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Limitations of glycosylated haemoglobin (HbA 1c ) in diabetes screening
Author(s) -
Hanna Fahmy WF,
Geen John,
Issa Basil G,
Tahrani Abd A,
Fryer Anthony A
Publication year - 2012
Publication title -
practical diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.205
H-Index - 24
eISSN - 2047-2900
pISSN - 2047-2897
DOI - 10.1002/pdi.1653
Subject(s) - medicine , diabetes mellitus , glycosylated haemoglobin , cohort , plasma glucose , glucose tolerance test , stepwise regression , anthropometry , endocrinology , type 2 diabetes , insulin resistance
Recently, glycosylated haemoglobin (HbA 1c ) has been recommended by the American Diabetes Association (ADA), the World Health Organisation and subsequently by many other professional bodies as a diagnostic tool for diabetes mellitus. However, the cut‐off values suggested vary between these groups and uncertainties remain regarding the limitations of this test and its effectiveness as a diagnostic tool. We wished to assess the effect of HbA 1c on detection rates for dysglycaemia in a high risk cohort of 200 patients with possible acute coronary syndrome not previously known to have diabetes. Anthropometric as well as HbA 1c , oral glucose tolerance tests (OGTT), random and fasting plasma glucose (RPG and FPG) concentrations, fasting lipids and high sensitivity C‐reactive protein data were obtained during admission. We examined each of the recommended cut‐off values for HbA 1c . Test accuracy was assessed by the degree of misclassification (both under‐ and over‐diagnosis) of patients into normal glycaemic control, impaired glucose tolerance and diabetes mellitus based on OGTT data using WHO criteria. A predictive index (PI) was generated using stepwise ordinal regression models (incorporating FPG, HbA 1c , HDL‐C, triglycerides, age and gender). HbA 1c alone, using the International Expert Committee cut‐off values, had unacceptably high misclassification rates (49.0% under‐ and 2.5% over‐diagnosed). This did not improve when ADA criteria were examined, despite their lower cut‐off values for normoglycaemia (44.4% under‐ and 7.1% over‐diagnosed). FPG was marginally better, misclassifying 44.4% (mostly under‐diagnosis; 41.4%). The PI had the lowest misclassification rate (35.9%; with 22.7% under‐ and 13.1% over‐diagnosed). In conclusion, our data suggest that HbA 1c alone offers little advantage over FPG in detecting dysglycaemia in this high risk population. Our approach using a predictive index to combine HbA 1c with other test data will enhance its performance. Copyright © 2012 John Wiley & Sons.