Purpose: The purpose of this study was to investigate the knowledge and learning needs on coronary artery disease in diabetic patients by glycemic control. Methods: The subjects consisted of 188 patients at the hospital who had diabetes mellitus. Data was obtained using a knowledge and learning needs questionnaire from January to April 2006. Results: Treatment method, the levels of fasting blood glucose(FBG), and 2-hour postprandial blood glucose(PP2hr) showed meaningful differences between normo-glycemic group(HbA1c < 7%) and hyper-glycemic group($HbA1c{\geq}7%$). The levels of knowledge on coronary artery disease by glycemic control tend to show higher in normo-glycemic group. Etiology and prevention of coronary artery disease were significantly higher in normo-glycemic group than in hyper-glycemic group. The levels of learning needs on coronary artery disease by glycemic control tend to show higher in normo-glycemic group. The learning needs on items of diet control were higher in normo-glycemic group than in hyper-glycemic group. Conclusion: On the base of these results, we should focus on the coronary artery disease education for hyper-glycemic group. Also individual coronary artery disease educational program should be developed for the patients with different level of knowledge and learning needs.
The possibility that high, long-term intake of carbohydrates that are rapidly absorbed as glucose may increase the risk of type 2 diabetes has been long-standing controversy. A high consumption of carbohydrates with a high glycemic index produces greater insulin resistance than did the intake of low glycemic index carbohydrates. This study was designed to evaluate the cabohydrate intake status include glycemic index and correlation carbohydrtae intake status with anthropometry factors & other nutrients in usual diet of the Korean type 2 diabetes mellitus. In 104 tpye 2 diabetes mellitus patients(mean age : 51.8yr, male=44.femal=60), we determined carbohydrte intake status include glycemic index with 24hr recall method and measured anthropometry. Mean daily carbohydrtae intakes and glycemic index were 307.3g(male 323.1g, female 295.5g) and 90.7(male 93.4, female 88.8), respectively. We found a strong and statistically significant association between carbohydrate ratio and glycemic index in obese factors, other nutrient. But carbohydrate intake/kg of body weight was low a significant differences in obese factors, other nutrient. Also glycemic index was effected by total energy intake and carbohydrate ratio than carbohydrate intake/kg of body weight. In conclusion, emphasis for dietary modification should be total energy intake and carbohydrate ratio in diabetes mellitus patient.
This study was intended to assess the effects of low glycemic index (LGI) nutrition education on dietary management and glycemic control of patients with type 2 diabetes mellitus. The subjects were 48 sex-matched patients with type 2 diabetes mellitus, aged $66.5\;{\pm}\;6.2$ years, visiting a public health center. They were divided into two groups: the control group (males 10, females 14) and the educated group (males 10, females 14). The educated group was provided with a LGI nutrition education program for 7 weeks. The control group was educated only one time for general diabetic education. Anthropometric indices, knowledge and perception of efficacy of low glycemic index carbohydrates, dietary glycemic index (DGI) and glycemic load (DGL), fasting blood glucose, and HbA1c were assessed. In the educated group body weight, body mass index and systolic blood pressure (from $138.0\;{\pm}\;18.9\;mmHg$ to $130.6\;{\pm}\;15.0\;mmHg$) were significantly reduced after the nutrition education (p < 0.05). The scores of knowledge and perception of efficacy of low glycemic index carbohydrates increased significantly in the educated group. Dietary glycemic index and glycemic load of the educated group decreased significantly from $103.4\;{\pm}\;67.6$ to $45.4\;{\pm}\;27.1$ (p < 0.001), and from $173.3\;{\pm}\;135.9$ to $66.8\;{\pm}\;50.4$ (p < 0.001), respectively. Also fasting blood glucose and HbA1c levels of the educated group significantly decreased from $124.5\;{\pm}\;28.8\;mg/dL$ to $96.7\;{\pm}\;21.6\;mg/dL$ (p < 0.001) and from $7.1\;{\pm}\;1.3%$ to $6.4\;{\pm}\;1.2%$ (p < 0.05), respectively. The score of knowledge and perception of efficacy of low glycemic index significantly correlated with fasting blood glucose and HbA1c levels negatively. DGI, DGL and duration of diabetes significantly correlated with HbA1c level positively. From stepwise multiple linear regression analysis, DGI, DGL and the duration of diabetes were extracted as factors influencing HbA1c level of the subjects. The results of this study suggest that low glycemic index nutrition education programs is an effective intervention measure for the glycemic control in type 2 diabetic patients.
Background: Bone fractures are high in elderly patients with type 2 diabetes mellitus (T2DM). Hyperglycemia and chronic kidney disease may increase the risk of fracture prevalence via altered bone metabolism, but whether glycemic control and kidney function are associated with the risk of fracture prevalence remains unclear. This study evaluated the relationship between glycemic control and baseline estimated glomerular filtration rate (eGFR) and risk of fracture prevalence in older and middle-aged patients with T2DM. Methods: Patients who underwent a general medical check-up between 2009 and 2013 were selected from the Korean National Health Insurance Sharing Service records. Chi-square test and multiple logistic regression analysis were used to assess the relationship between glycemic control and eGFR and risk of fracture prevalence. Results: Cumulative fracture prevalence were higher in patients with T2DM, irrespective of whether they had tight or less stringent glycemic control (fasting blood glucose [FBG] ${\geq}110mg/dL$). After adjustment for baseline age and FBG, tight and less stringent glycemic control was significantly associated with increased adjusted risk of fracture prevalence in middle-aged patients with T2DM (OR=1.13, 95% CI, 1.05-1.21, p=0.0005 vs OR=1.13, 95% CI, 1.06-1.20, p=0.0001), but not in older patients. Baseline eGFR was not significantly related to fracture prevalence in either older or middle-aged patients. Conclusion: Less stringent glycemic control significantly increased the adjusted risk of fracture prevalence in middle-aged patients with T2DM. Further studies are needed to confirm the effect of tight glycemic control on fracture prevalence.
The purpose of this study was to investigate the correlation between glycemic load and blood lipid profiles in some Korean according to age: The subjects were divided into four groups based on the following age ranges; $10\sim19$ age group(n=260), $20\sim49$ age group(n=129), $50\sim64$ age group(n=135), over 65 age group(n=87). They were measured for the anthropometric measurements, dietary intakes, glycemic load and blood analysis. The average ages of the $10\sim19$ age group, $20\sim49$ age group, $50\sim64$ age group and over 65 age group were 10.9 yrs, 40.8 yrs, 57.1 yrs and 70.8 yrs, respectively. The food intakes were increased according to age in the younger two groups but decreased in the elder two groups. The energy and carbohydrate intakes were the highest in the $10\sim19$ age group. The averages of serum HDL-cholesterol levels of the $20\sim49,\;50\sim64$ and over 65 age group were significantly lower than that of the $10\sim19$ age group. The food intake of the $10\sim19$ age group was positively correlated to the glycemic load(p<0.001). The food and energy intakes of the $20\sim49,\;50\sim64$ and over 65 age group were positively correlated to the glycemic load. And the serum triglyceride and AI levels of the over 65 age group were positively correlated to the glycemic load(p<0.001, p<0.05). These results suggest the need for further research into the relation between glycemic load and blood lipids in order to ensure proper carbohydrate intakes.
Sasa borealis leaf has been known to have anti-diabetic properties. In this study, we tried to evaluate the effects of Sasa borealis leaf extract (SBE) on the inhibition of $\alpha$-glucosidase activity and postprandial glycemic response following ingestion of four carbohydrate-rich foods; cooked rice, ramen (instant noodle), noodle, and bread. Fourteen healthy female adults consumed 50 g of glucose (control) or one of the four foods containing 50 g of available carbohydrate with or without 2,000 mg of SBE. The activity of $\alpha$-glucosidase was inhibited dose-dependently by SBE. With SBE, blood glucose concentration at 15 min and the positive area under the curve (AUC) of postprandial glycemic response at 15 min and 30 min after consuming each of the four foods were reduced significantly. As the result, total positive AUC during 120 min was decreased in case of taking cooked rice or bread. Glycemic index and glycemic load of the four foods were declined from 13% to 23% with SBE. The results of this study suggest that SBE may be effective for postprandial glucose control by inhibiting $\alpha$-glucosidase activity.
The relationship between food and nutrient intake, glycemic index (GI), glycemic load (GL), and body weight was investigated with high school girls residing in Seoul. As subjects, 159 girls were divided into a normal weight (NW) group (18.5 kg/$m^2$$\leq$ BMI < 23 kg/$m^2$, n = 110) and an overweight (OW) group (BMI $\geq$ 23kg/$m^2$, n = 49) by body mass index (BMI). The food and nutrient intake data obtained by the 3-day food record were analyzed by Can pro 3.0 software. Anthropometric measurements were collected from each subject. Daily dietary GI (DGI) and dietary GL (DGL) were calculated from the 3-day food record. Body weights and BMI of NW were 52.4 kg and 20.4 kg/$m^2$ and those of OW were 65.2 kg and 25.4 kg/$m^2$, respectively. Total food, animal food, and other food intakes of NW were higher than those of OW, and vegetable food intakes of NW were lower than those of OW. Sugars intake of NW was significantly higher than OW. Nutrient intakes were not different between the two groups. Dietary fiber, calcium, and folate intakes of NW and OW were under 65% of the dietary reference intakes (DRIs). Major food sources of energy intake for both groups were rice, pizza, ice cream, pork, instant noodle, and chicken. Mean adequacy ratio (MAR), an index of overall dietary quality, was higher in NW (0.82) than in OW (0.80). Mean daily DGI of NW and OW was 66.5 and 66.4, respectively. Mean daily DGL of NW and OW was higher in NW (162.0) than in OW (155.9). DGI and DGL adjusted to energy intake were not significantly correlated with anthropometric data.
The purpose of this study was to evaluate nutrients intakes, glycemic index (GI), glycemic load (GL) according to body mass index (BMI) in female college students (n = 320). The study subjects were divided into 3 groups based on their body mass index, an underweight group (BMI < 18.5 kg/$m^2$, n = 55), a normal group (18.5 kg/$m^2$${\leq}$ < 23 kg/$m^2$, n = 231), and an overweight group (23 kg/$m^2$${\leq}$ BMI < 25 kg/$m^2$, n = 34). The food and nutrition intake data obtained by administering a 3-day food record and were analyzed by using Can pro 3.0 software. Anthropometric measurements were collected from each subject. Body weights and BMI of the underweight group were 45.9 kg, 17.6 kg/$m^2$, those of the normal group were 53.8 kg, 20.5 kg/$m^2$, and those of overweight group were 62.6 kg, 23.8 kg/$m^2$, respectively. The mean daily dietary GI of underweight, normal and overweight groups was 66.2, 65.8 and 66.5, respectively. These differences were statistically non-significant. The mean daily dietary GL of underweight, normal and overweight groups were 159.2, 149.4, and 148.9, respectively. The major food source of dietary GI and GL was rice in the three groups. Dietary GI and GL were not significantly correlated with obesity when adjusted for energy, carbohydrate and dietary fiber intake.
Background: We aimed to estimate the proportion of patients with diabetes who achieved target glycemic control, to estimate diabetes-related costs attributable to poor control, and to identify factors associated with them in the United Arab Emirates. Methods: This retrospective cohort study used administrative claims data handled by Abu Dhabi Health Authority (January 2010 to June 2012) to determine glycemic control and diabetes-related treatment costs. A total of 4,058 patients were matched using propensity scores to eliminate selection bias between patients with glycosylated hemoglobin (HbA1c) <7% and HbA1c ${\geq}7%$. Diabetes-related costs attributable to poor control were estimated using a recycled prediction method. Factors associated with glycemic control were investigated using logistic regression and factors associated with these costs were identified using a generalized linear model. Results: During the 1-year follow-up period, 46.6% of the patients achieved HbA1c <7%. Older age, female sex, better insurance coverage, non-use of insulin in the index diagnosis month, and non-use of antidiabetic medications during the follow-up period were significantly associated with improved glycemic control. The mean diabetes-related annual costs were $2,282 and $2,667 for patients with and without glycemic control, respectively, and the cost attributable to poor glycemic control was $172 (95% confidence interval [CI], $164-180). The diabetes-related costs were lower with mean HbA1c levels <7% (cost ratio, 0.94; 95% CI, 0.88-0.99). The costs were significantly higher in patients aged ${\geq}65$ years than those aged ${\leq}44$ years (cost ratio, 1.45; 95% CI, 1.25-1.70). Conclusion: More than 50% of patients with diabetes had poorly controlled HbA1c. Poor glycemic control may increase diabetes-related costs.
Purpose: This study was performed to assess the level of blood glucose and to identify poor glycemic control groups among patients with type 2 diabetes mellitus (DM). Methods: Data of 1,022 Korean type 2 DM patients aged 30-64 years were extracted from the Korea National Health and Nutrition Examination Survey VII. Complex samples analysis and a decision-tree analysis were performed using the SPSS WIN 26.0 program. Results: The mean level of hemoglobin A1c (HbA1c) was 7.22±0.25%, and 69.0% of the participants showed abnormal glycemic control (HbA1c≥6.5%). The characteristics of participants associated with poor glycemic control groups were presented with six different pathways by the decision-tree analysis. Poor glycemic control groups were classified according to the patients' characteristics such as period after DM diagnosis, awareness of DM, sleep duration, gender, alcohol drinking, occupation, income status, low density lipoprotein-cholesterol, abdominal obesity, and number of walking days per week. Period of DM diagnosis with a cut-off point of 6 years was the most significant predictor of the poor glycemic control group. Conclusion: The findings showed the predictable characteristics of the poor glycemic control groups, and they can be used to screen the poor glycemic control groups among adults with type 2 DM.
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