Generation of an induced pluripotent stem cell line GWCMCi006-A from a patient with autosomal dominant neurodevelopmental disorder with or without hyperkinetic movements and seizures harboring GRIN1 c.389A > G mutation.

Autosomal dominant neurodevelopmental disorder with or without hyperkinetic movements and seizures (NDHMSD) is a rare neurological disorder characterized by neurodevelopmental disorder and hyperkinetic movement, with or without seizures. Heterozygous mutation in the GRIN1 encoding the subunit 1 of the N-methyl-D-aspartate receptor caused this disorder. We first established an induced pluripotent stem cell (iPSC) line from a male patient with c.389A > G mutation in the GRIN1, via reprogramming with KLF4, SOX2, OCT3/4, and c-MYC. Through identification examination, the iPSCs (GWCMCi006-A) stably expressed pluripotency-associated stem cell markers, maintained a normal karyotype, and showed proliferative potential for three-germ layers differentiation.

Untargeted urine metabolomics and machine learning provide potential metabolic signatures in children with autism spectrum disorder.

Background: Complementary to traditional biostatistics, the integration of untargeted urine metabolomic profiling with Machine Learning (ML) has the potential to unveil metabolic profiles crucial for understanding diseases. However, the application of this approach in autism remains underexplored. Our objective was to delve into the metabolic profiles of autism utilizing a comprehensive untargeted metabolomics platform coupled with ML. Methods: Untargeted metabolomics quantification (UHPLC/Q-TOF-MS) was performed for urine analysis. Feature selection was conducted using Lasso regression, and logistic regression, support vector machine, random forest, and extreme gradient boosting were utilized for significance stratification. Pathway enrichment analysis was performed to identify metabolic pathways associated with autism. Results: A total of 52 autistic children and 40 typically developing children were enrolled. Lasso regression identified ninety-two urinary metabolites that significantly differed between the two groups. Distinct metabolites, such as prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, were revealed to be associated with autism through the application of four different ML methods (p<0.05). The alterations observed in the phosphatidylinositol and inositol phosphate metabolism pathways were linked to the pathophysiology of autism (p<0.05). Conclusion: Significant urinary metabolites, including prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, exhibit associations with autism. Additionally, the involvement of the phosphatidylinositol and inositol phosphate pathways suggests their potential role in the pathophysiology of autism.

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OBJECTIVES: To explore the association between maternal gestational diabetes mellitus (GDM) exposure and the development of autism spectrum disorder (ASD) in offspring. METHODS: A case-control study was conducted, recruiting 221 children with ASD and 400 healthy children as controls. Questionnaires and interviews were used to collect information on general characteristics of the children, socio-economic characteristics of the family, maternal pregnancy history, and maternal disease exposure during pregnancy. Multivariate logistic regression analysis was used to investigate the association between maternal GDM exposure and the development of ASD in offspring. The potential interaction between offspring gender and maternal GDM exposure on the development of ASD in offspring was explored. RESULTS: The proportion of maternal GDM was significantly higher in the ASD group compared to the control group (16.3% vs 9.4%, P=0.014). After adjusting for variables such as gender, gestational age, mode of delivery, parity, and maternal education level, maternal GDM exposure was a risk factor for ASD in offspring (OR=2.18, 95%CI: 1.04-4.54, P=0.038). On the basis of adjusting the above variables, after further adjusting the variables including prenatal intake of multivitamins, folic acid intake in the first three months of pregnancy, and assisted reproduction the result trend did not change, but no statistical significance was observed (OR=1.94, 95%CI: 0.74-5.11, P=0.183). There was an interaction between maternal GDM exposure and offspring gender on the development of ASD in offspring (P<0.001). Gender stratified analysis showed that only in male offspring of mothers with GDM, the risk of ASD was significantly increased (OR=3.67, 95%CI: 1.16-11.65, P=0.027). CONCLUSIONS: Maternal GDM exposure might increase the risk of ASD in offspring. There is an interaction between GDM exposure and offspring gender in the development of ASD in offspring.

Autistic clinical profiles, age at first concern, and diagnosis among children with autism spectrum disorder.

Background: To explore the relationship between autistic clinical profiles and age at first concern and diagnosis among children with autism spectrum disorder. The clinical profiles included the severity of autism, cognition, adaptability, language development, and regression. Methods: The multivariate linear regression model was used to examine the association of diagnostic age and first-concern age with autistic clinical profiles and with further stratification analysis. Results: A total of 801 autistic children were included. Language delay and regression were associated with earlier diagnostic age (language delay: crudeß: -0.80, 95%CI%: -0.92--0.68; regression: crudeß: -0.21, 95%CI%: -0.43--0.00) and the age of first concern of autistic children (language delay: crudeß: -0.55, 95%CI%: -0.65--0.45; regression: crudeß: -0.17, 95%CI%: -0.34--0.00). After stratification by sex, language delay tended to be more associated with the earlier diagnostic age among boys (crudeß: -0.85, 95%CI%: -0.98--0.72) than among girls (crudeß: -0.46, 95%CI%: -0.77--0.16). After stratification by maternal education level or family income level, language delay was most associated with the earlier diagnostic age in autistic children from families with higher socioeconomic levels. Conclusion: Language delay, rather than other symptoms, promoted an earlier diagnostic age. Among male autistic children or children from families with higher socioeconomic levels, language delay was most significantly associated with an earlier age of diagnosis. Cognitive delay, or adaptive delay, was associated with a later age at diagnosis and presented only in autistic children from families with lower socioeconomic levels. There may be sex or socioeconomic inequality in the diagnostic age for autistic children. More publicity and public education about the diversity of autistic symptoms are urgently needed in the future, especially for low-socioeconomic families.