High Blood Sugar Levels Research Articles

Euglycemic Diabetic Ketoacidosis Presenting as Chest Pain and Vomiting in a Patient taking SGLT-2 inhibitor

Empagliflozin-induced Euglycemic Diabetic Ketoacidosis (EDKA) is a life-threatening metabolic emergency of Diabetes Mellitus. It is characterized by metabolic acidosis (i.e., pH <7.3 and Serum bicarbonate <15mEq/dl) with an increased anion gap, ketonemia and relatively normal serum glucose level. EDKA resembles Diabetic Keto Acidosis (DKA) but does not exhibit the typical high blood sugar levels. In EDKA, blood glucose is usually below 200 mg/dL. This condition is a diagnostic challenge as euglycemia masks the underlying diabetic ketoacidosis. Here we present a case of Empagliflozin-induced Euglycemic Diabetic Ketoacidosis presented with palpitation and several episodes of vomiting. The aim of this article is to create awareness among clinicians to consider this diagnosis in the differential, especially in the patients taking SGLT-2 inhibitors. Bangladesh Crit Care J September 2024; 12 (2): 172-174

Quercetin: Potential antidiabetic effects through enzyme inhibition and starch digestibility

AbstractDiabetes mellitus involves high blood sugar levels due to insufficient insulin action. Furthermore, enzymes such as α‐amylase and α‐glucosidase break down carbohydrates into glucose, leading to postprandial hyperglycemia. Flavonoids, particularly quercetin, inhibit these enzymes, slowing carbohydrate digestion and reducing glucose absorption. Quercetin has significant hypoglycemic effects with inhibitory concentration (IC50) values comparable to acarbose, a standard inhibitor, suggesting its potential as a natural alternative for diabetes management. In silico models, including molecular docking, molecular dynamics (MD) simulations, and quantitative structure‐activity relationship (QSAR) approaches, help researchers understand the molecular interactions of therapeutic agents. These techniques identify potential inhibitors, determine enzyme‐inhibitor structures, and calculate binding energies, correlating findings with in vitro or in vivo data. Molecular docking predicts molecular orientations, MD simulations offer insights into enzyme–inhibitor dynamics, and QSAR models predict inhibitory potential based on structural properties. Studies have shown that quercetin effectively inhibits α‐glucosidase and α‐amylase by forming hydrogen bonds with specific amino acid residues. Quercetin interacts with starches and reduces their digestibility, increases the formation of resistant starch, lowers the glycemic index, and inhibits digestive enzymes. Studies show that the effects of quercetin on starch digestion vary with concentration and type of starch, and its incorporation into foods such as bakery products, pasta, etc. can significantly decrease starch hydrolysis. The incorporation of quercetin into starch matrices may aid in the development of functional foods aimed at improving glycemic control.

Ayurveda’s Role in Managing Diabetes: A Case Study

Diabetes is also known as Diabetes mellitus (DM) which is characterised by high blood sugar levels over a long period. The raised level of Blood Glucose leads to frequent urination. This may occur when the pancreas is unable to produce the proper level of insulin or the inability of body cells to absorb the glucose properly. Insulin regulates the level of glucose in the bloodstream, so its deficiency leads to a rise in the glucose levels in the bloodstream. The condition of Diabetes may lead to various complications if it is not managed properly. A 50-year-old male patient presented with complaints of frequent urination, increased thirst and hunger. The clinical findings and symptoms of the patient are related to Type 2 Diabetes. Diabetes is correlated to Madhumeha in Ayurveda. The patient was given Dia-Beta Plus capsules, Karela capsules, Madhumehantak churna, Fenugreek capsules and Diaplan tea. After 2 months, the patient feels some relief in his symptoms. The random sugar levels of the patient decreased from 240 mg/dL to 130 mg/dL. After the complete course of Ayurveda medication, the blood sugar level of the patient was managed effectively.

Comparison of Classification Results of SVM, KNN, Decision Tree, and Ensemble Methods in Diabetes Diagnosis

This study aims to determine which algorithms and test techniques are the most optimal in detecting diabetes mellitus and obtaining the best results based on the value of accuracy, precision, and recall. In this study, approaches were used in early diagnosis of diabetes using KNN, SVM, Decision Tree, and Ensemble Majority Voting methods in Percentage Split and K-Fold Cross Validation methods. Diabetes is a disease characterized by high blood sugar (glucose) levels and can cause a variety of disease complications and damage to the body's organs if not treated immediately. Early diagnosis of diabetes is becoming crucial so that people can take immediate action to the hospital for immediate treatment. The data used is Healthcare-Diabetes from Kaggle. The results of this study have found that the K-Fold Cross Validation method is better because it can provide an average improvement in Ensemble accuracy of 13.42% compared to the Percentage Split method which only gives an average increase in Ensamble accuracy of 9.15%. The best algorithm for classifying diabetes disease is the Ensemble Majority Voting algorithm using the K-Fold Cross Validation method with a 98.81% accuracy rate. These excellent research results may contribute to detecting early symptoms of diabetes before it become too severe.

Relationship between Lipid and Blood Glucose Levels with Stroke Risk in Dupak Surabaya

Background: Stroke is a syndrome characterized by rapidly developing symptoms and/or clinical signs in the form of focal or global brain functional disorders lasting more than 24 hours which are not caused by causes other than vascular. Stroke is the number one cause of disability and the main cause of death in Indonesia (19.42% of total deaths). Dyslipidemia and diabetes mellitus are two-fold risk factors for stroke.This research is to determine the relationship between lipids and blood glucose and a high risk of stroke. Methods: This research uses descriptive analytical methods with a cross sectional approach. The population is residents in Dupak Surabaya. The sampling method is random sampling technique. The total research sample was 53 participants. The statistical test used is Chi Square. Result: Statistical results show that there is no relationship between high cholesterol levels and the risk of stroke and there is a relationship between high triglyceride and blood sugar levels and the risk of stroke.Conclusion:This research concluded that there was a significant relationship between high levels of triglycerides and blood glucose and the risk of stroke and there was no significant relationship between high cholesterol levels and the risk of stroke. This lack of significant association could be due to the heterogeneous population and the presence of previous anti-cholesterol medication.

Effect of madecassic acid on retinal oxidative stress, inflammation and Growth Factors in streptozotocin-induced diabetic rats

Diabetic retinopathy (DR) is the leading cause of blindness and visual loss in people with diabetes. It has been suggested that the progression of DR is associated with chronic inflammation and oxidative stress. The aim of the present work was to evaluate the ability of the natural compound madecassic acid (MEA) to reverse the negative impact of streptozotocin (STZ) on retinal injury in rats. Diabetic rats induced by STZ were treated with MEA at the doses of 10 and 20 mg/kg bw for 8 weeks. The study compared the efficacy of the drug in controlling high blood sugar levels and its impact on therapeutic targets such as SOD, CAT, GPx, NF-κB, TNF-α, IL-6, IL-1β, VEGF, IGF, bFGF and Keap1/Nrf-2 pathway. The results showed that the treatment with MEA significantly restored the retinal SOD, CAT, and GPx levels in diabetic rats to the near-normal levels. Moreover, the level of inflammatory mediators (TNF-α, IL-1β, IL-6) and growth factors (VEGF, IGF, bFGF) was significantly lower in retinas of animals treated with MEA as compared to retinas of diabetic animals. The study also established that MEA administration reduced the NF-κB protein and altered the Nrf-2/Keap1 pathway thereby reducing oxidative stress and inflammation. Furthermore, the use of MEA prevented the progression of the retinal capillary basement membrane thickening. It has been found that MEA offers significant protection to the retina and therefore, the compound may be useful in the treatment of DR in humans.

Ensemble machine learning algorithm for cost-effective and timely detection of diabetes in Maiduguri, Borno State

Diabetes is a serious medical condition that severely hinders the body's ability to produce or properly regulate insulin, leading to detrimental carbohydrate metabolism and dangerously high blood sugar levels. This ultimately causes inadequate carbohydrate metabolism and heightened blood glucose levels. Alarmingly, from 2000 to 2019, diabetes-related mortality rates rose by 3%. In the year 2019 alone, diabetes was tragically responsible for nearly 2 million deaths. This groundbreaking research introduces the improved weighted average ensemble learning (WAEL) model as an innovative solution for detecting diabetes. The enhanced WAEL model effectively addresses the overfitting challenge by integrating multiple models that have gained unique insights from the data. The proposed WAEL model ingeniously combines five feature spaces through the grey wolf optimisation (GWO) algorithm to uncover the optimal weight combination. GWO plays a vital role in weight optimization, enabling the reduction of weights in models that are particularly sensitive to noise. The results demonstrated that the improved WAEL achieved an astounding level of accuracy, soaring to 98.90%. The LGBM algorithm followed closely, achieving an impressive accuracy of 85.00%. The RF method recorded an accuracy of 81.00%. When it comes to accurately identifying diabetes, the improved WAEL ensemble model significantly outperformed the other five individual models, as evidenced by metrics such as accuracy, precision, recall, and F1-score. Therefore, the proposed model stands as a compelling alternative tool for healthcare professionals in the early detection of diabetes.

Ensemble machine learning framework for predicting maternal health risk during pregnancy

Maternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper identification and monitoring of risk factors can assist to reduce pregnancy complications. The primary goal of this research is to use real-world datasets to identify and predict Maternal Health Risk (MHR) factors. As a result, we developed and implemented the Quad-Ensemble Machine Learning framework to predict Maternal Health Risk Classification (QEML-MHRC). The methodology used a vacxsriety of Machine Learning (ML) models, which then integrated with four ensemble ML techniques to improve prediction. The dataset collected from various maternity hospitals and clinics subjected to nineteen training and testing tests. According to the exploratory data analysis, the most significant risk factors for pregnant women include high blood pressure, low blood pressure, and high blood sugar levels. The study proposed a novel approach to dealing with high-risk factors linked to maternal health. Dealing with class-specific performance elaborated further to properly understand the distinction between high, low, and medium risks. All tests yielded outstanding results when predicting the amount of risk during pregnancy. In terms of class performance, the dataset associated with the “HR” class outperformed the others, predicting 90% correctly. GBT with ensemble stacking outperformed and demonstrated remarkable performance for all evaluation measure (0.86) across all classes in the dataset. The key success of the models used in this work is the ability to measure model performance using a class-wise distribution. The proposed approach can help medical experts assess maternal health risks, saving lives and preventing complications throughout pregnancy. The prediction approach presented in this study can detect high-risk pregnancies early on, allowing for timely intervention and treatment. This study’s development and findings have the potential to raise public awareness of maternal health issues.

Explainable deep learning for diabetes diagnosis with DeepNetX2

Diabetes is a leading health global health challenge because of its high blood sugar levels and the risk of extensive damage to other internal organs. Early and accurate identification of diabetes is important because it may cause other diseases including heart diseases and nerve damage. Despite the success of using machine learning, especially deep learning in automated diabetes diagnosis. These models are mostly black boxes which rarely offer comprehensive explanations and interpretations of the results. This study introduces DeepNetX2, a proposed custom deep neural network designed to overcome these challenges by integrating Explainable Artificial Intelligence (XAI) techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These techniques make the decision-making process of the model transparent, thereby increasing the credibility of the predictions. The proposed methodology entails a comprehensive data preprocessing technique that involves a customized Spearman’s correlation coefficient feature selection strategy. This preprocessing restricts complexity to only relevant features that promote effectiveness, instead of oversimplification, to the point of decreasing efficiency. DeepNetX2 was rigorously tested on three datasets: the PIMA dataset, local private dataset, and Type-2 diabetes dataset, achieving test accuracies of 94.81%, 97.87%, and 97.50%, respectively. These results demonstrate not only the superior performance of DeepNetX2 compared with existing models, but also its enhanced interpretability. Based on the proposed model, an appropriate and rapid strategy for diabetes prediction was developed which is very useful for improving diagnostic integrity and patient health.

Tropical oils consumption and health: a scoping review to inform the development of guidelines in tropical regions

BackgroundTropical oils such as palm and coconut oils are renowned for their high saturated fat content and culinary versatility. However, their consumption has sparked debate regarding their health benefits and production concerns. The purpose of this review was to map existing evidence on the health benefits and challenges associated with the consumption of tropical oils.MethodThe recommendations for conducting a scoping review by Arksey and O’Malley were followed. PubMed, Dimensions AI, Central, JSTOR Google, Google Scholar, and ProQuest databases were searched for relevant papers. The predetermined keywords used were Consumption” AND “Tropical oil,” as well as “Health benefits” OR “Health challenges” AND “Tropical Countries.” Peer-reviewed and grey literature published in English were eligible for this review.ResultTropical oils, such as palm and coconut oils, provide health benefits including essential vitamins (A and E) that enhance ocular health, boost immunity, and support growth. They are also recognised for their role in managing high blood sugar, obesity, and cholesterol levels, while offering antioxidant and anti-inflammatory properties. These oils have wound-healing abilities and are commonly used in infant nutrition and traditional cooking. Nevertheless, prolonged and repeated use of tropical oils to high temperature can degrade vitamin E, whereas excessive intake may result in overdose. Health concerns include oxidative risks, diabetes, cancer, coronary heart disease, high blood pressure, and acrylamide formation due to production challenges excessive consumption. Additional issues include obesity, suboptimal oil production, misconceptions, regulatory obstacles, and preferences for alternative fats.ConclusionThis review suggest that tropical oils provide essential health benefits, including vitamins and antioxidant properties, but pose significant health risks and production challenges, particularly when exposed to high temperatures and through excessive intake. Guidelines on the consumption of tropical oils in the tropical regions are necessary to regulate their consumption.

Diabetic peripheral neuropathy and neuromodulation techniques: a systematic review of progress and prospects.

Neuromodulation for diabetic peripheral neuropathy represents a significant area of interest in the management of chronic pain associated with this condition. Diabetic peripheral neuropathy, a common complication of diabetes, is characterized by nerve damage due to high blood sugar levels that lead to symptoms, such as pain, tingling, and numbness, primarily in the hands and feet. The aim of this systematic review was to evaluate the efficacy of neuromodulatory techniques as potential therapeutic interventions for patients with diabetic peripheral neuropathy, while also examining recent developments in this domain. The investigation encompassed an array of neuromodulation methods, including frequency rhythmic electrical modulated systems, dorsal root ganglion stimulation, and spinal cord stimulation. This systematic review suggests that neuromodulatory techniques may be useful in the treatment of diabetic peripheral neuropathy. Understanding the advantages of these treatments will enable physicians and other healthcare providers to offer additional options for patients with symptoms refractory to standard pharmacologic treatments. Through these efforts, we may improve quality of life and increase functional capacity in patients suffering from complications related to diabetic neuropathy.

Study of Differences in Blood Sugar Levels of Diabetes Mellitus Patients Who Consume Rice Cooked with the Rice Cooker Method and The Steaming Method

The 2023 Indonesian Health Survey Report issued by the Ministry of Health revealed that there was an increase in the prevalence of diabetes mellitus (DM) in the population aged over 15 years based on the results of blood sugar level measurements. In the 2018 Basic Health Research, it was recorded that the prevalence of diabetes in Indonesia reached 10.9%. Now, the prevalence has reached 11.7% in 2023. The Aceh Health Office recorded that sufferers of Diabetes Mellitus until early 2023 reached 154,889 cases. The purpose of this study is to find out the difference in blood sugar levels in people with diabetes mellitus who consume rice after cooking with the rice cooker method and with the steaming method. This quantitative analytical research uses a cross-sectional study design. The population of the study was all people with diabetes mellitus at the Calang Health Center, Aceh Jaya Regency, totaling 50 people. Data collection was carried out from January 15 to March 15, 2024 using nonprobability sampling techniques. This study also took direct data by looking at the sugar content of the respondents by eating rice cooked by the rice cooker method and 1 month later by measuring the sugar content of the respondents consuming rice by cooking the rice by steaming. Data were analyzed using univariate, bivariate and multivariate statistical tests. The results of the study showed that factors related to blood sugar levels in DM patients were gender, age, education, occupation, income, physical activity, nutritional status, smoking behavior, and family history of diabetes. The factors most related to increased blood sugar levels in diabetes mellitus patients were steaming with OR=7.47, use of a rice cooker of 11.2 and family history of diabetes mellitus of 7.50. It is expected that respondents will control their blood sugar levels, so that DM sufferers and their families will cook rice using the steaming method, and control other risk factors that can cause high blood sugar levels.

Diabetes Disparities: Addressing Health Inequities and Improving Outcomes in Underserved Populations

Diabetes mellitus, characterized by high blood sugar levels due to insulin deficiency or resistance, is a global public health crisis with significant disparities in prevalence, management, and outcomes, particularly among underserved populations such as racial and ethnic minorities, low-income groups, and rural residents. These populations faced higher rates of diabetes and complications due to social determinants of health, including economic instability, inadequate education, and limited access to healthcare. Barriers such as lack of insurance, transportation challenges, and cultural and linguistic differences further hindered effective diabetes management. Lifestyle and behavioral factors, including diet, physical activity, medication adherence, and mental health, play critical roles in diabetes outcomes. Policy and system-level interventions, such as expanding healthcare coverage, improving access to care, and addressing social determinants of health, are essential to reducing disparities. Technological innovations like telehealth, mobile health applications, wearable devices, and data analytics offered significant potential to enhance diabetes management in underserved populations. This review was conducted using a comprehensive analysis of current literature and existing studies to provide insights into the multifaceted nature of diabetes disparities and to highlight successful interventions and necessary policy changes to improve health outcomes for underserved populations. Addressing these inequities required a multifaceted approach, including community-based interventions, policy reforms, and technological advancements to ensure equitable diabetes care for all. Keywords: Diabetes disparities, Underserved populations, social determinants of health, Healthcare access, Technological innovations.

The Analysis Study of Prevalence of Gastrointestinal Complication in Patients with Type 2 Diabetes Mellitus : A Comprehensive Systematic Review

Background: Diabetes, a chronic metabolic disorder, has a high prevalence worldwide due to changing lifestyles and urbanization. It is associated with life-threatening hazards and disabling complications, decreasing life expectancy. Gastroparesis, a well-known manifestation, is more prevalent in women. Understanding the prevalence of gastrointestinal complications in type 2 diabetes mellitus patients is crucial for creating effective treatment regimens. Methods: Adhering to the PRISMA 2020 principles, this systematic study focused on analyzing complete English literature texts published from 2011 to 2024. Several websites, such as ScienceDirect, PubMed, and SagePub, were used to collect the literature. Result: The study examined over 4000 publications sourced from recognized platforms such as Science Direct, SagePub, and PubMed. Following the decision to subject eight publications to further scrutiny, a comprehensive examination of the complete body of literature was conducted. Conclusion: Diabetes often leads to gastroparesis, a condition affecting the upper gastrointestinal tract, associated with metabolic imbalance, high blood sugar levels, low insulin, hormone release, and H. pylori infection. This condition is linked to the contractile activity of the duodenum, and is associated with various liver conditions, including diabetic fibrosis and cirrhosis.

Investigation of Pharmacological Screening Techniques for Diabetes Mellitus

Diabetes is the medical term for a condition in which the body fails to generate or use insulin as intended, leading to high blood sugar levels. Both in-vitro and in-vivo techniques are employed to assess the antidiabetic potential of synthetic or natural compounds that have been extracted from conventional plants. The rat paw oedema method is frequently used to screen anti-inflammatory medications. The use of animals in this procedure has drawbacks related to ethical concerns. We proposed an in vitro system in this context that would not have these drawbacks. Rat in vivo experiments are frequently used to screen anti-hyperlipidemic medications. Pharmacological screening currently entails testing novel chemical entities as plant extracts or any other type of material (synthetic or semisynthetic) in isolated preparations before testing on entire animals. Rats and mice are typically used, but occasionally higher animals like dogs and monkeys are as well. These techniques have been used to assess the majority of medications now used in therapy. Wherever possible, non-animal alternatives are being developed. Pharmacologists' constant struggle is to reconcile in vitro and in vivo results. A thorough grasp of the in vivo performance is one of the main objectives of pharmacological screening. Therefore, it would be praiseworthy to establish in vitro assessments that might be connected to in vivo performance.

Depression and Anxiety Screening of Patients With Type II Diabetes Mellitus Attending Primary Healthcare Centers in Saudi Arabia.

Diabetes mellitus is a chronic condition affecting both physical and mental health. High blood sugar levels can lead to serious complications if not managed properly. Individuals with diabetes are also at a higher risk of developing mental health disorders like depression and anxiety. The objective was to assess the prevalence of depression and anxiety among patients with type II diabetes attending primary healthcare centers in Makkah, Saudi Arabia. An analytical cross-sectional study design was employed, targeting adults with type II diabetes attending primary healthcare centers in Makkah. A total sample size of 412 participants was selected through systematic random sampling. Data were collected using validated Arabic versions of the patient health questionnaire (PHQ-9) and the general anxiety disorder (GAD-7) questionnaire, along with demographic information. Statistical analysis was conducted with non-parametric tests used to evaluate the significance of outcome variables. The median age of participants was 47 years, with 237 (57.5%) being male and 373 (90.5%) Saudi nationals. Neuropathy was the most prevalent complication, affecting 181 (43.9%) of participants. The median GAD-7 score was 5, and the median PHQ-9 score was 7. Mild to moderate depression was observed in 224 (54.4%), and 194 (47%) had mild-to-moderate anxiety. Significant associations were found between mental health outcomes and several factors: nationality (non-Saudis showed higher depression and anxiety scores, p = 0.010 and p = 0.020, respectively), marital status (divorced participants had the highest mean ranks for both conditions, p = 0.003 for depression and p < 0.001 for anxiety), and monthly income (p = 0.007 for depression and p = 0.008 for anxiety). Additionally, the presence of diabetes complications was strongly associated with higher depression and anxiety scores (p < 0.001 for both). The findings indicate a high prevalence of depression and anxiety among patients with type II diabetes in Makkah. These mental health issues are significantly associated with socio-demographic factors and diabetes complications. Integrated medical and psychological care is needed to improve the quality of life and treatment outcomes for diabetic patients.

Computational Simulation Studies, Synthesis, In Vitro, and In Vivo Evaluation of Heterocyclically Substituted Glycinamide Derivatives as DPP 4 Inhibitors

AbstractAccording to the International Diabetes Federation, the global prevalence of diabetes is projected to increase to 643 million individuals by 2030 and 783 million by 2045. DPP 4 inhibitors approved by regulatory authorities worldwide, efficiently to regulate high blood sugar levels by impeding the degradation of glucagon like peptide‐1 (GLP‐1), an incretin hormone, prolonging the physiological effects of GLP‐1. In the present research work, a total of 12 glycinamide‐based compounds with aromatic substituents on the N‐terminus and heterocycles like piperidine, 4‐phenylpiperazine, and 6‐fluorobenzo[d]isoxazol‐3‐piperidine on the C‐terminus were synthesized and characterized. The synthesized compounds were subjected to in vitro DPP 4 inhibition assay. The promising compound JAG‐D2 was evaluated in vivo using OGTT assay in streptozotocin‐induced diabetic rats. JAG‐D2 produced a significant reduction in blood glucose levels at 20 mg/kg and 40 mg/kg doses when compared with a 1 mg/kg dose of the reference standard sitagliptin. The synthesized compounds occupied the active site of DPP 4 as demonstrated in molecular docking and MM‐GBSA analysis for rescoring on the basis of binding free energy. Molecular dynamics studies displayed the stability of the docked pose of JAG‐D2 in the catalytic site of DPP 4. All compounds exhibited compliance with Lipinski's rule of five, suggesting drug‐like properties.

Knowledge and Awareness of Type 2 Diabetes Complications in Conflict-Affected Regions: A Cross-Sectional Study in Homs, Syria.

Background Diabetes mellitus is a significant global health problem characterized by high blood sugar levels due to either a lack of insulin or insensitivity to it. Concerns about diabetes complications are growing both globally and locally, making effective preventive measures crucial to tackle these complications. This research aimed to determine the level of knowledge and awareness regarding type 2 diabetes complications among patients in Homs, Syria, during the ongoing conflict. Methods A cross-sectional study was conducted among 1016 diabetic patients aged 16 years and above attending internal medicine and diabetes clinics in Homs City through a structured questionnaire administered via social media, telephone interviews, and paper forms. The data collected were analyzed using SPSS version 23.0 (IBM Corp., Armonk, USA). Descriptive statistics were used to summarize the demographic characteristics. In contrast, inferential statistics such as the chi-square test were used to test for associations between different variables with p-value < 0.05 as the significance level. Results The study found that 478 (47.1%) of participants were male, and 538 (53%) were female. The majority 652 (64.2%) were between 46 and 70 years old, and 422 (41.5%) had primary-level education. Most were married 750 (73.8%) and lived in urban areas 856 (84.2%). Diabetes knowledge varied significantly by age, education, occupation, and residence. Housewives and those with lower education levels had the least knowledge, while urban residents and those with 1-5 years since diagnosis had better awareness. Individuals with relatives in the medical field had higher knowledge levels.Results indicate that gender did not significantly influence diabetes knowledge (p = 0.19), while younger adults showed poorer awareness compared to older age groups (p < 0.05). Higher education levels were associated with better knowledge (p < 0.05), and a family history of diabetes correlated with greater awareness of complications. Occupation significantly impacted knowledge, with employed individuals and urban residents demonstrating higher awareness levels (p < 0.05). Diabetes knowledge peaked during the 6-10 year disease duration (p < 0.05). Conclusion This study assesses diabetes knowledge and management practices among 1016 participants in Homs, Syria, focusing on demographic and socioeconomic factors. Our findings underscore the need for targeted educational initiatives to improve diabetes management, particularly among younger, less educated, and rural populations in Homs.The analysis reveals significant knowledge gaps about diabetes across different demographics in Homs, highlighting the need for targeted educational programs, especially for younger individuals, the less educated, and rural residents. The study emphasizes the importance of education and medical practice in understanding diabetes, particularly in conflict-affected areas.

Phytochemicals and their Potential Mechanisms against Insulin Resistance.

Insulin's inception dates back to 1921 and was unveiled through a momentous revelation. Diabetes is a dangerous, long-term disease in which the body fails to generate enough insulin or utilize the insulin it creates adequately. This causes hyperglycemia, a state of high blood sugar levels, which can even put a person into a coma if not managed. Activation of the insulin receptor corresponds to two crucial metabolic functions, i.e., uptake of glucose and storage of glycogen. Type 2 diabetes mellitus (T2DM) exists as one of the most challenging medical conditions in the 21st century. The sedentary lifestyle and declining quality of food products have contributed to the rapid development of metabolic disorders. Hence, there is an urgent need to lay some reliable, significant molecules and modalities of treatment to combat and manage this epidemic. In this review, we have made an attempt to identify and enlist the major phytoconstituents along with the associated sources and existing mechanisms against insulin resistance. The conducted study may offer potential sustainable solutions for developing and formulating scientifically validated molecules and phytoconstituents as formulations for the management of this metabolic disorder.

A review on medicinal plants for the management of diabetes mellitus

Diabetes is a metabolic disorder in which there are high levels of sugar in the blood, a condition called hyperglycemia. Under normal conditions, food is broken down to glucose which then enters the bloodstream and acts as fuel for the body. The pancreas produces a hormone called insulin which helps to carry glucose from the bloodstream into muscle, fat and liver where it can be used as fuel. Diabetics are not able to move this sugar out of the bloodstream because of two primary reasons as their pancreas does not produce enough insulin and/or their cells do not respond normally to insulin, a condition called insulin resistance. This is why people with diabetes have high blood sugar levels. Diabetes mellitus can induce life-threatening health problems such as blindness, heart disease, kidney damage, and stroke. Although the cure of DM has improved over the past decades, its morbidity and mortality rates remain high. Hence, new therapeutic strategies are needed to overcome the burden of this disease. One such prevention and treatment strategy that is easily accessible to diabetic patients at low cost is the use of medicinal plants The research objective of this review article is to study DM and explore its treatment modalities based on medicinal plants. Results of numerous scientific investigations revealed that chemical constituents present in some medicinal plants possess anti-hypoglycemic activities and show promise for the prevention and/or control of DM.