Analysis of clinical and genetic characteristics of Chinese children with congenital hyperinsulinemia that is spontaneously relieved

IMPACT OF ABCC8 AND TRPM4 GENETIC VARIATION IN CENTRAL NERVOUS SYSTEM DYSFUNCTION ASSOCIATED WITH PEDIATRIC SEPSIS

ABSTRACT

Background: Sepsis-associated brain injury is associated with deterioration of mental status, persistent cognitive impairment, and morbidity. The SUR1/TRPM4 channel is a nonselective cation channel that is transcriptionally upregulated in the central nervous system with injury, allowing sodium influx, depolarization, cellular swelling, and secondary injury. We hypothesized that genetic variation in ABCC8 (SUR1 gene) and TRPM4 would associate with central nervous system dysfunction in severe pediatric sepsis. Methods: 326 children with severe sepsis underwent whole exome sequencing in an observational cohort. We compared children with and without central nervous system dysfunction (Glasgow Coma Scale <12) to assess for associations with clinical characteristics and pooled rare variants in ABCC8 and TRPM4. Sites of variation were mapped onto protein structure and assessed for phenotypic impact. Results: Pooled rare variants in either ABCC8 or TRPM4 associated with decreased odds of central nervous system dysfunction in severe pediatric sepsis (OR 0.14, 95% CI 0.003-0.87), P = 0.025). This association persisted following adjustment for race, organ failure, viral infection, and continuous renal replacement therapy (aOR 0.11, 95% CI 0.01-0.59, P = 0.038). Structural mapping showed that rare variants concentrated in the nucleotide-binding domains of ABCC8 and N-terminal melastatin homology region of TRPM4 . Conclusion : This study suggests a role for the ABCC8/TRPM4 channel in central nervous system dysfunction in severe pediatric sepsis. Although exploratory, the lack of therapies to prevent or mitigate central nervous system dysfunction in pediatric sepsis warrants further studies to clarify the mechanism and confirm the potential protective effect of these rare ABCC8/TRPM4 variants.

A loss-of-function mutation in KCNJ11 causing sulfonylurea-sensitive diabetes in early adult life

AIMS/HYPOTHESIS

The ATP-sensitive potassium (KATP) channel couples beta cell electrical activity to glucose-stimulated insulin secretion. Loss-of-function mutations in either the pore-forming (inwardly rectifying potassium channel 6.2 [Kir6.2], encoded by KCNJ11) or regulatory (sulfonylurea receptor 1, encoded by ABCC8) subunits result in congenital hyperinsulinism, whereas gain-of-function mutations cause neonatal diabetes. Here, we report a novel loss-of-function mutation (Ser118Leu) in the pore helix of Kir6.2 paradoxically associated with sulfonylurea-sensitive diabetes that presents in early adult life.

METHODS

A 31-year-old woman was diagnosed with mild hyperglycaemia during an employee screen. After three pregnancies, during which she was diagnosed with gestational diabetes, the patient continued to show elevated blood glucose and was treated with glibenclamide (known as glyburide in the USA and Canada) and metformin. Genetic testing identified a heterozygous mutation (S118L) in the KCNJ11 gene. Neither parent was known to have diabetes. We investigated the functional properties and membrane trafficking of mutant and wild-type KATP channels in Xenopus oocytes and in HEK-293T cells, using patch-clamp, two-electrode voltage-clamp and surface expression assays.

RESULTS

Functional analysis showed no changes in the ATP sensitivity or metabolic regulation of the mutant channel. However, the Kir6.2-S118L mutation impaired surface expression of the KATP channel by 40%, categorising this as a loss-of-function mutation.

CONCLUSIONS/INTERPRETATION

Our data support the increasing evidence that individuals with mild loss-of-function KATP channel mutations may develop insulin deficiency in early adulthood and even frank diabetes in middle age. In this case, the patient may have had hyperinsulinism that escaped detection in early life. Our results support the importance of functional analysis of KATP channel mutations in cases of atypical diabetes.

Genotype-phenotype correlation in Taiwanese children with diazoxide-unresponsive congenital hyperinsulinism

Objective

Congenital hyperinsulinism (CHI) is a group of clinically and genetically heterogeneous disorders characterized by dysregulated insulin secretion. The aim of the study was to elucidate genetic etiologies of Taiwanese children with the most severe diazoxide-unresponsive CHI and analyze their genotype-phenotype correlations.

Methods

We combined Sanger with whole exome sequencing (WES) to analyze CHI-related genes. The allele frequency of the most common variant was estimated by single-nucleotide polymorphism haplotype analysis. The functional effects of the ATP-sensitive potassium (KATP) channel variants were assessed using patch clamp recording and Western blot.

Results

Nine of 13 (69%) patients with ten different pathogenic variants (7 in ABCC8, 2 in KCNJ11 and 1 in GCK) were identified by the combined sequencing. The variant ABCC8 p.T1042QfsX75 identified in three probands was located in a specific haplotype. Functional study revealed the human SUR1 (hSUR1)-L366F KATP channels failed to respond to intracellular MgADP and diazoxide while hSUR1-R797Q and hSUR1-R1393C KATP channels were defective in trafficking. One patient had a de novo dominant mutation in the GCK gene (p.I211F), and WES revealed mosaicism of this variant from another patient.

Conclusion

Pathogenic variants in KATP channels are the most common underlying cause of diazoxide-unresponsive CHI in the Taiwanese cohort. The p.T1042QfsX75 variant in the ABCC8 gene is highly suggestive of a founder effect. The I211F mutation in the GCK gene and three rare SUR1 variants associated with defective gating (p.L366F) or traffic (p.R797Q and p.R1393C) KATP channels are also associated with the diazoxide-unresponsive phenotype.

Innovation in Hyperinsulinemia Diagnostics with ANN-L(atin square) Models

Hyperinsulinemia is a condition characterized by excessively high levels of insulin in the bloodstream. It can exist for many years without any symptomatology. The research presented in this paper was conducted from 2019 to 2022 in cooperation with a health center in Serbia as a large cross-sectional observational study of adolescents of both genders using datasets collected from the field. Previously used analytical approaches of integrated and relevant clinical, hematological, biochemical, and other variables could not identify potential risk factors for developing hyperinsulinemia. This paper aims to present several different models using machine learning (ML) algorithms such as naive Bayes, decision tree, and random forest and compare them with a new methodology constructed based on artificial neural networks using Taguchi's orthogonal vector plans (ANN-L), a special extraction of Latin squares. Furthermore, the experimental part of this study showed that ANN-L models achieved an accuracy of 99.5% with less than seven iterations performed. Furthermore, the study provides valuable insights into the share of each risk factor contributing to the occurrence of hyperinsulinemia in adolescents, which is crucial for more precise and straightforward medical diagnoses. Preventing the risk of hyperinsulinemia in this age group is crucial for the well-being of the adolescents and society as a whole.

Clinical and laboratory evaluation of children with congenital hyperinsulinism: a single center experience

OBJECTIVES

To evaluate and present the data regarding clinical, laboratory, radiological and the results of molecular genetic analysis of patients with hyperinsulinemic hypoglycemia in our clinics.

METHODS

A total of 9 patients with CHI followed at Istanbul Medipol University. Data related to gender, age at presentation, birth weight, gestational age, consanguinity, glucose and insulin levels at diagnosis, treatment modalities, response to treatment, the results of genetic analysis and radiological evaluation were gathered from the files.

RESULTS

The oldest age at presentation was 6 months. K_ATP channel mutation was detected in 55% (n: 5). Diazoxide unresponsiveness was seen in 55% (n: 5). Octreotide was effective in 3 of them. ¹⁸F-DOPA PET performed in 4 diazoxide unresponsive patients revealed focal lesion in 3 of them. Spontaneous remission rate was 66% (n:6). All the patients with normal genetic result achieved spontaneous remission. Spontaneous remission was even noted in diazoxide unresponsive patients and in patients with focal lesion on ¹⁸F-DOPA PET.

CONCLUSIONS

Clinical presentation of patients with congenital hypereinsulinism is heterogeneous. Spontaneous remission rate is quite high even in patients with severe clinical presentation. It is important to develop methods that can predict which patients will have spontaneous remission. Reporting the clinical and laboratory data of each patient is important and will help to guide the management of patients with hyperinsulinemic hypoglycemia.

A Novel Approach of Determining the Risks for the Development of Hyperinsulinemia in the Children and Adolescent Population Using Radial Basis Function and Support Vector Machine Learning Algorithm

Hyperinsulinemia is a condition with extremely high levels of insulin in the blood. Various factors can lead to hyperinsulinemia in children and adolescents. Puberty is a period of significant change in children and adolescents. They do not have to have explicit symptoms for prediabetes, and certain health indicators may indicate a risk of developing this problem. The scientific study is designed as a cross-sectional study. In total, 674 children and adolescents of school age from 12 to 17 years old participated in the research. They received a recommendation from a pediatrician to do an OGTT (Oral Glucose Tolerance test) with insulinemia at a regular systematic examination. In addition to factor analysis, the study of the influence of individual factors was tested using RBF (Radial Basis Function) and SVM (Support Vector Machine) algorithm. The obtained results indicated statistically significant differences in the values of the monitored variables between the experimental and control groups. The obtained results showed that the number of adolescents at risk is increasing, and, in the presented research, it was 17.4%. Factor analysis and verification of the SVM algorithm changed the percentage of each risk factor. In addition, unlike previous research, three groups of children and adolescents at low, medium, and high risk were identified. The degree of risk can be of great diagnostic value for adopting corrective measures to prevent this problem and developing potential complications, primarily type 2 diabetes mellitus, cardiovascular disease, and other mass non-communicable diseases. The SVM algorithm is expected to determine the most accurate and reliable influence of risk factors. Using factor analysis and verification using the SVM algorithm, they significantly indicate an accurate, precise, and timely identification of children and adolescents at risk of hyperinsulinemia, which is of great importance for improving their health potential, and the health of society as a whole.

Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents

Background and Objectives: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resistance in adolescents is monitored, they do not necessarily have hyperinsulinemia. It is considered a condition of pre-diabetes and represents a condition of increased risk of developing DM (diabetes mellitus); it can exist for many years without people having the appropriate symptoms. This study aims to determine the risk of developing hyperinsulinemia at an early age in adolescents by examining which factors are crucial for its occurrence. Materials and Methods: The cross-sectional study lasting from 2019 to 2021 (2 years) was realized at the school children’s department in the Valjevo Health Center, which included a total of 822 respondents (392 male and 430 female) children and adolescents aged 12 to 17. All respondents underwent a regular, systematic examination scheduled for school children. BMI is a criterion according to which respondents are divided into three groups. Results: After summary analyzes of OGTT test respondents and calculated values of HOMA-IR (homeostatic model assessment for insulin resistance), the study showed that a large percentage of respondents, a total of 12.7%, are at risk for hyperinsulinemia. The research described in this paper aimed to use the most popular AI (artificial intelligence) model, ANN (artificial neural network), to show that 13.1% of adolescents are at risk, i.e., the risk is higher by 0.4%, which was shown by statistical tests as a significant difference. Conclusions: It is estimated that a model using three different ANN architectures, based on Taguchi’s orthogonal vector plans, gives more precise and accurate results with much less error. In addition to monitoring changes in each individual’s risk, the risk assessment of the entire monitored group is updated without having to analyze all data.