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The prediction of the disease course is one of the main targets of ALS research, particularly considering its wide phenotypic heterogeneity. Despite many attempts to classify patients in prognostic categories according to the different spreading patterns at diagnosis, a precise regional progression rate and the time of involvement of each region has yet to be clarified.In a population-based dataset of ALS patients, we analyse the functional decline in different body regions according to time and order of regional involvement. We calculated the regional progression intervals (RPIs) from the initial involvement (Inv) to the severe functional impairment (SevI), using the ALS Functional Rating Scale revised (ALSFRS-r) subscores of bulbar, upper limbs, lower limbs and respiratory/thoracic regions. Time-to-event analyses, adjusted for age, sex, ALSFRS-r pre-slope (ΔALSFRS-R), cognitive and mutational status were performed.RPI duration differed significantly among ALS phenotypes, being the RPI of first region involved significantly longer than RPIs of regions involved later. Cox proportional hazard models showed that the time between disease onset and initial regional involvement is invariably related to a reduced RPI duration in each different body region (bulbar region, HR 1.11, 95% CI 1.06-1.16, p0.001; upper limbs region, HR 1.16, 95% CI 1.06-1.28, p=0.002; lower limbs region, HR 1.11, 95% CI 1.03-1.19, p=0.009; respiratory/thoracic region, HR 1.10, 95% CI 1.06-1.14, p=0.005).We found that the progression of functional decline accelerates in regions involved later during disease course. Our findings can be useful in patients' management and prognosis prediction.

ObjectiveTo detect the clinical characteristics of patients with amyotrophic lateral sclerosis (ALS) carrying an intermediateATXN2polyQ number of repeats in a large population-based series of Italian patients with ALS.MethodsThe study population includes 1330 patients with ALS identified through the Piemonte and Valle d’Aosta Register for ALS, diagnosed between 2007 and 2019 and not carryingC9orf72, SOD1, TARDBPandFUSmutations. Controls were 1274 age, sex and geographically matched Italian subjects, identified through patients’ general practitioners.ResultsWe found 42 cases and 4 controls with≥31 polyQ repeats, corresponding to an estimated OR of 10.4 (95% CI 3.3 to 29.0). Patients with≥31 polyQ repeats (ATXN2+) compared with those without repeat expansion (ATXN2−) had more frequently a spinal onset (p=0.05), a shorter diagnostic delay (p=0.004), a faster rate of ALSFRS-R progression (p=0.004) and King’s progression (p=0.004), and comorbid frontotemporal dementia (7 (28.0%) vs 121 (13.4%), p=0.037). ATXN2+ patients had a 1-year shorter survival (ATXN2+ patients 1.82 years, 95% CI 1.08 to 2.51; ATXN2− 2.84 years, 95% CI 1.67 to 5.58, p=0.0001).ATXN2polyQ intermediate repeats was independently related to a worse outcome in Cox multivariable analysis (p=0.006).ConclusionsIn our population-based cohort, ATXN2+ patients with ALS have a distinctive phenotype, characterised by a more rapid disease course and a shorter survival. In addition, ATXN2+ patients have a more severe impairment of cognitive functions. These findings have relevant implications on clinical practice, including the possibility of refining the individual prognostic prediction and improving the design of ALS clinical trials, in particular as regards as those targeted explicitly toATXN2.

Objective.Despite recent advances, it is not clear whether the various genes/genetic variants related to ALS interact in modifying patients phenotype. The aim of this study was to determine if the co-presence of genetic variants related to ALS has interactive effects on the course of the disease.Methods.The study population includes 1,245 ALS patients identified through the Piemonte Register for ALS between 2007 and 2016 and not carryingSOD1, TARDBPandFUSpathogenic variants. Controls were 766 Italian subjects age-, sex-, and geographically-matched to cases. We consideredUNC13A (rs12608932), CAMTA1 (rs2412208), SLC11A2 (rs407135) andZNF512B (rs2275294)variants, as well asATXN2polyQ intermediate repeats (≥31) andC9orf72GGGGCC intronic expansions( ≥30).Results.The median survival time of the whole cohort was 2.67 years (IQR 1.67-5.25). In univariate analysis onlyC9orf72(2.51 years, IQR 1.74-3.82; p=0.016),ATXN2(1.82 years, IQR 1.08-2.33; pUNC13AC/C(2.3 years, IQR 1.3-3.9; pCAMTA1emerged to be independently related to survival (HR 1.13, 95% c.i. 1.001-1.30, p=0.048). The co-presence of two detrimental alleles/expansions was correlated with shorter survival. In particular, the median survival of patients withCAMTA1G/G+G/TandUNC13AC/Calleles was 1.67 years (1.16-3.08) years compared to 2.75 years (1.67-5.26) of the patients not carrying these variants (pCAMTA1G/G+G/Talleles andATXN2≥31intermediate polyQ repeats was 1.75 years (0.84-2.18) (pATXN2≥31polyQ repeats andUNC13AC/Callele was 1.33 years (0.84-1.75) (pC9ORF72≥30andUNC13AC/Callele was 1.66 years (1.41-2.16). Each pair of detrimental alleles/expansions was associated to specific clinical phenotypes.Conclusions.We showed that gene variants acting as modifiers of ALS survival or phenotype can act on their own or in unison. Overall, 54% of patients carried at least one detrimental common variant or repeat expansion, emphasizing the clinical impact of our findings. In addition, the identification of the interactive effects of modifier genes represents a crucial clue for explaining ALS clinical heterogeneity and should be considered when designing and interpreting clinical trials results.

PURPOSE To study for the first time the incidence of adult-onset CNS tumors in Southern Sardinia, Italy. METHODS Clinical records of patients > 18 years old who were diagnosed with primary CNS tumors during 2016-2019 in the study area were reviewed. Meningiomas, cranial/paraspinal nerve tumors, lymphomas, and pituitary tumors were excluded. Cases were classified according to the 2016 WHO classification of CNS tumors and to the morphology codes from the International Classification of Diseases-Oncology, third edition. Age-adjusted incidence rates were calculated by the direct method to the 2011-2020 European standard population. Kulldorff's spatial scan statistic was used to identify geographic clusters of patients who shared increased/decreased tendency to develop CNS tumors. RESULTS CNS tumors were diagnosed in 234 incident patients, but histological diagnosis was available in 222/234 patients (95%) aged 64.3 ± 13.5 years at diagnosis. Crude incidence rate was 7.1 per 100,000 persons-year (95% CI, 6.2-8.1), 6.2 per 100,000 persons-year (95% CI, 5.4-7.0) when age-adjusted. CNS tumors were more frequent in men and after age 40. Glioblastoma accounted for 76% of the total (adjusted rate, 4.7 per 100,000 persons-year; 95% CI, 4.0-5.4). Spatial analysis revealed geographic variations of glioblastoma incidence within the study area. CONCLUSION Although the distribution of tumor diagnoses in Sardinia reflects expected age and gender-related patterns in western populations, our findings would indicate a slightly higher incidence of glial tumors, glioblastoma in particular, in Sardinia than in other European countries. The identification of spatial clusters of high/low risk will serve as a resource for etiological research.

Being exposed to electromagnetic fields has been suggested to increase the risk of developing Amyotrophic Lateral Sclerosis (ALS). Here, we investigated the effect of exposure to electromagnetic fields on ALS onset age and progression rate (ΔALSFRS-r). A large cohort of ALS patients (n = 1098) was geolocalized at the time of their diagnosis. Concomitantly, data on the distribution of power lines and repeater antennas (extremely low frequency electromagnetic fields) during the same period were retrieved. Exposure to each repeater antenna was calculated as the sum of 1/(distance from each antenna)^2. Exposure to power lines was calculated assuming each patient's address as the center of several circles of variable radius (100, 250, 500, 1000, and 2000 m). For each radius, the exposure was calculated as the length of the power lines included in the circle. Finally, patients were divided into low- and high-exposed based on the median of the exposure and compared using the Mann-Whitney test. A regression model (one for each radius) was also performed. Neither the onset age nor the ΔALSFRS-r differed among patients' low- and high-exposed to electromagnetic fields. Similarly, we could not find any significant relationship using the regression models. Our findings suggest that electromagnetic fields do not modify the ALS phenotype or progression.

ObjectiveTo determine if the co-presence of genetic polymorphisms related to ALS has additive effects on the course of the disease in a population-based cohort of Italian patients.MethodsThe study population includes 1245 ALS patients identified through the Piemonte Register for ALS, diagnosed between 2007 and 2016 and not carrying SOD1, TARDBP and FUS mutations. Controls were 766 age, sex, and geographically matched Italian subjects. We considered UNC13A (rs12608932), CAMTA1 (rs2412208), SLC112A (rs407135) and ZNF512B (ZNF512B) polymorphisms, as well as ATXN2 polyQ intermediate repeats and C9ORF72 GGGGCC intronic expansion.ResultsThe variants in C9orf72 (p=0.016), ATXN2 (pUNC13A (pCAMTA1 emerged to be independently related to survival. When assessing the interaction by pairs of genes, we found that the presence of both detrimental alleles/expansion was correlated with significantly shorter survival compared to subjects non-carrying both detrimental alleles/expansions. Each association of pairs of detrimental alleles was characterized by specific clinical phenotypes.Conclusionswe demonstrated that gene polymorphisms acting as genetic modifiers of ALS survival can act on their own or in unison. Overall, 54% of patients carried at least one detrimental common polymorphism or repeat expansion, highlighting the clinical impact of our findings. In addition, the identification of the synergic effects of modifier genes represents an essential clue for explaining ALS clinical heterogeneity and should be considered in designing and interpreting clinical trials.Key messagesWhat is already known on this topicBesides the disease-causing genes, several other genes have been reported to act as modifiers of ALS phenotype, especially patients’ survival. However, the interactions of these genes at clinical level have never been explored.What this study addsWe demonstrated that gene polymorphisms and expansions acting as genetic modifiers of ALS survival can act on their own or in unison. Overall, 54% of patients carried at least one detrimental allele at common polymorphism or repeat expansion, highlighting the clinical impact of our findings.How this study might affect research, practice, or policThe identification of the synergic effects of modifier genes represents an essential clue for explaining ALS clinical heterogeneity, will have deep effects on clinical trial design and interpretation and support the inclusion of these polymorphisms in ALS genetic panels.

BackgroundSystemic inflammation has been proposed as a relevant mechanism in Amyotrophic Lateral Sclerosis (ALS). Recent evidence shows that an increased inflammatory status correlates with survival in ALS. Still, comprehensive data on ALS patients’ innate and adaptive immune responses and their effect on the clinical phenotype are lacking. Here, we investigate the role and characteristics of systemic immunity in a population-based ALS cohort using readily available hematological indexes that reflect changes in innate and adaptive immunity.MethodsWe collected the complete blood count (CBC) at diagnosis in ALS patients from the Piemonte and Valle d’Aosta Register for ALS (PARALS) from 2007 to 2019. Demographic and clinical data were collected using registry data. Leukocytes populations, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII) and lymphocyte-to-monocyte ratio (LMR) were derived from CBC. All variables were analyzed for association with clinical features in the entire cohort and then in sex- and age-based subgroups. Logistic, linear and Cox regression models were used as appropriate.ResultsAfter adjustment for relevant covariates, neutrophils (p=0.001) and markers of increased innate immunity (NLR, p=0.008 and SII, p=0.006) were associated with a faster disease progression. Similarly, elevated innate immunity markers correlated with worse pulmonary function and shorter survival. Sex-based differences emerged, as the prognosis in women also correlated with a low lymphocyte (p=0.045) and a decreased LMR (p=0.013). ALS patients with cognitive impairment exhibited lower levels of monocytes (p=0.0415) and, although only in later-onset ALS (age at onset > 70 years), lower lymphocytes (p=0.006) and increased NLR (p=0.021) and SII (p=0.030).Conclusions and RelevanceOur results confirm that a dysregulated systemic immune system participates in ALS progression. More specifically, an elevated innate immune response is associated with faster progression and reduced survival. The immune response varied according to sex and age, thus prompting the speculation that involved immune pathways are patient-specific. Finally, we observed that ALS patients with greater cognitive impairment showed a reduction in monocytes count. Those data revealed that systemic inflammation plays a multifaceted role in ALS: further studies will help translate those findings into clinical practice or targeted treatments.

Objective To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival. Methods We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients’ disease trajectories and predict the probability of functional impairment and survival at different time points. Results DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80–0.93 and 0.84–0.89 for the two scenarios, respectively). Conclusions Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making.

Objective: To investigate the role of arterial blood gas (ABG) analysis parameters (blood carbon dioxide, pCO2; oxygen, pO2; carbonate, HCO3−; standard base excess, SBE) in monitoring respiratory f...

MRI studies reported that ALS patients with bulbar and spinal onset showed focal cortical changes in corresponding regions of the motor homunculus. We evaluated the capability of brain 2-[We classified as pure bulbar (PB) patients with bulbar onset and a normal score in the spinal items of the ALSFRS-R, and as pure spinal (PS) patients with spinal onset and a normal score in the bulbar items at the time of PET. Forty healthy controls (HC) were enrolled. We compared PB and PS, and each patient group with HC. Metabolic clusters showing a statistically significant difference between PB and PS were tested to evaluate their accuracy in discriminating the two groups. We performed a leave-one-out cross-validation (LOOCV) over the entire dataset. Four classifiers were considered: support vector machines (SVM), K-nearest neighbours, linear classifier, and decision tree. Then, we used a separate test set, including 10% of patients, with the remaining 90% composing the training set.We included 63 PB, 271 PS, and 40 HC. PB showed a relative hypometabolism compared to PS in bilateral precentral gyrus in the regions of the motor cortex involved in the control of bulbar function. SVM showed the best performance, resulting in the lowest error rate in both LOOCV (4.19%) and test set (9.09 ± 2.02%).Our data support the concept of the focality of ALS onset and the use of 2-[

The identification of prognostic tools in amyotrophic lateral sclerosis (ALS) would improve the design of clinical trials, the management of patients, and life planning. We aimed to evaluate the accuracy of brain 2-[A prospective cohort study enrolled 418 ALS patients, who underwent brain 2-[Data were discretized in three survival profiles: 0-2 years, 2-5 years, and5 years. SVM resulted in an error rate20% for two out of three classes separately. As for class one, the discriminant clusters included left caudate body and anterior cingulate cortex. The most discriminant regions were bilateral cerebellar pyramid in class two, and right cerebellar dentate nucleus, and left cerebellar nodule in class three.Brain 2-[

To characterize the clinical and cognitive behavioral phenotype and brainThe study population included 1,409 patients with ALS withoutThe C/C genotype was associated with an increased risk of ALS (odds ratio: 1.54, 95% confidence interval 1.18-2.01,C/C rs12608932 genotype of

Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive degenerative disease of upper and lower motor neurons. Approximately 15% of patients display clinical features consistent with frontotemporal dementia (FTD) and 35% display milder degrees of cognitive and behavioural impairment at some stage during their illness.1 Several genes have been reported to cause both ALS and FTD. Nevertheless, it remains unclear why some patients with ALS develop cognitive impairment, while other cases, often within the same family, remain unaffected. The GBA gene (OMIM *606463) encodes glucocerebrosidase (GCase), a lysosomal enzyme that converts glucocerebroside into glucose and ceramide. Heterozygous GBA mutations increase the risk of Parkinson’s disease (PD) and the risk of cognitive impairment in patients with PD.2 It is increasingly recognised that variants in genes causing Mendelian neurodegenerative diseases may exhibit pleiotropic effects and impact the phenotypic heterogeneity of those disorders. Moreover, lysosomal dysfunction has recently been associated with both Dementia with Lewy Bodies and FTD spectrum (online supplemental table 1). Based on this, we postulated that GBA variants may influence the cognitive status of patients with ALS. ### Supplementary data [jnnp-2021-327426supp001.pdf] We examined the GBA variants’ association with the risk of cognitive impairment in 751 patients with ALS from the population-based Piemonte and Valle d’Aosta Register for ALS that had undergone both a detailed neuropsychological evaluation and a whole-genome sequencing screening.3 Patients were classified as ALS with normal cognitive function (ALS-CN), ALS-FTD and ALS with intermediate cognitive deficits. The characteristics of the study population and a detailed description of neuropsychological testing and genetic screening are reported in online supplementarl materials in the Methods section. A mutational screening of GBA exonic variants was performed and their frequencies were compared with an internal control cohort (see online supplemental methods, Subjects). To assess whether pathogenic rare variants (minor allele frequency

BACKGROUND AND PURPOSE The purpose was to assess the prognostic role of neck muscle weakness at diagnosis in amyotrophic lateral sclerosis (ALS) patients with respect to survival and respiratory impairment. METHODS A retrospective cohort study was conducted. All ALS patients seen in the Turin ALS Centre from 2007 to 2014 were included. Muscle strength at diagnosis was evaluated using the Medical Research Council (MRC) scale. Survival was considered as the time from diagnosis to death or tracheostomy; time to respiratory impairment was considered as the interval from diagnosis to the first event amongst an ALS Functional Rating Scale revised item 10

ObjectiveTo identify the metabolic changes related to the various levels of cognitive deficits in amyotrophic lateral sclerosis (ALS) using 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG-PET) imaging.Methods274 ALS patients underwent neuropsychological assessment and brain 18F-FDG-PET at diagnosis. According to the criteria published in 2017, cognitive status was classified as ALS with normal cognition (ALS-Cn, n=132), ALS with behavioural impairment (ALS-Bi, n=66), ALS with cognitive impairment (ALS-Ci, n=30), ALS with cognitive and behavioural impairment (ALS-Cbi, n=26), ALS with frontotemporal dementia (ALS–FTD, n=20). We compared each group displaying some degree of cognitive and/or behavioural impairment to ALS-Cn patients, including age at PET, sex and ALS Functional Rating Scale-Revised as covariates.ResultsWe identified frontal lobe relative hypometabolism in cognitively impaired patients that resulted more extensive and significant across the continuum from ALS-Ci, through ALS-Cbi, to ALS–FTD. ALS–FTD patients also showed cerebellar relative hypermetabolism. ALS-Bi patients did not show any difference compared with ALS-Cn.ConclusionsThese data support the concept that patients with cognitive impairment have a more widespread neurodegenerative process compared with patients with a pure motor disease: the more severe the cognitive impairment, the more diffuse the metabolic changes. Otherwise, metabolic changes related to pure behavioural impairment need further characterisation.

ObjectiveTo assess the burden of rare genetic variants and to estimate the contribution of known amyotrophic lateral sclerosis (ALS) genes in an Italian population-based cohort, we performed whole genome sequencing in 959 patients with ALS and 677 matched healthy controls.MethodsWe performed genome sequencing in a population-based cohort (Piemonte and Valle d'Aosta Registry for ALS [PARALS]). A panel of 40 ALS genes was analyzed to identify potential disease-causing genetic variants and to evaluate the gene-wide burden of rare variants among our population.ResultsA total of 959 patients with ALS were compared with 677 healthy controls from the same geographical area. Gene-wide association tests demonstrated a strong association with SOD1, whose rare variants are the second most common cause of disease after C9orf72 expansion. A lower signal was observed for TARDBP, proving that its effect on our cohort is driven by a few known causal variants. We detected rare variants in other known ALS genes that did not surpass statistical significance in gene-wise tests, thus highlighting that their contribution to disease risk in our cohort is limited.ConclusionsWe identified potential disease-causing variants in 11.9% of our patients. We identified the genes most frequently involved in our cohort and confirmed the contribution of rare variants in disease risk. Our results provide further insight into the pathologic mechanism of the disease and demonstrate the importance of genome-wide sequencing as a diagnostic tool.

We describe the telemedicine experience of an Italian ALS tertiary Center during COVID-19 pandemic. A total of 144 visits were scheduled between 6th March and 6th April 2020. These mostly consisted of neurological or psychological visits (139, 96.5%). One hundred thirty-nine (96.5%) visits were performed as telemedicine and mostly via phone call (112, 80.6%). Three (2.1%) visits were considered as urgent and maintained as outpatient care. Additionally, patients were still able to telephone, being put through directly to their neurologists. Many requests of contact were addressed at getting information about the scheduled visits or examinations (45, 43.3%). Globally, patients and caregivers were satisfied with the telemedicine service. However, the majority (85, 65.9%) would prefer a face-to-face visit. In conclusion, telemedicine could be considered a good complement to face-to-face care, even after social restrictions have been eased.

Background Clinical registers constitute an invaluable resource in the medical data-driven decision making context. Accurate machine learning and data mining approaches on these data can lead to faster diagnosis, definition of tailored interventions, and improved outcome prediction. A typical issue when implementing such approaches is the almost unavoidable presence of missing values in the collected data. In this work, we propose an imputation algorithm based on a mutual information-weighted k-nearest neighbours approach, able to handle the simultaneous presence of missing information in different types of variables. We developed and validated the method on a clinical register, constituted by the information collected over subsequent screening visits of a cohort of patients affected by amyotrophic lateral sclerosis. Methods For each subject with missing data to be imputed, we create a feature vector constituted by the information collected over his/her first three months of visits. This vector is used as sample in a k-nearest neighbours procedure, in order to select, among the other patients, the ones with the most similar temporal evolution of the disease over time. An ad hoc similarity metric was implemented for the sample comparison, capable of handling the different nature of the data, the presence of multiple missing values and include the cross-information among features captured by the mutual information statistic. Results We validated the proposed imputation method on an independent test set, comparing its performance with those of three state-of-the-art competitors, resulting in better performance. We further assessed the validity of our algorithm by comparing the performance of a survival classifier built on the data imputed with our method versus the one built on the data imputed with the best-performing competitor. Conclusions Imputation of missing data is a crucial –and often mandatory– step when working with real-world datasets. The algorithm proposed in this work could effectively impute an amyotrophic lateral sclerosis clinical dataset, by handling the temporal and the mixed-type nature of the data and by exploiting the cross-information among features. We also showed how the imputation quality can affect a machine learning task.

Background and purpose The aim was to assess the frequency of plateaus in amyotrophic lateral sclerosis (ALS) progression using a large population-based cohort. Methods Data from the Piemonte and Valle d'Aosta ALS register were used. Patients who were diagnosed between 2007 and 2014 were considered. The follow-up period was extended until 31 December 2018. Visits after tracheostomy were excluded. A plateau was defined as a stable Amyotrophic Lateral Sclerosis Functional Rating Scale revised (ALSFRSr) score lasting at least 6, 12 or 18 months. Results Out of 1214 patients, 200 (16.5%), 93 (7.7%) and 52 (4.3%) showed at least one plateau lasting a minimum of 6, 12 and 18 months, respectively. Plateaus occurred mostly at high ALSFRSr scores and were more frequent during the initial phases of the disease course. Spinal onset [odds ratio (OR) 1.83, 95% confidence interval (CI) 1.16-2.95, P value 0.01) and predominant upper motor neuron phenotype (OR 2.18, 95% CI 1.36-3.48, P value 0.001) conferred a higher risk for the subsequent appearance of plateaus; conversely, older age at diagnosis (OR 0.25, 95% CI 0.11-0.54, P value 0.002 for >75 year age class) reduced this risk. Conclusions Plateaus in ALS progression lasting at least 6 months appear in about one out of six patients and could last even 12, 18 months or more in a smaller subgroup of patients. Plateau occurrence should not lead the neurologist to automatically reconsider ALS diagnosis and should be considered for future clinical trial design.

ObjectiveTo assess the determinants of amyotrophic lateral sclerosis (ALS) phenotypes in a population-based cohort.MethodsThe study population included 2,839 patients with ALS diagnosed in Piemonte, Italy (1995–2015). Patients were classified according to motor (classic, bulbar, flail arm, flail leg, predominantly upper motor neuron [PUMN], respiratory) and cognitive phenotypes (normal, ALS with cognitive impairment [ALSci], ALS with behavioral impairment [ALSbi], ALSci and ALSbi combined [ALScbi], ALS–frontotemporal dementia [FTD]). Binary logistic regression analysis was adjusted for sex, age, and genetics.ResultsBulbar phenotype correlated with older age (p < 0.0001), women were more affected than men at increasing age (p < 0.0001), classic with younger age (p = 0.029), men were more affected than women at increasing age (p < 0.0001), PUMN with younger age (p < 0.0001), flail arm with male sex (p < 0.0001) and younger age (p = 0.04), flail leg with male sex with increasing age (p = 0.008), and respiratory with male sex (p < 0.0001). C9orf72 expansions correlated with bulbar phenotype (p < 0.0001), and were less frequent in PUMN (p = 0.041); SOD1 mutations correlated with flail leg phenotype (p < 0.0001), and were less frequent in bulbar (p < 0.0001). ALS-FTD correlated with C9orf72 (p < 0.0001) and bulbar phenotype (p = 0.008), ALScbi with PUMN (p = 0.014), and ALSci with older age (p = 0.008).ConclusionsOur data suggest that the spatial–temporal combination of motor and cognitive events leading to the onset and progression of ALS is characterized by a differential susceptibility to the pathologic process of motor and prefrontal cortices and lower motor neurons, and is influenced by age, sex, and gene variants. The identification of those factors that regulate ALS phenotype will allow us to reclassify patients into pathologically homogenous subgroups, responsive to targeted personalized therapies.

BackgroundA genetic diagnosis in Amyotrophic Lateral Sclerosis (ALS) can inform genetic counselling, prognosis and, in the light of incoming gene-targeted therapy, management. However, conventional genetic testing strategies are often costly and time-consuming.ObjectiveTo evaluate the diagnostic yield and advantages of whole-genome sequencing (WGS) as a standard diagnostic genetic test for ALS.MethodsIn this population-based cohort study, 1043 ALS patients from the Piemonte and Valle d’Aosta Register for ALS and 755 healthy individuals were screened by WGS for variants in 42 ALS-related genes and for repeated-expansions in C9orf72 and ATXN2.ResultsA total of 279 ALS cases (26.9%) received a genetic diagnosis, namely 75.2% of patients with a family history of ALS and 21.5% of sporadic cases. The mutation rate among early-onset ALS patients was 43.9%, compared with 19.7% of late-onset patients. An additional 14.6% of the cohort carried a genetic factor that worsen prognosis.ConclusionsOur results suggest that, because of its high diagnostic yield and increasingly competitive costs, along with the possibility of retrospectively reassessing newly described genes, WGS should be considered as standard genetic testing for all ALS patients. Additionally, our results provide a detailed picture of the genetic basis of ALS in the general population.

Background and ObjectivesTo characterize the clinical and cognitive behavioral phenotype and brain18F-2-fluoro-2-deoxy-d-glucose-PET (18F-FDG-PET) metabolism of patients with amyotrophic lateral sclerosis (ALS) carrying the rs12608932 variant of theUNC13Agene.MethodsThe study population included 1,409 patients with ALS withoutC9orf72, SOD1, TARDBP, andFUSmutations identified through a prospective epidemiologic ALS register. Control participants included 1,012 geographically matched, age-matched, and sex-matched participants. Clinical and cognitive differences between patients carrying the C/C rs12608932 genotype and those carrying the A/A + A/C genotype were assessed. A subset of patients underwent18F-FDG-PET.ResultsThe C/C genotype was associated with an increased risk of ALS (odds ratio: 1.54, 95% confidence interval 1.18–2.01,p= 0.001). Patients with the C/C genotype were older, had more frequent bulbar onset, and manifested a higher rate of weight loss. In addition, they showed significantly reduced performance in the letter fluency test, fluency domain of Edinburgh Cognitive and Behavioural ALS Screen (ECAS) and story-based empathy task (reflecting social cognition). Patients with the C/C genotype had a shorter survival (median survival time, C/C 2.25 years, interquartile range [IQR] 1.33–3.92; A/A + C/C: 2.90 years, IQR 1.74–5.41;p= 0.0001). In Cox multivariable analysis, C/C genotype resulted to be an independent prognostic factor. Finally, patients with a C/C genotype had a specific pattern of hypometabolism on brain18F-FDG-PET extending to frontal and precentral areas of the right hemisphere.DiscussionC/C rs12608932 genotype ofUNC13Ais associated with a specific motor and cognitive/behavioral phenotype, which reflects on18F-FDG-PET findings. Our observations highlight the importance of adding the rs12608932 variant inUNC13Ato the ALS genetic panel to refine the individual prognostic prediction and reduce heterogeneity in clinical trials.

Background MRI studies reported that ALS patients with bulbar and spinal onset showed focal cortical changes in corresponding regions of the motor homunculus. We evaluated the capability of brain 2-[18F]FDG-PET to disclose the metabolic features characterizing patients with pure bulbar or spinal motor impairment. Methods We classified as pure bulbar (PB) patients with bulbar onset and a normal score in the spinal items of the ALSFRS-R, and as pure spinal (PS) patients with spinal onset and a normal score in the bulbar items at the time of PET. Forty healthy controls (HC) were enrolled. We compared PB and PS, and each patient group with HC. Metabolic clusters showing a statistically significant difference between PB and PS were tested to evaluate their accuracy in discriminating the two groups. We performed a leave-one-out cross-validation (LOOCV) over the entire dataset. Four classifiers were considered: support vector machines (SVM), K-nearest neighbours, linear classifier, and decision tree. Then, we used a separate test set, including 10% of patients, with the remaining 90% composing the training set. Results We included 63 PB, 271 PS, and 40 HC. PB showed a relative hypometabolism compared to PS in bilateral precentral gyrus in the regions of the motor cortex involved in the control of bulbar function. SVM showed the best performance, resulting in the lowest error rate in both LOOCV (4.19%) and test set (9.09 ± 2.02%). Conclusions Our data support the concept of the focality of ALS onset and the use of 2-[18F]FDG-PET as a biomarker for precision medicine-oriented clinical trials.

Purpose Neuropathological data suggest that ALS with SOD1 mutations (SOD1-ALS) is a distinct form of ALS. We evaluated brain metabolic changes characterizing SOD1-ALS as compared to sporadic ALS (sALS), employing 18fluorodeoxyglucose-positron-emission tomography (18F-FDG-PET). Methods We included 18 SOD1-ALS patients, 40 healthy controls (HC), and 46 sALS patients without mutations in SOD1, TARDBP, FUS, and C9ORF72, randomly selected from 665 subjects who underwent brain 18F-FDG-PET at diagnosis between 2008 and 2019 at the ALS Centre of Turin. We excluded patients with frontotemporal dementia. We used the full factorial design in SPM12 to evaluate whether differences among groups exist overall. In case the hypothesis was confirmed, group comparisons were performed through the two-sample t-test model of SPM12. In all the analyses, the height threshold was P < 0.001 (P < 0.05 FWE-corrected at cluster level). Results The full factorial design resulted in a significant main effect of groups. We identified a relative hypometabolism in sALS patients compared to SOD1-ALS cases in the right precentral and medial frontal gyrus, right paracentral lobule, and bilateral postcentral gyrus. SOD1 patients showed a relative hypermetabolism as compared to HC in the right precentral gyrus and paracentral lobule. As compared to HC, sALS patients showed relative hypometabolism in frontal, temporal, and occipital cortices. Conclusion SOD1-ALS was characterized by a relative hypermetabolism in the motor cortex as compared to sALS and HC. Since promising, targeted, therapeutic strategies are upcoming for SOD1-ALS, our data support the use of PET to study disease pathogenesis and to track its course in clinical trials, in both asymptomatic and symptomatic mutation carriers.

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.

Social cognition (SC) deficits are included in amyotrophic lateral sclerosis (ALS)-frontotemporal spectrum disorder revised diagnostic criteria. However, SC performance among ALS patients is heterogeneous due to the phenotypic variability of the disease and the wide range of neuropsychological tools employed. The aim of the present study was to assess facial emotion recognition and theory of mind in ALS patients compared to controls and to evaluate correlations with the other cognitive domains and degree of motor impairment.Eighty-three patients and 42 controls underwent a cognitive evaluation and SC assessment through the Ekman 60 Faces Test (EK-60F), the Reading the Mind in the Eyes Test-36 Faces (RMET-36), and the Story-Based Empathy Task (SET).ALS patients showed significantly worse performance compared to controls in EK-60F global score (p lt; 0.001), recognition of disgust (p = 0.032), anger (p = 0.038), fear (p lt; 0.001), and sadness (p lt; 0.001); RMET-36 (p lt; 0.001), and SET global score (p lt; 0.001). Also, cognitively normal patients (ALS-CN) showed significantly worse performance compared to controls in EK-60F global score (p lt; 0.001), recognition of fear (p = 0.002), sadness (p lt; 0.001), and SET (p lt; 0.001). RMET-36 showed a significant correlation with the Category Fluency Test (p = 0.041). SC tests showed no correlation with motor impairment expressed by Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised.ALS patients, also when categorized as ALS-CN, may show impairment in SC performance. The frequent identification of early SC impairment in ALS patients supports the need to routinely assess SC for its impact on end-of-life decisions and its potential influence on patients' quality of life.

Summary: Various genetic and environmental risk factors have been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, the cause of most ALS cases remains obscure. In this review, we describe the current evidence implicating genetic and environmental factors in motor neuron degeneration. While the risk exerted by many environmental factors may appear small, their effect could be magnified by the presence of a genetic predisposition. We postulate that gene-environment interactions account for at least a portion of the unknown etiology in ALS. Climate underlies multiple environmental factors, some of which have been implied in ALS etiology, and the impact of global temperature increase on the gene-environment interactions should be carefully monitored. We describe the main concepts underlying such interactions. Although a lack of large cohorts with detailed genetic and environmental information hampers the search for gene-environment interactions, newer algorithms and machine learning approaches offer an opportunity to break this stalemate. Understanding how genetic and environmental factors interact to cause ALS may ultimately pave the way towards precision medicine becoming an integral part of ALS care.

Few epidemiological studies have assessed the risk of parkinsonisms after prolonged use of neuroleptics. We aimed to examine the long-term risk of degenerative parkinsonisms (DP) associated with previous use of neuroleptics.All residents in Piedmont, Northern-west Italy, older than 39 years (2,526,319 subjects), were retrospectively followed up from 2013 to 2017. Exposure to neuroleptics was assessed through the regional archive of drug prescriptions. The development of DP was assessed using the regional archives of both drug prescriptions and hospital admissions. We excluded prevalent DP cases at baseline as well as those occurred in the first 18 months (short-term risk). The risk of DP associated with previous use of neuroleptics was examined through Cox regression, using a matched cohort design.The risk of DP was compared between 63,356 exposed and 316,779 unexposed subjects. A more than threefold higher risk of DP was observed among subjects exposed to antipsychotics, compared to those unexposed (HR = 3.27, 95% CI 3.00-3.57), and was higher for exposure to atypical than typical antipsychotics. The risk decreased after 2 years from therapy cessation but remained significantly elevated (HR = 2.38, 95% CI 1.76-3.21).These results indicate a high risk of developing DP long time from the start of use and from the cessation for both typical and atypical neuroleptics, suggesting the need of monitoring treated patients even after long-term use and cessation.

Since its first description by Charcot, amyotrophic lateral sclerosis (ALS) has been classified as a single neurodegenerative disease involving motor neurons. More recently, however, it has become clear that ALS is pleiotropic and its phenotype could vary depending on several factors: age of onset, prevalent damage to the upper (UMN) or the lower motor neuron, body region mostly affected, progression rate and presence of non-motor symptoms, the most frequent being cognitive impairment.1 As a consequence, different phenotypes have been described.2 It remains unclear whether these presentations should be considered as distinct clinical entities or as variants of the same disease.3 More importantly, we do not know if different aetiologies justify this variability.3 Despite these uncertainties, we approach clinical trials by administering the same treatment to all patients with ALS.3 Spatial epidemiology could give some clues on aetiology by identifying clusters and subsequently seeking for causative genetic or environmental factors. Previously, we analysed the spatial distribution of a large ALS cohort in Northwestern Italy. Patients resulted to be homogeneously distributed, suggesting either that a single environmental factor was evenly distributed or that multiple factors acted on distinct subgroups, the latter being the more likely.4 Moving from the hypothesis that aetiology could vary according to phenotypes, here we performed a spatial analysis of ALS cases stratified according to their clinical presentation. Data from the Piemonte and Valle d’Aosta ALS Register were used.4 All patients with ALS diagnosed between 2007 and 2014 and who were resident in Piemonte at the time of diagnosis were included. The cluster analysis was conducted considering municipalities; …

Recent advances in the genetic causes of ALS reveals that about 10% of ALS patients have a genetic origin and that more than 30 genes are likely to contribute to this disease. However, four genes are more frequently associated with ALS: C9ORF72, TARDBP, SOD1, and FUS. The relationship between genetic factors and ALS progression rate is not clear. In this study, we carried out a causal analysis of ALS disease with a genetics perspective in order to assess the contribution of the four mentioned genes to the progression rate of ALS.In this work, we applied a novel causal learning model to the CRESLA dataset which is a longitudinal clinical dataset of ALS patients including genetic information of such patients. This study aims to discover the relationship between four mentioned genes and ALS progression rate from a causation perspective using machine learning and probabilistic methods.The results indicate a meaningful association between genetic factors and ALS progression rate with causality viewpoint. Our findings revealed that causal relationships between ALSFRS-R items associated with bulbar regions have the strongest association with genetic factors, especially C9ORF72; and other three genes have the greatest contribution to the respiratory ALSFRS-R items with a causation point of view.The findings revealed that genetic factors have a significant causal effect on the rate of ALS progression. Since C9ORF72 patients have higher proportion compared to those carrying other three gene mutations in the CRESLA cohort, we need a large multi-centric study to better analyze SOD1, TARDBP and FUS contribution to the ALS clinical progression. We conclude that causal associations between ALSFRS-R clinical factors is a suitable predictor for designing a prognostic model of ALS.

Background Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord. Mean life expectancy is three to five years, with paralysis of muscles, respiratory failure and loss of vital functions being the common causes of death. Clinical manifestations of ALS are heterogeneous due to the mix of anatomic regions involvement and the variability in disease course; consequently, diagnosis and prognosis at the level of individual patient is really challenging. Prediction of ALS progression and stratification of patients into meaningful subgroups have been long-standing interests to clinical practice, research and drug development. Methods We developed a Dynamic Bayesian Network (DBN) model on more than 4500 ALS patients included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), in order to detect probabilistic relationships among clinical variables and identify risk factors related to survival and loss of vital functions. Furthermore, the DBN was used to simulate the temporal evolution of an ALS cohort predicting survival and the time to impairment of vital functions (communication, swallowing, gait and respiration). A first attempt to stratify patients by risk factors and simulate the progression of ALS subgroups was also implemented. Results The DBN model provided the prediction of ALS most probable trajectories over time in terms of important clinical outcomes, including survival and loss of autonomy in functional domains. Furthermore, it allowed the identification of biomarkers related to patients’ clinical status as well as vital functions, and unrevealed their probabilistic relationships. For instance, DBN found that bicarbonate and calcium levels influence survival time; moreover, the model evidenced dependencies over time among phosphorus level, movement impairment and creatinine. Finally, our model provided a tool to stratify patients into subgroups of different prognosis studying the effect of specific variables, or combinations of them, on either survival time or time to loss of autonomy in specific functional domains. Conclusions The analysis of the risk factors and the simulation allowed by our DBN model might enable better support for ALS prognosis as well as a deeper insight into disease manifestations, in a context of a personalized medicine approach. Electronic supplementary material The online version of this article (10.1186/s12859-019-2692-x) contains supplementary material, which is available to authorized users.