The Role of Herbal Supplements in Type 2 Diabetes Management: An Evidence from a Systematic Review

The pattern and factors which can be associated with the glycemic control of Saudi adult diabetic patients were examined in this study. Confirmed diabetic patients from all regions of Saudi Arabia constituted the study population. Random blood glucose <10 mmol/L and >10 mmol/L was used to categorize patients into good and poor glycemic control patients, respectively. There were 613 confirmed non-insulin dependent diabetic patients (NIDDM), 50% with good glycemic control. Patients with poor glycemic control were significantly older than patients with good glycemic control (51.5 vs. 47 years, P=0.0001). The insulin-treated diabetic population amounted to 13%, compared with 43% and 44% for oral agent and diet, respectively. The rate of insulin users among poor glycemic control diabetic population was 18%, compared with 50% for oral agents. There was a significant relationship between glycemic control and age, and treatment modalities of DM. Subjects who had good glycemic control of DM were younger and following a diet regimen, while those who had poor glycemic control were older and on insulin treatment. Multivariate analysis comprising 415 individuals was conducted to find out the factors that can potentially influence, or may be associated with, the control of DM. The association of insulin therapy with poor glycemic control is not a cause-effect relationship. Insulin therapy in our study population is underutilized, given the high rate of poor glycemic control and high rate of relative occurrence of complication among the Saudi diabetic population. There is a need to address the importance of maintaining good glycemic control, and the reason for the low rate of insulin users. Close periodic monitoring of glycemic control, utilizing laboratories and home glucose monitoring devices, is required. Effective implementation of these measures, in addition to diabetes education, will have an impact on the future outcome of the Saudi diabetic population.

Type 1 Diabetes in Children and Adolescents.

. The Framingham Risk Score (FRS) assesses coronary heart disease (CHD) risk and predicts acute coronary events. Metabolic markers like LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and TG/HDL ratio play critical roles in atherosclerosis and cardiovascular risk. Elevated LDL cholesterol, fasting blood glucose, and uric acid contribute to plaque formation, inflammation, and vascular damage, while high triglycerides and low HDL cholesterol exacerbate atherogenesis. This study explores the relationship between these markers and FRS to enhance CHD risk prediction and support targeted cardiovascular interventions. This study analyzed LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and TG/HDL ratio with Framingham Risk Score in 85 participants, excluding those with incomplete data or chronic illnesses. The analysis found significant correlations between metabolic parameters and the 10-year myocardial infarction risk. LDL cholesterol, triglycerides, and uric acid showed moderate positive associations with cardiovascular outcomes, while the triglyceride-to-HDL ratio and fasting blood glucose had weaker but significant correlations. These findings highlight lipid profiles and metabolic markers as key contributors to cardiovascular risk. This study highlights significant correlations between LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and the triglyceride/HDL ratio with 10-year cardiovascular risk. These findings emphasize the importance of lipid profiles, glycemic control, and metabolic markers in predicting coronary outcomes and guiding targeted preventive interventions for improved cardiovascular risk management.

Aim: Effective diabetes management is achieved with adequate glycemic control. Nutrition and physical activity have an important role in glycemic control. This study was conducted to determine the relationship between glycemic control and nutrition attitude, Mediterranean diet adherence and physical activity levels of women with Type 2 diabetes aged 20-64 years who applied to a state hospital in Konya province. Material and Methods: This correlational study was conducted with 246 women aged 20-64 years with Type 2 diabetes who applied to the Internal Medicine and Endocrinology outpatient clinics of a hospital in Konya. Individuals were selected by random sampling method. Data were collected using a questionnaire form, Attitudes Toward Healthy Eating Scale, Mediterranean Diet Adherence Scale and International Physical Activity Questionnaire Short Form (IPAQ-SF). Descriptive statistics, Chi-Square, Fisher's exact test, Kruskal Wallis test, Spearman correlation analysis and logistic regression analysis were used to analyze the data collected by face-to-face interviews. Results: In the study, glycated hemoglobin A1c (HbA1c) and fasting blood glucose (FBG) were used to assess glycemic control. 55.3% and 56.9% of the individuals had poor glycemic control in terms of HbA1c and FBG, respectively. According to logistic regression analysis, for HbA1c; having diabetes for more than 10 years (OR=0.291, 95% Cl=0.095-0.894), high triglycerides (OR=0.440, 95% Cl=0.190-1.017) and low high density lipoprotein (HDL) (OR=0.293, 95% Cl=0.114-0.753); For FBG, increased frequency of skipping medication and insulin use (OR=2.431, 95% Cl=1.090-5.424) and low HDL level (OR=0.269, 95% Cl=0.110-0.653) were significant risk factors. Among the individuals, 73.2% had a moderate attitude towards healthy eating, 46.3% had a moderate adherence to the Mediterranean diet and 60.6% were physically inactive. In addition, there was a very weak negative significant correlation (p

The significance of hyperglycaemia and other comorbidities during the <scp>COVID</scp>‐19 pandemic

Diabetes Management Demands Flexibility

Aim: The relationship between diabetes mellitus (DM) and lifestyle quality become important in diabetes research in last year. The present study aims to study the influence of metformin response in sleep in diabetes mellitus patients type 2. Materials and Methods: A cross sectional study was designed to achieve study goal, glycemic parameters included fasting blood glucose (FBG), glycated protein (HbA1c%), insulin (IN), insulin resistance (HOMA-IR) and insulin sensitivity (IS). PCR sequencing was used to detect SLC47A2 intronic variants and its related with glycemic control and sleep status. Results: Among the study population, about 26.3% achieved well glycemic control, 30% were moderately controlled, and 43.8% were poorly controlled. Sleep quality assessment showed that the majority of participants in all glycemic groups experienced intermediate sleep. The prevalence of insomnia increased with worsening glycemic control, from 4.8% in the well-controlled group to 17.1% in poorly controlled participants, in non- statistically significant (p = 0.722). Biochemical parameters confirmed significant differences in fasting blood glucose and HbA1c across the three glycemic categories (p < 0.001), insulin, HOMA-IR, and insulin sensitivity did not differ significantly. Multiple regression analyses indicated that none of the biochemical predictors significantly explained sleep in any group (p > 0.05), in poorly controlled patients, non-significant opposing trends were observed for insulin and insulin resistance, sociodemographic factors included supplement use, education level, and employment were associated with better sleep among poorly controlled patients. Genetic analysis of two intronic variants in the SLC47A2 gene (g.19716681G>C and rs1597652185) revealed no significant associations with glycemic control or sleep, though both showed similar distribution patterns across groups. Statistical analysis didn't find significant association between either variant and glycemic or sleep status (p > 0.05). Conclusions: Poor glycemic control was common and associated with higher insomnia prevalence. While demographic and clinical factors showed no clear links with glycemic control or sleep, supplement use emerged as a protective factor. FBG and HbA1c strongly differentiated control groups, but other biomarkers and SLC47A2 variants were not predictive. Findings suggest that combining metabolic management with supportive measures like supplementation may improve sleep and outcomes in type 2 diabetes..

ISPAD Clinical Practice Consensus Guidelines 2022: Diabetes in adolescence

Prediabetes: A Prevalent and Treatable, but Often Unrecognized, Clinical Condition

Organization of Diabetes Care

Efficacy of supplementation of probiotics on maternal glycaemic control – A systematic review and meta-analysis of randomized controlled trials

Effects of Dietary Approaches to Stop Hypertension (DASH) diet on some risk for developing type 2 diabetes: A systematic review and meta-analysis on controlled clinical trials

Background: There is a growing interest in the considerable benefits of dietary supplementations, such as folic acid, on the glycemic profile. We aimed to investigate the effects of folic acid supplementation on glycemic control markers in adults. Methods: Randomized controlled trials examining the effects of folic acid supplementation on glycemic control markers published up to March 2021 were detected by searching online databases, including Scopus, PubMed, Embase, and ISI web of science, using a combination of related keywords. Mean change and standard deviation (SD) of the outcome measures were used to estimate the mean difference between the intervention and control groups at follow-up. Meta-regression and non-linear dose-response analysis were conducted to evaluate the association between pooled effect size and folic acid dosage (mg/day) and duration of the intervention (week). From 1814 detected studies, twenty-four studies reported fasting blood glucose (FBG), fasting insulin, hemoglobin A1C (HbA1C), and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) as an outcome measure. Results: Results revealed significant reductions in FBG (weighted mean difference (WMD): −2.17 mg/dL, 95% CI: −3.69, −0.65, p = 0.005), fasting insulin (WMD: −1.63 pmol/L, 95% CI: −2.53, −0.73, p < 0.001), and HOMA-IR (WMD: −0.40, 95% CI: −0.70, −0.09, p = 0.011) following folic acid supplementation. No significant effect was detected for HbA1C (WMD: −0.27%, 95% CI: −0.73, 0.18, p = 0.246). The dose-response analysis showed that folic acid supplementation significantly changed HOMA-IR (r = −1.30, p-nonlinearity = 0.045) in non-linear fashion. However, meta-regression analysis did not indicate a linear relationship between dose, duration, and absolute changes in FBG, HOMA-IR, and fasting insulin concentrations. Conclusions: Folic acid supplementation significantly reduces some markers of glycemic control in adults. These reductions were small, which may limit clinical applications for adults with type II diabetes. Further research is necessary to confirm our findings.

A randomized clinical trial comparing basal insulin peglispro and insulin glargine, in combination with

Achieving glycemic control or reduction of hyperglycemia would significantly decrease most of the complications associated with hyperglycemia in diabetes mellitus. It has been stated that measurement of glycosylated hemoglobin (HbA1C) remains the gold standard for the assessment of glycemic control; there is no consensus whether the Fasting or Postprandial is a better predictor of glycemic control in poor resource setting where HbA1C is not easily accessed or available. The aim of this research is to determine fasting and postprandial plasma glucose and their correlation with HbA1C in glycemic control. A cross sectional case control study was carried out from January, 2023 to July, 2023; a total of 203 participants were recruited into the study. Fasting blood glucose (FBG), 2 hours post prandial blood glucose (2HPBG) were determined in all the subjects using the enzymatic glucose oxidase method for glucose estimation according to the instruction of the manufacturer, while HbA1C was determined using Boroaffinity Chromatographic method according to the instruction of the manufacturer. Statistical data analysis was carried out using SPSS software (Version 25.0, IBM Corp., Armonk, New York USA), and p&lt;0.001 were defined as statistically significant; the correlation between the parameters was carried out using Pearson’s correlation. Both the FBG and the 2HPBG showed positive correlation with HbA1C in the diabetic and control subjects; however, the level of correlation varies. The correlation of FBG and 2HPBG with HbA1C is directly proportional to the concentration of blood glucose level. The FBG for the control and the subjects are 7.40±1.59 and 143.67±5.01, with HbA1C of 4.45±0.05; 6.26±1.47 respectively. The 2HPBG for the control and the subjects are 120.70±1.75 and 192.92±7.05, with HbA1C of 5.56±0.07; 7.82±0.22 respectively. The FBG correlation to HbA1C is r= 0.875, p&lt;0.001; while 2HPBG is r= 0.908, NEWPORT INTERNATIONAL JOURNAL OF PUBLIC HEALTH AND PHARMACY (NIJPP) Volume 4 Issue 2 2023 OPEN ACCESS ©NIJPP ONLINE ISSN: 2992-5479 Publications 2023 PRINT ISSN: 2992-605X Page | 10 p&lt;0.001. The study showed positive correlation of FBG and 2HPBG with HbA1C, since HbA1C is the value of the percentage concentration of glucose at a given period of time; FBG, 2HPBG and HbA1C can be used to evaluate the degree of glycemic control in diabetic patients’ management, in order to minimize or avoid diabetic complications. Keywords: FBG, PBG, Diabetes, HbA1C, Correlation, Complications