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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 group...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2741575/ https://www.ncbi.nlm.nih.gov/pubmed/19771164 http://dx.doi.org/10.1371/journal.pone.0007107 |
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author | Ganesalingam, Jeban Stahl, Daniel Wijesekera, Lokesh Galtrey, Clare Shaw, Christopher E. Leigh, P. Nigel Al-Chalabi, Ammar |
author_facet | Ganesalingam, Jeban Stahl, Daniel Wijesekera, Lokesh Galtrey, Clare Shaw, Christopher E. Leigh, P. Nigel Al-Chalabi, Ammar |
author_sort | Ganesalingam, Jeban |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2741575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-27415752009-09-22 Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups Ganesalingam, Jeban Stahl, Daniel Wijesekera, Lokesh Galtrey, Clare Shaw, Christopher E. Leigh, P. Nigel Al-Chalabi, Ammar PLoS One Research Article 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. Public Library of Science 2009-09-22 /pmc/articles/PMC2741575/ /pubmed/19771164 http://dx.doi.org/10.1371/journal.pone.0007107 Text en Ganesalingam et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Ganesalingam, Jeban Stahl, Daniel Wijesekera, Lokesh Galtrey, Clare Shaw, Christopher E. Leigh, P. Nigel Al-Chalabi, Ammar Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups |
title | Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups |
title_full | Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups |
title_fullStr | Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups |
title_full_unstemmed | Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups |
title_short | Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups |
title_sort | latent cluster analysis of als phenotypes identifies prognostically differing groups |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2741575/ https://www.ncbi.nlm.nih.gov/pubmed/19771164 http://dx.doi.org/10.1371/journal.pone.0007107 |
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