HbA1c Blood Glucose Research Articles

Distress and Living with Diabetes: Defining Characteristics Through an Online Survey.

IntroductionThere is considerable evidence for diabetes reducing quality of life. The impact of such a diagnosis on mental health is less well understood and was subsequently explored here.MethodsOnline PHQ-9 scores (which calculate the severity of depression), Diabetes Distress Screening Scale (DDSS) and EQ-5D-5L (quality-of-life) questionnaires were completed by patients with diabetes, followed by the extraction of data where possible from responders’ clinical records.ResultsA total of 133 people submitted questionnaires. However, not all data items could be completed by each patient; 35% (45/130) had type I diabetes mellitus (T1DM); 55% (64/117) were women. The overall median age of 117 responders was 60 (IQR 50–68 years). The median aggregated response scores were: EQ-5D-5L 0.74 (IQR 0.64–0.85) (lower quality of life than UK population median of 0.83), DDSS 1.9 (IQR1.3–2.7) (≥ 2 indicates moderate distress) and PHQ-9 5 (IQR2-11) (≥ 5 indicates depression). Higher diabetes distress (DDSS)/lower quality of life EQ-5D-5L/higher depressive symptoms (PHQ-9) linked to female sex (DDSS 0.5/25% above median), younger age (< 50 years DDSS 0.7/35% above median), fewer years after diagnosis (< 10 years DDSS 0.8/40% above median), and obesity (BMI > 35 DDSS 0.6/30% above median). Additionally, a HbA1c reading of ≤ 48 mmol/mol was associated with higher DDSS scores, as did a reduction of more than 5 mmol/mol in HbA1c over the last three HbA1c measurements. The 30 individuals with a history of prescribed antidepressant medication also showed higher diabetes distress scores (DDSS 0.9, equating to 45% above the median). The DDSS score elevation came from an increase in emotional burden and regimen-related distress. DDSS scores were not significantly linked to diabetes type, insulin use, absolute level/change in blood glucose HbA1c. Physician-related distress showed a similar pattern.ConclusionsA low level of stress in relation to diabetes management may be associated with lower HbA1c. The larger impact of diabetes on mental health in younger women/people with shorter diabetes duration should be noted when considering psychosocial intervention/behavior change messaging. Physician-related distress is a potentially remediable factor. However, this sample was self-selecting, limiting generalization to other samples.

Neonatal outcomes in case of euglycemic control in gestational diabetes using insulin versus metformin. Randomized controlled trial

Background: Gestational diabetes mellitus (GDM) is a major global public health issue, with prevalence increasing in recent years due to the epidemic of obesity and type 2 diabetes. Aim of the Work: to compare different neonatal outcomes according to the different treatment modalities used in the management of GDM. Our hypothesis was that Metformin is as effective and safe as insulin in patients with gestational diabetes. Patients and Methods: The current non inferiority-Randomized controlled trial was conducted at Ain Shams Maternity hospital between June 2020 to February 2021. The study included 140 outpatient cases or admitted patients for antenatal care: Group A: women were given Metformin (Total 70) and Group B: Women were given insulin. (Total 70). Results: there was no significant difference between Metformin and Insulin groups regarding age, enrollment BMI, parity and family history of DM. There was no significant difference between Metformin and Insulin groups regarding gestational age at enrollment and delivery as well as pregnancy duration after intervention. BMI at delivery, BMI increase as well as BMI increase rate were significantly lower in Metformin group. There were no significant differences between Metformin and Insulin groups regarding fasting, two-hour postprandial and HbA1c blood glucose at enrollment and throughout treatment as well as their reduction after intervention. Maternal complications as hypoglycemia, hyperglycemia and preeclampsia were non-significantly less frequent among Metformin group than among Insulin group. Compliance to treatment was significantly more frequent among Metformin group than among Insulin group. Cesarean delivery was non­significantly less frequent among Metformin group than among Insulin group. There was no significant difference between Metformin and Insulin regarding birth weight APGAR-1, but APGAR-5 was significantly higher in Metformin group. Neonatal complications as IUFD, IUGR, macrosomia, congenital anomalies, neonatal hypoglycemia, respiratory distress and NICU admission were non-significantly less frequent among Metformin group. Conclusions: From the results of current study we can conclude that: Oral metformin was effective as insulin injection in control and management of GDM. BMI was controlled with oral metformin better than insulin injection. Maternal and neonatal complications specially birth weight were the same with both types of treatment. Women had better compliance to metformin treatment. Type of delivery wasn’t affected by type of treatment.

Using Hemoglobin A1c to Derive Mean Blood Glucose in Peritoneal Dialysis Patients

Background: Although hemoglobin A1c (HbA1c) has been widely used as a clinical assessment tool for outcome analyses related to glycemic control, the relationship between HbA1c and average blood glucose (BG) specific to peritoneal dialysis (PD) patients with diabetes has not been characterized. We sought to develop HbA1c-BG equation models for PD patients. Methods: We examined associations between HbA1c and random serum BG values over time in a contemporary 5-year (2001-2006) cohort of DaVita PD patients with diabetes. We identified 850 patients (mean age: 58 ± 13 years, 56% male) with 4,566 paired measurements of HbA1c and BG. The bootstrapping method was used to estimate average BG and corresponding HbA1c. Results: Linear regression analyses yielded the following HbA1c-BG equations: (1) BG (mg/dl) = 24.1 + 28.6 × HbA1c - 12.2 × albumin [adjusted R² (R²_adj = 0.454)], (2) BG = 55.3 + 28.8 × HbA1c - 10.2 × albumin - 3.3 × Hb (R²_adj = 0.457), and (3) BG = 69.5 + 28.7 × HbA1c - 10.1 × albumin - 3.7 × Hb - 0.1 × age + race/ethnicity (-10.1 African Americans, -5.4 other race/ethnicities; R²_adj = 0.457). All models showed greater explanatory power of BG variation than previously established HbA1c-BG equation models defined within non-PD cohorts [R²_adj = 0.446 for both the Diabetes Control and Complications Trial (DCCT) and the A1c-Derived Average Glucose (ADAG) equations]. Conclusions: The association between HbA1c and BG in PD patients is different than that of patients with normal kidney function. Our analysis suggests that equations incorporating serum albumin and/or Hb values better estimate the HbA1c-BG relationship in PD patients compared to equations using HbA1c alone.

Developing an HbA1c-Based Equation to Estimate Blood Glucose in Maintenance Hemodialysis Patients

OBJECTIVEHemoglobin A1c (HbA1c) has been widely used as a clinically important assessment tool for outcome analyses related to glycemic control. However, because of special conditions in dialysis patients, including the uremic milieu, there is no HbA1c blood glucose (BG) equation specific for patients on dialysis. In this study, we sought to develop HbA1c-BG equation models for hemodialysis patients.RESEARCH DESIGN AND METHODSWe examined associations between HbA1c and random serum BG over time in a contemporary cohort of diabetic patients with hemodialysis treated in DaVita dialysis clinics. We identified 11,986 patients (63 ± 12 years old and 49% male) with 69,764 paired measurements of HbA1c and BG over the course of 5 years (2001–2006). Bootstrapping method was used to estimate average BG and corresponding HbA1c levels. The association was adjusted by patient factors using linear regression.RESULTSLinear regression analyses yielded the following three regression equations: BG = 59.2 + 29.4 × HbA1c − 20.8 × Alb (R2 = 0.483); BG = 104.8 + 29.7 × HbA1c − 18.4 × Alb − 4.7 × Hb (R2 = 0.486); and BG = 82.9 + 30.7 × HbA1c − 16.5 × Alb − 5.4 × Hb + 0.3 × age + race (R2 = 0.491). All our models showed stronger association than previous equation models (R2 = 0.468 in the Diabetes Control and Complications Trial and A1c-Derived Average Glucose equations).CONCLUSIONSThe association between HbA1c and BG in hemodialysis patients is different than that of patients with normal kidney function. Our analysis suggests that equations including serum albumin or hemoglobin are better for hemodialysis patients.

DEVELOPING A NEW HEMOGLOBIN A1C BASED EQUATION TO ESTIMATE -BLOOD GLUCOSE IN MAINTENANCE HEMODIALYSIS PATIENTS.

HbA1c has been widely used as a clinically important assessment tool for outcome analyses related to glycemic control. However, there is no HbA1c-blood glucose equation formula specific for hemodialysis (HD) patients. In this study, we sought to develop HbA1c-blool glucose equation models for dialysis patients. We examined associations between HbA1c levels and random serum glucose levels over time in a contemporary cohort of 18,361 diabetic maintenance HD patients treated in DaVita dialysis clinics from July 2001 through June 2006 with 276,277 paired measurements of HbA1c and glucose. Time-averaged HbA1c values and blood glucose (BG) values were compared using regression models with and without adjustment for hemoglobin, albumin, patients' age, gender, and/or race/ethnicity to determine a new equation model. The new models were compared to the previously reported equations (DCCT and ADAG). modal formula adjust R 2 Our modal BG=53.7+28.8 * HbA1c-18.2 * Alb 0.3243 DCCT BG=35.6 * HbA1c-77.3 0.3141 ADAG BG=28.7 * HbA1c-46.7 0.3141 In conclusion, the association between HbA1c and BG in HD patients is different from non-HD patient. Our proposed model results in better estimated BG level in HD patients compared to previously reported models.

Pro-Active Call Center Treatment Support (PACCTS) to Improve Glucose Control in Type 2 Diabetes

To determine whether Pro-Active Call Center Treatment Support (PACCTS), using trained nonmedical telephonists supported by specially designed software and a diabetes nurse, can effectively improve glycemic control in type 2 diabetes. A randomized controlled implementation trial of 1-year duration was conducted in Salford, U.K. The trial comprised 591 randomly selected individuals with type 2 diabetes. By random allocation, 197 individuals were assigned to the usual care (control) group and 394 to the PACCTS (intervention) group. Lifestyle advice and drug treatment in both groups followed local guidelines. PACCTS patients were telephoned according to a protocol with the frequency of calls proportional to the last HbA(1c) level. The primary outcome was absolute reduction in HbA(1c), and the secondary outcome was the proportion of patients reducing HbA(1c) by at least 1%. A total of 332 patients (84%) in the PACCTS group and 176 patients (89%) in the control group completed the study. Final HbA(1c) values were available in 374 patients (95%) in the PACCTS group and 180 patients (92%) in the usual care group. Compared with usual care, HbA(1c) improved by 0.31% (95% CI 0.11-0.52, P = 0.003) overall in the PACCTS patients. For patients with baseline HbA(1c) >7%, the improvement increased to 0.49% (0.21-0.77, P < 0.001), whereas in patients with baseline HbA(1c) <7% there was no change. The difference in the proportions of patients achieving a >/=1% reduction in HbA(1c) significantly favored the PACCTS intervention: 10% (4-16, P < 0.001) overall and 15% (7-24, P < 0.001) for patients with baseline HbA(1c) >7%. In an urban Caucasian trial population with blood glucose HbA(1c) >7%, PACCTS facilitated significant improvement in glycemic control. Further research should extend the validity of findings to rural communities and other ethnic groups, as well as to smoking and lipid and blood pressure control.

Lipoproteins and diuretics in type II diabetes

This double-blind cross-over study was performed to investigate whether the lipoproteins in plasma were different on furosemide (Lasix Retard) and thiazide (hydrochlorthiazide) treatment in patients suffering from type II diabetes. Twenty-four patients were randomly allocated to either furosemide-hydrochlorthiazide (LR-HCT) or HCT-LR treatment. The treatment period was 12 months: 6 months on each sequence. After inclusion, the patients were seen every second month. Laboratory data were recorded at each visit. The only significant treatment effect was observed for high-density-lipoprotein 3 cholesterol concentration (HDL 3 cholesterol concentration), which was higher when patients were on furosemide therapy ( p < 0.05). We conclude from the present study that blood-glucose HbA 1c, and the concentration of lipoproteins connected to development of atherosclerosis is unaffected whether type II diabetes patients are treated with HCT or furosemide.