Amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by progressive muscular paralysis reflecting degeneration of motor neurones in the primary motor cortex, corticospinal tracts, brainstem and spinal cord. Incidence (average 1.89 per 100,000/year) and prevalence (average 5.2 per100,000) are relatively uniform in Western countries, although foci of higher frequency occur in the Western Pacific. The mean age of onset for sporadic ALS is about 60 years. Overall, there is a slight male prevalence (M:F ratio~1.5:1). Approximately two thirds of patients with typical ALS have a spinal form of the disease (limb onset) and present with symptoms related to focal muscle weakness and wasting, where the symptoms may start either distally or proximally in the upper and lower limbs. Gradually, spasticity may develop in the weakened atrophic limbs, affecting manual dexterity and gait. Patients with bulbar onset ALS usually present with dysarthria and dysphagia for solid or liquids, and limbs symptoms can develop almost simultaneously with bulbar symptoms, and in the vast majority of cases will occur within 1–2 years. Paralysis is progressive and leads to death due to respiratory failure within 2–3 years for bulbar onset cases and 3–5 years for limb onset ALS cases. Most ALS cases are sporadic but 5–10% of cases are familial, and of these 20% have a mutation of the SOD1 gene and about 2–5% have mutations of the TARDBP (TDP-43) gene. Two percent of apparently sporadic patients have SOD1 mutations, and TARDBP mutations also occur in sporadic cases. The diagnosis is based on clinical history, examination, electromyography, and exclusion of 'ALS-mimics' (e.g. cervical spondylotic myelopathies, multifocal motor neuropathy, Kennedy's disease) by appropriate investigations. The pathological hallmarks comprise loss of motor neurones with intraneuronal ubiquitin-immunoreactive inclusions in upper motor neurones and TDP-43 immunoreactive inclusions in degenerating lower motor neurones. Signs of upper motor neurone and lower motor neurone damage not explained by any other disease process are suggestive of ALS. The management of ALS is supportive, palliative, and multidisciplinary. Non-invasive ventilation prolongs survival and improves quality of life. Riluzole is the only drug that has been shown to extend survival

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

BACKGROUND Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001). CONCLUSION The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research

Machine learning and clinical neurophysiology

Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions

Motor neuron disease: Focusing the mind on ALS: updated practice parameters

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease and often known as Lou Gehrig disease in the US, is a devastating disease characterized by the progressive degeneration of motor neurons, which leads to an inability to control voluntary movement. The American Academy of Neurology (AAN) published the first comprehensive evidence-based guidelines on the management of ALS in 199

The sex ratio in amyotrophic lateral sclerosis: A population based study

Replicable risk factors for ALS include increasing age, family history and being male. The male: female ratio has been reported as being between 1 and 3. We tested the hypothesis that the sex ratio changes with age in a population register covering the south-east of England. The sex ratio before and after the age of 51 years was compared using a Z-test for proportions. Kendall's tau was used to assess the relationship between age group and sex ratio using incidence and prevalence data. Publicly available data from Italian and Irish population registers were compared with results. There was a significant difference in the proportion of females with ALS between those in the younger group (30.11%) and those in the older group (43.66%) (p = 0.013). The adjusted male: female ratio dropped from 2.5 in the younger group to 1.4 in the older group using prevalence data (Kendall's tau = -0.73, p = 0.039). Similar ratios were found in the Italian but not the Irish registry. We concluded that sex ratios in ALS may change with age. Over-representation of younger patients in clinic registers may explain the variation in sex ratios between studies. Menopause may also play a rol