Molecular and Phenotypic Expansion of Alström Syndrome in Chinese Patients

Obesity-Related Ciliopathies: Focus on Advances of Biomarkers

Obesity-related ciliopathies, as a group of ciliopathies including Alström Syndrome and Bardet-Biedl Syndrome, exhibit distinct genetic and phenotypic variability. The understanding of these diseases is highly significant for understanding the functions of primary cilia in the human body, particularly regarding the relationship between obesity and primary cilia. The diagnosis of these diseases primarily relies on clinical presentation and genetic testing. However, there is a significant lack of research on biomarkers to elucidate the variability in clinical manifestations, disease progression, prognosis, and treatment responses. Through an extensive literature review, the paper focuses on obesity-related ciliopathies, reviewing the advancements in the field and highlighting the potential roles of biomarkers in the clinical presentation, diagnosis, and prognosis of these diseases.

Artificial intelligence-based diagnosis in fetal pathology using external ear shapes

OBJECTIVE

Here we trained an automatic phenotype assessment tool to recognize syndromic ears in two syndromes in fetuses-=CHARGE and Mandibulo-Facial Dysostosis Guion Almeida type (MFDGA)-versus controls.

METHOD

We trained an automatic model on all profile pictures of children diagnosed with genetically confirmed MFDGA and CHARGE syndromes, and a cohort of control patients, collected from 1981 to 2023 in Necker Hospital (Paris) with a visible external ear. The model consisted in extracting landmarks from photographs of external ears, in applying geometric morphometry methods, and in a classification step using machine learning. The approach was then tested on photographs of two groups of fetuses: controls and fetuses with CHARGE and MFDGA syndromes.

RESULTS

The training set contained a total of 1489 ear photographs from 526 children. The validation set contained a total of 51 ear photographs from 51 fetuses. The overall accuracy was 72.6% (58.3%-84.1%, p < 0.001), and 76.4%, 74.9%, and 86.2% respectively for CHARGE, control and MFDGA fetuses. The area under the curves were 86.8%, 87.5%, and 90.3% respectively for CHARGE, controls, and MFDGA fetuses.

CONCLUSION

We report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool.

Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome

The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.

Clinical characteristics and genetic expansion of 46,XY disorders of sex development children in a Chinese prospective study

Diagnosis and management strategy of disorders of sex development (DSD) are difficult and various due to heterogeneous phenotype and genotype. Under widespread use of genomic sequencing technologies, multiple genes and mechanisms have been identified and proposed as genetic causes of 46,XY DSD. In this study, 178 46,XY DSD patients were enrolled and underwent gene sequencing (either whole-exome sequencing or targeted panel gene sequencing). Detailed clinical phenotype and genotype information were summarized which showed that the most common clinical manifestations were micropenis (56.74%, 101/178), cryptorchidism (34.27%, 61/178), and hypospadias (17.42%, 31/178). Androgen synthesis/action disorders and idiopathic hypogonadotropic hypogonadism were the most frequent clinical diagnoses, accounting, respectively, for 40.90 and 21.59%. From all next-generation sequencing results, 103 candidate variants distributed across 32 genes were identified in 88 patients. The overall molecular detection rate was 49.44% (88/178), including 35.96% (64/178) pathogenic/likely pathogenic variants and 13.48% (24/178) variants of uncertain significance. Of all, 19.42% (20/103) variants were first reported in 46,XY DSD patients. Mutation c.680G>A (p.R227Q) on SRD5A2 (steroid 5-alpha-reductase 2) (36.67%, 11/30) was a hotspot mutation in the Chinese population. Novel candidate genes related to DSD (GHR (growth hormone receptor) and PHIP (pleckstrin homology domain-interacting protein)) were identified. Overall, this was a large cohort of 46,XY DSD patients with a common clinical classification and phenotype spectrum of Chinese patients. Targeted gene panel sequencing covered most of the genes contributing to DSD, whereas whole-exome sequencing detected more candidate genes.

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes

Introduction

Mandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.

Methods

The training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.

Results

We trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838-0.999] (p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648-0.920] (p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544-0.960] (p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses).

Conclusion

This is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.

Genotypic and phenotypic features of dyslipidemia in a sample of pediatric patients in China

BACKGROUND

Dyslipidemia, especially hypercholesterolemia is of significant clinical interest. Precise diagnosis is not paid enough attention to about the management of pediatric patients with hypercholesterolemia, which is especially apparent in China. Given this, we designed this study to confirm the specific molecular defects associated with hypercholesterolemia using whole-exome sequencing (WES) to be helpful for precise diagnosis and treatment.

METHODS

Pediatric patients were enrolled using specific criteria and their clinical information were recorded for later evaluation in conjunction with the WES completed for each of these patients.

RESULTS

Our criteria allowed for the initial enrollment of 35 patients, 30 of whom (aged 1.02-12.99 years) underwent successful genetic sequencing and clinical investment. Positive results were obtained in 63.33% (19/30) of these patients. We identified 25 variants in 30 pediatric patients with persistent hypercholesterolemia, seven of them were novel and variants in LDLR and ABCG5/ABCG8 ranks first and second, respectively. Further analysis revealed that the levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (ApoB) and lipoprotein (a) were higher in patients with positive genetic results.

CONCLUSION

Our study enriched the genetic and phenotypic spectra for hypercholesterolemia in young patients. Genetic testing is important for the prognostics and treatment of pediatric patients. Heterozygous ABCG5/8 variants may be underestimated in pediatric patients with hypercholesterolemia.

Olfactory function changes and the predictive performance of the Chinese Smell Identification Test in patients with mild cognitive impairment and Alzheimer's disease

Objectives

Olfactory disorder is one of the sensory features that reflects a decline in cognitive function. However, olfactory changes and the discernibility of smell testing in the aging population have yet to be fully elucidated. Therefore, this study aimed to examine the effectiveness of the Chinese Smell Identification Test (CSIT) in distinguishing individuals with cognitive decline from those with normal aging and to determine whether the patients with MCI and AD show changes in their olfactory identification abilities.

Methods

This cross-sectional study included eligible participants aged over 50 years between October 2019 and December 2021. The participants were divided into three groups: individuals with mild cognitive impairment (MCI), individuals with Alzheimer's disease (AD), and cognitively normal controls (NCs). All participants were assessed using neuropsychiatric scales, the Activity of Daily Living scale, and the 16-odor cognitive state test (CSIT) test. The test scores and the severity of olfactory impairment were also recorded for each participant.

Results

In total, 366 eligible participants were recruited, including 188 participants with MCI, 42 patients with AD, and 136 NCs. Patients with MCI achieved a mean CSIT score of 13.06 ± 2.05, while patients with AD achieved a mean score of 11.38 ± 3.25. These scores were significantly lower than those of the NC group (14.6 ± 1.57; P < 0.001). An analysis showed that 19.9% of NCs exhibited mild olfactory impairment, while 52.7% of patients with MCI and 69% of patients with AD exhibited mild to severe olfactory impairment. The CSIT score was positively correlated with the MoCA and MMSE scores. The CIST score and the severity of olfactory impairment were identified as robust indicators for MCI and AD, even after adjusting for age, gender, and level of education. Age and educational level were identified as two important confounding factors that influence cognitive function. However, no significant interactive effects were observed between these confounders and CIST scores in determining the risk of MCI. The area under the ROC curve (AUC) generated from the ROC analysis was 0.738 and 0.813 in distinguishing patients with MCI and patients with AD from NCs based on the CIST scores, respectively. The optimal cutoff for distinguishing MCI from NCs was 13, and for distinguishing AD from NCs was 11. The AUC for distinguishing AD from MCI was 0.62.

Conclusions

The olfactory identification function is frequently affected in patients with MCI and patients with AD. CSIT is a beneficial tool for the early screening of cognitive impairment among elderly patients with cognitive or memory issues.