Faculty Opinions recommendation of Kinesin-associated protein 3 (KIFAP3) has no effect on survival in a population-based cohort of ALS patients.

To date, there are no reports studying the rate of amyotrophic lateral sclerosis (ALS) in relatives of multiple sclerosis (MS) patients and vice versa. This study was designed to look into this issue using two population-based databases of MS and ALS in Isfahan province of Iran. We have searched for any first, second or third degree familial kinship between the Isfahan MS Society database and Isfahan ALS population. We compared the rate of ALS among the population of first degree relatives of MS patients, with the crude prevalence of ALS in the general population of Isfahan. On the other hand, a reverse analysis was carried out to compare the prevalence of MS in Isfahan with its rate amongst the first degree relatives of ALS patients. We found 10 families among which five had first degree kinship. The rate of the diseases was significantly higher in both comparisons among the family members ( p < 0.00001) and an odds ratios of more than 67 in both calculations showed a several-fold increase of ALS occurrence in the first degree relatives of MS patients and vice versa. In our study relatives of MS patients were significantly more prone to ALS and vice versa. This could give clues about the common features that the two disease share. Both diseases have an environmental and genetic component and these results mostly point toward genetic similarities.

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Faculty Opinions recommendation of Factors Predicting Survival in ALS Patients--Data from a Population-Based Registry in Rhineland-Palatinate, Germany.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727419560.793529760 ◽

2017 ◽

Author(s):

Ammar Al-Chalabi

Keyword(s):

Population Based ◽

Als Patients ◽

Population Based Registry

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Stratification of ALS patients’ survival: a population-based study

Journal of Neurology ◽

10.1007/s00415-015-7940-z ◽

2015 ◽

Vol 263 (1) ◽

pp. 100-111 ◽

Cited By ~ 15

Author(s):

Benoît Marin ◽

Philippe Couratier ◽

Simona Arcuti ◽

Massimiliano Copetti ◽

Andrea Fontana ◽

...

Keyword(s):

Population Based ◽

Population Based Study ◽

Als Patients

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The Incidence of Amyotrophic Lateral Sclerosis in Ohio 2016–2018: The Ohio Population-Based ALS Registry

Neuroepidemiology ◽

10.1159/000515103 ◽

2021 ◽

pp. 1-10

Author(s):

Angeline S. Andrew ◽

Erik P. Pioro ◽

Meifang Li ◽

Xun Shi ◽

Jiang Gui ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Rural Areas ◽

Clinical Information ◽

Population Based ◽

Incidence Rates ◽

The State ◽

Newly Diagnosed ◽

Disease Etiology ◽

Als Patients ◽

Lateral Sclerosis

Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal, neuromuscular disease with no cure. ALS incidence rates have not been assessed specifically in Ohio, yet the state contains both metropolitan and rural areas with a variety of environmental factors that could contribute to disease etiology. We report the incidence of ALS in Ohio residents diagnosed from October 2016 through September 2018. Methods: We engaged practitioners from 9 Ohio sites to identify newly diagnosed ALS patients and to complete case report forms with demographic and clinical information. ALS was diagnosed according to the Awaji criteria and classified as either definite, probable, or possible. We developed a method to estimate missing cases using a Poisson regression model to impute cases in counties with evidence of undercounting. Results: We identified 333 newly diagnosed ALS patients residing in Ohio during the 2-year index period and found incidence rates varied in the 88 state counties. After incorporating the estimated 27% of missing cases, the corrected crude annual incidence was 1.96/100,000 person-years, and the age- and gender-standardized incidence was 1.71/100,000 person-years (standardized to the 2010 US census). Discussion/Conclusion: The estimated Ohio incidence of ALS is overall similar to that reported in other states in the USA. This study reveals a geospatial variation in incidence within the state, and areas with higher rates warrant future investigation.

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Epidemiology of Amyotrophic Lateral Sclerosis: A Population-Based Study in Israel

Neuroepidemiology ◽

10.1159/000448921 ◽

2016 ◽

Vol 47 (2) ◽

pp. 76-81 ◽

Cited By ~ 6

Author(s):

Clara Weil ◽

Neta Zach ◽

Shay Rishoni ◽

Varda Shalev ◽

Gabriel Chodick

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Health Maintenance Organization ◽

Healthcare Services ◽

Population Based ◽

Annual Incidence ◽

Health Maintenance ◽

High Prevalence ◽

Als Patients ◽

Lateral Sclerosis

Background: Globally, the annual incidence and prevalence of amyotrophic lateral sclerosis (ALS) are estimated at 1.9 and 4.5 per 100,000 population, respectively. This study is aimed at describing the epidemiology of ALS in Israel in a real-world setting. Methods: A retrospective study was performed using the databases of Maccabi Healthcare Services (MHS), a 2-million-member health maintenance organization in Israel. The study included all MHS adults diagnosed with ALS between 1997 and 2013. In 2013, characteristics of ALS patients were compared to those of age-sex-matched patients without ALS. Survival after ALS diagnosis was assessed until death and until tracheostomy or death (follow-up through 2014). Results: In 2013 (n = 158), the prevalence of ALS was 8.1 per 100,000 population in MHS. In 1997-2013, a total of 375 ALS patients were diagnosed, corresponding to an average annual incidence of 1.8 per 100,000 population in MHS. The median survival from diagnosis to death was 3.5 years (95% CI 2.9-4.1), with approximately 28% surviving at least 10 years. Median tracheostomy-free survival was 2.5 years (95% CI 2.1-2.9). Conclusions: Results suggest that there is a relatively high prevalence of ALS in Israel. Further research is needed to investigate factors that may contribute to the survival of patients with ALS in Israel.

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Factors Predicting Survival in ALS Patients - Data from a Population-Based Registry in Rhineland-Palatinate, Germany

Neuroepidemiology ◽

10.1159/000381625 ◽

2015 ◽

Vol 44 (3) ◽

pp. 149-155 ◽

Cited By ~ 15

Author(s):

Joachim Wolf ◽

Anton Safer ◽

Johannes C. Wöhrle ◽

Frederick Palm ◽

Wilfred A. Nix ◽

...

Keyword(s):

Rating Scale ◽

Population Based ◽

Predictor Variables ◽

Survival Prognosis ◽

Cox Proportional Hazard ◽

Kaplan Meier ◽

Bulbar Onset ◽

Cox Proportional Hazard Regression ◽

Als Patients ◽

Population Based Registry

Background: The possibility to survive with amyotrophic lateral sclerosis (ALS) varies considerably and survival extends from a few months to several years. A number of demographic and clinical factors predicting survival have been described; however, existing data are conflicting. We intended to predict patient survival in a population-based prospective cohort of ALS patients from variables known up to the time of diagnosis. Methods: Incident ALS patients diagnosed within three consecutive years were enrolled and regularly followed up. Candidate demographic and disease variables were analysed for survival probability using the Kaplan-Meier method. The Cox proportional hazard regression model was used to assess the influence of selected predictor variables on survival prognosis. Results: In the cohort of 193 patients (mean age 65.8, standard deviation 10.2 years), worse prognosis was independently predicted by older age, male gender, bulbar onset, probable or definite ALS according to El Escorial criteria, shorter interval between symptom onset and diagnosis, lower Functional Rating Scale, diagnosis of frontotemporal dementia, and living without a partner. Conclusions: Taking into account these predictor variables, an approximate survival prognosis of individual ALS patients at diagnosis seems feasible.

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Kinesin-associated protein 3 (KIFAP3) has no effect on survival in a population-based cohort of ALS patients

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.0914079107 ◽

2010 ◽

Vol 107 (27) ◽

pp. 12335-12338 ◽

Cited By ~ 19

Author(s):

B. J. Traynor ◽

M. Nalls ◽

S.-L. Lai ◽

R. J. Gibbs ◽

J. C. Schymick ◽

...

Keyword(s):

Population Based ◽

Als Patients

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Mutational analysis of known ALS genes in an Italian population-based cohort

Neurology ◽

10.1212/wnl.0000000000011209 ◽

2020 ◽

pp. 10.1212/WNL.0000000000011209

Author(s):

Maurizio Grassano ◽

Andrea Calvo ◽

Cristina Moglia ◽

Maura Brunetti ◽

Marco Barberis ◽

...

Keyword(s):

Genome Sequencing ◽

Genetic Variants ◽

Rare Variants ◽

Disease Risk ◽

Statistical Significance ◽

Geographical Area ◽

Population Based ◽

Italian Population ◽

Healthy Controls ◽

Als Patients

ObjectiveTo assess the burden of rare genetic variants and to estimate the contribution of known ALS genes in an Italian population-based cohort we performed whole genome sequencing in 959 ALS patients 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 ALS patients 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 utility.

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Identification and prediction of ALS subgroups using machine learning

10.1101/2021.04.02.21254844 ◽

2021 ◽

Author(s):

Faraz Faghri ◽

Fabian Brunn ◽

Anant Dadu ◽

Elisabetta Zucchi ◽

Ilaria Martinelli ◽

...

Keyword(s):

Machine Learning ◽

Classification System ◽

Population Based ◽

Classification Systems ◽

Supervised Machine Learning ◽

Population Substructure ◽

Clinical Parameters ◽

Independent Population ◽

Als Patients ◽

Clinical Subtype

Background The disease entity known as amyotrophic lateral sclerosis (ALS) is now known to represent a collection of overlapping syndromes. A better understanding of this heterogeneity and the ability to distinguish ALS subtypes would improve the clinical care of patients and enhance our understanding of the disease. Subtype profiles could be incorporated into the clinical trial design to improve our ability to detect a therapeutic effect. A variety of classification systems have been proposed over the years based on empirical observations, but it is unclear to what extent they genuinely reflect ALS population substructure. Methods We applied machine learning algorithms to a prospective, population-based cohort consisting of 2,858 Italian patients diagnosed with ALS for whom detailed clinical phenotype data were available. We replicated our findings in an independent population-based cohort of 1,097 Italian ALS patients. Findings We found that semi-supervised machine learning based on UMAP applied to the output of a multi-layered perceptron neural network produced the optimum clustering of the ALS patients in the discovery cohort. These clusters roughly corresponded to the six clinical subtypes defined by the Chiò classification system (bulbar ALS, respiratory ALS, flail arm ALS, classical ALS, pyramidal ALS, and flail leg ALS). The same clusters were identified in the replication cohort. A supervised learning approach based on ensemble learning identified twelve clinical parameters that predicted ALS clinical subtype with high accuracy (area under the curve = 0.94). Interpretation Our data-driven study provides insight into the ALS population's substructure and demonstrates that the Chiò classification system robustly identifies ALS subtypes. We provide an interactive website (https://share.streamlit.io/anant-dadu/machinelearningforals/main) so that clinical researchers can predict the clinical subtype of an ALS patient based on a small number of clinical parameters. Funding National Institute on Aging and the Italian Ministry of Health.

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