Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data

Analysis of genetic association of vitamin D receptor (VDR) gene FokI polymorphism in Bangladeshi patients with type 2 diabetes mellitus

AIMS

Type 2 diabetes mellitus (T2DM) is a complex and multifaceted disease that contributes significantly to Bangladesh's disease burden. Both polygene abnormalities and environmental factors contribute to this genetic condition. Vitamin D receptor (VDR) has immunomodulatory functions that may contribute to the developmentof type 2 diabetes. This investigation examined the association between the vitamin D receptor gene (rs2228570) FokI polymorphism and genetic susceptibility to T2DM in the Bangladeshi population.

METHODS

A total of 203 subjects (108 clinically identified T2DM patients and 95 healthy controls) participated in this research study with the ethical committee's approval. Genomic DNA was isolated from venous blood collected from the volunteers with prior consent. Extracted DNA was then genotyped for single nucleotide polymorphism (SNPs) for VDR (rs2228570) FokI gene by PCR-RFLP analysis, where the genotypes were assessed by agarose gel electrophoresis.

RESULTS

Genotype distribution for VDR (rs2228570) FokI polymorphism exhibited a significant difference between T2DM patients and the control group, whereas allele frequencies for both genes did not differ evidently between the patient and control group.

CONCLUSIONS

Our finding demonstrates a possible link between the risk of T2DM and the FokI polymorphism of the VDR (rs2228570) gene.

Delving the vitamin D receptor variation and expression profiles in the context of type 2 diabetes among families

BACKGROUND

Vitamin D is essential for insulin secretion and sensitivity. Consequently, its inadequacy is linked to higher insulin resistance and Type 2 Diabetes (T2D). The Vitamin D receptor (VDR) gene is one potential candidate for T2D, and multiple polymorphisms in VDR have been examined in various populations, but no conclusive answers have been provided.

OBJECTIVES

This study was designed to evaluate the susceptibility of VDR gene polymorphism and its expression in diabetic families in Pakistan.

METHODOLOGY

In this family-based study, twenty diabetic families with a positive family history of T2D and at least three T2D patients were recruited from outpatient clinics and public hospitals. The current study comprised 143 individuals with 55 affected and 88 unaffected individuals. Blood samples of the selected families were collected. DNA was extracted from the collected samples and the PCR-RFLP method was followed to identify the genotyping and RT-qPCR for expression. Phenotypic and genotypic pedigrees of the families were developed by the progeny online tool. The association values of SNPs were determined by TDT and DFAM analysis implemented on Plink software.

RESULTS

The results explained a significant familial aggregation among phenotypic characters including Age, Gender, BMI (body mass index), age of disease diagnosis, disease duration, and blood pressure in the probands, affected FDRs (First Degree Relatives) and affected SDRs (Second Degree Relatives). A significant association of rs731236 C/T (OR = 1.522), rs2228570 C/T (OR = 1.327) with p < 0.05. Whereas, for rs1544410 G/A (OR = 0.9706) and rs7975232 T/G (OR = 0.7368) no considerable association evidence was seen (p > 0.05) in families. The mRNA expression of VDR increased threefold (p = 0.0204) in patients compared to controls. Variation-based expression analysis exhibited that the rs2228570 genotype influences the expression.

CONCLUSION

A linkage was found among the FDRs with probands. Variation in the gene VDR at loci rs731236 and rs2228570 was associated with familial T2D. However further research is required to explore more genetic factors that could influence T2D risks in families.

Level of biochemical parameters in patients with type 2 diabetes mellitus depending on the genotype of the FokI polymorphism in the vitamin D3 receptor (VDR gene)

OBJECTIVES

Diabetes mellitus type 2 (T2DM) is a multifactorial and polygenic disorder characterised by chronic hyperglycaemia accompanied by impaired lipid, carbohydrate, and protein metabolism. The disease is associated with several genetic polymorphisms, including the FokI polymorphism in the vitamin D receptor (VDR) gene.

METHODS

We conducted a study of 327 probands (191 T2DM patients, 136 controls), with a mean age 65.06 (SD ± 10.88) years of patients with T2DM and 58.89 (SD ± 6.59) years in the healthy probands. We investigated the association between FokI polymorphism and biochemical parameters in T2DM patients in the Slovak population. Anthropometric measurements, biochemical, and genetic analysis were statistically evaluated by Statistica ver.13 software using t-tests.

RESULTS

Biochemical analysis confirmed significantly higher mean values of total cholesterol (TC), triglyceride (TG), glucose (GLU), and uric acid (UA) (p < 0.001) in T2DM probands and statistically significantly lower values of high-density lipoprotein (HDL), cholesterol and vitamin D (p < 0.001). Allele frequencies and genotype distributions of the FokI (rs2228570) polymorphism were not significantly different between T2DM patients and controls (p = 0.909). Patients with T2DM and TT genotype had the highest glucose level of 11.39 (SD ± 2.32) uU/ml (p < 0.001).

CONCLUSION

Our study did not provide evidence for an association of the investigated FokI polymorphism of the VDR gene with T2DM in the Slovak population. Further research is needed to evaluate the impact of single nucleotide polymorphisms (SNPs) in the VDR gene, focusing on related genetic analyses in a larger T2DM cohort.

Genetic association of vitamin D receptor gene with female infertility

BACKGROUND

Infertility is defined as failure to achieve a clinical pregnancy after 12 months of unprotected intercourse. It affects 15% of couples globally and 22% of couples within Pakistan. Female infertility can be caused by numerous genetic or environmental factors including hormone imbalances and exposure to chemicals or radiation. The prevalence of vitamin D deficiency among the adult population was reported to be 14-59% with a higher prevalence in Asian countries. Furthermore, the expression of Vitamin D receptor (VDR) can play a vital role in the reproductive organs of females. Hence, the aim of our present study was to check the association of VDR polymorphisms with infertile females. For this purpose, blood samples were collected for genotyping of four known VDR mutations [FokI (rs2228570), TaqI (rs731236), ApaI (rs7975232), and BsmI (rs1544410)] via PCR-based RFLP assay.

RESULTS

Genotyping indicated that FokI, TaqI, and ApaI are associated with infertility (p = 0.004*, p = 0.013*, and p = 0.033*, respectively). However, BsmI did not show any significance. Multinomial regression analysis indicated that FokI heterozygous genotypes increase the risk of infertility by 2.5 times (hetero: OR = 2.5, 95%, p = 0.001*) as compared to wild type. Heterozygous genotypes of TaqI and ApaI were found to play a protective role and reduce the risk of infertility by 58 and 52%, respectively [TaqI: OR = 0.42, 95%, p = 0.004*, ApaI: OR = 0.48, 95%, p = 0.01*, respectively] as compared to wild type. Multinomial logistic regression analysis was also performed for allelic data as well.

CONCLUSION

Thus, it could be summarized that among the studied polymorphisms of VDR, FokI SNP greatly increased the risk of infertility, while TaqI and ApaI genotypes protect from infertility. However, BsmI does not influence the risk of infertility in Pakistani females.

Influence of Self-Practice Oriented Teaching plus Psychological Intervention on Blood Glucose Level and Psychological State in Patients with Type 2 Diabetes Mellitus on Insulin Therapy

Background. The study aimed to examine the effect of self-practice oriented teaching plus psychological intervention on blood glucose level and psychological status of type 2 diabetic patients on first insulin therapy. Methods. A total of 80 patients with type 2 diabetes admitted from April 2020 to November 2020 were assessed for eligibility and included. They were then assigned to a control group and an observation group via the random number table method, with 40 cases in each group. In addition to insulin injection treatment in both groups prior to intervention, the control group received health education and psychological intervention, whereas the observation group adopted a self-practice oriented teaching strategy plus psychological intervention. Insulin injections, nursing satisfaction, blood glucose level, and disease awareness were compared between the two groups. The Exercise of Self-Care Agency (ESCA) scale was used to assess the patients’ self-care ability, the Generic Quality of Life Inventory-74 (GQOLI-74) scale was used to assess their quality of life, and the emotional state of patients was evaluated by the Hospital Anxiety and Depression (HAD) scale. Results. Patients in the observation group outperformed the control group in terms of insulin injection after intervention ( $P<0.05$ ). Significantly higher nursing satisfaction and ESCA scores were observed after intervention ( $P<0.05$ ). Self-practice oriented teaching plus psychological intervention resulted in remarkably lower postintervention glycemic indexes ( $P<0.001$ ). Markedly higher disease knowledge scores and GQOLI-74 scores were witnessed in the observation group in contrast to those of the control group ( $P<0.001$ ). The observation group patients showed lower HAD scores than those of the control group ( $P<0.001$ ). Conclusion. Self-practice oriented teaching plus psychological intervention could effectively alleviate the negative emotions of type 2 diabetic patients on first insulin therapy, stabilize glycemic indexes, and improve quality of life, demonstrating good potential for clinical promotion.

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.

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.

Side Effects and Perceptions Following COVID-19 Vaccination in Jordan: A Randomized, Cross-Sectional Study Implementing Machine Learning for Predicting Severity of Side Effects

Background: Since the coronavirus disease 2019 (COVID-19) was declared a pandemic, there was no doubt that vaccination is the ideal protocol to tackle it. Within a year, a few COVID-19 vaccines have been developed and authorized. This unparalleled initiative in developing vaccines created many uncertainties looming around the efficacy and safety of these vaccines. This study aimed to assess the side effects and perceptions following COVID-19 vaccination in Jordan. Methods: A cross-sectional study was conducted by distributing an online survey targeted toward Jordan inhabitants who received any COVID-19 vaccines. Data were statistically analyzed and certain machine learning (ML) tools, including multilayer perceptron (MLP), eXtreme gradient boosting (XGBoost), random forest (RF), and K-star were used to predict the severity of side effects. Results: A total of 2213 participants were involved in the study after receiving Sinopharm, AstraZeneca, Pfizer-BioNTech, and other vaccines (38.2%, 31%, 27.3%, and 3.5%, respectively). Generally, most of the post-vaccination side effects were common and non-life-threatening (e.g., fatigue, chills, dizziness, fever, headache, joint pain, and myalgia). Only 10% of participants suffered from severe side effects; while 39% and 21% of participants had moderate and mild side effects, respectively. Despite the substantial variations between these vaccines in the presence and severity of side effects, the statistical analysis indicated that these vaccines might provide the same protection against COVID-19 infection. Finally, around 52.9% of participants suffered before vaccination from vaccine hesitancy and anxiety; while after vaccination, 95.5% of participants have advised others to get vaccinated, 80% felt more reassured, and 67% believed that COVID-19 vaccines are safe in the long term. Furthermore, based on the type of vaccine, demographic data, and side effects, the RF, XGBoost, and MLP gave both high accuracies (0.80, 0.79, and 0.70, respectively) and Cohen’s kappa values (0.71, 0.70, and 0.56, respectively). Conclusions: The present study confirmed that the authorized COVID-19 vaccines are safe and getting vaccinated makes people more reassured. Most of the post-vaccination side effects are mild to moderate, which are signs that body’s immune system is building protection. ML can also be used to predict the severity of side effects based on the input data; predicted severe cases may require more medical attention or even hospitalization.