Biological variation of glycated haemoglobin in a paediatric population and its application to calculation of significant change between results

This version was published on 1 January 2010

Ann Clin Biochem 2010;47:35-38
© 2010 Association for Clinical Biochemistry


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Original Articles

Philippe Desmeules1,
Jocelyne Cousineau1 and
Pierre Allard1,2

1 Department of Clinical Biochemistry;
2 Department of Paediatrics, Medical Genetics Division, CHU Ste-Justine, Université de Montréal, 3175 Côte Ste-Catherine, Montréal, QC, Canada H3T 1C5

Corresponding author: Philippe Desmeules. Email: philippe.desmeules{at}

Background: To determine precisely the probability that a change betweentwo glycated haemoglobin A1c (HbA1c) results is significantand that clinical actions may be required, the biological variationof HbA1c must be known. However, it has not been evaluated ina paediatric population. We therefore determined the long-termbiological variation of HbA1c in a paediatric population andused it to generate a probability curve for significant changesbetween two consecutive HbA1c measurements.

Methods: A group of 24 boys and 14 girls with cystic fibrosis (CF) but without diabetes or impaired glucose tolerance has been selected. HbA1c has been measured at least five times over five consecutive years for all subjects. We have used the Fraser and Harris method to calculate within-subject biological variation (CVI), whichallowed the determination of the probability that a change issignificant between results.

Results: As within-subject variances are equivalent for girls and boys (P > 0.1), both genders were merged for biological variation analysis. The CVI calculated for HbA1c was 4.8% and the between-subject variation (CVG) was 12.8%. Then, a probability curve based on the CVI found was generated and showed that a change of 14%between two consecutive HbA1c results corresponding to a probabilityof 95% was significant.

Conclusions: We determined for the first time the biological variation ofHbA1c in a paediatric population, which is higher than the onesfound for adult populations. The probability curves generatedfrom these data could be invaluable tools for clinicians tobalance HbA1c results with other clinical parameters.

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