Prognostic value of haemoglobin A1c and fasting plasma glucose for incident diabetes and implications for screening
ABSTRACT The aim of this analysis is to compare screening strategies with haemoglobin A(1c) (HbA(1c)), fasting plasma glucose (FPG) or combined measures in the identification of individuals at high risk for diabetes. Applying American Diabetes Association thresholds for FPG and HbA(1c) screening, 6,803 subjects free of diabetes were classified as non-diabetic, pre-diabetic and possibly diabetic by FPG (<100, 100-125 and >125 mg/dl) and HbA(1c) (<5.7, 5.7-6.4 and >6.4%). Hazard ratios, sensitivity and specificity were estimated for individuals with pre-diabetes with respect to incident diabetes in the following 5 years. Areas under the receiver operating characteristic curves (AUC) were estimated for levels of FPG ≤ 125 mg/dl and HbA(1c) ≤ 6.4% in diabetes prediction. Although FPG and HbA(1c) screenings poorly agreed in classifying individuals as pre-diabetic, hazard ratios [95% confidence interval] for incident diabetes were similarly increased in univariate models in the two pre-diabetic groups: FPG 100-125 mg/dl, 4.72 [3.69; 6.05]; HbA(1c) 5.7-6.4%, 3.97 [3.05; 5.23]. HbA(1c) and FPG had comparable AUCs (FPG, 0.732; HbA(1c), 0.725) and consequently similar 5-year sensitivities and specificities for their pre-diabetes definitions (when the lower cut-off for HbA(1c)-defined pre-diabetes was increased to a level between 5.8 and 5.9%). Combining HbA(1c) and FPG increased the AUC to 0.778, and a further increase to 0.817 was seen with additional inclusion of conventional risk factors. FPG and HbA(1c) have comparable (yet insufficient) abilities in identifying individuals at high risk for diabetes. Effectiveness of a diabetes screening program could be improved by a risk score including FPG and HbA(1c).
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ABSTRACT: The prevalence of type 2 diabetes mellitus (T2DM) has been increasing, owing to increases in overweight and obesity, decreasing physical activity and the changing demographic structure of the population. People can develop T2DM without symptoms and up to 20% may be undiagnosed. They may have diabetic complications, such as retinopathy, by the time they are diagnosed, or may suffer a heart attack, without warning. Undiagnosed diabetes can be detected by raised blood glucose levels. The aim of this review was to provide an update for the UK National Screening Committee (NSC) on screening for T2DM. As this review was undertaken to update a previous Health Technology Assessment review published in 2007, and a more recent Scottish Public Health Network review, searches for evidence were restricted from 2009 to end of January 2012, with selected later studies added. The databases searched were MEDLINE, EMBASE, MEDLINE-in-Process & Other Non-Indexed Citations, Science Citation Index and Conference Proceedings Citation Index. The case for screening was considered against the criteria used by the NSC to assess proposed population screening programmes. Population screening for T2DM does not meet all of the NSC criteria. Criterion 12, on optimisation of existing management, has not been met. A report by the National Audit Office (NAO) gives details of shortcomings. Criterion 13 requires evidence from high-quality randomised controlled trials that screening is beneficial. This has not been met. The Ely trial of screening showed no benefit. The ADDITION trial was not a trial of screening, but showed no benefit in cardiovascular outcomes from intensive management in people with screen-detected T2DM. Criterion 18 on staffing and facilities does not appear to have been met, according to the NAO report. Criterion 19 requires that all other options, including prevention, should have been considered. A large proportion of cases of T2DM could be prevented if people avoided becoming overweight or obese. The first stage of selection would use risk factors, using data held on general practitioner computer systems, using the QDiabetes Risk Score, or by sending out questionnaires, using the Finnish Diabetes Risk Score (FINDRISC). Those at high risk would have a measure of blood glucose. There is no perfect screening test. Glycated haemoglobin (HbA1c) testing has advantages in not requiring a fasting sample, and because it is a predictor of vascular disease across a wider range than just the diabetic one. However, it lacks sensitivity and would miss some people with diabetes. Absolute values of HbA1c may be more useful as part of overall risk assessment than a dichotomous 'diabetes or not diabetes' diagnosis. The oral glucose tolerance test is more sensitive, but inconvenient, more costly, has imperfect reproducibility and is less popular, meaning that uptake would be lower. When considered against the NSC criteria, the case for screening is less strong than it was in the 2007 review. The main reason is the absence of cardiovascular benefit in the two trials published since the previous review. There is a case for selective screening as part of overall vascular risk assessment. Population screening for T2DM does not meet all of the NSC criteria. The National Institute for Health Research Health Technology Assessment programme.08/2013; 17(35):1-90. DOI:10.3310/hta17350
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ABSTRACT: Background Using high-normal levels of haemoglobin A(1C) (Abnormal-A(1C)) or fasting plasma glucose (FPG) (Abnormal-FPG) for diabetes screening are expected to improve the ability to detect persons with or at high risk of diabetes. We assessed the diagnostic and predictive capacity for diabetes of Abnormal-A(1C) and Abnormal-FPG. We compared these to the combined use of the two measures to the single use of either measurement. Methods We analysed 31 eligible cross-sectional or cohort studies that assessed diagnostic or predictive ability, respectively, by using lower A(1C) and FPG cutoff values than recommended by current diabetes criteria. Positive and negative likelihood ratios (LR+ and LR-) were calculated to assess the ability to confirm or exclude diabetes, respectively, on the basis of a bivariate random-effects model. ResultsWith both Abnormal-A(1C) and Abnormal-FPG, the pooled LR+ was above 4 for diagnosing diabetes and above 3 for predicting diabetes. However, the pooled LR- for predicting diabetes was higher with Abnormal-A(1C) (0.48) and Abnormal-FPG (0.49) in comparison with that for diagnosing diabetes (0.27, Abnormal-A(1C); 0.28, Abnormal-FPG). In eight studies that assessed the predictive ability of the combination of A(1C) and FPG, using either Abnormal-A(1C) or Abnormal-FPG could lower LR- to 0.17 from 0.43 for only Abnormal-A(1C) and from 0.38 for only Abnormal-FPG. Accordingly, LR+ was also lowered to 2.37 from 3.36 for only Abnormal-A(1C) and from 3.84 for only-Abnormal-FPG. Conclusion The use of the two blood glucose tests had insufficient capacity to identify subjects at high risk for diabetes but had considerable capacity to identify undiagnosed diabetes. Copyright (c) 2013 John Wiley & Sons, Ltd.Diabetes/Metabolism Research and Reviews 11/2013; 29(8). DOI:10.1002/dmrr.2445 · 3.59 Impact Factor
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ABSTRACT: With respect to clinical phenotype and pathophysiology, prediabetes is akin to diabetes. Prediabetes is prevalent in the global population, and those affected are at high risk of progression to overt diabetes, and also at risk of cardiovascular disease (CVD). Progression to diabetes can occur because of worsening insulin resistance, β-cell dysfunction, or both, but the timecourse can be non-linear and, therefore, unpredictable. Intervention-by lifestyle modification, glucose-lowering drugs, or a combination-can postpone deterioration of glucose control, but effects of intervention are variable and can be transient. Furthermore, to what extent interventions can reduce cardiovascular risk is uncertain. Lifestyle intervention mainly hinges on weight loss; as such, risk of failure in the long-term is high, and implementation at the community level is difficult. The ideal candidate for intervention is an individual with prediabetes-identified by targeted screening-with many well documented cardiovascular risk factors, and who is highly motivated to initiate and maintain multifactorial risk-control using a personalised mix of lifestyle-adaptation and pharmacological treatment.01/2014; 2(8). DOI:10.1016/S2213-8587(13)70175-X