4.5 Article

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

Journal

ORPHANET JOURNAL OF RARE DISEASES
Volume 16, Issue 1, Pages -

Publisher

BMC
DOI: 10.1186/s13023-021-02076-w

Keywords

Tuberous Sclerosis Complex; TAND; Natural TAND clusters; Neuropsychiatric; Autism spectrum disorder; Cluster analysis; Factor analysis; Precision medicine

Funding

  1. Tuberous Sclerosis Association (UK) [2014-S01]
  2. King Baudouin Foundation Fund [2019-J1120010-213544]
  3. National Research Foundation
  4. Struengmann Fund for endowment of the professorship
  5. Research Foundation -Flanders [FWO FKM 1805321N]
  6. Developmental Synaptopathies Consortium, which is a part of the National Center for Advancing Translational Sciences (NCATS) Rare Diseases Clinical Research Network (RDCRN) [NIH U54-NS092090]
  7. NCATS
  8. National Institute of Mental Health
  9. NINDS
  10. National Institute of Child Health and Human Development (NICHD)

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This study identified seven natural TAND clusters in individuals with TSC, providing a basis for tailored approaches to identification and treatment of TAND. The clusters showed good clinical face validity and may have differential responses to treatments.
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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