[ Nutrition ]

A1C for Screening and Diagnosis of Type 2 Diabetes in Routine Clinical Practice

We reviewed the charts of all HIV patients with diabetes mellitus (DM) at our clinic. The simple test, which measures average blood sugar levels for the previous two to three months, has long been used to help manage diabetes. Although outpatient studies identified HbA1c-screening cutoff values for diabetes and prediabetes, HbA1c-screening thresholds have not been determined for acute-care settings. and Europe. So when they fail to detect the disease early and receive due treatment, it would expose them to multiple complications. In particular, the section “Revisions to the Standards of Medical Care in Diabetes,” recommends that the A1C be used to identify people with “pre-diabetes,” those at increased risk for developing the type 2 form of disease. Abnormalities in glucose tolerance may be detectable for many years prior to the development of overt diabetes.

In the past, the A1C wasn’t recommended for use in the diagnosis of diabetes because the test wasn’t standardized from lab to lab, according to the ADA. Diabetes mellitus (DM) is a concerning health problem for the Asia Pacific region where late diagnosis and poor monitoring is associated with increased risk of microvascular and macrovascular disease, disability and mortality often prematurely[1, 2 and 3]. I have since read that one should confirm with a second fasting blood test and that an A1c is not necessarily used in the diagnosis. The clinical population, Melbourne Pathology (MP) group, included all patients referred by medical practitioners for an OGTT in 2003–2008 to a state-wide private pathology service (MP Services, Australia); the AusDiab population comes from a national population-based study (2004–2005 AusDiab follow-up) (5). Only people with concurrent A1C and OGTT results are included here (MP group: n = 2,494; AusDiab: n = 6,014). For instance, patient education regarding diabetic retinopathy may be instituted sooner. A1C was determined either by Diabetes Control and Complications Trial (DCCT)-aligned (7) cation-exchange chromatography (MP population) or by boronate affinity chromatography with values converted to DCCT-aligned A1C (5) (AusDiab).

Plasma glucose was measured using hexokinase. Interestingly, 49% mistakenly thought HbA1c was already an approved screening test (J. Uncontrolled diabetes is characterized by too much glucose in the bloodstream. Accessed Nov. Worldwide standardization efforts are moving forward with the development of a common standard. Applying the above cut offs, a total 35.2% of the MP population had diabetes ruled in or ruled out (Fig. Given the retrospective design of the current study, peripheral blood smear, haptoglobin, and lactate dehydrogenase levels were unavailable to evaluate hemolysis.

From those with impaired A1C, 61.9% had abnormal glucose status. The purpose of this study was to determine optimal HbA1c-screening cutoff points for undiagnosed dysglycemia in the emergency department setting using follow-up fasting blood glucose (FBS) and 2-h oral glucose tolerance tests (OGTTs) as the criterion gold standard. Using the recommended cut off of 6.5% (3), specificity decreased to 88.8% and the PPV decreased to 76.8%. Application of the A1C cut offs to screen or diagnose diabetes in a clinical group (MP population, n = 2,494, undiagnosed diabetes 34.6%) (A) and in a national population-based group (AusDiab population, n = 6,014, undiagnosed diabetes 4.6%) (B). “So, if you’re over the age of 45, or if you’re under the age of 45 and overweight and have any other risk factor for diabetes, the recommendation is that you be screened for diabetes to detect early cases,” Buse said. 1B), while the remaining 24.1% had impaired A1C. From those with impaired A1C, 69.3% had abnormal glucose status.

The method selected to measure HbA1C is dependent on the laboratory, with approximately 100 different ways of doing so. Last Updated: Tuesday April 06, 2010 15:10:04 This Internet site provides information of a general nature and is designed for educational purposes only. By dropping the cut off to 6.5%, specificity remained 99.9%, with PPV near 100%. Our study supports recommendations to use A1C for diabetes screening and diagnosis (2,3). Using two, rather than one, cut off values for A1C achieved high sensitivity for screening plus optimal specificity for diabetes diagnosis. We also show the high probability that those with impaired A1C have abnormal glucose status. Single A1C cut offs have limited clinical utility in identifying those people with abnormal blood glucose levels.

The most commonly reported single A1C cut off obtained from reported receiver-operated characteristic (ROC) curves was ∼6.1%, which gives sensitivities of 78–81%, with specificities of 79–84% (8). In our MP population, the ROC curve–identified optimal A1C of 6.2% gave a sensitivity of 82.2% and specificity of 78.8% (Z.X.L, unpublished data) but yielded a reduced PPV (67.2%) and NPV (89.3%). We therefore apply two proposed cut offs. As is the case when the test is used for monitoring diabetes control, any state of altered red cell turnover (hemolytic anemias, major blood loss, transfusions, pregnancy) will alter the relationship of Hb A1c to chronic glycemia, and in these patients glucose-based diagnostic testing should be used. Applying an A1C of 5.5% to National Health and Nutrition Examination Survey (NHANES) data generated a sensitivity of ∼92% and specificity of ∼30% from the published ROC curve (9). Our findings suggest a moderate correlation between HbA1c and aAG as well as between fructosamine and aAG. Our 7.0% cut off for diabetes diagnosis is higher than the 6.5% recommended cut off (2,3) but was chosen to optimize specificity.

For those with impaired A1C, the prevalence of diabetes increases as A1C increases. A1C is a continuous variable for diabetes and any cut off values chosen are somewhat arbitrary. From our data, people with A1C 5.6–6.0% were more likely to have either normoglycemia or pre-diabetes (impaired fasting glucose and/or impaired glucose tolerance) than diabetes, consistent with NHANES, where A1C 5.5–6.0% excluded diabetes in moderate but not high-risk individuals (10). Thus, those with an A1C of 5.6–6.0% would probably require education and lifestyle modification to prevent progression to diabetes (11) plus retesting every 6–12 months. Also in our study, people with an A1C of 6.1–6.4% were more likely to have pre-diabetes or diabetes than normoglycemia, while among those with a A1C of 6.5–6.9%, diabetes was highly probable. Thus, individuals with an A1C of 6.1–6.9% may require an OGTT to confirm their glycemic status plus lifestyle education and regular monitoring as for people with pre-diabetes. For those with an A1C ≥6.5%, screening for retinopathy is also necessary (2).

A1C as a screening/diagnostic tool has some limitations (3). The main issues are method bias, which is now being addressed by Internation Federation of Clinical Chemistry standardization (12) and certain confounding medical conditions (hemoglobinopathies and anemia). Most new A1C methods can identify or are unaffected by hemoglobinopathies. Anemia is also readily identifiable. The cost of A1C has also been raised as a concern. While A1C analysis, per se, is more expensive than for glucose, the overall differences are small once the costs for blood collection are accounted for. From our own estimates, total costs are AUD $10.20 for A1C compared with AUD $8.80 for FPG and AUD $12.10 for a two-point collection of OGTT.

These cost comparisons are consistent with reports from other countries (13–15). Further, the time and inconvenience to patients in having to fast for an OGTT cannot be ignored. A1C ≤5.5% and ≥7.0% predicts with 97.5% confidence the absence or presence of type 2 diabetes using the OGTT as a reference. Many with impaired A1C have pre-diabetes, while diabetes is highly probable when A1C reaches 6.5–6.9%. Performed statistical analyses: PF. Although the cost of A1C is slightly higher than for FPG, the overall efficiency of using A1C as a first line for diabetes screening may facilitate early diagnosis and reduce the health burden associated with diabetes complications. The AusDiab study, co-coordinated by the Baker IDI Heart and Diabetes Institute, gratefully acknowledges the generous support given by the National Health and Medical Research Council (Grant no.

233200); the Australian Government Department of Health and Ageing; Abbott Australasia; Alphapharm; AstraZeneca; Bristol-Myers-Squibb; City Health Centre, Diabetes Service, Canberra; the Department of Health and Community Services, Northern Territory; the Department of Health and Human Services, Tasmania; the Department of Health, New South Wales; the Department of Health, Western Australia; the Department of Health, South Australia; the Department of Human Services, Victoria; Diabetes Australia; Diabetes Australia Northern Territory; Eli Lilly Australia; the estate of the late Edward Wilson; GlaxoSmithKline; the Jack Brockhoff Foundation; Janssen-Cilag; Kidney Health Australia; the Marian & FH Flack Trust; the Menzies Research Institute; Merck Sharp & Dohme; Novartis Pharmaceuticals; Novo Nordisk Pharmaceuticals; Pfizer; the Pratt Foundation; Queensland Health; Roche Diagnostics Australia; Royal Prince Alfred Hospital, Sydney; Sanofi-Aventis; and Sanofi Synthelabo. The following individuals also made an invaluable contribution: A. Allman, B. Atkins, S. Bennett, A. Bonney, S. Chadban, M.

de Courten, M. Dalton, D. Dunstan, T. Dwyer, H. Jahangir, D. Jolley, D. McCarty, A.

Meehan, N. Meinig, S. Murray, K. O’Dea, K. Polkinghorne, P. Phillips, C. Reid, A.

Stewart, R. Tapp, H. Taylor, T. Whalen, F. Wilson, and P. Zimmet.

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