Relationships of geochemistry and multiple sclerosis
The main aim of this study has been to investigate how registers and databases of geochemistry can be combined with registers of patient data in epidemiological studies. By testing the hypothesis that Multiple Sclerosis (MS) varies with geography and investigating if the variation can be explained by natural variability of zinc in different media, difficulties have been identified and recommendations for future epidemiological studies with similar scope are given. Multiple sclerosis (MS) is a chronic neurological illness that affects nerve cells in the central nervous system (CNS). It belongs to a group of illnesses called autoimmune diseases where the immune system attacks the body's own tissue. The onset of autoimmune reactions is not fully understood. Autoimmune diseases are believed to be multifactorial where both intrinsic factors (e.g. genetics, age, hormones) and environmental factors (e.g. infections, diet, drugs, chemicals etc.) may contribute to the induction, development and progression of the disease. There is a general believe that the epidemiological pattern of MS vary with geography, but even though the systematic study of MS started in 1929 the comparison of prevalence studies over the world still is very difficult and the results are not reliable. Iron, zinc, manganese, copper and molybdenum are examples of important building blocks for almost all living organisms and are thus termed essential elements. They originally derive from the Earth's crust and are taken up in organisms from soil, air and water. For some metals, no biological, nutritional or biochemical function has been established (yet) and they are thus termed non-essentials. The level of exposure to essential and non-essential elements is of crucial importance for the effect on living organisms. A too high dose can be toxic while a too low dose of essential elements will cause deficiency and consequent higher vulnerability for the exposure to toxic compounds or non-essential elements. In this study we have initially focused to check if the occurrence of MS could be correlated to background levels of zinc (Zn) since zinc is an element that participates in several important reactions in the body. We used the Swedish MS-register, which includes almost all MS-patients in Sweden. The best resolution on where the patients live is given on post code areas. Spatially distributed census data over postcode areas, valid for December 2005 and compiled by Official Statistics of Sweden, Statistics Sweden (SCB), were used in this study. Geochemical data from soil (till), stream-water and groundwater from the Swedish Geological Survey have been compiled into postcode areas. The analyzed data on the distribution of MS-patients indicate that a geographical pattern could be found with higher prevalence of MS in the county of Västerbotten and clusters around larges cities. No north -south or east-west gradient of the prevalence was found. However, visual interpretation of prevalence measures is strongly biased towards large post code areas, masking the variation of prevalence measures of small areas. This effect is striking in larger cities always having a large number of small post code areas. Combination of the patient registers and the geochemical registers was evaluated with multivariate analysis (MVA) and as a univariate study for zinc solely, but no correlation between the prevalence of MS and the occurrence of natural background levels of elements were found. Registers were analyzed both separately and together, but none of these models increased the degree to which variance was explained. This does not mean that no relationship between MS and geochemistry is possible but that correlations could not be found with the data, methods and models used in this project. The most important conclusion from this study is that to combine patient data with any kind of exposure data with a geographical variation, the administrative division (i.e. parishes, post code areas etc) are less appropriate. Divisions with respect to natural (geographical) borders such as catchment areas would be more useful for epidemiological purposes where a geographic component is of interest. To fulfil this, population data for catchment areas is needed. The density of the patient data and the exposure data is also of crucial importance. Moreover, there must be a variation in the exposure data large enough to result in a difference between areas. It is recommended that also the areas where no disease is found to be included in epidemiological studies. In these regions high or low levels of elements can also be present. The use of average values over districts is problematic. A high density of sampling in an area does not necessarily mean that the calculated mean value is representative for the whole area. How well an average value for a district describes the actual value depends both on the natural variability of substances in the media as well as the sampling density (i.e. high variance but many samples could give the same average value as low variance and few samples).