Current diabetes reviews最新文献

Background: Diabetes Mellitus (DM) is an alarming health concern, affecting approximately 537 million people worldwide. As a leading cause of morbidity and mortality, DM demands a comprehensive understanding of its diverse pathophysiological mechanisms and disease progression.

Methods: This traditional review has consolidated literature on the pathogenesis of hyperglycemia, its progression into complications, and advances in optimal treatment strategies. The literature in the last two decades has been reviewed using several keywords, including "diabetes," "diabetes-associated complications", "novel therapeutic interventions for diabetes-associated diseases", "phyto-extracts as antidiabetic drugs", etc. in prominent databases, such as PubMed, Scopus, Google Scholar, Web of Science, and ClinicalTrials.gov.

Results: We have discussed macrovascular and microvascular complications, such as atherosclerosis, cardiovascular disease, Peripheral Arterial Disease (PAD), stroke, diabetic nephropathy, retinopathy, and neuropathy, as well as various pharmacological and non-pharmacological interventions that are currently available for the management of DM. We have also focused on the potential of natural products in targeting molecular mechanisms involved in carbohydrate metabolism, insulin production, repair of pancreatic cells, and reduction of oxidative stress, thereby contributing to their antidiabetic activity. Additionally, novel therapeutic approaches, like genetic, stem cell, and immunomodulatory therapies, have been explored. We have also discussed the benefits and limitations of each intervention, emerging research and technologies, and precision medicine interventions.

Conclusion: This review has emphasized the need for an improved understanding of these advancements, which is essential to enhance clinicians' ability to identify the most effective therapeutic interventions.

Aims: The objective of this study was to investigate the correlation between serum 25 hydroxyvitamin D [25(OH)D] levels and insulin resistance, as well as metabolic associated fatty liver disease (MAFLD) in newly diagnosed with type 2 diabetes mellitus (T2DM) patients.

Method: A retrospective analysis was conducted on 491 T2DM patients who were newly diagnosed between January 2017 and August 2022 at Peking University International Hospital. These patients were categorized into three groups based on their 25(OH)D levels.

Results: The prevalence of MAFLD was significantly elevated in both the Vitamin D (VD) deficiency group and the VD insufficiency group compared to the VD sufficiency group (χ2 = 6.51, p<0.05). The patients in the VD sufficiency group had lower levels of insulin resistance, as assessed by the homeostasis model assessment when compared to the VD deficiency group and the VD insufficiency group (F = 8.61, p<0.05). Additionally, the VD sufficiency group demonstrated higher levels of β cell function in comparison to the other two groups (p<0.05, respectively). A significant negative correlation was observed between 25(OH)D levels and insulin resistance, as assessed by the homeostasis model assessment in T2DM patients(r=-0.33, p<0.05 for females; r=-0.32, p<0.05 for males). In male patients, 25(OH)D was identified as a protective factor against MAFLD(OR = 0.42;95%CI:0.19-0.95;p <0.05). Meanwhile, in female patients, 25(OH)D was also associated with a reduced risk of MAFLD(OR = 0.35;95%CI 0.17-0.89; p<0.05). Additionally, the study determined that the threshold values for 25(OH)D were 15.06 ng/ml in female patients and 18.79 ng/ml in male patients for predicting MAFLD.

Conclusion: In newly diagnosed with T2DM patients, the level of 25(OH)D may be related to insulin resistance and β cell secretion function independently and VD deficiency is an independent risk factor for MAFLD in patients with newly diagnosed T2DM.

Background: Diabetes mellitus (DM), arising from pancreatic β-cell dysfunction and disrupted alpha-amylase secretion, manifests as hyperglycemia. Synthetic inhibitors of alphaamylase like acarbose manage glucose but pose adverse effects, prompting interest in plantderived alternatives rich in antioxidants and anti-inflammatory properties.

Objective: The current review investigates plant-based alpha-amylase inhibitors, exploring their potential therapeutic roles in managing DM. Focusing on their ability to modulate postprandial hyperglycemia by regulating alpha-amylase secretion, it assesses their efficacy, health benefits, and implications for diabetes treatment.

Methods: This review examines plant-derived alpha-amylase inhibitors as prospective diabetic mellitus treatments using PubMed, Google Scholar, and Scopus data.

Results: Plant-derived inhibitors, including A. deliciosa, B. egyptiaca, and N. nucifera, exhibit anti-inflammatory and antioxidant properties, effectively reducing alpha-amylase levels in diabetic conditions. Such alpha-amylase inhibitors showed promising alternative treatment in managing diabetes with reduced adverse effects.

Conclusion: The current literature concludes that plant-derived alpha-amylase inhibitors present viable therapeutic avenues for diabetes management by modulating alpha-amylase secretion by regulating inflammatory, oxidative stress, and apoptotic mechanisms involved in the pathogenesis of diabetes. Further investigation into their formulations and clinical efficacy may reveal their more comprehensive diabetes therapeutic significance, emphasizing their potential impact on glucose regulation and overall health.

Background/introduction: Albuminuria in Diabetes mellitus (DM) patients may lead to nephropathy and end-stage renal disease. Our study aimed to assess the prevalence of albuminuria and its associated predictors among type 2 DM patients in the United Arab Emirates.

Methods: A retrospective cross-sectional study was conducted among type 2 DM patients in the diabetic clinic at Fujairah Hospital from 1st January 2016 to 30th January 2020 after getting the ethical clearance. Data were collected electronically from the health information system and analyzed using SPSS version 26. Regression analysis and ANOVA were used for inferential analysis. A P-value of ≤0.05 was considered significant.

Results and discussion: Among the 200 patients included in the study, the mean age of the study population was 56 years, and the majority of them were females (71%). The prevalence of albuminuria was found to be 44%. By using regression analysis, glycated hemoglobin (HbA1c; P=0.038) and systolic blood pressure (SBP; P=0.003) were found to be predictors of albuminuria. One way ANOVA revealed that there were significant associations between the albumin levels and HbA1c (P=0.004), SBP (P= 0.002), diastolic blood pressure (DBP; P=0.028), serum creatinine (Scr) (P=0.039), and glomerular filtration rate (GFR; P=0.013).

Conclusion: To the best of our knowledge, this is the first study from Fujairah emirate that explored the prevalence and predictors of albuminuria in type 2 DM patients. We found a high prevalence of albuminuria among type 2 DM patients. HbA1c and SBP directly contributed to albuminuria. To improve glycemic control, patients need to improve physical activity, reduce overweight and, adherence to medications that improve overall therapeutic outcomes.

Background: Diabetes Mellitus is a chronic health condition (long-lasting) due to inadequate control of blood levels of glucose. This study presents a prediction of Type 2 Diabetes Mellitus among women using various Machine Learning Algorithms deployed to predict the diabetic condition. A University of California Irvine Diabetes Mellitus Dataset posted in Kaggle was used for analysis.

Methods: The dataset included eight risk factors for Type 2 Diabetes Mellitus prediction, including Age, Systolic Blood Pressure, Glucose, Body Mass Index, Insulin, Skin Thickness, Diabetic Pedigree Function, and Pregnancy. R language was used for the data visualization, while the algorithms considered for the study are Logistic Regression, Support Vector Machines, Decision Trees and Extreme Gradient Boost. The performance analysis of these algorithms on various classification metrics is also presented here, considering the Area Under the Curve and Receiver Operating Characteristics score is the best for Extreme Gradient Boost with 85%, followed by Support Vector Machines and Decision Trees.

Results: The Logistic Regression is showing low performance. But the Decision Trees and Extreme Gradient Boost show promising performance against all the classification metrics. But the Support Vector Machines offers a lower support value; hence it cannot be claimed to be a good classifier. The model showed that the most significant predictors of Type 2 Diabetes Mellitus were strongly correlated with Glucose Levels and mediumly correlated with Body Mass Index, whereas Age, Skin Thickness, Systolic Blood Pressure, Insulin, Pregnancy, and Pedigree Function were less significant. This type of real-time analysis has proved that the symptoms of Type 2 Diabetes Mellitus in women fall entirely different compared to men, which highlights the importance of Glucose Levels and Body Mass Index in women.

Conclusion: The prediction of Type 2 Diabetes Mellitus helps public health professionals to help people by suggesting proper food intake and adjusting lifestyle activities with good fitness management in women to make glucose levels and body mass index controlled. Therefore, the healthcare systems should give special attention to diabetic conditions in women to reduce exacerbations of the disease and other associated symptoms. This work attempts to predict the occurrence of Type 2 Diabetes Mellitus among women on their behavioral and biological conditions.

Background: Brevifoliol is a diterpenoid that occurs naturally in the plants of Taxus genus and is widely used as chemotherapy agent for the management of cancer. A series of semisynthetic esters analogues of brevifoliol were prepared by Steglich esterification and attempted for their pharmacological potential against insulin resistance conditions using in-vitro and in-silico assays.

Objective: The aim of this study is to understand the pharmacological potential of eighteen semisynthetic analogs through Steglich esterification of Brevifoliol against insulin resistance condition.

Methods: In the in-vitro study, insulin resistance condition was induced in skeletal muscle cells using TNF-α, pro-inflammatory cytokine and these cells were treated with brevifoliol analogues. The most potent analouge was further validated using in-silico docking study against the tumor necrosis factor (TNF-α) (PDB ID: 2AZ5) and Human Insulin Receptor (PDB ID: 1IR3), using the Auto dock Vina v0.8 program.

Results: Although, all the analogues of Brevifoliol significantly exhibited the pharmacological potential. Among all, analogue 17 was most potent in reversing the TNF-α induced insulin resistance condition in skeletal muscle cells and also to inhibit the production of TNF-α in LPSinduced inflammation in macrophage cells in a dose-dependent manner. Similarly, in-silico molecular docking studies revealed that analogue 17 possesses a more promising binding affinity than the selected control drug metformin toward the TNF-α and insulin receptor.

Conclusion: These findings suggested the suitability of analogue 17 as a drug-like candidate for further investigation toward the management of insulin resistance conditions.

Introduction: Type 2 Diabetes Mellitus (T2DM) accounts for more than 95% of all diabetes cases and is a leading cause of disability and death. This study aimed to evaluate the effectiveness and safety of a combination therapy involving metformin, teneligliptin, and glimepiride in patients diagnosed with T2DM.

Methods: The present quasi-experimental clinical trial involved 300 adult T2DM patients. They were divided into three groups: Group 1 (Metformin; n=100), Group 2 (Metformin + Teneligliptin; n=100), and Group 3 (Metformin + Teneligliptin +; n=100). Along with demographic data, we collected information on HbA1c, FBS, and PPBS levels, as well as fasting insulin, CPeptide, HOMA-IR, QUICKI-IR, and lipid, renal, and hepatic profiles at baseline and after 3, 6, and 12 months. Data analysis was performed using SPSS 21.0 software.

Results: A total of 300 patients participated in the study. At the end of 12 months, triple-drug therapy achieved significant glycemic control (HbA1c: 6.56±0.50%; P<0.0001) and reduced FBS (7.6±1.41 mg/dl; P<0.0001), PPBS (9.39±2.14 mg/dl; P<0.0001), and fasting insulin (11.26±2.5 IU; P<0.0001), C-peptide (2.01±2.29 ng/ml; P<0.0001), and insulin resistance by HOMA-IR (3.74±0.7; P<0.0001). Favorable lipid profiles (P<0.0001) were noted versus other groups. Despite renal and hepatic profile variations, values remained within the normal range.

Conclusion: The combination of teneligliptin with metformin and glimepiride in T2DM patients demonstrated significant improvements in glycaemic control, reduced insulin resistance, and positive effects on lipid, renal, and hepatic profiles. Importantly, the therapy did not result in serious adverse drug reactions, such as hypoglycemia. We need more RCTs to substantiate these findings.

Background: Recent research demonstrates that diabetes can lead to heart problems, neurological damage, and other illnesses.

Method: In this paper, we design a low-complexity Deep Learning (DL)-based model for the diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes Dataset (PIDD). To obtain a low-complexity and accurate DL architecture, we perform an accuracy-versus-complexity study on several DL models.

Result: The results show that the proposed DL structure, including Convolutional Neural Networks and Multi-Layer Perceptron models (i.e., CNN+MLP model) outperforms other models with an accuracy of 93.89%.

Conclusion: With these features, the proposed hybrid model can be used in wearable devices and IoT-based health monitoring applications.

Background: Metabolic syndrome comprises various conditions like abdominal obesity, insulin resistance, elevated triglyceride levels, reduced HDL, and high blood pressure, which pose significant health challenges globally. It's imperative to determine its prevalence in specific populations to formulate effective preventive measures.

Objective: This systematic review and meta-analysis aimed to determine the prevalence of metabolic syndrome in the Qatari population.

Methods: Using the PRISMA guidelines, a systematic search was executed on PubMed until July 2023 with keywords "Metabolic syndrome" and "Qatar." Eligibility criteria included human subjects, studies assessing metabolic syndrome components, and research conducted in Qatar or on Qatari subjects. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS). Pooled prevalence rates were calculated using the inverse variance weighting metaanalysis.

Results: Out of 237 studies, 14 met our inclusion criteria, with a combined sample size of 14,772 from the Qatari population. The overall pooled prevalence of metabolic syndrome was 26%. The ATP III and IDF criteria exhibited significant differences in prevalence rates, with the IDF criteria showing a higher prevalence. Patients in the age of 40 or older demonstrated a higher prevalence compared to the younger group. Studies post-2018 reported a decreasing trend in metabolic syndrome prevalence.

Conclusion: The prevalence of metabolic syndrome in the Qatari population is comparable to rates in the Middle East. The study underscores the need for tailored interventions and strategies, especially targeting the older age group. Continuous research and monitoring are essential to track and understand the disease's progression in Qatar.

Introduction: Recently, there has been a notable increase in interest in various forms of vegetarianism, which may be due to the growing prevalence of health issues, such as Type 2 Diabetes Mellitus (T2DM). Adhering to a vegan diet may have positive health outcomes. As a result, we conducted a review article to gather data from previous research studies on the effects of a vegan diet on different aspects of managing patients with T2DM.

Methods: We searched the PubMed website for research studies on how a vegan diet affects the outcomes of patients with T2DM. The research studies were categorized according to the type of data collected, such as prevalence, incidence, body weight, insulin resistance, glycemic control, and lipid profile.

Results: It was found that following a vegetarian diet can significantly reduce the risk of mortality from heart disease. Additionally, studies have demonstrated that a vegetarian diet is linked to several improvements in T2DM. However, long-term weight loss plans and managing T2DM is a comprehensive intervention that includes caloric restriction, exercise, and behavioral modification.

Conclusion: Incorporating a vegan diet can be a valuable factor to consider in managing T2DM, as it can offer numerous benefits, such as increased insulin sensitivity, weight loss, and reduced blood sugar levels. It helps to reduce cholesterol levels, LDL, and triglyceride levels, which are all risk factors associated with T2DM. By reducing these risk factors, the vegan diet can improve the overall health of T2DM patients.