Is It Feasible to Identify Natural Clusters of TSC-Associated Neuropsychiatric Disorders (TAND)?

Publication date: Available online 31 January 2018
Source:Pediatric Neurology
Author(s): Loren Leclezio, Sugnet Gardner-Lubbe, Petrus J. de Vries
BackgroundTuberous Sclerosis Complex (TSC) is a genetic disorder with multi-system involvement. The lifetime prevalence of TSC-Associated Neuropsychiatric Disorders (TAND) is in the region of 90% in an apparently unique, individual pattern. This 'uniqueness' poses significant challenges for diagnosis, psycho-education, and intervention planning. To date, no studies have explored whether there may be natural clusters of TAND. The purpose of this feasibility study was a) to investigate the practicability of identifying natural TAND clusters, and (b) to identify appropriate multivariate data analysis techniques for larger-scale studies.MethodsTAND Checklist data were collected from 56 individuals with a clinical diagnosis of TSC (n= 20 from South Africa; n = 36 from Australia). Using R, the open-source statistical platform, mean squared contingency coefficients were calculated to produce a correlation matrix, and various cluster analyses and exploratory factor analysis (EFA) were examined.ResultsWARD's method rendered six TAND clusters with good face validity and significant convergence with a six-factor EFA solution. The 'bottom-up' data-driven strategies identified a 'Scholastic' cluster of TAND manifestations, an 'ASD-like' cluster, a 'Dysregulated Behaviour' cluster, a 'Neuropsychological' cluster, a 'Hyperactive/Impulsive' cluster, and a 'Mixed/Mood' cluster.ConclusionThese feasibility results suggest that a combination of cluster analysis and exploratory factor analysis methods may be able to identify clinically-meaningful natural TAND clusters. Findings require replication and expansion in larger dataset, and could include quantification of cluster/factor scores at an individual level.



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