ArticleScientific reports2017
Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Continuous glucose monitoring as a tool in early-stage type 1 diabetes.Diabetologia · 2026Review
- Bridging Evidence and Practice: A Consensus Statement from the Korean Diabetes Association on Diabetes Screening, Pharmacological Treatment and Severe Diabetes.Diabetes & metabolism journal · 2025Review
- Budget impact analyses of hemoglobin A1c and lipid panel point-of-care testing with Afinion™ 2 in Canada and Italy.Journal of comparative effectiveness research · 2025Article
- Application of Clinical Blood Metabogram to Type 2 Diabetes Mellitus.Metabolites · 2024Article
- Thyroid Stimulating Hormone and Thyroid Hormones (Triiodothyronine and Thyroxine): An American Thyroid Association-Commissioned Review of Current Clinical and Laboratory Status.Thyroid : official journal of the American Thyroid Association · 2023Review
- Biological variation: Understanding why it is so important?Practical laboratory medicine · 2021Review
- Article
- Diagnostic testing for gestational diabetes mellitus during the COVID-19 pandemic: an opportunity to revisit Glucose-Based testing.Internal medicine journal · 2020Article
- Toward a Framework for Outcome-Based Analytical Performance Specifications: A Methodology Review of Indirect Methods for Evaluating the Impact of Measurement Uncertainty on Clinical Outcomes.Clinical chemistry · 2019Review
- Performance of HbA1c versus oral glucose tolerance test (OGTT) as a screening tool to diagnose dysglycemic status in high-risk Thai patients.BMC endocrine disorders · 2019Article
- Screening for diabetes with HbA1c: Test performance of HbA1c compared to fasting plasma glucose among Chinese, Malay and Indian community residents in Singapore.Scientific reports · 2018Article
- Non-communicable disease risk factor profile among public employees in a regional city in northern Ethiopia.Scientific reports · 2018Article
- Proposed guidelines for screening of hyperglycemia in patients hospitalized with COVID-19 in low resource settings.Diabetes & metabolic syndromeReview
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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