Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c

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 criter...

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Autores principales:	Jia Hui Chai, Stefan Ma, Derick Heng, Joanne Yoong, Wei-Yen Lim, Sue-Anne Toh, Tze Ping Loh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2da6fe4f820a436d9cdfd9d98a24486d
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spelling	oai:doaj.org-article:2da6fe4f820a436d9cdfd9d98a24486d2021-12-02T15:05:48ZImpact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c10.1038/s41598-017-14172-82045-2322https://doaj.org/article/2da6fe4f820a436d9cdfd9d98a24486d2017-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-14172-8https://doaj.org/toc/2045-2322Abstract 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.Jia Hui ChaiStefan MaDerick HengJoanne YoongWei-Yen LimSue-Anne TohTze Ping LohNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-7 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jia Hui Chai Stefan Ma Derick Heng Joanne Yoong Wei-Yen Lim Sue-Anne Toh Tze Ping Loh Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c
description	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.
format	article
author	Jia Hui Chai Stefan Ma Derick Heng Joanne Yoong Wei-Yen Lim Sue-Anne Toh Tze Ping Loh
author_facet	Jia Hui Chai Stefan Ma Derick Heng Joanne Yoong Wei-Yen Lim Sue-Anne Toh Tze Ping Loh
author_sort	Jia Hui Chai
title	Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c
title_short	Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c
title_full	Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c
title_fullStr	Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c
title_full_unstemmed	Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c
title_sort	impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and hba1c
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/2da6fe4f820a436d9cdfd9d98a24486d
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Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c

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