Abstract
Hyperuricemia, a chronic metabolic condition characterized by elevated serum uric acid levels, is a major risk factor for gout and has also been associated with cardiovascular disease, hypertension, chronic kidney disease, and metabolic syndrome. Although diet is widely recognized as an important modifiable factor in hyperuricemia, the associations between specific food groups and hyperuricemia remain incompletely understood. In this frequency-matched case-control analysis using data from the National Health and Nutrition Examination Survey 2001–2020, we included 886 individuals with hyperuricemia and 2171 matched controls. Dietary intake was assessed using 24-hour dietary recall data. Unconditional multivariable logistic regression models were applied to examine the associations between food group intake and hyperuricemia, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated by comparing the highest with the lowest intake quantiles. False discovery rate-adjusted P for trend values were additionally calculated to account for multiple comparisons. Higher intake of legumes, nuts, and seeds (OR, 0.74; 95% CI, 0.57–0.96), grain products (OR, 0.78; 95% CI, 0.62–0.96), whole-wheat bread, oats, and brown rice (OR, 0.76; 95% CI, 0.62–0.93), eggs (OR, 0.85; 95% CI, 0.77–0.94), and milk products (OR, 0.64; 95% CI, 0.51–0.79) were associated with lower odds of hyperuricemia. In contrast, higher intake of meat, poultry, and fish (OR, 1.22; 95% CI, 1.12–1.33), fish and seafood (OR, 1.09; 95% CI, 1.01–1.23), sugars, sweets, and beverages (OR, 1.60; 95% CI, 1.28–2.00), soft drinks (OR, 1.11; 95% CI, 1.03–1.21), sugar-sweetened soft drinks (OR, 1.13; 95% CI, 1.03–1.23), and sugar-sweetened tea (OR, 1.86; 95% CI, 1.11–3.12) were associated with higher odds of hyperuricemia. The overall directions of the observed associations were largely unchanged after false discovery rate adjustment, although fewer trend tests remained statistically significant. Several dietary factors were associated with hyperuricemia in this National Health and Nutrition Examination Survey-based frequency-matched case-control analysis. These findings may help inform dietary strategies for the prevention and management of hyperuricemia. However, the observed associations should be interpreted cautiously, particularly in light of multiple-comparison adjustment, and further studies are warranted to confirm the findings and clarify their clinical implications.
Keywords: dietary intake, food groups, hyperuricemia, National Health and Nutrition Examination Survey (NHANES), uric acid metabolism
1. Introduction
Hyperuricemia is a chronic metabolic disorder characterized by elevated levels of uric acid in the blood, often resulting from either excessive urate production or impaired renal and gastrointestinal excretion.[1] While often asymptomatic, hyperuricemia is considered a key risk factor for gout, a painful inflammatory condition caused by the deposition of urate crystals in the joints.[2] The condition could also lead to the formation of tophi, which are urate crystal deposits in soft tissues.[2] In addition to gout, hyperuricemia is associated with a variety of serious health complications, including cardiovascular disease, hypertension, chronic kidney disease, and metabolic syndrome.[3,4] If left unmanaged, hyperuricemia may contribute to chronic joint damage and kidney stones and is often associated with obesity, diabetes, and other long-term metabolic complications. Studies indicate that the global prevalence of hyperuricemia is rising, contributing to an increasing burden on public health.[5–7]
Recent epidemiological surveys have shown a significant increase in hyperuricemia prevalence in recent decades, with rates of 20.1% in the United States (US), 16.6% in Australia, and 11.4% in South Korea.[8–10] Consequently, hyperuricemia has become an important public health concern. A growing body of research has focused on identifying modifiable risk factors, particularly dietary factors that contribute to the development of hyperuricemia. Several studies have shown that excessive intake of alcohol, sugary beverages, and purine-rich foods can increase the risk of hyperuricemia, while higher intake of magnesium and zinc may offer protective effects.[11–13]
Although the importance of nutritional interventions in the management of gout and hyperuricemia is widely recognized, most previous studies have focused on selected nutrients or a limited number of food items.[14,15] As a result, the role of broader food group intake in hyperuricemia remains incompletely understood. In addition, dietary habits vary substantially across populations, and findings from earlier studies have not always been consistent.[16–18] Few studies have comprehensively examined these associations over an extended period using standardized dietary and laboratory data collected across multiple survey cycles. Therefore, a more comprehensive evaluation of multiple food groups in a large and diverse population is needed to better clarify these associations.
The National Health and Nutrition Examination Survey (NHANES) provides a valuable opportunity to address this question because it includes standardized dietary assessments, laboratory measurements, and detailed demographic and health-related information collected over multiple survey cycles. Using NHANES 2001–2020, we conducted a frequency-matched case-control analysis to examine the associations between intake of multiple primary and custom food groups and hyperuricemia among US adults. By matching cases and controls on key demographic and clinical characteristics, we aimed to improve comparability while efficiently evaluating a broad range of dietary exposures. We hypothesized that higher intake of plant-based foods and dairy products would be associated with lower odds of hyperuricemia, whereas higher intake of purine-rich animal foods and sugar-sweetened beverages would be associated with higher odds.
2. Materials and methods
2.1. Study population and sample
The NHANES is conducted by the National Center for Health Statistics, a division of the Centers for Disease Control and Prevention, with the purpose of evaluating the health and dietary patterns of the US population.[19] For this matched case-control analysis using NHANES 2001–2020 data, participants were selected from 9 survey cycles (2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2020), comprising a total of 84,144 individuals (Fig. 1). Additional information can be found on the Centers for Disease Control and Prevention website.[20] This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology statement.
Figure 1.
The flowchart of participants included in the NHANES case-control study of dietary characteristics and hyperuricemia. NHANES = National Health and Nutrition Examination Survey.
2.2. Case and control definitions
The overall dataset was limited to 57,736 individuals with available serum uric acid (SUA) data. Potential cases (n = 1236) were defined as individuals with hyperuricemia based on a SUA level of ≥ 8.5 mg/dL. This threshold was selected to identify more clinically meaningful hyperuricemia and to reduce potential misclassification of borderline SUA elevations. Although higher than thresholds used in some previous epidemiologic studies, this definition was intended to capture individuals with more pronounced hyperuricemia.[21,22] Potential controls (n = 56,500) were individuals with SUA levels lower than 8.5 mg/dL. Participants were excluded if they were under 18 years old (51 cases and 9774 controls), had missing energy intake data or implausible total energy intake (<500 or > 3500 kcal/d for females and < 800 or > 4000 kcal/d for males; 86 cases and 3515 controls),[23] or were missing matching variables (213 cases and 5155 controls), resulting in a total of 886 cases and 38,056 possible controls. Controls were frequency matched to cases at a 3:1 ratio based on 5 criteria: NHANES release, sex, race/ethnicity, age at interview, and body mass index (BMI) category (<18.5, 18.5–24.9, 25.0–29.9, and ≥ 30.0 kg/m2; Fig. 1). Frequency matching was used to improve comparability between cases and controls on key demographic and clinical characteristics associated with SUA while maintaining analytic efficiency in the case-control subset. The final analytic sample therefore consisted of a frequency-matched case-control subset derived from NHANES 2001–2020.
2.3. Descriptive characteristics
Descriptive characteristics were obtained from NHANES interview questionnaires and physical examinations.[20] BMI was measured through physical examination. Blood pressure, cholesterol levels, diabetes, and kidney failure were assessed using questionnaire data, physical examinations, and, where applicable, laboratory measurements.
2.4. Dietary data collection
Dietary intake was evaluated using a 24-hour dietary recall (DR), which recorded all foods and beverages consumed from midnight to midnight on the day before the NHANES interview. From the DR, NHANES provides individual estimates of total daily energy intake (kcal/d), macronutrients (g/d), and micronutrients. The DR also includes detailed information on specific food items, each identified by an 8-digit US Department of Agriculture (USDA) food code. The first digit represents the main food group, as outlined in the USDA Food and Nutrient Database for Dietary Studies: (1) milk and milk products; (2) meat, poultry, fish, and mixtures; (3) eggs; (4) legumes, nuts, and seeds; (5) grain products; (6) fruits; (7) vegetables; (8) fats, oils, and salad dressings; and (9) sugars, sweets, and beverages.[24] For further specificity, fruits were subdivided into (6a) fruits (excluding juice) and (6b) fruit juice, and a combined group for “fruits (excluding juice) and vegetables was created.”
To better capture dietary exposures relevant to hyperuricemia, we developed 14 custom food groups using prespecified USDA food code rules. Depending on the level of specificity required, groups were defined using USDA food code prefixes of varying lengths, and in selected instances by complete food codes or keyword-based identification. These custom groups were designed to identify specific foods within broader categories that may drive associations with hyperuricemia or that had previously been implicated in the literature. Details of the custom food groups, including code-based classification rules and representative food items, are provided in Supplementary Table S1, Supplemental Digital Content 1. For each food group – both primary and custom – we calculated each individual’s daily intake in grams. Intake was generally categorized into tertiles (Low/Medium/High). For food groups with highly skewed distributions, participants with no intake were classified as “None,” and the remaining consumers were divided into “Low” and “High” using the median as the cutoff.
Additionally, NHANES administered a comprehensive food frequency questionnaire (FFQ), which was used as a supplementary analysis in this study. The questionnaire collected data on the monthly intake of both primary and custom food groups. Monthly consumption (times per month) was categorized into tertiles (Low/Medium/High) or into “None/Low/High” based on the distribution observed in the control group. The 24-hour DR data were used for the primary analyses, whereas FFQ-based monthly intake frequency data were analyzed separately as supportive secondary analyses and were not combined with the recall-based exposure measures.
2.5. Statistical analysis
Differences in demographic and health-related characteristics between cases and controls were compared using Student’s t tests for continuous variables and Pearson chi-square tests for categorical variables. Because controls were frequency matched to cases rather than individually matched, unconditional multivariable logistic regression models were used to examine the associations between hyperuricemia and the daily intake of primary and custom food groups, with odds ratios (ORs) and 95% confidence intervals (CIs) as the measures of association. The multivariable models adjusted for the matching factors and other a priori covariates, including NHANES cycle, age at interview, sex, race/ethnicity, education level, physical activity, BMI category, cigarette history, history of high blood pressure, high cholesterol, diabetes, kidney failure, and total energy intake for dietary quality control. Because controls were frequency matched rather than individually matched, the matching variables were retained in the multivariable models to account for the sampling design and minimize residual confounding. Trend tests were performed by entering the median value of each exposure category as a continuous variable in the regression models. As secondary analyses, the associations between hyperuricemia and monthly intake frequency of both primary and custom food groups were assessed using models similar to those used in the primary analyses. Analyses were conducted using a complete-case approach, and participants with missing data on key exposure, outcome, matching, or covariate variables were excluded as described above. Because multiple food groups were evaluated in this exploratory analysis, the findings should be interpreted cautiously, with greater emphasis placed on the overall pattern and consistency of associations than on isolated statistically significant results. In addition, false discovery rate-adjusted P for trend values were calculated across the regression analyses to account for multiple comparisons. All statistical tests were 2-sided, and a P value of < .05 was considered statistically significant. All analyses were performed using R software (version 4.0 or higher).
2.6. Ethics statement
The NHANES protocol was approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent. Because this study was a secondary analysis of publicly available de-identified data, additional institutional review board approval was not required.
3. Results
3.1. Participant characteristics
This study includes 3057 participants (886 hyperuricemia cases and 2171 controls) with a mean age of 59.0 years. The participants are predominantly obese (62.0%), male (72.8%), and non-Hispanic White (48.6%), with no significant differences in matched factors between cases and controls. Significant differences were observed in marital status (P < .001), education level (P < .05), physical activity (P < .05), cigarette history (P < .05), and daily energy intake (P < .001), with cases being less likely to be married, have a college degree, engage in physical activity, or never smoke, and more likely to have lower daily energy intake. High blood pressure (P < .001) and kidney failure (P < .001) were also more prevalent in cases. Other demographic and lifestyle factors were comparable between cases and controls (Table 1).
Table 1.
Demographic characteristics of 3057 participants in the NHANES case-control study on dietary characteristics and hyperuricemia.
| Patient with hyperuricemia | Patient without Hyperuricemia* | Total | ||
|---|---|---|---|---|
| Number (%) | 886 (29.0) | 2171 (71.0) | 3057 | P-value† |
| Age at interview, mean (SD), y | 58.2 (17.1) | 59.2 (16.4) | 59.0 (16.6) | .15 |
| NHANES release | .99 | |||
| 2001–2002 | 71 (9.1) | 176 (8.1) | 247 (8.1) | |
| 2003–2004 | 63 (8.1) | 174 (8.0) | 237 (7.8) | |
| 2005–2006 | 71 (9.1) | 204 (9.4) | 275 (9.0) | |
| 2007–2008 | 102 (13.1) | 287 (13.2) | 389 (12.7) | |
| 2009–2010 | 94 (12.1) | 269 (12.4) | 363 (11.9) | |
| 2011–2012 | 108 (13.9) | 304 (14.0) | 412 (13.5) | |
| 2013–2014 | 99 (12.7) | 262 (12.1) | 361 (11.8) | |
| 2017–2020 | 170 (21.9) | 495 (22.8) | 665 (21.8) | |
| Sex | .50 | |||
| Female | 233 (26.3) | 597 (27.5) | 830 (27.2) | |
| Male | 653 (73.7) | 1574 (72.5) | 2227 (72.8) | |
| Race/Ethnicity | .31 | |||
| Non-Hispanic White | 413 (46.6) | 1072 (49.4) | 1485 (48.6) | |
| Non-Hispanic Black | 262 (29.6) | 662 (30.5) | 924 (30.2) | |
| Mexican American | 78 (8.8) | 176 (8.1) | 254 (8.3) | |
| Other Hispanic | 61 (6.9) | 116 (5.3) | 177 (5.8) | |
| Non-Hispanic Asian | 41 (4.6) | 81 (3.7) | 122 (4.0) | |
| Others | 31 (3.5) | 64 (2.9) | 95 (3.1) | |
| Marital status | <.001 | |||
| Married | 468 (52.8) | 1314 (60.5) | 1782 (58.3) | |
| Living with partner | 37 (4.2) | 63 (2.9) | 100 (3.3) | |
| Widowed | 142 (16.0) | 371 (17.1) | 513 (16.8) | |
| Divorced | 120 (13.5) | 227 (10.5) | 347 (11.4) | |
| Never married | 93 (10.5) | 155 (7.1) | 248 (8.1) | |
| Separated | 26 (2.9) | 41 (1.9) | 67 (2.2) | |
| Education | <.05 | |||
| <9th grade | 102 (11.5) | 194 (8.9) | 296 (9.7) | |
| 9–11th grade (includes 12th grade with no diploma) | 124 (14.0) | 278 (12.8) | 402 (13.2) | |
| High school graduate/GED or equivalent | 235 (26.5) | 546 (25.1) | 781 (25.5) | |
| Some college or AA degree | 250 (28.2) | 647 (29.8) | 897 (29.3) | |
| College graduate or above | 175 (19.8) | 506 (23.3) | 681 (22.3) | |
| Physical activity | <.05 | |||
| Vigorous | 130 (14.7) | 369 (17) | 499 (16.3) | |
| Moderate | 230 (26.0) | 631 (29.1) | 861 (28.2) | |
| Never | 526 (59.3) | 1171 (53.9) | 1697 (55.5) | |
| BMI category, kg/m2 | .06 | |||
| Underweight (<18.5) | 3 (0.3) | 0 (0) | 3 (0.1) | |
| Normal weight (18.5–24.9) | 77 (8.7) | 201 (9.3) | 278 (9.1) | |
| Overweight (25.0–29.9) | 254 (28.7) | 626 (28.8) | 880 (28.8) | |
| Obese (≥30) | 552 (62.3) | 1344 (61.9) | 1896 (62) | |
| Mean (SD) | 33.6 (8.2) | 32.0 (6.4) | 32.5 (7.0) | <.01 |
| Cigarette history | <.05 | |||
| Ever | 478 (54.0) | 1065 (49.1) | 1543 (50.5) | |
| Never | 408 (46.0) | 1106 (50.9) | 1514 (49.5) | |
| Tobacco history | .28 | |||
| Ever | 160 (18.1) | 429 (19.8) | 589 (19.3) | |
| Never | 726 (81.9) | 1742 (80.2) | 2468 (80.7) | |
| Total energy intake, kcal/d | 1924.5 (743.0) | 2050.0 (756.8) | 2013.6 (754.9) | <.001 |
| Alcohol history | .38 | |||
| Ever | 666 (75.2) | 1666 (76.7) | 2332 (76.3) | |
| Never | 220 (24.8) | 505 (23.3) | 725 (23.7) | |
| High blood pressure | <.001 | |||
| Yes | 613 (69.2) | 1137 (52.4) | 1750 (57.2) | |
| No | 273 (30.8) | 1034 (47.6) | 1307 (42.8) | |
| High cholesterol | .29 | |||
| Yes | 433 (48.9) | 1015 (46.8) | 1448 (47.4) | |
| No | 453 (51.1) | 1156 (53.2) | 1609 (52.6) | |
| Diabetes | .06 | |||
| Yes | 223 (25.2) | 473 (21.8) | 696 (22.8) | |
| Borderline | 31 (3.5) | 62 (2.9) | 93 (3.0) | |
| No | 632 (71.3) | 1636 (75.3) | 2268 (74.2) | |
| Kidney failure | <.001 | |||
| Yes | 126 (14.2) | 79 (3.6) | 205 (6.7) | |
| No | 760 (85.8) | 2092 (96.4) | 2852 (93.3) |
BMI = body mass index, NHANES = National Health and Nutrition Examination Survey, SD = standard deviation.
Matching of controls and cases at a 4:1 ratio based on 5 criteria: Age group (5-year categories), NHANES release, sex, race/ethnicity, and BMI category.
P value was calculated using Student t test for continuous variables and chi-square test for categorical variables.
3.2. Dietary intake and hyperuricemia
Multivariable models assessing the association between daily food intake and hyperuricemia, based on USDA primary and custom food groups, are presented separately for plant-based foods (Table 2), animal-based foods (Table 3), and fats, sweets, and beverages (Table 4).
Table 2.
Association between daily consumption of plant-based food groups, based on 24-hour DR data, and hyperuricemia in 3057 participants in the NHANES case-control study on dietary characteristics and hyperuricemia.
| Food group* | Hyperuricemia | Non-hyperuricemia | OR (95% CI) | Adjusted OR† (95% CI) | P for trend | FDR-adjusted P for trend‡ |
|---|---|---|---|---|---|---|
| Legumes, nuts, and seeds, g/d | ||||||
| None | 668 | 1503 | 1 (Ref) | 1 (Ref) | ||
| Low (0.1–63.2) | 115 | 328 | 0.79 (0.62–0.99) | 0.84 (0.66–1.07) | ||
| High (63.5–1012.0) | 103 | 340 | 0.68 (0.53–0.86) | 0.74 (0.57–0.96) | <.05 | .12 |
| Grain products, g/d | ||||||
| Low (0–143.5) | 350 | 669 | 1 (Ref) | 1 (Ref) | ||
| Medium (144.0–340.5) | 260 | 760 | 0.65 (0.54–0.79) | 0.70 (0.57–0.86) | ||
| High (341–1890.6) | 276 | 742 | 0.71 (0.59–0.86) | 0.78 (0.62–0.96) | <.05 | .23 |
| Whole-wheat bread, whole oats, and brown rice, g/d | ||||||
| None | 343 | 771 | 1 (Ref) | 1 (Ref) | ||
| Low (1.3–65.0) | 290 | 682 | 0.96 (0.79–1.15) | 0.94 (0.77–1.16) | ||
| High (65.3–870.0) | 253 | 718 | 0.79 (0.65–0.96) | 0.76 (0.62–0.93) | <.01 | .07 |
| Fruits (excluding juice) and vegetables, g/d | ||||||
| Low (0–120.2) | 317 | 703 | 1 (Ref) | 1 (Ref) | ||
| Medium (120.4–317.0) | 282 | 736 | 0.85 (0.70–1.03) | 0.92 (0.76–1.13) | ||
| High (317.2–2946.3) | 287 | 732 | 0.87 (0.72–1.05) | 1.01 (0.82–1.25) | .82 | .92 |
| Fruits (excluding juice), g/d | ||||||
| None | 495 | 1125 | 1 (Ref) | 1 (Ref) | ||
| Low (1.0–154.0) | 200 | 522 | 0.87 (0.72–1.06) | 0.93 (0.76–1.15) | ||
| High (154.2–1910.6) | 191 | 524 | 0.83 (0.68–1.01) | 0.89 (0.72–1.11) | .29 | .49 |
| Vegetables, g/d | ||||||
| Low (0–51.8) | 313 | 706 | 1 (Ref) | 1 (Ref) | ||
| Medium (51.9–200.2) | 279 | 740 | 0.85 (0.70–1.03) | 0.93 (0.76–1.14) | ||
| High (200.3–1928.0) | 294 | 725 | 0.91 (0.76–1.11) | 1.11 (0.90–1.36) | .25 | .46 |
| Cruciferous vegetables, g/d | ||||||
| None | 688 | 1658 | 1 (Ref) | 1 (Ref) | ||
| Low (0.9–46.0) | 96 | 263 | 0.88 (0.68–1.13) | 0.89 (0.68–1.16) | ||
| High (46.8–920.0) | 102 | 250 | 0.98 (0.77–1.25) | 0.99 (0.77–1.31) | .91 | .95 |
| Tomatoes, g/d | ||||||
| None | 629 | 1486 | 1 (Ref) | 1 (Ref) | ||
| Low (0.9–45.0) | 140 | 370 | 0.89 (0.72–1.11) | 0.92 (0.73–1.15) | ||
| High (45.5–930.3) | 117 | 315 | 0.88 (0.69–1.10) | 0.97 (0.76–1.24) | .80 | .93 |
| Fruit juice and nectar, g/d | ||||||
| None | 687 | 1669 | 1 (Ref) | 1 (Ref) | ||
| Low (1.2–248.0) | 115 | 270 | 1.03 (0.82–1.31) | 1.15 (0.90–1.49) | ||
| High (248.8–1992.0) | 84 | 232 | 0.88 (0.67–1.14) | 0.96 (0.72–1.27) | .93 | .95 |
BMI = body mass index, CI = confidence interval, DR = dietary recall, FDR = false discovery rate, NHANES = National Health and Nutrition Examination Survey, OR = odds ratio, Ref = reference, USDA = United States Department of Agriculture.
Italics categories are custom food groups, while regular font represents primary USDA food groups, except for “fruits (excluding juice)” and “fruit juice and nectar.”
Odd ratio (OR) and 95% confidence interval (CI) were calculated using unconditional multivariable logistic regression adjusted for age at interview, sex, race/ethnicity, education, physical activity, BMI category, cigarette history, high blood pressure history, high cholesterol history, diabetes history, kidney failure history and total energy intake for dietary quality controls.
P for trend values are nominal trend test P values. FDR-adjusted P for trend values were calculated using the false discovery rate method to account for multiple comparisons across regression analyses.
Table 3.
Association between daily consumption of animal-based food groups, based on 24-hour DR data, and hyperuricemia in 3057 participants in the NHANES case-control study on dietary characteristics and hyperuricemia.
| Food group* | Hyperuricemia | Non-hyperuricemia | OR (95% CI) | Adjusted OR† (95% CI) | P for trend | FDR-adjusted P for trend‡ |
|---|---|---|---|---|---|---|
| Eggs, g/d | ||||||
| None | 656 | 1602 | 1 (Ref) | 1 (Ref) | ||
| Low (2.3–94.0) | 122 | 278 | 0.96 (0.88–1.06) | 0.96 (0.87–1.06) | ||
| High (95.0–594.0) | 108 | 291 | 0.90 (0.82–0.99) | 0.85 (0.77–0.94) | <.01 | .64 |
| Milk and milk products, g/d | ||||||
| Low (0–20.0) | 352 | 670 | 1 (Ref) | 1 (Ref) | ||
| Medium (20.3–198.3) | 295 | 728 | 0.77 (0.64–0.93) | 0.80 (0.65–0.98) | ||
| High (198.4–3584.8) | 239 | 773 | 0.59 (0.48–0.71) | 0.64 (0.51–0.79) | <.001 | <.01 |
| Meat, poultry, fish, and mixtures, g/d | ||||||
| Low (0–116.0) | 280 | 740 | 1 (Ref) | 1 (Ref) | ||
| Medium (117.0–265.0) | 310 | 709 | 1.15 (1.07–1.25) | 1.15 (1.06–1.25) | ||
| High (265.6–1483.1) | 296 | 722 | 1.19 (1.11–1.29) | 1.22 (1.12–1.33) | <.001 | .29 |
| Processed meat, g/d | ||||||
| None | 578 | 1407 | 1 (Ref) | 1 (Ref) | ||
| Low (2.8–84.0) | 171 | 394 | 1.06 (0.86–1.29) | 1.1 (0.88–1.36) | ||
| High (84.3–684.8) | 137 | 370 | 0.90 (0.72–1.12) | 0.9 (0.71–1.14) | .51 | .66 |
| Fish & sea foods, g/d | ||||||
| None | 718 | 1796 | 1 (Ref) | 1 (Ref) | ||
| Low (4.0–148.1) | 85 | 187 | 1.02 (0.92–1.14) | 1.02 (0.91–1.14) | ||
| High (148.3–968.0) | 83 | 188 | 1.15 (1.03–1.28) | 1.09 (1.01–1.23) | .12 | .41 |
BMI = body mass index, CI = confidence interval, DR = dietary recall, FDR = false discovery rate, NHANES = National Health and Nutrition Examination Survey, OR = odds ratio, Ref = reference, USDA = United States Department of Agriculture.
Italics categories are custom food groups, while regular font represents primary USDA food groups.
Odd ratio (OR) and 95% confidence interval (CI) were calculated using unconditional multivariable logistic regression adjusted for age at interview, sex, race/ethnicity, education, physical activity, BMI category, cigarette history, high blood pressure history, high cholesterol history, diabetes history, kidney failure history and total energy intake for dietary quality controls.
P for trend values are nominal trend test P values. FDR-adjusted P for trend values were calculated using the false discovery rate method to account for multiple comparisons across regression analyses.
Table 4.
Association between daily consumption of fats, sweets, and beverages, based on 24-hour DR data, and hyperuricemia in 3057 participants in the NHANES case-control study on dietary characteristics and hyperuricemia.
| Food group* | Hyperuricemia | Non-hyperuricemia | OR (95% CI) | Adjusted OR† (95% CI) | P for trend | FDR-adjusted P for trend‡ |
|---|---|---|---|---|---|---|
| Fats, oils, and salad dressings, g/d | ||||||
| None | 447 | 1092 | 1 (Ref) | 1 (Ref) | ||
| Low (0.5–16.0) | 228 | 532 | 1.05 (0.86–1.27) | 1.09 (0.89–1.34) | ||
| High (16.3–297.4) | 211 | 547 | 0.94 (0.78–1.14) | 1.11 (0.90–1.36) | .38 | .61 |
| Sugars, sweets, and beverages, g/d | ||||||
| Low (0–1246.4) | 264 | 755 | 1 (Ref) | 1 (Ref) | ||
| Medium (1246.7–2252.6) | 284 | 735 | 1.11 (0.91–1.34) | 1.17 (0.94–1.44) | ||
| High (2255.0–15,195.7) | 338 | 681 | 1.42 (1.17–1.72) | 1.60 (1.28–2.00) | <.001 | <.001 |
| Foods and beverages (sugar-sweetened), g/d | ||||||
| Low (0–22.0) | 320 | 700 | 1 (Ref) | 1 (Ref) | ||
| Medium (22.0–346.5) | 260 | 758 | 0.75 (0.62–0.91) | 0.83 (0.67–1.02) | ||
| High (347.0–5183.8) | 306 | 713 | 0.94 (0.78–1.13) | 1.01 (0.81–1.24) | .48 | .65 |
| Foods (sugar-sweetened), g/d | ||||||
| None | 391 | 776 | 1 (Ref) | 1 (Ref) | ||
| Low (1–46.9) | 258 | 687 | 0.75 (0.62–0.90) | 0.77 (0.63–0.94) | ||
| High (47.0–769.2) | 237 | 708 | 0.66 (0.55–0.80) | 0.74 (0.60–0.91) | <.05 | .11 |
| Beverages (sugar-sweetened), g/d | ||||||
| None | 504 | 1262 | 1 (Ref) | 1 (Ref) | ||
| Low (2.0–493.8) | 184 | 462 | 1.05 (0.97–1.14) | 1.01 (0.93–1.10) | ||
| High (494.0–5183.8) | 198 | 447 | 1.13 (1.05–1.22) | 1.13 (1.03–1.23) | <.05 | .44 |
| Soft drinks, g/d | ||||||
| Low (0–620.0) | 281 | 740 | 1 (Ref) | 1 (Ref) | ||
| Medium (621.6–1494.0) | 284 | 734 | 1.05 (0.98–1.14) | 1.01 (0.93–1.09) | ||
| High (1494.5–13626.9) | 321 | 697 | 1.17 (1.08–1.26) | 1.11 (1.03–1.21) | <.01 | .28 |
| Soft drinks (sugar-sweetened), g/d | ||||||
| None | 504 | 1262 | 1 (Ref) | 1 (Ref) | ||
| Low (2.0–493.8) | 184 | 462 | 1.05 (0.97–1.14) | 1.01 (0.93–1.10) | ||
| High (494.0–5183.9) | 198 | 447 | 1.13 (1.05–1.22) | 1.13 (1.03–1.23) | <.05 | .44 |
| Soft drinks (artificially-sweetened), g/d | ||||||
| None | 750 | 1816 | 1 (Ref) | 1 (Ref) | ||
| Low (15.0–473.0) | 61 | 185 | 0.80 (0.59–1.07) | 0.74 (0.53–1.02) | ||
| High (473.3–4260.0) | 75 | 170 | 1.07 (0.80–1.41) | 1.10 (0.81–1.51) | .95 | .95 |
| Tea (sugar-sweetened), g/d | ||||||
| None | 842 | 2073 | 1 (Ref) | 1 (Ref) | ||
| Low (9.0–496.0) | 16 | 57 | 0.69 (0.38–1.18) | 0.75 (0.42–1.34) | ||
| High (518.0–4002.0) | 28 | 41 | 1.68 (1.02–2.72) | 1.86 (1.11–3.12) | .06 | .25 |
| Tea (unsweetened), g/d | ||||||
| None | 767 | 1820 | 1 (Ref) | 1 (Ref) | ||
| Low (44.4–429.2) | 56 | 179 | 0.74 (0.54–1.01) | 0.75 (0.54–1.05) | ||
| High (435.0–3433.6) | 63 | 172 | 0.87 (0.64–1.17) | 0.88 (0.63–1.21) | .24 | .46 |
| Coffee drink, g/d | ||||||
| None | 870 | 2121 | 1 (Ref) | 1 (Ref) | ||
| Low (6.8–360.0) | 6 | 28 | 0.52 (0.19–1.18) | 0.57 (0.23–1.43) | ||
| High (364.5–3240.0) | 10 | 22 | 1.11 (0.50–2.29) | 1.08 (0.50–2.35) | .70 | .87 |
| Energy and nutritional drinks, g/d | ||||||
| None | 856 | 2105 | 1 (Ref) | 1 (Ref) | ||
| Low (9.0–435.0) | 14 | 34 | 1.01 (0.52–1.86) | 1.01 (0.52–1.97) | ||
| High (465.0–1984.0) | 16 | 32 | 1.23 (0.65–2.22) | 1.31 (0.69–2.48) | .44 | .64 |
BMI = body mass index, CI = confidence interval, DR = dietary recall, FDR = false discovery rate, NHANES = National Health and Nutrition Examination Survey, OR = odds ratio, Ref = reference, USDA = United States Department of Agriculture.
Italics categories are custom food groups, while regular font represents primary USDA food groups.
Odd ratio (OR) and 95% confidence interval (CI) were calculated using unconditional multivariable logistic regression adjusted for age at interview, sex, race/ethnicity, education, physical activity, BMI category, cigarette history, high blood pressure history, high cholesterol history, diabetes history, kidney failure history and total energy intake for dietary quality controls.
P for trend values are nominal trend test P values. FDR-adjusted P for trend values were calculated using the false discovery rate method to account for multiple comparisons across regression analyses.
Daily intake of certain plant-based foods was inversely associated with hyperuricemia, as shown in Table 2. Legumes, nuts, and seeds showed a significant inverse association with hyperuricemia (High OR, 0.74; 95% CI, 0.57–0.96), as did grain products (High OR, 0.78; 95% CI, 0.62–0.96) and whole-wheat bread, whole oats, and brown rice (High OR, 0.76; 95% CI, 0.62–0.93). However, the corresponding trend tests were not statistically significant after false discovery rate adjustment. In contrast, fruits (excluding juice), vegetables, cruciferous vegetables, tomatoes, and fruit juice and nectar were not significantly associated with hyperuricemia.
Among animal-based foods (Table 3), higher egg consumption was inversely associated with hyperuricemia (High OR, 0.85; 95% CI, 0.77–0.94), although the trend across intake categories was not statistically significant after false discovery rate adjustment. Milk and milk products also exhibited a significant inverse association, and the trend remained significant after false discovery rate adjustment, with medium intake associated with 20% lower odds and high intake with 36% lower odds (Medium OR, 0.80; 95% CI, 0.65–0.98; High OR, 0.64; 95% CI, 0.51–0.79; Ptrend < .01). In contrast, meat, poultry, fish, and mixtures were associated with higher odds of hyperuricemia (High OR, 1.22; 95% CI, 1.12–1.33), although the false discovery rate-adjusted trend was not statistically significant. Higher odds of hyperuricemia were observed in the highest intake category of fish and seafood (OR, 1.09; 95% CI, 1.01–1.23); however, this association was not accompanied by a statistically significant trend across intake categories after false discovery rate adjustment.
Among fats, sweets, and beverages (Table 4), higher intake of sugars, sweets, and beverages was associated with higher odds of hyperuricemia (High OR, 1.60; 95% CI, 1.28–2.00; Ptrend < .001), showing a significant dose-dependent trend even after false discovery rate adjustment. In contrast, although higher intake of some specific sugar-related categories, including beverages (sugar-sweetened; High OR, 1.13; 95% CI, 1.03–1.23), soft drinks (High OR, 1.11; 95% CI, 1.03–1.21), soft drinks (sugar-sweetened; High OR, 1.13; 95% CI, 1.03–1.23), and tea (sugar-sweetened; OR, 1.86; 95% CI, 1.11–3.12), their trend tests were not statistically significant after false discovery rate adjustment. Artificially sweetened soft drinks, unsweetened tea, coffee, and energy/nutritional drinks showed no significant association with hyperuricemia.
3.3. Intake frequency and hyperuricemia
Our secondary analysis of NHANES data examined the association between monthly intake frequency of primary and custom food groups and hyperuricemia (Supplementary Table S2, Supplemental Digital Content 2). The results generally supported several associations observed in the primary analysis of daily intake, including inverse associations for milk and milk products, legumes, nuts and seeds, and whole-grain-related foods, as well as a positive association for meat, poultry, and fish. However, some findings differed between the 2 dietary assessment approaches. In particular, several beverage-related associations that were observed in the daily intake analysis were weaker or not evident in the FFQ-based analysis. Artificially sweetened soft drinks showed a borderline inverse association, while sugar-sweetened tea, unsweetened tea, coffee, and energy/nutritional drinks remained unrelated to hyperuricemia.
4. Discussion
In this matched case-control analysis using NHANES 2001–2020 data, several specific food groups were associated with hyperuricemia. Higher intake of legumes, nuts, seeds, grain products, whole-wheat bread, oats, brown rice, eggs, and milk products was associated with lower odds of hyperuricemia, whereas higher intake of meat, poultry, fish, sugar-sweetened beverages, soft drinks, and sugar-sweetened tea was associated with higher odds. Overall, these findings are broadly consistent with current dietary recommendations that emphasize greater intake of whole grains and dairy products and lower intake of purine-rich animal foods and sugar-sweetened beverages among individuals at risk of hyperuricemia.
The inverse associations observed for legumes, nuts, seeds, and grain products, including whole-wheat bread, oats, and brown rice, are consistent with prior epidemiologic studies.[12,25–27] These foods are rich in dietary fiber, unsaturated fatty acids, vitamins, minerals, polyphenols, and antioxidants, which may favorably influence uric acid metabolism and overall metabolic health.[28,29] Nuts and seeds have also been associated with lower obesity risk and better metabolic profiles, possibly because of their high unsaturated fat content.[30,31] Whole grains may additionally contribute through effects on insulin sensitivity and gut microbiota regulation.[1] Taken together, these factors may partly explain the protective associations observed for plant-based foods. However, although inverse associations were observed for several plant-based food groups, the corresponding trend tests did not remain statistically significant after false discovery rate adjustment, and these findings should therefore be interpreted cautiously.
Among animal-based foods, eggs and milk products were inversely associated with hyperuricemia, whereas higher intake of meat, poultry, fish, and seafood was associated with higher odds of hyperuricemia. These results are generally in line with previous epidemiologic evidence regarding animal-source foods and SUA levels.[12,32] Eggs are naturally low in purines and may therefore be less likely to increase SUA.[33] In contrast, meat, poultry, fish, and seafood are major dietary sources of purines, which are metabolized into uric acid and may contribute to elevated serum urate levels.[34] Dairy products, particularly those containing casein and lactalbumin, have been reported to exert uricosuric effects and may help lower SUA concentrations.[35] This effect may be related to milk components such as orotic acid, lactose, and galactose, which have been suggested to promote renal urate excretion.[36,37] Notably, among these animal-based food groups, only the inverse association for milk and milk products remained supported by a statistically significant trend after false discovery rate adjustment. By contrast, the inverse association for eggs and the positive association for fish and seafood were more modest, and the latter was observed only in the highest intake category without a statistically significant dose–response trend.
The positive associations observed for sugars, sweets, and beverages, especially sugar-sweetened soft drinks, are also consistent with recommendations from the American Heart Association and the World Health Organization to reduce added sugar intake.[38,39] In our analysis, sugar-sweetened soft drinks were associated with higher odds of hyperuricemia, whereas artificially sweetened soft drinks were not, suggesting that the sugar content itself may be particularly important. This distinction is biologically plausible because sugar-sweetened beverages are commonly sweetened with sucrose or high-fructose corn syrup, both of which provide fructose.[40,41] Fructose metabolism promotes ATP depletion and AMP degradation in the liver, thereby increasing uric acid production.[42] Excessive fructose intake may also contribute to hypertriglyceridemia and insulin resistance,[43–45] both of which are associated with elevated SUA. At the same time, the associations for individual soft drink and sugar-sweetened beverage categories were relatively modest in magnitude, whereas the broader sugars, sweets, and beverages category showed a stronger association that remained statistically significant after false discovery rate adjustment. This pattern suggests that the cumulative contribution of sugar-rich dietary exposures may be more informative than any single beverage item considered in isolation. From a clinical and public health perspective, even relatively modest associations may still be relevant when considered together within an overall dietary pattern and broader metabolic risk profile. These findings provide additional support for recommendations to reduce sugar-sweetened beverage intake as part of dietary strategies for hyperuricemia prevention and management.
Although the FFQ-based analysis generally supported the direction of the primary findings, some discrepancies were observed, particularly for beverage-related exposures. Associations identified in the daily intake analysis were not always replicated in the FFQ-based analysis, likely reflecting differences between recent intake captured by the 24-hour DR and longer-term intake frequency captured by the FFQ. Taken together, these findings suggest that the overall direction of several associations was reasonably consistent across assessment methods, while also highlighting the importance of considering the distinct measurement properties of short-term and longer-term dietary instruments when interpreting the results.
This study has several notable strengths. First, it provides a relatively comprehensive assessment of the associations between a wide range of specific food groups and hyperuricemia using NHANES data collected over nearly 2 decades (2001–2020). Second, dietary intake was assessed using both a 24-hour DR and a FFQ, enabling a more robust evaluation of both recent and habitual dietary patterns. Third, the inclusion of participants from multiple NHANES cycles and diverse racial and ethnic groups improved the heterogeneity of the study sample. Fourth, frequency matching on key demographic and clinical variables, along with multivariable adjustment for a wide range of potential confounders, improved the internal validity of the observed associations. Finally, both dietary and laboratory data were collected using standardized NHANES protocols within the same survey cycle.
This study is not without limitations. First, like all studies relying on self-reported dietary data, it is subject to recall bias and measurement error. In particular, the primary exposure assessment was based on 24-hour DR data, which may not adequately reflect usual long-term intake and may introduce measurement error and misclassification. Although FFQ-based analyses were used as a secondary approach, both methods remain subject to self-report bias. Second, some food groupings, particularly those combining heterogeneous items such as solid foods and beverages, may mask differential effects of individual components. Third, although we adjusted for a wide range of confounding variables, residual confounding from unmeasured or unknown factors cannot be completely excluded. In addition, because multiple food groups were evaluated, some statistically significant associations may have occurred by chance and should therefore be interpreted cautiously, particularly when not supported by a clear dose–response relationship. After false discovery rate adjustment, the overall directions of association remained similar, although fewer trend tests remained statistically significant. Fourth, the use of a complete-case approach may have introduced selection bias if missingness was not completely at random. Comparisons between included and excluded participants suggested that the final analytic sample differed from excluded individuals on several demographic and health-related characteristics; however, these differences should be interpreted cautiously because exclusions also reflected study eligibility criteria in addition to missing data. Fifth, we did not assess overall dietary patterns or individual food items within complex food groups, which may provide additional insight in future studies. Lastly, the observational design of this study precludes the establishment of temporality or causal inference between diet and hyperuricemia. In addition, the use of a relatively high SUA threshold (≥8.5 mg/dL) may have preferentially identified more severe hyperuricemia and may limit comparability with studies using conventional thresholds. These findings should therefore be interpreted as associative rather than causal.
In conclusion, this study provides additional evidence on the associations between specific food groups and hyperuricemia in US adults using NHANES data. Higher intake of legumes, nuts, seeds, grain products, eggs, and milk products was associated with lower odds of hyperuricemia, whereas higher intake of meat, fish/seafood, and sugar-sweetened beverages was associated with higher odds. Although causality cannot be inferred from this observational study, these findings highlight the potential relevance of dietary factors in hyperuricemia and may help inform future prospective studies and dietary prevention strategies.
Acknowledgments
The authors thank the staff and participants of the National Health and Nutrition Examination Survey for their valuable contributions. The authors also thank the School of Public Health, Guizhou Medical University, for administrative and academic support.
Author contributions
Conceptualization: Honggui Ma, Zinian Wang.
Data curation: Huixin Ge.
Formal analysis: Honggui Ma.
Funding acquisition: Zinian Wang.
Investigation: Huixin Ge, Quliang Zhong, Guangyu Li.
Methodology: Honggui Ma, Zinian Wang.
Project administration: Zinian Wang.
Supervision: Zinian Wang.
Writing – original draft: Honggui Ma, Zinian Wang.
Writing – review & editing: Honggui Ma, Huixin Ge, Quliang Zhong, Guangyu Li, Zinian Wang.
Abbreviations:
- BMI
- body mass index
- CI
- confidence interval
- DR
- dietary recall
- FFQ
- food frequency questionnaire
- NHANES
- National Health and Nutrition Examination Survey
- OR
- odds ratio
- SUA
- serum uric acid
- USDA
- United States Department of Agriculture
This work was supported by the National Natural Science Foundation of China (Youth Program) [Grant No. 82504229] and the High-Level Talent Research Start-up Fund Project of Guizhou Medical University (J[2024]072) to ZW. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000049364).
How to cite this article: Ma H, Ge H, Zhong Q, Li G, Wang Z. Dietary intake and hyperuricemia among US adults: A matched case-control analysis of NHANES 2001–2020. Medicine 2026;105:25(e49364).
Contributor Information
Honggui Ma, Email: mhgmhg@sina.com.
Huixin Ge, Email: 34865852@qq.com.
Quliang Zhong, Email: zhongquliang@126.com.
Guangyu Li, Email: lgu131939@sina.com.
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