Association between Nutrient Patterns and Hyperuricemia: Mediation Analysis Involving Obesity Indicators for NHANES study

doi:10.21203/rs.3.rs-1835179/v1

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Association between Nutrient Patterns and Hyperuricemia: Mediation Analysis Involving Obesity Indicators for NHANES study

https://doi.org/10.21203/rs.3.rs-1835179/v1

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Background: Diet had long been hypothesized to play an important role in hyperuricemia and weight gain was the stronger factor associated with the rise in serum urate. We aim to clarify the mediating role of obesity in the relationship between diet and hyperuricemia and further determine whether the weight-loss diet is an effective way to prevent hyperuricemia.

Methods: This cross-sectional study analyzed representative samples of the United States (n=20081; NHANES 2007-2016) adults. Nutrient patterns were derived with two methods: principal component analysis (PCA) and reduced rank regression (RRR) with obesity. Logistic regression and multivariable linear regression were applied to analyze the association between nutrient patterns in obesity and hyperuricemia. Mediation analyses were used to determine if four obesity indicators, including body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI) and lipid accumulation product Index (LAP), mediated nutrient patterns and hyperuricemia relationship.

Results: PCA revealed three nutrient patterns (including “low energy diet”, “lower vitamin A, C, K pattern” and “vitamin B group”) and only vitamin B group had a total effect on hyperuricemia. RRR resulted in one main nutrient pattern associated with obesity, which is characterized by high fat and low vitamin levels and was significantly associated with hyperuricemia. Mediation analysis showed that obesity mostly or even completely mediated the relationship between nutrient patterns and hyperuricemia, especially traditional obesity indicators play a key intermediary effect. Proportion of indirect effect for BMI and WC were as high as 53.34 and 59.69, respectively.

Conclusions: Our findings suggest that the direct effect of diet on hyperuricemia is weak, and obesity plays a critical mediated role in the pathway between diet and hyperuricemia, which confirms that weight-loss diet may be useful in preventing hyperuricemia.

obesity

hyperuricemia

mediation analysis

principal component analysis

reduced rank regression

Latest Global Burden of Disease (GBD) showed that gout, the most common cause of inflammatory arthritis, is affecting 41 million people worldwide [1]. Hyperuricemia, as the early stage and major etiologic factor of gout, needs to be given more attention. It is caused by the elevation of plasma uric acid concentration in the human body and is defined as blood uric acid levels higher than 7.0 mg/dL (416μmol/L) in men and 6.0 mg/dL (360μmol/L) in women under normal dietary condition [2, 3]. Besides, it is also the potential risk factor for cardiovascular disease, type 2 diabetes, chronic kidney disease and mortality [4]. Recently, the prevalence of hyperuricemia has increased markedly worldwide but the management remains suboptimal [5].

As an important factor of many chronic diseases, diet is also hypothesized to be a contributing factor in hyperuricemia, and an increase in dietary purines leads to increased urate production [6]. According to the update on gout management, dietary modifications may be useful adjuncts to urate-lowering therapy [7]. So there has been much interest in the potential effects of dietary approaches in hyperuricemia management and a large amount of literature focused on evaluating the association between diet and hyperuricemia. For example, red meat, seafood, sugar-sweetened beverages, alcohol, and animal protein have been identified to be associated with a greater risk of hyperuricemia [8]. Many famous dietary patterns such as Med Diet Score [9], approaches to Stop Hypertension (DASH) Diet [10] and plant-based diets [11] have also been studied in relation to hyperuricemia.

The relationship between obesity and urate has been extensively studied. For example, a longitudinal study among 2,611 young adults reported that baseline BMI was positively related to a 10-year change in serum uric acid (UA) [12]. Bidirectional Mendelian randomization analyses showed that BMI was causally associated with elevated serum UA but not vice versa [13]. A randomized controlled trial has found that bariatric surgery was associated with a significant urate reduction when compared with the traditional therapy [14]. Another controlled intervention trial evaluated that bariatric surgery reduced the incidence of gout compared with the traditional medical care in obese subjects [15].

The above relationships among diet, obesity and urate stimulate us to clarify the role of obesity in the relationship between diet and hyperuricemia. Further, we are also interested in whether a weight-loss diet has a preventive effect on hyperuricemia. In addition to body mass index(BMI) and waist circumference (WC), the other two new indicators of obesity were also low-cost and were often used to reflect obesity from different angles, including visceral adiposity index (VAI) [16] and lipid accumulation product Index (LAP) [17].

On the other hand, compared with a single dietary factor, dietary patterns have been widely used in nutritional research because they can reflect the overall dietary characteristics of individuals. Further, in an international research context, nutrients are worldwide and functionally not interchangeable, nutrient patterns may be more interpretable and much easier to translate into public health recommendations across populations [18], whereas dietary patterns may be affected by social, cultural and geographical scenarios [19]. Various approaches to dietary patterns were discussed in a review and each method has a unique feature and serve a distinct purpose [20]. In addition to investigator-driven methods such as Med Diet Score and Stop Hypertension (DASH) Diet, principal component analysis (PCA) and reduced rank regression (RRR) were also often used, where RRR is a hybrid method which combine priori professional knowledge on health outcomes and the relevant relational structure of nutrients and was often used to complement data-driven methods [20].

Therefore, to further explore the relationship among nutrient patterns, obesity and hyperuricemia, the current study first identifies the nutrient patterns based on two methods: principal component analysis and reduced rank regression with obesity. Furthermore, we aimed to examine the possible mediating role of multiple obesity indicators in the link between nutrient patterns and hyperuricemia by conducting mediation analyses.

Study Populations

The National Health and Nutrition Survey (NHANES) is an ongoing continuous survey conducted by the Centers for Disease Control and Prevention (NCHS) to describe the health and nutritional status of the United States population [21]. Data is collected by using a complex, stratified, multistage probability cluster sampling design and each survey cycle covers demographic data, body measurements, laboratory tests, and diet information [22]. The details of the programs, collection procedures and data files are publicly available at http://www.cdc.gov/nchs/nhanes.html. Participants of NHANES provided written informed consent, and the study protocol was approved by the Research Ethics Review Board of the National Center for Health Statistics and the US Army Research Institute of Environmental Medicine Human Use Review Committee [23].

For this study, a total of 22712 participants with reliable dietary data for NHANES from 2007 to 2016 aged 20 years or older constituted the initial sample. After excluded pregnant women, individuals with missing uric acid, BMI, WC and VAI information and those with extreme energy intake. Finally, 20081subjects (9537 men and 10544 women) were included in our final analyses (see Figure 1).

Dietary information

The data of dietary intakes were collected via two 24 h dietary recall interviews, the first dietary recall was collected with face-to-face inquiry, and the second dietary survey was conducted by the telephone 3 to 10 days after the initial recall interview [22]. Food energy and nutrients of each food were calculated using USDA's Food and Nutrient Database for Dietary Studies [24]. We calculated the average intake of all nutrients from the two 24 h recalls. For simplicity, we didn't take into account the specific saturated, monounsaturated and polyunsaturated fatty acids because we have considered the sum of them. Finally, we considered 41 major nutrients.

Assessment of mediators

Anthropometric and biochemical data were measured by NHANES researchers. Waist circumference (WC) was measured at the iliac crest by a tape measure to the nearest millimeter [22]. To assess the height and weight, participants wore the underwear, disposable paper robes and foam slippers [25]. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. A blood specimen was drawn from all study participant’s antecubital vein by a trained phlebotomist [25]. Laboratory testing details for Hemoglobin A1c (HbA1c), direct HDL-cholesterol, and fasting triglycerides are provided in the NHANES Laboratory/Medical Technician Procedures Manual [22].

Serum uric acid measurement and hyperuricemia

Uric acid concentration was detected on Beckman Synchron LX20 (Beckman Coulter, Inc., Brea, CA) using a colorimetric method [21]. Hyperuricemia was defined as uric acid ≥ 420 mmol/L in males and ≥360 mmol/L in females[28] or those taking uric acid-lowering drugs .

Confounders

Based on the associations with nutrient patterns, hyperuricemia and obesity measures, the following factors were considered as confounders: age (20-39, 40-59, >59years), gender (male, female), race (Mexican American, non-Hispanic white, non-Hispanic black, others), income status based on poverty index (0-1.3, 1.3-3.5, >3.5) [29], smoking status (smoking at least 100 cigarettes in life or not), drinking status (had at least 12 alcohol drinks/ year or not), vigorous physical activity (yes or no), creatinine level and energy intake, the history of diseases (including diabetes, hypertension, cardiovascular diseases, cancer, liver disease and dyslipidemia). All of them were obtained via standardized questionnaires or instrumental measurement. Hypertension was defined as the mean systolic blood pressure (SBP) ≥140 mm Hg, or the mean diastolic blood pressure (DBP) ≥90 mm Hg, or self-report hypertension diagnosis [30]. Cardiovascular diseases were defined as a positive answer to the question “Have you ever been told you had congestive heart failure/coronary heart disease/angina/heart attack/stroke?”[31]. Dyslipidemia was defined as taking lipid-lowering medications or a low-density lipoprotein cholesterol level of ≥140 mg/dL, a high-density lipoprotein cholesterol level of <40 mg/dL, or a triglyceride level of ≥150 mg/dL[32].

Statistical analysis

We considered masked variance and used the weighting methodology in all analyses[33]. Survey package of R (version 4.0) was used to account for the complex sampling design[34]. General characteristics of participants were summarized and compared according to hyperuricemia status. All continuous variables were described as mean with standard deviation and the categorical variables were expressed as frequencies and percentages. Student’s t-test (normally distributed data) or non-parametric test (non-normally distributed data) was applied for continuous variables and chi-squared tests were used for categorical variables.

Nutrient patterns were derived from 41 nutrients based on two main approaches: principal component analysis (PCA) and reduced rank regression (RRR) with obesity indicators as the response variable. PCA is a data-driven analysis and the number of factors was decided based on eigenvalues, scree test, and factor interpretability [35]. Nutrients with factor loadings ≥|0.2| were considered being major contributors to the corresponding pattern and were retained. Orthogonal varimax rotation was applied to increase interpretability between the patterns. RRR was the second statistical approach used to derive nutrient patterns. For this method, patterns are identified based on a set of pre-defined response variables [36]. 4 obesity indicators after log transformation were used as response variables and we retained main nutrient patterns in which coefficients of nutrients are below or above |0.15|. The number of nutrient patterns was determined by the number of response variables. Each participant can get a factor score for each pattern, which indicated the degree of adherence to the specific pattern. As simple linear dose-response relationships are unlikely to be found in nutritional epidemiology, we classify participants based on the quartile of the factor scores[37].

Both crude and adjusted weighted logistic regression models were used to investigate the association between the scores for each nutrient pattern derived by PCA and RRR with hyperuricemia: model 1 unadjusted; model 2 adjusted for age, gender, race; model 3 additionally adjusted for smoking, drinking, vigorous physical activity, pox ratio, creatinine level, energy intake, the history of diabetes, hypertension, cardiovascular diseases, cancer, liver disease and dyslipidemia. In addition, we used multivariable-adjusted models to identify nutrient patterns associated with obesity indicators, and the lowest quartile was used as the reference group, trend tests were also conducted. Finally, mediation analysis suggested by VanderWeele [38] was to examine the potential mediating role of four obesity indicators on the relationship between nutrient patterns and hyperuricemia. Odds ratio (OR) and 95% CI for direct effect and indirect effect were calculated using bootstrap method. Proportion of mediation was calculated using. where was OR for the direct effect, was OR for the indirect effect.

Sensitivity analyses were applied as the same steps above for mediation analysis in a new population that further excluded individuals who were taking uric acid-lowering drugs based on the above study populations and used continuous urate level as the outcome. All statistical analyses were conducted with SAS 9.4 and R 3.6.3. All tests were two-sided, and P <0.05 was considered statistically significant.

General characteristics of study participants

Baseline characteristics of participants according to hyperuricemia status are shown in Table 1. 18.38% of 20081 study participants had hyperuricemia. In general, participants with hyperuricemia were more likely to be older, male, Non-Hispanic black, had higher levels of obesity (BMI, WC, VAI and LAP), creatinine and lower levels of physical activity, as compared with those without hyperuricemia. In addition, a higher proportion of those classified as hyperuricemia smoked more, and a higher proportion of them suffered from other diseases.

Table 1. Baseline characteristics of participants according to Hyperuricemia status

Characteristics	All participants (n=20081)		Hyperuricemia				P value
	All participants (n=20081)		Yes (N = 3691)		No (N=16390)
	Mean, n	SD, %	Mean, n	SD, %	Mean, n	SD, %
Age group, n (%)							<0.001
20~39	6396	31.8	889	24.1	5507	33.6
40~59	6885	34.3	1099	29.8	5786	35.3
60~	6800	33.9	1703	46.1	5097	31.1
Gender, n (%)							<0.001
Male	9537	47.5	1965	53.2	7572	46.2
Female	10544	52.5	1726	46.8	8818	53.8
Race, n(%)							<0.001
Hispanic	5186	25.8	681	18.5	4505	27.5
Non-Hispanic white	3993	19.9	906	24.5	3087	18.8
Non-Hispanic black	8940	44.5	1762	47.7	7178	43.8
others	1962	9.8	342	9.3	1620	9.9
Poverty index, n(%)							0.001
<1.3	6336	31.6	1179	31.9	5157	31.5
1.3~3.5	7504	37.4	1454	39.4	6050	36.9
>=3.5	6241	31.1	1058	28.7	5183	31.6
Continued table 1. Baseline characteristics of participants according to Hyperuricemia status
Characteristics	All participants (n=20081)		Hyperuricemia				P value
	All participants (n=20081)		Yes (N = 3691)		No (N=16390)
	Mean, n	SD, %	Mean, n	SD, %	Mean, n	SD, %
Vigorous recreational activities, n(%)							<0.001
Yes	4435	22.1	610	16.5	3825	23.3
No	15646	77.9	3081	83.5	12565	76.7
Smoking at least 100 cigarettes in life, n(%)							<0.001
Yes	8840	44.0	1774	48.1	7066	43.1
No	11241	56.0	1917	51.9	9324	56.9
Hypertension, n(%)							<0.001
Yes	7491	37.3	1586	43.0	5386	32.9
No	12590	63.7	2105	57.0	11004	67.1
Diabetes, n (%)							<0.001
Yes	2531	12.6	678	18.4	1853	11.3
No	17550	87.4	3013	81.6	14537	88.7
Drinking, n(%)							0.932
Yes	5637	28.1	1034	28	4603	28.1
No	14444	71.9	2657	72	11787	71.9

Continue table 1. Baseline characteristics of participants according to Hyperuricemia status
Characteristics	All participants (n=20081)		Hyperuricemia				P value
	All participants (n=20081)		Yes (N = 3691)		No (N=16390)
	Mean, n	SD, %	Mean, n	SD, %	Mean, n	SD, %
CVD, n(%)							<0.001
Yes	2098	10.4	661	17.9	1437	8.8
No	17983	89.6	3030	82.1	14953	91.2
Liver disease, n (%)							<0.001
Yes	768	3.8	186	5.0	582	3.6
No	19313	96.2	3505	95.0	15808	96.4
Dyslipidemia, n(%)							<0.001
Yes	7595	37.8	1848	50.1	5747	35.1
No	12486	62.2	1843	49.9	10643	64.9
WC, mean (SD)	99.64	16.25	108.42	16.37	97.66	15.56	<0.001
BMI, mean (SD)	29.20	6.75	32.46	7.41	28.47	6.37	<0.001
VAI, mean (SD)	2.61	3.45	3.23	3.39	2.47	3.45	<0.001
LAP, mean (SD)	71.51	78.30	97.37	78.31	65.69	77.11	<0.001
Energery, mean (SD)	1971.06	695.81	1924.74	703.04	1981.49	693.76	<0.001
creatinine，mean (SD)	0.89	0.42	1.05	0.46	0.86	0.41	<0.001

Continuous variables are expressed as mean and standard deviation (SD), categorical variables presented are as counts and percentage.

^a WC means waist circumference.

^b BMI means body mass index.

^b VAI means Visceral adiposity index.

^d LAP means Lipid accumulation product Index.

In addition, in terms of demographics and health-related factors, there were no significant differences between the analytical sample and full NHANES (2007-2016) sample (Supplemental Table S1).

Nutrient patterns

Principal component analysis

We derived 3 independent nutrient patterns based on principal component analysis of complex survey, which explained 71.6% of the total variance.

The first pattern was negatively correlated with protein, fat, carbohydrate, cholesterol, choline, sodium and selenium, termed as “Low energy diet”; Pattern 2 was negatively correlated with vitamin A, vitamin C, vitamin K, carotene, lutein, termed as “Lower vitamin A, C, K pattern”; Pattern 3 was positively correlated with vitamin B6, B12, folate, and termed as “Vitamin B group”. The factor loadings for each nutrient pattern were shown in Supplemental Table S2.

Reduced rank regression

Only the first nutrient pattern was kept for further analyses based on RRR, since it explained the biggest variance (20.01%) of the response variables. It was positively correlated with fat, cholesterol and negatively associated with Vitamin A, C, D, K, fiber, folate, and termed as “High fat and low vitamin diet”. The factor loadings of the pattern and the correlation coefficients with the response variables were shown in Supplemental Table S3.

Nutrient patterns and the risk of hyperuricemia and obesity

Multivariate logistic regression analyses in the associations between 4 nutrient patterns with hyperuricemia are shown in Table 2. After adjusting for all confounders (Model 3), there are two patterns that are significantly related to hyperuricemia. Among them, the “Vitamin B group” was based on principal component analysis, compared to the first quartile as reference, the higher ORs were 0.81(0.67,0.99), 0.75(0.63,0.89) and 0.63(0.51,0.77) respectively. In addition, “High fat and low vitamin diet” based on RRR was significantly related to hyperuricemia, compared with the lowest quartile, the adjusted OR indicated a dose-dependent relation with each quartile increment (P for trend <0.001). The OR in the highest quartile was 1.23 (1.06, 1.41).

Table 2. Odds ratios and 95% confidence intervals for the association between nutrient patterns and hyperuricemia

Nutrient patterns	Quartiles of nutrient pattern scores				P-trend
	Q1	Q2	Q3	Q4
	Q1	OR (95% CI)	OR (95% CI)	OR (95% CI)
PCA^a
Lower energy diet
Model 1^c	Ref.	0.79 (0.67, 0.94)	0.88 (0.76, 1.02)	0.88 (0.74, 1.05)	0.296
Model 2^d	Ref.	0.85 (0.70, 1.02)	1.01 (0.85, 1.20)	1.04 (0.88 ,1.24)	0.267
Model 3^e	Ref.	0.84 (0.68, 1.03)	1.01 (0.78, 1.29)	1.00 (0.73, 1.37)	0.459
Low vitamin A, C, K pattern
Model 1^c	Ref.	1.07 (0.89, 1.29)	1.20 (1.01, 1.42)	1.25 (1.03, 1.52)	0.009
Model 2^d	Ref.	1.04 (0.87, 1.25)	1.16 (0.97, 1.39)	1.22 (0.98, 1.52)	0.036
Model 3^e	Ref.	1.01 (0.84, 1.22)	1.12 (0.93, 1.34)	1.17 (0.93, 1.46)	0.104
Vitamin B group
Model 1^c	Ref.	0.84 (0.70, 1.02)	0.74 (0.62, 0.89)	0.63 (0.51, 0.77)	<0.001
Model 2^d	Ref.	0.82 (0.67, 0.99)	0.74 (0.62, 0.88)	0.63 (0.51, 0.77)	<0.001
Model 3^e	Ref.	0.81 (0.67, 0.99)	0.75 (0.63, 0.89)	0.63 (0.51, 0.77)	<0.001
RRR^b
High fat and low vitamin diet
Model 1^c	Ref.	1.09 (0.92, 1.29)	1.27 (1.10, 1.46)	1.37 (1.18, 1.58)	<0.001
Model 2^d	Ref.	1.11 (0.93, 1.32)	1.27 (1.09, 1.48)	1.32 (1.14, 1.52)	<0.001
Model 3^e	Ref.	1.00 (0.84, 1.19)	1.14 (0.97, 1.32)	1.23 (1.06, 1.41)	<0.001

^a PCA is the method of principal component analysis and including “Lower energy diet”, “Low vitamin A, C, K pattern” and “Vitamin B group” 3 nutrient patterns.

^b RRR stands for Reduced Rank Regression and including “High fat and low vitamin diet” pattern which is related to obesity.

^c Model 1 was a crude model;

^d Model 2 was adjusted for age, race, gender;

^e Model 3 was further adjusted for adjusted for smoking, drinking, vigorous physical activity, pox ratio, creatinine level, energy intake, the history of diabetes, hypertension, cardiovascular diseases, cancer, liver disease and dyslipidemia.

The results of multivariable linear regression analysis were shown inTable 3. All four nutrient patterns were correlated with BMI, WC and LAP (P for trend <0.05). Further, VAI was also significantly correlated with “High fat and low vitamin diet”.

Table3. The association between nutrient patterns and obesity indicators

Nutrient patterns	Quartiles of nutrient pattern scores				P-trend
Nutrient patterns	Q1	Q2 (95% CI)	Q3 (95% CI)	Q4 (95% CI)	P-trend
PCA^a
Lower energy diet
BMI	0	-1.37 (-1.89,-0.84)	-1.93 (-2.59,-1.26)	-2.67 (-3.49,-1.85)	<0.001
WC	0	-2.87 (-4.12,-1.63)	-4.51 (-5.99,-3.02)	-6.06 (-7.90,-4.23)	<0.001
VAI	0	-0.22 (-0.41,-0.02)	-0.19 (-0.48,0.09)	-0.28 (-0.59,0.04)	0.211
LAP	0	-8.89 (-14.15,-3.63)	-11.02 (-18.38,-3.65)	-15.32 (-24.25,-6.38)	0.003
Low vitamin A, C, K pattern
BMI	0	0.29 (-0.08,0.66)	0.92 (0.55,1.28)	1.02 (0.61,1.42)	<0.001
WC	0	0.95 (0.10,1.80)	2.23 (1.38,3.07)	2.77 (1.85,3.70)	<0.001
VAI	0	-0.11 (-0.27,0.05)	-0.03 (-0.19,0.13)	0.11 (-0.10,0.31)	0.263
LAP	0	-0.74 (-4.35,2.87)	2.42 (-1.13,5.97)	3.70 (-0.48,7.88)	0.037
Vitamin B group
BMI	0	-0.39 (-0.86,0.07)	-0.95 (-1.4,-0.51)	-1.48 (-1.92,-1.05)	<0.001
WC	0	-1.22 (-2.31,-0.14)	-2.35 (-3.39,-1.31)	-3.61 (-4.59,-2.63)	<0.001
VAI	0	0.04 (-0.12,0.19)	-0.07 (-0.21,0.08)	-0.10 (-0.25,0.05)	0.075
LAP	0	-0.89 (-4.36,2.58)	-4.05 (-7.9,-0.19)	-6.47 (-9.9,-3.04)	<0.001
RRR^b
High fat and low vitamin diet
BMI	0	0.91 (0.59, 1.24)	1.78 (1.41, 2.15)	2.73 (2.35, 3.11)	<0.001
WC	0	2.44 (1.66, 3.23)	4.57 (3.68, 5.46)	6.89 (6.02, 7.77)	<0.001
VAI	0	0.24 (0.07, 0.41)	0.28 (0.09, 0.47)	0.24 (0.07, 0.40)	0.005
LAP	0	7.87 (3.92, 11.83)	9.94 (5.51, 14.37)	13.61 (9.54, 17.68)	<0.001

Survey linear regression models were used to estimate β and 95% CIs and adjusted for smoking, drinking, vigorous physical activity, pox ratio, creatinine level, energy intake, the history of diabetes, hypertension, cardiovascular diseases, cancer, liver disease and dyslipidemia.

^a PCA is the method of principal component analysis and including “Lower energy diet”, “Low vitamin A, C, K pattern” and “Vitamin B group” 3 nutrient patterns.

^b RRR stands for Reduced Rank Regression and including “High fat and low vitamin diet” pattern which is related to obesity.

Mediating role of obesity indicators in the association between nutrient patterns and hyperuricemia

Table 4 presents the direct and indirect effect of nutrient patterns on hyperuricemia with obesity measures as mediators. Overall, all four obesity indicators mediated the relationship between each nutrient pattern and hyperuricemia more or less. Further, the direct effects of the other three nutrient patterns in relation to hyperuricemia were almost insignificant except for the Vitamin B group. Conclusions suggest that the association of each nutrient pattern with hyperuricemia was mediated by obesity. Although the indirect and direct effects were in opposite directions for two nutrient patterns and the proportion of indirect effects in this case could not be explained, we found obesity measures (BMI, WC, LAP) fully mediated the relationship between “High fat and low vitamin pattern” based on RRR and hyperuricemia. In particular, two common obesity measures (BMI, WC) had significant mediating effects on the relationships between all four nutrient patterns and hyperuricemia and the mediating proportion were as high as 53.34 and 59.69, respectively. In addition, LAP also mediated the relationship between the three nutrient patterns and hyperuricemia, although the indirect effect was not as large as that of BMI and WC.

Table4. Mediating effects of obesity on the association between nutrient patterns and odds ratios of hyperuricemia

Nutrient patterns	Direct effects	Indirect effects	Proportion of indirect effect
Nutrient patterns	OR (95% CI)	OR (95% CI)	Proportion of indirect effect
PCA^a
Lower energy diet
BMI	1.14 (1.00, 1.28)	0.94 (0.92, 0.95)	NA^c
WC	1.13 (0.99, 1.28)	0.94 (0.92, 0.95)	NA^c
LAP	1.06 (0.94, 1.21)	0.98 (0.97, 1.00)	NA^c
VAI	1.05 (0.93, 1.08)	1.00 (0.99, 1.01)	0.12%
Low vitamin A, C, K pattern
BMI	1.03 (0.96, 1.11)	1.03 (1.02, 1.04)	53.34%
WC	1.03 (0.96, 1.10)	1.04 (1.02, 1.05)	59.69%
LAP	1.05 (0.99, 1.12)	1.01 (1.00, 1.01)	18.54%
VAI	1.06 (0.99, 1.13)	1.00 (0.99, 1.01)	7.81%
Vitamin B group
BMI	0.90 (0.84, 0.96)	0.95 (0.94, 0.97)	28.31%
WC	0.90 (0.83, 0.96)	0.95 (0.94, 0.97)	28.95%
LAP	0.87 (0.81, 0.93)	0.98 (0.97, 0.99)	8.49%
VAI	0.86 (0.81, 0.92)	0.99 (0.99, 1.00)	2.48%
RRR^b
High fat and low vitamin diet
BMI	0.99 (0.94, 1.05)	1.08 (1.06, 1.09)	NA^c
WC	0.98 (0.93, 1.04)	1.09 (1.07, 1.10)	NA^c
LAP	1.07 (0.99, 1.11)	1.01 (1.01, 1.03)	27.77%
VAI	1.07 (1.01, 1.13)	1.01 (1.00, 1.01)	6.49%

Mediation analysis was adjusted for smoking, drinking, vigorous physical activity, pox ratio, creatinine level, energy intake, the history of diabetes, hypertension, cardiovascular diseases, cancer, liver disease and dyslipidemia.

^a PCA is the method of principal component analysis and including “Lower energy diet”, “Low vitamin A, C, K pattern” and “Vitamin B group” 3 nutrient patterns.

^b RRR stands for Reduced Rank Regression and including “High fat and low vitamin diet” pattern which is related to obesity.

^c NA means the proportion of indirect effects could not be explained because the direction of indirect and direct effect is opposite.

Sensitivity Analyses

Sensitivity analysis showed similar results that “Vitamin B group” and “High fat and low vitamin pattern” were significantly associated with urate level (Supplemental Table S4). In addition, all four nutrient patterns were correlated with BMI, WC and LAP (Supplemental Table S5), which were also shown to be important mediators in the nutrient patterns and uric acid pathways (Supplemental Table S6).

In this study, we used both PCA and RRR to derive the nutrient patterns and explored their relationship with hyperuricemia and obesity. We found Vitamin B group and nutrient pattern related to weight loss have a significant total effect on hyperuricemia. Further, the associations between all four nutrient patterns and hyperuricemia were mediated by obesity in a large proportion. The significant mediating effect of obesity combined with the significant total effect of hyperuricemia based on a reduced-rank regression suggest that weight-loss diet may be an effective way to prevent elevated uric acid.

With principal component analysis, we obtained three nutrient patterns, and only Vitamin B group had a significant total effect on hyperuricemia after adjusting for all covariates. The relationship between Vitamin B group and urate level is still controversial, but there are some literatures about individual vitamin B may could support our conclusions. For example, another NHANES study on individual B vitamins indicated the intakes of folate and vitamin B12 were inversely related to the risk of HU in males, and only folate was found in females [39]. An animal study investigated in vitro and in vivo showed that Aster glehni with vitamin B6 might be used functional foods in reducing serum uric acid level in gout [40]. Another randomized controlled trial showed that uric acid was significantly decreased after 4 and 8 week supplementation of vitamin B-12 and fish oil [41]. Further, according to a substudy of China Stroke Primary Prevention Trial, compared with enalapril alone, the combination of enalapril and folic acid could reduce the magnitude of the increase in UA concentrations in hypertensive adults, which implied that high folic acid intake may be an adjuvant nutritional recommendation for preventing and treating hyperuricemia [42]. However, a Norwegian randomized controlled study in patients with coronary artery disease did not find any significant effect of folic acid and vitamin B-12 treatment on the risk of hyperuricemia [43]. This discrepancy could be due to important differences in the population characteristics and treatment regimen between the 2 studies according to literature [44]. Our study found that the overall vitamin B group has a certain effect on hyperuricemia, however, more clinical trials are required for further verification and need to further clarify which B vitamins should be included and at what dose to maximize uric acid reduction.

Further, the indirect effect of obesity was significant in the relationship of all four nutrient patterns to hyperuricemia, while the direct effect was small to none. This finding is consistent with recent studies on the relationship of diet to hyperuricemia by using the method of population attributable fractions (PAF). In a Mendelian randomized study, the effects of four dietary patterns on hyperuricemia were fully mediated by BMI. In addition, the article also points out BMI produced

PAF for hyperuricemia of 59%-69%, while diet had a relatively minor role in lowering uric acid in the three non-gout cohorts [45]. Another study of the Third National Health and Nutrition Examination Survey reported the corresponding PAFs of hyperuricemia cases for overweight or obese and nonadherence to a DASH-style diet were 44% and 8% respectively[46]. All these findings supported our results about the direct effect of diet on hyperuricemia was smaller than the indirect effect through BMI [47]. It’s well known that diet plays a critical role in obesity and further had the subsequent risk of hyperuricemia, so the total effect of diet is greater than the PAF estimated for the diet (direct effect and independently of BMI), as shown in previous studies [46, 48]. In addition, only the direct of vitamin B group on hyperuricemia is significant, which implied other potential mechanisms existed besides obesity. Previous studies have shown several other potential mechanisms. According to reports, folic acid can effectively reduce total homocysteine, thereby reducing intracellulars-adenosyl-homocysteine, which may induce marked DNA damage and release purine nucleotides that result in the generation of UA [43].

To further illustrate that the relationship between diet and hyperuricemia is largely mediated by obesity, we performed reduced-rank regression and obtain one main pattern associated with obesity: “High fat and low vitamin” diet, which has a significant promoting effect on hyperuricemia, and the effect is entirely caused by weight gain. In fact, a variety of studies show that greater BMI is associated with increased risk of hyperuricemia and weight loss is an important measure for the prevention and management of hyperuricemia and gout [46, 49]. For example, a cohort study including 1189 patients observed over 6.45 years showed that obesity (BMI >27) was an independent risk factor for incident gout among women with and without hyperuricemia [50]. Further, Mendelian randomization studies have found obesity is causally associated with serum urate levels in the general population [51], weight loss through bariatric surgery or through lifestyle intervention leads to reductions in serum urate [52, 53]. In addition, A meta-analysis included 20 cohort studies assessed the relation of bariatric surgery to gout and serum urate, the result showed that uric acid decreased by an average of 0.73 mg/dL three months after bariatric surgery and 1.91 mg/dL three years after surgery [54]. The 2020 ACR gout treatment guideline also recommends people with hyperuricemia gout who are obese or overweight should use weight loss program [49]. Furthermore, our research has also found that VAI and LAP have certain direct and indirect effects, suggesting that metabolism-related obesity has an important effect on hyperuricemia and we can’t ignore them. In conclusion, the results of the reduced-rank regression further suggest that obesity is an important mediator in the pathway between dietary patterns and hyperuricemia. The significant total effect suggests that nutrient pattern for weight loss may be an effective way to prevent and treat hyperuricemia. This pattern is consistent with current recommendations (e.g., Mediterranean, Dietary Approaches to Stop Hypertension (DASH), Nordic, vegan, vegetarian) which are associated with weight loss [55] and have a lower correlation with obesity-related chronic diseases [56].

The present study has several strengths. Firstly, we identified nutrient patterns based on two methods: principal component analysis and reduced rank regression with obesity. The two methods complement each other, and both suggest that obesity largely mediates the relationship between diet and hyperuricemia. So we further obtained one nutrient pattern for weight loss had a significant effect on the prevention of hyperuricemia. Secondly, a variety of obesity indicators (BMI, WC, VAI and LAP) were used to examine the potential mediating role of obesity in the relationship between nutrient patterns and hyperuricemia. Different obesity indicators can reflect multiple perspectives on obesity. In particular, VAI and LAP incorporate indicators such as gender and metabolism, which makes our analysis more comprehensive. Thirdly, our analyses were based on a large sample population and used complex sampling design, so the results can be extrapolated to the general population. Fourth, the patterns were based on nutrients rather than food groups, which makes it easier for nutritionists to provide public nutrition recommendation. Finally, we adjusted for many key confounding variables.

However, our study has some limitations that need to be noted. Firstly, due to the observational design, the possible causal relationship cannot be established and needs to be conducted in future cohort studies. Secondly, we adjusted for many potential confounders, but we cannot completely rule out unmeasured confounders. Thirdly, more detailed analysis needs to be discussed further. For example, future studies will consider subdividing the population into two subgroups: those suffering from gout and those not suffering from gout, and then analyze them separately. In addition, each obesity index was used as the response variable to perform reduced rank regression to obtain a specific nutrient pattern and analyze its effect on hyperuricemia.

In summary, this study explored the relationship between diet, various obesity indicators and hyperuricemia based on two methods. Studies have shown that obesity largely mediated the relationship between nutrient patterns and the risk of hyperuricemia. Our findings suggest that the direct effect of diet on hyperuricemia is weak, and obesity plays a critical mediated role in the pathway between diet and hyperuricemia, which confirms the recent studies that dietary factors contribute far less to hyperuricemia than obesity because diet has little direct effect on uric acid. Further, the significant mediating effect of obesity combined with the significant total effect of hyperuricemia based on a reduced-rank regression suggest that weight-loss diet may be a useful adjunct to urate-lowering therapy.

BMI: Body mass index

VAI: Visceral adiposity index

WC: Waist circumference

LAP: Lipid accumulation product Index

NHANES: The National Health and Nutrition Examination Surveys

OR: Odds ratio

PCA: Principal component analysis

RRR: Reduced rank regression

Ethical Standards Disclosure

This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the institutional review board of the National Center for Health Statistics (NCHS). Written informed consent was obtained from all subjects/patients.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and analysed during the current study are publicly available from the National Center for Health Statistics: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.

Conflict of interest

The authors declare that they have no conflict of interest.

Funding

This work was supported by the funds of the National Natural Science Foundation of China (Grant numbers: 82073674); This work was also supported in part by the Shanxi Science and Technology Research of China (202103021223234) and the Innovation Foundation for Graduate Student of Shanxi Province (2020BY065).

Authors’ contributions

Tong Wang designed the study and Juping Wang wrote the main manuscript text. Data preprocessing and analysis were carried out by Junkang Zhao and Jie Liang; Shuting Chen and Simin He prepared all the tables; Qian Gao, Xue Gao reviewed and edited the manuscript; Supervision was carried out by Tong Wang. All authors have read and approved the final manuscript.

Acknowledgments

The data used in this study was from NHANES. We thank all the staffs and the participants of NHANES for their contribution.

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Association between Nutrient Patterns and Hyperuricemia: Mediation Analysis Involving Obesity Indicators for NHANES study

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Abstract

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Introduction

Methods

Study Populations

Dietary information

Assessment of mediators

Serum uric acid measurement and hyperuricemia

Confounders

Statistical analysis

Results

General characteristics of study participants

Nutrient patterns

Principal component analysis

Reduced rank regression

Nutrient patterns and the risk of hyperuricemia and obesity

Mediating role of obesity indicators in the association between nutrient patterns and hyperuricemia

Discussion

Conclusion

Abbreviations

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References

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