Defining the Relationship Between Plasma Glucose and HbA1c: Analysis of glucose profiles and HbA1c in the Diabetes Control and Complications Trial

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

To define the relationship between HbA(1c) and plasma glucose (PG) levels in patients with type 1 diabetes using data from the Diabetes Control and Complications Trial (DCCT). The DCCT was a multicenter, randomized clinical trial designed to compare intensive and conventional therapies and their relative effects on the development and progression of diabetic complications in patients with type 1 diabetes. Quarterly HbA(1c) and corresponding seven-point capillary blood glucose profiles (premeal, postmeal, and bedtime) obtained in the DCCT were analyzed to define the relationship between HbA(1c) and PG. Only data from complete profiles with corresponding HbA(1c) were used (n = 26,056). Of the 1,441 subjects who participated in the study, 2 were excluded due to missing data. Mean plasma glucose (MPG) was estimated by multiplying capillary blood glucose by 1.11. Linear regression analysis weighted by the number of observations per subject was used to correlate MPG and HbA(1c). Linear regression analysis, using MPG and HbA(1c) summarized by patient (n = 1,439), produced a relationship of MPG (mmol/l) = (1.98 . HbA(1c)) - 4.29 or MPG (mg/dl) = (35.6 . HbA(1c)) - 77.3, r = 0.82). Among individual time points, afternoon and evening PG (postlunch, predinner, postdinner, and bedtime) showed higher correlations with HbA(1c) than the morning time points (prebreakfast, postbreakfast, and prelunch). We have defined the relationship between HbA(1c) and PG as assessed in the DCCT. Knowing this relationship can help patients with diabetes and their healthcare providers set day-to-day targets for PG to achieve specific HbA(1c) goals.

Related collections

Most cited references 13

Record: found
Abstract: found
Article: not found

A mathematical model for the determination of total area under glucose tolerance and other metabolic curves.

M. M. Tai (1994)

To develop a mathematical model for the determination of total areas under curves from various metabolic studies. In Tai's Model, the total area under a curve is computed by dividing the area under the curve between two designated values on the X-axis (abscissas) into small segments (rectangles and triangles) whose areas can be accurately calculated from their respective geometrical formulas. The total sum of these individual areas thus represents the total area under the curve. Validity of the model is established by comparing total areas obtained from this model to these same areas obtained from graphic method (less than +/- 0.4%). Other formulas widely applied by researchers under- or overestimated total area under a metabolic curve by a great margin. Tai's model proves to be able to 1) determine total area under a curve with precision; 2) calculate area with varied shapes that may or may not intercept on one or both X/Y axes; 3) estimate total area under a curve plotted against varied time intervals (abscissas), whereas other formulas only allow the same time interval; and 4) compare total areas of metabolic curves produced by different studies. The Tai model allows flexibility in experimental conditions, which means, in the case of the glucose-response curve, samples can be taken with differing time intervals and total area under the curve can still be determined with precision.

0 comments Cited 111 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

The clinical information value of the glycosylated hemoglobin assay.

Paul Nathan, K Hurxthal, Stacy D Singer … (1984)

We evaluated the clinical information value of the glycosylated hemoglobin assay by comparing it with practitioners' estimates of glucose control over the preceding 10 weeks in 216 patients with diabetes. Twenty-four per cent of the practitioners' estimates, which were based on historical and laboratory data collected during a routine office visit, differed by more than +/- 75 mg per deciliter from the actual mean blood glucose levels calculated with the glycosylated hemoglobin assay. One third of the mean blood glucose concentration fell outside the confidence intervals physicians used to bound their estimates. When examined individually or in the aggregate, historical information, such as polyuria, nocturia, or home urine testing for glucose, and laboratory information, such as fasting or random blood glucose levels, were weak predictors of the actual mean concentration of blood glucose. We conclude that the glycosylated hemoglobin assay provides information about the degree of long-term glucose control that is not otherwise obtainable in the usual clinical setting.

0 comments Cited 103 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Nonfasting plasma glucose is a better marker of diabetic control than fasting plasma glucose in type 2 diabetes.

L Monnier-Cholley, A Radauceanu, A Avignon (1997)

To evaluate the relative value of plasma glucose (PG) at different time points in assessing glucose control of type 2 diabetic patients. Glycemic profiles, i.e., PG at prebreakfast (8:00 A.M.), prelunch (11:00 A.M.), postlunch (2:00 P.M.), and extended postlunch (5:00 P.M.) times over the same day, were obtained in 66 type 2 diabetic patients on an ambulatory basis. The different time points of PG were compared with a measurement of HbA1c made in a reference laboratory. Extended postlunch PG was lower than prebreakfast PG (104 +/- 21 vs. 133 +/- 35 mg/dl, P < 0.02) in patients demonstrating good diabetic control (HbA1c < or = 7.0%), was not different from prebreakfast PG (149 +/- 47 vs. 166 +/- 26 mg/dl, NS) in patients demonstrating fair diabetic control (7.0% < HbA1c < or = 8.5%), and was higher than prebreakfast PG (221 +/- 62 vs. 199 +/- 49 mg/dl, P < or = 0.01) in those demonstrating poor diabetic control (HbA1c < or = 8.5%). Prebreakfast, prelunch, postlunch, and extended postlunch PG values were all significantly correlated with HbA1c. Multiple linear regression analysis demonstrated that postlunch PG and extended postlunch PG correlated significantly and independently with HbA1c, but that prebreakfast PG and prelunch PG did not. Moreover, postlunch PG and extended postlunch PG demonstrated better sensitivity, specificity, and positive predictive value in predicting poor glycemic control than did prebreakfast PG or prelunch PG. In type 2 diabetes, postlunch PG and extended postlunch PG are better predictors of glycemic control than fasting plasma glucose (FPG). We therefore suggest that they be more widely used to supplement, or substitute for, FPG in evaluating the metabolic control of type 2 diabetic patients.