To investigate the real postprandial plasma glucose status level and the relation between postprandial plasma glucose, fasting plasma glucose and A1c as well as diabetic complications in patients with type 2 diabetes mellitus. Subjects and methods: The study was descriptive and cross-sectional with the population of 125 people with type 2 diabetes mellitus. All participants were examined and measured postprandial plasma glucose, fasting plasma glucose and A1c. Results: Mean postprandial plasma glucose was 13.41 mmol/L and 77.6% of participants had poor postprandial plasma glucose control. When A1c was less than 7%, postprandial plasma glucose makes a predominant contribution to an accumulative A1c in comparison to fasting plasma glucose. Participants with poor postprandial plasma glucose control were more likely to have microvascular complications than those with good postprandial plasma glucose control. Conclusion: The present study provided new evidence of relation of 2-hour postprandial plasma glucose to A1c and some diabetic complications in patients with type 2 diabetes mellitus.
Journal of military pharmaco-medicine n02-2019 EVALUATION OF 2-HOUR POSTPRANDIAL PLASMA GLUCOSE LEVEL AND ITS RELATION TO A1c AND SOME DIABETIC COMPLICATIONS IN PATIENTS WITH TYPE DIABETES MELLITUS Nguyen Tien Son1; Le Van Quan1 SUMMARY Objectives: To investigate the real postprandial plasma glucose status level and the relation between postprandial plasma glucose, fasting plasma glucose and A1c as well as diabetic complications in patients with type diabetes mellitus Subjects and methods: The study was descriptive and cross-sectional with the population of 125 people with type diabetes mellitus All participants were examined and measured postprandial plasma glucose, fasting plasma glucose and A1c Results: Mean postprandial plasma glucose was 13.41 mmol/L and 77.6% of participants had poor postprandial plasma glucose control When A1c was less than 7%, postprandial plasma glucose makes a predominant contribution to an accumulative A1c in comparison to fasting plasma glucose Participants with poor postprandial plasma glucose control were more likely to have microvascular complications than those with good postprandial plasma glucose control Conclusion: The present study provided new evidence of relation of 2-hour postprandial plasma glucose to A1c and some diabetic complications in patients with type diabetes mellitus * Keywords: Type diabetes mellitus; Postprandial plasma glucose; Fasting plasma glucose INTRODUCTION Diabetes is a group of metabolic diseases which characterized by chronic hyperglycemia Diabetic prevalence has increased sharply recently (about 592 million people with diabetes) [1] Multifactor control is a pivotal factor in diabetic achievement goals, and among them, glycemic controls are of great concerns, including A1c, fasting plasma glucose (FPG) [2] Physicians regularly aim to decrease A1c and FPG and neglect to control PPG [1] In clinical practice, there are patients who achieve FPG goal but not A1c [2, 3] Some studies pointed out that it was the increase of PPG that makes a contribution to the increase of A1c and diabetic complications as well [1, 2, 4, 5, 9] In the current analysis, we aimed: To survey the real PPG control conditions and its relation between PPG, FPG and A1c as well as diabetic complications in patients with type diabetes 103 Military Hospital Corresponding author: Le Van Quan (levanquan2002@yahoo.com) Date received: 20/12/2018 Date accepted: 22/01/2019 200 Journal of military pharmaco-medicine n02-2019 SUBJECTS AND METHODS Subjects 145 patients with type diabetes mellitus (T2D) were enrolled in our study They were treated at Department of Rheumatology and Endocrinology, 103 Military Hospital They met the ADA 2014 criteria for diabetes mellitus [2, 10], WHO 1985 for type diabetes (T2D) and signed the Informed Consent Form We further excluded 20 people with T2D who were treated with insulin The present study was conducted from June 2016 to December 2016 Methods * Design research: Descriptive and cross-sectional * Study procedure: - Clinical characteristics: Data were collected at the first-time participants met researchers, including age, gender, family history, participant history, disease duration, height, weight, waist circumstance, blood pressure, and paraclinical profile, such as whole blood count, lipid profile, BUN, creatinine, microalbuminuria - Glucose profile: Measurements of glycemic profile were performed at Department of Biochemistry, 103 Military Hospital, which measured glycemic profile at fasting states on the second day of hospital administration including FPG, PPG, A1c and C-peptide All blood samples of glucose profile were taken at the same time each participant - Laboratory analyses: Blood samples taken at the clinical examinations were sent to laboratory at Department of Biochemistry, 103 Military Hospital Samples for determinations of A1c were analyzed with HPLC (Roche A1c) A1c reflects the average blood glucose over the prior of to 12 weeks Accordingly, A1c were used as outcome variables PPG samples were taken after participants‟ breakfast hours and we assigned them with 2-hour PPG After at least hours of fasting, participants were drawn blood for FPG samples Both PPG and FPG were sent to laboratory within 30 minutes and measured with glucose oxidase method Insulin resistance was calculated with HOMA2 calculator (downloaded from https://www.dtu.ox.ac.uk/homacalculator/) * Statistical analyses: Linear regression analysis was used to estimate the correlations of FPG, PPG as well as glycemic variability (FPG and PPG) with A1c and PPG with HOMA2-IR Analyses were performed without adjustment for age, sex, BMI We then calculated the proportion of variables in contribution to A1c explained by the following categories: FPG, PPG Firstly, we calculated the association between A1c and each of the two variables (FPG and PPG) Because the two variables to some extent were correlated to A1c, and then, we calculated the distribution to A1c of each variables using the equation: A1c = aPPG + bFPG + c Contributions of PPG to A1c = a/(a+b) Contributions of FPG to HbA1c = b/(a+b) Statistical analyses were performed in SPSS 18.0 A two-sided p (less than or equal to 0.05) was used as a criterion for statistical significance in all analyses 201 Journal of military pharmaco-medicine n02-2019 RESULTS General characteristics Table 1: Indices Value Percentage (%) Mean 64.46 10.71 < 60 years 34 27.2 > 60 years 91 72.8 Male 66 52.8 Female 59 47.2 Mean 6.49 6.14 < 10 years 100 80.0 > 10 years 25 20.0 Positive 33 59.0 Negative 16 41.0 Positive 51 40.8 Negative 74 59.2 Positive 29 23.2 Negative 96 76.8 FPG (mmol/L) Mean 7.25 3.20 A1c (%) Mean 8.15 2.00 Mean 13.41 5.39 Good control 28 22.4 Poor control 97 77.6 Age (years) Gender Disease duration (years) MAU Retinopathy Abnormalitites on ECG and cardiac sonography PPG (mmol/L) Table showed the clinical characteristics of the study populations at baseline There were the same proportions of male and female sex, and the majority of participants were > 60 year old Within patients with T2D, MAU accounted for highest percentage of all complications surveyed Though mean FPG was not higher than target for FPG control, mean of A1c (%) still remained high Nearly 80% of participants with T2D controlled PPG poorly and the mean of PPG was 13.41 mmol/L which exceeded the target for PPG according to ADA 2018 202 Journal of military pharmaco-medicine n02-2019 1.23 Changes on ECG and cardiac sonography 1.99 MAU 1.51 Diabetic retinopathy Figure 1: PPG control grades and diabetic complications Participants with poor PPG control were likely to suffer from microvascular complications (such as, microalbuminuria and diabetic retinopathy with OR = 1.99 and 1.51, respectively) though there were no statistical significances (p > 0.05) Relationship between glycemic measures and A1c Table 2: PPG control and A1c control grades FPG (n = 125) A1c (n = 125) Indices Good, n (%) Poor, n (%) Good, n (%) Poor, n (%) Good 27 (36.69) (1.98) 23 (82.14) (17.86) Poor 47 (63.51) 50 (98.02) 31 (31.96) 66 (68.04) PPG (n = 125) p < 0.01 < 0.01 Table 3: PPG control and A1c control grades stratifying by FPG control grades PPG HbA1C Good Indices Good (n = 28) Poor (n = 97) Total (n = 125) Good FPG control 22 24 46 Poor FPG control 23 (82.14) 31 (31.96) 54 Good FPG control 23 28 Poor FPG control 43 43 (17.86) 66 (68.04) 71 Poor Total Total Within the good FPG control group, there were up to 61% with poor PPG control To have an insight on the distributions of each variables (FPG and PPG) to A1c, we found 203 Journal of military pharmaco-medicine n02-2019 that the role of each variables varied from different A1c gaps and the lower A1c was, the higher amount of distributions to A1c due to PPG (A1c < 7%) (table and fig.2) Table 4: Distributions of glycemic variables to A1c HbA1c FPG PPG R p < 7% 0.096 0.166 0.069 > 0.05 - 9% 1.140 0.430 0.358 > 0.05 > 9% 0.091 0.019 0.009 > 0.05 Figure 2: DISCUSSION According to UKPDS, each 1% of A1c reduction could lead to 21% of mortal rate reduction [9] In our study, up to 77% of participants poorly controlled PPG, which showed that there was an inappropriate attention to the role of PPG Theoretically, blood glucose is mainly supplied to human body after meals, while FPG only accounts for small duration of time (4 - hours estimated) before breakfast [8] When put PPG control into consideration, we found that there were 28 participants with poor A1c control (> 7.5%), even 204 though they had an acceptable to good FPG control To investigate clearly about this fact, we found that in this special group, there were up to 23 people with poor PPG control These results were consistent with previous studies [1, 13] A1c reflexes an estimated average blood glucose over a 3-month period Due to the fluctuation of blood glucose after meals, many previous studies had tried to find out the role of PPG and FPG to A1c [2, 3, 13] and they pointed out there was a relation between PPG, FPG and A1c but there were no clear evidence for the role Journal of military pharmaco-medicine n02-2019 of each variables (FPG and PPG) to A1c In our study, we stratified A1c of all participants into categories: less than 7%, to 9% and above 9% and we found that 63% of accumulative A1c due to PPG if A1c < 7%, which was sustainable with most of recent studies [8] These results denoted that to get A1c target control, only good FPG control was not appropriate, which consistent with other studies [7, 8] Whilst some studies figured out that FPG had a strong correlation to macrovascular complications, PPG tended to relate to microvascular complications in patients with T2D In our study, participants with poor PPG control were likely to suffer from microvascular complications (such as microalbuminuria and diabetic retinopathies, OR = 1.99 and 1.51 respectively, but both p > 0.05) Some hypotheses were created to explain this fact After meals, the high fluctuation of PPG could lead to the changes of oxidative agents and decrease in NO secretion into circulation and VCAM-1, ICAM-1, E-selectin especially when PPG > 10 mmo/L [4] In Japan, a recent study has suggested that high PPG was an independent risk factor for diabetic retinopathy due to endothelium disorders [9] There were also some limitations in our study, due to A1c is accumulative blood glucose for about months, we need to take blood samples for PPG and FPG more times during the baseline visits of each participants and after months The number of diabetic complications was too small (3 complications) but these complications were common in patients with T2D CONCLUSION In our study, patients with T2D mainly cared about FPG and A1c and the vast majority of patients had poor PPG controls, which more likely led to microvascular diabetic complications When a physician judges glycemic control in patients with T2D, he/she should take PPG into consideration if A1c > % REFERENCES Nguyễn Thị Thùy Dương, Võ Hồng 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