Introduction

As we know Multiple sclerosis is a common neurological disease that affects 1 in 1,000 people, most commonly young women, although men too to a lesser extent. The typical disease progress includes attacks and remissions with slowly progressing disability. Current therapies can prevent the appearance of new events, but they have much less effect on disease progression and serve mainly to moderate the initial relapsing-remitting phase.

May 15th 2009 saw a new review published from Oxford University by Lars Fugger, Manuel A. Friese and John I. Bell. In it they describe the current and new approaches that can be applied to define the functional role of the known genes involved in multiple sclerosis but also point out that environmental factors have a bearing on the function of the genes.

Environmental factors

Unfortunately, these environmental factors have proved to be even more elusive than the genes. Why do different areas of the world have a different prevalence and incidence of MS ? Could this be climate? Diet ? Genetics ? Lifestyle ? Infections ? What could these infections be ? Numerous viral and bacterial infections are potential candidates such as those found in the respiratory airways and gastrointestinal or urinary tracts as they are often associated with relapses, but no single infection has been consistently associated with disease.

We do not know how so many different infections could have a role in MS and how they might interact with genetic risk factors but, it is also important to try to understand how non-infectious risk factors, such as sunlight, may interact with genetic risk factors.

Asking the questions

The biggest challenge will be to use genetic information to ask questions about the environmental factors that interact with gene pathways and contribute to disease development. The identification of the exact disease susceptibility gene does not necessarily define the pathway involved in disease development.

The insights gained from functional studies may help the study of environmental risk factors by using methodology that goes well beyond the conventional approaches of population epidemiology. The improving capacity for modelling and simulation using genetic data may lead to the identification of the additional environmental factors that interact with genetic factors to cause disease.