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Su Y, Xia C, Zhang H, Gan W, Zhang GQ, Yang Z, Li D. Emerging biosensor probes for glycated hemoglobin (HbA1c) detection. Mikrochim Acta 2024; 191:300. [PMID: 38709399 DOI: 10.1007/s00604-024-06380-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/22/2024] [Indexed: 05/07/2024]
Abstract
Glycated hemoglobin (HbA1c), originating from the non-enzymatic glycosylation of βVal1 residues in hemoglobin (Hb), is an essential biomarker indicating average blood glucose levels over a period of 2 to 3 months without external environmental disturbances, thereby serving as the gold standard in the management of diabetes instead of blood glucose testing. The emergence of HbA1c biosensors presents affordable, readily available options for glycemic monitoring, offering significant benefits to small-scale laboratories and clinics. Utilizing nanomaterials coupled with high-specificity probes as integral components for recognition, labeling, and signal transduction, these sensors demonstrate exceptional sensitivity and selectivity in HbA1c detection. This review mainly focuses on the emerging probes and strategies integral to HbA1c sensor development. We discussed the advantages and limitations of various probes in sensor construction as well as recent advances in diverse sensing strategies for HbA1c measurement and their potential clinical applications, highlighting the critical gaps in current technologies and future needs in this evolving field.
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Affiliation(s)
- Yang Su
- Key Laboratory of DrugTargeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, 610041, China
| | - Chengen Xia
- Key Laboratory of DrugTargeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, 610041, China
| | - He Zhang
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Wei Gan
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Guo-Qi Zhang
- Department of Chemistry, School of Science, Xihua University, Chengdu, 610039, People's Republic of China
| | - Zi Yang
- Key Laboratory of DrugTargeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, 610041, China
| | - Dapeng Li
- Key Laboratory of DrugTargeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, 610041, China.
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Maylor BD, Edwardson CL, Clarke-Cornwell AM, Davies MJ, Dawkins NP, Dunstan DW, Khunti K, Yates T, Rowlands AV. Physical Activity Assessed by Wrist and Thigh Worn Accelerometry and Associations with Cardiometabolic Health. SENSORS (BASEL, SWITZERLAND) 2023; 23:7353. [PMID: 37687813 PMCID: PMC10489920 DOI: 10.3390/s23177353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/20/2023] [Accepted: 08/08/2023] [Indexed: 09/10/2023]
Abstract
Physical activity is increasingly being captured by accelerometers worn on different body locations. The aim of this study was to examine the associations between physical activity volume (average acceleration), intensity (intensity gradient) and cardiometabolic health when assessed by a thigh-worn and wrist-worn accelerometer. A sample of 659 office workers wore an Axivity AX3 on the non-dominant wrist and an activPAL3 micro on the right thigh concurrently for 24 h a day for 8 days. An average acceleration (proxy for physical activity volume) and intensity gradient (intensity distribution) were calculated from both devices using the open-source raw accelerometer processing software GGIR. Clustered cardiometabolic risk (CMR) was calculated using markers of cardiometabolic health, including waist circumference, triglycerides, HDL-cholesterol, mean arterial pressure and fasting glucose. Linear regression analysis assessed the associations between physical activity volume and intensity gradient with cardiometabolic health. Physical activity volume derived from the thigh-worn activPAL and the wrist-worn Axivity were beneficially associated with CMR and the majority of individual health markers, but associations only remained significant after adjusting for physical activity intensity in the thigh-worn activPAL. Physical activity intensity was associated with CMR score and individual health markers when derived from the wrist-worn Axivity, and these associations were independent of volume. Associations between cardiometabolic health and physical activity volume were similarly captured by the thigh-worn activPAL and the wrist-worn Axivity. However, only the wrist-worn Axivity captured aspects of the intensity distribution associated with cardiometabolic health. This may relate to the reduced range of accelerations detected by the thigh-worn activPAL.
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Affiliation(s)
- Benjamin D. Maylor
- Diabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK; (B.D.M.); (M.J.D.); (N.P.D.); (K.K.); (T.Y.); (A.V.R.)
- Assessment of Movement Behaviours Group (AMBer), Leicester Lifestyle and Health Research Group, Diabetes Research Centre, University of Leicester, Leicester LE1 7RH, UK
- Leicester Diabetes Centre, University Hospitals of Leicester NHS Trust, Leicester LE5 4PW, UK
| | - Charlotte L. Edwardson
- Diabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK; (B.D.M.); (M.J.D.); (N.P.D.); (K.K.); (T.Y.); (A.V.R.)
- Assessment of Movement Behaviours Group (AMBer), Leicester Lifestyle and Health Research Group, Diabetes Research Centre, University of Leicester, Leicester LE1 7RH, UK
- Leicester Diabetes Centre, University Hospitals of Leicester NHS Trust, Leicester LE5 4PW, UK
| | | | - Melanie J. Davies
- Diabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK; (B.D.M.); (M.J.D.); (N.P.D.); (K.K.); (T.Y.); (A.V.R.)
- Leicester Diabetes Centre, University Hospitals of Leicester NHS Trust, Leicester LE5 4PW, UK
| | - Nathan P. Dawkins
- Diabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK; (B.D.M.); (M.J.D.); (N.P.D.); (K.K.); (T.Y.); (A.V.R.)
- Leicester Diabetes Centre, University Hospitals of Leicester NHS Trust, Leicester LE5 4PW, UK
- School of Sport and Wellbeing, Leeds Trinity University, Leeds LS18 5HD, UK
| | - David W. Dunstan
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia;
- Institute for Physical Activity and Nutrition, Faculty of Health, Deakin University, Geelong, VIC 3220, Australia
| | - Kamlesh Khunti
- Diabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK; (B.D.M.); (M.J.D.); (N.P.D.); (K.K.); (T.Y.); (A.V.R.)
- Leicester Diabetes Centre, University Hospitals of Leicester NHS Trust, Leicester LE5 4PW, UK
- NIHR Applied Research Collaboration East Midlands, Diabetes Research Centre, University of Leicester, Leicester LE1 7RH, UK
| | - Tom Yates
- Diabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK; (B.D.M.); (M.J.D.); (N.P.D.); (K.K.); (T.Y.); (A.V.R.)
- Leicester Diabetes Centre, University Hospitals of Leicester NHS Trust, Leicester LE5 4PW, UK
| | - Alex V. Rowlands
- Diabetes Research Centre, Population Health Sciences, College of Life Sciences, University of Leicester, Leicester LE1 7RH, UK; (B.D.M.); (M.J.D.); (N.P.D.); (K.K.); (T.Y.); (A.V.R.)
- Assessment of Movement Behaviours Group (AMBer), Leicester Lifestyle and Health Research Group, Diabetes Research Centre, University of Leicester, Leicester LE1 7RH, UK
- Leicester Diabetes Centre, University Hospitals of Leicester NHS Trust, Leicester LE5 4PW, UK
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Rosa LDS, Mistro S, Oliveira MG, Kochergin CN, Cortes ML, de Medeiros DS, Soares DA, Louzado JA, Silva KO, Bezerra VM, Amorim WW, Barone M, Passos LC. Cost-Effectiveness of Point-of-Care A1C Tests in a Primary Care Setting. Front Pharmacol 2021; 11:588309. [PMID: 33542687 PMCID: PMC7851089 DOI: 10.3389/fphar.2020.588309] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 11/23/2020] [Indexed: 12/18/2022] Open
Abstract
Objective: We evaluated the cost-effectiveness of the point-of-care A1c (POC-A1c) test device vs. the traditional laboratory dosage in a primary care setting for people living with type 2 diabetes. Materials and Methods: The Markov model with a 10-year time horizon was based on data from the HealthRise project, in which a group of interventions was implemented to improve diabetes and hypertension control in the primary care network of the urban area of a Brazilian municipality. A POC-A1c device was provided to be used directly in a primary care unit, and for a period of 18 months, 288 patients were included in the point-of-care group, and 1,102 were included in the comparison group. Sensitivity analysis was performed via Monte Carlo simulation and tornado diagram. Results: The results indicated that the POC-A1c device used in the primary care unit was a cost-effective alternative, which improved access to A1c tests and resulted in an increased rate of early control of blood glucose. In the 10-year period, POC-A1c group presented a mean cost of US$10,503.48 per patient and an effectiveness of 0.35 vs. US$9,992.35 and 0.09 for the traditional laboratory test, respectively. The incremental cost was US$511.13 and the incremental effectiveness was 0.26, resulting in an incremental cost-effectiveness ratio of 1,947.10. In Monte Carlo simulation, costs and effectiveness ranged between $9,663.20-$10,683.53 and 0.33-0.37 for POC-A1c test group, and $9,288.28-$10,413.99 and 0.08-0.10 for traditional laboratory test group, at 2.5 and 97.5 percentiles. The costs for nephropathy, retinopathy, and cardiovascular disease and the probability of being hospitalized due to diabetes presented the greatest impact on the model's result. Conclusion: This study showed that using POC-A1c devices in primary care settings is a cost-effective alternative for monitoring glycated hemoglobin A1c as a marker of blood glucose control in people living with type 2 diabetes. According to our model, the use of POC-A1c device in a healthcare unit increased the early control of type 2 diabetes and, consequently, reduced the costs of diabetes-related outcomes, in comparison with a centralized laboratory test.
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Affiliation(s)
- Lorena de Sousa Rosa
- Program of Post-Graduation in Medicine and Health, Federal University of Bahia, Salvador, Brazil
| | - Sóstenes Mistro
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Marcio Galvão Oliveira
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | | | - Mateus Lopes Cortes
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Danielle Souto de Medeiros
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Daniela Arruda Soares
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - José Andrade Louzado
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Kelle Oliveira Silva
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Vanessa Moraes Bezerra
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Welma Wildes Amorim
- Departament of Natural Sciences, State University of Southwest Bahia, Vitória da Conquista, Brazil
| | - Mark Barone
- Intersectoral Forum to Fight NCDs in Brazil, São Paulo, Brazil
| | - Luiz Carlos Passos
- Program of Post-Graduation in Medicine and Health, Federal University of Bahia, Salvador, Brazil
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Zhao X, Getmanenko A, Zhang Y, Mo Q, Yao C. A formula based on autonomic test using EZSCAN and anthropometric data for diagnosis of DM in China. Sci Rep 2020; 10:4870. [PMID: 32184464 PMCID: PMC7078247 DOI: 10.1038/s41598-020-61841-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 03/03/2020] [Indexed: 11/08/2022] Open
Abstract
Clinical diagnosis of diabetes mellitus (DM) is time-consuming and invasive. This study aimed to investigate the efficacy and accuracy of EZSCAN in detecting impaired glucose tolerance (IGT) and diabetes mellitus (DM) in Chinese population, and explore a diagnosis formula based on an autonomic test using EZSCAN measurement and anthropometric data. Eligible subjects (n = 1547) had the following data collected: those of anthropometric and EZSCAN measurements and biochemical tests including FPG, OGTT, HbA1c, and serum lipid tests. The support vector machine (SVM) algorithm method was used to derive a diagnostic formula. In this study, 452 and 263 subjects were diagnosed with T2DM and IGT, respectively, while 832 had normal glucose tolerance (NGT). The sensitivity rates for the formula were 77.2% for T2DM and 80.4% for IGT. The diagnostic formula was found to correlate strongly with EZSCAN values. The diagnostic formula based on autonomic test and anthropometric data appears to be a convenient and accurate routine screening option in the Chinese population.
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Affiliation(s)
- Xiaolan Zhao
- Center of Health Examination, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China
| | | | - Yalan Zhang
- Center of Health Examination, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China
| | - Qinyun Mo
- Department of Transfusion Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China
| | - Chunyan Yao
- Department of Transfusion Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
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Fang T, Li Y, Li F, Huang F. Rapid Diagnosis of Type II Diabetes Using Fourier Transform Mid-Infrared Attenuated Total Reflection Spectroscopy Combined with Support Vector Machine. ANAL LETT 2018. [DOI: 10.1080/00032719.2017.1375940] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Tao Fang
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Jinan University, Guangzhou, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, China
| | - Yuanpeng Li
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Jinan University, Guangzhou, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, China
| | - Fucui Li
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Jinan University, Guangzhou, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, China
| | - Furong Huang
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Jinan University, Guangzhou, China
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, China
- Research Institute of Jinan University in Dongguan, Dongguan, China
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