PENGARUH KONSELING ASUHAN KEFARMASIAN PADA RESPONDEN DIABETES MELLITUS II UNTUK PENINGKATAN KEPATUHAN MINUM OBAT DALAM PENGENDALIAN GLUKOSA DARAH DI RUMAH SAKIT SIDOARJO

The purpose of this study was to determine the glycemic profiles of drug-naïve type 2 diabetes patients according to hemoglobin A1c (HbA1c) level using continuous glucose monitoring. We aimed to clarify factors associated with HbA1c and average blood glucose level. Patients were divided into three groups according to their HbA1c level (< 7.0% n=23, 7.0% ≤ HbA1c < 8.0% n=17 and ≥ 8.0% n=31), and the factors associated with HbA1c and average glucose of each group were evaluated. Pre-meal glucose levels were the highest before lunch, and the 2 hour postprandial blood glucose level was the lowest after lunch. The pre-meal and postprandial blood glucose levels increased after each meal with increases in HbA1c. Average glucose level was the most significant determinant of HbA1c, whereas pre-meal glucose level at dinner was the most significant determinant of average glucose level, and the range of increase in glucose from pre-meal at dinner was the most significant determinant of standard deviation (SD) of 24 hour glucose levels. HbA1c subgroup analysis indicated that pre-meal glucose level at lunch significantly correlated with average glucose level in the HbA1c < 8.0% group, while pre-meal glucose level at dinner significantly correlated with average glucose level in the HbA1c ≥ 8.0% group. The range of increase in glucose from pre-meal in the morning significantly correlated with SD of 24 hour glucose levels in the HbA1c < 8.0% group, and the postprandial peak glucose level at lunch significantly correlated with SD of 24 hour glucose levels in the HbA1c ≥ 8.0% group. The results suggest that improvement of the average glucose level is necessary to improve the HbA1c levels. For patients with HbA1c < 7.0%, it is important to improve blood glucose level after breakfast and before lunch to decrease the average glucose level. For patients with 7.0% ≤ HbA1c < 8.0%, it is important to improve blood glucose level before lunch and after dinner to decrease the average glucose level. For patients with HbA1c ≥ 8.0%, it is important to improve blood glucose levels after lunch and before dinner to decrease the average glucose level.

To define the mathematical relationship between fructosamine levels and average glucose values. The study comprised laboratory data of 1,227 patients with type 1 or 2 diabetes mellitus. Fructosamine levels measured at the end of a 3-week period were compared against the average blood glucose levels of the previous 3 weeks. Average glucose levels were determined by the weighted average of the daily fasting capillary glucose results performed during the study period, and the plasma glucose measured in the same sample collected for fructosamine measurement. In total, 9,450 glucose measurements were performed. Linear regression analysis between fructosamine levels and average glucose levels showed that for each 1.0 μmol/L increase in fructosamine level there was a 0.5 mg/dL increase in average glucose level, as estimated by the equation Mean glucose level = (0.5157 × Fructosamine) - 20. The coefficient of determination (r2 = 0.353492, p < 0.006881) allowed the calculation of the estimated average glucose based on fructosamine level. Our study demonstrated a linear correlation between fructosamine level and mean blood glucose level, suggesting that fructosamine levels can be a proxy for the average glucose level in assessing the metabolic control of patients with diabetes.

Glucose management…a “must have”

BackgroundThe use of remote data capture for monitoring blood glucose and supporting digital apps is becoming the norm in diabetes care. One common goal of such apps is to increase user awareness and engagement with their day-to-day health-related behaviors (digital engagement) in order to improve diabetes outcomes. However, we lack a deep understanding of the complicated association between digital engagement and diabetes outcomes.ObjectiveThis study investigated the association between digital engagement (operationalized as tagging of behaviors alongside glucose measurements) and the monthly average blood glucose level in persons with type 2 diabetes during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first 6 months, blood glucose levels will drop faster and further in patients with increased digital engagement and that difference in outcomes will persist for the remainder of the year. Finally, we hypothesize that disaggregated between- and within-person variabilities in digital engagement will predict individual-level changes in blood glucose levels.MethodsThis retrospective real-world analysis followed 998 people with type 2 diabetes who regularly tracked their blood glucose levels with the Dario digital therapeutics platform for chronic diseases. Subjects included “nontaggers” (users who rarely or never used app features to notice and track mealtime, food, exercise, mood, and location, n=585) and “taggers” (users who used these features, n=413) representing increased digital engagement. Within- and between-person variabilities in tagging behavior were disaggregated to reveal the association between tagging behavior and blood glucose levels. The associations between an individual’s tagging behavior in a given month and the monthly average blood glucose level in the following month were analyzed for quasicausal effects. A generalized mixed piecewise statistical framework was applied throughout.ResultsAnalysis revealed significant improvement in the monthly average blood glucose level during the first 6 months (t=−10.01, P<.001), which was maintained during the following 6 months (t=−1.54, P=.12). Moreover, taggers demonstrated a significantly steeper improvement in the initial period relative to nontaggers (t=2.15, P=.03). Additional findings included a within-user quasicausal nonlinear link between tagging behavior and glucose control improvement with a 1-month lag. More specifically, increased tagging behavior in any given month resulted in a 43% improvement in glucose levels in the next month up to a person-specific average in tagging intensity (t=−11.02, P<.001). Above that within-person mean level of digital engagement, glucose levels remained stable but did not show additional improvement with increased tagging (t=0.82, P=.41). When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels (t=1.30, P=.20).ConclusionsThis study sheds light on the source of the association between user engagement with a diabetes tracking app and the clinical condition, highlighting the importance of within-person changes versus between-person differences. Our findings underscore the need for and provide a basis for a personalized approach to digital health.

Background Diabetes is a known independent risk factor for stroke. However, whether higher glucose levels (126–139.9 mg/dl) can increase the risk of stroke in people without diabetes is still unknown. Moreover, as a fluctuating parameter, long-term glucose levels may also be related to the risk of stroke outcome. It is important to explore the correlation between long-term average blood glucose, as well as its variability, and stroke. Methods We used 40,975 clinical measurements of glucose levels and 367 measurements of glycated hemoglobin A1c levels from 12,321 participants without stroke to examine the relationship between glucose levels and the risk of stroke. Participants were from the Weitang Geriatric Diseases study, including 5,707 men and 6,614 women whose mean age at baseline was 60.8 years; 1,011 participants had diabetes, and 11,310 did not. We estimated the long-term average blood glucose level based on the multilevel Bayesian model and fit in Cox regression models, stratified according to diabetes status. Results Over a median follow-up period of 5 years, stroke developed in 279 of the 12,321 participants (244 without diabetes and 35 with). For people with an average glucose level of 126–139.9 mg per deciliter, compared with 90–99.9 mg per deciliter, the adjusted hazard ratio (HR) for total stroke was 1.78 (95% confidence interval (CI), 1.16–2.75), and the HR for levels higher than 140 mg per deciliter was 1.89 (95% CI, 1.09–3.29). Among those without diabetes whose glucose level was higher than 140 mg per deciliter, compared with 90–99.9 mg per deciliter, the adjusted HRs for total stroke and fatal stroke were 3.66 (95% CI, 1.47–9.08) and 5 (95% CI, 1.77–14.15), respectively. For a glucose standard deviation level higher than 13.83 mg per deciliter, compared with that lower than 5.91 mg per deciliter, the adjusted HR for total stroke was 2.31 (95% CI, 1.19–4.48). Conclusions Our results suggest that higher average glucose levels (126–139.9 mg/dl) and variance may be risk factors for stroke, even among people without diabetes diagnosis.

Aims/hypothesisThis study aimed to examine the relationship between average glucose levels, assessed by continuous glucose monitoring (CGM), and HbA1c levels in pregnant women with diabetes to determine whether calculations of standard estimated average glucose (eAG) levels from HbA1c measurements are applicable to pregnant women with diabetes.MethodsCGM data from 117 pregnant women (89 women with type 1 diabetes; 28 women with type 2 diabetes) were analysed. Average glucose levels were calculated from 5–7 day CGM profiles (mean 1275 glucose values per profile) and paired with a corresponding (±1 week) HbA1c measure. In total, 688 average glucose–HbA1c pairs were obtained across pregnancy (mean six pairs per participant). Average glucose level was used as the dependent variable in a regression model. Covariates were gestational week, study centre and HbA1c.ResultsThere was a strong association between HbA1c and average glucose values in pregnancy (coefficient 0.67 [95% CI 0.57, 0.78]), i.e. a 1% (11 mmol/mol) difference in HbA1c corresponded to a 0.67 mmol/l difference in average glucose. The random effects model that included gestational week as a curvilinear (quadratic) covariate fitted best, allowing calculation of a pregnancy-specific eAG (PeAG). This showed that an HbA1c of 8.0% (64 mmol/mol) gave a PeAG of 7.4–7.7 mmol/l (depending on gestational week), compared with a standard eAG of 10.2 mmol/l. The PeAG associated with maintaining an HbA1c level of 6.0% (42 mmol/mol) during pregnancy was between 6.4 and 6.7 mmol/l, depending on gestational week.Conclusions/interpretationThe HbA1c–average glucose relationship is altered by pregnancy. Routinely generated standard eAG values do not account for this difference between pregnant and non-pregnant individuals and, thus, should not be used during pregnancy. Instead, the PeAG values deduced in the current study are recommended for antenatal clinical care.

Introduction: According to clinical risk assessment guidelines, a history of diabetes mellitus (DM) portends poor outcomes following acute MI. Elevated in-hospital glucose levels also predict early mortality in acute MI patients, but the degree to which glucose levels and diabetic history independently predict post-MI mortality is unclear. Methods and Hypothesis: We analyzed data from the combined cohort of the CREATE-ECLA and OASIS-6 randomized trials that evaluated the impact of glucose-insulin-potassium (GIK) infusion versus no infusion on 30-day mortality in 22,943 patients hospitalized with acute ST-elevation MI. We calculated the average in-hospital glucose level for each patient (mean of the admission, 6-hour, and 24-hour glucose levels). Logistic regression was performed to determine whether average glucose level and history of DM remained significant mortality predictors after adjusting for age, sex, and GIK allocation. Results: Glucose data were recorded in 22,860 (99.6%) patients; 10,050 (44%) had an average in-hospital glucose level ≥ 8 mmol/L (144 mg/dL), of whom 65% did not have known prior DM. Among patients with glucose &gt;8 mmol/L, 30-day mortality rates were similar in patients with and without known DM (Figure ). In-hospital glucose, but not history of DM, was a significant multivariable predictor of mortality (Table). Conclusions: By considering only history of DM and not in-hospital glucose levels, risk assessment guidelines for acute MI overlook a large proportion of patients at high risk for early death. Therefore, clinicians should emphasize elevated glucose levels in addition to history of DM as a risk marker in patients with acute MI.

The association between estimated average glucose levels and fasting plasma glucose levels

Brown seaweed is promising for the treatment of type 2 diabetes mellitus (T2DM). Its bioactive constituents can positively affect plasma glucose homeostasis in healthy humans. We investigated the effect of the brown seaweeds Sargassum (S.) fusiforme and Fucus (F.) vesiculosus in their natural form on glucose regulation in patients with T2DM. We conducted a randomized, double-blind, placebo-controlled pilot trial. Thirty-six participants with T2DM received, on a daily basis, either 5 g of dried S. fusiforme, 5 g of dried F. vesiculosus, or 0.5 g of dried Porphyra (control) for 5 weeks, alongside regular treatment. The primary outcome was the between-group difference in the change in weekly average blood glucose levels (continuous glucose monitoring). The secondary outcomes were the changes in anthropometrics, plasma lipid levels, and dietary intake. The data were analyzed using a linear mixed-effects model. The change in weekly average glucose levels was 8.2 ± 2.1 to 9.0 ± 0.7 mmol/L (p = 0.2) in the S. fusiforme group (n = 12) and 10.1 ± 3.3 to 9.2 ± 0.7 mmol/L (p = 0.9) in the F. vesiculosus group (n = 10). The between-group difference was non-significant. Similarly, no between-group differences were observed for the changes in the secondary outcomes. A daily intake of 5 g of fresh, dried S. fusiforme or F. vesiculosus alongside regular treatment had no differential effect on weekly average blood glucose levels in T2DM.

ObjectiveTo explore the efficacy of the hospital glycemic management system with information integration in patients with malignant tumors and hyperglycemia.MethodsThree hundred ninety-three patients diagnosed with malignant tumors with hyperglycemia and hospitalized in the non-endocrinology department of a specialized cancer hospital from March 2019 to November 2020 were recruited. All the patients were diagnosed and treated according to the clinical department and disease course. In total, 196 patients were divided into the control group, who received the conventional blood glucose management mode, and 197 patients were divided into the intervention group, who received the hospital glycemic management system with information integration. The average daily glucose levels were recorded before and after breakfast, lunch, and dinner, at bedtime and at night. The average glucose level, glucose compliance rate, hypoglycemia rate, hyperglycemia rate, glucose measurements per day, average number of hospitalization days and patient satisfaction were compared between the groups.ResultsIn the intervention group, the average glucose level was significantly lower than that in the control group (P<0.05). The hyperglycemia and hypoglycemia rates in the intervention group were lower than those in the control group (P<0.05). The glucose compliance rate in the intervention group was higher than that in the control group (P<0.05). The highest blood glucose level in the intervention group was lower than that in the control group (P<0.05), and the lowest blood glucose level was higher than that in the control group (P<0.05). The glucose measurements per day in the intervention group were higher than those in the control group, and the average number of hospitalization days in the intervention group was lower than that in the control group (P<0.05). Patient satisfaction in the intervention group was higher than that in the control group (P<0.05).ConclusionThe hospital glycemic management system with information integration significantly improved the glycemic management of patients with malignant non-endocrine tumors and hyperglycemia, including their glucose level and glucose compliance rate, as well as patient satisfaction, and reduced the average number of hospitalization days and risk of hyperglycemia/hypoglycemia.

The Interactions Between Physicians and Dentists in Managing the Care of Patients With Diabetes Mellitus

Checking blood glucose levels supports healthy lifestyle and pattern changes to avoid foods that contain high glucose, which can increase blood glucose levels. This study aims to analyze the administration of cocoa pod extract (Theobroma cacao L.) to describe serum blood glucose levels in white rats (Rattusnovergicus). This study used an RCT (randomized controlled trial) design involving five groups of cocoa pod extract samples through an evaporation process using a rotary vacuum evaporator after going through the maceration process for 3 x 24 hours divided into three concentrations of 50 mg/Kg body wt, 100 mg/Kg body wt, 150 mg/Kg body wt. Furthermore, serum blood glucose levels will be measured on the 3rd, 7th, and 14th day after administration of metformin and waste extract of cocoa pod shell (Theobroma cacao L.). Analysis of the research data used a paired t-test. The results showed that the average fasting blood glucose levels of K1 rats (Mus musculus) to the average fasting glucose levels in K2, K3, K4, and K5, respectively, were as follows: 0.037 (p &lt;0.05); 0.000 (p &lt; 0.05); 0.028 (p &lt; 0.05); and 0.015 (p &lt;0.05). This shows that the average fasting blood glucose level is significantly different (p &lt;0.05). The average fasting blood glucose levels of K2 (metformin group) to the average glucose levels in K3 (extract group 50 mg/kg body wt), K4 (extract group 100 mg/kg body wt), and K5 (extract group 150 mg/kg body wt) respectively are as follows: 0.157 (p &gt; 0.05); 1.000 (p &gt; 0.05); and 0.995 (p &gt; 0.05). This shows that the average blood glucose levels between K2, K3, K4, and K5 did not differ significantly (p &lt;0.05) after measuring cocoa pod extract for two days. Based on the results of data analysis from this experimental study, it can be concluded that there is an effect of giving cocoa pod extract (Theobroma cacao L.) on fasting blood glucose levels of mice (Mus musculus) with type 2 diabetes mellitus hypercholesterolemia model. Keywords: Diabetes mellitus; Cacao; Blood Glucose; Rats

To investigate the characteristic of dynamic glucose level in obstructive sleep apnea-hypopnea syndrome (OSAHS) patients with newly diagnosed type 2 diabetes mellitus (T2DM) and to evaluate the effect of continuous positive airway pressure (CPAP) treatment on the glucose level. A total of 65 cases of patients with T2DM who were newly diagnosed by oral glucose tolerance test (OGTT) were enrolled from April 2014 to April 2015 in Gansu Provincial Hospital, and divided into simple T2DM group (n=30) and OSAHS with T2DM group (n=35) according to aponea-hypopnea index (AHI) which was monitored by polysomnography (PSG). Their general clinical data were collected, and glucose level of different periods was monitored by continuous glucose moitoring system (CGMS). Changes of glucose level were compared between two groups before and after CPAP treatment. Age, gender proportion, BMI, smoking and drinking history, glycosylated hemoglobin (HbA1c) and blood lipid profile had no significantly difference between two groups. Longer neck circumstance and higher waist-hip ration (WHR), higher systolic blood pressure and diastolic blood pressure, higher fasting plasma glucose (FPG) [(9.4 ± 3.2) vs (7.3 ± 2.1) mmol/L, P=0.028] and fasting insulin (FINS) [(19.2 ± 8.7) vs (11.1 ± 4.7) mU/L, P=0.044] level, more serious homeostasis model assessment insulin resistance (HOMA-IR) were found in OSAHS patients with T2DM when compared to patients in simple T2DM group. The average dynamic glucose level of 24 hours, daytime, nocturnal and sleep time in OSAHS with T2DM group were higher than that in the simple T2DM group (all P<0.05). The alarming times when the average dynamic glucose level of nocturnal time was more than 0.1 mmol·L⁻¹·min⁻¹ in T2DM with OSAHS was more than that in control group (P=0.001). After treatment of CPAP, the level of AHI [(5.9 ± 3.6) vs (56.7 ± 11.4) times/h, P<0.001], average dynamic glucose level of 24 hours, day, nocturnal and sleep time were obviously decreased (all P<0.05); lowest saturation oxygen (LSpO₂) was significantly increased [(92.3 ± 3.7)% vs (81.5 ± 20.2)%, P<0.001]; the alarming times and HOMA-IR were obviously decreased (P=0.019, 0.043). According to multiple linear regression analysis, the AHI (β=0.736, P<0.001) in OSAHS with T2DM group was positively related to the average dynamic glucose level during sleep time, but the LSpO₂(β=-0.889, P<0.001) was negatively correlated. OSAHS patients with newly diagnosed T2DM have higher glucose level than that in simple T2DM patients, and CPAP therapy can obviously decrease the glucose level in newly diagnosed T2DM patients with OSAHS. AHI and LSpO₂may influence the average dynamic glucose level during sleep time.

Objective: The A1c assay, expressed as the percent of hemoglobin that is glycated, measures chronic glycemia and is widely used to judge the adequacy of diabetes treatment and adjust therapy. Day-to-day management is guided by self-monitoring of capillary glucose concentrations (milligrams per decilitre or millimoles per litter) as well as by using continuous glucose monitoring systems (CGMS). We found a mathematical relationship between A1c and average glucose (AG) levels measured by CGMS over 5 days and determined the correlation between the variable CGMS parameters and HbA1c in 50 children with type 1 diabetes mellitus (DM-1) on MDI therapy. Research design and methods: A total of 50 diabetic children randomly selected from a cohort of children with DM-1 were included in the analyses. A1c levels obtained at the end of 3 months and measured in a central laboratory were compared with the AG levels during the previous 5 days recorded by CGMS. AG was calculated by combining weighted results from 5 days of continuous glucose monitoring performed before measuring HbA1c, with 3-5 point daily self-monitoring of capillary (fingerstick) glucose. Results: Linear regression analysis between the A1c and AG values provided the tightest correlations HbA1c=0.0494 MG- 2E-14, R2=0.90, P<0.0001), allowing calculation of an estimated average glucose (eAG) for A1c values. Conclusions: Our study showed a linear relationship between HbA1C and AG values measured by CGMS for 5 days before HbA1c measurement. The AG can be easily calculated using a formula derived from linear regression analysis of HbA1c data obtained in our diabetic children. (www.actabiomedica.it)

To determine the adequacy of perioperative glycemic control in diabetic patients undergoing coronary artery bypass grafting (CABG) and to explore the association between glycemic control and in-hospital morbidity/mortality. Retrospective cohort study of consecutive patients with diabetes undergoing CABG between April 2000 and March 2001 who survived at least 24 h postoperatively. Of the 291 patients in this study, 95% had type 2 diabetes and 40% had retinopathy, nephropathy, or neuropathy at baseline. During hospitalization (median 7 days), 78 (27%) of these patients suffered a nonfatal stroke or myocardial infarction, septic complication, or died ("adverse outcomes"). Glycemic control was suboptimal (average glucose on first postoperative day was 11.4 [11.2-11.6] mmol/l) and was significantly associated with adverse outcomes post-CABG (P = 0.03). Patients whose average glucose level was in the highest quartile on postoperative day 1 had higher risk of adverse outcomes after the first postoperative day than those with glucose in the lowest quartile (odds ratio 2.5 [1.1-5.3]). Even after adjustment for other clinical and operative factors, average blood glucose level on the first postoperative day remained significantly associated with subsequent adverse outcomes: for each 1-mmol/l increase above 6.1 mmol/l, risk increased by 17%. Perioperative glycemic control in our cohort of diabetic patients undergoing CABG in a tertiary care facility was suboptimal. We believe closure of this care gap is imperative, because hyperglycemia in the first postoperative day was associated with subsequent adverse outcomes in our study patients.