Elevated uric acid level and metabolic syndrome in Non-Hispanic Black American adults

Abstract

Objectives

To ascertain the direct and indirect link between elevated uric acid (eUA) and metabolic syndrome (MetSyn) in Non-Hispanic Black (NHB) American adults.

Design

Structural equation modeling (SEM) was used to disentangle the U.S. National Health and Nutritional Examination Survey (2015–2018 NHANES) dataset and investigate the connection between eUA and components of MetSyn as per the criteria from the National Cholesterol Education Program (NCEP) Adult Treatment Panel III. The association between eUA and MetSyn was determined using odds ratios from sex-specific multivariable logistic regression analysis. The analysis was adjusted for age, physical activity, alcohol use, and smoking. SEM coefficients were used to measure the strength of the link between eUA and MetSyn components.

Results

NHB American men with eUA had 1.41-fold greater odds of MetSyn, and NHB American women with eUA had 2.70-fold greater odds of MetSyn after adjusting for confounding factors. Elevated uric acid was more strongly and directly linked to abdominal obesity (β = 0.320, p < 0.01) in NHB American men, and with abdominal obesity (β = 0.423, p < 0.01), dyslipidemia (β = 0.151, p < 0.01) and hypertension (β = 0.121, p < 0.01) in NHB American women than between eUA and other components of MetSyn.

Conclusions

This study's finding linking eUA to MetSyn components in NHB American adults needs reaffirmation through a robust prospective study design. If validated, eUA could help predict and prevent MetSyn in NHB American adults.

Introduction

Elevated blood values of uric acid (eUA) and metabolic syndrome (MetSyn) are critical metabolic abnormalities with unclear relationships in Non-Hispanic Black (NHB) Americans. Uric acid(UA) is a product of purine metabolism implicated in declining insulin resistance [1]. The prevalence of eUA has increased worldwide in the past decade [2], but it remained stable in the U.S. between 2007 and 2016 [3]. A possible reason for the increasing worldwide trend in eUA is the increased prevalence of obesity and increased use of medications, including diuretics and low-dose aspirin [4]. Increased UA may also be due to the increased use of favipiravir [5]. Favipiravir is a purine nucleic acid analog and an antiviral agent [5]. The substantial increase in soft drinks that are high in sugar also coincides with the secular trend of eUA [6]. A study of racial differences in gout (a condition caused by high levels of UA) indicated that the incidence among Black men and women was higher than their White counterparts [7]. The study found that the incidence rate of gout was 12.0 and 15.5 per 10,000 person-years in Black men and women, respectively, compared to 9.4 and 5.0 per 10,000 person-years for white men and women, respectively [7].

MetSyn is an assemblage of cardiometabolic risk factors that increase insulin-resistant-associated morbidity and all-cause mortality [8–10]. The principal components of MetSyn are abdominal obesity (AO), elevated blood pressure (BP), dyslipidemia (elevated triglycerides and low levels of HDL-C), and hyperglycemia (HG) [8–10]. Although, in general, Blacks have a lower prevalence of MetSyn when compared to Whites, a previous study from the National Health and Nutrition Examination Survey (NHANES) from 1988 through 2012 showed that the prevalence of MetSyn increased from 1988 to 2012 for every sociodemographic group. The analysis indicated that, except for White men, the most significant increase in the prevalence of MetSyn was observed among NHB American men (55%) and NHB American women (41%) compared to other racial/ethnic groups [11]. Also, NHB Americans have a higher prevalence of insulin resistance, obesity, and elevated blood pressure compared to Whites [12]. In 2012, the prevalence of MetSyn was 35.5% among HNB Americans [12].

Despite racial/ethnic differences in the prevalence of insulin resistance and eUA, there is a paucity of data associating eUA with MetSyn and its components in Blacks. Many studies have been conducted mainly on individuals of European origin. Findings from these studies support a strong relationship between eUA and components of MetSyn [13, 14]. For example, prospective studies show that eUA is independently associated with developing type 2 diabetes [15]. Additionally, eUA is independently associated with long-term risk for hypertension (HTN) among adolescents [16]. Indeed, a study found that increased UA level was associated with Fasting blood glucose (FBG), BP, and lipid profiles in Chinese adults [17]. The association was stronger between UA and lipid profiles than between UA and other components of MetSyn [17] in Chinese adults. However, emerging data suggest that hyperuricemia poses a significant burden on various demographic groups, including black populations. To our knowledge, there are currently no epidemiological studies connecting eUA to MetSyn in black populations, such as NHB Americans. Non-Hispanic Black American adults are at a much greater risk of many components of MetSyn, including obesity, elevated BP, and elevated blood glucose, than non-Hispanic white Americans [18, 19]. Hence, it is unclear if hyperuricemia would function differently in Blacks than MetSyn in Whites. Understanding the connection between UA and MetSyn may aid in developing effective prevention programs to lower the risk of MetSyn and its related components in NHB American adults.

This research focuses on adults and aims to investigate the connection between eUA levels and MetSyn in NHB American adults. We employed a nationally representative dataset of NHB American adults to test this connection and utilized structural equation modeling (SEM). SEM is a statistical technique that enables the inclusion of variables that cannot be directly measured (latent variables) but are assumed to exist based on measurable data. Like multivariable regression models, SEM presents an effective means of testing theories incorporating latent variables. Furthermore, SEM facilitates modeling interactions between correlated variables and multiple independent latent variables [20]. Another advantage of SEM is its ability to account for measurement errors [20, 21].

Materials and methods

Sources of data and participants

This study used data (2015–16, 2017–18) from the U.S. National Health and Nutritional Examination Surveys that collect health-related information from non-institutionalized American residents using cross-sectional sampling designs [23]. NHANES participants were interviewed in their homes and received physical and laboratory examinations in mobile examination centers. The focus of this study is on NHB American adults who are between the ages of 18 and 80 years and are assayed for FBG,TRG, HDL-C, and UA. Participants for this study were further restricted to those who provided physical activity, alcohol use, and smoking information and those with complete data on sex, age, waist circumference (WC), height, weight, and BP.

The NHANES surveys utilized stratified, multistage probability sampling techniques that were conducted in four stages: (i) Primary sampling units (PSUs) that included counties, groups of tracts within counties, or combinations of adjacent counties; (ii) segments within PSUs, consisting of census blocks or combinations of blocks; (iii) dwelling units (households) within segments; and (iv) individuals within households. The PSUs were sampled from all U.S. counties [22].The protocol for the NHANES was approved by the institutional review board of NCHS. Subjects with missing demographic and MetSyn-defining variables were excluded from the analysis. There were no significant demographic differences between those with complete data eligible for this study and those excluded due to missing data or study variables.

Study variables

This analysis relied on participants’ self-reported age, sex, and race/ethnicity. The study participants' clinical and body measurements were taken by NHANES staff. The descriptions of variables and assays are published online. WC was measured with a nonelastic tape above the right iliac crest at the mid-axillary line. Height was measured with a stadiometer. Weight was measured with a Toledo digital weight scale (Seritex, Carlstadt, NJ, USA) at the end of a normal expiration and to the nearest 0.1 kg [22]. Three systolic (SBP) and diastolic (DBP) readings were taken using a standard protocol during a one-time examination visit. Each blood pressure reading was taken with a 30-s interval between measurements. For this investigation, the averages of the second and third SBP and DBP readings were used to represent the participants' SBP and DBP values. In NHANES, TRG, HDL-C, and FBG were measured after an overnight fast of at least eight hours. TRG and FBG values were determined enzymatically in serum by a series of chemical reactions after hydroxylation into glycerol. HDL-C measurements were attained using a direct immunoassay technique [22, 23]. FBG measurements were performed using the Roche/Hitachi 600 Analyzer (Roche Diagnostics, Indianapolis, IN, USA) by the Collaborative Studies Clinical Laboratory, University of Minnesota Medical Center, Fairview-University Medical Center, Minneapolis, Minnesota. FBG was measured according to specification based on a series of enzymatic reactions.

Elevated uric acid level

A comprehensive summary of the methods used to measure UA levels in NHANES can be found elsewhere [24, 25]. The University of Minnesota Advanced Research and Diagnostic Laboratory in NHANES measured the UA levels in the blood. Per clinical recommendations, this research considered eUA to be a UA level greater than 7 mg/dL for men and 6.0 mg/dL or more for women [26].

Metabolic syndrome

In this study, we have defined MetSyn using the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria [27]. According to these criteria, a person is considered to have MetSyn if they have three or more of the following conditions: (a) AO (W.C. ≥ 102 cm in men and ≥ 88 cm in women), (b) elevated TRG (≥ 150 mg/dL or on drug treatment for elevated TRG), (c) decreased HDL-C (< 40 mm/dL in men and < 50 mm/dL in women or on drug treatment to reduce HDL-C, (d) elevated BP (> 130 mmHg SBP or > 85 mmHg DBP or on antihypertensive drug treatment in a patient with a history of HTN, and (e) elevated FBG (> 100 mg/dL or on drug treatment for elevated blood glucose).

Behavioral variables

The NHANES study assessed physical activity levels by asking participants about their weekly moderate to vigorous physical activity. Moderate intensity and vigorous activity were defined as any activity that causes slight to mild and heavy increases in breathing or heart rate, such as brisk walking or carrying light loads for at least 10 min continuously. The study converted physical activity levels to metabolic equivalents (METs) and calculated the total METs by multiplying the weekly physical activity volume (duration frequency) by the corresponding MET value. Participants were then grouped into three MET levels based on their weekly total METs: none (< 0 MET minutes/week), insufficient to meet the guidelines (1–499 MET minutes/week), and sufficient to meet the guidelines (> 500 MET minutes/week). These categories are based on health outcomes and risks for cardiovascular disease or premature death. The American Heart Association recommends a weekly exercise of 500 MET minutes or more for cardiovascular fitness [28, 29].

In the NHANES study, participants were asked about their current cigarette smoking habits and were classified into three groups: every day, some days, and not at all. In this study, individuals were categorized as current cigarette smokers if they reported smoking every day or some days. Those who reported not smoking at all were classified as non-smokers. Participants who reported consuming at least 12 alcoholic beverages in the past year were considered alcohol users.

Statistical and analytical approach

Several statistical analyses were done using STATA (Version 13.1) and SPSS (Version 29.0.1.0). In NHANES, study participants were assigned survey weights that denoted how many US adults each represented. Participants who contributed lipid samples had additional weights, extrapolating their lipid results to the US adult population due to the unequal probability of selection, oversampling, and nonresponse in NHANES. We used appropriate sample weights, strata, and cluster variables. First, the normality of the distribution of study variables was checked by descriptive measures, including skewness and kurtosis, as well as mean and standard deviation. Second, sex differences for continuous and categorical variables were determined with independent t-tests and chi-squared tests, respectively. Third, we performed Pearson partial correlation analysis to determine the strength of the relationship between UA and manifest continuous variables (WC, SBP, DBP, TRG, HDL-C, FBG) based on unadjusted (Model I), adjusted for age (Model II), adjusted for BMI (Model III), and adjusted for age and BMI (Model IV). Fourth, sex-specific multivariable logistic regression analysis was used to determine the association between eUA (defined using values above the clinically acceptable range) and MetSyn (binary outcome). In the multivariable logistic regression analysis, we compared the odds ratio (OR) of MetSyn of participants with eUA to participants with normoglycemia and controlled for age, physical activity, alcohol use, and smoking.

Fifth, SEM was used to validate our proposed theoretical model developed based on our findings from the literature review of the possible relationship between the UA and components of MetSyn [1, 2, 12–15]. Our hypothesized model of the link between the UA and components of MetSyn is depicted in Fig. 2. The directions of our hypothesized link between eUA and components of MetSyn are illustrated in the figure with arrows. This model classified hyperuricemia, AO, HG, HTN, and dyslipidemia as latent variables since the underlying concept cannot directly measure them. Thus, this study assigns these latent variables to directly measured variables. Specifically, abdominal obesity was assigned to WC, eUA to UA level, HTN to DBP and SBP, FBG to HG, and dyslipidemia to TRG and HDL-C. Sex-specific SEM was used to examine the link between UA and components of MetSyn separately due to sex differences in MetSyn and UA [21, 29]. We fitted factors in our proposed model according to our hypothesis. Path coefficients indicate the degree of the link between factors. We used the goodness of fit (GoF) index, the Tucker-Lewis index (TLI), and the root mean square error of approximation index (RMSEA) to evaluate the model fit. GoF and TLI values greater than 0.95 and RMSEA values of less than 0.05 were considered as evidence of satisfactory model fit [21].

Fig. 2.

Model depicting the relationship between Hyperuricemia and components of Metabolic Syndrome (MetSyn) in non-Hispanic Black Adults. [Values are path coefficients ($\beta$) with *p < .05; **p < .01]; [GoF, goodness of fit index; TLI, Tucker-Lewis index; RMSEA, root mean square error of approximation Index; WC, waist circumference; AO, abdominal obesity; eUA, elevated uric acid; FBG, fasting plasma glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; TRG, triglycerides; HDL-C, high-density lipoprotein cholesterol; HG, hyperglycemia; HTN, hypertension; DYSLIPID, dyslipidemia]

Results

Essential characteristics of study participants

The studied population's basic anthropometric and clinical characteristics are shown in Table 1. The study analyzed 2608 NHB American adult participants, including 1232 men and 1376 women. The participants were mainly obese, with a body mass index (BMI) above 30, and 10.7% of them had MetSyn. Significant sex differences were observed among the variables studied, except for age and FBG. NHB American men had higher mean values of SBP, DBP, Triglycerides (TRG), and UA, along with higher prevalence rates of hyperuricemia, HG, and HTN, compared to NHB American women. Conversely, NHB American women had higher values of WC, BMI, HDL-C, and a higher prevalence of abdominal obesity (AO), abnormal HDL-C, and MetSyn, compared to NHB American men. The study also compared the mean values of the UA by components of MetSyn in American men and women. As shown in Fig. 1, a clear gradient of increasing UA with increasing numbers of MetSyn components in both men and women. The mean UA values in men were 5.4, 5.8, 6.0, 6.5, 6.2, and 6.7 mg/dl for 0, 1, 2, 3, 4, and 5 components of MetSyn, respectively. The corresponding values in women were 3.9, 4.2, 4.9, 5.4, 5.2, and 5.8 mg/dl, respectively.

Table 1.

Characteristics of study populations of Non-Hispanic Black adults

Variables	Overall	Men	Women	P-value
Age (years)	48.6 $\pm$ 17.9	49.1 $\pm$ 18.1	48.1 $\pm$ 17.1	.073
Waist circumference (cm)	101.1 $\pm$ 14.4	99.6 $\pm$ 17.9	102.4 $\pm$ 18.8	< .001
Body mass index (kg/m2)	30.8 $\pm$ 8.2	29.0 $\pm$ 7.0	32.3 $\pm$ .8.8	< .001
Diastolic BP (mmHg)	72.1 $\pm$ 14.1	73.2 $\pm$ 14.6	71.2 $\pm$ 13.5	< .001
Systolic BP (mmHg)	129.1 $\pm$ 20.0	130.7 $\pm$ 20.3	127.7 $\pm$ 21.1	< .001
Triglycerides (mg/dL)	84.3 $\pm$ 47.05	92.8 $\pm$ 47.0	77.1 $\pm$ 49.6	< .001
High-density cholesterol (mg/dL)	57.4 $\pm$ 17.9	53.9 $\pm$ 16.6	60.4 $\pm$ 17.6	< .001
Fasting blood glucose (mg/dL)	111.4 $\pm$ 41.3	112.8 $\pm$ 43.7	111.0 $\pm$ 43.1	.235
Uric acid (mg/dL)	5.5 $\pm$ 1.6	6.1 $\pm$ 1.5	5.0 $\pm$ 1.5	< .001
Abdominal obesity (%)	46.6	13.1	77.1	< .001
Hyperuricemia (%)	23.7	25.1	22.5	.045
Hyperglycemia (%)	48.8	51.8	42.2	.050
Hypertension (%)	39.6	42.5	37.1	.005
Reduced HDL-C (%)	19.7	14.9	23.9	< .001
MetSyn) (%)	10.7	8.6	12.6	< .001

Continuous variables are mean ± standard deviation and sex differences were evaluated using independent t-tests; Categorical variables are rates (percentages) and sex differences were evaluated with chi-squared tests]; MetSyn, metabolic syndrome

Fig. 1.

Mean values of uric acid by components of Metabolic Syndrome (MetSyn) in non-Hispanic Black Adults

Correlation between uric acid and components of metabolic syndrome

Table 2 summarizes the correlation between the UA and components of MetSyn. As shown, UA revealed a positive correlation with WC in both men (r = 0.114) and women (r = 0.143), as well as with TRG in men (r = 0.175) and women (r = 0.213), These correlations were adjusted for age and BMI and were found to be statistically significant (P < 0.05). Furthermore, in men, UA exhibited a significant negative correlation with HDL-C in unadjusted (r = −0.162), age-adjusted (r = −0.224), BMI-adjusted (r = −0.108), and age- and-BMI-adjusted (r = −0.123) models (P < 0.05). In women, UA was negatively and significantly correlated with HDL-C only in the age-adjusted model (r = −0.124, P < 0.01). Additionally, the study found that the correlation of UA levels with the components of MetSyn was stronger with WC, SBP, TRG, and FBG in women compared to men, except for DBP and HDL-C.

Table 2.

Sex-specific correlation between uric acid and components of metabolic syndrome (MetSyn) in Non-Hispanic Black adults

	Model 1	Model II	Model III	Model IV
Men
WC	.321**	.321**	.164**	.114*
SBP	.070*	-.026	-.021	-.062
DBP	.086**	.114*	.081	.075
TRG	.202**	.212**	.168**	.175**
HDL-C	-.162**	-.224**	-.108*	-.123*
FBG	.023	.017	-.050	.068
Women
WC	.423**	.380**	.243**	.143**
SBP	.279**	.121**	.273**	.064
DBP	.050	.044	-.007	.028
TRG	.297**	.261**	.263**	.213**
HDL	-.090	−124**	-.021	-.050
FBG	.084*	.018	.047*	.019

WC waist circumference, SBP systolic blood pressure, DBP diastolic blood pressure, TRG triglycerides, HDL-C high-density lipoprotein cholesterol, FBG fasting blood glucose, Model I unadjusted, Model II adjusted for age, Model III adjusted for BMI**, Model IV, adjusted for both age and BMI, Values are Pearson’s partial correlation coefficients significant** at P < .01 or significant* at P < .05

Association between hyperuricemia and components of metabolic syndrome

The following table (Table 3) presents the results of sex-specific multivariable logistic regression models conducted to establish the relationship between eUA and MetSyn. The study showed that men and women with eUA had a higher prevalence of MetSyn than those without hyperuricemia. After adjusting for possible confounders, men with eUA had a 1.52-fold (95% CI: = 1.17 –3.24) increased odds of MetSyn, while women with hyperuricemia had a 2.59-fold (95% CI = 1.08 – 6.18) increased odds of MetSyn, as compared to men with normoglycemia. In both models, age was also found to be associated with the odds of MetSyn in men (OR = 1.07: 95% CI = 1.05–1.10) and women (OR = 1.10: 95% CI = 1.07–1.13), after adjusting for eUA, physical activity, alcohol use, and smoking. Sufficient exercise (≥ 500 METs-minutes/week) was associated with decreased odds of MetSyn in men and women.

Table 3.

Association between hyperuricemia and metabolic syndrome metabolic syndrome in Non-Hispanic Black adults

	Men		Women
	OR	95% CI	OR	95% CI
Hyperuricemia	1.52	1.17–3.24	2.59	1.08–6.18
Age	1.07	1.05–1.10	1.10	1.07–1.13
Physical activity
None (0 MET-minutes/week)	1.00	Reference	1.00	Reference
Insufficient (1– 499 METs-minutes/week)	0.84	0.70–1.02	0.78	0.71–1.88
Sufficient (≥ 500 METs-minutes/week)	0.64	0.61–0.83	0.71	0.65–0.85
Alcohol use	0.74	0.31–1.75	1.77	0.90–3.51
Smoking	1.80	0.79–4.06	1.44	0.56–3.78

MET metabolic equivalent- based on recommended physical activity guideline levels according to total weekly METs, OR odds ratio and 95% confidence Intervals (CI) from logistic regression analysis and adjusted for age, physical activity, alcohol use, and smoking

Structural equation modelling of uric acid on components of metabolic syndrome

According to our proposed model, we examined the connections between UA and MetSyn components using sex-specific SEM. The postulated path diagram and the associated beta (β) coefficients are presented in Fig. 2. In the figure, there are four crucial points related to eUA: (a) In men and women, each unit increase in BP was associated with a 0.007 and 0.121 mg/dl increase in UA, respectively; (b) In men and women, each unit increase in WC was associated with a 0.320 and 0.423 mg/dl increase in UA, respectively, (c), each unit increase in FBG was associated with a 0.104 and 0.021 mg/dl increase in UA, respectively, and (d) In men and women, each unit increase in dyslipidemia was associated with a 0.009 and 0.151 mg/dl increase in UA, respectively. The results from the SEM demonstrate significant increases in BP (β = 0.247, p < 0.01) and HG (β = 0.342, p < 0.01) with an increase in WC in men. Additionally, there is an increase in dyslipidemia (β = 0.129, p < 0.01) with an increase in FBG in men. Similarly, in women, there are significant increases in BP (β = 0.190, p < 0.01) and HG (β = 0.190, p < 0.01) with an increase in WC and an increase in dyslipidemia (β = 0.174, p < 0.01) with an increase in HG. The findings also reveal a significant increase in BP associated with an increase in FBG (β = 0.054, p < 0.05) in men and women (β = 0.074, p < 0.05), as well as an increase in BP with increases in dyslipidemia in women.

Regarding model fitness, the GoF and TLI were 0.946 and 0.960, respectively, and the RMSEA was 0.0341 (90% CI: 0.0230–0.0684) for men. In women, the GoF and TLI were 0.921 and 0.951, respectively, and the RMSEA was 0.0241 (90% CI: 0.0121–0.0621). These results suggest that our data fit well with the proposed model.

Discussion

The main findings

This study revealed sex differences in the relationship between eUA and increased odds of MetSyn, as well as in the correlation between UA and MetSyn components in NHB American adults. After accounting for age, physical activity, alcohol use, and smoking, NHB American men and women with eUA were respectively found to have 52% and 159% increased odds of MetSyn compared to those without eUA. WC showed the strongest correlation with UA in both men (r = 0.321) and women (r = 0.423). Additionally, in both men and women, higher UA levels were linearly associated with an increased number of MetSyn components. In men, eUA was positively linked with AO (β = 0.320; SE = 0.065), HG (β = 0.104; SE = 0.014), and dyslipidemia (β = 0.009; SE = 0.002). Meanwhile, in women, eUA was positively linked with HTN (β = 0.121; SE = 0.009), AO (β = 0.423; SE = 0.345), and dyslipidemia (β = 0.151; SE = 0.040). Therefore, eUA may be a crucial factor in the development of MetSyn in NHB American adults.

The link between eUA) and MetSyn is not fully understood, but we hypothesize that insulin resistance may be the key connection between them. Our hypothesis is supported by several studies showing an inverse association between UA levels and insulin sensitivity [30–33]. Previous research suggests that eUA levels may lead to insulin resistance by limiting nitric oxide availability and causing oxidative stress in the mitochondria [31]. Insulin resistance can also lead to eUA by increasing sodium reabsorption and UA absorption [31]. In fact, MetSyn patients with high eUA levels who were treated with uricosuric agents like allopurinol or benzbromarone were found to have improved insulin resistance [34].

The much stronger correlation between WC and UA, compared to the correlation between WC and lipid profiles in both men and women in this study, is in line with previous findings [35]. Our finding is consistent with experimental studies indicating a direct decrease in TRG levels with a reduction in serum UA [34]. Thus, the reduction in serum UA resulting from decreased TRG levels can be explained by earlier research showing that high intracellular UA levels may trigger mitochondrial oxidative stress, dysfunction, and citrate release to the cytosol, ultimately promoting lipid and TRG synthesis [36]. Similarly, other studies suggest that soluble and crystalline UA inhibits AMP-kinase, which reduces fatty acid oxidation and leads to TRG accumulation [37]. Additionally, eUA affects the activity of enzymes involved in TRG breakdown, contributing to TRG accumulation in the blood and the development of high triglycerides [35]. The findings from this investigation highlight the importance of further exploring eUA and MetSyn's development.

The results of our conceptual model are similar to findings in China that used generalized obesity, defined using BMI instead of abdominal obesity [17]. Our use of abdominal obesity rather than generalized obesity is in line with epidemiologic evidence that suggests that abdominal obesity is a more potent form of adiposity than generalized obesity in the development of diabetes [38, 39]. Indeed, studies show that abdominal obesity correlates with visceral obesity, which secretes many cytokines that contribute to the development of many chronic metabolic diseases [40].

Study strengths

This study's main strength lies in using data from the NHANES to determine the link between eUA and components of MetSyn. NHANES is a national and representative data source, making it an excellent and reliable resource to investigate this link in NHB American adults. The quality control measures implemented in NHANES to measure and collect study parameters add credibility to the data.

Weaknesses of this study

It is important to interpret the findings of this study with caution because there are several limitations. Firstly, this is a cross-sectional study, causal relationship between UA and the different components of MetSyn cannot be established. Secondly, the variables used in this analysis were limited to what was available in NHANES, which means that important factors such as hormonal and genetic parameters were not considered. These factors may have an impact on the relationship between eUA and MetSyn, particularly when it comes to sex differences. Thirdly, the effect of endothelial dysfunction and inflammatory markers, which could explain the link between eUA and MetSyn was not assessed. Although SEM provides valuable insights into causal relationships, it has certain limitations. For instance, our testing was limited to the overall model and didn't consider multiple between-population groups. While within-group analysis can be insightful in understanding the health of vulnerable minority groups, future studies should explore the impact of social and structural determinants of health on NHB adults to gain a comprehensive understanding of those at a higher risk of experiencing adverse health outcomes.

Public health significance of study findings

Our study has found a direct link between elevated levels of UA and AO, HTN, and dyslipidemia in both NHB American men and women. In addition, elevated levels of uric acid were found to be associated with hyperglycemia in NHB American women. These findings have important clinical and implications. While our study was cross-sectional and cannot establish causation, it supports the idea of evaluating elevated uric acid concentration as a component of metabolic syndrome. This idea aligns with the original definition of MetSyn proposed by the Swedish Kylin in 1923 [41]. However, it is still unclear whether eUA is a cause or simply a marker of metabolic syndrome. Our results suggest that reducing blood uric acid concentration could be a critical target for prevention strategies for metabolic syndrome, particularly in at-risk populations such as NHB American adults. One of the benefits of using uric acid as a target is that it is a routinely measured laboratory parameter in a clinical setting and thus can be used for predictive purposes in metabolic syndrome and its associated components. Although some clinicians may be hesitant to prioritize assessing eUA over more established risk factors for metabolic syndrome, such as the five elements of the metabolic syndrome, testing for eUA is much less expensive, costing $29 compared to $87 for the MetSyn test panel. (https://www.accesalabs.com).

Conclusion

The results of this study reveal a significant link between elevated uric acid levels and metabolic syndrome in NHB Americans. Addressing uric acid levels in conjunction with other risk factors specific to this population is advisable to mitigate the risk of metabolic syndrome. By implementing this strategy, we can effectively reduce the likelihood of metabolic syndrome and its associated components. Taking a proactive measure to lower uric acid levels may help to reduce the risk for metabolic syndrome in NHB Americans.

Funding

The authors did not receive support from any organization for the submitted work.

Data availability

The data for this study is available online at www.cdc.gov/nhanes.

Declarations

Ethical approval

This study does not require ethical approval since it was based on publicly available data that did not contain identifiable information.

Conflict of interest

The authors declared that they have no conflict of interest.