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Is Geo-Environmental Exposure a Risk Factor for Multiple Sclerosis? A Population-Based Cross-Sectional Study in South-Western Sardinia

View Article: PubMed Central - PubMed

ABSTRACT

Background: South-Western Sardinia (SWS) is a high risk area for Multiple Sclerosis (MS) with high prevalence and spatial clustering; its population is genetically representative of Sardinians and presents a peculiar environment. We evaluated the MS environmental risk of specific heavy metals (HM) and geographical factors such as solar UV exposure and urbanization by undertaking a population-based cross-sectional study in SWS.

Methods: Geochemical data on HM, UV exposure, urbanization and epidemiological MS data were available for all SWS municipalities. Principal Component Analysis (PCA) was applied to the geochemical data to reduce multicollinearity and confounding criticalities. Generalized Linear Mixed Models (GLMM) were applied to evaluate the causal effects of the potential risk factors, and a model selection was performed using Akaike Information Criterion.

Results: The PCA revealed that copper (Cu) does not cluster, while two component scores were extracted: 'basic rocks', including cobalt, chromium and nickel, and 'ore deposits', including lead and zinc. The selected multivariable GLMM highlighted Cu and sex as MS risk factors, adjusting for age and 'ore deposits'. When the Cu concentration increases by 50 ppm, the MS odds are 2.827 (95% CI: 1.645; 5.07) times higher; females have a MS odds 2.04 times (95% CI: 1.59; 2.60) higher than males.

Conclusions: The high frequency of MS in industrialized countries, where pollution by HM and CO poisoning is widespread, suggests a relationship between environmental exposure to metals and MS. Hence, we suggested a role of Cu homeostasis in MS. This is a preliminary study aimed at generating hypotheses that will need to be confirmed further.

No MeSH data available.


The 25 municipalities forming the study area, the sampling sites and the ore bodies.
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pone.0163313.g001: The 25 municipalities forming the study area, the sampling sites and the ore bodies.

Mentions: The study considered potential environmental risk factors, classified by their geochemical and geographical nature and defined in the 25 SWS municipalities (Fig 1). We collected six HM (Co, Cr, Cu, Ni, Pb, Zn), which were revealed by geochemical samplings (Fig 1), and also a proxy of UV exposure (% of the municipal areas exposed to the south) and urbanization (% of urban area included in the municipal area), revealed by Geographic Information System processing (data collection is detailed in S2 File).


Is Geo-Environmental Exposure a Risk Factor for Multiple Sclerosis? A Population-Based Cross-Sectional Study in South-Western Sardinia
The 25 municipalities forming the study area, the sampling sites and the ore bodies.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036813&req=5

pone.0163313.g001: The 25 municipalities forming the study area, the sampling sites and the ore bodies.
Mentions: The study considered potential environmental risk factors, classified by their geochemical and geographical nature and defined in the 25 SWS municipalities (Fig 1). We collected six HM (Co, Cr, Cu, Ni, Pb, Zn), which were revealed by geochemical samplings (Fig 1), and also a proxy of UV exposure (% of the municipal areas exposed to the south) and urbanization (% of urban area included in the municipal area), revealed by Geographic Information System processing (data collection is detailed in S2 File).

View Article: PubMed Central - PubMed

ABSTRACT

Background: South-Western Sardinia (SWS) is a high risk area for Multiple Sclerosis (MS) with high prevalence and spatial clustering; its population is genetically representative of Sardinians and presents a peculiar environment. We evaluated the MS environmental risk of specific heavy metals (HM) and geographical factors such as solar UV exposure and urbanization by undertaking a population-based cross-sectional study in SWS.

Methods: Geochemical data on HM, UV exposure, urbanization and epidemiological MS data were available for all SWS municipalities. Principal Component Analysis (PCA) was applied to the geochemical data to reduce multicollinearity and confounding criticalities. Generalized Linear Mixed Models (GLMM) were applied to evaluate the causal effects of the potential risk factors, and a model selection was performed using Akaike Information Criterion.

Results: The PCA revealed that copper (Cu) does not cluster, while two component scores were extracted: 'basic rocks', including cobalt, chromium and nickel, and 'ore deposits', including lead and zinc. The selected multivariable GLMM highlighted Cu and sex as MS risk factors, adjusting for age and 'ore deposits'. When the Cu concentration increases by 50 ppm, the MS odds are 2.827 (95% CI: 1.645; 5.07) times higher; females have a MS odds 2.04 times (95% CI: 1.59; 2.60) higher than males.

Conclusions: The high frequency of MS in industrialized countries, where pollution by HM and CO poisoning is widespread, suggests a relationship between environmental exposure to metals and MS. Hence, we suggested a role of Cu homeostasis in MS. This is a preliminary study aimed at generating hypotheses that will need to be confirmed further.

No MeSH data available.