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Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

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posted on 2023-06-08, 13:34 authored by Jeban Ganesalingam, Daniel Stahl, Lokesh Wijesekera, Clare Galtrey, Christopher E Shaw, Nigel LeighNigel Leigh, Ammar Al-Chalabi
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.

History

Publication status

  • Published

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  • Published version

Journal

PLoS ONE

ISSN

1932-6203

Publisher

Public Library of Science

Issue

9

Volume

4

Article number

e7107

Department affiliated with

  • Clinical and Experimental Medicine Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2012-11-09

First Open Access (FOA) Date

2016-03-22

First Compliant Deposit (FCD) Date

2016-11-15

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