Characteristic or classic phenotype of Cornelia de Lange syndrome (CdLS) is associated with a recognisable facial pattern. However, the heterogeneity in causal genes and the presence of overlapping syndromes have made it increasingly difficult to diagnose only by clinical features. DeepGestalt technology, and its app Face2Gene, is having a growing impact on the diagnosis and management of genetic diseases by analysing the features of affected individuals. Here, we performed a phenotypic study on a cohort of 49 individuals harbouring causative variants in known CdLS genes in order to evaluate Face2Gene utility and sensitivity in the clinical diagnosis of CdLS. Based on the profile images of patients, a diagnosis of CdLS was within the top five predicted syndromes for 97.9% of our cases and even listed as first prediction for 83.7%. The age of patients did not seem to affect the prediction accuracy, whereas our results indicate a correlation between the clinical score and affected genes. Furthermore, each gene presents a different pattern recognition that may be used to develop new neural networks with the goal of separating different genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdLS.

The aim of the study was to investigate the relationship between liver transaminase levels and metabolic syndrome (MS) features in obese children and adolescents. A total of 132 children and adolescents (73 males and 59 females) aged 8 - 16, participated in the study. All were studied at the department of Paediatrics, University Hospital of Zaragoza (Spain). Inclusion criteria were the existence of obesity as defined by body mass index (BMI) according to Cole cut-off values (when BMI was higher than the age and sex specific equivalent to 30 kg/m2). The definition of metabolic syndrome was according to the International Diabetes Federation criteria. Weight (kg), height (cm), waist circumference (cm), blood pressure and BMI were measured. Laboratory determinations after overnight fasting included: transaminases (ALT, AST, GGT), fasting glucose, insulin, triglycerides and HDL-C. The MS was found in 21.6% of the obese children and adolescents and the prevalence was higher in males (25.9%) than in females (15.9%). Serum transaminases (ALT, AST and GGT) mean concentrations were higher in males than in females, and decreased during pubertal development. The obese children and adolescents with the MS did not show higher transaminases concentrations when compared with those without the MS. Some MS manifestations (mainly waist circumference) showed a correlation with ALT, although all transaminases values were normal according to adult references. Liver transaminases, a surrogate marker of NAFLD, did not show an early and consistent manifestation of abnormalities in the obese children and adolescents studied. In order to define the presence of the disease, it would be necessary to obtain aminotransferase reference standards for children and adolescents, considering pubertal stage and gender.

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The aim of this study was to investigate fat distribution, mainly abdominal fat, and its relationship with metabolic risk variables in a group of 126 children and adolescents (60 males and 66 females) aged 5.0 to 14.9. According to IOTF criteria, 46 were classified as normal weight, 28 overweight and 52 obese. Weight, height, waist (WC) and hip circumferences were measured. The body mass index (BMI) was calculated. Total body fat, trunkal and abdominal fat were also assessed by dual energy x-ray absorptiometry (DXA). Glucose, insulin, HDL-Cholesterol, triglycerides (TG), ferritine, homocystein and C-reactive protein (CRP) were measured. Obesity status was related with insulin concentrations, CRP, TG and HDL. Obese patients had higher abdominal fat and higher CRP values than overweight and normal subjects. All markers of central body adiposity were related with insulin and lipid metabolism; however, they were not related with homocystein or ferritin. A simple anthropometric measurement, like waist circumference, seems to be a good predictor of the majority of the obesity related metabolic risk variables.