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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.


Scree plots of the principal component analysis (with and without copper).
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pone.0163313.g002: Scree plots of the principal component analysis (with and without copper).

Mentions: Therefore, to perform the multivariable GLMM on MS we first applied a PCA on HM. The PCA statistics, i.e., eigenvalue>1, Horn’s parallel analysis, the very simple structure (VSS) complexity 1 = 0.90 and the scree plot (Fig 2) identified two principal components.


Is Geo-Environmental Exposure a Risk Factor for Multiple Sclerosis? A Population-Based Cross-Sectional Study in South-Western Sardinia
Scree plots of the principal component analysis (with and without copper).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0163313.g002: Scree plots of the principal component analysis (with and without copper).
Mentions: Therefore, to perform the multivariable GLMM on MS we first applied a PCA on HM. The PCA statistics, i.e., eigenvalue>1, Horn’s parallel analysis, the very simple structure (VSS) complexity 1 = 0.90 and the scree plot (Fig 2) identified two principal components.

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.