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. 2020 Sep 1;12(1):24.
doi: 10.1186/s11689-020-09327-0.

Natural clusters of tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND): new findings from the TOSCA TAND research project

Petrus J de Vries  1 , Elena Belousova  2 , Mirjana P Benedik  3 , Tom Carter  4 , Vincent Cottin  5 , Paolo Curatolo  6 , Lisa D'Amato  7 , Guillaume Beure d'Augères  8 , José C Ferreira  9 , Martha Feucht  10 , Carla Fladrowski  11   12 , Christoph Hertzberg  13 , Sergiusz Jozwiak  14   15 , John A Lawson  16 , Alfons Macaya  17 , Ruben Marques  7   18 , Rima Nabbout  19 , Finbar O'Callaghan  20 , Jiong Qin  21 , Valentin Sander  22 , Matthias Sauter  23 , Seema Shah  24 , Yukitoshi Takahashi  25 , Renaud Touraine  26 , Sotiris Youroukos  27 , Bernard Zonnenberg  28 , J Chris Kingswood  29 , Anna C Jansen  30 ; TOSCA Consortium and TOSCA Investigators
Collaborators, Affiliations

Natural clusters of tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND): new findings from the TOSCA TAND research project

Petrus J de Vries et al. J Neurodev Disord. .

Abstract

Background: Tuberous sclerosis complex (TSC)-associated neuropsychiatric disorders (TAND) have unique, individual patterns that pose significant challenges for diagnosis, psycho-education, and intervention planning. A recent study suggested that it may be feasible to use TAND Checklist data and data-driven methods to generate natural TAND clusters. However, the study had a small sample size and data from only two countries. Here, we investigated the replicability of identifying natural TAND clusters from a larger and more diverse sample from the TOSCA study.

Methods: As part of the TOSCA international TSC registry study, this embedded research project collected TAND Checklist data from individuals with TSC. Correlation coefficients were calculated for TAND variables to generate a correlation matrix. Hierarchical cluster and factor analysis methods were used for data reduction and identification of natural TAND clusters.

Results: A total of 85 individuals with TSC (female:male, 40:45) from 7 countries were enrolled. Cluster analysis grouped the TAND variables into 6 clusters: a scholastic cluster (reading, writing, spelling, mathematics, visuo-spatial difficulties, disorientation), a hyperactive/impulsive cluster (hyperactivity, impulsivity, self-injurious behavior), a mood/anxiety cluster (anxiety, depressed mood, sleep difficulties, shyness), a neuropsychological cluster (attention/concentration difficulties, memory, attention, dual/multi-tasking, executive skills deficits), a dysregulated behavior cluster (mood swings, aggressive outbursts, temper tantrums), and an autism spectrum disorder (ASD)-like cluster (delayed language, poor eye contact, repetitive behaviors, unusual use of language, inflexibility, difficulties associated with eating). The natural clusters mapped reasonably well onto the six-factor solution generated. Comparison between cluster and factor solutions from this study and the earlier feasibility study showed significant similarity, particularly in cluster solutions.

Conclusions: Results from this TOSCA research project in an independent international data set showed that the combination of cluster analysis and factor analysis may be able to identify clinically meaningful natural TAND clusters. Findings were remarkably similar to those identified in the earlier feasibility study, supporting the potential robustness of these natural TAND clusters. Further steps should include examination of larger samples, investigation of internal consistency, and evaluation of the robustness of the proposed natural clusters.

Keywords: ASD; Cluster analysis; Factor analysis; Natural TAND clusters; Neuropsychiatric; Registry; TAND; TOSCA; Tuberous sclerosis complex.

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Conflict of interest statement

PJdV, EB, TC, VC, PC, GBd’A, JCK, JCF, MF, CF, CH, SJ, RN, FO’C, JQ, MS, RT, JAL, AM, SY, MPB, BZ, and ACJ received honoraria and travel support from Novartis. VC received personal fees for consulting; lecture fees and travel from Actelion, Bayer, Biogen Idec, Boehringer Ingelheim, Gilead, GSK, MSD, Novartis, Pfizer, Roche, and Sanofi; grants from Actelion, Boehringer Ingelheim, GSK, Pfizer, and Roche; and personal fees for developing educational material from Boehringer Ingelheim and Roche. PJdV has been on the steering group of the EXIST-1, 2, and 3 studies sponsored by Novartis and co-PI on two investigator-initiated studies part-funded by Novartis. RN received grant support, paid to her institution, from Eisai and lecture fees from Nutricia, Eisai, Advicenne, and GW Pharma. YT received personal fees from Novartis for lecture and for copyright of referential figures from the journals and grant from the Japanese government for intractable epilepsy research. SJ was partly financed by the EC Seventh Framework Programme (FP7/2007-2013; EPISTOP, grant agreement no. 602391), the Polish Ministerial funds for science (years 2013–2018) for the implementation of international co-financed project, and the grant EPIMARKER of the Polish National Center for Research and Development No. STRATEGMED3/306306/4/2016. JCK, PC, CH, JAL, and JQ received research grants from Novartis. RM and SS are employees of Novartis. LD’A was an employee of Novartis at the time of manuscript concept approval. VS reported no conflict of interest.

Figures

Fig. 1
Fig. 1
Dendrogram of natural TAND clusters. Hierarchical cluster analysis using the WARD method produced six natural TAND clusters
Fig. 2
Fig. 2
Exploratory factor analysis results of a six-factor solution to identify the latent constructs underlying the TAND variables. The figure shows the rotated factor pattern using the Varimax method. Coefficients in blue represent the largest coefficient values for each variable across all 6 factors. All other coefficients with values > 0.5 are shown in yellow
Fig. 3
Fig. 3
Visualization of the factor score graph showing factor scores of individual TAND variables in relation to the six-factor solution derived from exploratory factor analysis. The closer a factor score is to + 1 the stronger the influence of the factor is on that variable. Solid blue dots represent the largest coefficient values for each variable across all 6 factors and solid yellow dots represent all other coefficients with values > 0.5. Blue circles represent coefficients with values < 0.5
Fig. 4
Fig. 4
Comparison of cluster analysis and exploratory factor analysis to show the overlap between cluster and factor solutions. Dotted lines indicate natural TAND clusters; solid lines show factor analysis solutions
See this image and copyright information in PMC

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