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 groupi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2009-09-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC2741575?pdf=render |
| _version_ | 1811325330264162304 |
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| author | Jeban Ganesalingam Daniel Stahl Lokesh Wijesekera Clare Galtrey Christopher E Shaw P Nigel Leigh Ammar Al-Chalabi |
| author_facet | Jeban Ganesalingam Daniel Stahl Lokesh Wijesekera Clare Galtrey Christopher E Shaw P Nigel Leigh Ammar Al-Chalabi |
| author_sort | Jeban Ganesalingam |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T14:31:16Z |
| format | Article |
| id | doaj.art-023b8803336b4377a8d948c1b5c5a96c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T14:31:16Z |
| publishDate | 2009-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-023b8803336b4377a8d948c1b5c5a96c2022-12-22T02:43:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032009-09-0149e710710.1371/journal.pone.0007107Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.Jeban GanesalingamDaniel StahlLokesh WijesekeraClare GaltreyChristopher E ShawP Nigel LeighAmmar Al-ChalabiBACKGROUND: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.http://europepmc.org/articles/PMC2741575?pdf=render |
| spellingShingle | Jeban Ganesalingam Daniel Stahl Lokesh Wijesekera Clare Galtrey Christopher E Shaw P Nigel Leigh Ammar Al-Chalabi Latent cluster analysis of ALS phenotypes identifies prognostically differing groups. PLoS ONE |
| 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 |
| url | http://europepmc.org/articles/PMC2741575?pdf=render |
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