1. Introduction
An estimated 15% to 20% of the global population suffers from hyperuricemia (HUA).[1] HUA is defined by serum uric acid concentration, resulting predominantly from purine metabolism disorders or uric acid excretion disorders.[2] It links to higher risks of gout,[3] kidney diseases,[4] diabetes mellitus,[5] and cardiovascular disease.[6,7] HUA, as a burden of public healthcare on society globally, accounts for tremendous medical costs.[8]
The risk factors of HUA have been investigated and are not yet fully clear. Recently, epidemiologic and clinical data have shown what contributes to HUA, including genetic factors,[9] obesity,[10] environment,[11] diuretics use,[12,13] and diet,[14–16] and alterations in dietary patterns may have an influence on the HUA endpoints.[15,17] A dietary approaches to stop hypertension diet[18] and Mediterranean diet[19] can lower the level of serum uric acid, while a western diet is linked to a higher risk of HUA.[20] Furthermore, previous research suggests dietary patterns play a role in regulating inflammation. Plant-based dietary patterns are closely correlated with a low level of inflammation and oxidative stress.[21] The research community is growing increasingly concerned about the effect of diet inflammatory modulation in HUA for prevention and treatment.[22]
The dietary inflammatory index (DII) is an innovative dietary tool based on literature to measure the inflammatory potential of diet in all the population.[23] It is based on the pro-inflammatory and anti-inflammatory properties of 45 food parameters.[24] DII scores are regulated to global dietary intakes, and different cultures and their dietary patterns can benefit from their use.
Higher DII scores indicate a pro-inflammatory diet, while lower DII scores indicate an anti-inflammatory diet. Diet can modulate the inflammatory level via pro- or anti-inflammatory mechanisms.[25] Chronic inflammation is involved in the pathogenesis of HUA.[26]
However, the association between the consumption of a pro-inflammatory dietary and HUA has not been explored using a nationally representative sample in the United States before. We assumed that the higher dietary inflammatory index was correlated with increased risk of HUA in this cross-sectional study.
2. Materials and Methods
2.1. Data source and study population
The National Health and Nutritional Examination Survey (NHANES) is a nationwide cross-sectional survey with a multistage probability sample method to assess the health and nutrition state of the civilian in the United States updated every 2 years by interviews, physical examination and laboratory tests. The NHANES has been approved by the National Center for Health Statistics Ethics Review Board. All study participants signed informed consent. The detailed information is available on a site (www.cdc.gov/nchs/nhanes/).
We obtained the data from NHANES 2011 to 2018. The exclusion criteria for participants in this study were: Incomplete date of DII and serum uric acid; Younger than 18 years and pregnant women; The use of drug of urate-lowering therapy; Individuals with hemodialysis and peritoneal dialysis. In total, the sample size was 19,004 (Fig. 1).
;)
Figure 1.: Participants included in the study.
2.2. DII
We collected dietary intakes from the first 24-hour dietary recall interview conducted in person at the mobile examination center, which covered total nutrient intake for the first 24 hours. NHANES Dietary Interviewers Procedure Manuals provide detailed descriptions of dietary interview techniques. The DII, a literature-derived dietary index, was developed to estimate the inflammatory of populations diet potential.[24] Inflammatory biomarkers (IL-1β, IL-4, IL-6, IL-10, TNF-α, and C-reactive protein) were determined to assess the effect of food on inflammation. The pro-inflammatory food parameter is labeled in “+,” the anti-inflammatory parameter is labeled in “−” and the neutral parameter without any effect on inflammatory is labeled in “0.” DII scores in this study were calculated by 28 of 45 food parameters, including energy, carbohydrates, protein, alcohol, fiber, cholesterol, total fat, saturated fat, monounsaturated fatty acid, polyunsaturated fatty acid, n-3 fatty acids, n-6 fatty acids, niacin, vitamin A, vitamin B1, vitamin B2, vitamin B6, vitamin B12, vitamin C, vitamin D, vitamin E, iron, magnesium, zinc, selenium, folic acid, beta carotene, and caffeine. The dietary inflammation index was calculated based on dietary intake data from a regionally representative world database. First, subtracting the individual’s intake from the standard global mean to created z-score, and divided it by standard deviation. Secondly, this value is converted to a symmetrical distribution which is centered at zero, between −1 and 1. Thirdly, multiply that by the inflammatory effect score for each food and sum all food parameter DII values to create an overall DII score.
2.3. Definition of HUA
HUA was defined as ≥ 7 mg/dL (416.4 mmol/L) for males, or ≥ 6 mg/dL (356.9 mmol/L) for females.[27]
2.4. Assessment of other covariates
The covariates included: age (< 60 years and ≥ 60 years), gender (female and male), race (non-Hispanic White, non-Hispanic Black, Mexican American, or other race), body mass index (< 30 and ≥ 30 kg/m2), family income-to-poverty ratio (< 1.3, 1.3–3.5, ≥ 3.5), education background (less than high school, high school or equivalent and college or above), hypertension, diabetes, estimated glomerular filtration rate (eGFR), smoking status, alcohol intake, and energy intake. Smoking status and alcohol intake were divided into 3 groups: never, former and current. Energy intake was categorized as < 500 kcal, 500–4000 kcal and ≥ 4000 kcal, which corresponded to deficiency of intake, normal, and excessive energy intake participants, respectively. Except for eGFR, the details of the acquisition process as well as the determination of each variable are available at the following Web site: www.cdc.gov/nchs/nhanes. eGFR was measured applying the Chronic Kidney Disease Epidemiology Collaboration equation and was categorized as < 60 and ≥ 60 mL/minutes/1.73m2.[28]
2.5. Statistical analysis
Continuous variables were present as mean ± SD, and categorical variables were expressed as frequency and percentage. Student t test was used to analyze continuous variables. To examine categorical variables, the chi-square test was applied. Multivariate linear regression models were set up to assess the correlation between DII score, serum uric acid and HUA. Stratified analyses were carried out to evaluating the interaction effect. Dietary intakes were shown according to HUA status. We completed all analyses using R (Version 3.4.3) (http://www.R-project.org, The R Foundation) and Empower Stats software (http://www.empowerstats.com). In all analyses, a value of P < .05 (2-sided) was considered to indicate statistical significance.
3. Results
This study involved a total of 19,004 individuals (48.83% males vs 51.17% females). The prevalence of HUA was 19.83% and it was higher in males than in females (22.45% vs 17.33%). The participant with HUA was more likely to be male, old, non-Hispanic, hypertensive, diabetes, chronic kidney disease, and have higher BMI levels (P < .05). DII scores ranged from −5.03 to 5.79. Compared with the non-HUA group, participants in the HUA group had higher DII scores (P < .001). The details of the study participants characteristics are shown in Table 1.
Table 1 -
Baseline characteristics of the participants according to gender and hyperuricemia status.
| Characteristics |
Female |
Male |
| Total |
Non-Hyperuricemia |
Hyperuricemia |
P value |
Total |
Non-Hyperuricemia |
Hyperuricemia |
P value |
| (n = 9725) |
(n = 8040) |
(n = 1685) |
(n = 9279) |
(n = 7196) |
(n = 2083) |
| SUA (mmol/L) |
289.00 ± 77.20 |
262.83 ± 50.10 |
413.88 ± 59.67 |
<.001 |
359.66 ± 77.87 |
328.52 ± 52.65 |
467.25 ± 50.13 |
<.001 |
| DII |
1.84 ± 1.84 |
1.80 ± 1.86 |
2.02 ± 1.77 |
<.001 |
1.14 ± 1.91 |
1.08 ± 1.91 |
1.36 ± 1.89 |
<.001 |
| Age (yr) |
|
|
|
<.001 |
|
|
|
.002 |
| <60 |
6703 |
5876 (73%) |
827 (49%) |
|
6346 |
4978 (69%) |
1368 (66%) |
|
| >=60 |
3022 |
2164 (27%) |
858 (51%) |
` |
2933 |
2218 (31%) |
715 (34%) |
|
| Race (n, %) |
|
|
|
<.001 |
|
|
|
<.001 |
| Non-Hispanic white |
3634 |
2944 (37%) |
690 (41%) |
|
3601 |
2771 (39%) |
830 (40%) |
|
| Non-Hispanic black |
2185 |
1690 (21%) |
495 (29%) |
|
1983 |
1488 (21%) |
495 (24%) |
|
| Mexican American |
1382 |
1223 (15%) |
159 (9%) |
|
1332 |
1103 (15%) |
229 (11%) |
|
| Other hispanic |
1095 |
972 (12%) |
123 (7%) |
|
892 |
719 (10%) |
173 (8%) |
|
| Other race |
1429 |
1211 (15%) |
218 (13%) |
|
1471 |
1115 (15%) |
356 (17%) |
|
| Family income-to-poverty ratio (n, %) |
|
|
|
<.001 |
|
|
|
.01 |
|
<1.3 |
3051 |
2506 (34%) |
545 (36%) |
|
2637 |
2080 (32%) |
557 (30%) |
|
| 1.3–3.5 |
3284 |
2666 (36%) |
618 (41%) |
|
3184 |
2495 (38%) |
689 (37%) |
|
| ≥3.5 |
2521 |
2159 (29%) |
362 (24%) |
|
2622 |
1984 (30%) |
638 (34%) |
|
| Education background (n, %) |
|
|
|
.967 |
|
|
|
.021 |
| Less than high school |
1979 |
1640 (20%) |
339 (20%) |
|
2153 |
1714 (24%) |
439 (21%) |
|
| High school or equivalent |
2291 |
1893 (24%) |
398 (24%) |
|
2395 |
1859 (26%) |
536 (26%) |
|
| College or above |
5449 |
4502 (56%) |
947 (56%) |
|
4723 |
3618 (50%) |
1105 (53%) |
|
| BMI (kg/m2) |
|
|
|
<.001 |
|
|
|
<.001 |
| <30 |
5544 |
4950 (62%) |
594 (36%) |
|
6020 |
5016 (70%) |
1004 (49%) |
|
| ≥30 |
4086 |
3011 (38%) |
1075 (64%) |
|
3169 |
2120 (30%) |
1049 (51%) |
|
| Hypertension (n, %) |
|
|
|
<.001 |
|
|
|
<.001 |
| No |
5732 |
5185 (64%) |
547 (32%) |
|
5409 |
4448 (62%) |
961 (46%) |
|
| Yes |
3993 |
2855 (36%) |
1138 (68%) |
|
3870 |
2748 (38%) |
1122 (54%) |
|
| Diabetes (n, %) |
|
|
|
<.001 |
|
|
|
.002 |
| No |
8004 |
6882 (86%) |
1122 (67%) |
|
7498 |
5864 (81%) |
1634 (78%) |
|
| Yes |
1721 |
1158 (14%) |
563 (33%) |
|
1781 |
1332 (19%) |
449 (22%) |
|
| eGFR (mL/min/1.73m2) |
|
|
|
<.001 |
|
|
|
<.001 |
| <60 |
752 |
317 (4%) |
435 (26%) |
|
689 |
358 (5%) |
331 (16%) |
|
| ≥60 |
8973 |
7723 (96%) |
1250 (74%) |
|
8590 |
6838 (95%) |
1752 (84%) |
|
| Smoking status (n, %) |
|
|
|
<.001 |
|
|
|
<.001 |
| Never |
6450 |
5414 (68%) |
1036 (62%) |
|
4470 |
3447 (49%) |
1023 (50%) |
|
| Former |
1651 |
1255 (16%) |
396 (24%) |
|
2589 |
1933 (27%) |
656 (32%) |
|
| Current |
1496 |
1247 (16%) |
249 (15%) |
|
2087 |
1705 (24%) |
382 (19%) |
|
| Alcohol intake (n, %) |
|
|
|
<.001 |
|
|
|
.685 |
| Never |
1871 |
1546 (22%) |
325 (22%) |
|
861 |
670 (10%) |
191 (10%) |
|
| Former |
1060 |
803 (11%) |
257 (17%) |
|
1147 |
901 (14%) |
246 (13%) |
|
| Current |
5765 |
4837 (67%) |
928 (61%) |
|
6503 |
5034 (76%) |
1469 (77%) |
|
| Energy intake (kcal) |
|
|
|
.078 |
|
|
|
.004 |
| <500 |
113 |
87 (1%) |
26 (2%) |
|
48 |
29 (1%) |
19 (1%) |
|
| 500–4000 |
9466 |
7825 (97%) |
1641 (97%) |
|
8492 |
6575 (91%) |
1917 (92%) |
|
| ≥4000 |
146 |
128 (2%) |
18 (1%) |
|
739 |
592 (8%) |
147 (7%) |
|
DII = dietary inflammatory index, eGFR = estimated glomerular filtration rate.
The correlation between DII scores and serum uric acid examined by multivariate regression analysis is shown in Table 2. As the increase of DII scores, the serum uric acid tended to be higher. As continuous variable, per unit increased in DII score was associated with a 3 mmol/L increase in serum uric acid of male (β 3.00, 95% CI 2.05–3.94) and 0.92 mmol/L of female (β 0.92, 95% CI 0.07–1.77) adjusted for all covariates. We further converted DII score from a continuous variable to a categorical variable to conduct the sensitivity analysis. Compared with the lowest tertile of DII group (T1), the participants of the others have higher serum uric acid among males (T2: β 5.88, 95% CI 1.75–10.01; T3: β 12.74, 95% CI 8.41–17.08). However, this association is not significant among females (T2: β 3.12, 95% CI −0.54–6.79, T3: β 4.55, 95% CI 0.65–8.24).
Table 2 -
Association between DII and serum uric acid.
|
Tertile of DII |
P for trend |
Continuous DII |
|
T1 |
T2 |
T3 |
|
Female |
|
| Range of DII |
−4.83 to 1.23 |
1.23 to 2.92 |
2.92–5.79 |
|
|
| Model 1 |
Reference |
7.63 (3.88, 11.38) < 0.0001 |
11.84 (8.09, 15.59) < 0.0001 |
<.0001 |
2.69 (1.86, 3.52) < 0.0001 |
| Model 2 |
Reference |
5.92 (2.29, 9.55) 0.0014 |
10.30 (6.66, 13.94) < 0.0001 |
<.0001 |
2.35 (1.55, 3.16) < 0.0001 |
| Model 3 |
Reference |
3.12 (−0.54, 6.79) 0.0952 |
4.45 (0.65, 8.24) 0.0216 |
.0189 |
0.92 (0.07, 1.77) 0.0337 |
|
Male |
|
| Range of DII |
−5.03 to 0.28 |
0.28 to 2.21 |
2.21–5.48 |
|
|
| Model 1 |
Reference |
7.14 (3.27, 11.00) 0.0003 |
15.32 (11.46, 19.19) < 0.0001 |
<.0001 |
3.42 (2.59, 4.24) < 0.0001 |
| Model 2 |
Reference |
7.02 (3.16, 10.88) 0.0004 |
14.58 (10.69, 18.47) < 0.0001 |
<.0001 |
3.30 (2.47, 4.14) < 0.0001 |
| Model 3 |
Reference |
5.88 (1.75, 10.01) 0.0053 |
12.74 (8.41, 17.08) < 0.0001 |
<.0001 |
3.00 (2.05, 3.94) < 0.0001 |
DII = dietary inflammatory index.
We observed a positive association between DII score and HUA in Table 3. This association was significant among the whole participants (OR 1.05, 95% CI 1.02–1.07) and males (OR 1.06, 95% CI 1.03–1.10) after adjusting for all covariates, but not among females (OR 1.02, 95% CI 0.99–1.06). Compared with the lowest tertile of DII score, the rise of DII grade increased the risk of HUA among the whole participants (T2: OR 1.14, 95% CI 1.03, 1.27; T3: OR 1.20 [1.07, 1.34], P for trend = .0012) and males [T2: 1.15 (0.99, 1.33), T3: 1.29 (1.11, 1.50), P for trend = .0008].
Table 3 -
Association between DII and hyperuricemia.
|
Tertile of DII |
P for trend |
Continuous DII |
|
T1 |
T2 |
T3 |
|
Overall participants |
|
| Range of DII |
−5.03 to 0.73 |
0.73–2.60 |
2.60–5.79 |
|
|
| Cases |
1127/6335 |
1271/6334 |
1370/6335 |
|
|
| Model 1 |
Reference |
1.16 (1.06, 1.27) 0.0011 |
1.28 (1.17, 1.39) < 0.0001 |
.0001 |
1.06 (1.04, 1.08) < 0.0001 |
| Model 2 |
Reference |
1.19 (1.09, 1.30) 0.0002 |
1.32 (1.20, 1.44) < 0.0001 |
.0001 |
1.07 (1.05, 1.09) < 0.0001 |
| Model 3 |
Reference |
1.14 (1.03, 1.27) 0.0151 |
1.20 (1.07, 1.34) 0.0013 |
.0012 |
1.05 (1.02, 1.07) 0.0004 |
|
Female |
|
| Range of DII |
−4.83 to 1.23 |
1.23–2.92 |
2.92–5.79 |
|
|
| Cases |
484/3242 |
574/3241 |
627/3242 |
|
|
| Model 1 |
Reference |
1.23 (1.07, 1.40) 0.0025 |
1.37 (1.20, 1.56) < 0.0001 |
<.0001 |
1.07 (1.04, 1.10) < 0.0001 |
| Model 2 |
Reference |
1.18 (1.03, 1.35) 0.0192 |
1.32 (1.16, 1.51) < 0.0001 |
<.0001 |
1.06 (1.03, 1.10) < 0.0001 |
| Model 3 |
Reference |
1.10 (0.93, 1.29) 0.2529 |
1.11 (0.94, 1.31) 0.2158 |
.2015 |
1.02 (0.99, 1.06) 0.2381 |
|
Male |
|
| Range of DII |
−5.03 to 0.28 |
0.28–2.21 |
2.21–5.48 |
|
|
| Cases |
605/3093 |
687/3093 |
791/3093 |
|
|
| Model 1 |
Reference |
1.17 (1.04, 1.33) 0.0104 |
1.41 (1.25, 1.59) < 0.0001 |
<.0001 |
1.08 (1.05, 1.11) < 0.0001 |
| Model 2 |
Reference |
1.17 (1.03, 1.32) 0.0135 |
1.38 (1.22, 1.55) < 0.0001 |
<.0001 |
1.08 (1.05, 1.10) < 0.0001 |
| Model 3 |
Reference |
1.15 (0.99, 1.33) 0.0612 |
1.29 (1.11, 1.50) 0.0008 |
.0008 |
1.06 (1.03, 1.10) 0.0002 |
DII = dietary inflammatory index.
Meanwhile, we conducted subgroup analysis to further explore the association between DII and HUA (Fig. 2). For females, there was a statistically significant difference in the subgroup stratified by BMI (BMI < 30, OR 1.08, 95% CI 1.02–1.14, P for interaction = .0134), indicating that the association depends on BMI. For males, the interaction test showed that there was no significant difference in subgroup stratified by age, BMI, hypertension, diabetes, eGFR, smoking status and alcohol intake (P for interaction > .05).
Figure 2.: Subgroup analysis for the association between DII and hyperuricemia. DII = dietary inflammatory index.
The results show that the subgroup analysis was adjusted for all presented covariates except the effect modifier.
The characteristics of dietary intakes stratified by HUA are shown in Table 4. To eliminate the effect of dietary energy, all dietary intakes were adjusted for total energy intake. Energy and carbohydrate intake in individuals with HUA are lower than those without HUA. People with HUA have higher intake of alcohol, cholesterol, niacin, vitamin D, and caffeine compared to participants without HUA. But it was significantly higher in fiber, saturated fat, vitamin A, vitamin B1, vitamin B2, vitamin C, vitamin E, iron, magnesium, zinc, folic acid, and caffeine of non-HUA.
Table 4 -
Characteristics of the dietary intakes (adjusted by energy, per 1000 kcal).
| Dietary intakes |
Non-hyperuricemia |
Hyperuricemia |
P value |
| Energy (kcal) |
2137.22 ± 1006.70 |
2060.17 ± 991.10 |
<.001 |
| Carbohydrate (g) |
122.27 ± 27.95 |
118.57 ± 30.02 |
<.001 |
| Protein (g) |
39.30 ± 13.47 |
39.85 ± 14.39 |
.223 |
| Alcohol (g) |
3.53 ± 9.20 |
5.36 ± 12.33 |
<.001 |
| Fiber (g) |
8.57 ± 4.74 |
8.01 ± 4.66 |
<.001 |
| Cholesterol (g) |
142.59 ± 102.49 |
149.31 ± 113.74 |
<.001 |
| Total fat (g) |
38.00 ± 10.13 |
37.87 ± 10.94 |
.491 |
| Saturated fat (g) |
12.19 ± 4.41 |
11.94 ± 4.59 |
.002 |
| MUFAs (g) |
13.32 ± 4.37 |
13.29 ± 4.54 |
.734 |
| PUFAs (g) |
8.98 ± 3.90 |
9.12 ± 4.18 |
.284 |
| Niacin (mg) |
12.38 ± 6.39 |
12.65 ± 5.74 |
.018 |
| Vitamin A (mcg RAE) |
305.91 ± 334.42 |
303.02 ± 444.04 |
<.001 |
| Vitamin B1 (mg) |
0.78 ± 0.35 |
0.75 ± 0.33 |
<.001 |
| Vitamin B2 (mg) |
1.00 ± 0.56 |
0.96 ± 0.48 |
<.001 |
| Vitamin B6 (mg) |
1.04 ± 0.85 |
1.02 ± 0.66 |
.348 |
| Vitamin B12 (mcg) |
2.34 ± 2.72 |
2.39 ± 3.56 |
.273 |
| Vitamin C (mg) |
42.12 ± 48.94 |
40.77 ± 52.04 |
<.001 |
| Vitamin D (mcg) |
2.24 ± 2.77 |
2.29 ± 3.35 |
<.001 |
| Vitamin E (mg) |
4.23 ± 2.85 |
4.10 ± 2.66 |
.013 |
| Iron (mg) |
7.03 ± 3.49 |
6.81 ± 3.32 |
<.001 |
| Magnesium (mg) |
148.77 ± 60.71 |
144.91 ± 57.44 |
<.001 |
| Zinc (mg) |
5.28 ± 2.49 |
5.25 ± 3.57 |
<.001 |
| Selenium (mg) |
55.41 ± 22.86 |
56.62 ± 24.88 |
.082 |
| Folic acid (mcg) |
194.42 ± 107.40 |
185.59 ± 106.09 |
<.001 |
| Beta carotene (mg) |
1209.58 ± 2581.40 |
1240.41 ± 2725.09 |
.516 |
| Caffeine (mg) |
75.36 ± 137.17 |
77.85 ± 118.99 |
.008 |
4. Discwussion
We conducted cross-sectional analyses using NHANES data to explore the correlation between DII and HUA. Our finding showed that a higher DII score, indicative of a pro-inflammatory diet, was linked to a higher risk of HUA following adjustment for confounding factors. Compared with the lowest tertile of DII group, the highest tertile of DII group was associated with 20% increased risk of HUA in the whole participants, and 29% in males. For females, there was a significant correlation of BMI with DII score for risk of HUA (BMI < 30 vs BMI ≥ 30, P for interaction = .0134).
The correlations between dietary inflammation and HUA have been explored previously. In a recent study among a Chinese population, the DII scores had a highly positive association with the level of serum uric acid irrespective of gender.[22] However, another interesting finding of a Korean study is that participants with a higher DII score have a higher risk of HUA in females only.[29] Nevertheless, our study revealed this correlation was similar for both male and nonobese female subjects.
On the 1 hand, we propose that gap in dietary habits of different populations may contribute to this result. Korean traditional food is rich in fermented foods and seafood.[30] The traditional Chinese diet contains a vast amount of cereals and vegetables and a small number of meat.[31] The Western diet features in high fat consumption.[32] In western countries, processed meats and red meats have already become the main component of the meat pattern.[33] In China, the total meat intake largely is fresh pork.[34]
Pro-inflammatory dietary mainly contains red and processed meats, fried foods, high-sugar foods, and refined grains.[35] While anti-inflammatory dietary contains more soy products, whole grains, nuts, vegetables, and fruits. Individuals with a more pro-inflammatory diet are more prone to HUA. This may be because a pro-inflammatory diet has higher purine content.[31] The purine degradation leads to the formation of uric acid.[36] And the enzyme xanthine oxidoreductase facilitates this degradation.[37] Uric acid is excreted by the kidney.
On the other hand, previous studies have demonstrated estrogenic compounds could increase renal uric acid excretion.[38] Levels of serum uric acid in females varies with the course of the menstrual cycle and higher levels of endogenous estradiol could result in suppression of uric acid.[39] In the case of obesity, estrogen levels in the circulation increase by several pathways.[40] Fat cells produce estrogen and aromatase activity increases estrogen levels as well.[41] In addition, estrogen contributes to alleviating inflammation.[25] Thus, for obesity alone, estrogen exerts their biological effects on uric acid far beyond dietary inflammation itself.
The higher the DII score, the more pro-inflammatory the diet. And inflammation has a close relationship with HUA as well as. Inflammation may elevate serum uric acid through multiple potential mechanisms, and oxidative stress is thought to have a key influence in the inflammatory response.[42] The inflammatory response is a pathological characteristic of HUA.[43] Uric acid acts as a danger signal and triggers inflammatory reactions.[44] HUA has also been found to elicit an inflammatory process in kidneys.[45] A recent study suggested anti-inflammatory diets represented by the Mediterranean diet[21,46] enhance the plasma antioxidant potency[21] and reduce xanthine oxidase activity,[21] in turn, decreasing uric acid production. A recent study showed that parsley and celery have the potency to reduce inflammatory effects, increase antioxidant activities and improved renal dysfunction in hyperuricemic mice.[47] However, further studies are still needed to explore the underlying mechanisms.
Yet, there were certain limitations in the current study. First, our study cannot draw a conclusion about causality for a retrospective, cross-sectional design. Secondly, serum uric acid was measured based on a single blood sample and dietary data are subject to recall bias, which may affect the accuracy of the results. Thirdly, the subjects with the risk of HUA had the potential to adjust their dietary habits. We adjusted for relevant variables wherever possible in our study, but serum uric acid is affected by many factors, such as daily water intake and metabolic factors.
5. Conclusions
Our study suggests that a higher DII score was linked to the higher risks of HUA in the United States adult population. Large-scale, prospective studies are needed to confirm our findings further.
Author contributions
Conceptualization: Lijuan Wang.
Data curation: Xiaofan Hong.
Funding acquisition: Kun Bao.
Methodology: Huoliang Liu, Daixin Zhao.
Resources: Yi Wang, Ping Li, Xiaoyan Huang.
Supervision: Kun Bao, Daixin Zhao.
Validation: Lijuan Wang, Xiaofan Hong.
Visualization: Huoliang Liu, Dan Wang.
Writing – original draft: Lijuan Wang, Huoliang Liu, Dan Wang.
Writing – review & editing: Kun Bao, Daixin Zhao.
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