Tirzepatide Reduces Fat Mass and Provides Good Glycaemic Control in Type 2 Diabetes Patients Undergoing Haemodialysis: A Single-Centre Retrospective Study.

OBJECTIVE: Tirzepatide is an injectable peptide approved by the US Food and Drug Administration for the treatment of Type 2 diabetes (T2DM). Its weight-loss effect primarily targets fat reduction; however, such effect on patients with chronic kidney disease (CKD) undergoing haemodialysis (HD) has not been reported. METHODS: Nine patients with CKD undergoing HD received weekly tirzepatide doses (2.5-7.5 mg) once a week. Evaluations encompassed tirzepatide's impact on dry weight (DW) and body composition assessed at baseline and study conclusion using bioelectrical impedance analysis. This longitudinal study included nine patients, with a median age of 53 years and median HD duration of 4 years. RESULTS: Tirzepatide treatment significantly decreased glycated albumin compared with the value at baseline (22.7 ± 5.4 vs. 18.3 ± 2.5%, p = 0.028, respectively). Significant reductions were observed in DW (-1.0 kg, p = 0.024) and body mass index (-0.6 kg/m2, p = 0.050) following tirzepatide administration. Total fat mass was also reduced, but not significantly (- 2.51% from baseline, p = 0.214). In contrast, skeletal muscle mass was not decreased (-1.02% from baseline, p = 0.722). No serious side effects other than nausea were observed during the study period. CONCLUSION: Tirzepatide effectively provides good glycaemic control in T2DM patients undergoing HD, decreasing DW by reducing body fat mass without increasing frailty risk.

Association of RASGRP1 polymorphism with vascular complications in Chinese diabetic patients with glycemic control and antihypertensive treatment.

BACKGROUND: Studies have shown that RASGRP1 was potently associated with the onset of type 2 diabetes mellitus (T2DM), and RASGRP1 rs7403531 was significantly correlated with islet function in T2DM patients. However, the effect of RASGRP1 polymorphism on blood glucose and blood pressure in T2DM patients after continuous treatment has yet to be fully elucidated. OBJECTIVE: This study aimed to explore the association between RASGRP1 genetic polymorphism and cardiovascular complications in T2DM patients, so as to provide more evidence for the individualized treatment of T2DM patients. METHODS: We retrospectively analyzed a large-scale multicenter drug clinical study cohort that based on a 2 × 2 factorial (glucose control axis and blood pressure lowering axis) randomized controlled design, with follow-up for 5 years. The major vascular endpoint events included cardiovascular death, non-fatal stroke, coronary heart disease, new-onset or worsening renal disease, and diabetic retinopathy. RASGRP1 rs12593201, rs56254815 and rs7403531 were finally selected as candidate single nucleotide polymorphisms. Mixed linear model and Cox hazard ratio (HR) model were used for data analysis with IBM SPSS (version 20.0 for windows; Chicago, IL). RESULTS: Our study enrolled 1357 patients with high-risk diabetes, with a mean follow-up duration of 4.8 years. RASGRP1 rs7403531 was associated with vascular events in hypoglycemic and antihypertensive therapy. Specifically, compared with CC carriers, patients with CT/TT genotype had fewer major microvascular events (HR = 0.41, 95% confidence interval (CI) 0.21-0.80, P = 0.009), and reduced the risk of major eye disease events (HR = 0.44, 95% CI 0.20-0.94, P = 0.03). For glucose lowering axis, CT/TT carriers had a lower risk of secondary nephropathy (HR = 0.48, 95% CI 0.25-0.92, P = 0.03) in patients with standard glycemic control. For blood pressure lowering axis, all cerebrovascular events (HR = 2.24, 95% CI 1.11-4.51, P = 0.025) and stroke events (HR = 2.07, 95% CI 1.03-4.15, P = 0.04) were increased in patients with CC genotype compared to those with CT/TT genotype in the placebo group, respectively. Furthermore, patients with CC genotype showed a reduced risk of major cerebrovascular events in antihypertensive group (HR = 0.36, 95% CI 0.15-0.86, P = 0.021). For RASGRP1 rs56254815, compared with the AA genotype carriers, the systolic blood pressure of AG/GG carriers in the antihypertensive group decreased by 1.5mmhg on average (P = 0.04). In the placebo group, the blood pressure of AG/GG carriers was 1.7mmHg higher than that of AA carriers (P = 0.02). CONCLUSION: We found that patients with G allele of RASGRP1 (rs56254815) showed a better antihypertensive therapy efficacy in T2DM patients. The rs7403531 T allele could reduce the risk of major microvascular events and major eye diseases in T2DM patients receiving either hypoglycemic or antihypertensive therapy. Our findings suggest that RASGRP1 genetic polymorphism might predict the cardiovascular complications in T2DM patients.

Factors influencing optimal diabetes care and clinical outcomes in Thai patients with type 2 diabetes mellitus: a multilevel modelling analysis.

BACKGROUND: Increasing levels of poor glycaemic control among Thai patients with type 2 diabetes mellitus (T2DM) motivated us to compare T2DM care between urban and suburban primary care units (PCUs), to identify gaps in care, and to identify significant factors that may influence strategies to enhance the quality of care and clinical outcomes in this population. METHODS: We conducted a cross-sectional study involving 2160 patients with T2DM treated at four Thai PCUs from 2019 to 2021, comprising one urban and three suburban facilities. Using mixed effects logistic regression, we compared care factors between urban and suburban PCUs. RESULTS: Patients attending suburban PCUs were significantly more likely to undergo eye (adjusted OR (AOR): 1.83, 95% CI 1.35 to 1.72), foot (AOR: 1.61, 95% CI 0.65 to 4.59) and HbA1c (AOR: 1.66, 95% CI 1.09 to 2.30) exams and achieved all ABC (HbA1c, blood pressure (BP) and low-density lipoprotein cholesterol (LDL-C)) goals (AOR: 2.23, 95% CI 1.30 to 3.83). Conversely, those at an urban PCU were more likely to undergo albuminuria exams. Variables significantly associated with good glycaemic control included age (AOR: 1.51, 95% CI 1.31 to 1.79), T2DM duration (AOR: 0.59, 95% CI 0.41 to 0.88), FAACE (foot, HbA1c, albuminuria, LDL-C and eye) goals (AOR: 1.23, 95% CI 1.12 to 1.36) and All8Q (AOR: 1.20, 95% CI 1.05 to 1.41). Chronic kidney disease (CKD) was significantly linked with high triglyceride and HbA1c levels (AOR: 5.23, 95% CI 1.21 to 7.61). Elevated HbA1c levels, longer T2DM duration, insulin use, high systolic BP and high lipid profile levels correlated strongly with diabetic retinopathy (DR) and CKD progression. CONCLUSION: This highlights the necessity for targeted interventions to bridge urban-suburban care gaps, optimise drug prescriptions and implement comprehensive care strategies for improved glycaemic control, DR prevention and CKD progression mitigation among in Thai patients with T2DM. The value of the clinical target aggregate (ABC) and the process of care aggregate (FAACE) was also conclusively demonstrated.

A randomized controlled trial of an app-based intervention on physical activity and glycemic control in people with type 2 diabetes.

BACKGROUND: We investigated the effects of a physical activity encouragement intervention based on a smartphone personal health record (PHR) application (app) on step count increases, glycemic control, and body weight in patients with type 2 diabetes (T2D). METHODS: In this 12-week, single-center, randomized controlled, 12-week extension study, patients with T2D who were overweight or obese were randomized using ratio 1:2 to a group using a smartphone PHR app (control group) or group using the app and received individualized motivational text messages (intervention group) for 12 weeks. During the extension period, the sending of the encouraging text messages to the intervention group was discontinued. The primary outcome was a change in daily step count after 12 weeks and analyzed by independent t-test. The secondary outcomes included HbA1c, fasting glucose, and body weight analyzed by paired or independent t-test. RESULTS: Of 200 participants, 62 (93.9%) and 118 (88.1%) in the control and intervention group, respectively, completed the 12-week main study. The change in daily step count from baseline to week 12 was not significantly different between the two groups (P = 0.365). Among participants with baseline step counts < 7,500 steps per day, the change in the mean daily step count at week 12 in the intervention group (1,319 ± 3,020) was significantly larger than that in control group (-139 ± 2,309) (P = 0.009). At week 12, HbA1c in the intervention group (6.7 ± 0.5%) was significantly lower than that in control group (6.9 ± 0.6%, P = 0.041) and at week 24, changes in HbA1c from baseline were significant in both groups but, comparable between groups. Decrease in HbA1c from baseline to week 12 of intervention group was greater in participants with baseline HbA1c ≥ 7.5% (-0.81 ± 0.84%) compared with those with baseline HbA1c < 7.5% (-0.22 ± 0.39%) (P for interaction = 0.014). A significant reduction in body weight from baseline to week 24 was observed in both groups without significant between-group differences (P = 0.370). CONCLUSIONS: App-based individualized motivational intervention for physical activity did not increase daily step count from baseline to week 12, and the changes in HbA1c levels from baseline to week 12 were comparable. TRIAL REGISTRATION: ClinicalTrials.gov (NCT03407222).

Functional insulin therapy in type 1 diabetics: Short-term effects on weight and nutritional intake.

INTRODUCTION-AIM: Flexible insulin therapy is currently considered the gold standard therapy of type 1 diabetes. We aimed to study the evolution of glycemic control, weight and nutritional intake of a group of patients with type 1 diabetes, three months after the initiation of functional insulin therapy (FIT). METHODS: This was a prospective longitudinal study having included 30 type 1 diabetic patients hospitalized for education to FIT. Each patient underwent an assessment of glycemic control (glycated hemoglobin (A1C) and number of hypoglycemia), weight and nutritional intake before FIT and 3 months after the initiation of this educative approach. RESULTS: The mean age of patients was 21,8 ± 7,9 years and the sex ratio was 0,5. The mean duration of diabetes was 7,2 ± 6 years. Three months after initiation of FIT, we observed a significant lowering of A1C, which went from 9,2 ± 1,6% to 8,3 ± 1,4% (p<0,001) of the number of minor hypoglycemia (p=0,001) and that of severe hypoglycemia (p= 0,021). the average weight went from 64,6 ± 13,1 kg to 65,5 ± 13,5 kg (p = 0,040) with a significant increase in BMI (p = 0,041). Weight gain was observed in 67% of patients. This weight gain contrasted with a significant decrease in caloric (p = 0,040) and in carbohydrates intakes (p = 0,027). CONCLUSION: Weight gain, associated with better glycemic control, should encourage the healthcare team to strengthen therapeutic education of patients undergoing FIT in order to limit weight gain.

Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis.

OBJECTIVE: This study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC). DESIGN: In this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients. METHODS: The nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes. RESULTS: Amino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups. CONCLUSIONS: These findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.

Assessing glycemic variability in critically ill patients: A prospective cohort study comparing insulin infusion therapy with insulin sliding scale.

Glycemic variability (GV) has been associated with an increased mortality rate among critically ill patients. The clinical outcomes of having less GV even with slight hyperglycemia are better than those having tight glycemic control but higher GV. Insulin infusion remains the preferred method to control stress hyperglycemia in critically ill patients. However, its impacts on GV and clinical outcomes in critically ill patients still need further investigation. This study intended to evaluate the impact of insulin infusion therapy (IIT) compared to the insulin sliding scale (ISS) on the extent of GV and explore its impact on the clinical outcomes for critically ill patients. A prospective, single-center observational cohort study was conducted at a tertiary academic hospital in Saudi Arabia between March 2021 and November 2021. The study included adult patients admitted to ICUs who received insulin for stress hyperglycemia management. Patients were categorized into two groups based on the regimen of insulin therapy during ICU stay (IIT versus ISS). The primary outcome was the GV between the two groups. Secondary outcomes were ICU mortality, the incidence of hypoglycemia, and ICU length of stay (LOS). A total of 381 patients were screened; out of them, eighty patients met the eligibility criteria. The distribution of patients having diabetes and a history of insulin use was similar between the two groups. The GV was lower in the IIT group compared to the ISS group using CONGA (- 0.65, 95% CI [- 1.16, - 0.14], p-value = 0.01). Compared with ISS, patients who received IIT had a lower incidence of hypoglycemia that required correction (6.8% vs 2.77%; p-value = 0.38). In contrast, there were no significant differences in ICU LOS and ICU mortality between the two groups. Our study demonstrated that the IIT is associated with decreased GV significantly in critically ill patients without increasing the incidence of severe hypoglycemia. There is no survival benefit with the use of the IIT. Further studies with larger sample size are required to confirm our findings and elaborate on IIT's potential effect in reducing ICU complications in critically ill patients.

Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study.

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow established procedures, the approach for short-term targets during hospitalization varies due to a lack of clinical consensus. Our study aims to elucidate the impact of pre-hospitalization and intra-hospitalization glycemic indexes on in-hospital survival rates in individuals with T2DM, addressing this notable gap in the current literature. METHODS: In this pilot study involving 120 hospitalized diabetic patients, we used advanced machine learning and classical statistical methods to identify variables for predicting hospitalization outcomes. We first developed a 30-day mortality risk classifier leveraging AdaBoost-FAS, a state-of-the-art ensemble machine learning method for tabular data. We then analyzed the feature relevance to identify the key predictive variables among the glycemic and routine clinical variables the model bases its predictions on. Next, we conducted detailed statistical analyses to shed light on the relationship between such variables and mortality risk. Finally, based on such analyses, we introduced a novel index, the ratio of intra-hospital glycemic variability to pre-hospitalization glycemic mean, to better characterize and stratify the diabetic population. RESULTS: Our findings underscore the importance of personalized approaches to glycemic management during hospitalization. The introduced index, alongside advanced predictive modeling, provides valuable insights for optimizing patient care. In particular, together with in-hospital glycemic variability, it is able to discriminate between patients with higher and lower mortality rates, highlighting the importance of tightly controlling not only pre-hospital but also in-hospital glycemic levels. CONCLUSIONS: Despite the pilot nature and modest sample size, this study marks the beginning of exploration into personalized glycemic control for hospitalized patients with T2DM. Pre-hospital blood glucose levels and related variables derived from it can serve as biomarkers for all-cause mortality during hospitalization.

Diabetic peripheral neuropathy: age-stratified glycemic control.

Background: We explore the effect of suboptimal glycemic control on the incidence of diabetic peripheral neuropathy (DPN) in both non-elderly and elderly patients with type 2 diabetes mellitus (T2DM). Methods: A 6-year follow-up study (2013-2019) enrolled T2DM patients aged >20 without DPN. Participants were classified into two groups: those below 65 years (non-elderly) and those 65 years or older (elderly). Biochemical measurements, including glycated hemoglobin (HbA1C), were recorded regularly. DPN was diagnosed using the Michigan Neuropathy Screening Instrument examination. The outcome was DPN occurrence in 2019. Results: In 552 enrollments (69% non-elderly), DPN occurred in 8.4% non-elderly and 24.0% elderly patients. A higher initial HbA1C level was significantly linked with a higher risk of future DPN in the non-elderly group (adjusted odds ratio [AOR] 1.46, 95% CI 1.13-1.89, p=0.004). In comparison, HbA1c at the end of the study period was not associated with DPN in the non-elderly group (AOR 1.17, 95% CI 0.72-1.90, p=0.526). In the elderly group, no statistical relationship was found between HbA1C levels and DPN, either in 2013 or in 2019. Conclusion: Suboptimal glycemic control at baseline, rather than at the end of the study period, predicts an increased risk of future DPN in individuals with T2DM under age 65. This correlation is not seen in elderly patients. Therefore, we recommend implementing enhanced glycemic control early in middle-aged T2DM patients and propose individualized therapeutic strategies for diabetes in different age groups.

The Role of Telehealth in Improving Care Connections and Outcomes for Community Health Center Patients with Diabetes.

INTRODUCTION: Does telehealth decrease health disparities by improving connections to care or simply result in new barriers for vulnerable populations who often lack access to technology? This study aims to better understand the role of telehealth and social determinants of health in improving care connections and outcomes for Community Health Center patients with diabetes. METHODS: This retrospective analysis of Electronic Health Record (EHR) data examined the relationship between telehealth utilization and glycemic control and consistency of connection to the health care team ("connectivity"). EHR data were collected from 20 Community Health Centers from July 1, 2019 through December 31, 2021. Descriptive statistics were calculated, and multivariable linear regression was used to assess the associations between telehealth use and engagement in care and glycemic control. RESULTS: The adjusted analysis found positive, statistically significant associations between telehealth use and each of the 2 primary outcomes. Telehealth use was associated with 0.89 additional months of hemoglobin A1c (HbA1c) control (95% confidence interval [CI], 0.73 to 1.04) and 4.49 additional months of connection to care (95% CI, 4.27 to 4.70). DISCUSSION: The demonstrated increased engagement in primary care for telehealth users is significant and encouraging as Community Health Center populations are at greater risk of lapses in care and loss to follow up. CONCLUSIONS: Telehealth can be a highly effective, patient-centered form of care for people with diabetes. Telehealth can play a critical role in keeping vulnerable patients with diabetes connected to their care team and involved in care and may be an important tool for reducing health disparities.

The Impact of a Nutritional Intervention on Glycemic Control and Cardiovascular Risk Markers in Type 2 Diabetes.

INTRODUCTION: Nutritional management plays a crucial role in treating patients with type 2 diabetes (T2D), working to prevent and control the progression of chronic non-communicable diseases. OBJECTIVES: To evaluate the effects of individualized nutritional interventions on weight, body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), fasting blood glucose (FBG), hemoglobin A1c (HbA1c), total cholesterol (TC), LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), triglycerides (TGs), systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR)} over 12 months and subsequently at follow-up (15 months). METHODS: This longitudinal experimental study (without randomization and blinding) enrolled 84 sedentary participants with T2D (both sexes, aged 18-80 years). They were divided into a control group of 40 participants who received only medical consultations, and an intervention group of 44 participants who received the same medical care along with a nutritional assessment. Consultations occurred quarterly from August 2020 to November 2022 (first-twelfth month), with six to nine patients per session. Subsequently, a follow-up was conducted from December 2022 to November 2023, during which the intervention group had only medical care (during the 12th-15th months). Personalized dietary planning was inspired by the Mediterranean/DASH diets adapted to Brazilian foods and socioeconomic cultures. STATISTICAL ANALYSIS: Normal variables were compared between groups for each time point and also within each group across different time points using a two-way ANOVA (repeated measures for intragroup) followed by the Sídák post hoc test. Non-normal variables were compared between groups for each time point using Kruskal-Wallis followed by the Dunn post hoc test, and within each group across different time points using Friedman followed by the Dunn post hoc test. Data with a Gaussian distribution were presented as mean ± standard deviation (SD), and data with a non-Gaussian distribution were presented as median ± interquartile range (IQR). For all cases, α < 0.05 and p < 0.05 were adopted. RESULTS: In the intervention group, significant reductions were observed between the first and twelfth month for all parameters (p < 0.05), (except for TC), along with an increase in HDL-C (p = 0.0105). Conversely, in the control group, there was a significant increase in HbA1c, weight, BMI, FBG, and WHR (p < 0.05) between the first and twelfth months. Regarding the comparison between groups, there was a significant difference for all analyzed parameters (p < 0.05) from the first to the twelfth month. In the follow-up, differences were also observed (p < 0.05), except for BMI (p > 0.05). CONCLUSION: The individualized nutritional intervention improved eating habits, anthropometric, biochemical, and cardiovascular markers in T2D over 12 months, with sustained results during follow-up. The dietary plan inspired by the Mediterranean and DASH diets demonstrated good adaptation to the Brazilian food culture and the patients' socioeconomic contexts. Consistent monitoring and personalized nutritional management are essential for optimizing long-term outcomes. However, more clinical trials are necessary in order to optimize the level of evidence for longitudinal interventions.

Effects of Dietary Carbohydrate Concentration and Glycemic Index on Blood Glucose Variability and Free Fatty Acids in Individuals with Type 1 Diabetes.

Monitoring glycemic control status is the cornerstone of diabetes management. This study aimed to reveal whether moderate-carbohydrate (CHO) diets increase the risk of free fatty acid (FFA) levels, and it presents the short-term effects of four different diet models on blood sugar, glycemic variability (GV), and FFA levels. This crossover study included 17 patients with type 1 diabetes mellitus to identify the effects of four diets with different CHO contents and glycemic index (GI) on GV and plasma FFA levels. Diet 1 (D1) contained 40% CHO with a low GI, diet 2 (D2) contained 40% CHO with a high GI, diet 3 (D3) contained 60% CHO with a low GI, and diet 4 (D4) contained 60% CHO with a high GI. Interventions were performed with sensor monitoring in four-day periods and completed in four weeks. No statistical difference was observed among the groups in terms of blood glucose area under the curve (p = 0.78), mean blood glucose levels (p = 0.28), GV (p = 0.59), and time in range (p = 0.567). FFA and total triglyceride levels were higher in the D1 group (p < 0.014 and p = 0.002, respectively). Different diets may increase the risk of cardiovascular diseases by affecting GI, FFA, and blood glucose levels.

Study of Glycemic Variability in Well-controlled Type 2 Diabetic Patients Using Continuous Glucose Monitoring System.

INTRODUCTION: The world has changed tremendously for patients suffering from diabetes mellitus with the development of cutting-edge technologies like continuous glucose monitoring and flash glucose monitoring systems. Now, the details of constant fluctuations of glucose in their blood can be monitored not only by medical professionals but also by patients, and this is called glycemic variability (GV). Traditional metrics of glycemic control measurement, such as glycated hemoglobin (HbA1c), fail to reflect various short-term glycemic changes like postprandial hyperglycemia and hypoglycemic episodes, paving the way to the occurrence of various diabetic complications even in asymptomatic, well-controlled diabetic patients. This need for advanced management of diabetes and effective monitoring of these swings in blood glucose can be met by using a continuous glucose monitoring system (CGMS). AIM AND OBJECTIVE: To evaluate the extent of GV in well-controlled type 2 diabetes mellitus (T2DM) patients using a flash CGMS and to assess the correlation between GV and HbA1c. MATERIALS AND METHODS: A hospital-based prospective observational study was carried out from May 2020 to Oct 2021 at the Department of Medicine, SMS Hospital, Jaipur, Rajasthan (India), after approval from the Ethics Committee of the institution. A total of 30 patients with well-controlled T2DM (HbA1c was ≥6.5, but ≤7.5) were included in the study using simple random techniques after written informed consent from patients. Patients were studied for glycemic excursions over a period of 7 days by using FreeStyle® Libre Pro™, which is a flash glucose monitoring system. The CGM sensor was attached to the left upper arm of the patient on day 0 and removed on day 7. The data recorded in the sensor was then retrieved using pre-installed computer software and analyzed using standard CGM metrics like standard deviation (SD), percentage coefficient of variation (%CV), time above range (TAR), time below range (TBR), and time in range (TIR), out of which %CV was used to quantify GV. %CV has been used to cluster patients into four cohorts from best to worst, namely: best/low CV ≤ 10%, intermediate CV from 10 to 20%, high CV from 20 to 30%, and very high CV of >30%. Scatterplots are used to establish correlations between various parameters. RESULT: Data from a total of 30 patients were analyzed using CGMS and thus used for calculating standard CGM metrics; glucose readings every 15 minutes were recorded consecutively for 7-day periods, making it a total of 672 readings for each patient. Interpreting the CGM data of all 30 patients, the following results were found: the mean blood glucose of all cases is 134.925 ± 22.323 mg/dL, the mean SD of blood glucose of all cases is 35.348 ± 9.388 mg/dL, the mean of %CV of all cases is 26.376 ± 6.193%. CGM parameters of time are used in the form of percentages, and the following results were found: the mean of TAR, TBR, and TIR is 14.425 ± 13.211, 5.771 ± 6.808, and 82.594 ± 12.888%, respectively. Clustering the patients into cohorts, the proportion of patients exhibiting best/low %CV (10%) is 0, intermediate %CV (10-20%) is 16.67% (five out of 30 patients), high %CV (20-30%) is 50% (15 out of 30 patients) and very high %CV (>30%) is 33.33% (10 out of 30 patients). Also, there is no significant correlation found between HbA1c and %CV (ρ = 0.076, p-value = 0.690); a significant negative correlation was found between %CV and TIR (ρ = -0.604, p < 0.001S); a positive correlation of %CV with TAR and TBR is significant (ρ = 0.816, p-value of <0.001). CONCLUSION: Using a flash CGMS device and considering %CV as the parameter and primary measure of GV, the study demonstrated the overall instability of a person's glycemic control, making note of unrecognized events of hypoglycemia and hyperglycemia in asymptomatic well-controlled T2DM patients, revealing the overall volatile glycemic control. The most important finding of this study is that even those diabetics who are considered well-controlled experience a great degree of GV as assessed by CGM-derived metrics. This study also demonstrated that there is no significant correlation between HbA1c and GV, suggesting that patients may not have optimal control of their diabetes despite having "normal HbA1c" values; hence, GV can be considered an HbA1c-independent danger factor, having more harmful effects than sustained hyperglycemia in the growth of diabetic complications. So, by using CGM-derived metrics, the measurement of GV has the potential to complement HbA1c data. In this manner, a more comprehensive assessment of glycemic excursions can be provided for better treatment decisions, thereby facilitating optimal glycemic control, which is essential for reducing overall complications and promoting good quality of life.

Exercise Prescription for Postprandial Glycemic Management.

The detrimental impacts of postprandial hyperglycemia on health are a critical concern, and exercise is recognized a pivotal tool in enhancing glycemic control after a meal. However, current exercise recommendations for managing postprandial glucose levels remain fairly broad and require deeper clarification. This review examines the existing literature aiming to offer a comprehensive guide for exercise prescription to optimize postprandial glycemic management. Specifically, it considers various exercise parameters (i.e., exercise timing, type, intensity, volume, pattern) for crafting exercise prescriptions. Findings predominantly indicate that moderate-intensity exercise initiated shortly after meals may substantially improve glucose response to a meal in healthy individuals and those with type 2 diabetes. Moreover, incorporating short activity breaks throughout the exercise session may provide additional benefits for reducing glucose response.

Tailored Meal-Type Food Provision for Diabetes Patients Can Improve Routine Blood Glucose Management in Patients with Type 2 Diabetes: A Crossover Study.

This study aimed to determine whether patients with type 2 diabetes can benefit from a meal plan designed based on diabetes management guidelines to improve blood glucose levels. Participants were divided into intervention and control groups. The intervention group consumed a diabetic diet for 2 weeks, while the control group consumed their normal diet. After 2 weeks, the groups switched their dietary regimens. The participants' demographic and clinical characteristics were evaluated, including factors such as blood pressure, blood lipid levels, weight and waist circumference, blood glucose levels (self-monitored and continuously monitored), nutritional status, and blood-based markers of nutrient intake. The dietary intervention group improved waist circumference, body fat percentage, low-density lipoprotein cholesterol, triglyceride levels, and glucose. The energy composition ratio of carbohydrates and proteins changed favorably, and sugar intake decreased. In addition, the proportion of continuous glucose monitoring readings within the range of 180-250 mg/dL was relatively lower in the intervention group than that of the control group. Meals designed based on diabetes management guidelines can improve clinical factors, including stable blood glucose levels in daily life, significantly decrease the carbohydrate energy ratio, and increase the protein energy ratio. This study can help determine the role of dietary interventions in diabetes management and outcomes.

Exploring the Impact of Alternative Sources of Dietary Nitrate Supplementation on Exercise Performance.

An increase in the level of nitric oxide (NO) plays a key role in regulating the human cardiovascular system (lowering blood pressure, improving blood flow), glycemic control in type 2 diabetes, and may help enhance exercise capacity in healthy individuals (including athletes). This molecule is formed by endogenous enzymatic synthesis and the intake of inorganic nitrate (NO3-) from dietary sources. Although one of the most well-known natural sources of NO3- in the daily diet is beetroot (Beta vulgaris), this review also explores other plant sources of NO3- with comparable concentrations that could serve as ergogenic aids, supporting exercise performance or recovery in healthy individuals. The results of the analysis demonstrate that red spinach (Amaranthus spp.) and green spinach (Spinacia oleracea) are alternative natural sources rich in dietary NO3-. The outcomes of the collected studies showed that consumption of selected alternative sources of inorganic NO3- could support physical condition. Red spinach and green spinach have been shown to improve exercise performance or accelerate recovery after physical exertion in healthy subjects (including athletes).

Twenty-Four week Taichi training improves pulmonary diffusion capacity and glycemic control in patients with Type 2 diabetes mellitus.

This study evaluated the effect of 24-week Taichi training and Taichi plus resistance band training on pulmonary diffusion capacity and glycemic control in patients with Type 2 diabetes mellitus (T2DM). Forty-eight patients with T2DM were randomly divided into three groups: Group A-Taichi training: practiced Taichi 60 min/day, 6 days/week for 24 weeks; Group B-Taichi plus resistance band training: practiced 60-min Taichi 4 days/week plus 60-min resistance band training 2 days/week for 24 weeks; and Group C-controls: maintaining their daily lifestyles. Stepwise multiple regression analysis was applied to predict diffusion capacity of the lungs for carbon monoxide (DLCO) by fasting blood glucose, insulin, glycosylated hemoglobin (HbA1c), tumour necrosis factor alpha (TNF-α), von Willebrand Factor (vWF), interleukin-6 (IL-6), intercellular adhesion molecule 1 (ICAM-1), endothelial nitric oxide synthase (eNOS), nitric oxide (NO), endothelin-1 (ET-1), vascular endothelial growth factor, and prostaglandin I-2. Taichi with or without resistance band training significantly improved DLCO, increased insulin sensitivity, eNOS and NO, and reduced fasting blood glucose, insulin, HbA1c, TNF-α, vWF, IL-6, ICAM-1, and ET-1. There was no change in any of these variables in the control group. DLCO was significantly predicted (R2 = 0.82) by insulin sensitivity (standard-ß = 0.415, P<0.001), eNOS (standard-ß = 0.128, P = 0.017), TNF-α (standard-ß = -0.259, P = 0.001), vWF (standard-ß = -0.201, P = 0.007), and IL-6 (standard-ß = -0.175, P = 0.032) in patients with T2DM. The impact of insulin sensitivity was the most important predictor for the variation of DLCO based on the multiple regression modeling. This study demonstrates that 24-week Taichi training and Taichi plus resistance band training effectively improve pulmonary diffusion capacity and blood glycemic control in patients with T2DM. Variation of DLCO is explained by improved insulin sensitivity and endothelial function, and reduced inflammatory markers, including TNF-α, vWF, and IL-6.