Association of heavy metals and bio-elements blood level with metabolic syndrome: a systematic review and meta-analysis of observational studies

Motahareh Hasani; Maryam Khazdouz; Sahar Sobhani; Parham Mardi; Shirin Riahi; Fahimeh Agh; Armita Mahdavi-Gorabi; Sahar Mohammadipournami; Fatemeh Gomnam; Mostafa Qorbani

doi:10.1007/s40200-024-01500-9

. 2024 Nov 4;23(2):1719–1752. doi: 10.1007/s40200-024-01500-9

Association of heavy metals and bio-elements blood level with metabolic syndrome: a systematic review and meta-analysis of observational studies

Motahareh Hasani ¹, Maryam Khazdouz ², Sahar Sobhani ³, Parham Mardi ³, Shirin Riahi ⁴, Fahimeh Agh ⁵, Armita Mahdavi-Gorabi ⁹, Sahar Mohammadipournami ⁶, Fatemeh Gomnam ^6,^7,^✉, Mostafa Qorbani ^3,^8,^✉

PMCID: PMC11599521 PMID: 39610503

Abstract

Background and objectives

The literature has reported heavy metals might alter the physiological and biochemical functions of body organs and cause several health problems. So, the present systematic review and meta-analysis aimed to investigate the association of blood levels of essential or non-essential metals with metabolic syndrome (MetS).

Methods

In this systematic review, some international databases including PubMed, Embase, Scopus, and Web of Science were searched up to February 2024. All observational studies which assessed the association of three heavy metals (cadmium, mercury, lead) and bio-elements (chromium, iron, manganese, and magnesium, copper) with the risk of MetS were included. There was no limitation in the time of publication and language. A random-effects meta-analysis was performed to estimate the pooled effect sizes. Possible sources of heterogeneity were explored by meta-regression analysis.

Results

Totally, 29 studies were eligible for meta-analysis. Our results showed that increased level of cadmium (pooled OR: 1.24, 95% CI: 1.05, 1.46) and mercury (pooled OR: 1.22, 95% CI: 1.08, 1.38) significantly increased the risk of MetS. In contrast, increased level of chromium significantly reduced the risk of developing MetS (pooled OR: 0.68, 95% CI: 0.56, 0.83). Moreover, association between lead, iron, copper, magnesium, and manganese with MetS was not statistically significant (P > 0.05). However, elevated lead levels in men increased the odds of MetS.

Conclusion

Our results show a significant association between blood levels of some heavy metals, including cadmium, mercury, and lead, with increased odds of MetS. On the other hand, chromium as a biometal decreased the odds of MetS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01500-9.

Keywords: Metabolic syndrome, Heavy metals, Elements, Cadmium, Mercury, Chromium, Heavy metal poisoning, Trace elements

Introduction

Metals are naturally widespread throughout the earth and generally enter the body by air inhalation, food and water ingestion, exposure to a contaminated environment, and use of metal products [1, 2]. Today, exposure to heavy metals is increasing due to improper usage of potential sources, including industrial production, mining, or oil and coal combustion [3, 4]. Heavy metals (mercury (Hg), cadmium (Cd), and lead (Pb)) are known as toxic and non-essential metals that can induce broad harmful metabolic problems [3]. Although some metals are vital to maintaining the metabolism of human beings, excessive and insufficient concentrations of them could interfere with the normal regulation of biological functions via several mechanisms [5, 6]. Recently, there has been an increasing concern about public health associated with environmental contamination by metals [3]. Based on epidemiological data, of the over 1 million illnesses estimated to be caused by the four metals in 2015, 54% are due to lead, 22% are due to methyl mercury, 20% are due to arsenic, and 1% are due to cadmium [7]. Several studies have shown that metals may change the biochemical and physiological functions of body organs and lead to a number of health issues, including organ failure, immune system problems, cancer, heart disease, and metabolic disorders [4, 8, 9]. Previous studies have shown that exposure to heavy metals is associated with increased levels of lipid profiles and glycemic indices as components of the metabolic syndrome (MetS). Animal studies have illustrated that heavy metals increase the production of reactive oxygen species (ROS), induce pancreatic cell apoptosis, decrease insulin gene promoters’ activity, and directly disturb the regulation of glucose [10–12]. Moreover, several studies have demonstrated that increasing levels of heavy metals are related to the development of MetS [13–15]. On the other hand, there is an association between the concentration of some essential metals, including copper, chromium, magnesium, manganese, and iron, and MetS and diabetes [16, 17]. For instance, copper, as a key co-factor of oxidative stress-related enzymes, is involved in the balance maintenance of redox reactions through the rotation between copper (I), a reduced form, and copper (II), an oxidized form [18, 19]. Then, excessive levels of copper have been related to an increase in oxidative stress and cellular damage [3, 20]. While the previous evidence supported that chromium and magnesium might have protective effects by diminishing insulin resistance and dyslipidemia [21, 22].

MetS is a group of five conditions that can lead to cardiovascular disease, diabetes, and other health problems [23]. MetS is diagnosed with three or more risk factors in someone: impaired blood glucose, dyslipidemia, abdominal obesity, and hypertension [24]. MetS is one of the most critical general health problems, and its prevalence has been growing worldwide [25, 26]. According to the previous data, nearly one-quarter or one-third of adults in developed and developing countries struggle with the risk of MetS and its consequences [27, 28]. The underlying mechanisms of MetS are complex, and some environmental and genetic risk factors contribute to the incidence of the syndrome [29]. The association of some essential and heavy metals with the incidence of MetS was shown in researches [16, 30, 31]. Whereas the results are inconsistent in other studies [32]. A recent meta-analysis have reported that levels of heavy metals were significantly high in the participants with MetS [33]. Although some meta-analyses have evaluated the relation between heavy metals exposure and Mets [33–36], no review has investigated the association of blood levels of essential or non-essential metals with MetS. The present systematic review and meta-analysis aimed to pool the association of blood levels of heavy metals and bio-elements with MetS.

Methods

Search strategy

This systematic review and meta-analysis was designed and implemented to assess the association of heavy metals and bio-elements blood level with MetS in accordance with the Meta-analysis of Observational Studies protocol [34] and was presented based on PRISMA guidelines [35]. The present study was registered in PROSPERO with registration ID number CRD42021277129.

International databases, including Scopus, PubMed, Web of Science, Embase were searched systematically until February 30, 2024. The most courses for catchphrase combination extricated from ((("Mercury" OR "Cadmium") OR ("Lead" OR "Pb")) AND ("element" OR " metal " OR "Chromium" OR "Bio element" OR "Iron" OR "manganese" OR "Magnesium" OR "Copper"))) AND ("Metabolic syndrome" OR "Syndrome X") and all related terms. Moreover, to recognize additional studies, we checked the references cited inside the important articles. To distinguish qualified studies, two of researchers searched specified databases independently, and all retrieved articles were managed by EndNote X9 computer program (Clarivate Analytics, PA, USA).

Study selection

In the present study, articles that met the inclusion criteria and assessed the relationship(s) of at least one of the three heavy metals or six bio-elements with MetS in humans were included. There were no limitation for the language, the time of research or publication, and gender. Articles were excluded from the meta-analysis if they had any of the following criteria: 1) duplicated article; 2) full-text not available; 3) studies conducted in animals or cells; 4) those studies that evaluate the association of environmental exposure to heavy metal(s) or bio-elements with MetS.

Data extraction and risk of bias assessment

Two of the authors were assessed the results of searches and extracted data independently based on the inclusion and exclusion criteria. The checklist was used for the extraction step that included general information and various items such as the first author, publication year, study design, study country, sample size, scope of study (local study or survey), MetS diagnostic criteria, age and gender of participants, heavy metals or bioelements and their specimens, exposure levels, and detection methods. If a study reported results for different metals (in a specific gender or both genders separately), the reported effect sizes (ESs) were considered as different studies in the meta-analysis. The means and standard deviations (SDs) of metal concentrations in human blood, as well as sample size numbers of MetS patients and controls, were extracted. The Newcastle–Ottawa Quality Assessment Scale was used to assess the quality of the included studies [36]. The Kappa statistic for agreement on quality assessment was considered to be 0.94. Possible contradictions and disagreements were resolved with the supervision of a third expert's opinion.

Data analysis

The odds ratio (OR) with a 95% confidence interval (CI) was considered the effect size (ES) for the association of heavy metals and bio-elements with MetS. The results of the pooled estimate were calculated using ORs and prevalence ratios (PRs). Moreover, to take full advantage of the available data for comprehensive meta-analysis, the standardized mean difference (SMD) was calculated (random effect analysis, Cohenʼd) as an ES using the available means and SDs of metal or bio-element concentrations and sample size numbers, and then the SMD was converted to OR based on Murad et al. report [37].

The Chi-square-based Q-test and the I² index were used to look at the statistical heterogeneity between the studies. A Q-test result of P < 0.1 was considered statistically significant. Based on the value of I², the pooled ES was calculated using a random-effect meta-analysis model (with the Der-Simonian and Laird method) when I² was more than 75%. Moreover, a forest plot was used to present the result of the meta-analysis schematically. To identify the possible source of heterogeneity, subgroup meta-analyses were performed according to the type of heavy metals (Cadmium, Lead, or Mercury) and bio-elements (Chromium, Copper, Magnesium, Manganese, Iron, and Ferritin), gender, age group, sample size, and study design. Meta-regression was applied to investigate the effects of age, gender, female ratio, total sample size, and study design on heterogeneity between studies. Egger's test was used to assess the publication bias; it was presented schematically by funnel plots. Also, one-out-remove method sensitivity analysis was conducted to detect potential outliers. To assess the overall association between heavy metals and MetS, the results of all studies that evaluated the association between MetS and blood levels of cadmium, mercury, and lead were combined. The Stata Software Vision 17 (StataCorp, College Station, TX, USA) were used for analyzes. A P value < 0.05 was considered to have statistical significance.

Results

Search results

A total of 8,536 research studies were initially identified in the databases mentioned above. Of the initially retrieved articles, 762 duplicated articles were removed, and 7,775 remained. After reviewing titles and abstracts from the remaining 7,675 articles, they were excluded because their design and population were not interesting or relevant. Finally, after reviewing the retrieved full texts as well as the full text for cited references and then qualitative synthesis, twenty-nine observational studies were eligible for the meta-analysis (Fig. 1).

Study characteristics

Overall, 29 articles comprised 80 ESs on different populations, and metals were included. Of the eligible studies, there were 8 case–control studies [22, 38–44], one prospective cohort studies [45], and 20 cross-sectional studies [14, 16, 17, 30–32, 46–59].

Most studies were conducted in Asia, for the most part in Korea, while one study was conducted in the USA [14], one in South America [38], and five in Europe [39, 45, 51, 59, 60].

To define MetS, ninteen studies used the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria of the US, two studies defined according to the American Heart Association and the National Heart, Lung, and Blood Institute [47, 51], one study used International Diabetes Foundation [16], two defined it based on Chinese Diabetes Society [42, 50], one another used the Chinese guidelines for the prevention and treatment of dyslipidemia in adults (2007) [40], one used abdominal obesity diagnostic criteria published by the Korean Society for the Study of Obesity (KSSO) [57], and three studies defined MetS based on the harmonized definition for MetS(2009) [14, 22, 54].

The sample size ranged from 30 to 9,880 participants. Two studies only recruited males [16, 42], while one reported data on females [51]. All included studies studied adults (≥ 19 y). The characteristics of the included studies are presented in Table 1.

Table 1.

General characteristics of included studies

	Author, Year	Study design	Sample size	Study population	Mean Age (SD)	metal	Concentration mean (sd) /quartiles	measurement	sampling
1	Arredondo et al. 2011	Case control	MetS: 185 Control: 120	Chile	MetS: 54.2 (12.3) Control: 53.6 (10.1)	Iron (μg/dL)	MetS: 146.6 (59.2) Controls: 121.3 (51.9)	atomic absorption spectrometry with graphite furnace, Simaa 6100, Perkin Elmer	Blood Sample
2	Lee et al. 2012	Cross sectional	3783 > = 20 years old No MetS:3082 MetS:701	Representative sample of the non-institutionalized civilian population of South Korea	No MetS: 42.32 (0.294) MetS: 48.36 (0.574)	Cadmium (µg/dL)	Q1: ≤ 0.819 Q2:0.819–1.359 Q3: > 1.359	Cd and Pb were measured by graphite furnace atomic absorption spectrometry with Zeeman background correction (A AnalystTM 600; Perkin Elmer)	Blood Sample
						Lead (µg/dL)	Q1: ≤ 2.362 Q2:2.362–3.282 Q3: > 3.282
						Mercury (µg/dL)	Q1: ≤ 3.979 Q2:3.979–6.460 Q3: > 6.460	Hg was measured using the gold-amalgam collection method with DMA-80 (Milestone, Bergamo, Italy)
3	Rhee et al. 2013	Cross-sectional	1405 > = 20 years old	South Korea	No MetS: 40.3 (13.7) MetS: 47.1 (13.3)	lead (μg/dL)	Q1 = 0.42–1.73 Q2 = 1.74–2.35 Q3 = 2.35–3.06 Q4 = 3.07–19.43	Trace element EDTA tubes (BD, Franklin Lakes, NJ, USA)	Blood sample
						Mercury (μg/dL)	Mets: 4.96 (4.62–5.32) Control:4.62 (4.49–4.76)		Blood sample
						Cadmium (μg/dL)	Mets: 0.88 (0.82–0.95) Control:0.85 (0.83–0.88)		Blood sample
						Manganese (μg/dL)	Mets: 1.33 (1.28–1.38) Control:1.29 (1.27–1.31)		Blood sample
4	Jin et al. 2013	Cross sectional	1493 > = 20 years’ old No MetS:1351 MetS:142	China	50.7(9.8)	lead (μg/dL)	MetS: 0.17 (0.10) Controls: 0.15 (0.10)	Inductively Coupled Plasma atomic emission Spectrometry (ICP-AES)	Blood Sample
						Cadmium (μg/dL)	MetS: 17.16 (6.82) Controls: 14.49 (6.74)
						Manganese (μg/dL)	MetS: 0.23 (0.12) Controls: 0.23 (0.13)
						Chromium (μg/dL)	MetS: 5.72 (2.49) Controls: 6.36 (2.71)
						Copper (mg/dL)	MetS: 1.05 (0.36) Controls: 0.99 (0.21)
						Magnesium (mg/dL)	MetS: 0.20 (0.09) Controls: 0.19 (0.09)
						Iron (mg/dL)	MetS: 15.52 (7.29) Controls: 15.84 (7.54)
5	Eom et al. 2014	Cross-sectional	2114 > 19 years old	healthy adults	45.5 (14.6)	mercury (μg/dL)	Q1: < 2.99 Q2:2.99–4.88 Q3: ≥ 4.88	Thermal Decomposition Amalgamation Atomic Absorption Spectrophotometer (TDA/AAS)	Blood sample
6	Tavakoli-Hoseini et al. 2014	Case control	MetS: 176 Control: 209	Individuals aged 35–65 years	MetS: 54(7.9) Controls: 52.7(9.7)	Iron (μg/dL)	MetS: 133.2(64.8) Controls:102.4(44.7)	Serum ferritin levels were assayed with the using of commercial kits by standard enzyme-linked immunosorbent assay (Stat Fax 2100)	Blood sample
7	Moon et al. 2014	Cross-sectional	3950 > = 20 years old No MetS:3045 MetS:605	South Korea	No MetS: 40.3 (13.7) MetS: 47.1	lead (μg/dL)	Q1 = 123 ± 1.01 Q2 = 1.9 ± 1.00 Q3 = 2.5 ± 1.01 Q4 = 3.79 ± 1.01	Graphite-furnace atomic absorption spectrometry background correction (Perkin Elmer A Analyst was used for blood lead and cadmium A gold-amalgam collection method with a DMA-80 was used for blood mercury	Blood sample
						Mercury (μg/dL)	Q1 = 1.88 ± 1.01 Q2 = 3.15 ± 1.00 Q3 = 4.65 ± 1.00 Q4 = 8.73 ± 1.01
						Cadmium (μg/dL)	Q1 = 0.37 ± 1.01 Q2 = 0.78 ± 1.00 Q3 = 1.17 ± 1.00 Q4 = 1.94 ± 1.01
8	Chung et al. 2015	Cross sectional	Male: 2,976 Female: 3,074	participants in the KNHANES-V	Male: 46.3(0.6) Female: 47.8(0.7)	mercury (μg/dL)	Q1: ≤ 2.841 Q2:2.842–4.253 Q3:4.254–6.48 Q4: > 6.481	a cold-vapor atomic absorption spectrometric method using a dedicated mercury analyzer (M-6000A, CETAC Technologies, USA)	Blood sample
9	Rotter et al. 2015	Cross-sectional	MetS: 161 Control: 152	Volunteers men in north-western Poland	61.3(6.3)	lead (μg/dL)	MetS: 75.17 (21.4) Controls: 73.82 (21.7)	Inductively Coupled Plasma Mass Spectrometry using PerkinElmer ICP-MS	Blood sample
						Mercury (μg/dL)	MetS: 4.59(0.92) Controls: 4.51 (080)
						Cadmium (μg/dL)	MetS:1.55 (0.32) Controls: 1.53 (0.34)
						Manganese (μg/dL)	MetS: 1.90 (1.17) Controls: 1.79 (1.14)
						Chromium (μg/dL)	MetS: 0.46 (0.18) Controls: 0.47 (0.25)
						Copper (mg/L)	MetS: 1.08 (0.18) Controls: 1.09 (0.18)
						Magnesium (mg/L)	MetS: 20.6 (2.05) Controls: 21.28 (2.37)
						Iron (mg/L)	MetS: 1.19 (0.45) Controls: 1.23 (0.43)
10	Han et al. 2015	Cross sectional	200, 30–64 years’ old Men:96 Women:104	Healthy volunteers	51(7.3)	Cadmium (μg/dL)	NA	Graphite-furnace atomic absorption spectrometry background correction (Perkin Elmer A Analyst	Blood Sample
11	Kilani et al. 2015	Cohort	6733 participants aged 35–75 years Men: 3189 Female: 3544	Participants in CoLaus study	Men: 50.1(10.3) Women: 44(5.5)	Iron (μg/dL)	NA	Iron was assessed by colorimetric method (ferrozine, BioSystems)	Blood sample
12	Ghamarchehreh et al. 2016	Case control	MetS: 143 Control: 156	Patients with NAFLD	44.99(12.77)	Iron (μg/dL)	MetS: 151.86(490.62) Controls:108.92(39.37)	–	Blood Sample
13	Lee et al. 2016	Cross sectional	9880	Civilian population of South Korea	At least 20 years old	Cadmium (µg/dL)	Q1: ≤ 0.720 Q2:0.720–1.172 Q3: > 1.172	atomic absorption spectrometry with graphite furnace (Zeeman background correction analyst 600, Perkin Elmer, Turku, Finland)	Blood Sample
						Lead (µg/dL)	Q1: ≤ 2.199 Q2:2.199–3.011 Q3: > 3.011
						Mercury (µg/dL)	Q1: ≤ 3.521 Q2:3.521–5.933 Q3: > 5.933	Gold-amalgam collection method with DMA-80 (Milestone, Bergamo, Italy)
14	Lee et al. 2017	Cross-sectional	1827 > = 20 years old No MetS:1408 MetS:419	South Korea	-	Lead (μg/dL)	Q1: ≤ 1.48 Q2:1.48–2.57 Q3: > 2.57	Graphite-furnace atomic absorption spectrometry background correction (Perkin Elmer A Analyst was used for blood lead and cadmium A gold-amalgam collection method with a DMA-80 was used for blood mercury	Blood sample
						Mercury (μg/dL)	Q1: ≤ 2.10 Q2:2.10–4.87 Q3: > 4.87
						Cadmium (μg/dL)	Q1: ≤ 0.57 Q2:0.57–1.31 Q3: > 1.31
15	El Sayed et al. 2017	case–control	Total: 30 No MetS:15 MetS:15	patients having metabolic syndrome and healthy volunteer	60.47(4.47)	magnesium (mg/dL)	MetS: 1.03 (0.31) Controls: 2.19 (0.31)	colorimetric methods from biodiagnostic CO (Diagnostic and Research Reagents)	Blood Sample
15	El Sayed et al. 2017	case–control	Total: 30 No MetS:15 MetS:15	patients having metabolic syndrome and healthy volunteer	60.47(4.47)	Copper (μg/dL)	MetS: 69.73(14.69) Controls: 105.93(30.56)		Blood Sample
16	Stechemesser et al. 2017	Cross sectional	Total: 107 No MetS:53 MetS:54	Austria	56.4(6.4)	Iron (μg/dL)	MetS: 99.8(32.2) Controls:108.3(34.5)	–-	Blood Sample
17	Park et al. 2018	Cross sectional	2833 > = 20 years’ old No MetS:2265 MetS:568	participants in the 7th KNHANES	Female: 60.07 Male: 53.80	lead (μg/dL)	Q1: < 2 Q2:2- < 3 Q3: 3- < 4 Q4: ≥ 4	atomic absorption spectrophotometry using the PerkinElmer AAnalsyt 600	Blood Sample
18	Lee et al. 2018	Cross sectional	4530 > = 20 years’ old No MetS:3619 MetS:911	participants in the 6th KNHANES	44.7 (14.7)	Mercury (μg/dL)	Q1 = 1.59 ± 1.30 Q2 = 2.67 ± 1.12 Q3 = 3.40 ± 1.13 Q4 = 7.62 ± 1.43	a gold-amalgam collection method with a DMA-80	Blood Sample
19	Guo et al. 2019	Case–Control	145 male No MetS:65 MetS:80	Participants who refer to Physical Examination Center affiliated with Capital Medical University, China	39(12)	Cadmium (μg/dL)	Q1: ≤ 0.11 Q2:0.53 Q3: 1.13 Q4: ≥ 3.35	Inductively coupled plasma mass spectrometry (ICP-MS)	Blood Sample
						lead(μg/dL)	Q1: ≤ 27.6 Q2:59.9 Q3: 78.5 Q4: ≥ 100.8
						Copper (mg/L)	Q1: ≤ 0.57 Q2:0.68 Q3: 0.73 Q4: ≥ 1.108
20	Fang et al. 2019	A nested case–control	698 > = 18 years’ old No MetS:349 MetS:349	individuals who developed metabolic syndrome during a 3-year follow-up	64.5(7.8)	Copper (mg/L)	Female: Q1: < 0.90 Q2:0.90–0.98 Q3: > 0.98 Male: Q1: < 0.87 Q2:0.87–0.94 Q3: > 0.94	flame atomic absorption spectrometry (SpectrAA240FS; Varian, USA)	Blood Sample
21	Shim et al. 2019	Cross sectional	5251 > = 20 years’ old No MetS:4673 MetS:578	Participants in Korean National Environmental Health Survey II (2012–2014, KNEHS)	61.59(0.50)	lead (μg/dL)	MetS: 0.759(0.487) Controls:0.713(0.482)	Graphite-furnace atomic absorption spectrometry background correction (Perkin Elmer A Analyst was used for blood lead A gold-amalgam collection method with a DMA-80 was used for blood mercury	Blood Sample
21	Shim et al. 2019	Cross sectional	5251 > = 20 years’ old No MetS:4673 MetS:578		61.59(0.50)	mercury (μg/dL)	MetS: 1.165(0.664) Controls:1.18(0.640)		Blood Sample
22	Bulka et al. 2019	Cross sectional	Total: 1088	non-institutionalized civilian resident population of the U.S	Female: 47.3 Male: 52.7	lead (μg/dL)	NA	inductively coupled plasma mass spectrometry (ICP-MS)	Blood Sample
						methylmercury (μg/dL)
						Cooper (μg/dL)
						Manganese (μg/dL)
23	Kaminska et al. 2020	Cross sectional	169 No MetS:47 MetS:122	women between 44–65 years from general population	54.49(5.65)	lead (μg/dL)	MetS: 5.64 (2.03) Controls: 8.01 (4.38)	inductively coupled plasma optical emission spectrometry (ICP-OES	Blood Sample
24	Chen et al. 020	Case–Control	4282 > = 18 years’ old No MetS:2141 MetS:2141	general population undergoing a routine health checkup	52.6(10.8)	Chromium (μg/dL)	Q1: < 3.27 Q2:3.28–4.46 Q3: 4.47–5.87 Q4: > 5.87	inductively coupled plasma mass spectrometry (ICP-MS)	Blood Sample
25	Wen, et al. 020	Cross sectional	2444 > = 20 years’ old No MetS:1618 MetS:826	general population in southern Taiwan	55.1(13.2)	lead (μg/dL)	MetS: 1.6 (1.1–2.3) Controls: 1.5 (1.0,2.2)	Graphite-furnace atomic absorption spectrometry background correction (Perkin Elmer)	Blood Sample
26	Park et al. 2020	Cross-sectional	823,19–29 years old	Republic of Korea	23.57	lead (μg/dL)	MetS: 1.57 (0.821) Controls: 1.34 (0.548)	atomic absorption spectrophotometry on a PerkinElmer Analyst 600	Blood sample
						Mercury (μg/dL)	MetS: 3.29 (2.105) Controls: 2.95 (1.848)
						Cadmium (μg/dL)	MetS: 0.73 (0.428) Controls: 0.57 (0.325)
27	Lo et al. 2021	Cross-sectional	3335 > = 18 years’ old Male: 1605 Female: 1730	participants in the United States National Health and Nutrition Examination Survey 2011–2016	––	Manganese (μg/dL)	Q1: < 7.63 Q2:7.63–9.42 Q3: 9.42–11.91 Q4: > 11.91	Inductively Coupled Plasma Mass Spectrometry using PerkinElmer ICP-MS	Blood sample
28	Duc et al. 2021	Cross sectional	60256 > = 20 years’ old Men:27429 Women:32827	participated in the KNANES 2009–2103 and 2016– 2017 surveys	40.8(22.8)	Lead (μg/dL)	MetS: 2.34 (1.22) Controls: 1.96 (1.03)	Graphite-furnace atomic absorption spectrometry background correction (Perkin Elmer A Analyst was used for blood lead and cadmium A gold-amalgam collection method with a DMA-80 was used for blood mercury	Blood Sample
						Mercury (μg/dL)	MetS: 4.78 (3.87) Controls: 3.83 (3.37)
						Cadmium (μg/dL)	MetS: 1.26 (0.69) Controls: 0.93 (0.64)
29	Lu et al. 2021	Case–Control	1165 > = 18 years’ old No MetS:446 MetS:709	participants enrolled at a medical center in Northern Taiwan 2007–2017	65.7	Cu (μg/L)	MetS: 1101.2 (322.5) Controls: 949.5 (253.3)	Inductively Coupled Plasma Mass Spectrometry using PerkinElmer ICP-MS	Blood Sample
29	Lu et al. 2021	Case–Control	1165 > = 18 years’ old No MetS:446 MetS:709		65.7	Fe (μg/L)	MetS: 1370 (577.7) Controls: 1051.6 (403.6)		Blood Sample

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Data synthesis and statistical analysis

Among the 125,890 participants involved in the included studies, the number of studies on the association between MetS and exposure to cadmium, lead, and mercury were 11, 15, and 13, respectively. While the relationships between MetS and concentrations of chromium, iron, copper, magnesium, and manganese were assessed in 3, 9, 7, 3, and 5 studies, respectively. The results of the included studies are shown in Table 2.

Table 2.

Association between heavy metal exposure with risk of metabolic syndrome according to type of exposure and outcome

Author, Year	Type of metal	Unit of exposure (categorical/continues)	Outcome	Unit of outcome(categorical/continues)	Definition of outcome	Statistical measure (SE)	Results	Significant association	Country
Lead (pb)
Rotter et al.2015	lead (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on criteria of IDF	OR(95% CI)	1.12(0.75,1.68)	No	Poland
Rhee et al. 2013	lead (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	2.57 (1.46,4.51)	Yes	South Korea
Park et al. 2020	lead (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	Male:0.97(0.38,2.45) Female: 0.04(0.001,1.67)	No	Republic of Korea
Moon et al.2014	lead (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.07(0.79,1.5)	Yes	South Korea
Lee et al. 2012	lead (μg/dL)	Categorical(q3/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.97(0.819,1.141)	No	South Korea
Lee et al. 2017	lead (μg/dL)	Categorical(q3/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.37(1.02,1.84)	Yes	South Korea
Lee et al. 2016	lead (μg/dL)	Categorical(q3/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.80 (0.60,1.049)	No	South Korea
Jin et al. 2013	lead (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.61(1.15,2.24)	Yes	China
Guo et al. 2019	lead (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.81(0.83,3.92)	No	China
Duc et al. 2021	lead (μg/dL)	Continues	MetS	Categorical	American Heart Association/National Heart, Lung, and Blood Institute for clinical diagnosis	OR(95% CI)	1.14(1.03,1.26)	Yes	Korea
Bulka et al. 2019	lead (μg/dL)	Categorical(q3/q1)	MetS	Categorical	The definition of MetS was based on the harmonized definition for MetS	OR(95% CI)	0.82 (0.54, 1.04)	No	USA
Kaminska et al. 2020	lead (μg/dL)	Continues	MetS	Categorical	American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI), 2009	OR(95% CI)	0.33(0.17,0.60)	Yes	Poland
Park et al. 2018	lead (μg/dL)	Categorical(q4/q1)	MetS	Categorical	The definition of MetS using abdominal obesity diagnostic criteria published by the Korean Society for the Study of Obesity (KSSO)	OR(95% CI)	Male: 1.17(1.01,1.36) Female: 1.09(0.93,1.29)	Male: Yes Female: No	Korea
Shim et al. 2019	lead (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.86(0.65,1.14)	No	South Korea
Wen et al. 2020	lead (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.86(0.61,1.2)	No	Taiwan
Mercury
Rotter et al. 2015	Mercury (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on IDF	OR(95% CI)	1.18(0.79,1.77)	No	Poland
Rhee et al. 2013	Mercury (μg/dL)	Categorical(q4/q1)	Mets	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.19 (0.76–1.87)	No	South Korea
Park et al. 2020	Mercury (μg/dL)	Continues	Mets	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	Male:0.79(0.53,1.18) Female: 0.73(0.34,1.58)	No	Republic of Korea
Moon et al. 2014	Mercury (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.13(0.86,1.49)	No	South Korea
Lee et al. 2012	Mercury (μg/dL)	Categorical(q3/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.994(0.870,1.136)	No	South Korea
Lee et al. 2017	Mercury (μg/dL)	Categorical(q3/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.43 (1.05,1.94)	Yes	South Korea
Lee et al. 2016	Mercury (μg/dL)	Categorical(q3/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.96 (0.79,1.16)	No	South Korea
Duc et al. 2021	Mercury (μg/dL)	Continues	MetS	Categorical	American Heart Association/National Heart, Lung, and Blood Institute for clinical diagnosis	OR(95% CI)	1.03(1.02,1.06)	Yes	Korea
Bulka et al. 2019	methylmercury (µg/dL)	Categorical(q3/q1)	MetS	Categorical	The definition of MetS was based on the harmonized definition for MetS	OR(95% CI)	1.10 (0.91–1.33)	No	USA
Shim et al. 2019	Mercury (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.99(0.73,1.34)	No	South Korea
Chung et al. 2015	Mercury (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	Men: 1.62(1.15,1.36)	Yes	South Korea
Chung et al. 2015	Mercury (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	Women: 0.84(0.57,1.24)	No	South Korea
Eom et al. 2014	Mercury (μg/dL)	Categorical(q3/q1)	Mets	Categorical	Definition of MetS based on NECP ATP III	OR (95%CI)	1.68 (1.25–2.25)	Yes	South Korea
Lee et al. 2018	Mercury (μg/dL)	Categorical(q4/q1)	Mets	Categorical	The definition of MetS was based on the harmonized definition for MetS 2009	OR (95%CI)	Overall: 2.02(1.59,2.56)	Yes	South Korea
							Men: 1.97(1.39,2.79)
							Women: 1.90(1.34,2.70)
Cadmium
Rotter et al. 2015	Cadmium (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on criteria of IDF	OR(95% CI)	1.11(0.75,1.67)	No	Poland
Rhee et al. 2013	Cadmium (μg/dL)	Categorical(q4/q1)	Mets	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.09 (0.65–1.83)	No	South Korea
Park et al. 2020	Cadmium (μg/dL)	Continues	Mets	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	Male:1.28(0.13,12.20) Female: 33.79 (0.32,3596.31)	No	Republic of Korea
Moon et al. 2014	Cadmium (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.91(0.73,1.14)	No	South Korea
Lee et al. 2012	Cadmium (μg/dL)	Categorical(q3/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR ( 95% CI)	1.23(1.047,1.447)	Yes	South Korea
Lee et al. 2017	Cadmium (μg/dL)	Categorical(q3/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR ( 95% CI)	1.10 (0.76,1.59)	No	South Korea
Lee et al. 2016	Cadmium (μg/dL)	Categorical(q3/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.31 (1.05,1.62)	Yes	South Korea
Jin et al. 2013	Cadmium (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.58(1.15,2.2)	Yes	China
Han et al. 2015	Cadmium (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	Female: 0.52(0.23,1.19) Male: 3.05(1.38,6.70)	No	Republic of Korea
Guo et al. 2019	Cadmium (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	2.2(1.03,4.72)	Yes	China
Duc et al. 2021	Cadmium (μg/dL)	Continues	MetS	Categorical	American Heart Association/National Heart, Lung, and Blood Institute for clinical diagnosis	OR(95% CI)	1.01(1.04,1.14)	Yes	Korea
Chromium
Rotter et al. 2015	Chrome (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on IDF	OR(95% CI)	0.92(0.60,0.78)	Yes	Poland
Jin et al. 2013	Chrome (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.65(0.48,0.78)	Yes	China
Chen et al. 2020	Chromium (μg/dL)	Categorical(q4/q1)	MetS	Categorical	The definition of MetS was based on the harmonized definition for MetS in 2009	OR(95% CI)	0.62(0.49,0.78)	Yes	China
Manganese
Lo et al. 2021	Manganese (μg/dL)	Categorical(q4/q1)	Mets	Categorical	Definition of MetS based on NECP ATP III	OR (95%CI)	Overall: 0.66(0.38,1.14)	No	China
							Men: 0.5(0.21,1.17)	No
							Women: 0.89(0.45,1.77)	No
Rotter et al. 2015	Manganese (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on IDF	OR (95%CI)	1.19(0.79,1.78)	No	Poland
Rhee et al. 2013	Manganese (μg/dL)	Categorical(q4/q1)	Mets	Categorical	Definition of MetS based on NECP ATP III	OR (95%CI)	1.22 (0.76–1.97)	Yes	South Korea
Bulka et al. 2019	Manganese (μg/dL)	Categorical(q3/q1)	MetS	Categorical	The definition of MetS was based on the harmonized definition for MetS	PR (95% CI)	1.04 (0.87–1.23)	No	USA
Jin et al. 2013	Manganese (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR (95%CI)	1.48(1.09,2.18)	Yes	China
Copper
Rotter et al. 2015	Copper (mg/dL)	Continues	MetS	Categorical	Definition of MetS based on IDF	OR (95%CI)	0.90(0.60,1.35)	No	Poland
Jin et al. 2013	Copper (mg/dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR (95%CI)	1.4(1.1,2)	Yes	China
Guo et al. 2019	Copper (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	3.13(1.47,6.63)	Yes	China
Fang et al. 2019	Copper mg/L	Categorical(q3/q1)	MetS	Categorical	based on the Chinese guidelines for the prevention and treatment of dyslipidaemia in adults (2007)	OR(95% CI)	Male:0.91(0.49,1.70) Female:1.28(0.81,2.01)	No	China
El Sayed et al. 2017	Copper (μg/dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.91(0.62,1.12)	No	Egypt
Bulka et al. 2019	Copper (μg/dL)	Categorical(q3/q1)	MetS	Categorical	The definition of MetS was based on the harmonized definition for MetS	PR (95% CI)	0.94 (0.80–1.11)	No	USA
Lu et al. 2021	Iron (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	2.02 (1.25–3.25)	Yes	Taiwan
Magnesium
El Sayed et al. 2017	Magnesium (mg/L)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	0.32(0.15,0.56)	Yes	Egypt
Rotter et al. 2015	Magnesium (mg/L)	Continues	MetS	Categorical	Definition of MetS based on IDF	OR(95% CI)	0.57(0.38,0.85)	Yes	Poland
Jin et al. 2013	Magnesium (mg/L)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	1.23(0.89,1.68)	No	China
Iron
Rotter et al. 2015	Iron (μg /dL)	Continues	MetS	Categorical	Definition of MetS based on IDF	Mean (SD)	MetS: 1.19 (0.45) Controls: 1.23 (0.43)	MetS/Controls: No	Poland
Jin et al. 2013	Iron (μg /dL)	Continues	MetS	Categorical	Definition of MetS based on NECP ATP III	Mean (SD)	MetS: 15.52 (7.29) Controls: 15.84 (7.54)	No	China
Arredondo et al. 2011	Iron (μg/dL)	Continues	Mets	Categorical	Definition of MetS based on NECP ATP III	Mean (SD)	MetS: 146.6 (59.2) Controls: 121.3 (51.9)	MetS/Controls: Yes	Chile
Stechemesser et al. 2017	Iron (μg/dL)	Continues	Mets	Categorical	Definition of MetS based on NECP ATP III	Mean (SD)	MetS: 99.8(32.2) Controls:108.3(34.5)	No	Austria
Ghamarchehreh et al. 2016	Iron (μg/dL)	Continues	Mets	Categorical	Definition of MetS based on NECP ATP III	Mean (SD)	MetS: 151.86(490.62) Controls:108.92(39.37)	No	Iran
Tavakoli-Hoseini et al. 2014	Iron (μg/dL)	Continues	Mets	Categorical	Definition of MetS based on NECP ATP III	Mean (SD)	MetS: 133.2(64.8) Controls:102.4(44.7)	Yes	Iran
Kilani et al. 2015	Iron (μg/dL)	Categorical(q4/q1)	Mets	Categorical	Definition of MetS based on NECP ATP III	OR (95%CI)	Men: 0.81(0.53,1.24)	No	Switzerland
Kilani et al. 2015	Iron (μg/dL)	Categorical(q4/q1)	Mets	Categorical	Definition of MetS based on NECP ATP III	OR (95%CI)	Women: 0.51(0.33,0.80)	Yes	Switzerland
Chen et al. 2020	Iron (μg/dL)	Categorical(q4/q1)	MetS	Categorical	The definition of MetS was based on the harmonized definition for MetS in 2009	OR(95% CI)	0.79(0.69,0.91)	Yes	China
Lu et al. 2021	Iron (μg/dL)	Categorical(q4/q1)	MetS	Categorical	Definition of MetS based on NECP ATP III	OR(95% CI)	2.11 (1.24–3.62)	Yes	Taiwan

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Heavy metals and MetS

To assess the overall association between heavy metals and MetS, the results of all studies that evaluated the association between blood levels (cadmium, mercury, and lead) with MetS were combined and the pooled effect size is presented in Fig. 2A. Random effect meta-analysis showed that participants in the highest categories of heavy metal exposure were more likely to develop MetS [pooled OR 1.17, 95% CI: 1.07, 1.29; I² 89.7%, Q = 177.91; p < 0.001]. Due to considerable heterogeneity, the subgroup analyses was performed based on the potential factors including gender, study design, type of heavy metals, and sample size. The data obtained from the analysis of subgroups recognized that in the subgroup of male [pooled OR 1.37, 95% CI: 1.15,1.65; I² 48.9%, Q = 21.51; p = 0.02] as well as subgroup of both gender [pooled OR 1.14, 95% CI: 1.06,1.22; I² 73.9%, Q = 88.17; p < 0.001] a significant association was presented while in female subgroup [pooled OR 0.96, 95% CI: 0.72,1.29; I² 82.0%, Q = 49.89; p < 0.01] no association was revealed. The degree of heterogeneity in the subgroups of both gender and female did not change considerably, while in male it decreased to less than 50%.

Fig. 2 — Forests plots. A Forrest plots of association between blood level of 3 heavy metals and risk of metabolic syndrome. B Forrest plots of association between blood level of cadmium and risk of metabolic syndrome. C Forrest plots of association between blood level of lead and risk of metabolic syndrome. D Forrest plots of association between blood level of mercury and risk of metabolic syndrome. E Forrest plots of association between blood level of chromium and risk of metabolic syndrome. F Forrest plots of association between blood level of cooper and risk of metabolic syndrome. G Forrest plots of association between blood level of iron and risk of metabolic syndrome. H Forrest plots of association between blood level of magnesium and risk of metabolic syndrome. I Forrest plots of association between blood level of manganese and risk of metabolic syndrome

Also, the associations were significant in both subgroups of cross-sectional study [pooled OR 1.15, 95% CI: 1.07, 1.22; I² 74.8%, Q = 172.32; p < 0.01] and case–control study (one article with two ESʼs) [pooled OR 1.99, 95% CI: 1.16,3.44; I² 0.0%, Q = 0.12; p > 0.05]. Although the association between MetS and heavy metals was observed in case–control studies with a stronger effect and negligible heterogeneity. The results of subgroup analysis were presented in Table 3.

Table 3.

Subgroup meta-analysis of heavy metal exposure and risk of metabolic syndrome

Variable	Sub-grouped by		No. of arms	Pooled effect size	95% CI	I² (%)	P for heterogeneity
Overall-heavy metals	gender	Both	24	1.39	1.064,1.22*	73.9	< 0.001
		Male	12	1.37	1.14,1.65*	48.9	0.028
		Female	10	0.96	0.72,1.29	82.0	< 0.001
	Age	Senior Adult (> 50)	13	1.04	0.90,1.20	66.0	< 0.001
		Middle-aged Adult (30–50)	26	1.20	1.10,1.30*	79.0	< 0.001
		young adult (20–30)	7	1.21	0.79,1.83	71.6	0.002
	Study design	Cross-sectional	44	1.15	1.08,1.22*	75.0	< 0.001
	Study design	Case–control	2	1.99	1.16,3.44*	0.0	0.725
	region	Asia	40	1.19	1.10,1.27	75.9	< 0.001
		Europe	4	0.88	0.55,1.41	77.1	< 0.001
		USA	2	0.98	0.74,1.30	75.9	< 0.001
	Sample size	1500 > =	20	1.09	0.97,1.23	68.6	< 0.001
		1500–4000	17	1.26	1.09,1.44	75.1	< 0.001
		> 4000	9	1.15	0.99,1.35	82.1	< 0.001
Cadmium	gender	Both	7	1.15	1.003,1.31*	53.7	0.043
		Male	4	1.72	1.005,2.93*	51.6	0.103
		Female	2	1.41	0.20,6.85	91.3	0.001
	Age	Senior Adult (> 50)	3	1.23	1.03,1.45*	0.0	0.642
		Middle-aged Adult (30–50)	8	1.19	0.99,1.47	70.3	0.001
		young adult (20–30)	2	2.6	0.94,7.15	41.5	0.191
	Study design	Cross-sectional	12	1.21	1.03,1.42	64.7	0.001
	Study design	Case–control	1	2.2	1.03,4.71	0.0	–
Lead	gender	Both	9	1.11	0.95,1.30	69.9	0.001
		Male	4	1.18	1.03,1.35*	0.0	0.705
		Female	4	0.69	0.42,1.14	83.3	< 0.001
	Age	Senior Adult (> 50)	6	0.92	0.74,1.14	76.2	0.001
		Middle-aged Adult (30–50)	8	1.14	0.95,1.36	70.7	0.001
		young adult (20–30)	3	1.08	0.53,2.23	44.7	0.164
	Study design	Cross-sectional	16	1.04	0.92,1.18	69.6	< 0.001
	Study design	Case–control	1	1.81	0.83,3.93	–	–
Mercury	gender	Both	8	1.20	1.03,1.40*	83.6	< 0.001
		Male	5	1.35	1.01,1.80*	69.1	0.012
		Female	4	1.07	0.72,1.60	78.4	0.003
	Age	Senior Adult (> 50)	5	1.16	0.92,1.48	65.4	0.021
		Middle-aged Adult (30–50)	10	1.31	1.11,1.54*	86.6	< 0.001
		young adult (20–30)	2	0.78	0.54,1.11	0.0	0.862
Cooper	gender	Both	4	1.23	0.88,1.74	81.2	0.001
		Male	3	1.30	0.64,2.62	77.0	0.013
		Female	1	1.28	0.81,2.01	–	–
	Age	Senior Adult (> 50)	5	1.12	0.83,1.52	57.9	0.05
	Age	Middle-aged Adult (30–50)	3	1.52	0.86,2.70	87.3	< 0.001
	Study design	Cross-sectional	4	1.25	0.87,1.81	80.7	0.001
	Study design	Case–control	4	1.25	0.80,1.96	69.3	0.021
Manganese	gender	Both	4	1.12	0.82,1.51	65.9	0.032
		Male	2	0.84	0.36,1.93	68.8	0.074
		Female	1	0.89	0.45,1.76	–	–
	Age	Senior Adult (> 50)	1	1.19	0.79,1.77	–	–
	Age	Middle-aged Adult (30–50)	6	1.02	0.77,1.35	59.3	0.031
Iron	gender	Both	5	1.56	1.03,2.37*	77.0	0.002
		Male	3	0.88	0.67,1.16	0.0	0.574
		Female	2	1.14	0.23,5.67	95.2	< 0.001
	Age	Senior Adult (> 50)	6	1.50	0.90,2.49	82.8	< 0.001
	Age	Middle-aged Adult (30–50)	4	0.96	0.59,1.58	83.7	< 0.001
	Study design	Cross-sectional	4	1.19	0.73,1.95	76.7	0.005
		Cohort	2	0.64	0.41,1.01	54.2	0.140
		Case–control	4	1.85	1.16,2.94*	73.3	0.011

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Association between cadmium and MetS

Random effect meta-analysis on twelve eligible studies with 14 ES showed there was no significant association [pooled OR 1.19, 95% CI: 0.92, 1.54; I² 93.46%, Q = 160.58; p = 0.001] between cadmium and MetS. According to results of subgroup analysis this association was remarkably higher in males and older individuals (> 50 yrs.) [Pooled OR 1.72, 95% CI: 1.005, 2.93; I² 51.6%; p = 0.103] and [pooled OR 1.23, 95% CI: 1.03,1.45; I² 0.0%,; p = 0.642] in comparison to females [pooled OR 1.41, 95% CI: 0.20,6.85; I² 91.3%; p > 0.001] and younger ones [pooled OR 1.19, 95% CI: 0.99,1.47; I² 70.3%; p = 0.001] (Table 3, Fig. 2B).

Association between lead and MetS

The meta-analysis revealed that no significant association between lead and risk of MetS (16 studies and 18 ES) [pooled OR 0.99, 95% CI: 0.80, 1.21] with significant heterogeneity (I2% = 91.62; p = 0.001). By performing subgroup analysis based on age, gender, study design, there was no change in final results except in subgroup of males which a significant association [pooled OR 1.18, 95% CI: 1.03, 1.35] was observed with a considerable reduction in heterogeneity (I2% = 0.0; p = 0.705) (Table 3, Fig. 2C).

Association between mercury and MetS

Data about the association of mercury with MetS have been assessed in 14 studies including, eighteen ES. The pooled OR indicated that risk of MetS approximately 22 percent was elevated with higher exposure of mercury [pooled OR 1.18, 95% CI: 1.01, 1.38; I² 89.61%, Q = 92.97; p < 0.001]. The data obtained from the analysis of subgroups recognized that in male [pooled OR 1.35, 95% CI: 1.01, 1.80; I² 69.1%; p = 0.012] and both gender subgroups [pooled OR 1.20, 95% CI: 1.03,1.40; I² 83.6%; p < 0.001] the association between mercury and MetS was significant in comparison to female subgroup [pooled OR 1.07, 95% CI: 0.72,1.60; I² 78.4%; p = 0.003]. Although heterogeneity decreased slightly in the male group, it remained at significant levels. Mercury also caused a significant association with MetS in Middle-aged Adult (30–50) [pooled OR 1.08, 95% CI: 1.11, 1.54; I² 86.6%; p < 0.001] compared to those who were older or younger (Table 3, Fig. 2D).

The association of bio-elements with MetS

Association between chromium and MetS

According to the results of analysis on three ES which evaluated the association between blood level of chromium and risk of MetS in adults, chromium has a protective effect against MetS. In fact, a significant relationship was observed between high levels of chromium and a reduced risk of MetS without obvious heterogeneity [pooled OR 0.67, 95% CI: 0.57, 0.79; I² 0.00%; p = 0.27] (Table 3, Fig. 2E).

Association between copper and MetS

Our results illustrate that no significant association between higher blood level of copper and risk of MetS (OR = 1.24; 95% CI: 0.95, 1.63) with considerable heterogeneity (Q = 25.29; p = 0.001; I2% = 75.98). It should be noted that the subgroup analysis based on age, gender, and study design did not show a significant change in the pooled result (Table 3, Fig. 2F).

Association between iron and MetS

Meta-analysis on eight studies, including 10 ES failed to reach a significant association between level of iron and MetS (OR = 1.24; 95% CI: 0.86, 1.79) with substantial heterogeneity (Q = 57.11; p = < 0.001; I2 % = 84.2). Subgroup analyzed based on age didn’t show any differences in the relation of iron and MetS. However, in subgroup analyzed based on study design we observed that a significant association between higher blood level of iron and risk of MetS in case-control studies (OR = 1.85; 95% CI: 1.16, 2.94) whereas, this did not happen in cohort (OR = 0.64; 95% CI: 0.41, 1.01) and cross-sectional (OR = 1.20; 95% CI: 0.73,1.95) studies. Also, meta-analysis showed that in the studies that evaluated both genders, a significant relationship was observed between increased level of iron and MetS (OR = 1.56; 95% CI: 1.03, 2.37), while in other groups that assessed each gender separately, this did not happen (Table3, Fig 2G).

Association between magnesium and MetS

Random effect meta-analysis demonstrated that there was no significant association between blood level of magnesium and MetS [pooled OR 0.63, 95% CI: 0.30, 1.33; I² 88.87%; p <0.001] (Table 3, Fig. 2F).

Association between manganese and MetS

The analysis on articles which studied the association of level of manganese and MetS failed to show significant relationship (OR = 1.15; 95% CI: 0.97, 1.37) with negligible heterogeneity (Q = 9.31; p = 0.10; I2% = 40.73). In subgroup analysis did not show a significant difference in the results across levels of all included factors (Table 3, Fig. 2E).

Publication bias

Providing funnel plots and Egger's regression test was assessed to evaluate publication bias, presented in Fig. 3A-I. The results of Egger’s test supported the existence of significant publication bias on association between mercury and MetS (coefficient = 1.23, standard error = 0.56; p = 0.044, 95% CI = 0.038, 2.44). Due to the existence of a degree of asymmetry in the funnel plot, the trim and fill test was performed and the results suggested 4 missing studies on the left side of the funnel plot. Although, imputation for these potential missing studies yielded no substantial change in OR 1.05 (95% CI = 1.001, 1.20). Statistical Egger's test revealed no clue of considerable publication bias for overall effect of heavy metals (p = 0.97), cadmium (p = 0.06), lead (p = 0.47) as well as chromium (p = 0.18), copper (p = 0.10), iron (p = 0.33), magnesium (p = 0.27) and manganese (p = 0.51) in the overall analysis.

Sensitivity analysis

The sensitivity analysis using the "leave one out" method independently for both the overall effect of heavy metals, each of them, and for the impact of bio-metals. Based on the sensitivity analysis results, when each study was removed consecutively, it did not significantly influence the overall effect.

Quality assessment for included studies

The results of quality assessment of included studies based on the modified Newcastle–Ottawa scale (36) were presented in Table 4, 5 and 6. Most of the included studies scored high as scores of 7–10 across domains.

Table 4.

Quality Assessment of included cross-sectional studies on

Study	Score	Representativeness of the sample	Sample size	Non-respondents	Ascertainment of the exposure (risk factor)	Comparability of groups/ Adjustment of confounding factors	Assessment of the outcome	Statistical test
Lee et al. 2012	9	*	*	*	**	*	**	*
Rhee et al. 2013	9	*	*	*	**	*	**	*
Jin et al. 2013	8	*	*	*	**	–	**	*
Eom et al. 2014	9	*	*	*	**	*	**	*
Moon et al. 2014	9	*	*	*	**	*	**	*
Chung et al. 2015	9	*	*	*	**	*	**	*
Rotter et al. 2015	7	*	*	*	**	–	*	*
Han et al. 2015	8	*	–	*	**	*	**	*
Lee et al. 2016	9	*	*	*	**	*	**	*
Lee et al. 2017	9	*	*	*	**	*	**	*
Stechemesser et al. 2017	7	*	–	–	**	*	**	*
Park et al. 2018	9	*	*	*	**	*	**	*
Lee et al. 2018	9	*	*	*	**	*	**	*
Shim et al. 2019	9	*	*	*	**	*	**	*
Bulka et al. 2019	10	*	*	*	**	**	**	*
Kaminska et al. 2020	9	*	*	*	**	*	**	*
Wen et al. 2020	7	*	*	–	*	*	**	*
Park et al. 2020	8	*	*	*	**	–	**	*
Lo et al. 2021	10	*	*	*	**	**	**	*
Duc et al. 2021	9	*	*	*	**	*	**	*
Lu et al. 2021	9	*	*	*	**	*	**	*

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Table 5.

Quality Assessment of included case–control studies on

Study	Score	Adequacy of case definition	Representativeness of the cases	Selection of controls	Definition of controls	Comparability of cases and controls on the basis of the design or analysis	Ascertainment of exposure	Same method of ascertainment for cases and controls	Non-response rate
Arredondo 2011	8	*	*	*	*	*	*	*	*
Tavakoli-Hoseini,2014	7	*	*	*	*	*	*	*	–
Ghamarchehreh,2016	8	*	*	*	*	*	*	*	*
El Sayed,2017	8	*	*	*	*	*	*	*	*
Guo,2019	7	*	*	*	*	*	*	*	–
Fang,2019	8	*	*	*	*	*	*	*	*
Chen,2020	8	*	*	*	*	*	*	*	*

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Table 6.

Quality Assessment of included cohort studies on

Study	Score	Representativeness of exposed group	Selection of non-exposed cohort	Ascertainment of exposure	Demonstration that outcome of interest was not present at the start of the study	Comparability of cohorts on the basis of the design or analysis	Assessment of outcome	Was follow-up long enough for outcomes to occur?	Adequacy of follow up of cohorts
Kilani,2015	8	*	*	*	*	*	*	*	*

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Meta-regression

A random -effect meta-regression analysis indicated no effect of all influencing factors on the heterogeneity between studies (p > 0.05) (Table7).

Table 7.

Meta- regression analysis

Variable	Meta-regression by	Coefficient	95% CI	P value
Overall-heavy metals	total_N	-2.19e-06	-8.79e-06, 4.40e-06	0.506
	femaleRatio	-.0853662	-.1763016, 0.0055692	0.065
	Age	-.111928	-.2976255, .0737695	0.231
Cadmium	total_N	-8.75e-06	-.0000254, 7.93e-06	0.275
Cadmium	femaleRatio	-.0569174	-.3241129, 0.210278	0.651
Lead	total_N	-3.38e-06	-.0000196, 0.0000129	0.665
Lead	femaleRatio	-.1306292	-.3235252, 0.0622668	0.170
Mercury	total_N	-4.98e-06	-.0000159, 5.96e-06	0.349
Mercury	femaleRatio	-.0865714	-.2386543, 0.0655116	0.245
Cooper	total_N	.0002067	-.0005125,0.0009258	0.508
Cooper	femaleRatio	.0330494	-.2773116,0.3434104	0.803
Manganese	total_N	2.09e-06	-.0000325, 0.0000367	0.875
Manganese	femaleRatio	-.0182499	-.4745908, 0.438091	0.917
Iron	total_N	-.0002306	-.0005623, 0.000101	0.147
Iron	femaleRatio	.0225533	-.2968757, 0.3419823	0.875

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Discussion

This study evaluated the association of three heavy metals and five bio-elements with MetS. Overall, higher blood levels of heavy metals are associated with a higher risk of MetS. More specifically, while a significant association was seen between blood levels of mercury and cadmium with MetS, only males with a higher level of lead are at a higher level of MetS. In addition, this study showed that higher blood levels of chromium have protective effects on MetS. Nevertheless, no association of MetS with Iron, copper, magnesium, and manganese was evident.

Comparably, a recent meta-analysis by Xu et al. demonstrated a significant link between heavy metal exposure and MetS risk [33]. However, the current study is more comprehensive, especially in the type of specimen in which the heavy metal is measured and the type of included studies.

As stated previously, this study showed that higher blood levels of cadmium are associated with an increased risk of MetS, especially in males and older participants. Previous studies have demonstrated a considerable link between cadmium exposure and MetS components [61]. For instance, Li et al. meta-analysis revealed a positive association between cadmium concentrations and diabetes [62]. Not merely a higher level of cadmium is correlated with an increased risk of hypertension [63], obesity [64], and dyslipidemia [65–67] in cross-sectional studies, but also prospective experimental studies showed that cadmium intake adversely affects the blood sugar [68], lipid, and lipoprotein profiles in rats [69].

In human participants, male smoking prevalence and occupational exposure to cadmium are higher than females [70, 71]. Several previous studies revealed cadmium-related biological gender-specific mechanisms in males [72, 73]. These findings are consistent with the stronger association between MetS and cadmium in males demonstrated in this study. This study also showed a stronger association between MetS and cadmium in older participants, which can be justified because older participants have weaker antioxidant protection against cadmium, an oxidant [74].

Conversely, overall exposure to lead did not significantly enhance the risk of MetS in this study. However, in subgroup analysis, males with exposure to lead had a higher risk of MetS. Similarly, there is a controversy over the impact of lead exposure on MetS components. While previous studies did not elucidate a link between lead and diabetes [75–78], some small studies showed a significant relationship between blood lead level and dyslipidemia [79] and obesity [55]. It is noteworthy that, similar to the current study's findings, experimental studies showed a strong correlation between MetS and bodyweight exclusively in male rats, proposing a sexually dimorphic sensitivity [80].

Like cadmium, mercury is an inducer of inflammatory responses and oxidative stress in biological systems by increasing 8-hydroxy-20-deoxyguanosine, which may lead to the development of MetS compounds, especially diabetes [81]. Moreover, experimental studies have shown that mercury exposure leads to altered glutathione system ontogenesis, which justifies a delayed age-related increase in MetS incidence [82]. These findings are consistent with the observational studies pooled in the current paper, which show sufficient evidence of an association between the development of MetS and mercury exposure.

Similar to our findings, a systematic review by Roy et al. demonstrated the association between mercury and the increase in MetS risk [83]. Unlike our study, they failed to reveal a quantitative measure of the association between mercury exposure and MetS risk. Conversely, they focused on causality. Their study showed that mercury exposure and MetS association fulfill five out of nine of Bradford Hill's criteria, including strength, temporality, plausibility, coherence, and analogy [83, 84]. However, additional prospective studies would be crucial to evaluating causality. In contrast to heavy metals, some of the bio-elements showed promising results in decreasing the risk of MetS. For example, this study showed that chromium level is correlated with a reduced risk of MetS. Although initially chromium was reported to have a positive effect exclusively on glucose metabolism [85], further studies revealed its crucial role in the metabolism of fat, protein, and carbohydrates [86, 87]. A previous meta-analysis pooled randomized clinical trials in which patients were treated with chromium supplementation showed a 19.23 mg/dL (95%CI: -35.3 to -3.16) decrease in patients' fasting blood sugar previously diagnosed with diabetes [88]. Moreover, a significant cardiovascular risk reduction was evident in patients who underwent chromium supplementation [89]. It is noteworthy that the effects of chromium on other components of MetS, such as obesity and dyslipidemia, are non-significant [90–92].

On the other hand, this study did not show an overall significant effect of copper concentration on the risk of MetS. However, some previous human studies regarding this association showed harmful effects of copper on MetS. For instance, Lu et al. demonstrated that a higher serum level of copper is associated with an increased risk of MetS, independent of BMI and insulin resistance [93]. Contrariwise, experimental studies showed that copper supplementation reduces fat accumulation by enhancing fatty acid oxidation and inhibiting De novo lipogenesis in the liver [94]. Changes in how iron is used are linked to steatosis, insulin resistance, and inflammation that isn't obvious, especially when genetic factors make these conditions more likely. Moreover, abnormal iron levels facilitate the evolution of type 2 diabetes [95]. Elevated blood iron levels are associated with an increased risk of MetS components [96, 97]. In addition, there is a close correlation between iron overload and obesity. In other words, BMI and inflammation disturb iron absorption and affect iron metabolism. Nevertheless, a significant overall association between iron level and MetS risk was not evident in our study; however, our subgroup analysis revealed a substantial link between them.

Although magnesium and manganese do not significantly modify the risk of MetS, a considerable protective trend of these bio-elements on the risk of MetS was evident. It should be noted that hypomagnesemia is attributed to altered glucose homeostasis and hypertension [98]. Likewise, there was a controversy over the effects of manganese on MetS components [5, 99–101]. In this study, a notably but not significant declined in the p-value was seen in the association of magnesium with MetS. As the number of studies conducted on the impact of magnesium on MetS is limited, more studies of higher quality should be carried out to determine this association.

Our more comprehensive systematic search has led to different findings than previous studies. For instance, unlike Xu et al. [33], we demonstrated a significant link between heavy metal exposure and MetS risk in males, consistent with experimental studies [62–64]. Moreover, a notable decrease in heterogeneity was evident in our sub-groups. The current review not only evaluated the effects of heavy metals on MetS but also assessed the association of bio-elements with MetS.

It should be noted that this study had several limitations; first, probably a low number of included studies has led to higher p-values, and we believe that the conduct and inclusion of more high-quality studies may lead to more definitive results. Concerning the above-mentioned, the conduct of subgroup analysis was impossible in some cases because the number of studies was insufficient. Second, it is challenging to interpret any causality, as the majority (71.42%) of included studies were cross-sectional. Third, although most of the included studies were multivariable-adjusted, there was a variation of confounders in each study.

Conclusion

In conclusion, there is a significant association between exposure to heavy metals and an increased risk of MetS. In other words, higher concentrations of cadmium and mercury result in an increased risk of MetS, while only males with increased lead concentrations are at higher risk of MetS. Although chromium was attributed to a decreased risk, iron and copper in some sub-groups had detrimental effects on MetS risk. This study did not identify a significant link between magnesium and manganese concentrations and MetS risk.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 624 KB)^{(624KB, docx)}

Acknowledgements

The authors would like to acknowledge the Clinical Research Development Unit of Imam Ali Hospital, Karaj, for their administrative help. This study was approved and funded by Alborz University of Medical Sciences.

Funding

The study was funded by the Alborz University of Medical Sciences.

Data availability

Please contact author for data requests.

Declarations

Ethic approval

The study was approved by the ethical committee of Alborz University of Medical Sciences (Code: IR.ABZUMS. REC. 1403.14.6).

Conflict of interest

The authors declare no conflict of interest.

Footnotes

Publisher's Note

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

Contributor Information

Fatemeh Gomnam, Email: Fatemegomnam1401@yahoo.com.

Mostafa Qorbani, Email: mqorbani1379@yahoo.com.

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PERMALINK

Association of heavy metals and bio-elements blood level with metabolic syndrome: a systematic review and meta-analysis of observational studies

Motahareh Hasani

Maryam Khazdouz

Sahar Sobhani

Parham Mardi

Shirin Riahi

Fahimeh Agh

Armita Mahdavi-Gorabi

Sahar Mohammadipournami

Fatemeh Gomnam

Mostafa Qorbani

Abstract

Background and objectives

Methods

Results

Conclusion

Supplementary Information

Introduction

Methods

Search strategy

Study selection

Data extraction and risk of bias assessment

Data analysis

Results

Search results

Fig. 1.

Study characteristics

Table 1.

Data synthesis and statistical analysis

Table 2.

Heavy metals and MetS

Fig. 2.

Table 3.

Association between cadmium and MetS

Association between lead and MetS

Association between mercury and MetS

The association of bio-elements with MetS

Publication bias

Fig. 3.

Sensitivity analysis

Quality assessment for included studies

Table 4.

Table 5.

Table 6.

Meta-regression

Table 7.

Discussion

Conclusion

Supplementary Information

Acknowledgements

Funding

Data availability

Declarations

Ethic approval

Conflict of interest

Footnotes

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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