Minipuberty in Klinefelter syndrome:Current status and future directions

Klinefelter syndrome is highly underdiagnosed and diagnosis is often delayed. With the introduction of non-invasive prenatal screening, the diagnostic pattern will require an updated description of the clinical and biochemical presentation of infants with Klinefelter syndrome. In the first months of life, the hypothalamic-pituitary-gonadal (HPG)-axis is transiently activated in healthy males during the so-called minipuberty. This period represents a “window of opportunity” for evaluation of the HPG-axis before puberty and without stimulation tests. Infants with Klinefelter syndrome present with a hormonal surge during the minipuberty. However, only a limited number of studies exist, and the results are contradictory. Further studies are needed to clarify whether infants with Klinefelter syndrome present with impaired testosterone production during the minipuberty. The aim of this review is to describe the clinical and biochemical characteristics of the neonate and infant with Klinefelter syndrome with special focus on the minipuberty and to update the clinical recommendations for Klinefelter syndrome during infancy

Behavioral Health Diagnoses in Youth with Differences of Sex Development or Congenital Adrenal Hyperplasia Compared with Controls: A PEDSnet Study

Objective: To evaluate the odds of a behavioral health diagnosis among youth with differences of sex development (DSD) or congenital adrenal hyperplasia (CAH) compared with matched controls in the PEDSnet database. Study design: All youth with a diagnosis of DSD (n = 1216) or CAH (n = 1647) and at least 1 outpatient encounter were extracted from the PEDSnet database and propensity-score matched on 8 variables (1:4) with controls (n = 4864 and 6588, respectively) using multivariable logistic regression. The likelihood of having behavioral health diagnoses was examined using generalized estimating equations. Results: Youth with DSD had higher odds of a behavioral health diagnosis (OR, 1.7; 95% CI, 1.4-2.1; P \u3c.0001) and neurodevelopmental diagnosis (OR, 1.7; 95% CI, 1.4, 2.0; P \u3c.0001) compared with matched controls. Youth with CAH did not have an increased odds of a behavioral health diagnosis (OR, 1.0; 95% CI, 0.9, 1.1; P =.9) compared with matched controls but did have higher odds of developmental delay (OR, 1.8; 95% CI, 1.4, 2.4; P \u3c.0001). Conclusions: Youth with DSD diagnosis have higher odds of a behavioral health or neurodevelopmental diagnosis compared with matched controls. Youth with CAH have higher odds of developmental delay, highlighting the need for screening in both groups

The human inactive X chromosome modulates expression of the active X chromosome

The inactive X chromosome (Xi) has been assumed to have little impact, in , on the active X (Xa). To test this, we quantified Xi and Xa gene expression in individuals with one Xa and zero to three Xis. Our linear modeling revealed modular Xi and Xa transcriptomes and significant Xi-driven expression changes for 38% (162/423) of expressed X chromosome genes. By integrating allele-specific analyses, we found that modulation of Xa transcript levels by Xi contributes to many of these Xi-driven changes (≥121 genes). By incorporating metrics of evolutionary constraint, we identified 10 X chromosome genes most likely to drive sex differences in common disease and sex chromosome aneuploidy syndromes. We conclude that human X chromosomes are regulated both in , through Xi-wide transcriptional attenuation, and in , through positive or negative modulation of individual Xa genes by Xi. The sum of these and effects differs widely among genes

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance