Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c
Open accessAbstract
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.
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Analytical errors can contribute to erroneous laboratory results, which can lead to inappropriate disease classification and clinical decision making.
“Analytical errors can contribute to erroneous laboratory results, which, in turn, can lead to inappropriate disease classification and clinical decision making.”
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- Authors
- Chai, J. H., Ma, S., Heng, D., Yoong, J., Lim, W.-Y., Toh, S.-A., & Loh, T. P.
- Affiliations
- National University of Singapore (SG)
- Ministry of Health (SG)
- Agency for Integrated Care (SG)
- National University Hospital (SG)
- Corresponding author
- Loh, Tze Ping
- Venue
- Scientific Reports
- Published
- 2017-10-20
- Volume
- 7
- Issue
- 1
- Pages
- 13721-13721
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5651837
- OpenAlex
- W2766199104
- Cited by
- 50
- References
- 11
- Citations by year
- 2026: 8 · 2025: 5 · 2024: 4 · 2023: 5 · 2022: 3 · 2021: 12 · 2020: 5 · 2019: 4 · 2018: 4
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- diabetes classification, fasting blood glucose, oral glucose tolerance test, HbA1c, analytical variability, biological variation, analytical bias, diagnostic accuracy, numerical simulation
- MeSH terms
- Biological Variation, Individual, Adolescent, Adult, Aged, Blood Glucose, Computer Simulation, Cross-Sectional Studies, Diabetes Mellitus, Diagnostic Errors, Fasting, Female, Glucose Tolerance Test, Glycated Hemoglobin, Humans, Male, Middle Aged, Models, Theoretical, Reproducibility of Results, Young Adult
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