Abstract
Objective
Prospective studies evaluating diet in relation to the risk of gout in Asian populations are lacking. The purpose of this study was to examine the relationship between the consumption of dietary protein from each of its major sources and the risk of gout in a Chinese population.
Methods
We used data from the Singapore Chinese Health Study, a prospective cohort of 63,257 Chinese adults who were 45–74 years old at recruitment during the years 1993–1998. Habitual diet information was collected via a validated semiquantitative food frequency questionnaire, and physician-diagnosed gout was self-reported during 2 followup interviews up to the year 2010. Cox proportional hazards models were used to calculate the hazard ratios (HRs) and 95% confidence intervals (95% CIs), with adjustment for potential confounders, among 51,114 eligible study participants who were free of gout at baseline and responded to our followup interviews.
Results
A total of 2,167 participants reported physician-diagnosed gout during the followup period. The multivariate-adjusted HRs (with 95% CIs) of gout, comparing the first quartile with the fourth quartile, were as follows: 1.27 (1.12–1.44; P for trend < 0.001) for total protein, 1.27 (1.11–1.45; P for trend < 0.001) for poultry, 1.16 (1.02–1.32; P for trend = 0.006) for fish and shellfish, 0.86 (0.75–0.98; P for trend = 0.018) for soy food, and 0.83 (0.73–0.95; P for trend = 0.012) for nonsoy legumes. No statistically significant associations were found with protein intake from other sources (red meat, eggs, dairy products, grains, or nuts and seeds).
Conclusion
In this Chinese population living in Singapore, higher total dietary protein intake from mainly poultry and fish/shellfish was associated with an increased risk of gout, while dietary intake of soy and nonsoy legumes was associated with a reduced risk of gout.
Gout is estimated to affect ~5% of the middle-aged and elderly population worldwide (1). In recent decades, the prevalence of gout in Asian countries is approaching that observed in Western populations (2–5). The disease burden of gout results from the loss of physical function and work productivity (6,7), as well as from death from cardiovascular causes (4,8–10). Gout is also associated with other chronic diseases, such as obesity, diabetes mellitus, hypertension, and hyperlipidemia, which are on a rising trend worldwide (11).
Diet plays an important role in the development and management of gout (12,13). Since high-protein foods tend to contain large quantities of purines, patients with gout or hyperuricemia are generally advised to avoid food sources of protein, including meat, seafood, soy, and nonsoy legumes (14–17). However, the data concerning the association between food sources of protein and gout remain unclear, and prospective studies have been limited mainly to Caucasian populations. For example, two large prospective studies in Caucasian men showed a positive association between gout and intake of meat and seafood (18,19) and an inverse association with low-fat dairy products (18).
The Chinese diet is distinct from that in the West. However, data from prospective studies on diet and gout in the Chinese population are sparse. In this study, we examined the relationship between dietary intake of protein from each of its major sources (i.e., meat, seafood, poultry, soy foods and nonsoy legumes, dairy products, grains, and nuts/seeds) and incident gout in a prospective cohort of middle-aged and older Chinese adults living in Singapore.
SUBJECTS AND METHODS
Study population
The Singapore Chinese Health Study is a population-based cohort of 63,257 Chinese adults ages 45–74 years at baseline (1993–1998) (20). The participants were recruited from persons living in government housing estates and were restricted to the 2 major dialect groups, the Hokkien and Cantonese, who originated from the contiguous provinces of Fujian and Guangdong, respectively, in southern China. At recruitment, face-to-face interviews were conducted in the participants’ homes by trained interviewers using a structured, scanner-readable questionnaire that ascertained information on demographics, height, weight, tobacco use, physical activity, and medical history. Habitual diet during the preceding year was captured using a validated 165-item food frequency questionnaire (FFQ).
The study was approved by the institutional review boards of the National University of Singapore and the University of Pittsburgh, and all participants gave informed consent.
Between 1999 and 2004, 52,322 participants were contacted for telephone interviews to update information on lifestyle factors and medical history (21). Between 2006 and 2010, telephone interviews were conducted again among 39,528 participants for updated lifestyle factors and medical history.
Assessment of diet and covariates
The FFQ included 165 food items commonly consumed by this population. These foods were covered under the categories of rice and noodles, meats (red meat [pork/beef], poultry, and fish and shellfish), vegetables, fruits, soy foods, nonsoy legumes, nuts and seeds, dairy products, beverages, condiments, and preserved foods. The participants were instructed to select from 8 consumption frequency categories (ranging from “never or hardly ever” to “two or more times a day”) and 3 portion sizes (small, medium, or large; aided by the use of photographs). The dietary nutrients were derived according to the Singapore Food Composition Database, which was developed specifically for this cohort study and has been described in detail elsewhere (20).
The FFQ was validated in two 24-hour recall assessments as well as by re-administration to 810 cohort participants (20). The correlation coefficient for energy intake and selected nutrients from the FFQ versus the 24-hour recalls ranged between 0.24 and 0.79 (22). The differences between the mean values of most pairs of assessments for energy and nutrients were within 10% of each other.
Incident gout cases
At both followup interviews, the participants were asked “Have you been told by a doctor that you have gout?” If the response was “yes,” the participant was then asked to “Please also tell me the age at which you were first diagnosed.” The interviewers confirmed that the participants had gout but not another form of arthritis by verifying with the participants that the diagnosis of gout was based on joint pain and swelling attributed to reported hyperuricemia by their physicians. Dietary advice for gout patients was not a criterion for case definition because of the variation and inconsistency in dietary advice among physicians. All interviews were tape-recorded and subjected to quality checks.
A total of 54,341 participants participated in either or both followup interviews (52,322 in followup 1 and 39,528 in followup 2). A major reason for nonparticipation in the followup interview was that the subject had died; hence, as expected, compared to those who participated in at least 1 followup interview, those who did not participate (n = 8,916) were older at recruitment (61.1 years versus 55.8 years). They were also more likely to be male and to have smoked at some point in their lives and to have a lower education level but a higher prevalence of self-reported hypertension and diabetes mellitus at baseline (Supplementary Table 1, available on the Arthritis & Rheumatology web site at http://onlinelibrary.wiley.com/doi/10.1002/art.39115/abstract).
We further excluded 1,290 participants with cancer at baseline and 841 participants who reported extreme calorie intakes (<600 or >3,000 kcal/day for women and <700 or >3,700 kcal/day for men). Participants with prevalent gout that had been diagnosed before baseline (n = 1,087) or those with missing age at gout diagnosis (n = 9) were also excluded. Hence, the current analysis included data from 51,114 participants (Figure 1).
Figure 1.
Flow chart showing inclusion of study participants from the initial cohort of 63,257 participants in the Singapore Chinese Health Study, 1993–1998.
Statistical analysis
The number of person-years at risk contributed by each participant was calculated from the date of the baseline interview to the date of reported gout diagnosis, death, or the latest followup interview, whichever occurred first. The differences between baseline characteristics by physician-diagnosed gout were examined using the chi-square test for categorical variables and Student’s t-test for continuous variables. The dietary intake of protein and the food sources of the protein were analyzed in quartiles. Cox proportional hazards regression was used to calculate hazard ratios (HRs) and 95% confidence intervals (95% CIs) for developing gout by using the lowest quartile of dietary intake as the reference group.
The selection of potential confounders was based primarily on prior consideration of their associations with either dietary intake or risk of gout in this population. A basic model (model 1) included age (years), sex, and year of baseline interview (1993–1995 or 1996–1998). An additional model (model 2) included dialect (Hokkien/Cantonese), education level (none, primary school, secondary school, or higher), physical activity (<0.5 hours/week, 0.5–3.9 hours/week, or ≥4.0 hours/week), smoking status (never, former, or current), alcohol consumption (none, monthly, weekly, or daily), body mass index (BMI; <20.0 kg/m2, 20.0–23.9 kg/m2, 24.0–27.9 kg/m2, or ≥28.0 kg/m2), self-reported history of hypertension or diabetes mellitus, and total energy intake (kcal/day). We further adjusted for dietary intake of vegetables, fruits, red meat, poultry, fish and shellfish, eggs, dairy products, soy foods, nonsoy legumes, nuts and seeds, and all grain products (all in quartiles) (model 3). We used the multivariate nutrient density method in the analyses by computing the intake of protein or its major food sources per 1,000 kcal/day and including total energy as a covariate in the model. This method is an “isocaloric” analysis and controls for confounding by energy intake with an intuitive interpretation as a measure of dietary composition (23).
Tests for trend were performed by using median values of intake in the quartile categories as continuous variables in the Cox regression models. Heterogeneity of the diet–gout associations between men and women was tested with an interaction term in the models. All analyses were performed using SAS version 9.2 software (SAS Institute), and statistical significance was based on 2-sided probability of 0.05.
RESULTS
After a mean ± SD followup of 11.1 ± 3.7 years among 51,114 participants (568,976 person-years), a total of 2,167 subjects reported new-onset physician-diagnosed gout (1,151 at followup 1 and 1,016 at followup 2). The mean ± SD age at diagnosis was 61.3 ± 8.1 years (range 45–87 years). The incidence rates of gout standardized to the age structure of the whole cohort were 504 per 100,000 person-years in men and 294 per 100,000 person-years in women.
The distributions of selected characteristics and dietary exposures are shown in Table 1. Participants with incident gout were more likely to be male, ever smokers, weekly or daily alcohol drinkers, and to have higher education levels and higher BMI than their counterparts. Incident gout cases also had a higher prevalence of self-reported hypertension at baseline as compared to noncases. Participants who developed gout also had high levels of consumption of total protein, red meat (including pork, beef and lamb), poultry, and fish and shellfish at baseline than those who remained free of gout (Table 1).
Table 1.
Baseline characteristics of 51,114 participants in the Singapore Chinese Health Study 1993–2010, categorized according to those who developed gout and those who remained free of gout
| Characteristic | Subjects with gout (n = 2,167) |
Subjects without gout (n = 48,947) |
P* |
|---|---|---|---|
| Demographic features | |||
| Age, mean ± SD years | 55.0 ± 7.4 | 55.7 ± 7.7 | <0.001 |
| No. (%) male | 1,193 (55.1) | 20,528 (41.9) | <0.001 |
| No. (%) Cantonese dialect speakers | 1,067 (49.2) | 23,369 (47.7) | 0.17 |
| No. (%) secondary school or higher | 737 (34.0) | 14,252 (29.1) | <0.001 |
| No. (%) ever smoked | 651 (30.0) | 13,813 (28.2) | 0.07 |
| Comorbid conditions, no. (%) | |||
| Hypertension | 798 (36.8) | 10,470 (21.4) | <0.001 |
| Diabetes mellitus | 159 (7.3) | 3,686 (7.5) | 0.74 |
| Physical activity, no. (%) | <0.001 | ||
| <0.5 hours/week | 1,623 (74.9) | 38,151 (77.9) | |
| 0.5–3.9 hours/week | 329 (15.2) | 6,856 (14.0) | |
| ≥4 hours/week | 215 (9.9) | 3,940 (8.1) | |
| Body mass index, mean ± SD kg/m2 | 24.2 ± 3.3 | 23.1 ± 3.2 | <0.001 |
| Alcohol intake, no. (%) | <0.001 | ||
| Weekly consumption | 231 (10.7) | 3,983 (8.1) | |
| Daily consumption | 88 (4.1) | 1,571 (3.2) | |
| Daily dietary intake, mean ± SD | |||
| Calories, kcal | 1,620 ± 540 | 1,553 ± 518 | <0.001 |
| Red meat, gm/1,000 kcal | 19.7 ± 11.1 | 18.9 ± 11.0 | <0.001 |
| Poultry, gm/1,000 kcal | 14.0 ± 10.5 | 12.7 ± 9.6 | <0.001 |
| Fish and shellfish, gm/1,000 kcal | 37.6 ± 17.6 | 36.2 ± 17.5 | <0.001 |
| Total protein, % kcal | 15.4 ± 2.5 | 15.2 ± 2.4 | <0.001 |
| Soy protein, % kcal | 1.47 ± 0.99 | 1.50 ± 1.01 | 0.10 |
P values were determined by chi-square test for categorical variables and by Student’s t-test for continuous variables.
High total protein consumption was associated with an increased risk of gout, with an HR for comparison of the first and fourth quartiles of 1.27 (95% CI 1.12–1.44; P for trend < 0.001) (Table 2). In contrast, dietary intake of soy protein was associated with a marginally significant inverse association, with an HR for comparison of the first and fourth quartiles of 0.89 (95% CI 0.79–1.01; P for trend = 0.06).
Table 2.
Hazard ratios and 95% confidence intervals for the risk of gout in 2,167 participants in the Singapore Chinese Health Study 1993–2010, by dietary protein intake*
| Protein | Quartile of energy-adjusted protein intake
|
P for trend | |||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | ||
| Total protein, % kcal/day | |||||
| No. of cases/person-years | 480/138,767 | 564/142,931 | 515/144,094 | 608/143,184 | |
| Multivariate model 1 | 1.00 | 1.18 (1.04–1.33) | 1.09 (0.96–1.23) | 1.34 (1.19–1.51) | <0.001 |
| Multivariate model 2 | 1.00 | 1.15 (1.02–1.30) | 1.06 (0.93–1.20) | 1.27 (1.12–1.44) | <0.001 |
| Soy protein, % kcal/day | |||||
| No. of cases/person-years | 546/134,854 | 553/143,020 | 545/145,313 | 523/145,789 | |
| Multivariate model 1 | 1.00 | 0.99 (0.88–1.11) | 0.98 (0.87–1.10) | 0.96 (0.85–1.09) | 0.55 |
| Multivariate model 2 | 1.00 | 0.96 (0.85–1.08) | 0.93 (0.83–1.05) | 0.89 (0.79–1.01) | 0.06 |
Linear trend was tested by assigning to participants the median value for the quartile and assessing this as a continuous variable. Multivariate model 1 was adjusted for age, sex, and year of interview. Multivariate model 2 was adjusted for the same variables as for model 1 plus dialect, education level, body mass index, physical activity, smoking status, alcohol use, baseline presence of diabetes mellitus, baseline presence of hypertension, and total energy.
When analyzing the individual sources of dietary protein intake (Table 3), we observed a positive association with poultry and fish/shellfish. In the multivariate-adjusted model with dietary variables, the HR for comparison of the first and fourth quartiles was 1.27 (95% CI 1.11–1.45; P for trend < 0.001) for poultry, and 1.16 (95% CI 1.02–1.32; P for trend = 0.006) for fish and shellfish. The consumption of red meat was positively associated with the risk of gout, but this was attenuated after additional adjustment for other dietary variables. In contrast, we found that consumption of soy foods and nonsoy legumes was associated with a reduced risk of gout, and the HR comparing the first and fourth quartiles was 0.86 (95% CI 0.75–0.98; P for trend = 0.018) for soy foods and 0.83 (95% CI 0.73–0.95; P for trend = 0.012) for nonsoy legumes. There was no statistically significant association between other food groups (i.e., eggs, dairy products, nuts and seeds, or grain products) and risk of gout.
Table 3.
Hazard ratios and 95% confidence intervals for the risk of gout in 2,167 participants in the Singapore Chinese Health Study 1993–2010, by source of dietary protein*
| Source of dietary protein | Quartiles of energy-adjusted food intake
|
P for trend | |||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | ||
| Red meat, gm/1,000 kcal/day | |||||
| No. of cases/person-years | 473/140,474 | 539/143,728 | 561/144,406 | 594/140,368 | |
| Multivariate model 1 | 1.00 | 1.07 (0.95–1.22) | 1.09 (0.97–1.24) | 1.16 (1.03–1.31) | 0.019 |
| Multivariate model 2 | 1.00 | 1.06 (0.94–1.20) | 1.10 (0.97–1.24) | 1.18 (1.04–1.33) | 0.005 |
| Multivariate model 3 | 1.00 | 1.02 (0.90–1.16) | 1.03 (0.91–1.18) | 1.08 (0.94–1.24) | 0.25 |
| Poultry, gm/1,000 kcal/day | |||||
| No. of cases/person-years | 422/135,212 | 536/143,932 | 555/144,826 | 654/145,006 | |
| Multivariate model 1 | 1.00 | 1.17 (1.03–1.33) | 1.19 (1.05–1.35) | 1.38 (1.22–1.56) | <0.001 |
| Multivariate model 2 | 1.00 | 1.13 (0.99–1.28) | 1.14 (1.01–1.30) | 1.31 (1.16–1.48) | <0.001 |
| Multivariate model 3 | 1.00 | 1.12 (0.98–1.28) | 1.13 (0.99–1.28) | 1.27 (1.11–1.45) | <0.001 |
| Fish and shellfish, gm/1,000 kcal/day | |||||
| No. of cases/person-years | 498/137,796 | 510/142,338 | 545/145,450 | 614/143,392 | |
| Multivariate model 1 | 1.00 | 1.01 (0.89–1.14) | 1.09 (0.96–1.23) | 1.28 (1.14–1.44) | <0.001 |
| Multivariate model 2 | 1.00 | 0.98 (0.86–1.10) | 1.04 (0.92–1.18) | 1.22 (1.09–1.38) | <0.001 |
| Multivariate model 3 | 1.00 | 0.95 (0.83–1.07) | 1.00 (0.88–1.14) | 1.16 (1.02–1.32) | 0.006 |
| Eggs, gm/1,000 kcal/day | |||||
| No. of cases/person-years | 536/138,604 | 555/144,755 | 540/145,358 | 536/140,259 | |
| Multivariate model 1 | 1.00 | 0.97 (0.86–1.09) | 0.94 (0.83–1.06) | 0.95 (0.84–1.07) | 0.39 |
| Multivariate model 2 | 1.00 | 0.99 (0.88–1.11) | 0.96 (0.85–1.08) | 0.99 (0.88–1.12) | 0.96 |
| Multivariate model 3 | 1.00 | 0.96 (0.85–1.09) | 0.93 (0.82–1.05) | 0.96 (0.85–1.09) | 0.58 |
| Dairy products, gm/1,000 kcal/day | |||||
| No. of cases/person-years | 555/141,523 | 560/144,262 | 544/142,992 | 508/140,199 | |
| Multivariate model 1 | 1.00 | 0.96 (0.85–1.08) | 0.95 (0.85–1.07) | 0.97 (0.86–1.10) | 0.90 |
| Multivariate model 2 | 1.00 | 0.95 (0.84–1.06) | 0.96 (0.85–1.08) | 0.97 (0.86–1.10) | 0.97 |
| Multivariate model 3 | 1.00 | 0.96 (0.85–1.08) | 0.97 (0.86–1.10) | 0.99 (0.87–1.13) | 0.72 |
| Soy foods, gm/1,000 kcal/day | |||||
| No. of cases/person-years | 562/134,365 | 558/142,612 | 508/145,588 | 539/146,411 | |
| Multivariate model 1 | 1.00 | 0.96 (0.86–1.08) | 0.88 (0.78–0.99) | 0.95 (0.85–1.08) | 0.37 |
| Multivariate model 2 | 1.00 | 0.94 (0.84–1.06) | 0.84 (0.74–0.94) | 0.89 (0.79–1.00) | 0.008 |
| Multivariate model 3 | 1.00 | 0.92 (0.82–1.04) | 0.81 (0.72–0.92) | 0.86 (0.75–0.98) | 0.018 |
| Nonsoy legumes, gm/1,000 kcal/day | |||||
| No. of cases/person-years | 542/133,693 | 563/143,341 | 564/146,305 | 498/145,637 | |
| Multivariate model 1 | 1.00 | 0.94 (0.84–1.06) | 0.94 (0.83–1.06) | 0.83 (0.74–0.94) | 0.004 |
| Multivariate model 2 | 1.00 | 0.91 (0.81–1.02) | 0.92 (0.81–1.03) | 0.82 (0.73–0.93) | 0.004 |
| Multivariate model 3 | 1.00 | 0.91 (0.80–1.02) | 0.91 (0.81–1.03) | 0.83 (0.73–0.95) | 0.012 |
| Nuts and seeds, gm/1,000 kcal/day | |||||
| No. of cases/person-years | 500/138,321 | 558/143,179 | 519/144,945 | 590/142,531 | |
| Multivariate model 1 | 1.00 | 1.06 (0.94–1.19) | 0.96 (0.85–1.09) | 1.08 (0.96–1.22) | 0.29 |
| Multivariate model 2 | 1.00 | 1.04 (0.92–1.17) | 0.92 (0.81–1.04) | 1.03 (0.91–1.17) | 0.80 |
| Multivariate model 3 | 1.00 | 1.06 (0.94–1.20) | 0.94 (0.83–1.07) | 1.05 (0.92–1.20) | 0.67 |
| All grain products, gm/1,000 kcal/day | |||||
| No. of cases/person-years | 603/144,191 | 538/146,244 | 555/142,804 | 471/135,737 | |
| Multivariate model 1 | 1.00 | 0.87 (0.77–0.97) | 0.91 (0.81–1.03) | 0.80 (0.71–0.90) | 0.001 |
| Multivariate model 2 | 1.00 | 0.89 (0.79–1.00) | 0.95 (0.84–1.07) | 0.86 (0.76–0.97) | 0.19 |
| Multivariate model 3 | 1.00 | 0.91 (0.80–1.02) | 0.98 (0.86–1.12) | 0.93 (0.79–1.09) | 0.77 |
Linear trend was tested by assigning to participants the median value of the quartile and assessing this as a continuous variable. Multivariate model 1 was adjusted for age, sex, and year of interview. Multivariate model 2 was adjusted for the same variables as for model 1 plus dialect, education level, body mass index, physical activity, smoking status, alcohol use, baseline presence of diabetes mellitus, baseline presence of hypertension, and total energy. Multivariate model 3 was adjusted for the same variables as for model 2 plus dietary intake of vegetables, fruits, red meat, poultry, fish and shellfish, eggs, dairy products, soy foods, nonsoy legumes, nuts and seeds, and all grain products.
We further examined the associations in men and women separately (Table 4). The positive association with fish and shellfish, and the inverse association with nonsoy legumes persisted in women, but not in men (P for interaction = 0.02 and 0.05, respectively). In contrast, there was a non–statistically significant trend toward decreasing risk between dairy products and gout in men, but a marginally significant trend toward increasing risk in women (P for interaction = 0.04). No significant interactions were found for other food groups, and the results were similar between men and women.
Table 4.
HRs and 95% CIs for the risk of gout in 1,193 men and women who participated in the Singapore Chinese Health Study 1993–2010, by source of dietary protein*
| Source of dietary protein | Men
|
Women
|
P for interaction | ||
|---|---|---|---|---|---|
| No. of Cases | HR (95% CI) | No. of Cases | HR (95% CI) | ||
| Total protein, % kcal/day† | |||||
| Q1 | 305 | 1.00 | 175 | 1.00 | 0.27 |
| Q2 | 336 | 1.19 (1.01–1.39) | 228 | 1.10 (0.91–1.34) | |
| Q 3 | 283 | 1.08 (0.91–1.27) | 232 | 1.04 (0.86–1.27) | |
| Q 4 | 269 | 1.21 (1.03–1.43) | 339 | 1.32 (1.10–1.59) | |
| P for trend | 0.061 | 0.003 | |||
| Soy protein, % kcal/day† | |||||
| Q 1 | 346 | 1.00 | 200 | 1.00 | 0.47 |
| Q 2 | 318 | 0.92 (0.79–1.08) | 235 | 1.01 (0.84–1.22) | |
| Q 3 | 288 | 0.91 (0.77–1.06) | 257 | 0.97 (0.81–1.17) | |
| Q 4 | 241 | 0.85 (0.72–1.00) | 282 | 0.95 (0.79–1.14) | |
| P for trend | 0.058 | 0.46 | |||
| Red meat, gm/1,000 kcal/day‡ | |||||
| Q 1 | 227 | 1.00 | 246 | 0.76 | |
| Q 2 | 286 | 1.01 (0.84–1.21) | 253 | 1.04 (0.87–1.24) | |
| Q 3 | 315 | 1.04 (0.86–1.24) | 246 | 1.04 (0.86–1.26) | |
| Q 4 | 365 | 1.12 (0.92–1.35) | 229 | 1.06 (0.86–1.29) | |
| P for trend | 0.16 | 0.71 | |||
| Poultry, gm/1,000 kcal/day‡ | |||||
| Q 1 | 218 | 1.00 | 204 | 1.00 | 0.45 |
| Q 2 | 288 | 1.08 (0.90–1.29) | 248 | 1.17 (0.97–1.41) | |
| Q 3 | 321 | 1.14 (0.95–1.36) | 234 | 1.11 (0.91–1.34) | |
| Q 4 | 366 | 1.19 (0.99–1.43) | 288 | 1.37 (1.13–1.67) | |
| P for trend | 0.06 | 0.003 | |||
| Fish and shellfish, gm/1,000 kcal/day‡ | |||||
| Q 1 | 323 | 1.00 | 175 | 1.00 | 0.02 |
| Q 2 | 297 | 0.88 (0.75–1.03) | 213 | 1.07 (0.87–1.31) | |
| Q 3 | 296 | 0.93 (0.79–1.10) | 249 | 1.13 (0.92–1.37) | |
| Q 4 | 277 | 1.02 (0.86–1.22) | 337 | 1.36 (1.12–1.65) | |
| P for trend | 0.57 | ,0.001 | |||
| Eggs, gm/1,000 kcal/day‡ | |||||
| Q 1 | 292 | 1.00 | 244 | 1.00 | 0.84 |
| Q 2 | 302 | 0.92 (0.78–1.08) | 253 | 1.01 (0.85–1.21) | |
| Q 3 | 296 | 0.91 (0.77–1.07) | 244 | 0.95 (0.79–1.14) | |
| Q 4 | 303 | 0.93 (0.79–1.10) | 233 | 1.00 (0.83–1.20) | |
| P for trend | 0.54 | 0.90 | |||
| Dairy products, gm/1,000 kcal/day‡ | |||||
| Q 1 | 327 | 1.00 | 228 | 1.00 | 0.04 |
| Q 2 | 352 | 0.94 (0.81–1.09) | 208 | 0.98 (0.81–1.18) | |
| Q 3 | 300 | 0.88 (0.75–1.04) | 244 | 1.12 (0.93–1.35) | |
| Q 4 | 214 | 0.85 (0.71–1.02) | 294 | 1.19 (0.98–1.43) | |
| P for trend | 0.18 | 0.04 | |||
| Soy foods, gm/1,000 kcal/day‡ | |||||
| Q 1 | 358 | 1.00 | 204 | 1.0 | 0.49 |
| Q 2 | 314 | 0.86 (0.74–1.00) | 244 | 1.03 (0.85–1.25) | |
| Q 3 | 267 | 0.76 (0.64–0.89) | 241 | 0.90 (0.74–1.09) | |
| Q 4 | 254 | 0.82 (0.68–0.97) | 285 | 0.93 (0.77–1.14) | |
| P for trend | 0.02 | 0.29 | |||
| Nonsoy legumes, gm/1,000 kcal/day‡ | |||||
| Q 1 | 272 | 1.00 | 270 | 1.00 | 0.05 |
| Q 2 | 319 | 0.91 (0.77–1.07) | 244 | 0.92 (0.77–1.10) | |
| Q 3 | 324 | 0.99 (0.83–1.17) | 240 | 0.83 (0.69–1.00) | |
| Q 4 | 278 | 0.89 (0.75–1.07) | 220 | 0.77 (0.64–0.94) | |
| P for trend | 0.39 | 0.007 | |||
| Nuts and seeds, gm/1,000 kcal/day‡ | |||||
| Q 1 | 241 | 1.00 | 259 | 1.00 | 0.55 |
| Q 2 | 313 | 1.15 (0.97–1.37) | 245 | 0.97 (0.81–1.16) | |
| Q 3 | 292 | 1.00 (0.84–1.20) | 227 | 0.88 (0.73–1.06) | |
| Q 4 | 347 | 1.10 (0.92–1.31) | 243 | 1.02 (0.84–1.23) | |
| P for trend | 0.71 | 0.78 | |||
| All grain products, gm/1,000 kcal/day‡ | |||||
| Q 1 | 299 | 1.00 | 304 | 1.00 | 0.93 |
| Q 2 | 300 | 0.94 (0.80–1.11) | 238 | 0.87 (0.73–1.04) | |
| Q 3 | 306 | 0.97 (0.81–1.16) | 249 | 1.00 (0.82–1.21) | |
| Q 4 | 288 | 0.92 (0.74–1.14) | 183 | 0.95 (0.75–1.21) | |
| P for trend | 0.78 | 0.92 | |||
Linear trend was tested by assigning to participants the median value of the quartile and assessing this as a continuous variable. HRs = hazard ratios; 95% CIs = 95% confidence intervals.
The multivariate model was adjusted for age, dialect, year of interview, education level, body mass index, physical activity, smoking status, alcohol use, baseline presence of diabetes mellitus, baseline presence of hypertension, and total energy.
The multivariate model was adjusted for age, dialect, year of interview, education level, body mass index, physical activity, smoking status, alcohol use, baseline presence of diabetes mellitus, baseline presence of hypertension, and total energy, as well as for dietary intake of vegetables, fruits, red meat, poultry, fish and shellfish, eggs, dairy products, soy foods, nonsoy legumes, nuts and seeds, and all grain products.
We further performed a 4-year lag sensitivity analysis (Supplementary Table 2, available on the Arthritis & Rheumatology web site at http://onlinelibrary.wiley.com/doi/10.1002/art.39115/abstract). A total of 48,805 participants with 1,279 gout cases were included in this analysis. The positive relationship of total protein, red meat, poultry, and fish/shellfish with gout remained largely unchanged. For example, the HR for comparison of the first and fourth quartiles of total protein intake was 1.32 (95% CI 1.13–1.55; P for trend = 0.005). However, the inverse relationship of soy protein and soy foods with gout was attenuated to null (P for trend = 0.86 and P for trend = 0.36, respectively), while the association between nonsoy legumes and gout was not materially changed, with an HR for comparison of the first and fourth quartiles of 0.83 (95% CI 0.70–0.98; P for trend = 0.06).
During followup 1 and followup 2, the participants were also asked if they had been diagnosed as having any other form of arthritis (including but not limited to osteoarthritis and rheumatoid arthritis). A total of 9,317 subjects responded positively at either or both followup interviews. We did a secondary analysis excluding these 9,317 subjects, and the results remained essentially the same for the positive association with intake of total protein, poultry, and fish/shellfish. In the multivariate-adjusted model with dietary variables, the HR for comparison of the first and fourth quartiles was 1.34 (95% CI 1.15–1.57; P for trend = 0.001) for total protein, 1.35 (95% CI 1.14–1.59; P for trend < 0.001) for poultry, and 1.16 (95% CI 0.99–1.36; P for trend = 0.027) for fish and shellfish. For the inverse association with soy foods and nonsoy legumes, although the risk estimates were attenuated to become nonsignificant, all of the higher quartiles (quartiles 2–4) still exhibited reduced risk as compared to the lowest quartile of intake. In the multivariate-adjusted model with dietary variables, as compared to the lowest quartile of intake, the HR of the higher 3 quartiles combined was 0.89 (95% CI 0.78–1.01) for soy foods and 0.92 (95% CI 0.80–1.04) for nonsoy legumes.
DISCUSSION
The present study demonstrated that among the Chinese population in Singapore, higher consumption of poultry and fish/shellfish, but not eggs, nuts, or seeds and grain products, was associated with an increased risk of gout. This study is the first prospective study to show a possible protective association of soy foods and nonsoy legumes on the risk of developing gout.
We previously analyzed data from 24-hour recall interviews with 986 cohort members who represented a randomly selected 3% of the study population recruited between February 1993 and August 1996 (20) and found that fish, pork, poultry (chicken and duck), and crustaceans (primarily prawns/shrimp and cuttlefish) accounted for 98.7% of all meats consumed. Among these, fish and pork were the 2 most common types in both dialect groups and sexes, constituting 38.0% and 30.6% of all meat intake, respectively. Poultry constituted 21.0%, with the majority being chicken (78% versus 22% duck). In contrast, other meats, such as beef and lamb or mutton, which are commonly consumed by Western populations, constituted only ~1% of all meats consumed by our study participants (24). Hence, the red meat intake in this population consisted mainly of pork (97%), with only a small proportion of beef and lamb (3%).
Our finding of a positive association between meat intake and risk of gout is consistent with evidence from other epidemiologic studies. From the Health Professionals Followup Study of 47,150 Caucasian men who were followed up over a period of 12 years, Choi et al (18) showed that meat intake, including red meat and seafood, was associated with an increased risk of gout. In a prospective study of 28,990 male runners followed up over a period of 8 years, the risk of gout was shown to increase with higher intake of meat (19). In a cross-sectional study of Taiwanese men older than 65 years, consumption of poultry and shellfish was associated with an increased likelihood of being treated for hyperuricemia or gout (25), while a recent prospective study in mainland China showed that shrimp and shellfish intake, as obtained from 7-day dietary records, was associated with an increased risk of developing gout in adults with hyperuricemia over a 5-year followup (26). Our finding of increased risk of gout with intake of meat, such as poultry and fish/shellfish, are thus consistent with previous studies in both Western and Asian populations.
The intake of dairy products in this Asian population is generally lower than that in Western populations (20). Furthermore, the mean energy-adjusted intake of dairy products in men (35.2 gm/1,000 kcal/day) was much lower than that in women (54.8 gm/1,000 kcal/day). To the best of our knowledge, no study has examined the association between intake of dairy products and risk of gout in women specifically. We are unable to offer any reason for the biologic plausibility of the disparate findings of the association of dairy product with gout risk in men versus women, and we acknowledge that this result may be due to chance. Hence, our finding of a possible interaction between sex and dairy products in association with a risk of gout needs to be validated in future studies.
In contrast, our finding of an inverse association between the intake of plant-based food sources of protein, such as soy foods and nonsoy legumes, and the risk of gout in this cohort was consistent with the findings of previous studies. The Health Professionals Followup Study did not report any significant association between the intake of purine-rich vegetables, which included nonsoy legumes such as peas, beans, and lentils, and the risk of gout (18). In a cross-sectional study of Taiwanese men, high intake of soy products was associated with a low likelihood of receiving treatment for hyperuricemia or gout (25). A national health and dietary survey in Taiwan evaluated the dietary trends for hyperuricemia using a 28-item FFQ and showed that higher frequency of intake of soy products was associated with reduced blood urate levels (3). Another study among 55 young Chinese Buddhist vegetarians and 59 Chinese medical students (nonvegetarians) in Taiwan found that vegetarians, whose consumption of soybean products was 3.5 times that of nonvegetarians, had lower blood urate levels (27). Finally, intervention studies administering tofu or isolated soy protein to gout or healthy individuals have not shown clinically relevant elevation of urate levels (28,29).
Soy products, such as soy milk and tofu, are important plant sources of protein in many Asian countries, and the intake of soy products contributed ~5–10% of the overall dietary protein intake in Asian countries such as Japan, China, and Singapore (30). As soy foods are also gaining popularity in the Western countries, it is therefore important to determine the effect of soy on the risk of gout. To our knowledge, our study is the first prospective study to suggest a possibly protective effect of soy foods on the risk of developing gout. The protective association between soy and gout, if true, may be mediated through the promotion of weight loss (31) and an enhanced uricosuric effect (32,33).
A recent International Life Science Institute survey found that an overwhelming 50–80% of health care professionals believed that soy foods should be avoided and advised their gout patients accordingly (34). The myths about restricting plant-based foods that are high in purines, such as beans, lentils, soy, and certain vegetables, are also perpetuated by non–evidence-based patient education materials (14,35). Counteracting these popular myths, our data provide further evidence that plant sources of protein, such as soy and nonsoy legumes, are not associated with an increased risk of gout. Given the protective effect of soy products and nonsoy legumes on the risk of other undesirable health outcomes, such as cardiovascular diseases (36), soy and nonsoy legumes are plausible vegetable-based meat substitutes with possible beneficial effects for patients with gout.
The strength of this study is the large number of incident gout cases identified from a population-based prospective cohort with a relatively long followup period. A comprehensive approach to measuring and controlling for multiple potential risk factors for gout minimized the likelihood of spurious associations. Another strength is the lack of recall bias in exposures, given that information on dietary intake was collected from participants many years before the diagnosis of gout was made.
Our study has some limitations. One potential limitation is that gout was self-reported, and we did not collect information on the treatment of the disease. In this study, we adopted the same methodology used in other cohort studies (9,37), whereby gout was defined by an affirmative answer to the question “Have you been told by a doctor that you have gout?” In addition, to increase the accuracy of self-reported physician-diagnosed gout, we had trained our interviewers to further enquire if the joint pain and swelling from gout had been attributed by their physicians to hyperuricemia. As with population-based studies, requiring the presence of intraarticular urate crystals or tophus as the gold standard for the diagnosis of gout was not feasible. The high specificity of the American College of Rheumatology (ACR) preliminary criteria for a diagnosis of gout is often not met in epidemiologic studies (38). Even in English-speaking health professionals, 30% of cases who reported to have gout did not fulfill the ACR criteria (18).
The validity of classifying patients with gout in the primary care setting using the ACR criteria is further limited (39). Validation using drug prescription data and medical records was not feasible in our study. Although large-scale cohort studies in the US (37) have shown that 94% of self-reported gout can be validated through review of medical records for fulfillment of the ACR criteria, low concordance between physician assessments and various gout criteria is common (40). Two population-based cohorts in the US showed that self-report of physician-diagnosed gout had moderate-to-good reliability and sensitivity, and investigators have suggested that self-report of physician-diagnosed gout is appropriate for epidemiologic studies (41).
Other limitations of this study include the possible misclassification of dietary intake from the use of a FFQ. Since such misclassifications are likely to be non-differential, however, it would result in an underestimation (as opposed to an overestimation) of the true relative risk. Finally, our study excluded subjects who did not participate in the followup interviews, and since the main reason for nonparticipation was the death of the study subject, these nonparticipants would be expected to have been older and to have had other baseline characteristics that were different from those who participated. To remove possible confounding bias due to these factors, all of our statistical models included these factors as covariates. Hence, even though there were differences between the subjects who were included in the study and those who were excluded, the conclusions of the present study should be valid.
In conclusion, this prospective study demonstrated that total protein intake of poultry and fish/shellfish was associated with an increased risk of gout. Contrary to popular belief, consumption of soy foods and nonsoy legumes may have beneficial effects on the risk of gout. Our findings from this population-based cohort of Chinese subjects living in Singapore provide evidence for establishing dietary guidelines for the prevention of gout that would be applicable to other Asian populations.
Supplementary Material
Acknowledgments
We thank Siew-Hong Low (National University of Singapore) for supervising the field work of the Singapore Chinese Health Study and Kazuko Arakawa and Renwei Wang for the development and maintenance of the cohort study database. We acknowledge Mimi C. Yu, the founding Principal Investigator of the Singapore Chinese Health Study for creating the food and nutrient variables used in this study.
Supported by the NIH (grants R01-CA-144034 and UM1-CA-182876).
Footnotes
AUTHOR CONTRIBUTIONS
All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Koh had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study conception and design. Teng, Pan, Koh.
Acquisition of data. Yuan, Koh.
Analysis and interpretation of data. Teng, Pan, Yuan, Koh.
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