Current diabetes reviews最新文献

Background: Dating violence is a prevalent issue among Mexican women, as is the incidence and prevalence of Type 2 diabetes mellitus (T2DM). The effects of dating violence can negatively impact lifestyle and, consequently, increase the risk of T2DM.

Objective: This study aimed to explore the influence of dating violence on lifestyle and the risk of T2DM in women university students from Mexico.

Methods: The study employed a cross-sectional and correlational design. The study population consisted of women university students. The sample size included 255 participants. Women aged 18 to 39 with current dating relationships and residency in Mexicali, Baja California, Mexico, were included. Data collection was conducted from February to May 2023. Correlations and multiple linear regression models were conducted.

Results: A total of 255 women participated, with an average age of 21.6 years (SD = 3.2), and 32.2% had a history of intrafamily violence during childhood. 58.8% of the participants exhibited some level of risk of T2DM, and 56.7% of the lifestyle was mostly categorized as poor/fair. Detachment was the most prevalent type of dating violence, followed by coercion. Dating violence was correlated with lifestyle (r = -.430) and the risk of T2DM (r = .321). In the multiple linear regression model, dating violence influenced the risk of T2DM.

Conclusion: Women who reported higher levels of dating violence have a less healthy lifestyle and a greater risk of T2DM. It is important to consider dating violence to improve lifestyle and prevent T2DM in Mexican women university students.

The Central nervous system (CNS) is the prime regulator of signaling pathways whose function includes regulation of food intake (consumption), energy expenditure, and other metabolic responses like glycolysis, gluconeogenesis, fatty acid oxidation, and thermogenesis that have been implicated in chronic inflammatory disorders. Type 2 diabetes mellitus (T2DM) and obesity are two metabolic disorders that are linked together and have become an epidemic worldwide, thus raising significant public health concerns. Fibroblast growth factor 21 (FGF21) is an endocrine hormone with pleiotropic metabolic effects that increase insulin sensitivity and energy expenditure by elevating thermogenesis in brown or beige adipocytes, thus reducing body weight and sugar intake. In contrast, during starvation conditions, FGF21 induces its expression in the liver to initiate glucose homeostasis. Insulin resistance is one of the main anomalies caused by impaired FGF21 signaling, which also causes abnormal regulation of other signaling pathways. Tumor necrosis factor alpha (TNF-α), the cytokine released by adipocytes and inflammatory cells in response to chronic inflammation, is regarded major factor that reduces the expression of FGF21 and modulates underlying insulin resistance that causes imbalanced glucose homeostasis. This review aims to shed light on the mechanisms underlying the development of insulin resistance in obese individuals as well as the fundamental flaw in type 2 diabetes, which is malfunctioning obese adipose tissue.

Recent studies have found that a link between people with type 1 diabetes mellitus (T1DM) are at higher risk of morbidity as well as mortality from COVID-19 infection, indicating a need for vaccination. T1DM appears to impair innate and adaptive immunity. The overabundance of pro-inflammatory cytokines produced in COVID-19 illness that is severe and potentially fatal is known as a "cytokine storm." Numerous cohorts have revealed chronic inflammation as a key risk factor for unfavorable COVID-19 outcomes. TNF-α, interleukin (IL)-1a, IL-1, IL-2, IL-6, and other cytokines were found in higher concentrations in patients with T1DM. Even more importantly, oxidative stress contributes significantly to the severity and course of COVID- 19's significant role in the progression and severity of COVID-19 diseases. Severe glucose excursions, a defining characteristic of type 1 diabetes, are widely recognized for their potent role as mediating agents of oxidative stress via several routes, such as heightened production of advanced glycation end products (AGEs) and activation of protein kinase C (PKC). Furthermore, persistent endothelial dysfunction and hypercoagulation found in T1DM may impair microcirculation and endothelium, which could result in the development of various organ failure and acute breathing syndrome.

Background: Gestational diabetes mellitus (GDM) constitutes a common metabolic disorder that could lead to deleterious short- and long-term complications in both the mother and her infant. Probiotic supplementation seems to exert diverse, healthy effects by acting as a preventive agent against various human diseases, including GDM.

Objective: The purpose of the current narrative review was to critically summarize and scrutinize the available clinical studies during the last 15 years (2008-2023) concerning the use of probiotic supplementation during pregnancy as a protecting agent against GDM.

Methods: A thorough and in-depth search was performed in the most accurate scientific databases, e.g., PubMed., Scopus, Web of Science, and Google Scholar applying effective, and relevant keywords.

Results: There are currently some clinical studies suggesting the potential beneficial impact of probiotic supplementation in the prevention and/or co-treatment of GDM. Nevertheless, there is a high heterogeneity amongst the available clinical studies concerning the dosage, the administration duration, the probiotic species types, the method designs and protocols, and the study populations.

Conclusion: Probiotic supplementation at conventional dosages and in combination with a balanced healthy diet, and lifestyle seems to reduce the the risk of developing GDM, while ameliorating the severity of its symptoms. Further clinical studies taking into account the above considerations should be performed to establish conclusive results, while the future meta-analyses should include studies with the feasibly lowest heterogeneity.

Diabetic neuropathy, also known as diabetic peripheral sensorimotor neuropathy (DPN), is a consequential complexity of diabetes, alongside diabetic nephropathy, diabetic cardiomyopathy, and diabetic retinopathy. It is characterized by signs and symptoms of peripheral nerve damage in diabetes patients after ruling out other causes. Approximately 20% of people with diabetes are affected by this painful and severe condition. The development of diabetic neuropathy is influenced by factors such as impaired blood flow to the peripheral nerves and metabolic issues, including increased polyol pathway activation, myo-inositol loss, and nonenzymatic glycation. The present review article provides a brief overview of the pathological changes in diabetic neuropathy and the mechanisms and types of DPN. Various diagnostic tests and biomarkers are available to assess nerve damage and its severity. Pharmacotherapy for neuropathic pain in diabetic neuropathy is complex. This review will explore current treatment options and potential future developments to improve the quality of life for patients suffering from diabetic neuropathy.

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.

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: 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.

The link between Type 2 Diabetes (T2DM) and Parkinson's Disease (PD) dates back to the early 1960s, and ongoing research is exploring this association. PD is linked to dysregulation of dopaminergic pathways, neuroinflammation, decreased PPAR-γ coactivator 1-α, increased phosphoprotein enriched in diabetes, and accelerated α-Syn amyloid fibril production caused by T2DM. This study aims to comprehensively evaluate the T2DM-PD association and risk factors for PD in T2DM individuals. The study reviews existing literature using reputable sources like Scopus, ScienceDirect, and PubMed, revealing a significant association between T2DM and worsened PD symptoms. Genetic profiles of T2DM-PD individuals show similarities, and potential risk factors include insulin-resistance and dysbiosis of the gut-brain microbiome. Anti-diabetic drugs exhibit neuroprotective effects in PD, and nanoscale delivery systems like exosomes, micelles, and liposomes show promise in enhancing drug efficacy by crossing the Blood-Brain Barrier (BBB). Brain targeting for PD uses exosomes, micelles, liposomes, dendrimers, solid lipid nanoparticles, nano-sized polymers, and niosomes to improve medication and gene therapy efficacy. Surface modification of nanocarriers with bioactive compounds (such as angiopep, lactoferrin, and OX26) enhances α-Syn conjugation and BBB permeability. Natural exosomes, though limited, hold potential for investigating DM-PD pathways in clinical research. The study delves into the underlying mechanisms of T2DM and PD and explores current therapeutic approaches in the field of nano-based targeted drug delivery. Emphasis is placed on resolved and ongoing issues in understanding and managing both conditions.

Background: Diabetes technology in the form of digital health or medical devices holds a promise for improving the quality of life and glycemic outcomes. A comprehensive insight into diabetes technology and its impact in Saudi Arabia and the MENA region may improve type 1 diabetes mellitus (T1DM) management.

Objective: This study aimed to assess the impact of different DM-specific technologies: Insulin pump therapy, continuous glucose monitoring (CGM), and automated insulin delivery system in terms of glycemic control and QoL among T1DM patients in Saudi Arabia and the MENA region.

Method: A systematic literature search was performed in PubMed and Scopus from 2005 until August 2023. The search was based on the PICO strategy, focusing on T1DM patients, diabetes technology, and QoL. The inclusion criteria were studies illustrating the effect of diabetes technologies on glycemic control or quality of life or both among T1DM patients. Systematic reviews, books, letters, or studies, including type 2 diabetes mellitus, were excluded.

Results: From 101 articles, eighteen studies were duplicated, and thirty-three studies were excluded after reading the title and abstract. Of the 50 articles analyzed, twenty-five articles did not meet the inclusion criteria. Therefore, 25 articles involving a total of 3088 participants were enrolled in the study. It was shown that a continuous glucose monitoring system and continuous subcutaneous insulin infusion improved the glycemic control and the QoL of T1DM patients.

Conclusion: There was a positive impact of insulin pumps, continuous glucose monitoring (CGM) systems, and telemedicine in achieving optimal glucose control and better QoL. Further studies are recommended to clarify the significant role of advanced diabetes technologies.