Abstract
Podoconiosis is a disease whose etiology remains murky. Currently, the disease is attributed to particles that are believed to move through the skin and into the lymphatic system causing swelling of the lower legs. Identity of these particles or their composition remains unclear, though the presence of silicon and/or aluminum is often noted and frequently cited as causal agents. We applied multivariate analyses to the bedrock compositions of a large set of cases from an online database in an effort to identify underlying patterns or combinations of relative element abundances associated with podoconiosis-endemic regions. Using a combination of principal component analysis, discriminant function analysis, and ANOVA, we analyzed ten oxides from five regions on the African continent known to be associated with podoconiosis. The Hawaiian Islands were included as a control group since they are not known to have cases of podoconiosis despite similarity in geology and agricultural practices. Our analyses suggest that a unique alkaline- and silicon-rich geochemistry underlies regions associated with podoconiosis. Our results also imply that minerals enriched in incompatible elements, such as Ca, K, Mg, and Na, may be stronger predictors of the presence of the disease than either silicon or aluminum.
Highlights
One of the greatest challenges in many areas of research is uncovering meaningful patterns and relationships within large, complex data sets
Our analyses suggest that a unique alkaline- and silicon-rich geochemistry underlies regions associated with podoconiosis
The current model of podoconiosis posits fine-grained minerals entering the lymphatic system leading to an inflammatory response
Summary
One of the greatest challenges in many areas of research is uncovering meaningful patterns and relationships within large, complex data sets. Multivariate statistical analyses are valuable tools for probing such data sets, allowing investigators to, among other things, identify underlying structure present in a set of variables, as with principal component analysis (PCA), and classify subjects into groups, as with discriminant function analysis (DFA). The use of these methods is not new to geology. Comparing the results of multivariate analyses to more traditional geological approaches, they found that did the multivariate approaches group and classify the data the same as more traditional approaches but they retained and portrayed the petrogenic significance of several mineralogical groupings such as mixing and control line populations We employed this approach to look for distinguishing characteristics of regions known to have podoconiosis
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