Literature DB >> 29534534

Hair as a Biomarker of Long Term Mercury Exposure in Brazilian Amazon: A Systematic Review.

Nathália Santos Serrão de Castro1, Marcelo de Oliveira Lima2.   

Abstract

Many studies have assessed mercury (Hg) exposure in the Amazonian population. This article performs a literature search of the studies that used hair as a biomarker of Hg exposure in the Brazilian Amazonian population. The search covered the period from 1996 to 2016 and included articles which matched the following criteria: (1) articles related to Hg exposure into Brazilian Amazon; (2) articles that used hair as a biomarker of Hg exposure; (3) articles that used analytical tools to measure the Hg content on hair and (4) articles that presented arithmetic mean and/or minimum and maximum values of Hg. 36 studies were selected. The findings show that most of the studies were performed along margins of important rivers, such as Negro, Tapajós and Madeira. All the population presented mean levels of Hg on hair above 6 µg g-1 and general population, adults, not determined and men presented levels of Hg on hair above 10 µg g-1. The results show that most of the studies were performed by Brazilian institutions/researchers and the majority was performed in the State of Pará. The present study identified that Amazonian population has long-term been exposed to Hg. In terms of future perspectives, this study suggests the implementation of a strategic plan for environmental health surveillance in the region in order to promote health and benefit Amazonian population.

Entities:  

Keywords:  Amazon; hair Amazonia; mercury; methylmercury

Mesh:

Substances:

Year:  2018        PMID: 29534534      PMCID: PMC5877045          DOI: 10.3390/ijerph15030500

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   3.390


1. Background

The health effects of methylmercury (MeHg) exposure have been investigated since the accident that occurred in Minamata Bay, Japan. The clinical features of MeHg poisoning were classified as acute or chronic, based on the symptoms observed in patients living around the bay in the vicinity of the pollution source (a factory) and in patients living on the coast of the Shiranui Sea; both populations had consumed contaminated fish for almost 20 years [1]. Clinical studies of Japanese patients affected by dietary MeHg poisoning showed that mercury (Hg) had long-term effects on health. The patients from Goshoura Island, an area close to Minamata Bay, who had mean levels of total hair Hg of 37 µg g−1 in 1960 (n = 16) and 2.4 µg g−1 in 2002 (n = 23) showed persistent sensory disorders caused by their past history of MeHg exposure [2,3]. Evaluation of data from a population-based study performed in 1971 at Goshoura showed an increased incidence of neurological signs, such as ataxia (12%), dysarthria (5.9%), and paresthesia of extremities (5.7%) [4]. A study performed at Niigata, a town located along the Agano River that was affected by MeHg poisoning in 1965, showed that even people with chronic exposure to levels of Hg of less than 20 µg g−1 (n = 24) presented neurologic signs associated with MeHg poisoning [5]. Recently, a population-based study in Minamata and neighboring areas identified the association between neurological sign and the development of psychiatric symptoms. In spite of the findings observed after the Minamata disaster, there is no clear consensus yet about a dose-response relationship between Hg exposure and health effects given that the genetic characteristics of the populations vary, the modes and times of exposure are diverse, and that life-styles and behavior can influence on the toxic effects of Hg exposure [6,7]. However, vulnerable populations (i.e., pregnant women, human fetuses, neonates, and children) are under the potential risks of Hg effects [8,9,10,11]. Analysis of scalp hair has been a valuable method used to assess the Hg exposure of different populations because hair is easy to collect, store, and manipulate [12]. Hair Hg levels strongly correlate with an individual’s dietary intake of MeHg. Moreover, its chemical stability facilitates retrospective studies [13]. However, Hg can be incorporated into hair in other ways, such as the sorption of volatile species (i.e., elemental Hg), and its level can be affected by hair color and growth rates, which are considered to be pre-analytical sources of variation that may cause bias and misleading interpretations [14,15]. In spite of these limitations, the versatility provided by scalp hair for assessing Hg exposure, especially in remote areas, has been valuable to access the Hg exposure in different populations. In Brazil, many studies had been performed in the Amazonian region and hair has been selected as a biomarker of Hg exposure. The present article performs a systematic review of publications that analyzed Hg on hair of different populations into Brazilian Amazon. The objective of this article is to provide an overview of long-term exposure to Hg into Brazilian Amazon, identify populations under risk of Hg effects and give future perspectives for environmental health surveillance for the region.

2. Methods

This systematic review was registered in PROSPERO (registration number CRD42017056584). The search was performed using the following electronic databases: Pubmed, EBSCO, VHL (Virtual Health Library) and Scielo. Both authors performed independently the virtual search for articles titles and abstract using the search strategy showed in the Table 1. The search covered the studies that were published in the period from 1996 to 2016. The present study followed the following inclusion criteria for articles: (1) articles related to Hg exposure into Brazilian Amazon; (2) articles that used hair as a biomarker of Hg exposure; (3) articles that used analytical tools to measure the Hg content on hair and (4) articles that presented arithmetic mean and/or minimum and maximum values of Hg. As exclusion criteria, the present study followed: (1) review articles of Hg exposure and (2) articles which methodology was not clear, such as sample size, locality and analytical tools. The potential studies were screened and duplicates were removed. Articles not excluded were read by the two authors who extracted in detail the geographic location where the study was performed, sample size and type, the age range and/or mean age of the populations, the first author of the study, the year of the publication, and the arithmetic mean of Hg observed.
Table 1

Search strategies in electronic database.

StrategyKeywords
#1Mercury and hair and Amazon
#2Methylmercury and hair and Amazon
#3Mercury and Brazil and Amazonia
#4Mercury and Amazon
The present study classified the type of the studied populations as follows: general population (0 to >60 years old), adult population (>15 years old), children (<15 years old), women, men, indigenous and not determined (nd) when the study did not identify the type and/or the age range of the target population. According to the type of each population, the data of the mean level of Hg was used in order to determine a weighted average in which the number of individuals studied contributed equally to a final average (Equation (1)). where: WA = weighted average; X = arithmetic mean of Hg (µg g−1); I = Number of Individuals. A cartographic quali-quantitative analysis of the data was performed. The classification of the populations determined for this study were considered for a qualitative evaluation and a quantitative analysis based on the degree of exposure followed the following criteria: lower (less than 2 µg g−1), medium (between 2 and 6 µg g−1) and higher (above 6 µg g−1). The ArcMap 10.1 software (ESRI, Redlands, CA, USA) was used for georeferencing for studied where the geographic coordinates were not available. The geographic location was matched with the Brazilian Institute of Geography and Statistics (IBGE). The quantitative analysis was evaluated by a “choropleth map” where the differences of Hg exposure according to different populations and regions were shaded: yellow representing populations with low exposure to Hg and red high exposure. Among the major source of risk of bias of the present study we can cite here: (1) The period of the study. The present study searched for studies that were published from 1996 to 2016 (20 years). Thus, the concentration of Hg on hair among the populations can vary, and, thus, the general visualization provided by the cartographic quali-quantitative analysis could not represent the current status. (2) The methodologies for Hg measurement were not an exclusion or inclusion criteria. Once the present study considered valid all the methodologies that measured Hg on hair, the results can not reflect a “gold standard” for Hg exposure in Amazon. Instead, all the selected articles were peer reviewed, thus the data could be considered to give an overview of long term Hg exposure in Amazonian population.

3. Results

The database search identified 1283 articles, of which 973 were duplicated and, consequently, removed. The potential 310 articles were screened and 274 of them were removed given that did not attended to some inclusion/exclusion criteria: 169 used other matrices rather than hair, 42 which methodology was not clear, 27 were review articles, 27 were performed in other regions rather than Brazilian Amazon (7 in Peru, 6 in Bolivia, 4 in Ecuador, 3 in French Guiana, 1 in Venezuela, 1 in Suriname, 1 in Peru and Ecuador, 1 Faroe Island, 1 in Colombia, 1 in Africa and 1 in the Northwest of Brazil), 5 were published out of the period of investigation and 4 contained generalist subject about Hg exposure. The study selection is summarized in the flowchart described in Figure 1.
Figure 1

Study selection flowchart.

In this context, the present study comprised 36 articles, comprising a total of 11,827 individuals (Table 2). According to the year of publication, the articles were published as follows: three in 1998 [16,17,18], two in 1999 [19,20], three in 2000 [21,22,23], two in 2001 [24,25], one in 2002 [26], two in 2003 [27,28], four in 2005 [29,30,31,32], three in 2006 [33,34,35], two in 2007 [36,37], one in 2008 [38], one in 2009 [39], two in 2010 [40,41], three in 2012 [42,43,44], one in 2013 [45], one in 2014 [46], three in 2015 [47,48,49] and two in 2016 [50,51]. Most of the studies (61%) were performed exclusively by Brazilians Institution [16,18,20,21,22,24,28,29,30,31,32,33,34,37,41,42,43,44,45,46,49,51] and others (36%) had been performed in collaboration with them [17,19,23,25,26,35,36,38,39,40,47,48,50]. Only one study (3%) was developed for a foreign country solely [27].
Table 2

Characteristics of the included studies (n = 36).

YearAuthorLocalityStatenType of Population According to the AuthorPopulation SpecificityType of the Population According to the Present StudyRange Hg (µg g−1)Mean Hg (µg g−1)
1998Barbosa A.C. [16]Fresco RiverPA28Kayapo womenchildbearing womenI0.8–13.78.11
Madeira RiverRO98non-indigenous womenchildbearing womenW2.6–94.714.08
Fresco RiverPA54Kayapo childrenndI2.0–20.47.30
Madeira RiverRO71non-indigenous childrenndC0.8–44.410.82
1998Kehrig H.A. [17]Balbina VillageAM53total of population studiedndGnd6.54
16childrenfemaleC1.3–22.07.7
12childrenmaleC2.5–11.45.3
12adultsfemaleA2.2–15.57.4
13adultsmaleA1.2–12.25.5
1998Barbosa A.C. [18]Madeira RiverRO37womenndW2.0–37.214.3
37children0.5–15 monthsC1.4–34.29.8
1999Silva-Forsberg M.C. [19]total (all the populations studied)AM154total of population studied0.2–66 y.oG5.76–171.2475.46
Acariquara, Rio Urubaxi15nd0.3–56 y.oG14.37–146.2569.18
Tupuruquara, Rio Marie57nd0.2–66 y.oG10.44–171.2497.44
Macuna, Rio Uneiuxi17nd0.7–52 y.oG22.17–129.1976.75
Perseverança, Rio Negro23nd0.2–65 y.oG15.77–122.3265.72
Ilha do Pinto. Rio Negro12nd1.6–37 y.oG19.02–100.9569.58
Tapera. Rio Padauari11nd2–59 y.oG19.20–55.5937.48
Tapereira. Rio Negro10nd2–47 y.oG24.94–110.5169.10
Aldeia Maia. Rio Maia7nd13–40 y.oG5.76–63.0228.02
Sitio Velho. Rio Marauia2nd42–62 y.oA13.93–62.5738.25
1999Guimaraes J.R.D. [20]Pracuuba LakeAP15fishermen and their familyndndnd16.7
Duas Bocas Lake15fishermen and their familyndndnd28
2000Hacon S. [21]Alta FlorestaMT75pregnant women14–45 y.oW0.051–8.21.12
2000Santos E.C.O. [22]Brasília LegalPA220total of population studied0–>65 y.oG0.53–49.9911.75
30nd0–5 y.oC1.09–20.465.84
68nd6–10 y.oC0.70–35.8013.06
33nd11–15 y.oC1.22–47.0014.2
12nd16–20 y.oA5.56–19.9013.39
10nd21–25 y.oA1.40–29.5015.25
9nd26–30 y.oA3.70–21.4011.06
16nd31–35 y.oA2.84–37.2012.57
12nd36–40 y.oA5.0–33.014.21
1nd41–45 y.oAnd11.7
7nd46–50 y.oA1.02–14.247.06
8nd51–55 y.oA3.57–49.9911.53
5nd56–60 y.oA0.53–7.074.93
6nd61–65 y.oA5.01–15.9411.33
3nd>65 y.oA2.78–16.467.45
São Luiz do Tapajós327total of population studied0–>65 y.oG0.10–94.5019.91
75nd0–5 y.oC0.10–94.5021.06
74nd6–10 y.oC2.40–52.5022.1
51nd11–15 y.oC3.90–61.8023.24
21nd16–20 y.oA2.10–33.6019.11
21nd21–25 y.oA1.73–32.015.68
15nd26–30 y.oA3.90–34.9015.34
15nd31–35 y.oA5.10–38.018.98
15nd36–40 y.oA2.60–27.814.31
14nd41–45 y.oA3.20–33.6015.13
8nd46–50 y.oA4.0–47.021.71
4nd51–55 y.oA5.90–20.6015.4
6nd56–60 y.oA7.90–27.6017.13
3nd61–65 y.oA3.8–27.8012.6
5nd>65 y.oA3.20–20.8012.6
Santana de Ituqui321total of population studied0–>65 y.oG0.40–11.604.33
37nd0–5 y.oC0.50–8.503.67
81nd6–10 y.oC0.40–10.94.44
62nd11–15 y.oC2.0–11.64.47
25nd16–20 y.oA2.5–9.605
17nd21–25 y.oA1.30–7.103.34
16nd26–30 y.oA1.70–9.204.69
19nd31–35 y.oA1.90–9.605.36
18nd36–40 y.oA1.20–6.03.44
10nd41–45 y.oA2.70–6.804.18
10nd46–50 y.oA1.90–6.404.02
9nd51–55 y.oA2.30–9.05.39
6nd56–60 y.oA3.10–6.904.37
4nd61–65 y.oA1.90–9.04.15
7nd>65 y.oA0.70–5.703.61
2000Dolbec J. [23]CametáPA68total of population studied12–79 y.oGnd10.8
2001Barbosa A.C. [24]Negro RiverAM73children<15 y.oC0.51–45.8918.52
76adults>15 y.oA1.66–59.0121.4
2001Harada M. [25]BarreirasPA76fisherman and family1–67, mean 28 y.oG1.8–53.816.4
Rainhas12fisherman and family7–53, mean 31 y.oG3.1–34.514.1
São Luiz do Tapajós44fisherman and family3–47, mean 21 y.oG5.1–42.220.8
Special group from Barreiras, Rainha and São Luiz do Tapajós50eligible subjects examined clinically that presented high level of Hg (>20 ppm) from March 1994 to February 19983–65, mean 25 y.oG5.1–42.723.6
2002Crompton P. [26]JacareacangaPA205total of population studiedgeneral population, except children under 2 y.oG0.3–83.28.6
ndmenndMnd11.0
ndwomenndWnd6.7
2003Passos C.J. [27]Brasília LegalPA26adults women23–62, mean 41 y.oW4.0–20.010.0
2003Santos E.C.O. [28]Guajará Mirim e Nova MamoréRO910total of studied population (Pakaanova indigenous)0–>45 y.oI0.52–83.898.37
57nd0–2 y.oI1.48–83.8910.54
115nd3–5 y.oI1.67–47.229.34
152nd6–10 y.oI0.52–63.818.16
114nd11–15 y.oI0.65–31.116.86
177nd16–25 y.oI0.65–39.428.45
114nd26–35 y.oI1.37–28.648.56
50nd36–45 y.oI1.49–21.258.39
131nd>45 y.oI1.37–25.847.84
2005Dorea J.G. [29]Teles Pires (Tapajós Basin)PA47Kayabi indigenousKayabi community from Teles PiresInd12.8
249Munduruku indigenousMunduruku community from Teles PiresInd3.4
2005Santos E.C.O. [30]São Gabriel da CachoeiraAM157total of population studied0–>40 y.oG0.30–83.1113.02
9nd0–5 y.oC1.01–14.405.71
8nd6–10 y.oC2.05–15.007.35
37nd11–20 y.oNC0.94–22.817.346
45nd21–30 y.oA0.30–59.1611.67
33nd31–40 y.oA1.03–60.0016.56
26nd>40 y.oA2.41–83.1122.88
Barcelos242total of population studied0–>40 y.oG0.07–52.049.671
17nd0–5 y.oC0.83–25.897.46
25nd6–10 y.oC0.76–27.466.85
44nd11–20 y.oNC0.07–21.537.00
62nd21–30 y.oA0.25–42.649.05
27nd31–40 y.oA0.23–52.0412.02
67nd>40 y.oA2.60–32.8612.67
2005Tavares L.M.B. [31]Riverine communities of Bocas de Conchas, Cuiabá Mirim, Estirão Cumprido and Porto Brandão located near of Barão de MelgaçoMT72riverine children3–7 y.oC0.58–17.145.37
Barão de Melgaço114urban children3–7 y.oC0.38–7.572.08
2005Klautau-Guimaraes M.N. [32]Teles PiresPA65total of population studied (Kayabi indigenous)0–>61, mean 24.53 y.oInd14.75
33Kayabi indigenous0–20 y.oInd17.86
25Kayabi indigenous21–40 y.oInd11.97
5Kayabi indigenous41–60 y.oInd14.35
2Kayabi indigenous>61 y.oInd15.17
117total of population studied (Munduruku indigenous)0–>61, mean 30.90 y.oIndnd
34Munduruku indigenous0–20 y.oInd4.26
56Munduruku indigenous21–40 y.oInd3.65
20Munduruku indigenous41–60 y.oInd3.75
7Munduruku indigenous>61 y.oInd3.72
2006Alves M.F.A. [33]total (all the riverine populations studied)AM105total of adult studied population18–50, mean 32 y.oAnd35.4
Mariuá (Negro River)3adultsndAnd24.9
Marará (Negro River)17adultsndAnd27.3
Piloto (Negro River)25adultsndAnd33.2
Ponta da Terra (Cuiuni River)12adultsndAnd38.2
São Luiz (Negro River)21adultsndAnd41.4
Cumaru (Negro River)16adultsndAnd43.7
Baturité (Negro River)11adultsndAnd33
Manaus105total of studied population18–50, mean 28 y.oAnd1.0
2006Bastos W.R. [34]total (all the populations studied)RO713total of population studiedndnd5.99–15015.22
Calama34ndndnd0.50–22.489.02
Boa Vitoria3ndndnd10.86–17.0513.82
Cujubim12ndndnd1.55–14.676.30
Firmesa4ndndnd9.40–14.8011.21
Itacoa6ndndnd5.28–16.0011.97
Nazaré64ndndnd0.63–22.6010.65
Papagaios13ndndnd4.76–27.2213.72
Santa Rosa19ndndnd7.68–20.7813.99
São Carlos15ndndnd1.84–22.839.51
Terra Caida7ndndnd5.01–14.619.61
Sto Antônio do Pau Queimado14ndndnd5.87–26.8614.69
PuruzinhoAM28ndndnd4.57–28.2714.83
Livramento15ndndnd18.96–63.5436.89
Valparaiso21ndndnd2.98–82.3818.93
Auxiliadora34ndndnd1.12–22.789.34
Curralinho5ndndnd10.70–34.4919.69
Nazaré do Retiro15ndndnd9.69–24.7717.90
Novos Prazeres20ndndnd2.77–24.2811.90
São Pedro14ndndnd6.61–28.0015.77
Barreiras do Manicoré9ndndnd1.45–23.0410.82
Cachoeirinha14ndndnd1.54–37.2214.74
São Lazaro6ndndnd2.50–23.379.48
Maraca II6ndndnd8.57–15.6911.37
Vista Nova4ndndnd21.40–28.5425.69
Vista Alegre17ndndnd7.28–26.2816.02
Bom Suspiro12ndndnd6.43–30.0616.29
Carara39ndndnd4.18–34.7118.13
Miriti16ndndnd6.70–50.3722.34
São Sebastiao (Lago Lucio)17ndndnd6.61–18.5212.84
Boca do Carapanatuba18ndndnd3.43–19.2210.45
São Sebastiao do Tapuru18ndndnd20.43–150.0062.76
Moanenses13ndndnd3.26–20.4912.73
Três Casas9ndndnd5.62–70.7033.07
Boa Ventura7ndndnd4.73–35.7916.55
Auara Grande19ndndnd6.21–24.9815.97
Fazenda Tabocal2ndndnd0.50–1.501.00
Remanso12ndndnd8.36–29.0218.16
Arapapa7ndndnd10.43–21.3316.56
Axinim13ndndnd3.27–23.028.65
Espirito Santo18ndndnd3.51–21.2812.47
Santa Maria7ndndnd6.70–16.849.28
Caicara23ndndnd1.94–17.9810.04
Paquique6ndndnd7.49–11.579.23
Uricurituba46ndndnd0.36–19.129.09
Santa Rosa II12ndndnd5.81–16.8911.65
2006Fillion M. [35]Tapajós (São Luiz do Tapajós, Nova Canaã, Santo Antônio, Mussum, Vista Alegre, Açaituba)PA251adults15–89, mean 35.2 y.oA0.21–77.217.8
2007Passos C.J. [36]Tapajós (São Luiz do Tapajós, Nova Canaã, Santo Antônio, Ipaupixuna, Novo Paraiso, Teca, Timbó, Açaituba, Campo Alegre, Sumauma, Vista Alegre, Mussum, Santa Cruz)PA449adults15–89, mean 38.6 y.oA0.2–58.316.8
2007Pinheiro M.C.N. [37]PanacaueraPA8children0–1 y.oC0.39–4.661.11
13children2–6 y.oC0.65–5.162.27
15children7–12 y.oC0.86–9.462.99
Barreiras (Tapajós Basin)17children0–1 y.oC1.80–15.705.35
45children2–6 y.oC1.43–23.606.21
22children7–12 y.oC1.63–14.506.72
São Luiz do Tapajós11children0–1 y.oC1.99–30.305.97
23children2–6 y.oC2.76–53.8013.22
14children7–12 y.oC1.34–38.8010.83
2008Passos C.J.S. [38]Tapajós (São Luiz do Tapajós, Nova Canaã, Santo Antônio, Vista Alegre, Mussum, Açaituba)PA256adults15–89, mean 35.3 y.oA0.2–58.317.9
2009Fillion M. [39]Tapajós (São Luiz do Tapajós, Nova Canaã, Santo Antônio, Mussum, Vista Alegre, Açaituba, Santa Cruz, Sumauma, Campo Alegre, Ipaupixuna, Novo Paraiso, Curi-Teca, Curi-Timbó)PA456adults15–>65 y.oA0.2–77.717.8
2010Grotto D. [40]TapajósPA108total of population studiedmean 41.1 y.ond1–57.813.7
54menndMnd11.5
54womenndWnd8.8
2010Bortoli M.C. [41]Novo AirãoAM55womenmean 32.3 y.oW0.04–18.675.67
2012Barcelos G.R.M. [42]TapajósPA144adults15–83, mean 43 y.oA1–43.310.4
2012Dutra M.D.S. [43]ItaitubaPA90childrenpopulation from urban area, samples collected in 2004Cnd1.01
47childrenpopulation from urban area, samples collected in 2006Cnd1.18
90childrenpopulation from urban area, samples collected in 2010Cnd1.18
2012Farias L.A. [44]ManausAM201children2–7 y.oC0.02–34.41.93
2013Khoury E.D.T. [45]BarreirasPA78general population13–53 y.oGnd8.66
São Luiz do Tapajós30general population13–53 y.oGnd9.19
Furo do Maracujá49general population13–53 y.oGnd0.73
2014Rocha A.V. [46]Demarcação—Machado RiverRO10children3–9 y.oCnd3.57
Gleba do Rio Preto10children3–9 y.oCnd6.24
2015Faial K. [47]ItaitubaPA6male0–2 y.oC4.14–9.796.85
6male3–5 y.oC16.01–23.8019.57
10male6–10 y.oC12.59–24.9318.58
6male11–15 y.oC5.83–15.5713.08
7male16–20 y.oA17.82–24.1620.87
4male21–25 y.oA9.42–20.0915.52
ndmale26–30 y.oAndnd
3male31–35 y.oA8.55–10.839.69
5male36–40 y.oA10.92–20.0315.29
1male41–45 y.oA14.81–14.8114.81
1male46–50 y.oA2.07–2.072.07
1male51–55 y.oA13.89–13.8913.89
3male56–60 y.oA14.00–14.1614.08
5male>60 y.oA12.04–21.4714.57
4female0–2 y.oC8.25–12.8910.38
7female3–5 y.oC12.20–19.2915.83
7female6–10 y.oC11.46–23.2115.84
8female11–15 y.oC8.41–21.7113.12
5female16–20 y.oA13.52–22.0116.74
14female21–25 y.oA6.33–17.6611.22
7female26–30 y.oA4.84–10.67.16
8female31–35 y.oA4.95–20.7813.52
3female36–40 y.oA7.30–15.7311.09
3female41–45 y.oA14.69–23.0218.62
3female46–50 y.oA7.31–14.9810.27
4female51–55 y.oA15.52–18.5217.02
1female56–60 y.oA14.29–14.2914.29
9female>60 y.oA14.60–27.0220.39
2015Castilhos Z. [48]São ChicoPA172ndndnd0.14–35.903.44
Creporizinho146ndndnd0.23–10.492.25
2015Hoshino A. [49]Lago do PuruzinhoAM58general population1–47, mean 17.3 y.oGnd12.78
2016Rocha A.V. [50]Porto VelhoRO200women18–48, mean 26.60 y.oWnd0.60
2016Carvalho L.V.B. [51]BelmontRO42ndmean 11.3 y.ondnd2.71
Cunia52ndmean 11.3 y.ondnd7.18
11.827

Legend. NC: not included for map classification, nd: not determined; G: general population; C: children; A: adults; W: women; M: men; I: Indigenous, >older than. y.o: year(s) old.

There was a predominance of studies that were performed in the State of Pará (47%) [22,23,25,26,27,29,35,36,37,38,39,40,42,43,45,47,48]. Some studies had been performed in the State of Amazonas (22%) [17,19,24,30,33,41,44,49], Rondônia (14%) [18,28,46,50,51], Mato Grosso (8%) [21,31,32] and Amapá (3%) [20]. Two studies were performed using samples from populations from two different States: Pará and Rondônia (3%) [16] and Rondônia and Amazonas (3%) [34]. The results shows that general population presented the highest mean level of Hg exposure (29.59 µg g−1, ranging from 0.73 to 97.44 µg g−1), followed by adult population (21.08 µg g−1, ranging from 1.00 to 43.70 µg g−1), not determined (14.60 µg g−1, ranging from 1.00 to 62.76 µg g−1), men population (11.25 µg g−1, ranging from 11.00 to 11.50 µg g−1), children population (7.95 µg g−1, ranging from 1.11 to 22.00 µg g−1), women population (7.66 µg g−1, ranging from 0.60 to 14.30 µg g−1) and Indigenous population (6.95 µg g−1, ranging from 4.90 to 8.37 µg g−1) (Figure 2).
Figure 2

Mean level (µg g−1) of Hg exposure in populations of Brazilian Amazon according to the present study.

The analysis of the georeferencing data is presented as a map which shows a geospatial distribution of the populations and their respective degree of Hg exposure. The results show that most of the studies were performed along the margins of the Amazonian rivers and that most of these populations are highly exposed (Figure 3).
Figure 3

The georeferencing results showing a geospatial distribution of the Brazilian Amazonian populations and their respective degree of Hg exposure on hair.

4. Discussion

The theme of Hg exposure in Amazonian population is an intriguing form of environmental contamination for some reasons: (1) the mean level of Hg exposure in Amazonian population usually exceed the normal limit preconized by WHO [24,30,33,52,53,54]; (2) there is a dichotomy in the clinical findings in different Amazonian populations, where some studies associate Hg exposure with the development of clinical symptoms [35,55,56,57] while others do not [45,58,59,60,61] and (3) even “non-exposed” populations are at risk of Hg effects [58]. The present study performed a systematic review of Hg exposure in Brazilian Amazon population. The results show that the majority of the studies were performed by Brazilian Institutions and researchers, reflecting the low international insertion despite that Hg is a global problem [62]. The high number of studies that were performed in the State of Pará is in agreement with the high prevalence of gold miners in the region (legal and illegal miners), especially in the Tapajós Basin [26,63]. The analysis shows that most of the studies were performed along the margins of important rivers, such as Negro, Tapajós and Madeira Rivers. The results show that all the populations presented mean levels above 6 µg g−1 of Hg on hair and that general population, adults, not determined and men presented mean levels above 10 µg g−1 of Hg on hair. Thus, the findings support the idea that Amazonian population present (along the period of time covered by this study) mean levels of Hg above the normal limit preconized by WHO (1–2 µg g−1 and levels above 10 µg g−1 for daily fish consumers) [64]. Although, affirm that this population is under risk of Hg effect is premature. Discussions about this issue should be evaluated based on other studies. As future perspectives, the present study suggests an implementation of a strategic plan for the region in order to promote health and benefit the population. As strategies, we propose: Increase the technical capacity for Hg determination in the region; Implement an environmental health surveillance program that considerers the Amazonian life style, behavior and ecosystem dynamics; A follow up program to monitor the Hg content and health of individuals that presents high levels of Hg on biological matrices.

5. Conclusions

The Hg exposure in the Amazonian population is a fact. The high level of Hg on hair revealed by this study shows that this population has been long-term exposed to this metal. The data reveals that the studies focused on population that lives along the margins of the rivers and, of utmost importance, populations under risk of Hg exposure from gold mining activities, especially from Tapajós Basin located in the State of Pará.
  53 in total

1.  The source and fate of sediment and mercury in the Tapajós River, Pará, Brazilian Amazon: Ground- and space-based evidence.

Authors:  Kevin Telmer; Maycira Costa; Rômulo Simões Angélica; Eric S Araujo; Yvon Maurice
Journal:  J Environ Manage       Date:  2006-07-07       Impact factor: 6.789

2.  Reconstruction of methylmercury intakes in indigenous populations from biomarker data.

Authors:  Nathalie H Gosselin; Robert C Brunet; Gaétan Carrier; Michèle Bouchard; Mark Feeley
Journal:  J Expo Sci Environ Epidemiol       Date:  2006-01       Impact factor: 5.563

3.  [Mercury exposure evaluation among Pakaanóva Indians, Amazon Region, Brazil].

Authors:  Elisabeth C Oliveira Santos; Volney de Magalhães Câmara; Edilson da Silva Brabo; Edvaldo Carlos Brito Loureiro; Iracina Maura de Jesus; Kleber Fayal; Fernanda Sagica
Journal:  Cad Saude Publica       Date:  2003-04-01       Impact factor: 1.632

4.  Mercury in the environment and riverside population in the Madeira River Basin, Amazon, Brazil.

Authors:  Wanderley Rodrigues Bastos; João Paulo Oliveira Gomes; Ronaldo Cavalcante Oliveira; Ronaldo Almeida; Elisabete Lourdes Nascimento; José Vicente Elias Bernardi; Luiz Drude de Lacerda; Ene Glória da Silveira; Wolfgang Christian Pfeiffer
Journal:  Sci Total Environ       Date:  2005-10-19       Impact factor: 7.963

5.  Mercury exposures in riverside Amazon communities in Pará, Brazil.

Authors:  E C Santos; I M Jesus; E S Brabo; E C Loureiro; A F Mascarenhas; J Weirich; V M Câmara; D Cleary
Journal:  Environ Res       Date:  2000-10       Impact factor: 6.498

Review 6.  Minamata disease revisited: an update on the acute and chronic manifestations of methyl mercury poisoning.

Authors:  Shigeo Ekino; Mari Susa; Tadashi Ninomiya; Keiko Imamura; Toshinori Kitamura
Journal:  J Neurol Sci       Date:  2007-08-02       Impact factor: 3.181

7.  Selenium status and hair mercury levels in riverine children from Rondônia, Amazonia.

Authors:  Ariana Vieira Rocha; Bárbara Rita Cardoso; Cristiane Cominetti; Rafael Barofaldi Bueno; Maritsa Carla de Bortoli; Luciana Aparecida Farias; Déborah Inês Teixeira Favaro; Luís Marcelo Aranha Camargo; Silvia Maria Franciscato Cozzolino
Journal:  Nutrition       Date:  2014-03-30       Impact factor: 4.008

Review 8.  Evidence on the human health effects of low-level methylmercury exposure.

Authors:  Margaret R Karagas; Anna L Choi; Emily Oken; Milena Horvat; Rita Schoeny; Elizabeth Kamai; Whitney Cowell; Philippe Grandjean; Susan Korrick
Journal:  Environ Health Perspect       Date:  2012-01-24       Impact factor: 9.031

9.  Mercury exposure in a riverside Amazon population, Brazil: a study of the ototoxicity of methylmercury.

Authors:  Ana Hoshino; Heloisa Pacheco-Ferreira; Seisse Gabriela G Sanches; Renata Carvallo; Nathália Cardoso; Maurício Perez; Volney de Magalhães Câmara
Journal:  Int Arch Otorhinolaryngol       Date:  2015-02-19

10.  Mercury in human hair due to environment and diet: a review.

Authors:  D Airey
Journal:  Environ Health Perspect       Date:  1983-10       Impact factor: 9.031

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  10 in total

1.  Genetic Polymorphism of Delta Aminolevulinic Acid Dehydratase (ALAD) Gene and Symptoms of Chronic Mercury Exposure in Munduruku Indigenous Children within the Brazilian Amazon.

Authors:  Jamila Alessandra Perini; Mayara Calixto Silva; Ana Claudia Santiago de Vasconcellos; Paulo Victor Sousa Viana; Marcelo Oliveira Lima; Iracina Maura Jesus; Joseph William Kempton; Rogério Adas Ayres Oliveira; Sandra Souza Hacon; Paulo Cesar Basta
Journal:  Int J Environ Res Public Health       Date:  2021-08-19       Impact factor: 3.390

2.  Economic Impacts on Human Health Resulting from the Use of Mercury in the Illegal Gold Mining in the Brazilian Amazon: A Methodological Assessment.

Authors:  Leonardo Barcellos de Bakker; Pedro Gasparinetti; Júlia Mello de Queiroz; Ana Claudia Santiago de Vasconcellos
Journal:  Int J Environ Res Public Health       Date:  2021-11-12       Impact factor: 3.390

3.  Mercury Exposure in Munduruku Indigenous Communities from Brazilian Amazon: Methodological Background and an Overview of the Principal Results.

Authors:  Paulo Cesar Basta; Paulo Victor de Sousa Viana; Ana Claudia Santiago de Vasconcellos; André Reynaldo Santos Périssé; Cristina Barroso Hofer; Natalia Santana Paiva; Joseph William Kempton; Daniel Ciampi de Andrade; Rogério Adas Ayres de Oliveira; Rafaela Waddington Achatz; Jamila Alessandra Perini; Heloísa do Nascimento de Moura Meneses; Gustavo Hallwass; Marcelo de Oliveira Lima; Iracina Maura de Jesus; Cleidiane Carvalho Ribeiro Dos Santos; Sandra de Souza Hacon
Journal:  Int J Environ Res Public Health       Date:  2021-09-01       Impact factor: 3.390

4.  An Assessment of Health Outcomes and Methylmercury Exposure in Munduruku Indigenous Women of Childbearing Age and Their Children under 2 Years Old.

Authors:  Joeseph William Kempton; André Reynaldo Santos Périssé; Cristina Barroso Hofer; Ana Claudia Santiago de Vasconcellos; Paulo Victor de Sousa Viana; Marcelo de Oliveira Lima; Iracina Maura de Jesus; Sandra de Souza Hacon; Paulo Cesar Basta
Journal:  Int J Environ Res Public Health       Date:  2021-09-25       Impact factor: 3.390

5.  Methylmercury exposure during prenatal and postnatal neurodevelopment promotes oxidative stress associated with motor and cognitive damages in rats: an environmental-experimental toxicology study.

Authors:  Beatriz Helena Fernandes Fagundes; Priscila Cunha Nascimento; Walessa Alana Bragança Aragão; Victória Santos Chemelo; Leonardo Oliveira Bittencourt; Luciana Eiró-Quirino; Marcia Cristina Freitas Silva; Marco Aurelio M Freire; Luanna Melo Pereira Fernandes; Cristiane do Socorro Ferraz Maia; Maria Elena Crespo-Lopez; Rafael Rodrigues Lima
Journal:  Toxicol Rep       Date:  2022-02-26

6.  Health Risk Assessment Attributed to Consumption of Fish Contaminated with Mercury in the Rio Branco Basin, Roraima, Amazon, Brazil.

Authors:  Ana Claudia Santiago de Vasconcellos; Sylvio Romério Briglia Ferreira; Ciro Campos de Sousa; Marcos Wesley de Oliveira; Marcelo de Oliveira Lima; Paulo Cesar Basta
Journal:  Toxics       Date:  2022-08-31

7.  Oral methylmercury intoxication aggravates cardiovascular risk factors and accelerates atherosclerosis lesion development in ApoE knockout and C57BL/6 mice.

Authors:  Janayne L Silva; Paola C L Leocádio; Jonas M Reis; Gianne P Campos; Luciano S A Capettini; Giselle Foureaux; Anderson J Ferreira; Cláudia C Windmöller; Flávia A Santos; Reinaldo B Oriá; Maria E Crespo-López; Jacqueline I Alvarez-Leite
Journal:  Toxicol Res       Date:  2020-11-05

8.  RNA sequencing and proteomic profiling reveal different alterations by dietary methylmercury in the hippocampal transcriptome and proteome in BALB/c mice.

Authors:  Ragnhild Marie Mellingen; Lene Secher Myrmel; Kai Kristoffer Lie; Josef Daniel Rasinger; Lise Madsen; Ole Jakob Nøstbakken
Journal:  Metallomics       Date:  2021-05-24       Impact factor: 4.526

9.  Mercury Contamination: A Growing Threat to Riverine and Urban Communities in the Brazilian Amazon.

Authors:  Heloisa do Nascimento de Moura Meneses; Marcelo Oliveira-da-Costa; Paulo Cesar Basta; Cristiano Gonçalves Morais; Romulo Jorge Batista Pereira; Suelen Maria Santos de Souza; Sandra de Souza Hacon
Journal:  Int J Environ Res Public Health       Date:  2022-02-28       Impact factor: 3.390

Review 10.  Mercury and Prenatal Growth: A Systematic Review.

Authors:  Kyle Dack; Matthew Fell; Caroline M Taylor; Alexandra Havdahl; Sarah J Lewis
Journal:  Int J Environ Res Public Health       Date:  2021-07-03       Impact factor: 3.390

  10 in total

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