Comparison of a point-of-care analyser for the determination of HbA1c with HPLC method

Emerging biosensor probes for glycated hemoglobin (HbA1c) detection

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.

Physical Activity Assessed by Wrist and Thigh Worn Accelerometry and Associations with Cardiometabolic Health

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.

Cost-Effectiveness of Point-of-Care A1C Tests in a Primary Care Setting

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.

A formula based on autonomic test using EZSCAN and anthropometric data for diagnosis of DM in China

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.