level were combined. When available, we chose multivariable-adjusted risk estimates; otherwise, we used unadjusted RRs or calculated them from the raw data presented in the article.

Pooled measure was calculated as the inverse variance-weighted mean of the logarithm of multivariate-adjusted RRs with 95 % CIs to assess the strength of association between alcohol consumption and gout risk. I 2 of Higgins and Thompson was used to assess heterogeneity among studies [24]. The DerSimonian and Laird random effects model was selected as the pooling method if substantial heterogeneity was present (I 2 > 50 %) [25]; otherwise, the fixed effects model was adapted. Influence analysis was conducted [26] to describe whether or not an individual study affected the pooled estimator. Additionally, if the main estimate of an individual study's omitted analysis lay outside the 95 % CIs of the combined analysis, it was suspected of excessive influence. Egger's regression asymmetry test [27] was used to estimate publication bias. Subgroup analysis was performed by the type of study design (case–control and cohort studies) and ethnicity (East Asia and Caucasian).

Stata version 10.0 (Stata Corporation, College Station, TX, USA) was used to carry out statistical analyses. All reported probabilities (p values) were two-sided with p value less than 0.05 considered statistically significant.

Results

Study characteristics

Briefly, 12 articles [14, 15, 16, 17, 18, 19, 20, 28, 29, 30, 31, 32] were included with 17 studies involving 42,924 cases meeting the inclusion criteria. Seven articles [14, 15, 18, 19, 29, 30, 32] with 12 studies involving 24,419 cases have a cohort design. Two articles [14, 32] included three studies and one article [30] reported two studies. Five articles [16, 17, 20, 28, 31] with five studies including 18,505 cases have a case–control design. The detailed characteristics of included studies are shown in Table 1.
Table 1

General characteristics of included studies

Ref.

Author (year)

Country (follow-up)

Study design

Gender

Age range (mean)

Participants (cases)

Alcohol drinking RR (95 % CI)

Diagnosis

Adjustment forcovariates (match for)

[28]

Chang et al. (1997)

China

Case–control

Botha

>40

1,044 (38)

Nondrinker/1.00

Clinical diagnostic

Matched for age, sex

Drinker/1.2 (0.4–3.4)

[29]

Lin et al. (1999)

China (1991–1992, 1996–1997)

Cohort

Male

>30

1,515 (391)

Nondrinker/1.00

Clinical diagnostic

Adjusted for uric acid, follow-up, alcohol, diuretics, and BMI

Drinker/3.45 (1.58–7.56)

[16]

Lyu et al. (2003)

China

Case–control

Male

20–70

184 (92)

Ethanol intake (g): 0.00/1.00, 0.01–2.99/1.14 (0.60–2.16), ≥3/3.27 (1.35–7.92)

ACR criteria

Matched for age range and dietary intake distributions

[14]

Choi et al. (2004)

America (1986–1998)

Cohort

Male

40–75

47,150 (730)

Beer: <1/month/1.00, 1/month–1/week/1.01 (082–1.24), 2–4/week/1.27 (1–1.62), 5/week–1/day/1.75 (1.32–2.32), >2/day/2.51 (1.77–3.55)

ACR criteria

Adjusted for the other two alcoholic beverages and for age, total energy intake, body mass index, diuretic, hypertension, history of renal failure, intake of total meats, seafood, purine-rich vegetables, dairy foods, and fluid

Spirits: 1/month/1.00, 1/month–1/week/1.27 (1.03–1.56), 2–4/week/1.25 (0.98–1.59), 5/week–1/day/1.22 (0.93–1.6), >2/day/1.6 (1.19–2.16)

Wine: <1/month/1.00, 1/month–1/week/0.84 (0.69–1.01), 2–4/week/0.90 (0.71–1.15), 5/week–1/day/0.82 (0.61–1.11), >2/day/1.05 (0.64–1.72)

[18]

Williams et al. (2008)

America (1991–1993, 1998–2001)

Cohort

Male

44.63

28,990 (228)

Ethanol intake (g): 0/1.00, 0–5/1.33 (0.78–1.87), 5–10/1.26 (0.7–1.81), 10–15/2.04 (1.18–2.9), >15/2.11 (1.39–2.82)

Clinical diagnostic

Adjusted for age, hypertension, and weekly intakes of other foods and aspirin

[19]

Cohen et al. (2008)

America (1999–2003)

Cohort

Botha

69.8

259,209 (328)

Ethanol intake (g): 6/1.33 (1.17–1.52)

Na

Adjusted for dialysis and censored for renal transplantation

[30]

Bhole et al. (2010)

America (1950–2002)

Cohort

Female

47

2,476 (200)

29.57 ml/week/1.00, 236.56 ml/week/1.30 (0.80–2.12), 532.26 ml/week/3.10 (1.69–5.68)

Clinical diagnostic

Adjusted for age, education, body mass index, hypertension, use of diuretics, blood glucose level, blood cholesterol level, and menopausal status

Male

46

1,951 (104)

29.57 ml/week/1.00, 236.56 ml/week/1.44 (0.99–2.08), 532.26 ml/week/2.21 (1.56–3.14)

[15]

Cea Soriano et al. (2011)

UK

Case–control

Botha

20–89

1,775,505 (24,768)

Ethanol intake (g/week): 0/1.00, 40/1.06 (1.01–1.11), 136/1.56 (1.49–1.65), 268/2.45 (2.27–2.63), 404/3.00 (2.66–3.38)

ICD code

Adjusted for sex, age, calendar year, number of GP visits, BMI, alcohol consumption, IHD, hypertension, hyperlipidemia, diabetes, chronic renal failure, and use of diuretics

[17]

Lee et al. (2013)

Korea

Case–control

Botha

≥40

36,246 (18,123)

Nearly none/1.00: <1/week/1.01 (0.95–1.08), 1–2/week 1.1 (1.04–1.16), ≥3–4/week/1.28 (1.2–1.37)

ICD code

Match for age and gender

[31]

Li et al. (2012)

China

Case–control

Botha

53.95

504 (252)

Nondrinker/1.00

ACR criteria

Matched for age, sex, area and ethnic

Drinker/5.19 (3.55–7.59)

[32]

Wang et al. (2012)

China (2004–2009)

Cohort

Botha

≥15

659 (75)

Beer: 0/1.00, drinker/0.603 (0.261–1.393)

ARA criteria

Adjusted for education, labor intension, family history of gout, diabetes, hypertension, smoking, obesity, age

Spirit: 0/1.00, drinker/0.766 (0.333–1.762)

Wine: 0/1.00, drinker/13.716 (0.598–314.715)

[20]

Cheng et al. (2012)

China

Case–control

Botha

56.79

255 (120)

Times/week: <4/1.00, ≥4/7.081 (1.229–40.807)

ARA criteria

Matched for age and sex

Na not available

aSeparate results were not available for gender

Quantitative synthesis

For highest vs. non/occasional alcohol drinking, the pooled RR of overall data was 1.98 (95 % CI, 1.52–2.58; I 2 = 93.1 %, p for heterogeneity < 0.001). In the subgroup analysis by study design, the pooled RRs for case–control studies and cohort studies were 2.64 (95 % CI, 1.11–6.29; I 2 = 93.1 %, p for heterogeneity < 0.001) and 1.83 (95 % CI, 1.35–2.48; I 2 = 90.0 %, p for heterogeneity < 0.001), respectively. With regard to the subgroup of ethnicity, the pooled RRs for highest vs. non/occasional alcohol drinking were 1.98 (95 % CI, 1.43–2.74; I 2 = 88.7 %, p for heterogeneity < 0.001) in East Asia studies and 2.05 (95 % CI, 1.12–3.77; I 2 = 92.5 %, p for heterogeneity < 0.001) in Caucasian studies.

For different exposed amounts vs. non/occasional alcohol drinking, nine studies for light intake [14, 15, 16, 17, 18, 19, 20], eight studies for moderate intake [14, 15, 18, 28, 30], and three studies for heavy intake [15, 30] were available. The pooled RRs for light, moderate, and heavy drinking vs. non/occasional alcohol drinking were 1.16 (95 % CI, 1.07–1.25), 1.58 (95 % CI, 1.50–1.66), and 2.64 (95 % CI, 2.26–3.09), respectively, in the absence of a significant heterogeneity.

Table 2 presented the pooled RRs (95 % CIs) of gout for highest, light, moderate, and heavy intake vs. non/occasional alcohol drinking in overall data and among subgroups by the type of study design and ethnicity. Figure 2 showed the study-specific pooled RRs of gout along with 95 % CIs for highest (a), light (b), and moderate (c) drinking vs. non/occasional alcohol drinking.
Table 2

Pooled RRs and corresponding 95 % CIs of gout for different amounts of alcohol drinking vs. non/occasional drinking, according to selected subgroups

Data

Numbera

Pooled RR (95 % CI)b

I 2 (%)

Articles included (refs.)

Highest vs. non/occasional

Overall

17

1.98 (1.52–2.58)*

93.1

[14, 15, 16, 17, 18, 19, 20, 28, 29, 30, 31, 32]

Cohort

12

1.83 (1.35–2.48)*

90.0

[14, 15, 18, 19, 29, 30, 32]

Case–control

5

2.64 (1.11–6.29)*

93.1

[16, 17, 20, 28, 31]

East Asia

9

1.98 (1.43–2.74)*

88.7

[16, 17, 20, 28, 29, 31, 32]

Caucasian

8

2.05 (1.12–3.77)*

92.5

[14, 15, 18, 19, 30]

Light vs. non/occasional

Overall

9

1.16 (1.07–1.25)*

77.7

[14, 15, 16, 17, 18, 19, 20]

Cohort

6

1.16 (1.05–1.29)*

73.3

[14, 15, 18, 19]

Case–control

3

1.17 (1.02–1.34)*

86.1

[16, 17, 20]

East Asia

3

1.17 (1.02–1.34)*

86.1

[16, 17, 20]

Caucasian

6

1.16 (1.05–1.29)*

73.3

[14, 15, 18, 19]

Moderate vs. non/occasional

Overall

8

1.58 (1.50–1.66)

45.1

[14, 15, 18, 28, 30]

Cohort

7

1.67 (1.43–1.95)**

51.2

[14, 15, 18, 30]

Case–control

1

1.20 (0.40–3.40)

[28]

East Asia

1

1.20 (0.40–3.40)

[28]

Caucasian

7

1.67 (1.43–1.95) **

51.2

[14, 15, 18, 30]

Heavy vs. non/occasional

Overall

3

2.64 (2.26–3.09)**

67.0

[15, 30]

Cohort

3

2.64 (2.26–3.09)**

67.0

[15, 30]

Case–control

0

East Asia

0

Caucasian

3

2.64 (2.26–3.09)**

67.0

[15, 30]

aThe number of studies included

bWhen I 2 ≤ 50 %, pooled RR (95 % CI) was for fixed effects model; otherwise, it was for random effects model

*p < 0.01; **p < 0.05

Fig. 2

Forest plots for pooled relative risks and the corresponding 95 % confidence intervals (CIs) of gout risk for highest (a), light (b), and moderate (c) alcohol consumption. White diamond denotes the pooled OR. Black squares indicate the OR in each study, with square sizes inversely proportional to the standard error of the OR. Horizontal lines represent 95 % CIs

Sources of heterogeneity

To explore the sources of heterogeneity, meta-regression with covariates and stratified analyses were performed. Univariate and multivariate meta-regression with the covariates of publication year, country, and study design showed that no covariates had a significant impact on between-study heterogeneity (data not shown).

Influence analysis and publication bias

Influence analysis showed that two studies [15, 17] had excessive influence on the pooled estimate for highest alcohol drinking vs. none/occasional drinking. One individual study [15] had excessive influence on the pooled estimate for moderate and heavy alcohol drinking vs. none/occasional drinking. After excluding the excessive influential studies, the pooled results did not change obviously. The pooled RRs for highest, moderate, and heavy drinking vs. non/occasional alcohol drinking were 1.99 (95 % CI, 1.48–2.67, I 2 = 82.9 %, p for heterogeneity < 0.001), 1.72 (95 % CI, 1.49–1.98, I 2 = 46.2 %, p for heterogeneity = 0.072), and 2.91 [95 % CI, 2.61–3.26, I 2 = 25 %, p for heterogeneity = 0.263], respectively.

No evidence of significant publication bias was detected by Egger's tests for highest, light, moderate, and heavy drinking vs. none/occasional drinking (data not shown).

Discussion

Recently, studies have been performed to evaluate the correlation between alcohol consumption and the risk of gout. However, the results remained controversial. In this meta-analysis, the results of 12 articles on the association of alcohol consumption and gout risk were summarized. Findings from this meta-analysis indicated that alcohol consumption is linked with the increased risk of gout. The RRs for highest, light, moderate, and heavy drinking vs. non/occasional drinking were 1.98 (95 % CI, 1.52–2.58), 1.16 (95 % CI, 1.07–1.25), 1.58 (95 % CI, 1.50–1.66), and 2.64 (95 % CI, 2.26–3.09), respectively. The different amounts of alcohol consumption comparison analysis suggested that alcohol consumption is associated with increased risk of gout, even in light intake. As the amount of alcohol consumption increases from light to moderate to heavy, the risk of gout is increasing gradually.

Between-study heterogeneity is common in meta-analysis [33], and exploring the potential sources of between-study heterogeneity is the essential component of meta-analysis. Our meta-analysis showed significant between-study heterogeneity. Although most studies in this meta-analysis used multivariate regression to adjust confounders, other indeterminate characteristics that varied among studies, such as study design, publication year, country, etc., could be the causes of between-study heterogeneity. Hence, we used meta-regression to explore the potentially important covariate which exerts substantial impact on between-study heterogeneity. However, no aforementioned covariates had a significant impact on between-study heterogeneity. Considering meta-analysis of all included studies was fraught with the problem of heterogeneity. Subgroup analyses by the type of study design (case–control and cohort studies) and ethnicity (East Asia and Caucasian) were performed to explore the source of heterogeneity. However, the between-study heterogeneity persisted in subgroups, suggesting the presence of other unknown confounding factors. Gout is a complex etiology and pathophysiology disease generated by the combined effects of genes and environment factors. Thus, other genetic and environment variables, as well as their possible interaction, may well be potential contributors to the heterogeneity observed.

In this meta-analysis, there were no significant differences observed across subgroups stratified by ethnicity and different exposure amount. As to influence analysis, significant individual study influence on the pooled effect was observed. After excluding the excessive influential studies, the results did not change substantially. Moreover, the Egger's test did not support the presence of publication bias, suggesting that the association observed was stable.

A major strength of the present meta-analysis is the large number of participates included, allowing a much greater possibility of reaching reasonable conclusions. Other strengths are the extensive search of the literature and the use of a statistical method [34] to combine RRs falling in more than one exposure levels. On the other aspect, the major limitation might be the rough. Choi et al. [14] found that different alcoholic beverages (beer, wine, and liquor) have different effects on gout risk. However, few studies reported separate effect estimates for beer, wine, and liquor consumption. Therefore, we did not perform the subgroup analyses according to the species of alcoholic beverages. Further studies should be conducted to examine the effect of different alcoholic beverages on gout risk.

In summary, results from this meta-analysis indicated that alcohol consumption might be associated with increased risk of gout. Reducing alcohol intake should be advocated for the primary prevention of gout.

Notes

Disclosures

None.

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Copyright information

© Clinical Rheumatology 2013

Authors and Affiliations

  • Meiyun Wang
    • 1
  • Xiubo Jiang
    • 1
  • Wenlong Wu
    • 1
  • Dongfeng Zhang
    • 1
  1. 1.Department of Epidemiology and Health StatisticsMedical College of Qingdao UniversityQingdaoPeople’s Republic of China

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