From gestalt to gene: early predictive dysmorphic features of PMM2-CDG
| Autor: | Antonio, Martinez-Monseny, Daniel, Cuadras, Mercè, Bolasell, Jordi, Muchart, César, Arjona, Mar, Borregan, Adi, Algrabli, Raquel, Montero, Rafael, Artuch, Ramón, Velázquez-Fragua, Alfons, Macaya, Celia, Pérez-Cerdá, Belén, Pérez-Dueñas, Belén, Pérez, Mercedes, Serrano, Oscar, Garcia |
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| Rok vydání: | 2018 |
| Předmět: |
Male
0301 basic medicine Pediatrics medicine.medical_specialty Adolescent 030105 genetics & heredity Correlation Young Adult 03 medical and health sciences Congenital Disorders of Glycosylation Neuroimaging Genetics Humans Medicine Genetic Predisposition to Disease Cerebellar disorder Genetic Testing Pmm2 cdg Child Strabismus Genetic Association Studies Genetics (clinical) Receiver operating characteristic business.industry Facies medicine.disease Phenotype 030104 developmental biology ROC Curve Phosphotransferases (Phosphomutases) Spain Child Preschool Gestalt psychology Female Lipodystrophy business |
| Zdroj: | Journal of Medical Genetics. 56:236-245 |
| ISSN: | 1468-6244 0022-2593 |
| Popis: | IntroductionPhosphomannomutase-2 deficiency (PMM2-CDG) is associated with a recognisable facial pattern. There are no early severity predictors for this disorder and no phenotype–genotype correlation. We performed a detailed dysmorphology evaluation to describe facial gestalt and its changes over time, to train digital recognition facial analysis tools and to identify early severity predictors.MethodsPaediatric PMM2-CDG patients were evaluated and compared with controls. A computer-assisted recognition tool was trained. Through the evaluation of dysmorphic features (DFs), a simple categorisation was created and correlated with clinical and neurological scores, and neuroimaging.ResultsDysmorphology analysis of 31 patients (4–19 years of age) identified eight major DFs (strabismus, upslanted eyes, long fingers, lipodystrophy, wide mouth, inverted nipples, long philtrum and joint laxity) with predictive value using receiver operating characteristic (ROC) curveanalysis (pConclusionsPMM2-CDG patients’ DFs are consistent and inform about clinical severity when no clear phenotype–genotype correlation is known. We propose a classification of DFs into major and minor with diagnostic risk implications. At present, Face2Gene is useful to suggest PMM2-CDG. Regarding the prognostic value of DFs, we elaborated a simple severity dysmorphology categorisation with predictive value, and we identified five major DFs associated with clinical severity. Both dysmorphology and digital analysis may help physicians to diagnose PMM2-CDG sooner. |
| Databáze: | OpenAIRE |
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