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Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND)

BACKGROUND: Tuberous Sclerosis Complex (TSC), a multi-system genetic disorder, is associated with a wide range of TSC-Associated Neuropsychiatric Disorders (TAND). Individuals have apparently unique TAND profiles, challenging diagnosis, psycho-education, and intervention planning. We proposed that i...

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Autores principales: de Vries, Petrus J., Leclezio, Loren, Gardner-Lubbe, Sugnet, Krueger, Darcy, Sahin, Mustafa, Sparagana, Steven, De Waele, Liesbeth, Jansen, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543869/
https://www.ncbi.nlm.nih.gov/pubmed/34689816
http://dx.doi.org/10.1186/s13023-021-02076-w
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author de Vries, Petrus J.
Leclezio, Loren
Gardner-Lubbe, Sugnet
Krueger, Darcy
Sahin, Mustafa
Sparagana, Steven
De Waele, Liesbeth
Jansen, Anna
author_facet de Vries, Petrus J.
Leclezio, Loren
Gardner-Lubbe, Sugnet
Krueger, Darcy
Sahin, Mustafa
Sparagana, Steven
De Waele, Liesbeth
Jansen, Anna
author_sort de Vries, Petrus J.
collection PubMed
description BACKGROUND: Tuberous Sclerosis Complex (TSC), a multi-system genetic disorder, is associated with a wide range of TSC-Associated Neuropsychiatric Disorders (TAND). Individuals have apparently unique TAND profiles, challenging diagnosis, psycho-education, and intervention planning. We proposed that identification of natural TAND clusters could lead to personalized identification and treatment of TAND. Two small-scale studies showed cluster and factor analysis could identify clinically meaningful natural TAND clusters. Here we set out to identify definitive natural TAND clusters in a large, international dataset. METHOD: Cross-sectional, anonymized TAND Checklist data of 453 individuals with TSC were collected from six international sites. Data-driven methods were used to identify natural TAND clusters. Mean squared contingency coefficients were calculated to produce a correlation matrix, and various cluster analyses and exploratory factor analysis were examined. Statistical robustness of clusters was evaluated with 1000-fold bootstrapping, and internal consistency calculated with Cronbach’s alpha. RESULTS: Ward’s method rendered seven natural TAND clusters with good robustness on bootstrapping. Cluster analysis showed significant convergence with an exploratory factor analysis solution, and, with the exception of one cluster, internal consistency of the emerging clusters was good to excellent. Clusters showed good clinical face validity. CONCLUSIONS: Our findings identified a data-driven set of natural TAND clusters from within highly variable TAND Checklist data. The seven natural TAND clusters could be used to train families and professionals and to develop tailored approaches to identification and treatment of TAND. Natural TAND clusters may also have differential aetiological underpinnings and responses to molecular and other treatments.
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spelling pubmed-85438692021-10-25 Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND) de Vries, Petrus J. Leclezio, Loren Gardner-Lubbe, Sugnet Krueger, Darcy Sahin, Mustafa Sparagana, Steven De Waele, Liesbeth Jansen, Anna Orphanet J Rare Dis Research BACKGROUND: Tuberous Sclerosis Complex (TSC), a multi-system genetic disorder, is associated with a wide range of TSC-Associated Neuropsychiatric Disorders (TAND). Individuals have apparently unique TAND profiles, challenging diagnosis, psycho-education, and intervention planning. We proposed that identification of natural TAND clusters could lead to personalized identification and treatment of TAND. Two small-scale studies showed cluster and factor analysis could identify clinically meaningful natural TAND clusters. Here we set out to identify definitive natural TAND clusters in a large, international dataset. METHOD: Cross-sectional, anonymized TAND Checklist data of 453 individuals with TSC were collected from six international sites. Data-driven methods were used to identify natural TAND clusters. Mean squared contingency coefficients were calculated to produce a correlation matrix, and various cluster analyses and exploratory factor analysis were examined. Statistical robustness of clusters was evaluated with 1000-fold bootstrapping, and internal consistency calculated with Cronbach’s alpha. RESULTS: Ward’s method rendered seven natural TAND clusters with good robustness on bootstrapping. Cluster analysis showed significant convergence with an exploratory factor analysis solution, and, with the exception of one cluster, internal consistency of the emerging clusters was good to excellent. Clusters showed good clinical face validity. CONCLUSIONS: Our findings identified a data-driven set of natural TAND clusters from within highly variable TAND Checklist data. The seven natural TAND clusters could be used to train families and professionals and to develop tailored approaches to identification and treatment of TAND. Natural TAND clusters may also have differential aetiological underpinnings and responses to molecular and other treatments. BioMed Central 2021-10-24 /pmc/articles/PMC8543869/ /pubmed/34689816 http://dx.doi.org/10.1186/s13023-021-02076-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
de Vries, Petrus J.
Leclezio, Loren
Gardner-Lubbe, Sugnet
Krueger, Darcy
Sahin, Mustafa
Sparagana, Steven
De Waele, Liesbeth
Jansen, Anna
Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND)
title Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND)
title_full Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND)
title_fullStr Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND)
title_full_unstemmed Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND)
title_short Multivariate data analysis identifies natural clusters of Tuberous Sclerosis Complex Associated Neuropsychiatric Disorders (TAND)
title_sort multivariate data analysis identifies natural clusters of tuberous sclerosis complex associated neuropsychiatric disorders (tand)
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543869/
https://www.ncbi.nlm.nih.gov/pubmed/34689816
http://dx.doi.org/10.1186/s13023-021-02076-w
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