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Amyotrophic lateral sclerosis (ALS) is known to represent a collection of overlapping syndromes. Various classification systems based on empirical observations have been proposed, but it is unclear to what extent they reflect ALS population substructures. We aimed to use machine-learning techniques to identify the number and nature of ALS subtypes to obtain a better understanding of this heterogeneity, enhance our understanding of the disease, and improve clinical care.In this retrospective study, we applied unsupervised Uniform Manifold Approximation and Projection [UMAP]) modelling, semi-supervised (neural network UMAP) modelling, and supervised (ensemble learning based on LightGBM) modelling to a population-based discovery cohort of patients who were diagnosed with ALS while living in the Piedmont and Valle d'Aosta regions of Italy, for whom detailed clinical data, such as age at symptom onset, were available. We excluded patients with missing Revised ALS Functional Rating Scale (ALSFRS-R) feature values from the unsupervised and semi-supervised steps. We replicated our findings in an independent population-based cohort of patients who were diagnosed with ALS while living in the Emilia Romagna region of Italy.Between Jan 1, 1995, and Dec 31, 2015, 2858 patients were entered in the discovery cohort. After excluding 497 (17%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 2361 (83%) patients were available for the unsupervised and semi-supervised analysis. We found that semi-supervised machine learning produced the optimum clustering of the patients with ALS. These clusters roughly corresponded to the six clinical subtypes defined by the Chiò classification system (ie, bulbar, respiratory, flail arm, classical, pyramidal, and flail leg ALS). Between Jan 1, 2009, and March 1, 2018, 1097 patients were entered in the replication cohort. After excluding 108 (10%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 989 patients were available for the unsupervised and semi-supervised analysis. All 1097 patients were included in the supervised analysis. The same clusters were identified in the replication cohort. By contrast, other ALS classification schemes, such as the El Escorial categories, Milano-Torino clinical staging, and King's clinical stages, did not adequately label the clusters. Supervised learning identified 11 clinical parameters that predicted ALS clinical subtypes with high accuracy (area under the curve 0·982 [95% CI 0·980-0·983]).Our data-driven study provides insight into the ALS population substructure and confirms that the Chiò classification system successfully identifies ALS subtypes. Additional validation is required to determine the accuracy and clinical use of these algorithms in assigning clinical subtypes. Nevertheless, our algorithms offer a broad insight into the clinical heterogeneity of ALS and help to determine the actual subtypes of disease that exist within this fatal neurodegenerative syndrome. The systematic identification of ALS subtypes will improve clinical care and clinical trial design.US National Institute on Aging, US National Institutes of Health, Italian Ministry of Health, European Commission, University of Torino Rita Levi Montalcini Department of Neurosciences, Emilia Romagna Regional Health Authority, and Italian Ministry of Education, University, and Research.For the Italian and German translations of the abstract see Supplementary Materials section.

We performed in vivo phosphorus magnetic resonance spectroscopy on the occipital lobes of 15 patients with multiple system atrophy (MSA; eight with olivopontocerebellar atrophy [OPCA] and seven with the striatonigral degeneration variant [SND]), 13 patients with idiopathic Parkinson's disease (PD), and 16 age-matched healthy subjects. The MSA group showed significantly reduced phosphocreatine (PCr), increased inorganic phosphate (Pi), and unchanged cytosolic free [Mg2+], and pH. We did not find any significant difference between the OPCA and SND variants. However, patients with PD showed significantly increased content of Pi, decreased cytosolic free [Mg2+], and unchanged [PCr] and pH. Comparing the MSA and PD groups, [PCr] was significantly lower in MSA than in PD, whereas cytosolic free [Mg2+] was significantly lower in PD. Despite a certain degree of overlap of [PCr] and [Mg2+] values between the two groups, by considering both variables at the same time it was possible to classify correctly 93% of cases by discriminant analysis. We conclude that phosphorus magnetic resonance spectroscopy discloses abnormal phosphate metabolite and ion contents in both MSA and PD, respectively, and may provide noninvasive diagnostic help to differentiate MSA from PD.

A patient with neuromyotonia, peripheral neuropathy and myasthenia gravis (MG) is described. Neurophysiological studies, at rest, showed continuous muscle discharges of motor unit action potentials (MUAPs) in duplets and triplets. Motor (MNCV) and sensory (SNCV) nerve conduction studies revealed mild axonal and demyelinating peripheral neuropathy. Plasma exchange was followed by disappearance of clinical and electrophysiological signs of neuromyotonia and MG, as well as peripheral neuropathy.

We used phosphorus magnetic resonance spectroscopy (31P MRS) to investigate in vivo the brain and skeletal muscle energy metabolism of 15 children with migraine with aura in interictal periods. Brain 31P MRS disclosed low phosphocreatine and high inorganic phosphate contents, and high intracellular pH in all patients. Calculated [ADP] and the relative rate of mitochondrial oxidation were higher in the brain of patients than in control subjects, whereas the phosphorylation potential was lower. Brain intracellular free Mg2+ concentration was reduced by 25% in patients. Abnormal skeletal muscle mitochondrial respiration was also disclosed in 7 of 15 patients as shown by the slow rate of phosphocreatine postexercise recovery. The multisystem bioenergetic failure found in patients with juvenile migraine is comparable to that found in adults with different types of migraine.