{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["7(1)"],"submitter":["Chai JH"],"pubmed_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 cla"],"journal":["Scientific reports"],"pagination":["13721"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5651837"],"repository":["biostudies-literature"],"pubmed_title":["Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c."],"pmcid":["PMC5651837"],"pubmed_authors":["Chai JH","Toh SA","Ma S","Heng D","Yoong J","Lim WY","Loh TP"],"additional_accession":[]},"is_claimable":false,"name":"Impact of analytical and biological variations on classification of diabetes using fasting plasma glucose, oral glucose tolerance test and HbA1c.","description":"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 cla","dates":{"release":"2017-01-01T00:00:00Z","publication":"2017 Oct","modification":"2026-05-05T23:29:20.822Z","creation":"2019-03-27T02:59:37Z"},"accession":"S-EPMC5651837","cross_references":{"pubmed":["29057963"],"doi":["10.1038/s41598-017-14172-8"]}}