Literature DB >> 30464053

From gestalt to gene: early predictive dysmorphic features of PMM2-CDG.

Antonio Martinez-Monseny1, Daniel Cuadras2, Mercè Bolasell1, Jordi Muchart3,4, César Arjona1, Mar Borregan1, Adi Algrabli5, Raquel Montero3,4, Rafael Artuch3,4, Ramón Velázquez-Fragua6, Alfons Macaya7, Celia Pérez-Cerdá8, Belén Pérez-Dueñas7, Belén Pérez8, Mercedes Serrano1,3,4.   

Abstract

INTRODUCTION: Phosphomannomutase-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.
METHODS: Paediatric 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.
RESULTS: Dysmorphology 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 (p<0.001). Dysmorphology categorisation using lipodystrophy and inverted nipples was employed to divide patients into three groups that are correlated with global clinical and neurological scores, and neuroimaging (p=0.005, 0.003 and 0.002, respectively). After Face2Gene training, PMM2-CDG patients were correctly identified at different ages.
CONCLUSIONS: PMM2-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. © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  automated facial analysis software; cerebellar disorders; congenital disorders of glycosylation; dysmorphology; phosphomannomutase

Mesh:

Substances:

Year:  2018        PMID: 30464053     DOI: 10.1136/jmedgenet-2018-105588

Source DB:  PubMed          Journal:  J Med Genet        ISSN: 0022-2593            Impact factor:   6.318


  7 in total

1.  Evaluation of Face2Gene using facial images of patients with congenital dysmorphic syndromes recruited in Japan.

Authors:  Hiroyuki Mishima; Hisato Suzuki; Michiko Doi; Mutsuko Miyazaki; Satoshi Watanabe; Tadashi Matsumoto; Kanako Morifuji; Hiroyuki Moriuchi; Koh-Ichiro Yoshiura; Tatsuro Kondoh; Kenjiro Kosaki
Journal:  J Hum Genet       Date:  2019-05-29       Impact factor: 3.172

2.  The facial dysmorphology analysis technology in intellectual disability syndromes related to defects in the histones modifiers.

Authors:  Giulia Pascolini; Nicole Fleischer; Alessandro Ferraris; Silvia Majore; Paola Grammatico
Journal:  J Hum Genet       Date:  2019-05-13       Impact factor: 3.172

3.  Molecular Modelling Hurdle in the Next-Generation Sequencing Era.

Authors:  Guerau Fernandez; Dèlia Yubero; Francesc Palau; Judith Armstrong
Journal:  Int J Mol Sci       Date:  2022-06-28       Impact factor: 6.208

4.  A Community-Led Approach as a Guide to Overcome Challenges for Therapy Research in Congenital Disorders of Glycosylation.

Authors:  Rita Francisco; Sandra Brasil; Carlota Pascoal; Andrew C Edmondson; Jaak Jaeken; Paula A Videira; Cláudia de Freitas; Vanessa Dos Reis Ferreira; Dorinda Marques-da-Silva
Journal:  Int J Environ Res Public Health       Date:  2022-06-02       Impact factor: 4.614

5.  Genotype-Phenotype Correlations in PMM2-CDG.

Authors:  Laurien Vaes; Daisy Rymen; David Cassiman; Anna Ligezka; Nele Vanhoutvin; Dulce Quelhas; Eva Morava; Peter Witters
Journal:  Genes (Basel)       Date:  2021-10-21       Impact factor: 4.096

Review 6.  Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Authors:  Sandra Brasil; Carlota Pascoal; Rita Francisco; Vanessa Dos Reis Ferreira; Paula A Videira; And Gonçalo Valadão
Journal:  Genes (Basel)       Date:  2019-11-27       Impact factor: 4.096

7.  Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study.

Authors:  Jean Tori Pantel; Nurulhuda Hajjir; Magdalena Danyel; Jonas Elsner; Angela Teresa Abad-Perez; Peter Hansen; Stefan Mundlos; Malte Spielmann; Denise Horn; Claus-Eric Ott; Martin Atta Mensah
Journal:  J Med Internet Res       Date:  2020-10-22       Impact factor: 5.428

  7 in total

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