Contributions of fasting and postprandial plasma glucose levels to glycosylated hemoglobin and diabetes mellitus-related complications: Treating hyperglycemia with insulin

Insulin ◽  
2006 ◽  
Vol 1 (4) ◽  
pp. 148-157
Author(s):  
George E. Dailey
2021 ◽  
Vol 17 (1) ◽  
pp. 44-54
Author(s):  
Prija Poudyal ◽  
Kabina Shrestha ◽  
Lily Rajbanshi ◽  
Afaque Anwar

Introduction: Diabetes Mellitus describes a group of metabolic disorders characterized by hyperglycemia. Uncontrolled glycemic state often leads to micro and macro vascular complications. Diabetes is the foremost cause of new blindness in adults. Constant screening of the diabetic profile through blood tests of the affected people and prompt actions to control them can help to improve the quality of life of these patients. The study was done to evaluate the correlation between fasting and postprandial plasma glucose levels with glycosylated hemoglobin for diagnosis of diabetes and to determine the prevalence of diabetes in different age groups with sex predilection. Methods: A descriptive cross sectional study was conducted and the data collection was carried out in the Department of Ophthalmic Pathology and Laboratory Medicine, Biratnagar Eye Hospital. Ethical approval was obtained from Institutional Review Committee of this hospital. All 275 patients who attended the laboratory from January 2019 to June 2019 for fasting plasma glucose, postprandial plasma glucose and glycosylated hemoglobin values estimation were included in this study. The data obtained were computed and analyzed using Statistical Package for the Social Sciences version 20.0 Results: A significant correlation between fasting plasma glucose, postprandial plasma glucose and glycosylated hemoglobin was observed in this study (p value <0.001). The correlation coefficient between fasting plasma glucose and glycosylated hemoglobin (r= 0.728) is stronger than the correlation coefficient between postprandial plasma glucose and glycosylated hemoglobin (r= 0.709). Conclusions: Fasting plasma glucose correlated better than postprandial plasma glucose with glycosylated hemoglobin.    


Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 772-P
Author(s):  
MARIKO HIGA ◽  
AYANA HASHIMOTO ◽  
MOE HAYASAKA ◽  
MAI HIJIKATA ◽  
AYAMI UEDA ◽  
...  

2020 ◽  
Vol 2 (2) ◽  
pp. 1-4
Author(s):  
Gerald C Hsu ◽  

The author describes the results of segmentation and pattern analyses of postprandial plasma glucose levels (PPG) and carbs/sugar intake amount (carbs), which are associated with his three daily meals. In this paper, there are three consistent ranges of low, medium, and high for PPG values and carbs/sugar amounts that are used for each meal but with different units. One of the final objectives for this analysis is to calculate the most reasonable and effective conversion ratio between measured PPG in mg/dL and carbs/sugar intake amount in grams, by discovering how much PPG amount would be generated from 1 gram of carbs/sugar intake. This investigation utilized the PPG data and carbs/sugar amount collected during a period of 2+ years from 5/5/2018 to 9/6/2020 with a breakdown of 855 days, including 2,565 meals, 33,345 glucose data, and 33,345 carbs/sugar data. By using the segmentation analysis of his 33,345 PPG data and 2,565 carbs/sugar data, the author has conducted a pattern recognition and segmentation analysis from his PPG profiles with its associated carbs/sugar intake of his food and meals in the past 855 days. Since 12/8/2015, he ceased taking any diabetes medications. In other words, his diabetes control is 100% dependent on his lifestyle management program with no chemical intervention from any medications. Subsequently, he has maintained a stringent exercise program after each meal; therefore, the development of his simplified PPG prediction model, excluding the exercise factor, can be expressed solely with carbs/sugar intake amount. Predicted PPG = (baseline glucose) + (conversion ratio * carbs/sugar amount) In his research work, he found the reasonable and effective conversion ratio between PPG and carbs that ranges from 1.8 mg/dL per gram to 2.5 mg/dL per gram. This simple equation could assist many type 2 diabetes (T2D) patients in controlling their diabetes via carbs/sugar intake amount. During this particular time period, his PPG control via a stringent lifestyle management without medication is highly successful. His estimated mathematically derived HbA1C values should be between 5.56% to 6.05%, which is a satisfactory HbA1C level for a 73-year-old male with a 25-year history of severe diabetes. It should be mentioned that he had an average daily glucose of 280 mg/dL and HbA1C of 11% in 2010. This segmented pattern analyses based on his PPG data and carbs/sugar intake amount offer a useful tool for analyzing other types of biomarkers in a deeper investigation with a wider entry point of research.


Author(s):  
Rakesh Kumar Jha ◽  
Badade ZG ◽  
Sandeep Rai ◽  
Badade VZ

Introduction: Diabetes is a chronic disease that occurs when not enough insulin is produced by the pancreas or the body does not use the insulin produced. Because of increased blood glucose levels in the body, serious heart, kidneys, blood vessels, nerves and eyes damage are caused. Report says about 400 million people suffer from diabetes. Therefore present study is aimed to assess levels of HbA1c, Lipid profile and Cyclophilin A in diabetic patient. Material and Methods: The present study includes total 126 subjects comprising of 66 type 2 Diabetes Mellitus patients and 60 healthy individual. Blood samples are collected from the all subjects were processed for HbA1c, Lipid Profile and Cyclophilin A estimation, from OPD and General Medicine Wards. HbA1c is estimated by HPLC, lipid Profile by AU480 and the Cyclophilin A by ELISA method using commercially available Qayee-bio ELISA kit. Conclusion: Present study showed significantly increased levels of HbA1c, Lipid Profile and Cyclophilin A in T2DM patients. The elevated lipid profile may be due to the complication of Diabetic mellitus. CyA is increased as an inflammation marker. Keywords: T2DM: Type 2 diabetes mellitus, HbA1c: Glycosylated Hemoglobin, CyA: Cyclophilin-A


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