Recent advances in glycated hemoglobin test methods: From lab to point of care testing devices

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dc.contributor.authorLakhera, Praveen
dc.contributor.authorChaudhary, Vikas
dc.contributor.authorKush, Preeti
dc.contributor.authorKumar, Parveen
dc.contributor.authorUghade, Yash
dc.contributor.authorAgrawal, Labi
dc.contributor.authorPatel, Gautam
dc.contributor.authorDeshmukh, Kalim Abdul Rashid
dc.date.accessioned2026-04-21T11:44:25Z
dc.date.available2026-04-21T11:44:25Z
dc.date.issued2025
dc.description.abstract-translatedGlobally, the threat of diabetes mellitus causes health issues and economic burdens on families. Glycated hemoglobin (HbA1c) is an internationally recommended and reliable gold-standard marker to assess the presence and severity of diabetes. It can be measured using both lab-based standard tests and point-of-care testing (POCT) devices. This review explores published literature from 2018 to July 2025 across Scopus, PubMed Central, Google Scholar, Science Direct, and PubMed, using various keywords such as HbA1c detection, diabetes, POCT devices, artificial intelligence (AI), and biosensors. Some sources, including letters to editors, encyclopedias, conference materials, abstracts, and proceedings, were excluded. It covers the history and standardization of HbA1c, as well as recent advances in testing techniques, including standard laboratory methods, various biosensors (electrochemical, optical, electrochemiluminescent, mass-based, and colorimetric), and cutting-edge approaches like colorimetric, fluorescent assays, and chip-based techniques. Additionally, AI-based methods (deep learning and machine learning) are discussed for predicting HbA1c levels. The review highlights technological developments and concludes with a comparative evaluation of publicly available POCT devices. It also details the process flow from ideation to lab testing, approval, and recognition by medical agencies worldwide. Furthermore, this work can serve as a useful resource for understanding different technology readiness levels. Based on this study, POCTs are increasingly essential, but a solid understanding of detection methods is necessary for working in this field. Moreover, integrating mobile apps with deep machine learning algorithms and AI, microfluidics/lab-on-chip systems, various methods, wearable sensors, and the Internet of Wearable Things (IoWT) can enhance analytical performance and automation.en
dc.description.sponsorship5/3/8/38/ITR-F/2020-ITR ICMR “Design and development of colorimetric technique-based Point-of-Care device for the detection of glycated hemoglobin”
dc.format28 s.cs
dc.identifier.doihttps://doi.org/10.1016/j.ijbiomac.2025.148742
dc.identifier.urihttp://hdl.handle.net/11025/67736
dc.language.isoenen
dc.publisherElsevieren
dc.rights© 2025 Elsevieren
dc.rights.accessrestrictedAccessen
dc.subjectHbA1ccs
dc.subjectbiosenzorycs
dc.subjectdiabetescs
dc.subjectumělá inteligencecs
dc.subjectzařízení POCTcs
dc.subject.translatedHbA1cen
dc.subject.translatedbiosensorsen
dc.subject.translateddiabetesen
dc.subject.translatedartificial intelligenceen
dc.subject.translatedPOCT devicesen
dc.titleRecent advances in glycated hemoglobin test methods: From lab to point of care testing devicesen
dc.typearticleen
dc.typečlánekcs
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen
local.files.count1*
local.files.size12522919*
local.has.filesyes*

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