2010 Consensus Statement on the Worldwide Standardization of the Hemoglobin A1C Measurement

Hanås, Ragnar; John, Garry

doi:10.2337/dc10-0953

Cited by 167 publications

(69 citation statements)

References 10 publications

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“…[5] Second sample was collected in the tube containing Ethylene Diamine Tetraacetic Acid(EDTA) for the estimation of HbA 1c by immunoinhibition method International Federation of Clinical Chemistry and Laboratory Medicine,(IFCC) using the Diasys kit. [6] Blood sample from the anticoagulant (EDTA)-containing vacutainer was centrifuged at 3000 rpm for 10 min; supernatant plasma was used for ascorbic acid estimation by nonenzymatic method, using 2, 4-dinitrophenyl hydrazine (DNPH). [7] The buffy coat was discarded.…”

Section: Methodsmentioning

confidence: 99%

Oxidative stress in diabetic patients with retinopathy

et al. 2014

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Section: Methodsmentioning

confidence: 99%

Oxidative stress in diabetic patients with retinopathy

et al. 2014

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“…HbA 1c was measured with an enzyme immunoassay using a monoclonal antibody. The HbA 1c values obtained were converted to DCCT values [20] and International Federation of Clinical Chemistry and Laboratory Medicine (mmol/mol) values in line with the European Association for the Study of Diabetes recommendations [21]. Lipids (fasting triglycerides, cholesterol and HDL) were measured with standard enzymatic methods.…”

Section: Methodsmentioning

confidence: 99%

Predicting major outcomes in type 1 diabetes: a model development and validation study

et al. 2014

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Aims/hypothesis Type 1 diabetes is associated with a higher risk of major vascular complications and death. A reliable method that predicts these outcomes early in the disease process would be helpful in risk classification. We therefore developed such a prognostic model and quantified its performance in independent cohorts. Methods Data were analysed of 1,973 participants with type 1 diabetes who were followed for seven years in the EURODIAB Prospective Complications Study. Strong prognostic factors of major outcomes were combined in a Weibull regression model. The model performance was tested in three different prospective cohorts: Pittsburgh Epidemiology of Diabetes Complications study (EDC, n=554), Finnish Diabetic Nephropathy study (FinnDiane, n=2,999) and Coronary Artery Calcification in Type 1 Diabetes study (CACTI, n=580). Major outcomes included major coronary heart disease, stroke, end-stage renal failure, amputations, blindness and all-cause death. Results 95 EURODIAB patients with type 1 diabetes developed major outcomes during follow-up. Prognostic factors were age, glycated haemoglobin, waist-hip ratio, albumin/creatinine ratio, and HDL cholesterol. A high risk group could be identified with 15% risk after 3-years of follow-up, 24% after 5-years and 32% after 7-years. The discriminative ability of the model was adequate with a C-statistic of 0.74. Discrimination was similar or even better in the independent cohorts: EDC, C-statistic = 0.79; FinnDiane, 0.82; and CACTI, 0.73. Conclusions/Interpretation Our prognostic model that uses easily accessible clinical features can discriminate between type 1 diabetes patients with good and poor prognosis. Such a prognostic model may be helpful in clinical practice and for risk stratification in clinical trials.

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“…Before starting the TPN infusion, a blood sample was drawn to measure the glycated hemoglobin, following the international recommendations for standardization of the HbA 1c measurement (17). Measurements were also made of fasting plasma blood glucose, albumin, and C-reactive protein (CRP) levels (with an autoanalyzer) at the laboratories of each hospital.…”

Section: Methodsmentioning

confidence: 99%

Parenteral Nutrition–Associated Hyperglycemia in Non–Critically Ill Inpatients Increases the Risk of In-Hospital Mortality (Multicenter Study)

Olveira

Tapia²,

Ocón

et al. 2013

Diabetes Care

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OBJECTIVEHyperglycemia may increase mortality in patients who receive total parenteral nutrition (TPN). However, this has not been well studied in noncritically ill patients (i.e., patients in the nonintensive care unit setting). The aim of this study was to determine whether mean blood glucose level during TPN infusion is associated with increased mortality in noncritically ill hospitalized patients.RESEARCH DESIGN AND METHODSThis prospective multicenter study involved 19 Spanish hospitals. Noncritically ill patients who were prescribed TPN were included prospectively, and data were collected on demographic, clinical, and laboratory variables as well as on in-hospital mortality.RESULTSThe study included 605 patients (mean age 63.2 ± 15.7 years). The daily mean TPN values were 1.630 ± 323 kcal, 3.2 ± 0.7 g carbohydrates/kg, 1.26 ± 0.3 g amino acids/kg, and 0.9 ± 0.2 g lipids/kg. Multiple logistic regression analysis showed that the patients who had mean blood glucose levels >180 mg/dL during the TPN infusion had a risk of mortality that was 5.6 times greater than those with mean blood glucose levels <140 mg/dL (95% CI 1.47–21.4 mg/dL) after adjusting for age, sex, nutritional state, presence of diabetes or hyperglycemia before starting TPN, diagnosis, prior comorbidity, carbohydrates infused, use of steroid therapy, SD of blood glucose level, insulin units supplied, infectious complications, albumin, C-reactive protein, and HbA1c levels.CONCLUSIONSHyperglycemia (mean blood glucose level >180 mg/dL) in noncritically ill patients who receive TPN is associated with a higher risk of in-hospital mortality.

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2010 Consensus Statement on the Worldwide Standardization of the Hemoglobin A1C Measurement

Cited by 167 publications

References 10 publications

Oxidative stress in diabetic patients with retinopathy

Oxidative stress in diabetic patients with retinopathy

Predicting major outcomes in type 1 diabetes: a model development and validation study

Parenteral Nutrition–Associated Hyperglycemia in Non–Critically Ill Inpatients Increases the Risk of In-Hospital Mortality (Multicenter Study)

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