Effect of Social Factors and the Natural Environment on the Etiology and Pathogenesis of Diabetes Mellitus

Health Benefits of Antioxidant Bioactive Compounds in Ginger (Zingiber officinale) Leaves by Network Pharmacology Analysis Combined with Experimental Validation

Network pharmacology is an ideal tool to explore the effects of therapeutic components derived from plants on human metabolic diseases that are linked to inflammation. This study investigated the antioxidant effects of ginger leaves (GLs) and predicted targets for antioxidant activity. Quantitative and free radical scavenging analyses were performed to detect the main bioactive compounds of GLs and evaluate their antioxidant activities. Chemical diversity and network pharmacology approaches were used to predict key antioxidant components of GLs and their molecular targets. Nine major bioactive compounds of GLs were quantified using an internal standard method, and the antioxidant activity was evaluated using the DPPH and ABTS free radical scavenging methods. We first built the compound-gene-pathways and protein-protein interaction networks of GLs-related antioxidant targets and then conducted gene ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses. Molecular docking results show that astragalin, a compound isolated from GLs, had the highest level of connectivity in the compound-target network and was involved in inflammation-related biosynthesis by directly impacting cytokine gene expression and PTGS2 inhibition markers. These findings not only suggest that the compounds isolated from GLs can be developed as potential antioxidants, but also demonstrate the applicability of network pharmacology to assess the potential of foods for disease treatment.

Mushrooms and Fungi and Their Biological Compounds with Antidiabetic Activity: A Review

Mushrooms have been used by humans for centuries as food and medicine because they have been shown to affect certain diseases. Mushrooms for medicinal purposes have been consumed in the form of extracts and/or biomass of the mycelium or fruiting body. The beneficial health effects of mushrooms are due to their content of bioactive compounds (polysaccharides, proteins, ergosterol, lectins, etc.). On the other hand, diabetes is one of the metabolic diseases that affects the population worldwide, characterized by hyperglycemia that involves a defective metabolism of insulin, a hormone secreted by β cells and that mainly stimulates glucose absorption by the cells. However, it also affects the metabolism of carbohydrates, fats and proteins; poor control of this disease leads to serious damage to eyesight, kidneys, bones, heart, skin, blood vessels, nerves, etc. It has been reported that the consumption of some mushrooms helps control and treat diabetes, since among other actions, they promote the secretion of insulin by the pancreas, help reduce blood glucose and have α-glucosidase inhibitory activity which improves glucose uptake by cells, which are effects that prescription medications have for patients with diabetes. In that sense, this manuscript shows a review of scientific studies that support the abilities of some mushrooms to be used in the control and/or treatment of diabetes.

Quantitative study of 3T MRI qDixon-WIP applied in pancreatic fat infiltration in patients with type 2 diabetes mellitus

OBJECTIVE

To investigate the application value of 3T MRI qDixon-WIP technique in the quantitative measurement of pancreatic fat content in patients with type 2 diabetes mellitus (T2DM).

METHODS

The 3T MRI qDixon-WIP sequence was used to scan the livers and the pancreas of 47 T2DM patients (experimental group) and 48 healthy volunteers (control group). Pancreatic fat fraction (PFF), hepatic fat fraction (HFF), Body mass index (BMI) ratio of pancreatic volume to body surface area (PVI) were measured. Total cholesterol (TC), subcutaneous fat area (SA), triglyceride (TG), abdominal visceral fat area (VA), high density lipoprotein (HDL-c), fasting blood glucose (FPC) and low-density lipoprotein (LDL-c) were collected. The relationship between the experimental group and the control group and between PFF and other indicators was compared. The differences of PFF between the control group and different disease course subgroups were also explored.

RESULTS

There was no significant difference in BMI between the experimental group and the control group (P=0.231). PVI, SA, VA, PFF and HFF had statistical differences (P<0.05). In the experimental group, PFF was highly positively correlated with HFF (r=0.964, P<0.001), it was moderately positively correlated with TG and abdominal fat area (r=0.676, 0.591, P<0.001), and it was weakly positively correlated with subcutaneous fat area (r=0.321, P=0.033). And it had no correlation with FPC, PVI, HDL-c, TC and LDL-c (P>0.05). There were statistical differences in PFF between the control group and the patients with different course of T2DM (P<0.05). There was no significant difference in PFF between T2DM patients with a disease course ≤1 year and those with a disease course <5 years (P>0.05). There were significant differences in PFF between the groups with a disease course of 1-5 years and those with a disease course of more than 5 years (P<0.001).

CONCLUSION

PVI of T2DM patients is lower than normal, but SA, VA, PFF, HFF are higher than normal. The degree of pancreatic fat accumulation in T2DM patients with long disease course was higher than that in patients with short disease course. The qDixon-WIP sequence can provide an important reference for clinical quantitative evaluation of fat content in T2DM patients.

Low-Dose Radiation Affects Cardiovascular Disease Risk in Human Aortic Endothelial Cells by Altering Gene Expression under Normal and Diabetic Conditions

High doses of ionizing radiation can cause cardiovascular diseases (CVDs); however, the effects of <100 mGy radiation on CVD remain underreported. Endothelial cells (ECs) play major roles in cardiovascular health and disease, and their function is reduced by stimuli such as chronic disease, metabolic disorders, and smoking. However, whether exposure to low-dose radiation results in the disruption of similar molecular mechanisms in ECs under diabetic and non-diabetic states remains largely unknown; we aimed to address this gap in knowledge through the molecular and functional characterization of primary human aortic endothelial cells (HAECs) derived from patients with type 2 diabetes (T2D-HAECs) and normal HAECs in response to low-dose radiation. To address these limitations, we performed RNA sequencing on HAECs and T2D-HAECs following exposure to 100 mGy of ionizing radiation and examined the transcriptome changes associated with the low-dose radiation. Compared with that in the non-irradiation group, low-dose irradiation induced 243 differentially expressed genes (DEGs) (133 down-regulated and 110 up-regulated) in HAECs and 378 DEGs (195 down-regulated and 183 up-regulated) in T2D-HAECs. We also discovered a significant association between the DEGs and the interferon (IFN)-I signaling pathway, which is associated with CVD by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein−protein network analysis, and module analysis. Our findings demonstrate the potential impact of low-dose radiation on EC functions that are related to the risk of CVD.

Transcultural Lifestyle Medicine in Type 2 Diabetes Care: Narrative Review of the Literature

Disparities in type 2 diabetes (T2D) care is a global problem across diverse cultures. The Dysglycemia-Based Chronic Disease (DBCD) model promotes early and sustainable interventions along the insulin resistance (stage 1), prediabetes (stage 2), T2D (stage 3), and complications (stage 4) spectrum. In this model, lifestyle medicine is the cornerstone of preventive care to reduce DBCD progression and the socioeconomic/biological burden of disease. A comprehensive literature review, spanning 2000 to 2021, was performed and 55 studies were included examining the effects of lifestyle medicine and their cultural adaptions with different prevention modalities. In stage 1, primordial prevention targets modifiable primary drivers (behavior and environment), unhealthy lifestyles, abnormal adiposity, and insulin resistance with educational and motivational health promotion activities at individual, group, community, and population-based scales. Primary, secondary, and tertiary prevention targets individuals with mild hyperglycemia, severe hyperglycemia, and complications, respectively, using programs that incorporate structured lifestyle interventions. Culturally adapted lifestyle change in primary and secondary prevention improved quality of life and biomarkers, but with a limited impact of tertiary prevention on cardiovascular events. In conclusion, lifestyle medicine with cultural adaptations is an integral part of preventive care in patients with T2D. However, considerable research gaps exist, especially for tertiary prevention.

Diabetes and Familial Hypercholesterolemia: Interplay between Lipid and Glucose Metabolism

Familial hypercholesterolemia (FH) is a genetic disease characterized by high low-density lipoprotein (LDL) cholesterol (LDL-c) concentrations that increase cardiovascular risk and cause premature death. The most frequent cause of the disease is a mutation in the LDL receptor (LDLR) gene. Diabetes is also associated with an increased risk of cardiovascular disease and mortality. People with FH seem to be protected from developing diabetes, whereas cholesterol-lowering treatments such as statins are associated with an increased risk of the disease. One of the hypotheses to explain this is based on the toxicity of LDL particles on insulin-secreting pancreatic β-cells, and their uptake by the latter, mediated by the LDLR. A healthy lifestyle and a relatively low body mass index in people with FH have also been proposed as explanations. Its association with superimposed diabetes modifies the phenotype of FH, both regarding the lipid profile and cardiovascular risk. However, findings regarding the association and interplay between these two diseases are conflicting. The present review summarizes the existing evidence and discusses knowledge gaps on the matter.

Integrated computational approaches to screen gene expression data to determine key genes and therapeutic targets for type-2 diabetes mellitus

There is a rapid rise in cases of Type-2-diabetes mellitus (T2DM) globally, irrespective of the geography, ethnicity or any other variable factors. The molecular mechanisms that could cause the condition of T2DM need to be more thoroughly analysed to understand the clinical manifestations and to derive better therapeutic regimes. Tools in bioinformatics are used to trace out key gene elements and to identify the key causative gene elements and their possible therapeutic agents. Microarray datasets were retrieved from the Gene expression omnibus database and studied using R to derive different expressed gene (DEG) elements. With the comparison of the expressed genes with disease specific genes in DisGeNET, the final annotated genes were taken for analysis. Gene Ontology studies, Protein–protein interaction (PPI), Co-expression analysis, Gene-drug interactions were performed to scale down the hub genes and to identify the novelty across the genes analysed so far. In vivo and invitro analysis of key genes and the trace of interaction pathway is crucial to better understand the unique outcomes from the novel genes, forming the basis to understand the pathway that ends up causing T2DM. Afterwards, docking was executed enabling recognition of interacting residues involved in inhibition. The complex CCL5-265 and CD8A-40585 thus docked showed best results as is evident from its PCA analysis and MMGBSA calculation. There is now scope for deriving candidate drugs that could possibly detect personalized therapies for T2DM.

Identifying key genes and screening therapeutic agents associated with diabetes mellitus and HCV-related hepatocellular carcinoma by bioinformatics analysis

Objective

Incidence of both Type 2 diabetes mellitus (T2DM) and hepatocellular carcinoma (HCC) are rapidly increasing worldwide. One of the leading causes of HCC is hepatitis C virus (HCV), which is a resource of blood-borne viral infection. HCV increases the risk for HCC probably by promoting fibrosis and cirrhosis. Association among T2DM and HCV related HCC remains significant, indicating that such association is clinically reliable and robust. Lawson was the first who uncovered HCC in person suffered from T2DM. Until now, genetic association between HCV related HCC and T2DM is poorly known. Current work was designed to figure out the molecular mechanisms of both diseases by identifying the hub genes and therapeutic drugs using integrated bioinformatics analysis.

Methods

Four microarray datasets were downloaded from GEO database and analyzed using R in order to obtain different expressed genes (DEGs). Protein–protein interaction (PPI) networks was constructed using STRING tool and visualized by Cytoscape. Moreover, hub genes were identified on the basis of their degree of connectivity. Finally, Networkanalyst and DGIdb were used for the identification of transcription factors (TFs) and selection of candidate drugs, respectively.

Results

A total of 53 DEGs were identified, of which 41 were upregulated genes and 12 were downregulated genes. PPI network obtained from STRING were subjected to Cytoscape plugin cytoHubba, and top 10 genes (AURKA, JUN, AR, MELK, NCOA2, CENPF, NCAPG, PCK1, RAD51AP1, and GTSE1) were chosen as the target hub genes based on the highest degree of connectivity. Furthermore, 47 drugs of AURKA, JUN, AR, MELK, and NCOA2 were found having therapeutic potential to treat HCV-HCC in patients with T2DM.

Conclusion

This study updates the information and yield a new perspective in context of understanding the pathogenesis and development of HCV related HCC in affected persons with T2DM. In vivo and in vitro investigation of hub genes and pathway interaction is essential to delineate the specific roles of the novel hub genes, which may help to reveal the genetic association between HCV-HCC and T2DM. In future, hub genes along with their candidate drugs might be capable of improving the personalized detection and therapies for both diseases.

Psychosocial stress, sleep deprivation, and its impact on type II diabetes mellitus: Policies, guidelines, and initiatives from Malaysia

Type II Diabetes Mellitus (T2DM) is a serious public health issue, affecting the global population, particularly those living in low‐ and middle‐income countries. Worldwide, the prevalence of T2DM ranges between 10.4% and 13.5%, depending on the domiciliary. T2DM negatively affects individuals' quality of life and causes high economic burden due to the increasing cost of treatment and management of the disease. Risk factors associated with T2DMs include aging, lifestyle or behavior, genetics, and important biopsychological aspects, which are psychological stress and sleep deprivation. By understanding the associations of psychological stress and sleep deprivation, which contribute to pathophysiology of T2DM, policies, programs, and guidelines were developed in Malaysia to combat the issue among population at large. This narrative review examines 19 national public health policies, programs, and guidelines from the past 20 years in Malaysia that aimed to mitigate the negative health effects of psychological stress, sleep deprivation, and T2DM, both from the government and non‐governmental organizations. Both psychological stress and sleep deprivation works independently or as combined effects in the pathophysiology of T2DM. Besides, in Malaysia, the government, in collaboration with non‐governmental organizations, have been developing and implementing policies, programs, and guidelines to combat mental health and T2DM issues, targeted to population at large. Integration of digital technology, such as usage of social media for health promotion and dissemination of public health messages to the community and good governance from government were deemed important in the effective implementation of health policies and guidelines, resulting in better health outcome.

Genomics, Proteomics and Metabolomics Approaches for Predicting Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients

BACKGROUND

There is a continuous rise in the prevalence of type 2 diabetes mellitus (T2DM) worldwide and most patients are unaware of the presence of this chronic disease at the early stages. T2DM is associated with complications related to long-term damage and failure of multiple organ systems caused by vascular changes associated with glycated end products, oxidative stress, mild inflammation, and neovascularization. Among the most frequent complications of T2DM observed in about 20-40% of T2DM patients is diabetes nephropathy (DN).

METHODS

A literature search was made in view of highlighting the novel applications of genomics, proteomics and metabolomics, as the new prospective strategy for predicting DN in T2DM patients.

RESULTS

The complexity of DN requires a comprehensive and unbiased approach to investigate the main causes of disease and identify the most important mechanisms underlying its development. With the help of evolving throughput technology, rapidly evolving information can now be applied to clinical practice.

DISCUSSION

DN is also the leading cause of end-stage renal disease and comorbidity independent of T2DM. In terms of the comorbidity level, DN has many phenotypes; therefore, timely diagnosis is required to prevent these complications. Currently, urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are gold standards for assessing glomerular damage and changes in renal function. However, GFR estimation based on creatinine is limited to hyperfiltration status; therefore, this makes albuminuria and eGFR indicators less reliable for early-stage diagnosis of DN.

CONCLUSION

The combination of genomics, proteomics, and metabolomics assays as suitable biological systems can provide new and deeper insights into the pathogenesis of diabetes, as well as discover prospects for developing suitable and targeted interventions.

Efficacy of individualized education in patients with type 2 diabetes mellitus: A randomized clinical study protocol

OBJECTIVE

To assess the effect of the program of individualized diabetes education on type 2 diabetes mellitus (DM) patients.

METHODS

This is a single-center randomized controlled trial that will be implemented from December 2020 to April 2021. The experiment was granted through the Research Ethics Committee of People's Hospital of Chengyang District (03982765). Patients are randomly assigned to the study group and control group with 50 cases in each group. Patients who meet the following criteria will be included in our study: patients diagnosed with type 2 DM based on the World Health Organization diagnostic criteria in 1999; patients who can take part in the follow-up researches after discharge; patients who can provide the written informed consent. And the exclusion criteria include: the known mental or psychological disorders, for instance, severe anxiety disorders or depression; severe comorbidities, e.g. liver dysfunction, kidney failure, stroke, and cancer; Uncontrolled diabetes complications, for instance, infection, acidosis, as well as peripheral vascular disease. The clinical examination shall be conducted during each follow-up period, and the laboratory examination is implemented as necessary in the process of each hospital visit. At the end of the 6-month study, each patient's blood pressure, waist circumference, body mass index, blood lipids, as well as fasting blood glucose are evaluated.

RESULTS

Table 1 reveals the comparison of biochemical results and clinical results between the control group and the study group.

CONCLUSION

Individualized diabetes education may improve the clinical outcomes in patients with type 2 DM.

TRIAL REGISTRATION

The protocol was registered in Research Registry (researchregistry6232).

Acarbose protects from central and peripheral metabolic imbalance induced by benzene exposure

Benzene is a well-known human carcinogen that is one of the major components of air pollution. Sources of benzene in ambient air include cigarette smoke, e-cigarettes vaping, and evaporation of benzene containing petrol processes. While the carcinogenic effects of benzene exposure have been well studied, less is known about the metabolic effects of benzene exposure. We show that chronic exposure to benzene at low levels induces a severe metabolic imbalance in a sex-specific manner, and is associated with hypothalamic inflammation and endoplasmic reticulum (ER) stress. Benzene exposure rapidly activates hypothalamic ER stress and neuroinflammatory responses in male mice, while pharmacological inhibition of ER stress response by inhibiting IRE1α-XBP1 pathway significantly alleviates benzene-induced glial inflammatory responses. Additionally, feeding mice with Acarbose, a clinically available anti-diabetes drug, protected against benzene induced central and peripheral metabolic imbalance. Acarbose imitates the slowing of dietary carbohydrate digestion, suggesting that choosing a diet with a low glycemic index might be a potential strategy for reducing the negative metabolic effect of chronic exposure to benzene for smokers or people living/working in urban environments with high concentrations of exposure to automobile exhausts.

Artificial Intelligence-Based Neural Network for the Diagnosis of Diabetes: Model Development

Background

The incidence of diabetes is increasing in China, and its impact on national health cannot be ignored. Smart medicine is a medical model that uses technology to assist the diagnosis and treatment of disease.

Objective

The aim of this paper was to apply artificial intelligence (AI) in the diagnosis of diabetes.

Methods

We established an AI diagnostic model in the MATLAB software platform based on a backpropagation neural network by collecting data for the cases of integration and extraction and selecting an input feature vector. Based on this diagnostic model, using an intelligent combination of the LabVIEW development platform and the MATLAB software-designed diabetes diagnosis system with user data, we called the neural network diagnostic module to correctly diagnose diabetes.

Results

Compared to conventional diagnostic procedures, the system can effectively improve diagnostic efficiency and save time for physicians.

Conclusions

The development of AI applications has utility to aid diabetes diagnosis.

A nomogram for predicting 5-year incidence of type 2 diabetes in a Chinese population

PURPOSE

To develop a nomogram for predicting 5-year incidence of type 2 diabetes (T2D) in Chinese adults.

METHODS

This is a retrospective cohort study from a prospectively collected database. We included a total 32,766 adults free of T2D at baseline with a median follow-up of 3 years. Univariate and multivariate Cox regression analyses were applied to identify independent predictors. A nomogram was constructed to predict 5-year incident rate of T2D based on the multivariate analysis results. Harrell's C-indexes and calibration plots were used to evaluate the accuracy of the nomogram in both internal and external validations.

RESULTS

The overall prevalence of T2D was 2.1%. Participants were randomly divided into a training set (n = 21,844) and a validation set (n = 10,922). After multivariate analysis in the training set, age, sex, BMI, hypertension, dyslipidemia, smoking status, and family history were found as risk predictors and integrated into the nomogram. Harrell's C-indexes were 0.815 (95% CI: 0.797-0.834) and 0.779 (95% CI: 0.747-0.811) in the training and validation sets, respectively. The calibration plots demonstrated good agreement between the estimated probability and the actual observation.

CONCLUSION

Our nomogram could be a simple and reliable tool for predicting 5-year risk of developing T2D in high-risk Chinese. Through the model, early identifying high-risk individuals is helpful for timely intervention to reduce the incidence of T2D.

The Play of Genes and Non-genetic Factors on Type 2 Diabetes

Diabetes has been a disease of public health concern for a number of decades. It was in the 1930s when scientists made an interesting discovery that the disease is actually divided into two types as some patients were insensitive to insulin treatment then. Type 2 Diabetes which happens to be the non-insulin dependent one is the most common form of the disease and is caused by the interaction between genetic and non-genetic factors. Despite conflicting results, numerous studies have identified genetic and non-genetic factors associated with this common type of diabetes. This review has summarized literature on some genes and non-genetic factors which have been identified to be associated with Type 2 diabetes. It has sourced literature from PubMed, Web of Science and Medline without any limitation to regions, publication types, or languages. The paper has started with the introduction, the play of non-genetic factors, the impact of genes in general, and ended with the interaction between some genes and environmental factors.