The association between heavy metal exposure and obesity: A systematic review and meta-analysis

Abstract

Background

Obesity and metabolic syndrome are global health concerns associated with development of different types of diseases and serious health threats in the long term. Their metabolic imbalance can be attributable to inherited and environmental factors. As a considerable environmental agent, heavy metals exposure can predispose individuals to diseases like obesity. This systematic review and meta-analysis aimed to evaluate the association between heavy metals exposure and the risk of obesity.

Methods

PubMed/MEDLINE, EMBASE and Web of Science were systematically searched until December 17, 2022. Only observational studies that evaluated heavy metals exposure and obesity were included. Studies were excluded if they assessed maternal or prenatal exposure, the mixture of heavy metals and other chemicals, reported the association with overweight or other diseases, and undesirable study designs. The Joanna Briggs Institute checklist was used for quality assessment. The pooled adjusted odds ratio (aOR) and the pooled standardized mean difference (SMD) with their 95% confidence intervals (CIs) were calculated, respectively. The publication bias was evaluated using Egger's and Begg's tests.

Results

Twenty studies (n = 127755), four case–control and sixteen analytical cross-sectional studies, were included. Lead exposure was significantly associated with a lower risk of obesity (aOR: 0.705, 95% CI: 0.498–0.997), while mercury (aOR: 1.458, 95% CI: 1.048–2.031) and barium (aOR: 1.439, 95% CI: 1.142–1.813) exposure increased the risk of obesity. No significant publication bias was found and the studies had a low risk of bias.

Conclusion

Overall, lead exposure reduced obesity risk, while mercury and barium exposure raised it. Further large-scale observational studies are recommended to determine the roles of heavy metals in obesity.

Study registration ID: CRD42023394865.

Supplementary information

The online version contains supplementary material available at 10.1007/s40200-023-01307-0.

Introduction

According to the World Health Organization (WHO), "Overweight and obesity are defined as abnormal or excessive fat accumulation that presents a health risk". In this regard, body mass index (BMI) over 25 is considered overweight, and more than 30 is sub grouped in the obese category [1].

The global prevalence of overweight and obesity has been twofold since 1980 [2]. On March 4, 2022, WHO declared that around the world, 650 million adults, 340 million teenagers, and 39 million children are obese, reaching 1 billion obese people worldwide [3]. Over the past 50 years, obesity has developed into a global pandemic due to its prevalence. Obesity is a substantial public health concern because it significantly increases the risk of diseases such as hypertension, myocardial infarction [4], type 2 diabetes, fatty liver disease, stroke, dementia, osteoarthritis, obstructive sleep apnea [5, 6], and various cancers [7] all of which lead to a decline in both life expectancy and quality of life. Beyond that, obesity is companied by low social competence, low self-esteem, social isolation, unemployment and low socioeconomic status, which aggravate the quality of life in the patients moreover [8].

The pathogenesis of obesity entails regulating calorie use, appetite, and physical activity. Still, it has complicated interplay with the accessibility of healthcare systems, the role of socioeconomic conditions, and underlying hereditary and environmental factors [9].

Environmental endocrine-disrupting chemicals are "exogenous factors that intervene with synthesis, secretion, transport, metabolism, binding action or removal of natural blood-borne hormones present in the body and are responsible for homeostasis, reproduction and developmental processes" [10]. Several chemical compounds have been identified as environmental risk factors for obesity, including phthalates [11], bisphenols, parabens, and persistent organic pollutants [12]. Heavy metals are naturally present in the environment and can cause systemic diseases like chronic kidney disease and have a tremendous attributable burden [13, 14]. As such, metabolic syndrome has been shown to be linked with exposure to mercury [15], lead [16], and arsenic [17] exposure. On the other hand, the results of a study showed that serum mercury levels have a negative relationship with body mass index (BMI) in adults, while no correlation was observed between serum mercury levels and BMI in children [18]. A negative relationship between the urinary levels of lead and cadmium with BMI and waist circumference (WC) was also found in another study [19].

Previous studies have evaluated heavy metals exposure and the development of osteopenia/osteoporosis [20] and chronic kidney disease [13]. Also, considering that several studies have investigated the relationship between heavy metals and metabolic disorders, including obesity, and have reached controversial findings regarding the effect of exposure to heavy metals and obesity, a systematic review and meta-analysis study is necessary to find an accurate statistical relationship. Herein, this systematic review and meta-analysis study was conducted to shed light on the association between heavy metals exposure and the risk of obesity.

Methods

This study was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [21]. The study was registered in the Systematic Review Registration: PROSPERO (registration ID: CRD42023394865).

Search strategy

We searched PubMed/Medline, EMBASE and Web of Science for studies reporting the association between heavy metals exposure and risk of obesity published up to December 17, 2022. Observational studies written in English were selected. We used the following Medical Subject Heading (MeSH) terms: ("Environmental Exposure" OR "Inhalation Exposure" OR "Occupational Exposure" OR "Dietary Exposure") AND ("Obesity" OR "Pediatric Obesity" OR "Obesity, Abdominal" OR "Obesity, Morbid" OR "Overweight") AND ("Metals, Heavy" OR "Cadmium" OR "Arsenic" OR "Mercury" OR "Lead") (Table S1-S3). Backwards and forwards citation searching was performed.

Study selection

The records found through database searching were merged, and the duplicates were removed using EndNote X8 (Thomson Reuters, Toronto, ON, Canada). Two reviewers (MZ and AGJ) independently screened the records by title/abstract and full texts according to the inclusion/exclusion criteria. Any disagreements were resolved by the lead investigator (MS). Included studies met the following criteria: (i) participants with exposure to heavy metals that were divided into high and low exposure groups according to serum and/or urine heavy metals levels, (ii) identification of patients with general or abdominal obesity and lean individuals, and (iii) comparison of the number of obese participants or levels of heavy metals in each group.

According to the World Health Organization (WHO) definitions [22], general obesity was defined as BMI ≥ 30 kg/m² in adults and ≥ 95^th percentile for children and adolescents. On the other hand, the definition of abdominal obesity was WC greater than 102 cm for men and greater than 88 cm for women according to the WHO definition [22], WC ≥ 90 cm in men and WC ≥ 80 cm in women according to the criteria of the International Diabetes Federation [23] or waist-to-height ratio ≥ 0.5. Heavy metal was defined as an element with an atomic number greater than 20 and an atomic density of more than 5 g per cubic meter, with metal-like properties [24].

The exclusion criteria were as follows: (i) assessing the maternal or prenatal (early life) exposure or pregnant women with obesity, (ii) assessing the exposure through heavy metals levels in soil, water, or participants' nail or hair, (iii) mixture of heavy metals with other chemicals, (iv) reporting the association between heavy metals and overweight or combination of overweight and obesity, (v) combined effect of heavy metals and obesity on other diseases, (vi) association between heavy metals and BMI, not obesity, and (vii) studies with common datasets and participants. Also, editorials, reviews, study protocols, meeting or conference abstracts and animal and molecular articles, were excluded.

Data extraction

Two reviewers (MZ and AGJ) designed a data extraction form. These reviewers collected data from all relevant studies, and consensus settled disagreements. The following data were extracted: first author name; year of publication; study design; duration and data source; countries where the research was conducted; demographics (i.e., age and sex); obesity type; source of sampling; heavy metals detection limit; the lowest and highest levels of heavy metals or the mean level in samples; the total number of participants, and the number of participants with obesity.

Quality assessment

Two reviewers (MZ and AGJ) assessed the quality of the studies using the Joanna Briggs Institute (JBI) critical appraisal tools for case controls and analytical cross-sectional studies [25]. A third reviewer (MS) was consulted if there were any discrepancies. Items such as study population, the measure of exposures, confounding factors, the extent of outcomes, follow-up data, and statistical analysis were evaluated.

Statistical analysis

The pooled adjusted odds ratio (aOR) with 95% confidence intervals (CIs) for dichotomous data and the pooled standardized mean difference (SMD) with 95% CIs for continuous data were assessed using random or fixed-effects models. Multiple effect sizes in a study with different groups were pooled with a fixed-effects model. The random-effects model was used because of the estimated methodological heterogeneity of the true effect sizes. The between-study heterogeneity was assessed by Cochran's Q and the I² statistic. I² values of more than 50% were considered high heterogeneity [26]. Subgroup and subset analyses were performed to compare the role of age, sampling source and type of obesity in statistical heterogeneity and final effect size. The median and inter-quartile range (IQR) were converted to mean and standard deviation (SD) for SMD calculation [27]. In order to avoid data overlap, different heavy metals from studies with common data sets were chosen. Meta-analysis was done for heavy metals with at least three studies. Effect sizes with the most adjustments were pooled to modify potential confounding factors. Publication bias was evaluated statistically by using Egger's and Begg's tests (p < 0.05 was considered indicative of statistically significant publication bias) [28]. The funnel plot was not used for publication bias assessment because fewer than ten studies were in each analysis [29]. All analyses were performed using "Comprehensive Meta-Analysis" software, Version 3.7 (Biostat, Englewood, NJ).

Results

We identified 737 papers through online database searching and screened 522 papers after removing duplicates. First, we excluded 470 papers by title and abstract screening. Then, we assessed 52 studies by full-text review. Fifteen articles were selected, and five studies were found by hand searching and backward/forward citation searching. Finally, four case–control [30–33] and sixteen analytical cross-sectional studies [34–49] were included (Fig. 1).

Fig. 1.

Flow chart of study selection for inclusion in the systematic review and meta-analysis

Study characteristics

Seven studies were conducted in the United States of America (USA) [37, 39, 43–46, 48], five in South Korea [34–36, 38, 40], four in China [33, 42, 47, 49] and others in Taiwan [41], Turkey [32], Egypt [31], and Russia [30]. All the USA studies [37, 39, 43–46, 48] were from US National Health and Nutrition Examination Survey data sources in the period from 1999 to 2016. The data source of the four Korean studies [34, 35, 38, 40] and one Egyptian article [31] was the Korean National Health and Nutrition Examination Survey from 2005 to 2017. Two Chinese studies [42, 47] were from the Survey on Prevalence in East China for Metabolic Diseases and Risk Factors 2014. Ten studies [30–32, 37, 42, 44–48] had assessed general obesity, and ten articles [33–36, 38–41, 43, 49] had reported abdominal obesity (Table 1).

Table 1.

Study characteristics

First author	Publication year	country	Study design	Study duration	Data source	Type of obesity
Tascilar et al. [32]	2011	Turkey	Case control	NA	Healthy children	General
Scinicariello et al. [44]	2013	USA	Analytical cross-sectional	1999–2006	NHANES	General
Lee and Kim. [38]	2013	South Korea	Analytical cross-sectional	2005–2010	KNHANES	Abdominal
moon et al. [40]	2014	South Korea	Analytical cross-sectional	2009–2010	KNHANES	Abdominal
Eom et al. [36]	2014	South Korea	Analytical cross-sectional	2010–2011	Healthy adult	Abdominal
Azab et al. [31]	2014	Egypt	Case control	2011–2013	KNHANES	General
Wang et al. [47]	2015	China	Analytical cross-sectional	2014	SPECT-China	General
Nie et al. [42]	2016	China	Analytical cross-sectional	2014	SPECT-China	General
Shao et al. [45]	2017	USA	Analytical cross-sectional	1999–2012	NHANES	General
Fan et al. [37]	2017	USA	Analytical cross-sectional	2011–2014	NHANES	General
Noor et al. [43]	2018	USA	Analytical cross-sectional	2001–2014	NHANES	Abdominal
Wang et al. [48]	2018	USA	Analytical cross-sectional	2003–2014	NHANES	General
Zhang et al. [33]	2020	China	Case control	2017	Beijing Population Health Cohort	Abdominal
Lo et al. [39]	2021	USA	Analytical cross-sectional	2001–2016	NHANES	Abdominal
Duc et al. [35]	2021	South Korea	Analytical cross-sectional	2009–2013 2016–2017	KNHANES	Abdominal
Swayze et al. [46]	2021	USA	Analytical cross-sectional	1999–2016	NHANES	General
Cho et al. [34]	2021	South Korea	Analytical cross-sectional	2010–2013	KNHANES	Abdominal
Tinkov et al. [30]	2021	Russia	Case control	NA	Healthy adult	General
Zhao et al. [49]	2022	China	Analytical cross-sectional	2017–2019	Healthy adult	Abdominal
Ngu et al. [41]	2022	Taiwan	Analytical cross-sectional	NA	Healthy adult	Abdominal

NA: not available/ USA: United States of America/ NHANES: National Health and Nutrition Examination Survey/ KNHANES: Korean National Health and Nutrition Examination Survey/ SPECT-China: Survey on Prevalence in East China for Metabolic Diseases and Risk Factors

Participant characteristics

There were 127,755 participants in the included studies that 81% were adults. According to nineteen studies [30–45, 47–49], 48.4% (46,421/95743) were males and 23.8% (12,273/51502) of participants were obese according to nine studies [30–32, 35, 41, 42, 44, 45, 47] (Table 2).

Table 2.

Participant characteristics

First author	Total participants		Age (year)		Obese participants (%)
	Male (%)	Female (%)
Tascilar et al. [32]	67		10.6		34 (50.7)
	36 (53.7)	31 (46.3)
Scinicariello et al. [44]	26,592		30.7		7299 (27.4)
	13,291 (49.9)	13,301 (50.1)	Children: 11	Adult: 44
Lee and Kim. [38]	7559		≥ 20		-
	3783 (50.0)	3776 (50.0)
moon et al. [40]	3950		≥ 20		-
	1961 (49.6)	1989 (50.4)
Eom et al. [36]	2114		45.5		-
	920 (43.5)	1194 (56.5)
Azab et al. [31]	160		7.9		80 (50.0)
	84 (52.5)	76 (47.5)
Wang et al. [47]	5558		53.3		300 (5.3)
	2235 (40.2)	3323 (59.8)
Nie et al. [42]	5544		≥ 18		299 (5.3)
	2229 (40.2)	3315 (59.8)
Shao et al. [45]	6602		12.8		1127 (17.0)
	3443 (52.1)	3159 (47.9)
Fan et al. [37]	5404		11.9		-
	2745 (50.7)	2659 (49.3)
Noor et al. [43]	3981		44.7		-
	1996 (50.1)	1985 (49.9)
Wang et al. [48]	9537		49.2		-
	4841 (50.7)	4696 (49.3)
Zhang et al. [33]	4134		60.0		-
	2043 (49.4)	2091 (50.6)
Lo et al. [39]	5048		> 18		-
	2452 (48.5)	2596 (51.5)
Duc et al. [35]	6434		61.6		2870 (44.6)
	3063 (47.6)	3371 (52.4)
Swayze et al. [46]	32,012		≥ 20		-
	-	-
Cho et al. [34]	1327		14.3		-
	672 (50.6)	655 (49.4)
Tinkov et al. [30]	395		45.2		196 (49.6)
	103 (26.0)	292 (74)
Zhao et al. [49]	1187		62.4		-
	418 (28.3)	769 (71.7)
Ngu et al. [41]	150		42.6		68 (45.3)
	106 (70.7)	44 (29.3)

Exposure characteristics

Eight studies [37, 38, 40, 44–47, 49] reported the effect of lead exposure on obesity. This number was eight for cadmium exposure [38, 40–43, 45, 46, 49], six for mercury exposure [33, 34, 36, 37, 40, 41] and zinc exposure [30–33, 37, 41], five for manganese exposure [33, 37, 39, 41, 49], four for exposure to the mixture of heavy metals, three for barium exposure [33, 45, 46], and three for the exposure to chromium [30–32]. Thirteen studies assessed the exposure through blood samples [31–38, 40–42, 44, 47], urine samples were evaluated in three articles [43, 45, 49], and four studies assessed both samples [30, 39, 46, 48] (Table 3).

Table 3.

Exposure characteristics

First author	Heavy metal (unit)		Detection limit (unit)	sample
	Lower or case level	Higher or control level
Tascilar et al. [32]	Zn (μg/dl) Cr (μg/L)		–	Blood
	Case: 67.45 ± 18.42 Case: 13.59 ± 2.47	Control: 74.45 ± 18.42 Control: 13.84 ± 1.61
Scinicariello et al. [44]	Pb (μg/dL)		0.25–0.30 (μg/dl)	Blood
	Children (q1): ≤ 0.7 Adult (q1): ≤ 1	Children(q4): ≥ 1.61 Adult(q4): ≥ 2.4
Lee and Kim. [38]	Pb (μg/L) Cd (μg/L)		Pb: 0.12 (mg/dl) Cd: 0.056 (mg/L)	Blood
	Q1: ≤ 2.362 Q1: ≤ 0.819	Q3: ≥ 3.282 Q3: ≥ 1.359
moon et al. [40]	Pb (μg/dl) Cd (μg/L) Hg (μg/L)		≥ 20	Blood
	Q1: 1.23 ± 1.01 Q1: 0.37 ± 1.01 Q1: 1.88 ± 1.01	Q4: 3.79 ± 1.01 Q4: 1.94 ± 1.01 Q4: 8.73 ± 1.01
Eom et al. [36]	Hg (μg/L)		0.2 (μg/L)	Blood
	Q1: < 2.99	Q3: ≥ 4.88
Azab et al. [31]	Zn (μg/dl) Cr (ng/ml)		–	Blood
	Case: 57 ± 14 Case: 0.2 ± 0.08	Control: 75 ± 17 Control: 0.18 ± 0.05
Wang et al. [47]	Pb (μg/L)		–	Blood
	Men (q1): ≤ 29 Women(q1): ≤ 25.13	Men (q4): ≥ 62.17 Women(q4): ≥ 54.36
Nie et al. [42]	Cd (μg/L)		0.01 (μg/L)	Blood
	Q1: ≤ 0.8	Q3: ≥ 2.95
Shao et al. [45]	Cd (ng/ml) Pb (ng/ml) Ba (ng/ml)		–	Urine
	Q1: ≤ 0.049 Q1: ≤ 0.35 Q1: ≤ 0.9	Q4: > 0.173 Q4: > 1.04 Q4: > 3.25
Fan et al. [37]	Pb Zn Mn Hg		–	Blood
	Q1	Q4
Noor et al. [43]	Cd (μg/L)		0.056 (μg/L)	Urine
	Q1: < 0.13	Q4: > 0.60
Wang et al. [48]	18 heavy metals		–	Blood (3 metals) Urine (15 metals)
	10th %	90th %
Zhang et al. [33]	Zn (μg/L) Hg (μg/L) Mn (μg/L) Ba (μg/L)		Zn: 11.75 (μg/L) Hg: 0.0112 (μg/L) Mn: 0.0347 (μg/L) Ba: 0.1087 (μg/L)	Blood
	Q1: < 763.87 Q1: < 0.32 Q1: < 0.95 Q1: < 2.17	Q4: > 973.89 Q4: > 0.90 Q4: > 1.69 Q4: > 4.89
Lo et al. [39]	Blood Mn (μg/L) Urine Mn (10–2 μg/g creatinine)		–	Blood & Urine
	Q1: < 7.63 Q1: < 6.97	Q4: > 11.91 Q4: > 18.60
Duc et al. [35]	Cd (μg/L) Pb (μg/dl) Hg (μg/L)		Cd: 0.087 μg/L Pb: 0.223 μg/dL Hg: 0.05 μg/L	Blood
	5th %: 0.55 5th %: 1.14 5th %: 1.31	95th %: 2.55 95th %: 4.41 95th %: 11.36
Swayze et al. [46]	Blood Cd (ng/ml) Urine Cd (ng/ml) Urine Pb (ng/ml) Urine Ba (ng/ml)		–	Blood & Urine
	Lower level: 2.4 Lower level: 0.16 Lower level: 0.4 Lower level: 0.88	Higher level: 11.2 Higher level: 1.01 Higher level: 1.8 Higher level: 4.76
Cho et al. [34]	Hg (μg/L)		0.158 (µg/L)	Blood
	Q1	Q4
Tinkov et al. [30]	Blood Zn (μg/dl) Blood Cr (ng/ml)		–	Blood & Urine
	Case: 91 ± 10 Case: 0.89 ± 0.56	Control: 92 ± 10 Control: 1.64 ± 0.82
Zhao et al. [49]	Cd (μg/L) Pb (μg/L) Mn (μg/L)		1 (μg/L)	Urine
	Q1: 1.30 Q1: 1.20 Q1: 2.84	Q4: 3.65 Q4: 2.90 Q4: 7.13
Ngu et al. [41]	Zn (μg/L) Cd (μg/L) Hg (μg/L) Mn (μg/L)		–	Blood
	Q1: < 764.5 NA Q1: < 0.9 Q1: < 1.4	Q3: > 951.9 NA Q3: > 1.4 Q3: > 1.7

Zn: zinc/ Cr: chromium/ Pb: lead/ Cd: cadmium/ Hg: mercury/ Ba: barium/ Mn: manganese/ NA: not available

Meta-analysis results

Lead exposure

Obesity was significantly lower in participants with high exposure to lead (aOR: 0.705, 95% CI: 0.498–0.997, Begg's test: 0.265, Egger's test: 0.398) (Fig. 2). This association remained significant in children and general obesity. Subset analysis showed that children had the most role in statistical heterogeneity in lead exposure (Table S4).

Fig. 2.

Forest plot of the adjusted odds ratio for the association between lead exposure and obesity

Cadmium exposure

Those with high exposure to cadmium had non-significantly lower odds of obesity development compared to those with low exposure (aOR: 0.769, 95% CI: 0.564–1.048, Begg's test: 0.536, Egger's test: 0.123) (Fig. 3). But after subgroup and subset analysis, a statistically significant association between general obesity, urine sampling and cadmium exposure was found. Also, the difference between adults and children was significant (Table S5).

Fig. 3.

Forest plot of the adjusted odds ratio for the association between cadmium exposure and obesity

Mercury exposure

Mercury exposure was significantly related to an increased risk of obesity (aOR: 1.458, 95% CI: 1.048–2.031, Begg's test: 1.000, Egger's test: 0.987) (Fig. 4). This association remained significant only for abdominal obesity (Table S6).

Fig. 4.

Forest plot of the adjusted odds ratio for the association between mercury exposure and obesity

Manganese exposure

Manganese exposure could insignificantly increase the risk of obesity (aOR: 1.280, 95% CI: 0.917–1.789, Begg's test: 0.806, Egger's test: 0.867) (Fig. 5). This relation was inverted for a urine sample, and there were significant differences between adult/children age groups and general/abdominal obesity (Table S7).

Fig. 5.

Forest plot of the adjusted odds ratio for the association between manganese exposure and obesity

Other heavy metals

Obesity was about 10% more in participants with high zinc exposure, but there was no statistical significance (Figure S1 and Table S8). On the other hand, barium exposure could significantly increase the risk of obesity by up to 44% (Figure S2 and Table S9). The mean of serum zinc (SMD: -0.542, 95% CI: -1.245/0.161, Begg's test: 1.000, Egger's test: 0.597) and chromium (SMD: -0.303, 95% CI: -1.258/0.651, Begg's test: 1.000, Egger's test: 0.493) levels were insignificantly lower in obese participants than lean people (Figures S3-S4 and Tables S10-S11).

The mixture of heavy metals

The mixture of different heavy metals could insignificantly increase the risk of obesity (aOR: 1.508, 95% CI: 0.748–3.041, Begg's test: 0.574, Egger's test: 0.734) (Fig. 6).

Fig. 6.

Forest plot of the adjusted odds ratio for the association between exposure to heavy metals mixture and obesity

Quality assessment

Overall, the included analytical cross-sectional and case–control studies had a low risk of bias (Table 4 and Table 5) except Wang et al. [47] study in the cases of sampling inclusion criteria, the definition of the study setting and exposure measurement along with the Azab et al. [31], and Tascilar et al. [32] studies for dealing with confounding factors.

Table 4.

Quality assessment of analytical cross-sectional studies

Author	1	2	3	4	5	6	7	8
Moon et al. [40]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Lee and Kim. [38]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Scinicariello et al. [44]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Shao et al. [45]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Wang et al. [47]	No	No	No	Yes	Yes	Yes	Yes	Yes
Ngu et al. [41]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Eom et al. [36]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Nie et al. [42]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Fan et al. [37]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Noor et al. [43]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Wang et al. [48]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Cho et al. [34]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Swayze et al. [46]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Duc et al. [35]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Zhao et al. [49]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Lo et al. [39]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

1. Were the criteria for inclusion in the sample clearly defined?

2. Were the study subjects and the setting described in detail?

3. Was the exposure measured in a valid and reliable way?

4. Were objective, standard criteria used for measurement of the condition?

5. Were confounding factors identified?

6. Were strategies to deal with confounding factors stated?

7. Were the outcomes measured in a valid and reliable way?

8. Was appropriate statistical analysis used?

Table 5.

Quality assessment of case control studies

Author	1	2	3	4	5	6	7	8	9	10
Tinkov et al. [30]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Unclear	Yes
Azab et al. [31]	Yes	Yes	Yes	Yes	Yes	No	Unclear	Yes	Yes	Yes
Tascilar et al. [32]	Yes	Yes	Yes	Yes	Yes	No	Unclear	Yes	Unclear	Yes
Zhang et al. [33]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

1. Were the groups comparable other than the presence of disease in cases or the absence of disease in controls?

2. Were cases and controls matched appropriately?

3. Were the same criteria used for identification of cases and controls?

4. Was exposure measured in a standard, valid and reliable way?

5. Was exposure measured in the same way for cases and controls?

6. Were confounding factors identified?

7. Were strategies to deal with confounding factors stated?

8. Were outcomes assessed in a standard, valid and reliable way for cases and controls?

9. Was the exposure period of interest long enough to be meaningful?

10. Was appropriate statistical analysis used?

Discussion

The present study's findings showed that mercury and barium exposure significantly increased the odds of obesity, while lead exposure significantly decreased the risk. The subgroup and subset analysis results showed that lead exposure in children and participants with general obesity, in addition to cadmium exposure in urine samples and participants with general obesity, was significantly associated with decreased obesity development. However, exposure to mercury in participants with abdominal obesity and exposure to barium in those with general obesity, urine samples, and adults significantly increased the risk of obesity. Also, the overall quality of the included studies was high.

Heavy metal poisoning often arises as a result of exposure in the workplace, like in mining and metallurgical activities, or due to interaction with industrial waste. There are also specific sources of heavy metal exposure in the general population [50]. For example, inorganic arsenic is taken into through pesticides, the smelting of copper and lead, wood preservation methods, and volcanic emissions [50]. Also, significant origins of lead exposure include lead-based paint, materials in the production of batteries, and the use of certain cosmetics [51]. Cadmium exposure sources are smoking, vegetables, seeds, shellfish, the manufacturing of plastics, as well as nickel–cadmium battery production [52]. Fish contaminated due to pollution is the primary origin of mercury, mainly in the form of methylmercury. Moreover, in countries that still permit the production of amalgam used for dental fillings, mercury poses a professional risk for dentists [50, 53].

Results of a survey of 884 women showed a significant association between blood lead levels and body mass index (B = 0.66; 95% CI = 0.09–1.22, p = 0.02) [54]. A prospective birth cohort study on 1442 mother–child pairs showed that increased red blood cell lead levels were associated with 1.65 times increased odds of overweight or obesity in children (95% CI: 1.18–2.32) [55]. On the other hand, several studies pointed out that lead exposure during the prenatal period has been associated with lower body weight in human infants [56, 57]. Complex interactions between neurodevelopmental effects of lead exposure on appetite and energy utilization, gut microbiome dysbiosis, and epigenetic changes have been suggested for the development of obesity [58]. A study by Pilsner et al. demonstrated that the methylation of genomic DNA in cord blood is inversely correlated with prenatal lead exposure. These findings indicate that maternal cumulative lead burden can impact the developing fetus' epigenome [59]. The animal study revealed that lead exposure causes gene expression alterations of methyltransferases and methyl cytosine-binding proteins, both of which regulate the methylation of DNA [60] and alter insulin and leptin gene expression associated with food intake and body glucose [61].

Our results showed that lead exposure decreased the risk of obesity (OR = 0.71; 95% CI: 0.50–1.00), and the results were also significant for children and those with general obesity subgroup analysis. It should be noted that there was a remarkable heterogeneity between the studies (I-square = 93.24%), and the effect size was so close to the significant cut-off (p = 0.048). Furthermore, the inverse association between lead exposure and obesity might be due to the not inclusion of cohort studies in the meta-analysis. As a result, further large-scale prospective cohort studies and in-vitro and in-vivo studies are recommended to determine whether there is an association between lead exposure and obesity.

We found that mercury exposure significantly increased the odds of obesity development by 1.5 times. In this regard, a systematic review and meta-analysis showed that participants with metabolic syndrome had higher mercury exposure (pooled effect size: 1.26; 95% CI: 1.06–1.48) [62]. Furthermore, the findings of a meta-analysis of 14 studies with 34,000 participants revealed a non-significant higher risk of nonfatal ischemic heart disease (risk ratio: 1.21; 95% CI: 0.98–1.50) [63]. Moreover, no significant association was found between mercury exposure and type 2 diabetes mellitus (OR: 1.00; 95 CI: 0.85–1.17) [64]. Overall, there is controversy about the association between mercury and the development of the metabolic syndrome and other metabolic diseases. Another study also emphasized the epidemiological studies' lack of consistency about the association between mercury exposure and diabetes mellitus and metabolic syndrome [65]. Mercury may play a role in affecting the adipogenesis, specifically, when 3T3-L1 preadipocytes were exposed to MeHg, the lipid content increased, while the expression of certain markers related to adipogenesis showed varying patterns. Additionally, mercury can influence the activity of genes associated with adipogenesis when combined with TCDD in BEAS-2B cells. The impact of mercury on adipogenesis was also observed in terms of adipokine secretion. Moreover, mature adipocytes exposed to MeHg showed increased production of vascular endothelial growth factor which play roles in metabolic syndrome [66]. Additionally, mercury can induce oxidative stress by activating free radical oxidation processes and suppressing the antioxidant system in biological systems regarding its physic-chemical properties [67]. Mercury can cause a significant decrease in Glutathione production and activity and decreased catalase and Thioredoxin system activity [68]. All of these transformations can aggravate oxidative stress. Mercury is also supposed to induce an inflammatory response by increasing the pro-inflammatory cytokines. Some cytokines which play a crucial role in inflammation, like (IL)-1b and tumor necrosis factor a (TNFa), are upregulated in the elevated levels of mercury [69]. Also, it was demonstrated in the animal studies that peroral administration of mercuric chloride resulted in a dose-dependent manner can induce TNFa, IL-2 and IFN concentration in liver [70]. Several studies have been demonstrated a direct correlation between mercury levels and serum total cholesterol, and trig and LDL concentration [71]. Also in a study by Melt a negative correlation between mercury and HDL has been revealed [72]. Previously it was established that mercury exposure increases the risk of cardiovascular disease [73]. So, mercury’s adverse effects by inducing oxidative stress, inflammation development and metabolic syndrome and another related condition, can be supposed to relate with obesity but more explicit investigations are needed to conduct.

The article by Xu and colleagues showed an increased non-significant association between cadmium exposure and metabolic syndrome (pooled effect size = 1.10; 95% CI: 0.95–1.27) [62]. In addition, a cohort study on mothers in their first trimester revealed a 25-time increased risk of obesity development at age five of their children for every one ng/g elevation in the blood weight of cadmium [74]. We found a protective non-significant role for cadmium in obesity (aOR = 0.77; 95% CI: 0.56–1.05). The findings implicate the limited number of primary studies that evaluated the effects of cadmium on metabolic disorders and obesity. Also, data from human studies on the interplay between Cd exposure and obesity are somewhat contradictory, ranging from negative to positive. However, in regression models incorporating different heavy metals, cadmium content was inversely associated with anthropometric indices of obesity [19]. A recent study found that cadmium significantly reduced the size of adipocytes in metallothionein-null mice [75]. Also, the inhibition of adipogenesis at a concentration of 3 μM of Cd, which resulted in a gradual reduction in the protein expression of C/EBPα and PPARγ in 3T3-L1 preadipocytes can explain the protective roles of Cd against obesity [76]. Furthermore, the inconsistency in the results can be due to the evaluation of different diseases and variations in inclusion criteria and sample populations.

Regarding manganese exposure and metabolic syndrome, a systematic review and meta-analysis of cross-sectional and case–control studies showed a non-significant association between metabolic syndrome and manganese level from the diet (OR = 0.83; 95% CI: 0.57–1.21), serum (OR = 0.87; 95% CI: 0.66–1.14), urine (OR = 0.84; 95% CI: 0.59–1.19), and whole blood (OR = 0.92; 95% CI: 0.53–1.60) [77]. A longitudinal prospective cohort study of 1478 participants showed a non-significant association between manganese level quartiles and the body mass index (p = 0.61) [78]. Following the findings of the previously mentioned studies, we found a non-significant association between manganese exposure and obesity (aOR = 1.28; 95% CI: 0.92–1.79). On the other hand, Ngu et al. found that manganese levels can predict abdominal obesity (OR = 1.62; 95% CI: 1.07–2.46) [79]. One of the reasons for the differences could be the low sample size in the original study, which is 150, while our meta-analysis has a much greater number of participants. Additionally, we included both urine and blood samples, children and adults, while the abovementioned study merely included blood samples of middle-aged adults [79].

Manganese is essential for synthesizing and activating several enzymes and regulating human glucose and lipid metabolism. Mn is also needed for Mn superoxide dismutase (MnSOD), which scavenges ROS in mitochondrial oxidative stress. Mn insufficiently and intoxication both affect metabolism and neuropsychiatry. Metabolic diseases, including type 2 diabetes mellitus, insulin resistance, hyperlipidemia, atherosclerosis, and obesity, correlate considerably with oxidative stress and inflammation. So more investigation of the probable effect of Manganese on metabolic syndrome and obesity is inevitable [80].

Previous studies found a positive association between arsenic, cadmium, iron, lead, mercury, and fatty liver disease [81]. Moreover, a significantly increased risk of proteinuria was found for exposures to lead (p = 0.02) and cadmium (p < 0.001) [13]. Jalili et al. also revealed that lead (p = 0.05) and cadmium (p ≤ 0.001) exposure, unlike mercury, were associated with an elevated risk of osteopenia or osteoporosis [20]. Also, those patients with breast cancer had higher levels of copper, cadmium, and lead levels than healthy individuals, based on findings of a meta-analysis [82]. Overall, we also found a non-significant increased risk of obesity in those exposed to a combination of different heavy metals (aOR = 1.51; 95% CI: 0.75–3.04). From the molecular point of view, there is an upregulated of adipogenic factors (i.e., CCAAT/enhancer-binding protein and peroxisome proliferator-activated receptor gamma), which lead to adipose tissue hypertrophy and expansion and finally obesity [66]. On the other hand, exposure to a high dose of heavy metals can inhibit adipogenesis because of the toxic effects of heavy metals [66].

Our study also evaluated the effects of other heavy metals like zinc, barium and chromium on the occurrence of obesity, while none of them showed a significant result, except for barium (aOR = 1.44; 95% CI: 1.14–1.81). However, primary studies are very limited on them, and to our knowledge, no previous meta-analysis has evaluated the association between exposure to them and the development of obesity.

Our systematic review and meta-analysis study is one of the most recent and up-to-date secondary studies that comprehensively evaluated the effects of different types of heavy metals separately and in combination on obesity development by age groups and type of obesity. Moreover, the serum and/or urine levels of heavy metals were compared among those with high or low exposure to them. Although it might not be helpful for clinical practice, it provides some valuable insights for health policymakers, environmental epidemiologists, and researchers. Nevertheless, the study has different limitations that should be considered in interpreting the results. First, the included studies in the meta-analysis were case–control and cross-sectional. So, it might lead to recall and selection bias in the findings and results should be interpreted with caution. Although we performed backwards and forward citation searching and hand searching for grey literature, searching Google Scholar and some other online databases like Scopus was not done. Therefore, we cannot rule out the possibility of not finding some relevant articles. Second, the study only focused on obesity, while other types of excess body weight, like overweight, were not evaluated in the present study. Third, different definitions for obesity and exposure cut-offs were used in the included studies, and it can lead to the inclusion of a heterogeneous population in the analysis. Fourth, due to the limited number of included studies for each heavy metal, the subgroup analysis only included one study for some. We tried to search and include all heavy metals, while some heavy metals like arsenic were not evaluated in at least three studies.

Conclusion

Although barium and mercury exposure were associated with increased odds of obesity occurrence, lead exposure decreased the odds of obesity development. Lead and cadmium exposure in children and individuals with general obesity were substantially associated with lower obesity development. On the other hand, mercury and barium exposure in participants with abdominal obesity and general obesity, respectively, significantly elevated the risk of obesity. However, no significant association was found for other heavy metals. The controversy between the findings could be due to the low number of included studies that evaluated each type of heavy metal and the high heterogeneity between the study findings. Further large-scale observational studies and meta-analysis are recommended.

Key points

• Obesity is significantly lower in participants with high exposure to lead.

• Those with high exposure to cadmium have non-significantly lower odds of obesity development.

• Mercury and barium exposure are significantly related to an increased risk of obesity.

• Manganese and zinc exposure could insignificantly increase the risk of obesity.

• The mean serum zinc and chromium levels are insignificantly lower in obese population.

Supplementary information

Below is the link to the electronic supplementary material.

Author contribution

MZ and SAN conceptualized the topic; MZ searched the databases; MZ and AGJ performed screening and full-text review; MZ and AGJ performed data extraction; MZ and AGJ performed quality assessment; MZ, SAN and LA prepared the first draft of the manuscript; MS critically revised and edited the manuscript; MS supervised this project. All authors reviewed and approved the final version of the manuscript.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethical approval

The present study was approved by the ethics committee of Kerman University of Medical Sciences, Kerman, Iran (Ethics code: IR.KMU.REC.1401.396).

Conflict of interests

No conflict of interest declared.