Fasting cut-points in determining prevalence of diabetes in an Arab population of the Middle East

We used two cross-sectional surveys involving 6356 Omanis aged >or= 20 years to estimate the effect of the 1997 American Diabetes Association (ADA) criteria on the prevalence of diabetes mellitus in Oman and develop a validated optimal fasting plasma glucose (FPG) cut-point which best predicts diabetes diagnosed 2-h post oral glucose tolerance test. Applying the 1997 ADA criteria to Oman would underestimate diabetes by 18%. The sensitivity of the ADA criteria was 68.3% (95% CI 64.0-72.4%) and specificity was 98.6% (95% CI 98.2-98.9%). Receiver-operating characteristic (ROC) curve depicted FPG>5.9 mmol/l to best predict 2-h post-load glucose >or=11.1 mmol/l. The area under the ROC curve was 0.95 (95% CI 0.94-0.95%) with no significant difference between obese and non-obese individuals. This cut-point had a sensitivity of 87.5% (95% CI 84.3-90.3%), specificity of 90.8% (95% CI 89.9-91.7%) and likelihood ratio of 9.5. On validation in an independent population, the sensitivity and specificity of the depicted cut-point remained high 84.2% (95% CI 77.0-89.8%) and 80.2% (95% CI 78.0-82.4%) compared to the ADA values 60.4% and 96.6%, respectively. Our study identified a lower cut-point to diagnose diabetes than that suggested by the 1997 ADA criteria.

Evaluation of systematic and random factors in measurements of fasting plasma glucose as the basis for analytical quality specifications in the diagnosis of diabetes. 3. Impact of the new WHO and ADA recommendations on diagnosis of diabetes mellitus

On behalf of the Danish Society of Clinical Endocrinology and the Danish Society of Clinical Chemistry we were commissioned to evaluate the influence of analytical and pre-analytical systematic and random factors on the diagnosis of diabetes, in order to provide a tool for conclusions on the analytical quality specifications needed to diagnose diabetes. A systems analysis was performed in accordance with the principles for evaluation of analytical quality specifications. The clinical setting was defined--diagnosis of diabetes in accordance with the WHO and ADA criteria with determination of fasting plasma glucose concentration (FPG) > or =7.0 mmol/L in two independent samples--with well-documented data on In (loge)-Gaussian distribution of reference values from a low-risk population and values for within-subject biological variation taken from the literature. An investigation was made of the consequences for the clinical setting of assumed errors related to the measurement of FPG. Four approaches were investigated for a single sampling and measurement and also for two independent samples: one showing the percentage of healthy individuals who had values > or = 7.0 mmol/L, one illustrating the origin of biological set-points for results > or = 7.0 mmol/L, one showing the risk of being measured > or =7.0 mmol/L when the biological set-point is known, and one showing the combined bias and imprecision for assumed percentages of false-positive (FP), defined as measurements > or = 7.0 mmol/L for the low-risk population and false-negative (FN), defined as measurements <6.4 mmol/L (the upper reference limit) for diabetics. This leaves a "grey zone" which includes the upper part of low-risk individuals, and defined by ADA and WHO as "impaired fasting glucose" (IFG). In the analysis, increasing systematic and random errors (combined analytical and pre-analytical) were assumed, and for each error condition the fractions of FP and FN were calculated. This gave plots from which the combined allowable systematic and random errors could be read off for pre-determined clinically acceptable fractions of FP and FN. The analysis does not distinguish between pre-analytical and analytical errors, as specified information on one of these is needed for specification of the other. The investigation provides a reliable basis for estimation of the needed analytical quality, and thereby for decisions about analytical quality specifications for analysis of FPG in relation to diagnosis of diabetes under optimized pre-analytical and analytical conditions. Consequences of deviations from these ideal conditions are illustrated in the figures, and should be considered for the different approaches with different performance conditions.