Introduction

Metabolic syndrome (MetS) is defined as a set of metabolic abnormalities, including dysglycaemia, central obesity, dyslipidaemia (elevated triglycerides and decreased HDL-cholesterol) and hypertension. These alterations increase the risk of type 2 diabetes mellitus and cardiovascular disease1. The pathogenesis of Mets is not well understood but involves complex interactions between genetic background, hormones, and environmental factors such as air pollution, toxins and nutrients2. Previous evidence supports that insulin resistance (IR), oxidative stress and low-grade inflammation play a central role3.

Chronic low-grade systemic inflammation appears to be a central mechanism underlying the pathophysiology of MetS3,4. This inflammation is characterised by an increase in pro-inflammatory mediators and the activation of several inflammatory pathways that are significantly associated with cardiovascular events5. In addition, the increased concentration of pro-inflammatory substances is primarily related to obesity, especially central obesity, resulting in altered endocrine function of visceral adipose tissue6.

Due to the increasing prevalence of obesity, the prevalence of MetS has grown worldwide, and it is expected to continue increasing in the coming years7. In this respect, the adult population with MetS is estimated between 20 and 30% in most countries8.Due to the complexity of MetS, with diverse influences and implications for other diseases, it is not easy to make a clear-cut distinction of the diagnostic ability of the various biomarker groups. Moreover, the subdivision has limitations: the complexity of the syndrome, interactions of various biochemical pathways and the overlap of markers9.

Nevertheless, some studies have shown an association between MetS and the following variables indicative of inflammatory processes: uric acid (UA), C-reactive protein (CRP), liver transaminases (ALT), erythrocyte sedimentation rate (ESR), leukocytes, among others10,11,12. Likewise, through magnetic resonance spectroscopy, different metabolites have been identified in urine, highlighting glucose, lipids, aromatic amino acids, salicylic acid, maltitol, trimethylamine N-oxide and p-cresol sulphate, which have been associated with the progression of MetS13.

UA is an enzymatic end product of purine metabolism in humans14. Hyperuricaemia is a metabolic disease caused by increased formation or reduced serum uric acid (SUA) excretion. Alterations in SUA homeostasis have been correlated with several diseases such as gout, MetS, cardiovascular disease, diabetes, hypertension and kidney disease15.

Although SUA levels are often associated with MetS16,17, hyperuricaemia is not included among the diagnostic criteria that have been proposed internationally for the definition of this pathology. However, the pro-oxidant action of hyperuricaemia may induce inflammation and endothelial dysfunction by decreasing the availability of nitric oxide, thus promoting the development of the pathologies discussed above18,19,20,21.

Given that the prevalence of MetS increases worldwide and raises the risk of morbidity and mortality, identifying biomarkers for the early detection of this pathology is of great importance22. Therefore, the main Aim is to provide the best evidence on the association between MetS and UA by determining the effect size of this biomarker.

Methods

Literature search and selection

A systematic review and meta-analysis were carried out, following the criteria established by the PRISMA statement23. The search covered the PubMed and Scopus databases. The search strategy was developed by combining the following Medical Subject Headings (MeSH) descriptors: "metabolic syndrome", "uric acid", using the Boolean operator AND. The review was carried out from 2015 to May 2021. In addition, hand searching the reference lists of included studies supplemented the tracking of the available literature. The systematic review was registered in PROSPERO with ID CRD42021231124.

Eligibility criteria

We included longitudinal, cross-sectional, case–control and cohort studies, which investigated the association between MetS and UA. In addition, their results had to include the mean and standard deviation of the study parameters. Furthermore, only papers in English and Spanish and those articles collected data in subjects older than 18 years were considered. Finally, abstracts and unpublished studies comparing subjects with and without MetS were excluded.

Data collection

Two authors (E.R.C. and M.R.S.) separately screened all articles obtained in the search to eliminate duplicates. Then, two other authors (D.P.J. and R.M.L.) independently read the title and abstract and applied the eligibility criteria to select the articles that were finally included in the review. Finally, a fifth authors (M.V.A.) acted as a judge in case of discrepancy. One researcher (E.R.C.) oversaw extracting the data, verified by a second researcher (G.M.R.). A third researcher (M.R.S.) resolved the disagreement in case of a tie.

The extracted articles were drawn up with a table with the main characteristics (author, year, country, study design, reporting guidelines, age of participants, MetS, Aims, conclusions).

The following data were extracted from each study: citation, details of the study population (including age and sex), study design, sample size, study, aims, the mean and standard deviation of UA in those subjects with and without MetS.

Evaluation of the qualitative synthesis

Four authors (R.M.L., D.P.J., G.M.R. and E.R.C.) were responsible for the evaluation of the qualitative synthesis through a triple analysis:

  1. 1.

    Assessment of methodological quality. The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement24 was used for observational studies.

  2. 2.

    Risk of bias assessment. Researchers were using the Cochrane Collaboration25 tool included in the REVMAN 5.4.2. software, the risks of selection, conduct, detection, attrition, and reporting were analysed.

  3. 3.

    Assessment of the quality of evidence. With the help of the Grade Protool, the evidence profile table was developed, establishing the following levels26:

    • High: high confidence in the match between the actual and estimated effect.

    • Moderate: moderate confidence in the effect estimate. There is a possibility that the actual effect is far from the estimated effect.

    • Low: limited confidence in the estimate of the effect. The actual effect may be far from the estimated effect.

    • Very low: low confidence in the estimated effect. The actual effect is very likely to be different from the estimated effect.

Statistical analysis (evaluation of the quantitative synthesis or meta-analysis)

For the meta-analysis, the Cochrane Review Manager software (RevMan 5.4.2) was used to perform the statistical calculations and create the forest plots and funnel plots. Due to the difference in effect size of the included studies, a meta-analysis was performed using the Mantel–Haenszel random-effects method according to the DerSimonian and Laird model. The difference between arithmetic means with a 95% confidence interval was used to measure effect size. The risk of publication bias was assessed using the funnel plot. Heterogeneity was analysed using the Chi-square test and the inconsistency index (I2). According to the Cochrane Collaboration tool, heterogeneity was classified as: unimportant (0–40%), moderate (30–60%), substantial (50–90%) and considerable (75–100%).

Results

Characteristics of the studies

Initially, 1582 articles were identified. Then, after excluding duplicates and reviewing titles and abstracts, 1529 articles were excluded from applying the eligibility criteria. Finally, a total of 43 articles were selected for systematic review and meta-analysis (Fig. 1).Given the large number of articles found in the search, it was divided into three subgroups: (i) articles providing UA data globally without distinction of sex (n = 24); (ii) articles with disaggregated data for men (n = 17) and (iii) women (n = 15). The detailed characteristics of the selected studies are shown in Table 1. Regarding research design, all studies were observational. Twenty-seven studies27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53 defined MetS according to the third report of the National Cholesterol Education Program (NCEP-Adult Treatment Panel (ATP III)54. Seven studies55,56,57,58,59,60,61 assessed metabolic syndrome using the International Diabetes Federation (IDF) criteria62. Four studies63,64,65,66 used the harmonised criteria67. Three studies68,69,70 used Chinese Medical Association criteria71; Sumiyoshi et al.72 used the Japanese criteria73 and, finally, Osadnik74 used the criteria defined in the study by Buscemi et al.75.

Figure 1
figure 1

PRISMA flowchart. MetS: metabolic syndrome; SD: standard deviation.

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Table 1 Characteristics of included studies (n = 43).
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Concerning the articles' origin, twelve (27.9%) were conducted in China34,38,39,42,48,50,61,64,66,68,69,70. In total, the 43 selected papers compared UA concentrations between 91,845 subjects with MetS and 259931controls. The age of study participants ranged from 18 to 90 years.

Methodological quality assessment

All papers scored 16 points or more out of the 22 items included (highest tercile). No article was excluded for insufficient methodological quality. Table 1 shows a column with the score for each of the reports.

Bias risk analysis

Overall (Fig. 2), the main biases were: random sequential generation, allocation and participant and staff concealment, and blinding of outcome assessment, affecting 72% of the reports. Figure 3 represents the individual assessment of the included studies.

Figure 2
figure 2

Overall risk of bias of the studies.

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Figure 3
figure 3

Summary of risk of bias by study.

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Quantitative analysis. Meta-analysis

Meta-analysis 1

This analysis comprises 43 papers, including men and women, together or separately, resulting in 56 groups (Fig. 4). Subjects with MetS had a mean UA 8.2% higher than those without this syndrome (5.89 mg/dl vs. 5.44 mg/dl; p < 0.00001). The funnel plot (Fig. 5) shows a low risk of publication bias. The sensitivity analysis performed to assess the pooled estimate's stability concerning each meta-analysis study did not show that any study significantly affected the heterogeneity of the meta-analysis; therefore, none was excluded. Given the heterogeneity of the included studies, it was decided to perform subgroup analysis.

Figure 4
figure 4

Results and summary statistics of studies analysing uric acid levels in the total population with and without metabolic syndrome (MetS) (meta-analysis 1).

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Figure 5
figure 5

Funnel plot (meta-analysis 1).

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Meta-analysis 2

Figure 6, which includes 17 studies, represents the results obtained when analysing the presence of UA in men with and without MetS. In this case, men with MetS showed a higher mean UA, (0.53 mg/dl; 95% CI 0.45 − 0.62; p < 0.00001; I2 = 97%). Figure 7 shows that there is a low risk of publication bias.

Figure 6
figure 6

Results and summary statistics of studies analysing uric acid levels in men with and without metabolic syndrome (MetS) (meta-analysis 2).

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Figure 7
figure 7

Funnet plot (meta-analysis 2).

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Meta-analysis 3

Figure 8 compiles the results of 15 studies that examined the association between UA in women and the presence of MetS. The results show that UA level was associated with the diagnosis of METS (0.57 mg/dl; 95% CI 0.48–0.66; p < 0.00001; I2 = 97%). This meta-analysis also observed a low risk of publication bias (Fig. 9).

Figure 8
figure 8

Results and summary statistics of studies analysing uric acid levels in women with and without metabolic syndrome (MetS) (meta-analysis 3).

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Figure 9
figure 9

Funnet plot (meta-analysis 3).

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Quality of evidence

Table 2 shows the evidence profile of the three meta-analyses, providing specific information regarding the overall certainty of the evidence of the studies included in the comparison, the magnitude of the studies examined and the sum of the data available for the outcomes assessed.

Table 2 Evidence profile with GRADE pro for the three meta-analyses.
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Discussion

A systematic review and meta-analysis were conducted to analyse the most recent evidence on the relationship between MetS and UA. Forty-three studies were selected, the effect size and the limitations that have conditioned the results of the different studies were quantified.

Of the included papers, 26 directly associated UA with MetS28,29,30,33,35,36,38,40,41,42,43,44,45,46,48,49,50,56,57,59,60,61,63,65,66,72, and 17 reports collected data indirectly27,31,32,34,37,39,47,51,52,53,57,58,64,68,69,70,74, i.e. they study parameters related to MetS and collect data associated with UA. These studies had limitations, but overall, all demonstrated a sufficient degree of methodological reliability and quality in terms of the association of UA and MetS.

This meta-analysis provides evidence of a relationship between UA level and MetS. The concentration of UA in subjects with MetS was significantly higher than in the control group. The meta-analysis is notable for its large sample size, with 91,845 subjects in the MetS group and 259,931 in the control group. Given the heterogeneity of the included studies, it was decided to perform subgroup analysis. The results obtained show that men with MetS have a higher UA concentration than those without MetS (mean difference (MD): mg/dl 0.53; 95% CI 0.45–0.62; p < 0.00001). This was also observed in women (MD 0.57 mg/dl; 95% CI 0.48–0.66, p < 0.00001).

Changes in the UA concentrations in human fluids can reflect the metabolic state, immunity, and other human body functions. If the concentration of UA in the blood exceeds normal, the human body fluid becomes acidic, which affects the normal function of human cells, leading to long-term metabolic disease76. UA correlates with obesity, diabetes mellitus76, hypertension77, cardiovascular disease78 and chronic kidney disease79, where UA acts as an oxidant, inducing oxidative stress and endothelial dysfunction80.

Previous studies have reported significant associations between hyperuricaemia and individual elements of the metabolic syndrome81,82. The study by Norvik et al.83 showed that elevated UA levels are associated with components of the MetS, such as hypertriglyceridaemia, insulin resistance, elevated blood pressure and low high-density lipoprotein cholesterol. Xu et al.84 concluded that the relationship between SUA and elevated body mass index, hypertension and hyperglycaemia varies by sex. Reducing SUA levels by adopting a healthier lifestyle may be a valuable strategy to reduce the burden of MetS84.

Overall, the results have shown that people with MetS have 8.2% more UA, so reducing UA could positively impact the development of this syndrome. The results found by several authors85,86,87 support this. Yuan et al.85, in a meta-analysis based on prospective studies of various populations, suggest that for every 1 mg/dl increase in SUA level, the risk of MetS increases by 30% with a linear dose–response relationship. Liu et al.86 observed a consistent and linear causality of increased UA on the incidence of MetS, concluding that SUA could be an individualised predictor in detecting systemic/hepatic metabolic abnormalities. It is estimated that people with high UA are 1.6 times more likely to develop MetS87. Therefore, reducing SUA levels could be a potential treatment to prevent comprehensive metabolic disorders.

At the methodological level, the assessment of risks of bias in studies is a major issue in this type of research, in line with PRISMA recommendations. Studies with similar methodologies but with discrepancies in quality may have biased results. Among all the papers included in this review, only ten studies29,35,38,41,42,50,56,63,65,68 had performed this step correctly. The quality of the evidence obtained is "very low" since observational studies have been analysed where there is a high risk of bias and, in addition, they present a very high inconsistency (heterogeneity).

One of the main strengths of this review is the comprehensive search that covered a wide geographical area. In addition, a large sample size of subjects with and without MetS was included, which strengthened the study's statistical power.

The interpretation of the findings in this systematic review and meta-analysis must be made considering some limitations. First, most of the studies are from China, making it difficult to generalise the results to other countries. Author bias should also be a limitation since the same research team wrote several studies. Finally, it should be noted that there is still a lack of uniformly accepted diagnostic criteria for the diagnosis of MetS.

Conclusions

Current diagnostic criteria for MetS vary, although there is a consensus on the main components of the syndrome. None of these criteria includes UA levels in the definition of MetS.

The results have shown that UA levels are associated with the presence of MetS. In particular, subjects with MetS have been found to have higher plasma UA. The assessment of UA concentration could provide a new avenue for early diagnosis, identifying new biomarkers, and discovering new therapeutic targets.

A detailed understanding of the components of MetS is essential for the development of effective prevention strategies and appropriate intervention tools, which could curb its increasing prevalence and limit its comorbidity.

However, well-designed, high-quality randomised controlled trials are needed to confirm these findings.