The Role of Garlic, Turmeric and Ginger in Preventing Chronic Disease in Prediabetic and Pre-Hyperlipidemia Population (Published)
This study looks up to the preventive role of garlic, turmeric, and ginger for delaying or reducing the progression of metabolic disturbances among individuals diagnosed with pre-diabetes and pre-hyperlipidaemia. These intermediary metabolic states are characterised by impaired glucose regulation, elevated lipid levels, increased oxidative stress, and low-grade chronic inflammation, which significantly increase the risk of developing type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD). Early nutritional interventions during this reversible stage may therefore offer an effective strategy to slow disease advancement. By integrating findings from experimental animal studies, human clinical trials, and phytochemical investigations, this research explores the multifaceted mechanisms through which these bioactive spices exert therapeutic effects. Garlic contains organosulfur compounds such as allicin, turmeric is rich in curcumin, and ginger provides gingerols and shogaols each possessing compelling antioxidant and anti-inflammatory properties. These composites are believed to reduce oxidative damage, suppress inflammatory pathways, enhance insulin sensitivity, improve pancreatic β-cell function, and regulate lipid metabolism. Evidence from preclinical and clinical studies indicates that supplementation with these spices may improve glycaemic parameters such as blood glucose in fasting and HbA1c, optimize lipid reports by controlling the cholesterol, good and bad cholesterol profile that results in reducing biomarkers of oxidative stress and systemic inflammation. Collectively, these findings suggest that garlic, turmeric, and ginger may serve as valuable nutraceutical agents in preventing or delaying the transition from pre-metabolic disorders to overt chronic diseases. However, despite promising findings, variations in dosage, bioavailability, and study design highlight the necessity for larger, well-controlled clinical tries to confirm long-term efficiency and ssecurity. Continued investigation will help establish standardized therapeutic guidelines and clarify the synergistic potential of combined supplementation.
Keywords: Diabetes Mellitus, cardiovascular disease, low density lipoprotein
Application of Artificial Intelligence in Continuous Blood Glucose Monitoring Techniques and Management of Diabetes: A Review (Published)
Emerging technologies and control systems revolutionized healthcare services which is very evident in self-management of diabetes mellitus by integrating continuous glucose monitors (CGMs), insulin pumps, and hybrid closed-loop systems, which significantly improve glycemic control and reduce hypoglycemia risk. In diabetes management, artificial intelligence (AI) technologies are used for three primary application which are closed-loop control algorithms, glucose prediction through continuous glucose monitoring (CGM) biosensors and AI algorithms, and the calibration of CGM biosensors with the assistance of AI algorithms. Integration of AI technologies into diabetes care supports better clinical outcomes, thereby reducing administrative burden and costs associated with diabetes management. Continuous Glucose Monitoring (CGM) systems, which plays vital role for immediate glucose data delivery, have shown effectiveness in improving diabetes management by reducing HbA1c levels and empowering self-care skills. This has nurtured an increased sense of confidence among patients in managing their medical condition. However, the successful adoption of these technologies requires substantial support from healthcare professionals and family members to ensure adherence and effective use, especially considering factors like family income, educational background, and technological proficiency. In spite of all the research advancements made continuous glucose monitoring (CGM) technologies are still evolving towards compactness, flexibility, sustained functionality, calibration-free operation, and closed-loop systems and free energy harvestability for prolong operation life.
Keywords: Artificial Intelligence, Diabetes Mellitus, biosensors, continuous glucose monitoring, hypoglycemia.
Factors Influencing Medication Adherence among Patients With Diabetes Mellitus And Hypertension In Nigeria (Published)
Medication non-adherence results in increased morbidity, mortality and financial loss. Reasons for medication non-adherence are multifactorial. This cross-sectional study was conducted to determine the prevalence of, and factors contributing to medication non-adherence among patients with diabetes mellitus and hypertension attending some secondary and tertiary health care facilities in Lagos, Nigeria. Of the 100 patients, 32% were compliant with their medications. Most (39%) respondents were noncompliant because of lack of funds and cost of medication, 19% due to forgetfulness, 16% because they felt well, and 15% due to non-availability of drugs at the pharmacy. Other reasons for non-compliance include illnesses (9%), side effects of medications (1%) and misinterpretation of prescription (1%). Among the socio-demographic variables studied, only male gender was positively associated with medication compliance. Adherence to anti-diabetics and anti-hypertensives was low. Both health system and patients’ related issues contributed to poor compliance and these should be addressed to improve medication adherence.
Keywords: Adherence, Diabetes Mellitus, Hypertension, Nigeria