Dataset Information

Analysis of amyotrophic lateral sclerosis as a multistep process: a population-based modelling study.

ABSTRACT:

Background

Amyotrophic lateral sclerosis shares characteristics with some cancers, such as onset being more common in later life, progression usually being rapid, the disease affecting a particular cell type, and showing complex inheritance. We used a model originally applied to cancer epidemiology to investigate the hypothesis that amyotrophic lateral sclerosis is a multistep process.

Methods

We generated incidence data by age and sex from amyotrophic lateral sclerosis population registers in Ireland (registration dates 1995-2012), the Netherlands (2006-12), Italy (1995-2004), Scotland (1989-98), and England (2002-09), and calculated age and sex-adjusted incidences for each register. We regressed the log of age-specific incidence against the log of age with least squares regression. We did the analyses within each register, and also did a combined analysis, adjusting for register.

Findings

We identified 6274 cases of amyotrophic lateral sclerosis from a catchment population of about 34 million people. We noted a linear relationship between log incidence and log age in all five registers: England r(2)=0·95, Ireland r(2)=0·99, Italy r(2)=0·95, the Netherlands r(2)=0·99, and Scotland r(2)=0·97; overall r(2)=0·99. All five registers gave similar estimates of the linear slope ranging from 4·5 to 5·1, with overlapping confidence intervals. The combination of all five registers gave an overall slope of 4·8 (95% CI 4·5-5·0), with similar estimates for men (4·6, 4·3-4·9) and women (5·0, 4·5-5·5).

Interpretation

A linear relationship between the log incidence and log age of onset of amyotrophic lateral sclerosis is consistent with a multistage model of disease. The slope estimate suggests that amyotrophic lateral sclerosis is a six-step process. Identification of these steps could lead to preventive and therapeutic avenues.

Funding

UK Medical Research Council; UK Economic and Social Research Council; Ireland Health Research Board; The Netherlands Organisation for Health Research and Development (ZonMw); the Ministry of Health and Ministry of Education, University, and Research in Italy; the Motor Neurone Disease Association of England, Wales, and Northern Ireland; and the European Commission (Seventh Framework Programme).

SUBMITTER: Al-Chalabi A

PROVIDER: S-EPMC4197338 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Publications

Analysis of amyotrophic lateral sclerosis as a multistep process: a population-based modelling study.

Al-Chalabi Ammar A Calvo Andrea A Chio Adriano A Colville Shuna S Ellis Cathy M CM Hardiman Orla O Heverin Mark M Howard Robin S RS Huisman Mark H B MHB Keren Noa N Leigh P Nigel PN Mazzini Letizia L Mora Gabriele G Orrell Richard W RW Rooney James J Scott Kirsten M KM Scotton William J WJ Seelen Meinie M Shaw Christopher E CE Sidle Katie S KS Swingler Robert R Tsuda Miho M Veldink Jan H JH Visser Anne E AE van den Berg Leonard H LH Pearce Neil N

The Lancet. Neurology 20141007 11

<h4>Background</h4>Amyotrophic lateral sclerosis shares characteristics with some cancers, such as onset being more common in later life, progression usually being rapid, the disease affecting a particular cell type, and showing complex inheritance. We used a model originally applied to cancer epidemiology to investigate the hypothesis that amyotrophic lateral sclerosis is a multistep process.<h4>Methods</h4>We generated incidence data by age and sex from amyotrophic lateral sclerosis population ...[more]

PMID: 25300936

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Project description:BackgroundAmyotrophic 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.MethodsIn 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.FindingsBetween 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]).InterpretationOur 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.FundingUS 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.TranslationsFor the Italian and German translations of the abstract see Supplementary Materials section.

Dataset Information

Analysis of amyotrophic lateral sclerosis as a multistep process: a population-based modelling study.

Background

Methods

Findings

Interpretation

Funding

Publications

Analysis of amyotrophic lateral sclerosis as a multistep process: a population-based modelling study.

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