No impact of soluble epoxide hydrolase rs4149243, rs2234914 and rs751142 genetic variants on the development of type II diabetes and its hypertensive complication among Jordanian patients

SERCA2 dysfunction triggers hypertension by interrupting mitochondrial homeostasis and provoking oxidative stress

BACKGROUND AND AIM

Sarcoplasmic/endoplasmic reticulum Ca2+ ATPase 2 (SERCA2) is critical in maintaining Ca2+ homeostasis. The cysteine 674 (C674) is the key redox regulatory cysteine in regulating SERCA2 activity, which is irreversibly oxidized in the renal cortex of hypertensive mice. We have reported that the substitution of C674 by serine causes SERCA2 dysfunction and increases blood pressure by induction of endoplasmic reticulum stress (ERS). This study is to explore whether the dysfunction of SERCA2 causes hypertension by interrupting mitochondrial homeostasis and inducing oxidative stress.

METHODS & RESULTS

We used heterozygous SERCA2 C674S gene mutation knock-in (SKI) mice, where one copy of C674 was substituted by serine to represent partial C674 oxidation. In renal proximal tubule (RPT) cells, the substitution of C674 by serine decreased mitochondrial Ca2+ content, increased mitochondrial membrane potential, ATP content, and reactive oxygen species (ROS), which could be reversed by ERS inhibitor 4-phenylbutyric acid or SERCA2 agonist CDN1163. In SKI RPT cells, the redox modulator Tempol alleviated oxidative stress, downregulated the protein expression of ERS markers and soluble epoxide hydrolase, upregulated the protein expression of dopamine D1 receptor, and reduced Na+/K+- ATPase activity. In SKI mice, SERCA2 agonists CDN1163 and [6]-Gingerol, or the redox modulator Tempol increased urine output and lowered blood pressure.

CONCLUSION

The irreversible oxidation of C674 is not only an indicator of increased ROS, but also further inducing oxidative stress to cause hypertension. Activation of SERCA2 or inhibition of oxidative stress is beneficial to alleviate hypertension caused by SERCA2 dysfunction.

Clinical Significance of NAT2 Genetic Variations in Type II Diabetes Mellitus and Lipid Regulation

Background

N-acetyltransferase 2 (NAT2) enzyme is a Phase II drug-metabolizing enzyme that metabolizes different compounds. Genetic variations in NAT2 can influence the enzyme's activity and potentially lead to the development of certain diseases.

Aim

This study aimed to investigate the association of NAT2 variants with the risk of Type II diabetes mellitus (T2DM) and the lipid profile among Jordanian patients.

Methods

We sequenced the whole protein-coding region in NAT2 using Sanger's method among a sample of 45 Jordanian T2DM patients and 50 control subjects. Moreover, we analyzed the lipid profiles of the patients and examined any potential associations with NAT2 variants.

Results

This study revealed that the heterozygous NAT2*13 C/T genotype is significantly (P = 0.03) more common among T2DM (44%) than non-T2DM subjects (23.5%). Furthermore, the frequency of homozygous NAT2*13 T/T genotype was found to be significantly higher (P = 0.03) among T2DM patients (26.7%) compared to that of non-T2DM subjects (11%). The heterozygous NAT2*7 G/A genotype was exclusively observed in T2DM patients (11.1%) and absent in the control non-T2DM group. Moreover, among T2DM patients, those with a homozygous NAT2*11 T/T genotype exhibited significantly higher levels of triglycerides (381.50 ± 9.19 ng/dL) with a P value of 0.01 compared to those with heterozygous NAT2*11 C/T (136.23 ± 51.12 ng/dL) or wild-type NAT2*11 C/C (193.65 ± 109.89 ng/dL) genotypes. T2DM patients with homozygous NAT2*12 G/G genotype had a significantly (P = 0.04) higher triglyceride levels (275.67 ± 183.42 ng/dL) than the heterozygous NAT2*12 A/G (140.02 ± 49.53 ng/dL) and the wild NAT2*12 A/A (193.65 ± 109.89 ng/dL).

Conclusion

The finding in this study suggests that the NAT2 gene is a potential biomarker for the development of T2DM and changes in triglyceride levels among Jordanians. However, it is important to note that our sample size was limited; therefore, further clinical studies with a larger cohort are necessary to validate these findings.

Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence

Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for individuals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for individuals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for individuals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%.

MICA Polymorphism and Genetic Predisposition to T1D in Jordanian Patients: A Case-Control Study

Type 1 diabetes (T1D) is an autoimmune disorder whose etiology includes genetic and environmental factors. The non-classical Major Histocompatibility Complex (MHC) class I chain-related gene A (MICA) gene has been associated with increased susceptibility to T1D as the interaction of MICA to the Natural Killer Group 2D (NK2GD) receptors found on the cell surface of natural killer (NK) cells and T cells is responsible for inducing immune responses. MICA polymorphisms were reported in association with T1D among different ethnic groups. However, data from different populations revealed conflicting results, so the association of MICA polymorphisms with predisposition to T1D remains uncertain. The aim of this sequencing-based study was to identify, for the first time, the possible MICA alleles and/or genotypes that could be associated with T1D susceptibility in the Jordanian population. Polymorphisms in exons 2–4 and the short tandem repeats (STR) in exon 5 of the highly polymorphic MICA gene were analyzed. No evidence for association between T1D and MICA alleles/genotypes was found in this study, except for the MICA*011 allele which was found to be negatively associated with T1D (p = 0.023, OR = 0.125). In conclusion, MICA polymorphisms seem not to be associated with increasing T1D susceptibility in Jordanian patients.

Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen's kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen's κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.