International Journal of Diabetes Management

Unveiling a Novel Frontier: Exploring Creatine Applications in Diabetes Management

2024-06-28T06:32:35-05:00

In the dynamic realm of diabetes management, the quest for innovative strategies continually evolves. This article embarks on an exploration of a groundbreaking frontier, introducing a new approach that extends beyond conventional paradigms. We delve into the intriguing intersection of creatine applications and diabetes care, unveiling a novel perspective that holds potential to redefine how we approach the multifaceted challenges posed by this metabolic condition. Traditionally renowned for its role in enhancing muscular energy metabolism, creatine has emerged from the realms of sports nutrition to captivate the attention of researchers and practitioners alike in the field of diabetes management. This paradigm shift invites us to reconsider the scope of creatine supplementation, moving beyond its established association with athletic performance to explore its nuanced impact on glucose regulation and insulin sensitivity in individuals grappling with diabetes. As we navigate this uncharted territory, this article aims to provide a comprehensive overview of the intricate interplay between creatine and diabetes, weaving together current research findings, emerging treatment trends, and the potential synergies that could shape the future of diabetes care. From the molecular intricacies of energy metabolism to the broader landscape of contemporary diabetes treatments, our exploration seeks to illuminate a path towards a more holistic and personalized approach to diabetes management.

Revolutionary Role of Stem Cell Therapy Coupled with Modern AI Based Technologies in Diabetes Management and Remission

2024-06-28T11:15:03-05:00

Type 2 diabetes is a chronic metabolic disorder characterized by high blood sugar levels over a prolonged period. It is a significant global health issue, affecting millions of people worldwide. The traditional treatment for diabetes involves medication, lifestyle changes, and regular monitoring of blood sugar levels to decide on treatment modifications accordingly. However, recent advancements in modern technologies/techniques involving artificial intelligence (AI) and stem cell therapy have shown promising results in achieving excellent diabetes care outcomes and even remission, which was never conceivable a few decades ago. AI-driven interventions enable the development of tailored treatment plans, leveraging patient data to optimize glycaemic control and predict complications. Whole Body Digital Twin (WBDT) models provide holistic insights, facilitating significant rates of diabetes remission. Stem cell therapy when coupled with newer technologies has shown to be revolutionary towards this. Especially therapies targeting the mammalian Target of Rapamycin (mTOR) pathway show a great potential for regenerating damaged pancreatic beta cells and improving insulin production. However, challenges such as data privacy concerns with AI models utilising big data and ethical considerations in stem cell research persist; there is a need for regulatory norms towards this especially with the availability of these advanced treatment modalities, which would be the face of medical science in the coming years. For sure, combining AI and stem cell-based therapies present an innovative approach to enhance diabetes management, enabling the identification of suitable candidates for treatment and predicting treatment success. Diabetes was considered a lifelong disease that carried a huge burden of secondary organ complications over a period. This manuscript explores the potential of these innovative approaches in treating diabetes and discusses the scientific evidence supporting their utility.

The Effect of Atorvastatin Intensity, Obesity, Gender, and Age upon New-onset Type 2 Diabetes Incidence among Libyan Coronary Heart Disease Patients

2024-06-28T11:17:04-05:00

Background: Atorvastatin is one of the statins family of lipid-lowering drugs. Statin has been linked to protective actions against cardiovascular disease; however, the use of statin has been linked to an increased risk of diabetes.

Aim of the study: To assess the prevalence of diabetes following the use of Atorvastatin and also to evaluate the effect of the statin intensity, BMI, age, and gender upon the glycemic control and incidence of diabetes.

Material and methods: 200 CHD patients divided into 2 groups. The atorvastatin group (using Atorvastatin (40 or 80 mg/day) for<2 months and the control group (non-atorvastatin users). FBG, HbA1c, total cholesterol, triglyceride, ALT, and AST were investigated, and BMI was calculated for all participants.

Results: Generally, the NOD incidence was 17% (17 patients had NOD out of 100 Atorvastatin-consuming patients), whereas, there were no NOD cases in the non-Atorvastatin group (100 cases). The oldest age ( ≥ 70 years) Atorvastatin-using patients showed the highest incidence of NOD (43.8%). The NOD incidence was higher in the male group with 22 NOD cases (27.5%) than that of the female group which showed 3 NOD cases (15%). Regarding the effect of BMI, the obese group (BMI ≥ 30) showed a higher incidence of NOD (23 cases, 45.1%) than the non-obese group (BMI<30) which showed 13 NOD cases (26.5%) of the atorvastatin using patients. In regards to statin intensity, the 80 mg/day-Atorvastatin subgroup showed 7 NOD cases (30%) which was higher than the 40 mg/ day-Atorvastatin subgroup which showed 18 NOD cases (23%) of the atorvastatin using patients.

Conclusion: Atorvastatin treatment was significantly implicated in the development of NOD. NOD incidence increases with higher doses of statin. The risk of NOD was also affected by other factors including; obesity, gender, and old age.

Gut Microbiota as Therapeutic Targets in Diabetes Management: Opportunities and Challenges

2024-06-28T06:32:35-05:00

This article delves into the complex interplay between Type 2 Diabetes Mellitus (T2DM), gut microbiota, and dietary strategies for effective diabetes management. T2DM, characterized by insulin resistance and β-cell dysfunction, is influenced by genetic and environmental factors. The gut-brain axis and alterations in incretin functioning contribute to gastrointestinal permeability in T2DM. Plant-based diets offer substantial benefits for managing T2DM by improving emotional well-being, HbA1c levels, weight, and cholesterol. High-fiber diets positively impact gut microbiota, serum metabolism, and emotional health in T2DM individuals. Probiotics, prebiotics, synbiotics, and postbiotics (PPSP) are emerging as pivotal interventions. Probiotics improve serum fructosamine, HbA1c, and cholesterol levels, while prebiotics like oligofructose-enriched inulin and synbiotics enhance glycemic control and lipid profiles. Insights from microbiome studies in diverse dietary populations provide personalized approaches for diabetes management. Integrating plant-based nutrition, PPSP interventions, and microbiome-focused strategies may offer a comprehensive and effective approach to T2DM management, addressing physiological aspects and empowering individuals in their health journey.

Analysis of the Profile of Primary Diagnosis Codes for Diabetes Mellitus Inpatients at Dr. Soekardjo Regional General Hospital Tasikmalaya Based on ICD-10, ICD-11, and SNOMED CT

2024-06-28T06:32:35-05:00

Background: the accuracy of a diagnosis code is essential in financing health services and diseases and procedure indexing and hospital management information. Based on the results of the preliminary study, diabetes mellitus is among the top 10 diseases. While coding 10 medical record documents, three consisted of 30% which were accurate, and the seven recorded 70% were found to be accurate. The four-character of the code was predominantly the character with many inaccuracies. Therefore, the researchers conducted a study on the accuracy of a diagnosis code at Dr. Soekardjo Regional General Hospital specifically on inpatient cases of diabetes mellitus in 2022.

Methods: A quantitative type of study with a descriptive research design was implied in the study. The study object is data coding of diabetes mellitus cases. Data are collected by observation and interviews

Results: According to the research results, diabetes mellitus is one of the top 10 diseases, while 40 medical record documents were coded, 20 (50%) were inaccurate and 20 (50%) were accurate, while the highest percentage of unaccuracy occurs in the fourth character of the code. The alignment of codes based on ICD-11 revealed that 10 documents (25%) were not aligned due to lack of specificity regarding ulcer complications and gastropathy.The alignment of codes based on SNOMED CT showed that 40 documents were aligned with the SNOMED CT clinical phrase standards.

Conclusion: The inaccuracies in diabetes mellitus diagnosis coding at Dr. Soekardjo Regional General Hospital are attributed to less specific diagnoses, unclear handwriting by doctors in patient medical records, and coding personnel still facing difficulties in determining complication coding. The researchers suggest solutions such as involving coders and medical personnel in training and socialization activities related to diagnosis codes, particularly for Diabetes Mellitus.