Skip to main content
Download PDF

The association of selenium exposure with the odds of metabolic syndrome: a dose-response meta-analysis

BMC Endocrine Disorders volume 25, Article number: 49 (2025) Cite this article

Abstract

Background

Selenium is a key regulator of metabolic homeostasis. It has been proposed that exposure to selenium might be associated with metabolic syndrome (MetS). However, the results are contradictory. This meta-analysis was carried out to analyze the relationships between selenium levels in biological samples and odds of Mets.

Methods

We searched Scopus and PubMed databases up until September 2024 to identify relevant studies. Odds ratio (OR) and 95% confidence interval was used to pool the data using a random effects model.

Results

The meta-analysis encompassed 18 observational studies involving 21,481 participants. It found that higher selenium exposure was related to an elevated likelihood of MetS (OR = 1.30, 95% CI = 1.12–1.51), even after controlling for covariates, such as smoking, age, alcohol consumption, and physical activity. Heterogeneity was significant among the studies (I² = 88.9%, P = 0.001). While elevated serum selenium levels linked to a higher odds of MetS, no such relationship was observed for selenium in urine or toenails. Subgroup analyses indicated that this association was evident only in females (OR = 2.0, 95% CI = 1.17–1.43) and particularly pronounced in individuals aged ≥ 50 years. A dose-response relationship was identified, showing a 6% increase in MetS odds for each additional 10 µg/L of serum selenium, with the odds rising non-linearly when serum levels surpassed 80 µg/L.

Conclusions

This study suggests that selenium may associated with the odds of MetS, following a dose-response relationship.

Peer Review reports

Background

Metabolic syndrome (MetS) is a multifaceted condition characterized by a combination of metabolic disturbances, including abdominal obesity, hypertension, impaired glucose regulation, and abnormal lipid levels [1]. Affecting approximately 20-25% of the global population, MetS remarkably increases the odds of type 2 diabetes, cardiovascular diseases, kidney failure, and overall mortality [2, 3]. It is also linked to heightened odds of developing reproductive disorders, nonalcoholic steatohepatitis, and various cancers [4, 5]. Although the precise etiology of MetS remains largely unclear, it is recognized to arise from a complex interplay of genetic, metabolic, and environmental factors, such as unhealthy dietary patterns [6,7,8]. As lifestyle and dietary patterns shift in tandem with the growing obesity epidemic, the prevalence of MetS is rapidly growing [9]. Therefore, preventing MetS is essential, making the identification of its risk factors critically important.

Oxidative stress plays a leading role in the pathophysiology of MetS, either prompting or exacerbating the mechanisms associated with its pathology [10]. Selenium, an essential trace element primarily sourced from the diet, contributes to the preservation of redox balance and endocrine and metabolic processes [11]. Recent studies have indicated a correlation between circulating selenium levels and metabolic risk factors, such as glycemic indices [12] and lipid profiles [13]. Selenium is crucial for human health due to its incorporation into selenoproteins, which are vital for antioxidant defense, insulin function, and thyroid hormone metabolism [14]. It is a key component of glutathione peroxidase (GSH-PX), which protects the body from oxidative damage by reducing lipid peroxidation and preserving the integrity and functionality of cell membranes [15].

Given the roles of selenium-dependent enzymes in oxidative stress and metabolic pathways, there has been significant interest in exploring the relationship between selenium exposure and MetS. However, the association of selenium levels in various biological samples, such as serum, urine, and nails, with MetS remains debated, with studies yielding inconsistent results. Some research indicates that elevated selenium levels may be linked to an increased odds of MetS [16, 17], while other studies suggest a protective effect or no significant association [18]. This could stem from the differences in study designs, types of samples collected, population characteristics, and the methodologies used to measure selenium exposure. This meta-analysis aimed to comprehensively synthesize the existing literature on the relationship between selenium concentrations in various biological samples and the odds of developing MetS to determine whether selenium exposure can be regarded as a modifiable risk factor for MetS and identify any potential dose-response relationships.

Methods

Information sources and search strategy

The meta-analysis was performed following the guidelines established by the PRISMA [19]. Relevant publications were recruited by searching the Scopus and PubMed databases up until September 2024, utilizing the following keywords: (“Selenium“[Mesh]) OR selenium OR selenite OR seleno OR selenious acid) AND (“Metabolic Syndrome“[Mesh] OR Metabolic Syndrome). The search was restricted to English-language articles. Furthermore, we manually reviewed the reference lists of eligible studies and relevant reviews to uncover any further qualifying studies. In cases where clarification was required or additional data were not present in the published manuscripts, the authors of the included studies were contacted directly. All literature was organized using EndNote X7 software. Two authors independently assessed the titles and abstracts of each article to determine their relevance, eligibility for inclusion, data extraction, and quality evaluation. Any disagreements were settled by consulting a third author.

Eligibility criteria

Studies were included if they satisfied the predefined inclusion criteria based on the PICO framework, which were as follows: (1) the population consisted of individuals at risk of MetS, (2) the exposure involved selenium levels in various biological samples, (3) the controls were individuals with the lowest selenium exposure, (4) the outcome measured was the odds of MetS, and (5) the studies were observational in design, encompassing cohort, case-control, and cross-sectional studies. Additionally, to qualify for inclusion, the studies had to provide risk estimates for the relationship between selenium concentrations in biological samples and MetS. We excluded animal studies, those with irrelevant exposures or outcomes, clinical trials, reviews, letters, protocols, short combinations, and editorials.

Data extraction

Data was gathered from each included study using a standardized protocol and reporting form, which encompassed the following information: country, first author’s name, publication year, sample size, number of patients with MetS, definition of MetS, method of selenium measurement, study design, mean age and gender of participants, and the covariates adjusted for in the analyses.

Quality assessment

Two investigators (RY and YZ) independently measured the quality of the eligible publications with the use of the Newcastle-Ottawa Scale (NOS) [20], which assesses various domains, including selection, comparability, and assessment of exposure and outcome. The final quality scores were determined by the total number of criteria met, classified as follows: 7–9 asterisks (high quality), 4–6 asterisks (medium quality), and 1–3 asterisks (low quality).

For cross-sectional studies, a modified version of NOS was used according to the previous studies [21]. The NOS had the following domains to assess the quality of cohort studies: representativeness of the exposed cohort, selection of the non-exposed cohort, ascertainment of exposure, demonstration that the outcome of interest was not present at the start of the study, study controls for the main covariate, study controls for any additional factors, assessment of outcome, adequacy of follow-up duration for outcomes occurrence, and loss-to-follow up. For case-control studies, NOS had the following domains: selection of cases and controls, sample size, comparability of cases and controls, ascertainment of exposures, study controls for the main covariate, study controls for any additional factor, ascertainment of the outcome, and statistical test. For cross-sectional studies, NOS had the following domains: representativeness of the sample, sample size, non-respondents, ascertainment of exposure, study controls for the main covariate, study controls for any additional factor, ascertainment of the outcome, and statistical test [21] (Table S1).

Statistical analysis

The odds ratio (OR) and 95% confidence intervals (CI), adjusted for a comprehensive set of covariates, were utilized as the effect size to pool the data. In instances where the study did not directly report risk estimates, we estimated the unadjusted ORs using the raw data provided. To evaluate heterogeneity across the articles, Cochran’s Q test and the I² statistic were employed, categorizing heterogeneity as low (0–25%), moderate (25–50%), or high (> 50%) [22, 23]. A random-effects model was implemented for data pooling. Publication bias was examined using Egger’s statistics and funnel plots, with a p-value < 0.05 showing statistically significant [24, 25]. Sensitivity analyses were conducted by sequentially excluding individual studies to evaluate the reliability of the results. Subgroup analyses utilizing random-effects models were performed based on sample size, gender, age, quality of study, source of population, geographic region, definitions of MetS, and methods of selenium assessment to explore potential sources of heterogeneity. Additionally, meta-regression analysis was executed to determine if pooled estimates were influenced by participants’ age.

The analysis of linear dose-response relationship was conducted using varying cut-off points for categories in each study to calculate the (OR and 95%CI for MetS associated with every 10 µg/L increment in serum selenium levels. This was performed through generalized least squares for trend estimation, following the methodologies proposed by Greenland and Longnecker [26] and Orsini [27]. The median serum selenium within each category was utilized as the corresponding dose. The relationship between selenium concentration and MetS odds was delineated for each distribution of cases and controls according to this method. Studies that failed to quantify the number of cases and controls within each category or that reported selenium concentrations with OR and 95% CI for fewer than three categories were removed from this dose–response analysis. Furthermore, we evaluated a non-linear dose–response curve relating serum selenium concentration to the summary OR and 95% CI for MetS. A restricted cubic spline model featuring four knots at the 5th, 35th, 65th, and 95th percentiles of selenium concentration was employed. Both linear and nonlinear models were derived by testing the null hypothesis, with the spline coefficients set to zero [28]. All statistical analyses were done with the use of Stata version 14 (Stata Corp, College Station, TX), with significance for pooled estimates established at p < 0.05.

Results

Study characteristics

Our preliminary literature review identified 667 publications. After removing 73 duplicates and 558 irrelevant studies, we conducted a full-text screening of 36 potentially relevant articles. Ultimately, 18 studies were excluded for failing to meet al.l eligibility criteria, as illustrated in Fig. 1. The meta-analysis included 18 studies [9, 11, 16,17,18, 29,30,31,32,33,34,35,36,37,38,39,40,41], comprising 9 case-control studies, 8 cross-sectional studies, and one cohort study. The sample sizes of these studies ranged from 145 to 3,272 participants, totaling 21,481 individuals. Among these, there were 6,231 cases of MetS. One study focused exclusively on women [39], three on men [9, 29, 31], while five provided separate results for both genders [9, 16, 17, 30, 40]; the remaining studies presented combined data. Out of the included studies, 15 evaluated serum selenium levels [9, 11, 16,17,18, 29,30,31, 33, 35, 36, 38,39,40,41], two examined urinary selenium [32, 37], and one assessed toenail selenium concentrations [34]. Measurement techniques varied: 11 studies utilized inductively coupled plasma mass spectrometry (ICP-MS), four employed atomic absorption spectrometry, one used spectrofluorimetry, and another utilized neutron activation analysis; one study did not specify the method of selenium assessment. Participants’ mean ages ranged from 15.3 ± 2.6 to 73.4 ± 5.74 years. MetS was defined using the National Cholesterol Education Program Expert Panel and Adult Treatment Panel (III NCEP-ATP III) in 5 studies, IDF in 5 studies, Chinese criteria in 5 studies, The Joint Interim Statement in one study, and one study did not report the criteria used to define MetS. According to the Newcastle-Ottawa Scale (NOS), the quality of the included studies ranged from low to high, with scores between 3 and 9 (Table S1). Detailed characteristics of each study are summarized in Table 1.

Fig. 1
figure 1

Flow diagram of study selection

Full size image
Table 1 Characteristics of the studies included in the meta-analysis
Full size table

Meta-analysis

The forest plot depicting the pooled association between selenium concentrations and MetS odds is shown in Fig. 2. The pooled odds ratio (OR) was calculated at 1.30 (95% CI = 1.12–1.51) for higher versus lower selenium concentrations, indicating significant heterogeneity among the studies (I² = 88.9%, P = 0.001) (Fig. 2). The results of subgroup analyses are presented in Table 2. The results were supported by hospital-based studies and studies with a low risk of bias. Subgroup analyses revealed the increased odds of MetS in females (OR = 2.0, 95% CI = 1.17–1.43), but not in males. Elevated serum selenium levels were associated with increased odds of MetS; however, no such association was found for urinary or toenail selenium concentrations. This association was particularly evident among individuals aged ≥ 50 years and in studies employing the NCEP-ATP III and Chinese definitions for MetS, as well as within Asian populations and those measuring selenium via ICP-MS or atomic absorption spectrometry. In subgroup analyses adjusting for covariates, higher selenium levels were linked to increased odds of MetS when controlling for at least one potential covariate or when age was accounted for; however, associations remained unchanged when not adjusted for energy intake, body mass index (BMI), or education level. The relationship did not vary significantly in the stratified analysis according to the adjustment for alcohol consumption, sex, physical activity level, and smoking status among participants (Table 2).

Fig. 2
figure 2

Meta-analysis of the association between selenium levels and metabolic syndrome for the highest compared with the lowest exposure

Full size image
Table 2 Overall and subgroup analyses for the association between selenium levels and the odds of metabolic syndrome
Full size table

A total of seven studies [9, 11, 17, 33, 36, 40, 41] provided data for dose-response meta-analysis. The OR for MetS was found to be 1.06 (95% CI = 1.003–1.12) per 10 µg/L increase in serum selenium concentration (Fig. 3). Notably, a direct nonlinear association between serum selenium concentration and MetS odds was observed (P < 0.001) (Fig. 4), indicating that the odds of MetS ascended when serum selenium surpassed 80 µg/L.

Fig. 3
figure 3

Linear dose-response meta-analysis of the association between serum selenium levels and metabolic syndrome per 10 µg/L increment in selenium levels

Full size image
Fig. 4
figure 4

Non-linear dose-response meta-analysis of the association between serum selenium levels and metabolic syndrome

Full size image

Meta-regression and sensitivity analysis

The meta-regression analysis indicated that pooled effect sizes were unaffected by sample size, participant age, or publication year. Sensitivity analyses confirmed that no individual study significantly affected the pooled effect sizes, indicating the reliability of the findings.

Publication bias

A significant publication bias was detected (P = 0.007) (Fig. 5). However, following a trim-and-fill analysis to adjust for this bias, the pooled effect size remained unchanged, suggesting that publication bias had a negligible impact on the results.

Fig. 5
figure 5

Funnel plot for publication bias

Full size image

Discussion

This analysis identified a direct relationship between serum selenium levels and the odds of MetS in females, particularly among older individuals (≥ 50 years), while no such association was observed in males. The relationship remained significant even after controlling for covariates such as age, smoking habits, alcohol intake, and levels of physical activity. Specifically, a dose-response relationship was identified, indicating a 6% rise in MetS odds for every 10 µg/L increase in serum selenium concentration. Notably, this association exhibited a non-linear trend, with the likelihood of MetS increasing when serum selenium levels exceeded 80 µg/L.

The overall results of this study align with several previous observational studies that reported positive correlations between serum selenium levels and MetS [16, 40]. It has been observed that individuals with MetS have higher serum selenium levels compared to healthy controls [40]. Furthermore, serum selenium levels have been positively linked to various components of MetS, including insulin resistance, fasting glucose, obesity, and triglycerides [17]. The dual function of selenium as an essential nutrient and a possible risk factor for metabolic disorders has been previously proposed. Numerous studies highlight this contradiction, indicating that while selenium is vital for various physiological functions, including antioxidant defense and thyroid hormone metabolism, excessive selenium exposure may be linked to adverse health outcomes. For instance, some research suggests that selenium deficiency is associated with metabolic disorders due to oxidative stress and impaired signaling pathways, while other studies indicate that high serum selenium levels correlate with a heightened risk of these same conditions. Recent studies indicate that elevated blood selenium concentrations may adversely impact cardiovascular metabolic risk factors [13] and type 2 diabetes (T2DM) [42]. Elevated serum selenium levels have been correlated with increased concentrations of total cholesterol, low-density lipoprotein-cholesterol (LDL-c), high-density lipoprotein-cholesterol (HDL-c) triglyceride (TG), and increased odds of dyslipidemia [43]. High serum selenium levels are associated with impaired fasting glucose and elevated fasting serum glucose [44]. While deficiency of selenium might be a risk factor for the development of high blood pressure [45], chronic overexposure to environmental selenium may increase blood pressure [46]. Moreover, a significant association between adiposity indices and selenium status has been suggested [47]. In contrast, reduced levels of selenium in urine and nails can be found in overweight or obese individuals [48].

In our study, we further identified the dose-response relationship between selenium and MetS, particularly in certain demographics such as females and older adults. These findings highlight the role of serum selenium as a biomarker for assessing MetS odds. The findings can inform public health policies aimed at addressing nutritional deficiencies or excesses in selenium, particularly in populations at higher odds for MetS. Identifying individuals with elevated selenium levels may help in early intervention strategies aimed at reducing MetS prevalence. Given the dose-response relationship observed, healthcare providers may consider monitoring selenium levels in patients at odds for MetS. This could lead to tailored dietary or supplementation recommendations to optimize selenium intake and potentially mitigate the associated risks. Given the potential risks associated with excessive selenium intake, public health policies may need to emphasize balanced dietary sources of selenium rather than high-dose supplementation. Future research should further explore the mechanisms behind this association and consider the implications for dietary guidelines to mitigate the risk of MetS while ensuring adequate selenium levels for overall health.

Supporting our findings, previous research has highlighted significant sex-specific effects of selenium on MetS odds, with associations observed either exclusively in females or showing a stronger correlation in females compared to males [17, 49]. The observed sex-specific differences could stem from a complex interplay of hormonal influences, metabolic processes, nutritional requirements, genetic factors, and age-related changes. Estrogen and other sex hormones may modulate how selenium affects metabolic processes. In females, estrogen can influence insulin sensitivity, adiposity, and lipid metabolism, potentially enhancing the impact of selenium on these pathways [50]. This hormonal interaction may explain why higher selenium levels are more strongly associated with MetS in women compared to men, who may not experience the same hormonal effects [17, 49]. Research indicates that selenium is more closely related to adiposity and lipid metabolism in females [49]. Differences in dietary patterns and nutritional needs between sexes could influence serum selenium levels and their health implications [51]. Women may require different amounts of selenium for optimal metabolic function compared to men [52], leading to differing associations with MetS. Females often present with different comorbid conditions associated with MetS compared to males [53]. The interaction between selenium levels and these conditions may vary by sex [54], influencing how each gender responds to changes in selenium status. The study highlights that the association is particularly pronounced in older individuals (≥ 50 years). Age-related hormonal changes in women, such as menopause, can alter metabolic processes and potentially enhance the impact of selenium on MetS odds [55]. Additionally, biosynthesis of selenoenzymes and selenoproteins displays sex-specific differences in a dose-dependent manner [56]. In animal studies, the overexpression of glutathione peroxidase 1 (GPx1) in the context of hyperinsulinemia was observed exclusively in male mice [57], while GPx1 expression in the liver was found to be higher in female-derived cells compared to those from males [58]. Additionally, increased levels of selenoprotein P and insulin resistance were noted solely in female mice [59]. In human genetic studies, the England SELGEN study indicated that women exhibited higher expression levels of GPx1 and selenoprotein P genes associated with obesity [60]. Conversely, GPx1 polymorphisms were linked to a greater incidence of MetS in men within a Japanese adult cohort [61]. Furthermore, in a Finnish cohort, variations in the selenoprotein S gene locus were associated with the odds of cardiovascular disease exclusively in females [62]. These factors could explain the gender-specific relationship observed in our study. However, the underlying mechanisms warrant further investigation.

The relation of selenium to MetS could be mediated through mechanisms involving insulin resistance, lipid metabolism, adiposity, and hormonal regulation. Selenium plays a critical role in insulin signaling pathways, which are essential for glucose metabolism [63]. Elevated selenium levels have been associated with increased insulin resistance, increased triglycerides, and altered lipid profiles, particularly in females [17], which are the components of MetS. The direct effects of elevated blood selenium levels on glucose intolerance and dyslipidemia are mediated by various mechanisms, including the mevalonate pathway (which plays a role in the synthesis of selenoproteins and cholesterol), the regulation of lipoprotein synthesis by selenoprotein P, and the influence of selenium compounds, particularly GPx1, on protein tyrosine phosphatase 1B [13, 16]. Excess selenium disrupts the redox-methylation balance through the methionine-homocysteine cycle, resulting in excessive expression of GPx1 [64, 65]. Animal studies have shown that overexpression of GPx1 is linked to insulin resistance, hyperglycemia, and obesity [66]. In vitro and in vivo experiments suggest that selenoprotein P interferes with insulin signaling in the liver and muscle by reducing the phosphorylation of insulin receptors and protein kinase B (Akt) in response to insulin, while also decreasing insulin sensitivity by lowering phosphorylation of AMP-activated protein kinase (AMPK) [67, 68]. This disruption contributes to insulin resistance (IR) and the development of MetS. Regarding adiposity, serum selenium has been reported to have an inverse relationship with body mass index (BMI) in both men and women; however, it was associated with body fat percentage only in women according to the third NHANES study [69]. Such fat accumulation can exacerbate metabolic disturbances and increase the likelihood of developing MetS. Additionally, overexpression of GPx1 has been linked to the onset of insulin resistance and obesity [66], further promoting MetS development. A recent study indicated that a genetic variant of selenoprotein P is associated with glutathione peroxidase 1 (GPX1) activity and fasting insulin levels, suggesting that this mechanism may also play a role in the development of MetS [70]. Selenium may also influence the expression and activity of peroxisome proliferator-activated receptor gamma (PPAR-γ), a key regulator of glucose and lipid metabolism. Changes in PPAR-γ activity due to high selenium levels could affect metabolic processes and contribute to insulin resistance and dyslipidemia [39]. Furthermore, chronic overexposure to selenium may increase blood pressure, another component of MetS [46].

To our knowledge, this is the first meta-analysis exploring the link between selenium exposure across various biological samples and MetS. The strengths of the study include its dose-response analysis and subgroup evaluations across different factors while accounting for covariate effects on the findings. However, several limitations exist. First, most studies included were cross-sectional or case-control in design, preventing the establishment of causality and making it difficult to determine if elevated serum selenium levels in women with MetS are a cause or a consequence of metabolic abnormality. Future research should focus on prospective cohort studies to clarify the relationship between selenium concentrations and MetS. Additionally, the results for some subgroup analyses, such as urinary and toenail selenium concentrations were limited by the small number of studies available, warranting cautious interpretation of these results. Nonetheless, serum selenium remains a reliable measure of selenium status and is a standard method used in epidemiological investigations [71]. Moreover, significant heterogeneity was found across studies; we identified that variations in sample type, participant demographics (age and gender), study design, geographic location, definitions of MetS, methods for assessing selenium levels, sample sizes, and the level of adjustments for covariates contributed to this heterogeneity. Morovere, the test for publication bias indicated that some smaller studies may have been overlooked. The search was restricted to English-language articles and some studies in other languages may be ignored. Lastly, we did not investigate the association of selenium exposure with individual components of MetS. Of the included studies, only a few studies provided risk estimates for specific components of MetS. Future studies could benefit from stratified analyses by MetS components to better understand these relationships and their implications for public health.

Conclusions

In conclusion, this meta-analysis revealed that higher serum concentrations of selenium are directly related to the elevated odds of MetS in females, exhibiting a dose-response relationship even after adjusting for covariates such as age, alcohol consumption, physical activity level, and smoking status. However, no significant association was found in males. Prospective cohort studies are required to confirm these findings and to clarify the mechanisms underlying these associations.

Data availability

The datasets used and/or analysed during the current study are available from the correspondingauthor on reasonable request.

Abbreviations

MetS:

Metabolic syndrome

OR:

Odds ratio

GSH-PX:

Glutathione peroxidase

NOS:

Newcastle-Ottawa Scale

CI:

Confidence intervals

ICP-MS:

Inductively coupled plasma mass spectrometry

III NCEP-ATP III:

National Cholesterol Education Program Expert Panel and Adult Treatment Panel

BMI:

Body mass index

T2DM:

Type 2 diabetes

GPx1:

Glutathione peroxidase 1

AMPK:

AMP-activated protein kinase

PPAR-γ:

Peroxisome proliferator-activated receptor gamma

References

  1. Alizadeh S, Ahmadi M, Ghorbani Nejad B, Djazayeri A, Shab-Bidar S. Metabolic syndrome and its components are associated with increased chronic kidney disease risk: evidence from a meta‐analysis on 11 109 003 participants from 66 studies. Int J Clin Pract. 2018;72(8):e13201.

    Article  Google Scholar 

  2. Rashidbeygi E, Safabakhsh M, Mohammed SH, Alizadeh S. Metabolic syndrome and its components are related to a higher risk for albuminuria and proteinuria: Evidence from a meta-analysis on 10,603,067 subjects from 57 studies. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2019;13(1):830– 43.

  3. Alizadeh S, Esmaeili H, Alizadeh M, Daneshzad E, Sharifi L, Radfar H, et al. Metabolic phenotypes of obese, overweight, and normal weight individuals and risk of chronic kidney disease: a systematic review and meta-analysis. Archives Endocrinol Metabolism. 2019;63:427–37.

    Google Scholar 

  4. Razi B, Alizadeh S, Omidkhoda A, Imani D, Rezaei R. Association of chronic hepatitis B infection with metabolic syndrome and its components: meta-analysis of observational studies. Diabetes Metabolic Syndrome: Clin Res Reviews. 2017;11:S939–47.

    Article  Google Scholar 

  5. Goodarzi G, Mozaffari H, Raeisi T, Mehravar F, Razi B, Ghazi ML, et al. Metabolic phenotypes and risk of colorectal cancer: a systematic review and meta-analysis of cohort studies. BMC Cancer. 2022;22(1):89.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Askari M, Dehghani A, Abshirini M, Raeisi T, Alizadeh S. Glycemic index, but not glycemic load, is associated with an increased risk of metabolic syndrome: Meta-analysis of observational studies. Wiley Online Library; 2021. p. e14295.

  7. Alizadeh S, Pooyan S, Mirzababaei A, Arghavani H, Hasani H, Mirzaei K. Interaction of MC4R rs17782313 variants and dietary carbohydrate quantity and quality on basal metabolic rate and general and central obesity in overweight/obese women: a cross-sectional study. BMC Endocr Disorders. 2022;22(1):121.

    Article  CAS  Google Scholar 

  8. Maddahi NS, Yarizadeh H, Setayesh L, Nasir Y, Alizadeh S, Mirzaei K. Association between dietary energy density with mental health and sleep quality in women with overweight/obesity. BMC Res Notes. 2020;13:1–6.

    Article  Google Scholar 

  9. Fang C, Wu W, Gu X, Dai S, Zhou Q, Deng H, et al. Association of serum copper, zinc and selenium levels with risk of metabolic syndrome: a nested case-control study of middle-aged and older Chinese adults. J Trace Elem Med Biol. 2019;52:209–15.

    Article  CAS  PubMed  Google Scholar 

  10. Masenga SK, Kabwe LS, Chakulya M, Kirabo A. Mechanisms of oxidative stress in metabolic syndrome. Int J Mol Sci. 2023;24(9):7898.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zhou L, Luo C, Yin J, Zhu Y, Li P, Chen S, et al. Diverse associations of plasma selenium concentrations and SELENOP gene polymorphism with metabolic syndrome and its components. Oxidative Med Cell Longev. 2020;2020(1):5343014.

    Google Scholar 

  12. Jablonska E, Reszka E, Gromadzinska J, Wieczorek E, Krol MB, Raimondi S, et al. The effect of selenium supplementation on glucose homeostasis and the expression of genes related to glucose metabolism. Nutrients. 2016;8(12):772.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Stranges S, Laclaustra M, Ji C, Cappuccio FP, Navas-Acien A, Ordovas JM, et al. Higher selenium status is associated with adverse blood lipid profile in British adults. J Nutr. 2010;140(1):81–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Papp LV, Holmgren A, Khanna KK. Selenium and selenoproteins in health and disease. Antioxid Redox Signal. 2010;12(7):793–5.

    Article  CAS  PubMed  Google Scholar 

  15. Spallholz JE. Selenium and glutathione peroxidase: essential nutrient and antioxidant component of the immune system. Antioxidant nutrients and immune functions. 1990:145– 58.

  16. Arnaud J, De Lorgeril M, Akbaraly T, Salen P, Arnout J, Cappuccio F, et al. Gender differences in copper, zinc and selenium status in diabetic-free metabolic syndrome European population–the IMMIDIET study. Nutr Metabolism Cardiovasc Dis. 2012;22(6):517–24.

    Article  CAS  Google Scholar 

  17. Lu C-W, Chang H-H, Yang K-C, Chiang C-H, Yao C-A, Huang K-C. Gender differences with dose–response relationship between serum selenium levels and metabolic Syndrome—A case-control study. Nutrients. 2019;11(2):477.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Feng X, Li L, Huang L, Zhang H, Mo Z, Yang X. Associations between serum multiple metals exposures and metabolic syndrome: a longitudinal cohort study. Biol Trace Elem Res. 2021;199:2444–55.

    Article  CAS  PubMed  Google Scholar 

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

  20. Luchini C, Stubbs B, Solmi M, Veronese N. Assessing the quality of studies in meta-analyses: advantages and limitations of the Newcastle Ottawa Scale. World J Meta-Analysis. 2017;5(4):80–4.

    Article  Google Scholar 

  21. Mozaffari H, Ajabshir S, Alizadeh S. Dietary approaches to stop hypertension and risk of chronic kidney disease: a systematic review and meta-analysis of observational studies. Clin Nutr. 2020;39(7):2035–44.

    Article  PubMed  Google Scholar 

  22. Khorshidi M, Zarezadeh M, Moradi Moghaddam O, Emami MR, Kord-Varkaneh H, Mousavi SM, et al. Effect of evening primrose oil supplementation on lipid profile: a systematic review and meta‐analysis of randomized clinical trials. Phytother Res. 2020;34(10):2628–38.

    Article  CAS  PubMed  Google Scholar 

  23. Mohammed SH, Shab-Bidar S, Abuzerr S, Habtewold TD, Alizadeh S, Djafarian K. Association of anemia with sensorineural hearing loss: a systematic review and meta-analysis. BMC Res Notes. 2019;12:1–6.

    Article  Google Scholar 

  24. Raeisi T, Rezaie H, Darand M, Taheri A, Garousi N, Razi B, et al. Circulating resistin and follistatin levels in obese and non-obese women with polycystic ovary syndrome: a systematic review and meta-analysis. PLoS ONE. 2021;16(3):e0246200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Emami MR, Safabakhsh M, Khorshidi M, Moghaddam OM, Mohammed SH, Zarezadeh M, et al. Effect of bariatric surgery on endogenous sex hormones and sex hormone-binding globulin levels: a systematic review and meta-analysis. Surg Obes Relat Dis. 2021;17(9):1621–36.

    Article  PubMed  Google Scholar 

  26. Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol. 1992;135(11):1301–9.

    Article  CAS  PubMed  Google Scholar 

  27. Orsini N, Li R, Wolk A, Khudyakov P, Spiegelman D. Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. Am J Epidemiol. 2012;175(1):66–73.

    Article  PubMed  Google Scholar 

  28. Mohseni R, Mohammed SH, Safabakhsh M, Mohseni F, Monfared ZS, Seyyedi J, et al. Birth weight and risk of cardiovascular disease incidence in adulthood: a dose-response meta-analysis. Curr Atheroscler Rep. 2020;22:1–13.

    Article  Google Scholar 

  29. Deng Q, Wei Y, Liu K, Wu D, Zhu X, Xu M, et al. Essential metals modified the effects of polycyclic aromatic hydrocarbons on the metabolic syndrome: mediation effects of miRNA. Sci Total Environ. 2024;906:167506.

    Article  CAS  PubMed  Google Scholar 

  30. Guo S, Hua L, Liu W, Liu H, Chen Q, Li Y, et al. Multiple metal exposure and metabolic syndrome in elderly individuals: a case-control study in an active mining district, Northwest China. Chemosphere. 2023;326:138494.

    Article  CAS  PubMed  Google Scholar 

  31. Guo X, Yang Q, Zhang W, Chen Y, Ren J, Gao A. Associations of blood levels of trace elements and heavy metals with metabolic syndrome in Chinese male adults with microRNA as mediators involved. Environ Pollut. 2019;248:66–73.

    Article  CAS  PubMed  Google Scholar 

  32. Huang M, Chen J, Yan G, Yang Y, Luo D, Chen X, et al. Plasma titanium level is positively associated with metabolic syndrome: a survey in China’s heavy metal polluted regions. Ecotoxicol Environ Saf. 2021;208:111435.

    Article  CAS  PubMed  Google Scholar 

  33. Huang S, Zhong D, Lv Z, Cheng J, Zou X, Wang T, et al. Associations of multiple plasma metals with the risk of metabolic syndrome: a cross-sectional study in the mid-aged and older population of China. Ecotoxicol Environ Saf. 2022;231:113183.

    Article  CAS  PubMed  Google Scholar 

  34. Jang H, Morris J, Park K. Correlates of toenail selenium and its cross-sectional association with metabolic syndrome. J Hum Nutr Dietetics. 2018;31(5):603–11.

    Article  CAS  Google Scholar 

  35. Kelishadi R, Ataei E, Motlagh ME, Yazdi M, Tajaddini MH, Heshmat R, et al. Association of serum concentrations of magnesium and some trace elements with cardiometabolic risk factors and liver enzymes in adolescents: the CASPIAN-III Study. Biol Trace Elem Res. 2015;163:97–102.

    Article  CAS  PubMed  Google Scholar 

  36. Lu C-W, Chang H-H, Yang K-C, Kuo C-S, Lee L-T, Huang K-C. High serum selenium levels are associated with increased risk for diabetes mellitus independent of central obesity and insulin resistance. BMJ Open Diabetes Res Care. 2016;4(1):e000253.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ma J, Zhou Y, Wang D, Guo Y, Wang B, Xu Y, et al. Associations between essential metals exposure and metabolic syndrome (MetS): exploring the mediating role of systemic inflammation in a general Chinese population. Environ Int. 2020;140:105802.

    Article  CAS  PubMed  Google Scholar 

  38. Pang Y, Wang Y, Hao H, Zhu W, Zou M, Liu Q, et al. Associations of multiple serum metals with the risk of metabolic syndrome among the older population in China based on a community study: a mediation role of peripheral blood cells. Ecotoxicol Environ Saf. 2024;284:116981.

    Article  CAS  PubMed  Google Scholar 

  39. Schneider-Matyka D, Cybulska AM, Szkup M, Pilarczyk B, Panczyk M, Tomza-Marciniak A, et al. Selenium as a predictor of metabolic syndrome in middle age women. Aging. 2023;15(6):1734.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yuan Z, Xu X, Ye H, Jin L, Zhang X, Zhu Y. High levels of plasma selenium are associated with metabolic syndrome and elevated fasting plasma glucose in a Chinese population: a case-control study. J Trace Elem Med Biol. 2015;32:189–94.

    Article  CAS  PubMed  Google Scholar 

  41. Zhang W, Du J, Li H, Yang Y, Cai C, Gao Q, et al. Multiple-element exposure and metabolic syndrome in Chinese adults: a case-control study based on the Beijing population health cohort. Environ Int. 2020;143:105959.

    Article  CAS  PubMed  Google Scholar 

  42. Wang X-l, Yang T-b, Wei J, Lei G-h, Zeng C. Association between serum selenium level and type 2 diabetes mellitus: a non-linear dose–response meta-analysis of observational studies. Nutr J. 2015;15:1–9.

    Article  Google Scholar 

  43. Ju W, Ji M, Li X, Li Z, Wu G, Fu X, et al. Relationship between higher serum selenium level and adverse blood lipid profile. Clin Nutr. 2018;37(5):1512–7.

    Article  CAS  PubMed  Google Scholar 

  44. Li Z, Li X, Ju W, Wu G, Yang X, Fu X, et al. High serum selenium levels are associated with impaired fasting glucose and elevated fasting serum glucose in Linyi, China. J Trace Elem Med Biol. 2018;45:64–9.

    Article  CAS  PubMed  Google Scholar 

  45. Nawrot TS, Staessen JA, Roels HA, Den Hond E, Thijs L, Fagard RH, et al. Blood pressure and blood selenium: a cross-sectional and longitudinal population study. Eur Heart J. 2007;28(5):628–33.

    Article  CAS  PubMed  Google Scholar 

  46. Vinceti M, Chawla R, Filippini T, Dutt C, Cilloni S, Loomba R, et al. Blood pressure levels and hypertension prevalence in a high selenium environment: results from a cross-sectional study. Nutr Metabolism Cardiovasc Dis. 2019;29(4):398–408.

    Article  CAS  Google Scholar 

  47. Soares de Oliveira AR, Jayanne Climaco Cruz K, Beatriz Silva Morais J, Rocha Dos Santos L, Rodrigues de Sousa Melo S, Fontenelle LC, et al. Selenium status and oxidative stress in obese: influence of adiposity. Eur J Clin Invest. 2021;51(9):e13538.

    Article  CAS  PubMed  Google Scholar 

  48. Fontenelle LC, de Araújo DSC, da Cunha Soares T, Cruz KJC, Henriques GS, do Nascimento Marreiro D. Nutritional status of selenium in overweight and obesity: a systematic review and meta-analysis. Clin Nutr. 2022;41(4):862–84.

  49. Weening EH, Al-Mubarak AA, Dokter MM, Dickstein K, Lang CC, Ng LL, et al. Sexual dimorphism in selenium deficiency is associated with metabolic syndrome and prevalence of heart disease. Cardiovasc Diabetol. 2023;22(1):8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kim JH, Cho HT, Kim YJ. The role of estrogen in adipose tissue metabolism: insights into glucose homeostasis regulation. Endocr J. 2014;61(11):1055–67.

    Article  CAS  PubMed  Google Scholar 

  51. Seale LA, Ogawa-Wong AN, Berry MJ. Sexual dimorphism in selenium metabolism and selenoproteins. Free Radic Biol Med. 2018;127:198–205.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Thomson C. Assessment of requirements for selenium and adequacy of selenium status: a review. Eur J Clin Nutr. 2004;58(3):391–402.

    Article  CAS  PubMed  Google Scholar 

  53. Onat A, Can G, Çakır H, Özpamuk-Karadeniz F, Karadeniz Y, Yüksel H, et al. Sex-specific predictors of metabolic syndrome independent of its components. J Investig Med. 2015;63(6):796–801.

    Article  CAS  PubMed  Google Scholar 

  54. Schomburg L. Sex-specific differences in biological effects and metabolism of selenium. Selenium: Its molecular biology and role in human health. 2016:377– 88.

  55. Ma X-M, Li K-X, Guo Y-M, Jiang S-Y, Liao W-Z, Guo X-G. Serum selenium and fasting blood glucose: a cross-sectional study in women of different menopause status. BMC Womens Health. 2024;24(1):341.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Schomburg L. Selene, the goddess of the moon: does she shine on men only? Eur Heart J. 2007;28(16):2043–a.

    Article  PubMed  Google Scholar 

  57. Wang X, Vatamaniuk M, Wang S, Roneker C, Simmons R, Lei X. Molecular mechanisms for hyperinsulinaemia induced by overproduction of selenium-dependent glutathione peroxidase-1 in mice. Diabetologia. 2008;51:1515–24.

    Article  CAS  PubMed  Google Scholar 

  58. Schomburg L, Schweizer U. Hierarchical regulation of selenoprotein expression and sex-specific effects of selenium. Biochim et Biophys Acta (BBA)-General Subj. 2009;1790(11):1453–62.

    Article  CAS  Google Scholar 

  59. Misu H, Takamura T, Takayama H, Hayashi H, Matsuzawa-Nagata N, Kurita S, et al. A liver-derived secretory protein, selenoprotein P, causes insulin resistance. Cell Metabol. 2010;12(5):483–95.

    Article  CAS  Google Scholar 

  60. Méplan C, Crosley LK, Nicol F, Beckett GJ, Howie AF, Hill KE, et al. Genetic polymorphisms in the human selenoprotein P gene determine the response of selenoprotein markers to selenium supplementation in a gender-specific manner (the SELGEN study). FASEB J. 2007;21(12):3063–74.

    Article  PubMed  Google Scholar 

  61. Kuzuya M, Ando F, Iguchi A, Shimokata H. Glutathione peroxidase 1 Pro198Leu variant contributes to the metabolic syndrome in men in a large Japanese cohort. Am J Clin Nutr. 2008;87(6):1939–44.

    Article  CAS  PubMed  Google Scholar 

  62. Alanne M, Kristiansson K, Auro K, Silander K, Kuulasmaa K, Peltonen L, et al. Variation in the selenoprotein S gene locus is associated with coronary heart disease and ischemic stroke in two independent Finnish cohorts. Hum Genet. 2007;122:355–65.

    Article  CAS  PubMed  Google Scholar 

  63. Casanova P, Monleon D. Role of selenium in type 2 diabetes, insulin resistance and insulin secretion. World J Diabetes. 2023;14(3):147.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Joseph J, Loscalzo J. Selenistasis: epistatic effects of selenium on cardiovascular phenotype. Nutrients. 2013;5(2):340–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Metes-Kosik N, Luptak I, DiBello PM, Handy DE, Tang SS, Zhi H, et al. Both selenium deficiency and modest selenium supplementation lead to myocardial fibrosis in mice via effects on redox‐methylation balance. Mol Nutr Food Res. 2012;56(12):1812–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. McClung JP, Roneker CA, Mu W, Lisk DJ, Langlais P, Liu F et al. Development of insulin resistance and obesity in mice overexpressing cellular glutathione peroxidase. Proceedings of the National Academy of Sciences. 2004;101(24):8852-7.

  67. Fontenelle LC, Feitosa MM, Morais JBS, Severo JS, Freitas TECd, Beserra JB, et al. The role of selenium in insulin resistance. Brazilian J Pharm Sci. 2018;54:e00139.

    Google Scholar 

  68. Steinbrenner H. Interference of selenium and selenoproteins with the insulin-regulated carbohydrate and lipid metabolism. Free Radic Biol Med. 2013;65:1538–47.

    Article  CAS  PubMed  Google Scholar 

  69. Zhong Q, Lin R, Nong Q. Adiposity and serum selenium in US adults. Nutrients. 2018;10(6):727.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Hellwege JN, Palmer ND, Ziegler JT, Langefeld CD, Lorenzo C, Norris JM, et al. Genetic variants in selenoprotein P plasma 1 gene (SEPP1) are associated with fasting insulin and first phase insulin response in hispanics. Gene. 2014;534(1):33–9.

    Article  CAS  PubMed  Google Scholar 

  71. Stefanowicz FA, Talwar D, O’Reilly DS, Dickinson N, Atkinson J, Hursthouse AS, et al. Erythrocyte selenium concentration as a marker of selenium status. Clin Nutr. 2013;32(5):837–42.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Kaifeng Science and Technology Development Program, Project No. 2403029.

Author information

Authors and Affiliations

  1. Department of Endocrinology, Huaihe Hospital of Henan University, Kaifeng, 475000, China

    Rongrong Yuan & Jiakai Han

  2. Department of Nephrology, Huaihe Hospital of Henan University, Kaifeng, 475000, China

    Yu Zhang

Authors
  1. Rongrong Yuan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yu Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jiakai Han
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

This article was put forward by RY and JH performed data collection, data analysis, and article preparation. YZ and JH performed the analysis and collation of the literature. RY conceived of the study idea and article review. All authors read and approved the fnal manuscript.

Corresponding author

Correspondence to Jiakai Han.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

Kaifeng Science and Technology Development Plan in 2024 (Key science and technology projects)-2403029.

Additional information

Publisher’s note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuan, R., Zhang, Y. & Han, J. The association of selenium exposure with the odds of metabolic syndrome: a dose-response meta-analysis. BMC Endocr Disord 25, 49 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12902-025-01858-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12902-025-01858-8

Keywords

BMC Endocrine Disorders

ISSN: 1472-6823

Contact us