PROJECT SUMMARY/ABSTRACT
Amyotrophic lateral sclerosis (ALS) is a fatal and primarily sporadic neurodegenerative disease involving the
death of upper and lower motor neurons. There are a myriad of pathological mechanisms underlying this disease,
which makes identifying a single target for treatment a great challenge. It is largely accepted that ALS is caused
by a combination of genetic susceptibility and environmental factors. The importance of environmental factors is
supported by inter alia discordance in monozygotic twins, conjugal ALS and increased risk of ALS with specific
occupations and toxic exposures. While there remains an urgent need to define the pathological etiology of ALS,
exposure assessments within etiologically-relevant, pre-symptom time intervals has proven difficult due to
differences in methodology and poor assessment methods. In regards physiological assessments, certain
chemical elements have been linked to neurotoxicity (e.g. lead). To date, lead represents one of the strongest
environmental risk factors connected to ALS. The identification of additional ALS-associated elements however
has been stymied by (1) discrepancies in reported measurements and (2) the fact that peripheral measurements
rarely reflect the physiological load within the central nervous system. In light of these challenges, our preliminary
work indicates an association between toxic element exposures and ALS in epochs prior to diagnosis for lead,
mercury and manganese. We have further detected the presence of these same elements in ALS patient brain
tissue. We now propose to validate these and other elements as environmental risk factors in Massachusetts
(MA), the only state in the country with a reportable (mandatory) ALS registry. Utilizing the national residential
history of MA ALS Registry participants, we will estimate subject exposure to potentially toxic and persistent
elements prior to diagnosis to identify elements associated with increased ALS risk in a case-control analysis
using spatiotemporal geographical information systems (GIS) techniques. In parallel, we will evaluate the
concentrations, combinations and spatial distribution of a suite of elements in disease-relevant brain tissue from
MA ALS Registry patients and non-neurodegenerative MA autopsy controls and correlate these findings to our
GIS studies. We will also characterize the subcellular distribution and composition of nanoparticles in ALS cases
relative to controls. Together, this unique and novel research will identify well-founded, risk-related exposures
for ALS as well as provide novel insight into the etiology of this disease.
