Western studies suggest that beverages may affect serum urate (SU) levels, but data from Asian populations are scarce. We evaluated the associations between beverages and SU levels in Singaporean Chinese.
Western studies suggest that beverages may affect serum urate (SU) levels, but data from Asian populations are scarce. We evaluated the associations between beverages and SU levels in Singaporean Chinese.
The study population consisted of 483 subjects ages 45–74 years from the Singapore Chinese Health Study cohort, recruited between 1993 and 1998. Lifestyle factors, medical histories, and diet were collected through in-person interviews. SU levels and other biomarkers were measured from blood collected between 1994 and 1996.
The mean age was 57.6 years and 44% were men. The geometric mean SU level was 321 μmoles/liter (range 157–719). Mean SU levels increased with alcohol consumption (P = 0.024 for trend). The mean SU level of daily alcohol drinkers was 42.6 μmoles/liter higher than that of nondrinkers. Similarly, increasing frequency of green tea intake was associated with rising SU levels. The highest mean SU level was observed in daily green tea drinkers (difference of 25.0 μmoles/liter) relative to nondrinkers (P = 0.009 for trend). Compared to nondrinkers, daily alcohol drinkers had an almost 5-fold increase in association with hyperuricemia (odds ratio [OR] 4.83, 95% confidence interval [95% CI] 1.10–21.23), whereas daily green tea drinkers had a 2-fold increase in association with hyperuricemia (OR 2.12, 95% CI 1.03–4.36). The present study did not show elevated levels of SU in individuals who consumed black tea, coffee, fruit juice, or soda.
Alcohol consumption increases SU levels. The finding that daily drinking of green tea is associated with hyperuricemia needs validation in future studies.
Hyperuricemia is a risk factor for gout and is closely associated with hypertension ([1, 2]), insulin resistance ([3-6]), and cardiovascular ([7-9]) and renal diseases ([10, 11]), as well as mortality ([12, 13]). Serum urate (SU) levels vary markedly among individuals, and are dependent on genetic, dietary, and lifestyle factors that regulate renal urate excretion and urate synthesis. Beverages make up an important part of one's diet and may contribute to both purine and nonpurine sources of SU or affect SU excretion. Recent epidemiologic studies have shown that ingestions of alcohol ([14-16]), sugar-sweetened soft drinks, and fruit juice ([17, 18]) may contribute significantly to SU levels and the risk of developing gout ([19, 20]). In addition to its rich purine content, alcohol, beer in particular, is metabolized from ATP to AMP, a uric acid precursor. Alcohol-induced lactate production also hinders urinary urate secretion and leads to urate retention. Green tea, black tea, and coffee are among the most common beverages consumed worldwide, but their effects on SU levels are less clear. From previous studies, coffee ([21-23]) was found to be associated with lower SU levels, whereas diet soft drinks () and tea () have no effect.
Most studies on the associations between alcohol and beverages and SU levels or risk of gout were conducted in Western populations ([14, 15, 17, 20, 21, 24]), and few population-based studies in Asia have examined these associations ([16, 22, 23, 25]). In addition, green tea and black tea may have differential effects on SU level. In this study, we examined whether intake of alcohol, green and black tea, soda, and fruit juices was associated with SU concentrations among Singaporean Chinese.
This study was conducted among a subpopulation of participants from the Singapore Chinese Health Study (SCHS), a population-based cohort of 63,257 Chinese women and men ages 45–74 years enrolled between April 1993 and December 1998 from government housing estates, where 86% of the entire Singapore population resided at the time of recruitment (). At recruitment, each subject was interviewed face to face in their home by a trained interviewer using a structured questionnaire for information on demographics, height, weight, tobacco use, usual physical activity, menstrual and reproductive history (women only), medical history, and dietary intake. Information about usual diet in the preceding 1 year was captured using a structured questionnaire that included a 165-item food frequency questionnaire that was subsequently validated against a series of 24-hour dietary recall interviews () and selected biomarkers ([27, 28]) conducted on random subsets of cohort participants. The Singapore Food Composition 1, developed in conjunction with this cohort study, allows for the computation of intake levels of ∼100 nutritive and non-nutritive compounds per study subject (). The SCHS was approved by our institutional review board.
In April 1994, 1 year after the initiation of cohort subject recruitment, we began to collect blood and single-void urine specimens from a random 3% sample of study enrollees. Details of the biologic specimen collection, processing, and storage procedures have been described (). The biologic specimens used in this study were collected from the first 483 subjects (214 men and 269 women) who donated blood for our research. All blood specimens were processed and separated into their various components (plasma, serum, red cells, buffy coat) prior to storage at −80°C. The first 483 cohort participants who donated blood for research were included in this study.
Participants were asked to choose from 8 frequency categories (never or hardly, once a month, 2–3 times a month, once a week, 2–3 times a week, 4–6 times a week, once a day, and ≥2 times a day) and 4 defined portion sizes for the consumption of each of the 4 types of alcoholic beverages (beer, wine, Western hard liquor, and Chinese hard liquor). For beer, the portion sizes were 1 small bottle (375 ml) or less, 2 small bottles or 1 large bottle (750 ml), 2 large bottles, and 3 large bottles or more. For wine, the portion sizes were 1 glass (118 ml) or less, 2 glasses, 3 glasses, and 4 glasses or more. For Chinese or Western hard liquor, the portion sizes were 1 shot (30 ml) or less, 2 shots, 3 shots, and 4 shots or more. One drink was defined as 375 ml of beer (13.6 gm of ethanol), 118 ml of wine (11.7 gm of ethanol), and 30 ml of Western or Chinese hard liquor (10.9 gm of ethanol).
Study subjects were asked to choose the intake frequency of a standard serving of coffee, green tea, and black tea from 9 predefined categories (never or hardly ever, 1–3 times a month, once a week, 2–3 times a week, 4–6 times a week, once a day, 2–3 times a day, 4–5 times a day, and ≥6 times a day). The standard serving size was assigned on the questionnaire as “1 cup” for coffee and “1 glass” for tea. Subjects were also asked to report the intake frequency of a standard serving of soft drinks and fruit/vegetable juices from 9 predefined categories (never or hardly ever, 1–3 times a month, once a week, 2–3 times a week, 4–6 times a week, once a day, 2–3 times a day, 4–5 times a day, and ≥6 times a day). The standard serving size was assigned on the questionnaire as “1 glass” or “1 packet.”
SU level was measured using the direct enzymic assay where SU was oxidized by uricase to allantoin and hydrogen peroxide, and the resultant intensity of the red chromogen measured at absorbance of 545 nm (). Serum creatinine was measured using the Enzymatic Creatinine_2 method (). Glycosylated hemoglobin (HbA1c) was measured using the Bio-Rad Variant II, a fully automated HbA1c analyzer that utilized the principles of ion exchange high-performance liquid chromatography, and detection was performed at 415 nm and 690 nm. Total cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides were measured on the Bayer Advia 1650 Autoanalyzer using standard reagents ([32, 33]). Total cholesterol was measured using a method based on an enzymatic method utilizing cholesterol esterase and cholesterol oxidase conversion followed by a Trinder end point. HDL cholesterol plasma concentrations were measured using the direct HDL cholesterol method in serum and plasma without prior separation (). The Friedewald formula was used to calculate the plasma concentrations of low-density lipoprotein cholesterol (). Triglycerides were measured based on the Fossati 3-step enzymatic reaction with a Trinder end point.
The distribution of the concentration of SU, cholesterol, creatinine, HbA1c, and triglycerides was positively skewed, which was corrected to a large extent by performing a logarithmic transformation. All statistical analyses were performed on the log-transformed values, and we reported their geometric means and corresponding 95% confidence intervals (95% CIs) by taking the antilog of the mean and 95% CIs of the log-transformed values.
To examine the relationship between SU levels and beverage consumption, we used the multiple linear regression model for assessing simultaneous effects of consumption patterns in various beverages while adjusting for potential confounders. The following covariates were included in all models: age at blood collection; sex; body mass index (BMI; kg/m2); education (none, primary, and secondary and above); weekly moderate activity (none, 0.5–3 hours/week, and ≥4 hours/week); daily consumptions of fish (gm/day), red meat (gm/day), and dairy products (gm/day); self-reported history of physician-diagnosed hypertension; and levels of serum cholesterol (mmoles/liter), triglycerides (mmoles/liter), creatinine (μmoles/liter), and HbA1c (percentage). Age, BMI, and dietary and biochemical variables were analyzed as continuous variables, while the remaining variables were analyzed as categorical variables. Our exposure of interest was beverage consumption, which was categorized into 4 ordinal levels (nondrinkers, monthly drinkers, weekly drinkers, and daily drinkers). To examine the linear trend in the beverage–SU level association, we used the linear contrast to assess the linear trend in beverage consumption ().
Hyperuricemia was defined as an SU level >356.88 μmoles/liter (∼6 mg/dl) in either sex (). We used the logistic regression model to examine the associations between beverage consumption and hyperuricemia (). The same covariates were included as potential confounders in the model. We estimated the odds ratio (OR) of hyperuricemia and the corresponding 95% CI in association with the level of individual beverages consumed.
The analyses were performed with the statistical program R, version 2.13.1 (http://www.r-project.org/). The regression model assumptions were assessed using graphical plots and residual diagnostics. All P values quoted are 2-sided and P values less than 0.05 were considered statistically significant.
The demographic, lifestyle, and biochemical characteristics, as well as the patterns of beverage consumption of the 483 subjects in this study, are shown in Table 1. The mean ± SD age was 57.6 ± 7.9 years and 44% were men. Comparing the characteristics of the participants in this study against those of the cohort from which they were drawn, the participants in this study were very similar in the distributions of age at recruitment, sex, BMI, level of education, and intake of selected foods to the entire cohort, whereas there were slightly fewer smokers (72.5% versus 69.4% never smokers). The geometric and arithmetic mean SU levels among the subjects were 321 μmoles/liter and 331 μmoles/liter, respectively. Men were more likely than women to be hyperuricemic (52% versus 22%; P < 0.001 for difference). Participants with hyperuricemia were older and more likely to have ever been smokers or have higher levels of BMI, serum creatinine, and triglycerides, but consumed fewer dairy products. Subjects with a history of hypertension were also more likely to have hyperuricemia.
|Overall (n = 483)||Normouricemia (n = 312)||Hyperuricemia (n = 171)||Pa|
|Age at specimen collection, mean ± SD years||57.6 ± 7.9||56.6 ± 7.6||59.5 ± 8.1||< 0.001|
|Body mass index, mean ± SD kg/m2||22.9 ± 3.0||22.5 ± 3.1||23.6 ± 2.8||< 0.001|
|Men||214 (44.3)||102 (32.7)||112 (65.5)|
|Women||269 (55.7)||210 (67.3)||59 (34.5)|
|History of hypertension||119 (24.6)||67 (21.5)||52 (30.4)||0.036|
|History of diabetes mellitus||46 (9.5)||31 (9.9)||15 (8.8)||0.747|
|Cigarette smoking||< 0.001|
|Never||350 (72.5)||240 (76.9)||110 (64.3)|
|Former||57 (11.8)||24 (7.7)||33 (19.3)|
|Current||76 (15.7)||48 (15.4)||28 (16.4)|
|Weekly moderate activity||0.56|
|None||359 (74.3)||234 (75.0)||125 (73.1)|
|0.5–3 hours/week||74 (15.3)||44 (14.1)||30 (17.5)|
|≥4 hours/week||50 (10.4)||34 (10.9)||16 (9.4)|
|No formal education||128 (26.5)||86 (27.6)||42 (24.6)|
|Primary school only||200 (41.4)||127 (40.7)||73 (42.7)|
|Secondary school and above||155 (32.1)||99 (31.7)||56 (32.7)|
|Dairy product intake, mean ± SD gm/day||76.7 ± 110.9||83.9 ± 116.6||63.7 ± 98.7||0.045|
|Red meat intake, mean ± SD gm/day||30.0 ± 25.5||30.4 ± 26.9||29.3 ± 22.7||0.632|
|Fish intake, mean ± SD gm/day||56.2 ± 29.1||56.2 ± 29.4||56.2 ± 28.7||0.994|
|Serum creatinine, geometric mean (95% CI) μmoles/liter||64.0 (62.3–65.8)||58.1 (56.7–59.6)||76.4 (72.5–80.5)||< 0.001|
|Plasma triglycerides, geometric mean (95% CI) mmoles/liter||1.6 (1.6–1.7)||1.5 (1.4–1.6)||2.0 (1.9–2.2)||< 0.001|
|Plasma cholesterol, geometric mean (95% CI) mmoles/liter||5.6 (5.5–5.7)||5.6 (5.4–5.7)||5.7 (5.5–5.9)||0.221|
|HbA1c, geometric mean (95% CI) %||6.2 (6.1–6.3)||6.2 (6.0–6.3)||6.2 (6.0–6.3)||0.781|
The majority (>80%) of the subjects were alcohol nondrinkers (Table 2), with 8.5% and 4.5% who drank beer and hard liquor daily to weekly, respectively. Approximately 70% of the subjects drank at least 1 cup of coffee every day, whereas ∼30% reported daily to weekly consumption of green or black tea. Daily soda and fruit juice intake was infrequent, and was reported in <3% of the subjects.
|Overall (n = 483), no. (%)||Exposure, median (range)||Serum urate level, μmoles/liter|
|Model 1*||Model 2†|
|Nondrinkers||397 (82.2)||0.0 (0.0–0.0)||310.9 (300.3–321.9)||310.5 (299.9–321.4)|
|Monthly drinkers||35 (7.2)||0.5 (0.2–0.8)||325.3 (302.2–350.0)||323.6 (300.6–348.4)|
|Weekly drinkers||36 (7.5)||2.5 (1.0–5.6)||323.0 (301.4–346.3)||320.6 (299.0–343.8)|
|Daily drinkers||15 (3.1)||7.3 (7.0–9.5)||353.4 (318.5–392.2)||353.1 (318.2–391.8)|
|P for trend||0.024||0.024|
|Nondrinkers||418 (86.5)||0.0 (0.0–0.0)||310.9 (300.5–321.7)||310.3 (299.9–321.1)|
|Monthly drinkers||24 (5.0)||0.5 (0.2–0.6)||322.1 (296.1–350.4)||319.0 (293.1–347.3)|
|Weekly drinkers||27 (5.6)||1.2 (1.0–6.0)||325.9 (300.7–353.1)||324.3 (299.2–351.4)|
|Daily drinkers||14 (2.9)||14.0 (7.0–30.0)||365.2 (327.7–407.1)||366.2 (328.6–408.1)|
|P for trend||0.005||0.004|
|Hard liquor, times/week|
|Nondrinkers||441 (91.3)||0.0 (0.0–0.0)||313.7 (303.3–324.5)||313.1 (302.7–323.9)|
|Monthly drinkers||20 (4.2)||0.2 (0.2–0.9)||324.3 (295.3–356.0)||323.1 (294.1–354.9)|
|Weekly drinkers||18 (3.7)||2.4 (1.0–5.0)||318.3 (289.4–350.1)||316.1 (287.3–347.8)|
|Daily drinkers||4 (0.8)||7.3 (7.0–10.0)||356.7 (292.5–434.9)||354.4 (290.4–432.4)|
|P for trend||0.239||0.264|
|Green tea, cups/month|
|Nondrinkers||292 (60.5)||0.0 (0.0–0.0)||310.3 (299.2–321.8)||309.5 (298.4–321.0)|
|Monthly drinkers||46 (9.5)||2.0 (2.0–2.0)||312.5 (293.3–333.0)||309.5 (290.4–329.9)|
|Weekly drinkers||84 (17.4)||10.7 (4.3–21.4)||314.7 (299.4–330.8)||311.6 (296.3–327.7)|
|Daily drinkers||61 (12.6)||30.0 (30.0–180.0)||334.3 (316.0–353.6)||334.5 (316.2–353.9)|
|P for trend||0.012||0.009|
|Black tea, cups/month|
|Nondrinkers||301 (62.3)||0.0 (0.0–0.0)||313.8 (302.8–325.3)||312.4 (301.4–323.8)|
|Monthly drinkers||42 (8.7)||2.0 (2.0–4.0)||311.4 (291.5–332.7)||308.8 (289.0–329.9)|
|Weekly drinkers||85 (17.6)||10.7 (4.3–21.4)||318.3 (302.6–334.9)||314.3 (298.6–330.7)|
|Daily drinkers||55 (11.4)||30.0 (30.0–135.0)||318.3 (299.6–338.3)||318.4 (299.6–338.4)|
|P for trend||0.500||0.442|
|Nondrinkers||84 (17.4)||0.0 (0.0–0.0)||312.3 (296.5–329.0)||309.8 (294.1–326.3)|
|Monthly drinkers||10 (2.1)||0.5 (0.5–0.5)||308.2 (271.2–350.3)||304.4 (267.8–346.0)|
|Weekly drinkers||55 (11.4)||2.5 (1.0–6.0)||306.7 (288.7–325.8)||302.2 (284.4–321.2)|
|Daily drinkers||334 (69.1)||11.0 (7.0–63.2)||316.7 (305.8–327.9)||315.7 (304.9–326.9)|
|P for trend||0.717||0.624|
|Fruit juice, times/week|
|Nondrinkers||337 (69.8)||0.0 (0.0–0.0)||313.6 (302.7–325.0)||312.3 (301.4–323.6)|
|Monthly drinkers||83 (17.2)||0.5 (0.5–0.9)||318.0 (302.4–334.3)||314.6 (299.2–330.9)|
|Weekly drinkers||50 (10.3)||2.0 (1.0–5.0)||317.4 (298.4–337.7)||316.0 (297.1–336.0)|
|Daily drinkers||13 (2.7)||7.0 (7.0–14.0)||310.8 (277.8–347.7)||306.2 (273.6–342.8)|
|P for trend||0.866||0.752|
|Nondrinkers||380 (78.7)||0.0 (0.0–0.0)||313.3 (302.5–324.5)||312.0 (301.3–323.1)|
|Monthly drinkers||42 (8.7)||0.5 (0.5–0.5)||324.0 (303.7–345.6)||321.0 (300.9–342.5)|
|Weekly drinkers||51 (10.5)||2.5 (1.0–5.0)||317.9 (298.8–338.2)||315.2 (296.2–335.3)|
|Daily drinkers||10 (2.1)||7.0 (7.0–17.6)||303.7 (267.3–345.0)||297.0 (261.2–337.6)|
|P for trend||0.569||0.403|
Compared to subjects who drank black tea less frequently, daily black tea drinkers were significantly younger at the time of the blood draw (age 55.9 years in daily drinkers versus 58.4 years in nondrinkers), more likely to be men (61.8% in daily drinkers versus 37.9% in nondrinkers), and more likely to be educated (12.7% without formal education in daily drinkers versus 32.2% in nondrinkers). Otherwise, there were no statistically significant differences by frequency of black tea drinking in selected dietary intakes, lifestyle habits (including smoking and physical activity), medical history, and biomarkers included as covariates in this study. Conversely, there were no statistically significant differences in the same list of covariates by intake frequency of green tea, except for a higher proportion of subjects who were physically active among daily drinkers compared to nondrinkers (36.1% with at least half an hour of moderate activity per week in daily drinkers versus 21.9% in nondrinkers).
After adjusting for confounders, SU levels increased with the frequency of alcohol consumption in a dose-dependent manner (P = 0.024 for trend, β = 0.084), with the highest level observed among daily drinkers compared to nondrinkers, and mainly due to the consumption of beer (Table 2). Compared with nondrinkers, daily alcohol drinkers had higher SU levels by a magnitude of 42.6 μmoles/liter. Most of the alcohol consumed was beer, and the increase in frequency of beer intake corresponded with an increase in SU level (P = 0.004 for trend). Compared to nondrinkers, the mean SU level of daily beer drinkers increased by 55.9 μmoles/liter. Due to the small sample size, the association between increasing frequency of hard liquor intake and increasing SU levels did not reach statistical significance (P = 0.264 for trend). There were too few rice wine and grape wine drinkers (5 and 7 drinkers, respectively) for a separate analysis on these alcoholic beverages.
Compared to nondrinkers, increasing frequency of green tea intake from monthly to weekly to daily was associated with a monotonic increase in SU level after adjustment for alcohol intake and other covariates (Table 2). The greatest increase of SU level was observed in daily drinkers (increased by 25.0 μmoles/liter) relative to nondrinkers (P = 0.009 for trend, β = 0.054).
There was no significant association between consumption of black tea, coffee, fruit juice, or soda and SU levels. However, for coffee consumption, among daily drinkers, although the mean SU level in those who drank 1 cup/day was 319.7 μmoles/liter (95% CI 301.7–338.7), there was a stepwise decrease in SU level with increasing cups per day, and the lowest mean SU level was among those who drank ≥4 cups/day at 310.1 μmoles/liter (95% CI 277.2–346.9) (data not shown). We repeated our analysis for the effect of green tea on SU level in 84 subjects who were coffee nondrinkers (drank less than once a month). In this subgroup of coffee nondrinkers, the adjusted geometric means of SU level for green tea nondrinkers and daily drinkers were 299.6 μmoles/liter (95% CI 269.1–333.5) and 344.5 μmoles/liter (95% CI 300.3–395.2), respectively (P = 0.099 for trend), which are very similar to those based on the entire data set (see Supplementary Table 1, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.21999/abstract).
We divided the subjects into subgroups of normouricemia and hyperuricemia, defined as SU levels of ≥356.88 μmoles/liter (∼6 mg/dl), which was the definition used by many National Health and Nutrition Examination Survey (NHANES) studies of hyperuricemia ([17, 21]). The geometric mean SU level was 418.5 μmoles/liter (95% CI 410.3–426.9) for the hyperuricemic group and 278.0 μmoles/liter (95% CI 273.0–283.2) for the normouricemic group. Compared to nondrinkers, daily drinkers of alcohol had an almost 5-fold increase in association with hyperuricemia (OR 4.83, 95% CI 1.10–21.23) (Table 3). Relative to nondrinkers, daily drinkers of green tea had a 2-fold increase in association with hyperuricemia (OR 2.12, 95% CI 1.03–4.36). There was no association between the intake of other beverages and hyperuricemia (Table 3). We also repeated our analysis using sex-specific cutoffs of 416.36 μmoles/liter (7.0 mg/dl) for men and 339.04 μmoles/liter (5.7 mg/dl) for women, which have been used in other studies ([17, 21]), and observed persisting increased associations of hyperuricemia with daily alcohol or green tea drinking. Compared to nondrinkers, daily drinkers of alcohol still had an almost 3-fold increase in association with hyperuricemia (OR 2.62, 95% CI 0.67–10.26), although this did not reach statistical significance due to the reduction in the number of subjects considered to be hyperuricemic using sex-specific cutoffs. Conversely, relative to nondrinkers, daily drinkers of green tea still had a statistically significant 4-fold increase in association with hyperuricemia (OR 4.27, 95% CI 2.00–9.08), suggesting that the association between daily green tea drinking and hyperuricemia was robust. When limited to those who were coffee nondrinkers, daily green tea drinkers still had the highest association with hyperuricemia compared to less frequent drinkers (see Supplementary Table 2, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.21999/abstract).
|Normouricemia (n = 312), no.||Hyperuricemia (n = 171), no.||Model 1, OR (95% CI)a||Model 2, OR (95% CI)b|
|Monthly drinkers||20||15||1.53 (0.60–3.88)||1.48 (0.59–3.76)|
|Weekly drinkers||22||14||1.00 (0.42–2.37)||0.96 (0.41–2.29)|
|Daily drinkers||5||10||4.55 (1.07–19.32)||4.83 (1.10–21.23)|
|P for trend||0.075||0.069|
|Monthly drinkers||16||8||0.85 (0.28–2.59)||0.76 (0.25–2.32)|
|Weekly drinkers||14||13||1.22 (0.47–3.15)||1.23 (0.48–3.15)|
|Daily drinkers||4||10||5.13 (1.15–22.96)||5.63 (1.21–26.24)|
|P for trend||0.029||0.022|
|Monthly drinkers||11||9||1.51 (0.43–5.27)||1.47 (0.43–5.10)|
|Weekly drinkers||12||6||0.92 (0.27–3.15)||0.87 (0.26–2.99)|
|Daily drinkers||2||2||4.65 (0.25–87.33)||5.10 (0.25–102.61)|
|P for trend||0.371||0.353|
|Monthly drinkers||33||13||0.91 (0.39–2.09)||0.84 (0.36–1.98)|
|Weekly drinkers||55||29||1.18 (0.64–2.17)||1.15 (0.62–2.14)|
|Daily drinkers||31||30||2.18 (1.06–4.45)||2.12 (1.03–4.36)|
|P for trend||0.03||0.033|
|Monthly drinkers||28||14||0.72 (0.31–1.69)||0.68 (0.28–1.67)|
|Weekly drinkers||50||35||1.40 (0.76–2.58)||1.27 (0.68–2.37)|
|Daily drinkers||36||19||0.60 (0.27–1.33)||0.56 (0.25–1.27)|
|P for trend||0.514||0.400|
|Monthly drinkers||6||4||0.45 (0.06–3.32)||0.45 (0.06–3.49)|
|Weekly drinkers||32||23||1.32 (0.56–3.13)||1.23 (0.52–2.94)|
|Daily drinkers||220||114||0.94 (0.51–1.76)||0.93 (0.49–1.76)|
|P for trend||0.522||0.584|
|Monthly drinkers||54||29||1.32 (0.70–2.48)||1.27 (0.67–2.42)|
|Weekly drinkers||33||17||1.08 (0.49–2.38)||1.06 (0.47–2.37)|
|Daily drinkers||7||6||1.71 (0.43–6.86)||1.38 (0.34–5.57)|
|P for trend||0.520||0.726|
|Monthly drinkers||27||15||1.33 (0.58–3.05)||1.33 (0.57–3.12)|
|Weekly drinkers||30||21||1.47 (0.68–3.14)||1.48 (0.68–3.22)|
|Daily drinkers||5||5||1.64 (0.32–8.24)||1.51 (0.30–7.72)|
|P for trend||0.534||0.596|
In our sample of middle-aged and elderly Singaporean Chinese, we found a dose-dependent increase in SU levels with alcohol and green tea intake, with the highest levels in daily drinkers compared to nondrinkers. The association was independent of other beverages and other known factors for hyperuricemia such as age, sex, BMI, physical activity, dietary factors, renal function, and triglyceride status. Black tea consumption did not significantly affect urate levels in our population.
The mean SU level in our study population was higher than the level of 319 μmoles/liter recorded for the US population in the Third NHANES (NHANES-III), and the mean age of our study subjects was a decade older (). In contrast, a study consisting of middle-aged Japanese men recorded a mean SU level of 349 μmoles/liter (), whereas the level among 2,176 middle-aged participants from a population-based study in Taiwan was 365 μmoles/liter (). The difference observed in the SU levels can be explained by the variation in the prevalence of the determinants of uric acid in these populations. One of the best-studied factors is alcohol consumption, which has been shown to increase SU level by altering urate production and excretion ([38, 39]). Alcohol intake has been associated with increased SU levels or risk of hyperuricemia in a dose-dependent manner, although direct comparison of the magnitude of increase across studies is difficult due to different quantification of alcohol intake ([15, 16, 22, 40]). In this study, we have standardized the categories of intake frequency for comparison across all beverages. In examining the association between alcohol intake and risk of hyperuricemia, a study among middle-aged and elderly men in Shanghai also noted statistically significant risk increase in daily drinkers compared to nondrinkers (). In our study, only 3% of participants drank alcohol daily, whereas an overwhelming majority drank none or less than monthly. Therefore, it was difficult to study the dose effect of alcohol beyond at least 1 drink/day. Nevertheless, even with a small percentage of daily drinkers, the majority of whom (93%) consume fewer than 2 drinks/day, we were able to demonstrate a considerable increase in SU level by 42.6 μmoles/liter compared to nondrinkers. This effect was higher than an increase of 25.8 μmoles/liter in a US cohort, where 50% drank 2 or more servings of alcohol per day compared to nondrinkers ([14, 15]). Similarly, the magnitude of increase in SU level among daily drinkers in our study population was higher compared to an elevation of 16.2 μmoles/liter per serving per day in the NHANES-III study ([14, 15]), suggesting that Chinese may be more susceptible to increase in SU levels induced by alcohol. Nevertheless, we acknowledge that there were other differences in the characteristics of the participants in the NHANES-III study and our study, such as racial differences and age, that could account for the discrepancy in the 2 studies.
The NHANES-III study has demonstrated a null association between tea intake and SU level and hyperuricemia (), although the authors did not differentiate green tea from black tea. Since Americans consume predominantly black tea ([42, 43]), it was likely that the null association between tea drinking and SU level was driven by the null effect of black tea. In a Japanese study, although no statistically significant trend in SU level was observed among men with 1 to ≥5 cups of green tea per day, individuals in the lowest intake level (<1 cup/day) had the lowest SU level (). In our study, the much wider range and a lower-frequency referent group allowed us to examine categories ranging from a very low intake of less than monthly to the highest intake of daily drinking, and our results showed a dose-dependent increase in SU level with increasing intake frequency.
To our knowledge, our study is the first to demonstrate that daily drinking of green tea increases SU levels, although the impact was much less than that with alcohol. The increase in association with hyperuricemia was also modest in daily green tea drinkers versus alcohol drinkers. Although green and black tea is derived from the plant Camellia sinensis, they are processed differently. Fresh tea leaves are steamed or heated immediately after harvest, resulting in minimal oxidation of the naturally occurring polyphenols in green tea. The major polyphenols found in green tea belong to the family of catechins, of which the most abundant are epigallocatechin gallate (EGCG). However, for black tea, the tea leaves are dried and crushed upon harvesting to encourage oxidation, which converts the catechins and gallocatechins to other polyphenols (mainly theaflavins and thearubigins) (). Our findings seem to concur with the observation in an intervention study where the SU level increased approximately 10% from baseline to 6 hours postingestion in healthy volunteers given a single large dose of EGCG in purified form or as a mixed green tea extract (). Nevertheless, we acknowledge that the positive association between green tea intake and SU level in this study may be explained by other unaccounted confounders. Given the increasing prevalence of green tea consumption worldwide, the report of this novel finding will motivate future studies to study this association and to examine the possible mechanism linking green tea catechins and urate metabolism.
Studies among Japanese ([22, 23]), Taiwanese (), and whites () have shown that SU levels may be lower in individuals with a high level of coffee consumption. In the Japanese men, the adjusted mean SU level was 0.27 mg/dl (16 μmoles/liter) lower in men consuming ≥7 cups of coffee/day than in nondrinkers (). Similarly, it required ≥4 cups of coffee/day to result in a small but significant increase of 0.22 mg/dl (13.2 μmoles/liter) in both sexes in the NHANES-III (). In our study, among daily coffee drinkers, the SU level was also lowest among those who drank ≥4 cups/day.
Although fructose-laden soft drinks have been linked with high SU levels from the NHANES-III and risk of incident gout ([17, 20]), we did not find an association between fructose-rich soft drinks or fruit juices with SU levels in our study. This is consistent with a recent meta-analysis that showed that controlled feeding trials of fructose under isocaloric conditions did not increase SU levels (). In addition, when adjusted for energy and nonfructose carbohydrates, high fructose intake did not affect SU levels in the NHANES 1999–2004 (). A recent study in Taiwanese adolescents also showed that only heavy soft drink consumption of >500 ml/day resulted in significantly raised SU levels ().
Singaporean Chinese are well suited for this study that could possibly differentiate effects of green tea and black tea consumption on SU levels due to divergent intake profiles in this population. Among the 483 subjects in this study, 434 were re-contacted for a followup interview between 1999 and 2004. Among them, only 4 subjects had a self-reported history of physician-diagnosed gout before giving blood for this study, who might therefore be receiving urate-lowering therapy. To limit the problem with diurnal variation in plasma urate levels (), blood was collected from all subjects in the morning. A limitation of the study is the relatively low level of alcohol use in this cohort, especially in women, which precludes any meaningful examination of the sex effect on the alcohol–SU level association. Most of the alcohol consumed was beer, which limited the analysis by subtype. We also lack information on duration of regular coffee, tea, or alcohol consumption.
In conclusion, our results suggest that daily alcohol intake, particularly beer, confers a significant increase in SU level and association with hyperuricemia. In addition, green tea may be associated with an increase in SU level, although the increase is moderate. This novel finding requires confirmation in other population-based studies with varying levels of green tea intake.
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. Teng 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, Yuan, Koh.
Acquisition of data. Teng, Yuan, Koh.
Analysis and interpretation of data. Teng, Tan, Santosa, Saag, Yuan, Koh.
The authors would like to thank Siew-Hong Low of the National University of Singapore for supervising the field work of the SCHS and Renwei Wang for maintenance of the cohort study database. Finally, we acknowledge the founding, longstanding principal investigator of the SCHS, Mimi C. Yu.