Amyotrophic lateral sclerosis (ALS) is characterized by degeneration of motor neurons. We tested the hypothesis that proteomic analysis will identify protein biomarkers that provide insight into disease pathogenesis and are diagnostically useful. To identify ALS specific biomarkers, we compared the proteomic profile of cerebrospinal fluid (CSF) from ALS and control subjects using surface-enhanced laser desorption/ ionization-time of flight mass spectrometry (SELDI-TOF-MS). We identified 30 mass ion peaks with statistically significant (p < 0.01) differences between control and ALS subjects. Initial analysis with a rule-learning algorithm yielded biomarker panels with diagnostic predictive value as subsequently assessed using an independent set of coded test subjects. Three biomarkers were identified that are either decreased (transthyretin, cystatin C) or increased (carboxy-terminal fragment of neuroendocrine protein 7B2) in ALS CSF. We validated the SELDI-TOF-MS results for transthyretin and cystatin C by immunoblot and immunohistochemistry using commercially available antibodies. These findings identify a panel of CSF protein biomarkers for ALS.
Any beneficial effect of creatine at 5 g per day in ALS must be small. Other agents should be considered in future studies of therapeutic agents to address mitochondrial dysfunction in ALS. In addition, motor unit number estimation may be a useful outcome measure for future clinical trials in ALS.
We conducted case-control and prospective longitudinal studies to examine risk factors and predictors of disease progression for ALS. Ninety-five subjects with ALS and 106 healthy control subjects were enrolled. All subjects completed a risk factor questionnaire at enrollment. The ALS subjects were prospectively followed for one year to define factors that influence the rate of disease progression, measured by rate of change in percent predicted forced vital capacity (%FVC) and the ALS functional rating scale (ALSFRS) score. The association of each potential risk factor with ALS was determined using univariate logistic regression. A random slope model was used to determine the association of each risk factor with disease progression. The demographic characteristics of ALS subjects and controls at enrollment did not differ. Significant risk factors for ALS included reported exposure to lead (p = 0.02) and pesticides (p = 0.03). Disease progression was faster in the ALS subjects having bulbar onset and a shorter time period between onset of symptoms and diagnosis. Pertinent variables not associated with either causation or progression of ALS included physical activity, cigarette smoking and a history of physical trauma or other clinical disorders.
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