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A correlation study between multiple sclerosis and type 1 diabetes incidences and geochemical data in Europe

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A correlation study between multiple sclerosis and type 1 diabetes incidences and geochemical data in Europe
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  A correlation study between multiple sclerosis and type 1diabetes incidences and geochemical data in Europe Paolo Valera  • Patrizia Zavattari  • Stefano Albanese  • Domenico Cicchella  • Enrico Dinelli  • Annamaria Lima  • Benedetto De Vivo Received: 11 October 2012/Accepted: 25 March 2013   Springer Science+Business Media Dordrecht 2013 Abstract  Complex multifactorial disorders usuallyarise in individuals genetically at risk in the presenceof permissive environmental factors. For many of these diseases, predisposing gene variants are partlyknown while the identification of the environmentalcomponent is much more difficult. This study aims toinvestigate whether there are correlations between theincidence of two complex traits, multiple sclerosis andtype 1 diabetes, and some chemical elements andcompounds present in soils and stream sediments inEurope. Data were obtained from the publishedliterature and analyzed by calculating the mean valuesof each element and of disease incidence for eachCountry, respectively, 17 for multiple sclerosis and 21for type 1 diabetes. Correlation matrices and regres-sion analyses were used in order to compare incidencedata and geochemical data. R correlation index andsignificance were evaluated. The analyses performedin this study have revealed significant positive corre-lations between barium and sodium oxide on one handand multiple sclerosis and diabetes incidences on theother hand that may suggest interactions to beevaluated between silicon-rich lithologies and/ormarine environments. The negative correlationsshown by cobalt, chromium and nickel (typical of silicon-poor environment), which in this case can beinterpreted as protective effects against the twodiseases onset, make the split between favorable andprotective environments even more obvious. In con-clusion, if other studies will confirm the involvementof the above elements and compounds in the etiologyof these pathologies, then it will be possible to plan Paolo Valera and Patrizia Zavattari: equal contributors. Electronic supplementary material  The online version of this article (doi:10.1007/s10653-013-9520-4) containssupplementary material, which is available to authorized users.P. Valera ( & )Department of Civil-Environmental Engineering andArchitecture, University of Cagliari, Via Marengo 3,09123 Cagliari, Italye-mail: pvalera@unica.itP. ZavattariDepartment of Biomedical Sciences, University of Cagliari, Cittadella Universitaria di Monserrato, SP 8,Km 0.700, 09042 Cagliari, ItalyS. Albanese    A. Lima    B. De VivoDipartimento di Scienze della Terra, Universita` di NapoliFederico II, Via Mezzocannone 8, 80134 Naples, ItalyD. CicchellaDepartment of Biological, Geological and EnvironmentalSciences, University of Sannio, Via dei Mulini 59/A,Benevento, ItalyE. DinelliDepartment of Earth Science, University of Bologna,Piazza di Porta San Donato 1, 40126 Bologna, Italy  1 3 Environ Geochem HealthDOI 10.1007/s10653-013-9520-4  strategies to reduce the spread of these seriouspandemics. Keywords  Multiple sclerosis    Type 1 diabetes   Elements and compounds    Correlation studies Abbreviations MS Multiple sclerosisT1D Type 1 diabetesFOREGS Forum of European Geological SurveysAR Aqua regiaXRF X-ray fluorescence spectrometryICP Inductively coupled plasma Introduction Disorders or complex traits such as multiple sclerosis(MS), type 1 (childhood) and type 2 diabetes (T1D,T2D),obesityandmanyothersowetheirbeginningstotheconcomitantintakeofpredisposingfactors,geneticand/or epigenetic, and environmental factors thatmake possible their expression, in fact causing theonset of the disease. The knowledge of the eventsunderlying the onset of such complex traits in recentyears has seen a rapid improvement from the genetic/ biological point of view. This is made possible thanksto the recent revolution in biotechnology that hasallowed to extend the investigation into the entiregenome simultaneously for huge series of samples. Onthe contrary, knowledge of environmental permissivefactors is still largely unknown. There are not manystudies reported in the literature that have addressedthese issues, both because of the difficulty of inter-pretation of putative correlations and because of thelack of openness to interdisciplinary collaborations.This study takes into account epidemiological datarelated to MS, presented on the European Journal of Neurology (Pugliatti et al. 2006), and T1D publishedon Diabetologia (Green et al. 2001) in Europe andanalyses the possible correlation with geochemicaldata collected in the same Countries, within Forum of European Geological Surveys (FOREGS) Project (DeVos et al. 2006; Salminen et al. 2005). Obviously, the situation is much more complex andthe data considered in this paper (geochemical andincidence) were undoubtedly influenced by manyconfounding factors (primarily genetic one and oth-ers). However, in this paper, we will discuss these datawithout considering other possible covariates.Althoughthesourcesfromwhichdataweretakenarereliable,weareawareofthelimitationsthatsuchastudymay present. However, what we show is an interestingpreliminarystudy,withstrongscientificfoundationthatwill hopefully stimulate other groups to seek theinvolvement of environmental factors in addition tothe undisputed biological basis of the studied diseases.Afeaturecommontobothdiseases,MSandT1D,isrepresented by the autoimmune origin of the twodisorders. In both cases, they are diseases resultingfrom the destruction of cells or tissues by the immunesystem, myelin in the case of MS and pancreatic betacells in the case of T1D, the first deputed to the rapidtransmission of nerve signals and the second to theproduction of endogenous insulin.The scientific literature (Bastos et al. 2011; Schir-aldi and Monestier 2009; Rana 2008) reports evidence on the possible involvement of different elements,mainly metals, in processes that lead to an autoim-mune response. For some metals such as mercury(Hg), this correlation is well documented and aputative mechanism has been hypothesized (Schiraldiand Monestier 2009). It is not excluded that othermetals can cause similar reactions in cells.In general, however, trying to understand theenvironmental factors working together with genesto increase the probability that an individual willbecome ill with MS or T1D, they have focused mainlyon factors like viral or bacterial infections, physicalagents (radiation) or eating habits. There is now a vastliterature on possible environmental factors that favorthe onset of T1D, such as infections, nutrition in earlychildhood, exposure to ultraviolet radiation (UVB)and vitamin D levels, environmental pollutants,growth rate, obesity and insulin resistance (Vehik and Dabelea 2011; Forlenza and Rewers 2011; Ahadi et al. 2011); with regard to MS, viral agents and othermicrobes, exposure deficiency to vitamin D andsmoking have been proposed as risk factors (Coma-bella and Khoury 2011; Kakalacheva and Lu¨nemann2011). Furthermore, recent advances in the geneticand environmental contributions to autoimmunitysuggest that interactions between genetic elementsand epigenetic changes caused by environmentalagents may be responsible for inducing autoimmunediseases (Hewagama and Richardson 2009). Environ Geochem Health  1 3  However, there is little literature on the possibleeffects of chemical elements in relation to the etiologyof the considered complex traits, although, as above,some items deemed to be potentially involved inautoimmunity. Nevertheless, there are interestingpoints to consider such a course of study to beaddressed in more detail. Purdey (2004b) described a hypothesis that progressive neurodegenerative dis-eases may have a predisposition component in thecombined exposure to high levels of ferromagnetic/ ferroelectric compounds incorporating aluminum(Al), iron (Fe), manganese (Mn), strontium (Sr),barium (Ba) (due to natural or industrial sources)and low levels of magnesium (Mg) and calcium (Ca).The author concluded that genetic factors, chemicalelements and compounds, would lead to the develop-ment of a neuropathology rather than another one(Purdey 2004b). In another study, the same author hypothesized that a chronic intoxication by Ba inter-feres with the synthesis of proteoglycans and may beinvolved with the srcin of MS (Purdey 2004a, b). Melø et al. (2003) have investigated on the levels of  Mn, copper (Cu) and zinc (Zn) in the cerebrospinalfluid of patients affected by MS. Domingo (2000) suggested that the use of vanadium (V) in diabeticpatients is not recommended, given the intrinsictoxicity of the element itself. In 2007, in a reviewpaperonenvironmentalexposuresandgeneregulationin disease etiology, Edwards and Myers (2007) summed up some work describing the effects of cadmium (Cd) on DNA repair and diabetes.Another extremely interesting aspect is repre-sented by the possible protective effect of certainelements against some diseases. With regard to MS,for example, Chora et al. (2007) have described the protective role that Heme oxygenase-1, upregulatedby cobalt protoporphyrin IX, plays in suppressing theprogression of the experimental autoimmune enceph-alomyelitis (EAE), the equivalent of human MS inmouse. Bosco et al. (2010) described how Zn plays a central role in cellular protection against apoptosisand oxidative stress, being Zn essential for normalinsulin production, therefore, concluding that Zntransporter is an important target for autoimmunity inT1D.This studystems from the desiretocontribute to theunderstanding of the factors that are causing the rapidincrease in the incidence of strongly debilitatingdiseases, such as MS and T1D, now real pandemicones. Therefore, we decided to investigate possiblecorrelations between levels of geochemical elementsand compounds in the soil and the incidence of thesediseases. We believe that sharing knowledge andexperiences in different fields may lead to the solutionof puzzle considered virtually unsolvable. Methods PreambleGiven the particular aim of this work and especiallythe spatial characteristics of the analyzed samples andclinicaldata,itwasconsideredappropriatetocalculatethe mean values of each element and of diseaseincidenceforeachconsidered Country.Thisapproach,while does not allows to exactly locate and overlap thetwo distinct populations of data, due to the size of geographical units (Nations), offers the advantage of eliminating the behavioral individual variables (kindof work, eating habits, etc.) that would be taken intoaccount in conducting a more detailed study. Inaddition, geochemical data derive from low-densitysampling, but it has been demonstrated that they areable to provide the necessary information to establishthe geochemical background in soils, surface waters,streamandfloodplainsediments(Salminenetal.2005;De Vos et al. 2006; Cicchella et al. 2012). In conducting the analysis on the data that will bepresented in this paper, we are aware that by calcu-lating a mean value for each Country, we aresmoothing outliers which may instead be representa-tive of local situations (e.g., the case of the highincidence of MS and even more of T1D in Sardiniacompared to mainland Italy).The elements and compounds considered in thisstudy are as follows: aluminum oxide (Al 2 O 3 ), arsenic(As), barium (Ba), beryllium (Be), calcium oxide(CaO), cadmium (Cd), cerium (Ce), cobalt (Co),chromium (Cr), cesium (Cs), copper (Cu), dysprosium(Dy), erbium (Er), europium (Eu), iron (Fe), ferricoxide (Fe 2 O 3 ), gallium (Ga), gadolinium (Gd), haf-nium (Hf), holmium (Ho), potassium oxide (K  2 O),lanthanum (La), lithium (Li), lutetium (Lu), manga-nese oxide (MgO), manganese (Mn), magnesiumoxide (MnO), molybdenum (Mo), sodium oxide(Na 2 O), niobium (Nb), neodymium (Nd), nickel(Ni), phosphorus pentoxide (P 2 O 5 ), lead (Pb), Environ Geochem Health  1 3  praseodymium (Pr), rubidium (Rb), sulfur (S), anti-mony (Sb), scandium (Sc), silicon dioxide (SiO 2 ),samarium (Sm), tin (Sn), strontium (Sr), tantalum(Ta), terbium (Tb), thorium (Th), titanium dioxide(TiO 2 ), thallium (Tl), thulium (Tm), uranium (U),vanadium (V), tungsten (W), yttrium (Y), ytterbium(Yb), zinc (Zn), zirconium (Zr).In addition to the FOREGS database, it will also beavailable soon, at European scale, the GEMAS(geochemical mapping of agricultural and grazing Fig. 1 a  MS Europeanincidence values (fromPugliatti et al. 2006,modified);  b  T1D Europeanincidence values (fromGreen et al. 2001, modified)Environ Geochem Health  1 3  land soil of Europe) database. The latter has beenproduced, under the aegida of the EuroGeoSurveysby the Geochemistry Experts Group, and is now stillin progress under the control of GEMAS ProjectTeam. The complete set of analytical data will beavailable by the end of 2013, but different elementsgeochemical maps are in progress and will be soonavailable in the literature (Reimann et al. 2009, 2011, 2012a, b; de Caritat et al. 2012; Sadeghi et al. 2012; Cicchella et al. 2012). Still at European scale isavailable the bottled water data set (Reimann et al.2010; Birke et al. 2010), which furnish information on more than 70 parameters, including the levels of many toxic elements.Thanks to the projects listed above, will soon bepossible to make appropriate analytical and spatialcomparisons between different types of samples,making it possible to have not only a broad informa-tion on the geochemical characteristics of differentsampling media, but also to have knowledge on thepervasiveness of the considered elements in differentgeological and environmental contexts. This willmake it possible to identify quite easily, at least in apreliminary way, the biotic availability of chemicalelements considered in this study.Applied methods and sources  MS epidemiological data Incidence data (number of cases per 100,000/year)related to MS were derived from an epidemiologicalwork conducted in collaboration between severalEuropean research groups. The data we used for theanalyses presented in this paper refer to 23 EuropeanCountries (Fig. 1a). The source we are referringpreferably collected data from large population-basedstudies (i.e., 50,000 peoples and over, registry-basedand nation-wide surveys); if more epidemiologicalstudies were available for a given Country, the datawereselectedfromthelargestpopulationsandthemostrecent studies. For Countries for which there were nodata published on peer-reviewed journals, local obser-vational studies were used (Pugliatti et al. 2006).Inthepresentstudy,usingdatareportedbyPugliattiet al. (2006), where some Countries had more than oneseries,wecalculatedthe average valueofincidence,inorder to interface epidemiological data with geochem-ical ones. T1D epidemiological data Data concerning the incidence of T1D (number of cases per 100,000/year) were derived from a study of 26 European Countries conducted by the EURODIABassociation (Green et al. 2001) (Fig. 1b). Data are referred to a period between 1989 and 1998. Theauthors of the work to which we refer, used annualestimates of the population size of each center’sgeographically defined area as denominators for thecalculation of rates (Green et al. 2001).To conduct our analysis, even for this disease, weconsidered an average value for each Country, in thecase of multiple samples. Geochemical data Geochemical data from FOREGS projects (Salminenet al. 1998, 2005; De Vos et al. 2006) aim, with Global Geochemical Baselines Program of IUGS/IAGC 360(Darnley et al. 1995; Plant et al. 1997), at the compilation of the environmental geochemical map-pingoftheentireplanet.Toachievethisaim,theglobewas divided into cells of 160  9  160 km (GlobalReference Network—GRN) within which varioustypes of samples are collected (soils, water, riversediments and active floodplains). The sampling GRNhas already been completed in China, in 27 EuropeanCountries and parts of Russia.The Global Geochemical Baselines Program aimsto establish a global geochemical reference baselinefor at least 60 elements and compounds in a range of media for environmental and other applications (i.e.,health, sensu lato). For the present study, we choosethe analytical data from stream sediment and soilsamples because they are the most representative,among the environmental matrices considered in theFOREGS program, especially relatively to humanhealth. There are two main reasons for this choice: onone hand, we considered that soils and streamsediments have a close correlation between them(Pereira et al. 2010; Grunsky et al. 2009; Ranasinghe et al. 2009; Marcello et al. 2005), so it is important to compare the elaboration results of data from both soilsand stream sediments collected in the same district; ontheotherhand,streamsedimentsandsoilsarethemostcommon matrices involved in human life, since mostof the European towns built until 100 years ago arenormally localized near streams and on ‘‘fertile’’ soils. Environ Geochem Health  1 3
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