Abstract
Historically, diabetes is diagnosed by measuring fasting (FPG) and two-hour post oral glucose load (OGTT) plasma concentration and interpreting it against recommended clinical thresholds of the patient. More recently, glycated haemoglobin A1c (HbA1c) has been included as a diagnostic criterion. Within-individual biological variation (CVi), analytical variation (CVa) and analytical bias of a test can impact on the accuracy and reproducibility of the classification of a disease. A test with large biological and analytical variation increases the likelihood of erroneous classification of the underlying disease state of a patient. Through numerical simulations based on the laboratory results generated from a large population health survey, we examined the impact of CVi, CVa and bias on the classification of diabetes using fasting plasma glucose (FPG), oral glucose tolerance test (OGTT) and HbA1c. From the results of the simulations, HbA1c has comparable performance to FPG and is better than OGTT in classifying subjects with diabetes, particularly when laboratory methods with smaller CVa are used. The use of the average of the results of the repeat laboratory tests has the effect of ameliorating the combined (analytical and biological) variation. The averaged result improves the consistency of the disease classification.
Highlights
Diabetes mellitus is a chronic disease characterized by impaired glucose metabolism
Through numerical simulations based on the laboratory results generated from a large population health survey, we examined (1) the impact of biological and analytical variations and between-laboratory method bias on the classification of diabetes using fasting plasma glucose (FPG), oral glucose tolerance test (OGTT) and haemoglobin A1c (HbA1c), and (2) the best strategy to interpret limited repeat blood testing to reduce the variation in result to achieve more accurate disease classification
The impact of biological and analytical variation on diabetes classification is related to the distribution of the laboratory results in the population examined as well as the diagnostic thresholds applied
Summary
Diabetes mellitus is a chronic disease characterized by impaired glucose metabolism. The hallmark of this disease is persistent elevation of glucose concentration in the blood[1]. A test with large biological and analytical variation increases the probability of the result of a patient falling further from his true homeostatic set point This increases the likelihood of erroneous classification of the underlying disease state of a patient[5]. Through numerical simulations based on the laboratory results generated from a large population health survey, we examined (1) the impact of biological and analytical variations and between-laboratory method bias on the classification of diabetes using fasting plasma glucose (FPG), oral glucose tolerance test (OGTT) and HbA1c, and (2) the best strategy to interpret limited repeat blood testing to reduce the variation in result to achieve more accurate disease classification
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
