Skip to main content
Download PDF

Prevalence of metabolic syndrome in Brazilian adults in the last 10 years: a systematic review and meta-analysis

BMC Public Health volume 22, Article number: 327 (2022) Cite this article

Abstract

Background

A cluster of interconnected cardiometabolic risk factors characterizes metabolic Syndrome (MS). The prevalence of MS is increasing worldwide, but there is not a meta-analysis of this prevalence in the Brazilian population. We aimed to determine the prevalence of metabolic syndrome among adult general population in Brazil through a meta‑analysis study.

Methods

Original research studies were searched at PubMed, Scopus, Web of Science, and SciELO databases, from 2011 to 2021. We used the Joanna Briggs Institute tool to assess the quality of included studies. The random effect model was used to estimate the pooled prevalence of MS. Subgroup and meta-regression analysis were conducted for explored heterogeneity and used the Funnel Plot and Egger’s test to assess publication bias. The study was performed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).

Results

The search in electronic databases identified 1598 records. From this total, 26 studies were eligible to be included in the final analysis. The overall pooled prevalence among the general population of Brazil was 33% with high heterogeneity observed. By gender, the prevalences were 26% in males and 38% in females. By criteria that was used to define MS, the prevalence were 31% in NCEP ATP III, 25% in JIS, 37% in IDF/NHLBI/AHA/WHF/IAS/IASO and 33% in IDF criteria. The prevalence in different habitat was 34% in urban, 15% in rural, 28% in quilombola and 37% in indigenous. In different regions was 37% in the South, 30% in Southeast, 38% in North, 31% in Northeast and 39% in Midwest. The pooled prevalence of MS with age was < 45 years: 43% and ≥ 45 years: 42% and the prevalence based on year of study implementation was 31% in 2015–2019, 35% in 2010–2014 and 28% in 2005–2009. There were no statistically significant differences between subgroups. Most of the studies showed high quality assessment criteria’s except adequate sample size criteria and many studies participants were not sampled in an appropriate way.

Conclusions

Our review indicates a high prevalence of MS in the healthy Brazilian adult population, when compared to others countries and with a world estimate.

Peer Review reports

Background

Metabolic syndrome (MS) is a complex disorder characterized by the association of cardiovascular risk factors and insulin resistance [1]. The components that define MS include hyperglycemia, hypertension, high triglyceride levels, low high density lipoprotein (HDL) cholesterol levels and abdominal obesity [2].

Most of these components are used as diagnostic criteria by some guidelines, such as the International Diabetes Federation (IDF) [3] and the National Cholesterol Education Program (Adult Treatment Panel III) (NCEP-ATPIII) [4], in addition to the World Health Organization (WHO) [5]. Generally, studies that used more than one guideline to define the prevalence of MS, observed a discrepancy in the results found [6, 7]. This difference occurs because there are divergent points between the assessment factors used by each of the definitions [8]. In the case of the WHO and the NCEP-ATPIII, for example, the main difference is that the former considers microalbuminuria and obesity to be diagnostic factors for the metabolic syndrome, and the NCEP-ATPIII requires that, among the components used for diagnosis, for a confirmation of a case of MS, at least three are altered [9]. Unlike the NCEP-ATPIII and IDF criteria, the WHO also considers the presence of type 2 diabetes mellitus (DM2) a mandatory factor for diagnosis which, probably, when compared with the other two methods, makes this one find a smaller number of MS patients [10].

Regardless of the criteria used for diagnosis, it is well accepted that the prevalence of MS is increasing at epidemic proportions in developed and developing countries [11]. The global prevalence of this condition in the adult population is estimated at around 20 to 25% [12]. In relation to Latin America, the general prevalence found was similar, around 24.9%, with a greater predominance of women and in the age group above 50 years old [13]. In Brazil, the prevalence was estimated in 2013, in the adult population at around 28.9 and 29.6% [14].

MS demands high expenses of the health system, in addition to causing considerable damage to the quality of life of patients, and is therefore considered a serious public health problem worldwide [15, 16]. In this regard, studies demonstrate a high prevalence of MS in the general population and particularly among participants with pre-diabetes, DM2 and patients at high risk for CVD. Therefore, screening for MS in health centers can identify patients at higher risk for these conditions, and multifactorial interventions can benefit this population [6]. Thus, it emphasizes the importance of studies on the prevalence of the syndrome to assist in designing and directing measures to prevent the development of this condition. Several systematic reviews and meta-analyses on the prevalence of MS have been published in various parts of the world [17,18,19], including a systematic review in Brazilian adults in 2013 [14]. However, as far as we know, no quantitative data analysis (meta-analysis) of this prevalence in Brazil was evaluated. Therefore, our objective was to develop a systematic review and meta-analysis summarizing available epidemiological data on the prevalence of MS among adults in the Brazilian population.

Methods

Data sources and searches

The present systematic review and meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines [20] and the PRISMA 2020 check list [21]. The review has been registered at PROSPERO (www.crd.york.ac.uk/prospero/), registration number CRD42021241890. A literature search was carried out to identify prevalence of MS in Brasilian adults. The studies were identified through systematically search at PubMed, Scopus, Web of Science, and SciELO, for relevant studies published before april 2021. “The following keywords were used in combination: ‘‘metabolic syndrome” or Syndrome X or MS, and ‘‘prevalence,’’ and ‘‘Brazil.’’ No language restrictions were imposed. A manual review of the reference lists, in gray literature and research in unpublished data was also conducted.

Study selection

The search was performed independently by three authors (LTSV, LSBS, and VASJ). This reviewers independently identified potentially eligible articles by performing an initial screen of titles and abstracts. All potentially relevant titles and abstracts were selected for full text examination. Any discrepancies among the reviewers were resolved through consensus. Then, the following inclusion criteria were applied: (I) original type studies (II) studies that were conducted among 18 years of age or older [22] and reportedly healthy individuals of both sexes (III) There were no restrictions geographic region (urban, rural) and (IV) to define MS, studies that used any defined criteria to determine the prevalence of MS.

The exclusion criteria for our study were as follows: (I) the reviews and letters to the editors, (II) studies that used animal models or in vitro, (III) studies performed outside of Brazil, (IV) the study population comprising individuals who were reported to have other health complications, (V) studies with incomplete information [6] or in a specific population.

Data extraction

The three investigators extracted the data independently. The following information collected from each study was: first author’s name, year of publication, gender, age range, city and region of study and area in which the study was carried out, population, study design, criteria for diagnosis of metabolic syndrome, and the prevalence of metabolic of syndrome and its components.

Quality of studies

Study quality was assessed independently and blindly by three reviewers using the Joanna Briggs Institute tool for cross-sectional studies (JBI Critical Appraisal Checklist for Analytical Cross Sectional Studies) [23]. This tool consists of a checklist of nine items, which determine the adequacy of the inclusion criteria; sample description; were study participants recruited in an appropriate way, was the sample size adequate, were the study subjects and setting described in detail, sufficient coverage of the identified sample, standardization of diagnostic criteria, reliability and validity of the results, use of adequate statistical analysis, and response rate adequate. The answer options were yes, no, unclear and not applicable. The divergences in the analysis were resolved by consensus.

Statistical analysis

The meta-analysis was performed using R software (R Foundation for Statistical Computing, Vienna, Austria, URL http://www.R-project.org, 2020). The prevalence of MS reported in the selected studies among healthy Brasilian adult populations was analyzed based on different diagnostic criteria used. In each study, we extracted the total number of participants and the number of individuals with the outcome. If one of these data was not provided by the article, we obtained this value through the prevalence of metabolic syndrome.

We used random effect models to calculate pooled prevalence and 95% confidence intervals. Inter-study heterogeneity was explored quantitatively using Cochran's Q and \({I}^{2}\) tests [24]. In this regard, an \({I}^{2}\) of 50% and 75% indicated substantial and considerable heterogeneity, respectively. We used the fixed effect for \({I}^{2}\)< 50% (low heterogeneity). We explored sources of heterogeneity by comparing MS prevalence across subgroups defined by several study-level characteristics and meta-regression analyses according to the year of implementation of study and age of the participants. We assessed the presence of publication bias graphically using the funnel plot. Publication bias also was evaluated using Egger’s Test, the significance level was set at a P value of less than 0.05 [25].

Results

The flow of the literature search is shown in Fig. 1. An initial search of the electronic databases identified 1598 records. Overall, 1560 records were excluded that did not meet the inclusion criteria. Therefore, 38 studies were assessed for eligibility through full-text reading. Of these, 12 studies were excluded for consisting of specific population. Finally, 26 studies were selected for systematic review and meta-analysis.

Fig. 1
figure 1

Flow diagram of studies included in the systematic review

Full size image

Characteristics of the included studies

The characteristics of studies published between 2011 and 2021 on the prevalence of MS in Brazil are included in Table 1. Most of the studies were performaded in urban populations [6, 7, 26,27,28,29,30,31,32,33,34,35,36,37,38,39]. All included studies were cross-sectional design. One study carried out only on female participants [30]. Eight studies used the criteria for diagnosing metabolic syndrome proposed by the NCEP-ATP III [30, 35, 36, 40,41,42,43,44]; three the criteria of the IDF [31, 38, 39]; ten studies used International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesit (IDF/NHLBI/AHA/WHF/IAS/ IASO) [26,27,28, 32,33,34, 37, 45,46,47]; two studies used Joint Interim Statement (JIS) [29, 48]; one study used NCEP ATP III and IDF criteria [7]; one study used modified NCEP, IDF and JIS criteria for diagnosing MS [6]; and one study did not present the criteria it used for the diagnosis of MS [49].

Table 1 Characteristics of studies that evaluated the prevalence of metabolic syndrome in the Brasilian population
Full size table

The studies selected in this systematic review comprised 84,522 subjects, 57.5% of whom were women and 42.5% men. The prevalence of metabolic syndrome is reported by all the studies ranged from 8.9% to 66.1%. Most of the studies where participants were both male and female, reported prevalence data not only for all but also for males and females separately. Many studies presented prevalence of individual components of MS [7, 26,27,28, 30,31,32,33, 35, 38, 40, 42, 43, 45,46,47, 49]; The component with the highest prevalence was increased waist circunference (WC), ranging from 37.8% to 92.6% [26, 27, 30,31,32, 42, 43, 45, 47, 49] followed by high blood pressures, ranging from 46.2% to 66.5% [28, 35, 40].

Analysis of quality of studies

The quality of the studies was assessed according to the set of criteria based on JBI guidance and are summarized in Table 2. A set of nine criteria was used to assess the quality of the studies. The sample frame was appropriate to address a target population in almost all articles with one exception [35]. Fourteen study participants were sampled appropriately [6, 7, 27, 28, 31,32,33,34, 36, 37, 39, 43, 47, 48]. The sample size was adequate in 19 studies [6, 7, 26, 27, 29,30,31,32, 34,35,36, 40, 41, 44,45,46,47,48,49]. Study subjects and setting was described in detail in all articles. The data analysis was conducted with sufficient coverage of the identified sample in 77% studies [6, 7, 26, 27, 29, 30, 32, 34,35,36, 38, 40, 42,43,44,45,46,47,48,49]. Valid methods were used of identify of the condition in almost all articles with one exception [49]. The condition was measured in a standard and reliable way for all participants and there was an appropriate statistical analysis in all the studies. The response rate was adequate and, if not, the low response rate was adequately managed in almost all articles with two exceptions [34, 47].

Table 2 Study quality assessment of studies that evaluated the prevalence of metabolic syndrome in the brazilian population
Full size table

General prevalence of MS and analysis based on the gender of study participants

To calculate the general prevalence, a meta-analysis was performed with the 26 studies that reported the prevalence of MS in Brazilian adults, using the random effects model. The general prevalence estimate was 33% (95% CI: 27%; 39%). There was a large amount of heterogeneity in the prevalence of metabolic syndrome (\({I}^{2}\)= 99.56%; Cochran Q-statistic p < 0.01; Fig. 2). In the funnel graph, there is an asymmetry, which suggests a possible publication bias (Fig. 3), however Egger’s test (p = 0.4851) suggested no significant publication bias.

Fig. 2
figure 2

Forest plot of prevalence of metabolic syndrome in Brazilian population. * prevalence according to the JIS criteria, ** prevalence according to the IDF criteria, *** prevalence according to the modified NCEP_ATPIII criteria and **** prevalence according to the NCEP-ATPIII criteria

Full size image
Fig. 3
figure 3

Funnel plot of the studies that evaluated the prevalence of metabolic syndrome in Brazilian population

Full size image

The prevalence of MS in female and male was respectively 38% (95% CI: 31%; 46%) and 26% (95% CI: 21%; 32%). However, there was no statistical difference between the two groups. There was significant heterogeneity (\({I}^{2}\)= 99.48%; Cochran Q-statistic p < 0.01; Fig. 4) in the prevalence of MS in females and in male (\({I}^{2}\)= 98.60%; Cochran Q-statistic p < 0.01; Fig. 5). The results of this study suggested no significant publication bias using Egger test for female (p = 0.0992) and male (p = 0.0589).

Fig. 4
figure 4

Forest plot of prevalence of metabolic syndrome in adult males in Brazilian population

Full size image
Fig. 5
figure 5

Forest plot of prevalence of metabolic syndrome in adult females in Brazilian population

Full size image

Subgroup analysis

Subgroup analysis based on criteria used to define metabolic syndrome

Studies that used the NCEP-ATP III criteria to define MS had the pooled prevalence of metabolic syndrome of 31% (95% CI: 18%; 45%) with high heterogeneity (\({I}^{2}\) = 99.20%; Cochran Q-statistic p < 0.01; Fig. 6). The pooled prevalence of metabolic syndrome of studies that used JIS criteria to diagnose metabolic syndrome was 25% (95% CI: 11%; 38%) with high heterogeneity (\({I}^{2}\) = 98.81%; Cochran Q-statistic p < 0.01). The weighted pooled prevalence of metabolic syndrome of studies that used IDF/NHLBI/AHA/WHF/IAS/IASO criteria was 37% (95% CI: 27%; 47%), with with high heterogeneity (\({I}^{2}\) = 99.71%; Cochran Q-statistic p < 0.01). The prevalence of MS in studies that used the IDF criteria was 33% (95% CI: 22%; 45%), with high heterogeneity (\({I}^{2}\) = 97.65%; Cochran Q-statistic p < 0.01) There was not statistically significant difference between studies based on diagnostic criteria (p = 0.71). In addition, there was high heterogeneity in prevalence estimates across studies (all heterogeneity p < 0.01). The results of this study suggested no significant publication bias using Egger test for IDF/NHLBI/AHA/WHF/IAS/IASO criteria (p = 0.5906), NCEP – ATPIII (p = 0.7054) and IDF (p = 0.8432), however, the results of this study indicated the possibility of statistically significant bias using Egger test for JIS criteria (p < 0.001).

Fig. 6
figure 6

Forest plot of prevalence according criteria used to define metabolic syndrome in Brazilian population. * prevalence according to the JIS criteria, ** prevalence according to the IDF criteria and **** prevalence according to the NCEP-ATPIII criteria

Full size image

Subgroup analysis based on habitat of study participants

The pooled prevalence of MS in the population living in urban, rural, quilombola and indigenous areas were respectively (34%, 95% CI: 27%; 40%), (15%, 95% CI: 12%; 18%), (28%, 95% CI: 22%; 34%), and (37%, 95% CI: 19%; 56%). There was high heterogeneity in studies: in urban area (\({I}^{2}\) = 99.59%; Cochran Q-statistic p < 0.01; Fig. 7), in quilombola area (\({I}^{2}\) = 66.37%; Cochran Q-statistic p < 0.01) and in indigenous area (\({I}^{2}\) = 98.62%; Cochran Q-statistic p < 0.01). There was not statistically significant difference between studies based on habitat (p = 0.36). In addition, there was high heterogeneity in prevalence estimates across studies (p < 0.01). The results of this study suggested no significant publication bias using Egger test for urban area (p = 0.0684) and indigenous area (p = 0.4279). Egger's test was not performed in the quilombola and rural subgroups because the number of studies included in these subgroups was small.

Fig. 7
figure 7

Forest plot of prevalence of metabolic syndrome according habitat of study participants in Brazilian population. * prevalence according to the JIS criteria, ** prevalence according to the IDF criteria, *** prevalence according to the modified NCEP_ATPIII criteria and **** prevalence according to the NCEP-ATPIII criteria

Full size image

Subgroup analysis based on Brazilian regions of study participants

The pooled prevalence of MS in the Brazilian population in the South, Southeast, North, Northeast and Midwest regions were respectively (37%, 95% CI: 17%; 56%), (30%, 95% CI: 20%; 30%), (38%, 95% CI: 29%; 48%), (31%, 95% CI: 18%; 44%) and (39%, 95% CI: 22%; 57%). There was high heterogeneity in South region (\({I}^{2}\) = 98.72%; Cochran Q-statistic p < 0.01; Fig. 8), in Southeast region, (\({I}^{2}\) = 98.86%; Cochran Q-statistic p < 0.01), in North region (\({I}^{2}\) = 94.02%; Cochran Q-statistic p < 0.01); in Northeast region (\({I}^{2}\) = 98.92%; Cochran Q-statistic p < 0.01) and in Midwest regions (\({I}^{2}\) = 99.14%; Cochran Q-statistic p < 0.01). There was not statistically significant difference between studies based on regions (p = 0.87). In addition, there was high heterogeneity in prevalence estimates across studies (p < 0.01). The results, using Egger test, suggested no significant publication bias for South (p = 0.7032), Southeast (p = 0.3542), North (p = 0.4542), Northeast (p = 0.3004) and Midwest regions (p = 0.5850).

Fig. 8
figure 8

Forest plot of prevalence of metabolic syndrome according regions of study participants in Brazilian population. * prevalence according to the JIS criteria, ** prevalence according to the IDF criteria, *** prevalence according to the modified NCEP_ATPIII criteria and **** prevalence according to the NCEP_ATPIII criteria

Full size image

Subgroup analysis based on age of study participants

The pooled prevalence of MS among studies with participants 45 years of age or older was 42% (95% CI: 30%; 53%) with high heterogeneity (\({I}^{2}\) = 98.88%; Cochran Q-statistic p < 0.01; Fig. 9). The studies that the participants had less than 45 years old, the pooled prevalence of MS was 43% (95% CI: 19%; 66%), with high heterogeneity (\({I}^{2}\) = 99.03%; Cochran Q-statistic p < 0.01). There was not statistically significant difference between studies based on age of participants (p = 0.92). In addition, there was high heterogeneity in prevalence estimates across studies (p < 0.01). Analyses using Egger’s test based on participants 45 years of age or older and less than 45 years old (p values were 0.4142 and 0.3639, respectively) indicated the absence of publication bias.

Fig. 9
figure 9

Forest plot of prevalence of metabolic syndrome according age of study participants in Brazilian population. * prevalence according to the JIS criteria, ** prevalence according to the IDF criteria, *** prevalence according to the modified NCEP_ATPIII criteria and **** prevalence according to the NCEP_ATPIII criteria

Full size image

Subgroup analysis based on year of study implementation

The prevalence of metabolic syndrome among the studies that was implementation in 2015–2019 was 31% (95% CI: 19%; 43%) with high heterogeneity (\({I}^{2}\) = 99.39%; Cochran Q-statistic p < 0.01; Fig. 10). The studies that were impletmentation in 2010–2014 presented the prevalence of metabolic syndrome in 35% (95% CI: 25%; 46%) with high heterogeneity (\({I}^{2}\) = 99.55%; Cochran Q-statistic p < 0.01). The studies that were impletmentation in 2005–2009, weighted pooled prevalence of metabolic syndrome was 28% (95% CI: 20%; 36%), with high heterogeneity (\({I}^{2}\) = 98.35%; Cochran Q-statistic p < 0.01). There was not statistically significant difference between studies based on year of study implementation (p = 0.82). In addition, there was high heterogeneity in prevalence estimates across studies (p < 0.01). Analyses using Egger’s test based on years of study implementation in 2015—2019, 2010 – 2014 and 2005 – 2009 (p values were 0.7205, 0.3082 and 0.5632, respectively) indicated the absence of publication bias.

Fig. 10
figure 10

Forest plot of prevalence of metabolic syndrome according year of study implementation in Brazilian population

Full size image

Meta-regression analyses

To assess the sources of heterogeneity, we performed a meta-regression. In these analyses, year of implementation and age of participants variables were not significantly associated with heterogeneity, p = 0.5291, p = 0.7369, meta-regression coefficient 0.0051, 0.0025 and confidence interval 95% CI -0.0108; 0.0211, -0.0122; 0.0172 respectively (Table 3).

Table 3 Results of meta-regression for the prevalence of metabolic syndrome
Full size table

Discussion

We have conducted this review including studies performed in the last decade to obtain a comprehensive estimate of burden of MS in Brazilian adult population. In total, we analysed data from 26 studies that involved 84,522 participants. We have also captured the gender distribution, habitat differences, geographical region, criteria used to define metabolic syndrome, age of study participants and year of the study implementation estimates to find any significant difference in the estimates of MS.

Our meta-analysis revealed that the pooled estimate of MS prevalence among subjects in Brazil was 33%. This estimate was higher than the prevalence of 29.6% observed in Brazil in 2013 [14] and approached the worldwide prevalence of 20–25% [3]. The prevalence was also higher than that found in Malaysia (27.5%) [50], in the Philippines (19.7%) [51], Bangladesh (30.0%) [19] and Nigeria, whose prevalence was 31.7%, 27.9% and 28.1%, according to the definitions of WHO, ATPIII and IDF, respectively [52]. In the South Asia region, the weighted average prevalence of metabolic syndrome was 14.0% according to the WHO definition, 26.1% according to ATPIII, 29.8% according to the IDF and 32.5% according to the criteria modified from NCEP-ATPIII [53]. On the other hand, the prevalence of metabolic syndrome found in this study was lower than that reported in countries like the USA, 34.5% (NCEP-ATPIII) and 39% (IDF) [54], Turkey, 36.6% (ATPIII) and 44.0% (IDF) [55] and Iran, 36.9% (ATPIII), 34.6% (IDF) and 41.5% (JIS) [56].

Despite the scarcity of studies on the prevalence of MS in Latin American countries, a meta-analysis encompassing these countries found an estimate lower than that found in our study, 24.5% [57]. In addition, countries such as Argentina and Venezuela also found values lower than those observed in this study, 27.5% and 26.1% respectively. [58, 59]. However, in Bolivia, a prevalence of 44.1% was observed [60] and in Peru, according to the definitions of the IDF and ATPIII, respectively, the prevalence of MS found was 35.3% and 28.2% [61]. The variation in the prevalence of MS around the world can be explained by marked cultural differences, which directly influence the lifestyle and consumption patterns of populations [62].

This study demonstrated increased waist circumference as the most frequent individual component of metabolic syndrome, and high blood pressure was shown the second most prominent metabolic syndrome component. The increased prevalence of abdominal obesity and high blood pressure on Brazilian population can have numerous causes. A study, with data from three cohorts, revealed that WC can predict the deterioration of other MS components, indicating that visceral obesity plays a central role in the development of the syndrome syndrome [63]. However, in countries such as Malaysia [50], Bangladesh [19] and Turkey [55], hypertension was reported as the most frequent component, representing 38%, 30% and 87.5% respectively, this large variation in the percentages of this component and in the waist circumference was also verified in our study. In Latin America [13], the prevalence of MS components varied greatly from one country to another. Overall, the component-weighted mean showed low HDL cholesterol as the most frequent component (62.9%), followed by hypertriacylglycerolemia (46.7%).

Environmental factors related to lifestyle, such as physical inactivity, unbalanced food and stress and are closely linked with higher prevalence of obesity and especially for the accumulation of adipose tissue in the abdominal region, tissue directly involved in the genesis of insulin resistance, which is a possible connection with MS. The decrease in insulin action in tissues, such as adipose tissue, leads to an increase in the inflammatory process, which induces this resistance. As a consequence, the accumulation of visceral adipose tissue in the body generating a high-risk cardiometabolic condition [64]. In addition, insulin promotes renal sodium reabsorption and, in hyperinsulinemic conditions, an exacerbation of this action is expected. In fact, comparing individuals with and without MS, it was observed that patients with the syndrome had significantly greater proximal sodium reabsorption, which can cause hypertension [65].

Study quality assessment shows that in many studies participants were not sampled in an appropriate way and the sample size was inadequate, which is a concern. Furthermore, some studies did not present sufficient coverage of the identified sample for data analysis. These criteria for evaluating the quality of studies demonstrate that some studies may have publication bias, which corroborates with evidente asymmetry on the funnel plot.

We observed considerable heterogeneity among the included studies to estimate the prevalence of MS in the Brazilian adult population. Prevalence of metabolic syndrome was the same in males and females, remaining with high heterogeneity. The wide variation in the prevalence of MS among populations in Brazil can be attributed to heterogeneity among the included studies. The country, in addition to being continental in size, has great epidemiology, demographic and socio-economic variability and multicultural characteristics, which makes the population very diverse, making it difficult to generalize the findings of this study in Brazil.

The subgroup analysis based on habitat, geographical region, criteria, age and year of study implementation was conducted in order to try to overcome this limitation. However, heterogeneity remained even after theses subgroup analysis. Hence, we tried to explain the between-study variability using meta-regression and found the potential sources of heterogeneity. However, meta-regression analyses did not indicate enough factors to explain the observed heterogeneity. We suggest that other factors such as lifestyle, alcohol and tobacco consumption, stress, diet and physical inactivity may influence MS heterogeneity. Furthermore, the small number of studies in some regions of Brazil did not allow for a more robust analysis of the prevalence in these areas.

Other studies that assessed the prevalence of MS in different countries also observed high heterogeneity among their data. Meta-analyses performed with data from the general population of Bangladesh [19], Iran [56], China [66], Middle East [67] and Mexico [18] showed heterogeneity greater than 90%. The study carried out in Bangladesh identified that the main source of heterogeneity was the geographical area of ​​the population. In the study conducted in China, the age of participants was associated with lack of homogeneity. In Mexico, the diagnostic criteria used were significantly associated with the heterogeneity. However, as in our work, the studies carried out in Iran and the Middle East, after performing analyzes by subgroups such as habitat, genus and diagnostic criteria, it was not possible to identify the source of this heterogeneity.

The high prevalence of MS found in this study has significant clinical and epidemiological implications, as, as mentioned, MS increases the risk of morbidity and mortality from cardiovascular diseases, in addition to being associated with a higher occurrence of diabetes [68], therefore, it directly interferes with the pattern and curve of illness in the country. This fact explains why the MS epidemic is considered a serious public health problem in Brazil, contributing to the exponential increase in spending in the health area. Thus, the results shown in this study are essential to guide strategies in the area of ​​primary care aimed at the prevention, screening and early treatment of MS.

Like other studies, this our systematic review and meta-analysis study has some limitations, like there is no uniformity of metabolic syndrome definitions, age groups, waist circumference and hyperglycemia cut-offs, and study settings in the studies included in the present review, resulting in limitations in comparability. Features also noted by de Carvalho Vidigal et al. in its systematic review carried out with Brazilian adults [14]. Furthermore, we could not estimate the role of important risk factors on MS such as physical activity and diet, since the studies included had not measured the effects of these factors. This review, we conduct some subgroup analyzes with limited data, such as MS prevalence based on age of participants, because many included studies did not present this information.

The major strength of the study is that we have tried to provide the first review with metanalisys on burden of MS among adult population in Brazil. In addition, the strength is the comprehensiveness of the process, which included a search of four different databases, well-defined inclusion/exclusion criteria, and extensive use of reference lists.

Conclusion

This systematic review and meta-analysis evaluated the scientific literature on the prevalence of metabolic syndrome in Brazil. Our review indicates a high prevalence of MS in the healthy Brazilian adult population, when compared to numerous countries and with a world estimate. Furthermore, the high prevalence remained when we subdivided the data according to different criteria, such as diagnostic, gender, age and geographic area of subjects studied, which suggests urgent attention from both the clinical and public health viewpoint. Information on how MS and its components are distributed could provide a great deal of insight into MS and assist in the planning and implementation of future prevention and control programmes.

Availability of data and materials

The datasets used and/or analysed during the current study available are available in the "supplementary file" section.

Abbreviations

AHA:

American Heart Association

BMI:

Body mass index; DM2 type 2 diabetes mellitus

HDL:

High density lipoprotein

IDF:

International Diabetes Federation

IAS:

International Atherosclerosis Society

IASO:

International Association for the Study of Obesit

JBI:

Joanna Briggs Institute

JIS:

Joint Interim Statement

MS:

Metabolic syndrome

NCEP-ATP III:

National Cholesterol Education Program Adult Treatment Panel III

NHLBI:

National Heart, Lung, and Blood Institute

PRISMA:

Preferred Reporting Items for Systematic Review and Meta-analysis

WC:

Waist circumference

WHF:

World Heart Federation

WHO:

World Health Organization

References

  1. Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med. 2011;9:48.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5.

    Article  CAS  PubMed  Google Scholar 

  3. Alberti G, Zimmet P, Shaw J, Grundy SM. The IDF consensus worldwide definition of the metabolic syndrome. Brussels Int Diab Fed. 2006;23(5):469–80.

    CAS  Google Scholar 

  4. Expert Panel on Detection E. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Jama. 2001;285(19):2486–97.

    Article  Google Scholar 

  5. Consultation W. Definition, diagnosis and classification of diabetes mellitus and its complications. 1999.

    Google Scholar 

  6. do Vale Moreira NC, Hussain A, Bhowmik B, Mdala I, Siddiquee T, Fernandes VO, et al. Prevalence of Metabolic Syndrome by different definitions, and its association with type 2 diabetes, pre-diabetes, and cardiovascular disease risk in Brazil. Diabetes Metab Syndr. 2020;14(5):1217–24.

    Article  PubMed  Google Scholar 

  7. Gronner MF, Bosi PL, Carvalho AM, Casale G, Contrera D, Pereira MA, et al. Prevalence of metabolic syndrome and its association with educational inequalities among Brazilian adults: a population-based study. Braz J Med Biol Res. 2011;44(7):713–9.

    Article  CAS  PubMed  Google Scholar 

  8. Penalva DQF. Síndrome metabólica: diagnóstico e tratamento. Revista de Medicina. 2008;87(4):245–50.

    Article  Google Scholar 

  9. Luna RL. Síndrome metabólica. Arq Bras Cardiol. 2007;88:e124–6.

    Article  PubMed  Google Scholar 

  10. do Monte IP, França SL, de Vasconcelos RNO, dos Santos Vieira JR. Comparação entre quatro diferentes critérios de diagnóstico de síndrome metabólica em indivíduos do Arquipélago do Marajó (Pará, Brasil). Revista Da Associação Brasileira De Nutrição-RASBRAN. 2019;10(1):96–102.

    Google Scholar 

  11. Cornier MA, Dabelea D, Hernandez TL, Lindstrom RC, Steig AJ, Stob NR, et al. The metabolic syndrome. Endocr Rev. 2008;29(7):777–822.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Federation ID. The IDF consensus worldwide definition of the metabolic syndrome. IDF Communications. 2006:1–24. http://www.idf.org/webdata/docs/IDF_Meta_def_final.pdf.

  13. Márquez-Sandoval F, Macedo-Ojeda G, Viramontes-Hörner D, Fernández Ballart JD, Salas Salvadó J, Vizmanos B. The prevalence of metabolic syndrome in Latin America: a systematic review. Public Health Nutr. 2011;14(10):1702–13.

    Article  PubMed  Google Scholar 

  14. de Carvalho VF, Bressan J, Babio N, Salas-Salvadó J. Prevalence of metabolic syndrome in Brazilian adults: a systematic review. BMC Public Health. 2013;13:1198.

    Article  Google Scholar 

  15. Schlenk EA, Erlen JA, Dunbar-Jacob J, McDowell J, Engberg S, Sereika SM, et al. Health-related quality of life in chronic disorders: a comparison across studies using the MOS SF-36. Qual Life Res. 1997;7(1):57–65.

    Article  Google Scholar 

  16. Williams MA, Haskell WL, Ades PA, Amsterdam EA, Bittner V, Franklin BA, et al. Resistance exercise in individuals with and without cardiovascular disease: 2007 update: a scientific statement from the American Heart Association Council on Clinical Cardiology and Council on Nutrition, Physical Activity, and Metabolism. Circulation. 2007;116(5):572–84.

    Article  PubMed  Google Scholar 

  17. Fatahi A, Doosti-Irani A, Cheraghi Z. Prevalence and incidence of metabolic syndrome in Iran: A systematic review and meta-analysis. International Journal of Preventive Medicine. 2020;11.

  18. Gutiérrez-Solis AL, Datta Banik S, Méndez-González RM. Prevalence of metabolic syndrome in Mexico: a systematic review and meta-analysis. Metab Syndr Relat Disord. 2018;16(8):395–405.

    Article  PubMed  Google Scholar 

  19. Chowdhury MZI, Anik AM, Farhana Z, Bristi PD, Abu Al Mamun BM, Uddin MJ, et al. Prevalence of metabolic syndrome in Bangladesh: a systematic review and meta-analysis of the studies. BMC Public Health. 2018;18(1):308.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med. 2009;151(4):W65.

    Article  Google Scholar 

  21. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, The PRISMA, et al. statement: an updated guideline for reporting systematic reviews. BMJ. 2020;2021:372.

    Google Scholar 

  22. Saúde Md. Marco legal: saúde, um direito de adolescentes. Ministério da Saúde Brasília (DF); 2007.

  23. Munn Z MS, Lisy K, Riitano D, Tufanaru C. Chapter 5: Systematic reviews of prevalence and incidence. In: Aromataris E, Munn Z (Editors) . JBI Manual for Evidence Synthesis.; 2020.

  24. Borenstein M, Hedges LV, Higgins J, Rothstein H. A basic introduction to fixed-effect and random-effects models for meta- analysis. Research Synthesis Methods. 2010;1(2):97–111.

    Article  PubMed  Google Scholar 

  25. Sterne JA, Egger M. Regression methods to detect publication and other bias in meta-analysis. Publication bias in meta-analysis: Prevention, assessment and adjustments. 2005;99:110.

    Google Scholar 

  26. Gouveia É, Gouveia BR, Marques A, Peralta M, França C, Lima A, et al. Predictors of Metabolic Syndrome in Adults and Older Adults from Amazonas, Brazil. Int J Environ Res Public Health. 2021;18(3):1303–14.

  27. Oliveira LVA, Santos BNSD, Machado Í, Malta DC, Velasquez-Melendez G, Felisbino-Mendes MS. Prevalence of the metabolic syndrome and its components in the Brazilian adult population. Cien Saude Colet. 2020;25(11):4269–80.

    Article  PubMed  Google Scholar 

  28. Santos FAAd, Back IDC, Giehl MWC, Fassula AS, Boing AF, González-Chica DA. Nível de atividade física de lazer e sua associação com a prevalência de síndrome metabólica em adultos: estudo de base populacional. Revista Brasileira de Epidemiologia. 2020;23:e200070.

    Article  PubMed  Google Scholar 

  29. Carvalho CA, Silva AAM, Assunção MCF, Fonseca PCA, Barbieri MA, Bettiol H, et al. The dietary inflammatory index and insulin resistance or metabolic syndrome in young adults. Nutrition. 2019;58:187–93.

    Article  CAS  PubMed  Google Scholar 

  30. Moreira MA, Vafaei A, da Câmara SMA, Nascimento RAD, de Morais MDSM, Almeida MDG, et al. Metabolic syndrome (MetS) and associated factors in middle-aged women: a cross-sectional study in Northeast Brazil. Women Health. 2020;60(6):601–17.

    Article  PubMed  Google Scholar 

  31. Mulatinho LM, Ferraz APC, Francisqueti FV, Garcia JL, de Souza CF, Cardoso MD, et al. Prevalence of metabolic syndrome and associated factors in adults living in Fernando de Noronha, Brazil. Diabetes Metab Syndr. 2019;13(1):554–8.

    Article  PubMed  Google Scholar 

  32. Ramires EKNM, Menezes RCE, Longo-Silva G, Santos TGD, Marinho PM, Silveira JACD. Prevalence and Factors Associated with Metabolic Syndrome among Brazilian Adult Population: National Health Survey - 2013. Arq Bras Cardiol. 2018;110(5):455–66.

    PubMed  PubMed Central  Google Scholar 

  33. França SL, Lima SS, Vieira JR. Metabolic syndrome and associated factors in adults of the Amazon Region. PLoS One. 2016;11(12):e0167320.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Bortoletto MSS, Souza RKTd, Cabrera MAS, González AD. Síndrome metabólica, componentes e fatores associados em adultos de 40 anos ou mais de um município da Região Sul do Brasil. Cadernos Saúde Coletiva. 2016;24:32–40.

    Article  Google Scholar 

  35. Martini F, Borges MB, Guedes DP. Eating habit and metabolic syndrome in a sample of Brazilian adults. Arch Latinoam Nutr. 2014;64(3):161–73.

    PubMed  Google Scholar 

  36. Moreira GC, Cipullo JP, Ciorlia LAS, Cesarino CB, Vilela-Martin JF. Prevalence of metabolic syndrome: association with risk factors and cardiovascular complications in an urban population. PloS One. 2014;9(9):e105056.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Dutra ES, de Carvalho KM, Miyazaki É, Merchán-Hamann E, Ito MK. Metabolic syndrome in central Brazil: prevalence and correlates in the adult population. Diabetol Metab Syndr. 2012;4(1):1–9.

    Article  Google Scholar 

  38. Gomes PM, Andrade RC, Figueiredo RC, Pace AE, Dal Fabbro AL, Franco LJ, et al. Cardiovascular risk in Japanese-Brazilian subjects. Arq Bras Endocrinol Metabol. 2012;56(9):608–13.

    Article  PubMed  Google Scholar 

  39. Silva EC, Martins IS, Araújo EACd. Síndrome metabólica e baixa estatura em adultos da região metropolitana de São Paulo (SP, Brasil). Ciência & Saúde Coletiva. 2011;16:663–8.

    Article  Google Scholar 

  40. Pimenta AM, Felisbino-Mendes MS, Velasquez-Melendez G. Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population. Sao Paulo Med J. 2013;131(4):213–9.

    Article  PubMed  Google Scholar 

  41. da Rocha AKS, Bós ÂJG, Carnenaz G, Machado DC. Bone mineral density, metabolic syndrome, and vitamin D in indigenous from south of Brazil. Arch Osteoporos. 2013;8(1):1–5.

    Google Scholar 

  42. da Rocha A, Bós A, Huttner E, Machado DC. Prevalence of metabolic syndrome in indigenous people over 40 years of age in Rio Grande do Sul. Brazil Revista Panamericana de Salud Pública. 2011;29(1):41–5.

    PubMed  Google Scholar 

  43. Anjos HNKd, Toledo MJdO, Mota LT, Previdelli ITS, Anjos AFd, Saruhashi TR, et al. Prevalence of metabolic syndrome among Kaingang native Americans in Southern Brazil. Braz Arch Biol Technol. 2011;54(1):81–9.

    Article  Google Scholar 

  44. Silva KF, Prata A, Cunha DFd. Frequency of metabolic syndrome and the food intake patterns in adults living in a rural area of Brazil. Revista da Sociedade Brasileira de Medicina Tropical. 2011;44(4):425–9.

    Article  PubMed  Google Scholar 

  45. Soares LP, Fabbro AL, Silva AS, Sartorelli DS, Franco LF, Kuhn PC, et al. Prevalence of metabolic syndrome in the Brazilian Xavante indigenous population. Diabetol Metab Syndr. 2015;7:105.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Santos KMd, Tsutsui MLdS, Galvão PPdO, Mazzucchetti L, Rodrigues D, Gimeno SGA. Grau de atividade física e síndrome metabólica: um estudo transversal com indígenas Khisêdjê do Parque Indígena do Xingu. Brasil Cadernos de Saúde Pública. 2012;28:2327–38.

    Article  PubMed  Google Scholar 

  47. Oliveira G, Oliveira T, Rodrigues FF, Corrêa LF, Arruda T, Casulari LA. Prevalence of metabolic syndrome in the indigenous population, aged 19 to 69 years, from Jaguapiru Village, Dourados (MS). Brazil Ethn Dis. 2011;21(3):301–6.

    PubMed  Google Scholar 

  48. Mussi RFF, Petróski EL. Metabolic syndrome and associated factors in quilombolas (inhabitants of black communities) from Bahia. Brazil Cien Saude Colet. 2019;24(7):2481–90.

    Article  PubMed  Google Scholar 

  49. Luisi C, Figueiredo FWdS, Sousa LVdA, Quaresma FRP, Maciel EdS, Adami F. Prevalence of and factors associated with metabolic syndrome in afro-descendant communities in a situation of vulnerability in Northern Brazil: a cross-sectional study. Metab Syndr Relat Disord. 2019;17(4):204–9.

    Article  PubMed  Google Scholar 

  50. Rampal S, Mahadeva S, Guallar E, Bulgiba A, Mohamed R, Rahmat R, et al. Ethnic differences in the prevalence of metabolic syndrome: results from a multi-ethnic population-based survey in Malaysia. PLoS One. 2012;7(9):e46365.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Sy RG, Llanes EJ, Reganit PF, Castillo-Carandang N, Punzalan FE, Sison OT, et al. Socio-demographic factors and the prevalence of metabolic syndrome among filipinos from the LIFECARE cohort. J Atheroscler Thromb. 2014;21(Suppl 1):S9-17.

    Article  CAS  PubMed  Google Scholar 

  52. Oguoma VM, Nwose EU, Richards RS. Prevalence of cardio-metabolic syndrome in Nigeria: a systematic review. Public Health. 2015;129(5):413–23.

    Article  CAS  PubMed  Google Scholar 

  53. Aryal N, Wasti SP. The prevalence of metabolic syndrome in South Asia: a systematic review. Int J Diabetes Dev Ctries. 2016;36(3):255–62.

    Article  Google Scholar 

  54. Ford ES. Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the US. Diabetes Care. 2005;28(11):2745–9.

    Article  PubMed  Google Scholar 

  55. Gundogan K, Bayram F, Gedik V, Kaya A, Karaman A, Demir O, et al. Metabolic syndrome prevalence according to ATP III and IDF criteria and related factors in Turkish adults. Arch Med Sci. 2013;9(2):243–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Amirkalali B, Fakhrzadeh H, Sharifi F, Kelishadi R, Zamani F, Asayesh H, et al. Prevalence of metabolic syndrome and its components in the Iranian Adult Population: a systematic review and meta-analysis. Iran Red Crescent Med J. 2015;17(12):e24723.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Márquez-Sandoval F, Macedo-Ojeda G, Viramontes-Hörner D, Ballart JF, Salvadó JS, Vizmanos B. The prevalence of metabolic syndrome in Latin America: a systematic review. Public Health Nutr. 2011;14(10):1702–13.

    Article  PubMed  Google Scholar 

  58. Diaz A, Espeche W, March C, Flores R, Parodi R, Genesio M, et al. Prevalencia del síndrome metabólico en Argentina en los últimos 25 años: revisión sistemática de estudios observacionales poblacionales. Hipertensión y riesgo vascular. 2018;35(2):64–9.

    Article  CAS  PubMed  Google Scholar 

  59. González JP, Martínez RN, de González TM, García RJ, Ugel E, Osuna D, et al. Prevalencia de síndrome metabólico, obesidad y alteración de la glucemia en ayunas en adultos del páramo del Estado Mérida, Venezuela (estudio VEMSOLS). Medicina Interna. 2011;27(4):262-7.

  60. Mamani Ortiz Y, Armaza Cespedes A, Medina Bustos M, Luizaga Lopez JM, Abasto Gonzales DS, Argote Omonte M, et al. Caracterización del perfil epidemiológico del síndrome metabólico y factores de riesgo asociados. Cochabamba Bolivia. Gaceta Médica Boliviana. 2018;41(2):24–34.

    Article  Google Scholar 

  61. Aliaga E, Tello T, Varela L, Seclén S, Ortiz P, Chávez H. Frecuencia de síndrome metabólico en adultos mayores del Distrito de San Martin de Porres de Lima, Perú según los criterios de ATP III y de la IDF. Revista Medica Herediana. 2014;25(3):142–8.

    Article  Google Scholar 

  62. Hajian-Tilaki K, Heidari B, Firouzjahi A, Bagherzadeh M, Hajian-Tilaki A, Halalkhor S. Prevalence of metabolic syndrome and the association with socio-demographic characteristics and physical activity in urban population of Iranian adults: a population-based study. Diabetes Metab Syndr. 2014;8(3):170–6.

    Article  CAS  PubMed  Google Scholar 

  63. Cameron AJ, Boyko EJ, Sicree RA, Zimmet PZ, Söderberg S, Alberti KGM, et al. Central obesity as a precursor to the metabolic syndrome in the AusDiab study and Mauritius. Obesity. 2008;16(12):2707–16.

    Article  CAS  PubMed  Google Scholar 

  64. Barbalho SM, Bechara MD, Quesada K, Gabaldi MR, Goulart RdA, Tofano RJ, et al. Síndrome metabólica, aterosclerose e inflamação: tríade indissociável? Jornal Vascular Brasileiro. 2015;14:319–27.

    Article  Google Scholar 

  65. Strazzullo P, Barbato A, Galletti F, Barba G, Siani A, Iacone R, et al. Abnormalities of renal sodium handling in the metabolic syndrome. Results of the Olivetti Heart Study. J Hypertens. 2006;24(8):1633–9.

    Article  CAS  PubMed  Google Scholar 

  66. Li R, Li W, Lun Z, Zhang H, Sun Z, Kanu JS, et al. Prevalence of metabolic syndrome in Mainland China: a meta-analysis of published studies. BMC Public Health. 2016;16(1):1–10.

    Article  Google Scholar 

  67. Ansarimoghaddam A, Adineh HA, Zareban I, Iranpour S, HosseinZadeh A, Kh F. Prevalence of metabolic syndrome in Middle-East countries: meta-analysis of cross-sectional studies. Diabetes Metab Syndr. 2018;12(2):195–201.

    Article  PubMed  Google Scholar 

  68. de Carvalho VF, Ribeiro AQ, Babio N, Salas-Salvadó J, Bressan J. Prevalence of metabolic syndrome and pre-metabolic syndrome in health professionals: LATINMETS Brazil study. Diabetol Metab Syndr. 2015;7(1):1–9.

    Google Scholar 

Download references

Acknowledgements

Not applicable

Funding

The research received funding from the Pro-Rectory of Extension, scientific initiation BIC/UFJF and academic professional training grants from the Federal University of Juiz de Fora.

Author information

Authors and Affiliations

  1. Universidade Federal de Juiz de Fora, Campus Governador Valadares, Governador Valadares, Minas Gerais, Brazil

    Letícia Teixeira de Siqueira Valadares, Luiza Siqueira Barreto de Souza & Valdir Alves Salgado Júnior

  2. Department of Pharmacy, Universidade Federal de Juiz de Fora, Governador Valadares, Brazil

    Larissa de Freitas Bonomo

  3. Department of Economy, Universidade Federal de Juiz de Fora, Governador Valadares, Brazil

    Leandro Roberto de Macedo

  4. Department of Basic Life Sciences, Universidade Federal de Juiz de Fora, Governador Valadares Campus, Avenida Moacir Paleta, nº 1167, no bairro São Pedro.CEP 35020-360, Governador Valadares, MG, Brazil

    Maísa Silva

Authors
  1. Letícia Teixeira de Siqueira Valadares
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luiza Siqueira Barreto de Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Valdir Alves Salgado Júnior
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Larissa de Freitas Bonomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Leandro Roberto de Macedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maísa Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MS conceptualize the idea and supervise the overall project. LFB, LRM and MS designed this study. LTSV, LSBS, VASJ and MS played a major role in searching literature, data compilation and preparing the first draft of the manuscript. MS reviewed the selected literature for analysis. LTSV, LSBS, VASJ, LFB and MS prepared the bias table for quality assessment. LRM and MS played a major role in data analysis. LFB, LRM and MS critically revised the manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Maísa Silva.

Ethics declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Siqueira Valadares, L.T., de Souza, L.S.B., Salgado Júnior, V.A. et al. Prevalence of metabolic syndrome in Brazilian adults in the last 10 years: a systematic review and meta-analysis. BMC Public Health 22, 327 (2022). https://doi.org/10.1186/s12889-022-12753-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-022-12753-5

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us