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- All Subjects: Diabetes
- Creators: Harrington Bioengineering Program
ormoglycemic controls (NC), dyslipidemic
ormoglycemic (DN), dyslipidemic/prediabetic (DPD) and dyslipidemic/diabetic (DD). Total cholesterol (TC) was 30% higher among DD than in NC participants (p<0.0001). The DPD group had 27% and 12% higher LDL-C concentrations than the NC and DN groups, respectively. Similarly, LDL-C was 29% and 13% higher in DD than in NC and DN participants (p=0.013). An increasing trend was observed in %10-year CVD risk with increasing degree of hyperglycemia (p<0.0001). The NC group had less cholesterol in sdLDL particles than dyslipidemic groups, regardless of glycemic status (p<0.0001). When hyperglycemia was part of the phenotype (DPD and DD), there was a greater proportion of total and HDL-C in sHDL particles in dyslipidemic individuals than in NC (p=0.023; p<0.0001; respectively). Percent 10-year CVD risk was positively correlated with triglyceride (TG) (r=0.384, p<0.0001), TC (r=0.340, p<0.05), cholesterol in sdLDL(r=0.247; p<0.05), and TC to HDL-C ratio (r=0.404, p<0.0001), and negatively correlated with HDL-C in intermediate and large HDL(r=-0.38, p=0.001; r=0.34, p=0.002, respectively). The TC/HDL-C was positively correlated with cholesterol in sdLDL particles (r=0.698, p<0.0001) and HDL-C in sHDL particles (r=0.602, p<0.0001), and negatively correlated with cholesterol in small (r=-0.35, p=0.002), intermediate (r=-0.91, p<0.0001) and large (r=-0.84, p<0.0001) HDL particles, and HDL-C in the large HDL particles (r=-0.562, p<0.0001). No significant association was found between %10-year CVD risk and hsCRP. Collectively, these results corroborate that dyslipidemic Mexican-American adults have higher CVD risk than normolipidemic individuals. Hyperglycemia may further affect CVD risk by modulating cholesterol in LDL and HDL subfractions.
The purpose of this study, which was done in conjunction with the Arizona Heart Foundation, was to evaluate whether pyridoxine accelerates ulcer wound healing in diabetic patients with ulcers in the lower extremities. In this study, 100 mg of pyridoxine per day was given to patients in the experimental group (while they receive normal wound treatment) while patients in the control group received normal treatment of wounds without the pyridoxine. Over time, wound healing was evaluated by photographing and then measuring the size of patients' ulcer wounds on the photographs. Results from the experimental group were compared with those of the control group to evaluate the efficacy of the pyridoxine treatment. In addition, comparisons of the healing rates were made with respect to whether the patients smoked, had hypertension or hypotension, and the patients' body mass indexes. It has been found that there was no statistically significant difference in the mean healing rates between the control groups and experimental groups. In addition, it has been found that smoking, BMI and blood pressure did not have a statistically appreciable effect on the difference in mean healing rates between the control and experimental groups. This is evidence that pyridoxine did not have a statistically significant effect on wound healing rates.
Carbohydrate counting has been shown to improve HbA1c levels for people with diabetes. However, the learning curve and inconvenience of carbohydrate counting make it difficult for patients to adhere to it. A deep learning model is proposed to identify food from an image, where it can help the user manage their carbohydrate counting. This early model has a 68.3% accuracy of identifying 101 different food classes. A more refined model in future work could be deployed into a mobile application to identify food the user is about to consume and log it for easier carbohydrate counting.
Purpose: To evaluate the acute effects of a 15-min postmeal walk on glucose control and markers of oxidative stress following a high-carbohydrate meal.
Methods: Ten obese subjects (55.0 ± 10.0 yrs) with impaired fasting glucose (107.1 ± 9.0 mg/dL) participated in this repeated measures trial. Subjects arrived at the laboratory following an overnight fast and underwent one of three conditions: 1) Test meal with no walking or fiber (CON), 2) Test meal with 10g fiber and no walking (FIB), 3) Test meal with no fiber followed by a 15-min treadmill walk at preferred walking speed (WALK). Blood samples were taken over four hours and assayed for glucose, insulin, thiobarbituric reactive substances (TBARS), catalase, uric acid, and total antioxidant capacity (TAC). A repeated measures ANOVA was used to compare mean differences for all outcome variables.
Results: The 2hr and 4hr incremental area under the curve (iAUC) for glucose was lower in both FIB (2hr: -93.59 mmol∙120 min∙L-1, p = 0.006; 4hr: -92.59 mmol∙240 min∙L-1; p = 0.041) and WALK (2hr: -77.21 mmol∙120 min∙L-1, p = 0.002; 4hr: -102.94 mmol∙240 min∙L-1; p = 0.005) conditions respectively, compared with CON. There were no differences in 2hr or 4hr iAUC for glucose between FIB and WALK (2hr: p = 0.493; 4hr: p = 0.783). The 2hr iAUC for insulin was significantly lower in both FIB (-37.15 μU ∙h/mL; p = 0.021) and WALK (-66.35 μU ∙h/mL; p < 0.001) conditions, compared with CON, and was significantly lower in the WALK (-29.2 μU ∙h/mL; p = 0.049) condition, compared with FIB. The 4hr iAUC for insulin in the WALK condition was significantly lower than both CON (-104.51 μU ∙h/mL; p = 0.001) and FIB (-77.12 μU ∙h/mL; p = 0.006) conditions. Markers of oxidative stress were not significantly different between conditions.
Conclusion: A moderate 15-minute postmeal walk is an effective strategy to reduce postprandial hyperglycemia. However, it is unclear if this attenuation could lead to improvements in postprandial oxidative stress.