An In-Vitro Optical Sensor Designed to Estimate Glycated Hemoglobin Levels

Design and Validation of a Monte Carlo Method for the Implementation of Noninvasive Wearable Devices for HbA1c Estimation Considering the Skin Effect

To diagnose diabetes early or to maintain stable blood glucose levels in diabetics, blood glucose levels should be frequently checked. However, the only way to check blood glucose levels regularly is to use invasive methods, such as pricking the fingertip or using a minimally invasive patch. These invasive methods pose several problems, including being painful and potentially causing secondary infections. This study focuses on noninvasively measuring glycated hemoglobin (HbA1c) using PPG signals. In particular, the study relates to a method and a hardware design technology for removing noise that may be present in a PPG signal due to skin contact with a noninvasive HbA1c measurement device. The proposed HbA1c measurement device consists of the first sensor (PPG sensor) module including an optical barrier and the second sensor (cylindrical sensor) module for removing the skin effect. We have developed a Monte Carlo method to implement accurate, noninvasive HbA1c measurement by considering different skin properties among different subjects. Implementing this model in wearable devices will allow end users to not only monitor their glycated hemoglobin levels but also control diabetes with higher accuracy without needing any blood samples. This will be a groundbreaking advancement in modern wearable medical devices.

Comparison of Glycated Hemoglobin (HbA1c) Values Estimated by High-Performance Liquid Chromatography and Spectrophotometry: A Pilot Study

Background Invasive blood sample collection followed by high-performance liquid chromatography (HPLC) based analysis is the gold standard for estimating glycated hemoglobin level or HbA1c currently. Spectrophotometry could be an alternative that holds the potential to be translated into a portable, non-invasive device for glycated hemoglobin level estimation. This study compares HbA1c values obtained from HPLC and spectrophotometry. Methods Venous blood samples were collected from both diabetic and non-diabetic participants in a cross-sectional study. The samples were subjected to both HPLC and spectrophotometry-based estimation of HbA1c%. The results obtained were compared, and the relationship between the two estimations were assessed. Results About 15 diabetic and non-diabetic individuals participated in the study and 28 samples were included in the final analysis. The Pearson's correlation coefficient was 0.65 (95% CI, 0.37-0.82), indicating that there was a strong positive association. This was further supported by the findings from linear regression analysis with a p-value of <0.001. Conclusions The positive correlation between the HPLC and spectrophotometric values supports the hypothesis that spectrophotometry could be an alternative to conventional HPLC for the measurement of HbA1c. This needs to be further validated through larger, well-powered studies.

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians

Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.

Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation

The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor's approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.

Trends in Quantification of HbA1c Using Electrochemical and Point-of-Care Analyzers

Glycated hemoglobin (HbA1c), one of the many variants of hemoglobin (Hb), serves as a standard biomarker of diabetes, as it assesses the long-term glycemic status of the individual for the previous 90-120 days. HbA1c levels in blood are stable and do not fluctuate when compared to the random blood glucose levels. The normal level of HbA1c is 4-6.0%, while concentrations > 6.5% denote diabetes. Conventionally, HbA1c is measured using techniques such as chromatography, spectroscopy, immunoassays, capillary electrophoresis, fluorometry, etc., that are time-consuming, expensive, and involve complex procedures and skilled personnel. These limitations have spurred development of sensors incorporating nanostructured materials that can aid in specific and accurate quantification of HbA1c. Various chemical and biological sensing elements with and without nanoparticle interfaces have been explored for HbA1c detection. Attempts are underway to improve the detection speed, increase accuracy, and reduce sample volumes and detection costs through different combinations of nanomaterials, interfaces, capture elements, and measurement techniques. This review elaborates on the recent advances in the realm of electrochemical detection for HbA1c detection. It also discusses the emerging trends and challenges in the fabrication of effective, accurate, and cost-effective point-of-care (PoC) devices for HbA1c and the potential way forward.

Non-Invasive In Vivo Estimation of HbA1c Using Monte Carlo Photon Propagation Simulation: Application of Tissue-Segmented 3D MRI Stacks of the Fingertip and Wrist for Wearable Systems

The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with pricking the fingertip or using minimally invasive patches. In this study, we devise a noninvasive method to estimate the percentage of the in vivo glycated hemoglobin (HbA1c) values from Monte Carlo photon propagation simulations, based on models of the wrist using 3D magnetic resonance (MR) image data. The MR image slices are first segmented for several different tissue types, and the proposed Monte Carlo photon propagation system with complex composite tissue support is then used to derive several models for the fingertip and wrist sections with different wavelengths of light sources and photodetector arrangements. The Pearson r values for the estimated percent HbA1c values are 0.94 and 0.96 for the fingertip transmission- and reflection-type measurements, respectively. This is found to be the best among the related studies. Furthermore, a single-detector multiple-source arrangement resulted in a Pearson r value of 0.97 for the wrist. The Bland-Altman bias values were found to be -0.003 ± 0.36, 0.01 ± 0.25, and 0.01 ± 0.21, for the two fingertip and wrist models, respectively, which conform to the standards of the current state-of-the-art invasive point-of-care devices. The implementation of these algorithms will be a suitable alternative to the invasive state-of-the-art methods.

Optical Measurement of Molar Absorption Coefficient of HbA1c: Comparison of Theoretical and Experimental Results

Diabetes can cause dangerous complications if not diagnosed in a timely manner. The World Health Organization accepts glycated hemoglobin (HbA1c) as a measure of diagnosing diabetes as it provides significantly more information on the glycemic behavior from a single blood sample than the fasting blood sugar reading. The molar absorption coefficient of HbA1c is needed to quantify the amount of HbA1c present in a blood sample. In this study, we measured the molar absorption coefficient of HbA1c in the range of 450 nm to 700 nm using optical methods experimentally. We observed that the characteristic peaks of the molar absorption coefficient of HbA1c (at 545 nm and 579 nm for level 1, at 544 nm and 577 nm for level 2) are in close agreement with those reported in previous studies. The molar absorption coefficient values were also found to be close to those of earlier reports. The average molar absorption coefficient values of HbA1c were found to be 804,403.5 M−1cm−1 at 545 nm and 703,704.5 M−1cm−1 at 579 nm for level 1 as well as 503,352.4 M−1cm−1 at 544 nm and 476,344.6 M−1cm−1 at 577 nm for level 2. Our experiments focused on calculating the molar absorption coefficients of HbA1c in the visible wavelength region, and the proposed experimental method has an advantage of being able to easily obtain the molar absorption coefficient at any wavelength in the visible wavelength region. The results of this study are expected to help future investigations on noninvasive methods of estimating HbA1c levels.

Noninvasive Estimation of Glycated Hemoglobin In-Vivo Based on Photon Diffusion Theory and Genetic Symbolic Regression Models

The diagnosis and management of diabetes require frequent monitoring of blood sugar levels. Prolonged exposure of most of the monosaccharides in the bloodstream results in the glycation of hemoglobin. This glycated hemoglobin (HbA1c) based test plays an important role to avoid diabetic complications. However, noninvasive estimation of HbA1c is a very new, promising, and challenging topic in modern bioengineering scopes. The purpose of this study is to develop and verify mathematical models in order to quantify the glycated hemoglobin in-vivo percentage non-invasively. This research utilized photon diffusion theory to develop the finger models and genetic symbolic regression methods to solve the models to estimate the level of glycated hemoglobin in the blood. The validation of these models with human participants indicated a high degree of correlation (0.887 and 0.907 Pearsons r value), and high precision (2.56% and 2.96% coefficient of variation (%CV)) for transmission and reflection type noninvasive digital volume pulse-based signals. This research will be a breakthrough for the application of noninvasive HbA1c estimation.

Clinical assessment of W-band spectroscopy for non-invasive detection and monitoring of sustained hyperglycemia

HbA1c is the gold standard test for monitoring medium/long term glycemia conditions in diabetes care, which is a critical factor in reducing the risk of chronic diabetes complications. Current technologies for measuring HbA1c concentration are invasive and adequate assays are still limited to laboratory-based methods that are not widely available worldwide. The development of a non-invasive diagnostic tool for HbA1c concentration can lead to the decrease of the rate of undiagnosed cases and facilitate early detection in diabetes care. We present a preliminary validation diagnostic study of W-band spectroscopy for detection and monitoring of sustained hyperglycemia, using the HbA1c concentration as reference. A group of 20 patients with type 1 diabetes mellitus and 10 healthy subjects were non-invasively assessed at three different visits over a period of 7 months by a millimeter-wave spectrometer (transmission mode) operating across the full W-band. The relationship between the W-band spectral profile and the HbA1c concentration is studied using longitudinal and non-longitudinal functional data analysis methods. A potential blind discrimination between patients with or without diabetes is obtained, and more importantly, an excellent relation (R-squared = 0.97) between the non-invasive assessment and the HbA1c measure is achieved. Such results support that W-band spectroscopy has great potential for developing a non-invasive diagnostic tool for in-vivo HbA1c concentration monitoring in humans.

Quantitative Analysis of Different Multi-Wavelength PPG Devices and Methods for Noninvasive In-Vivo Estimation of Glycated Hemoglobin

Diabetes is a serious disease affecting the insulin cycle in the human body. Thus, monitoring blood glucose levels and the diagnosis of diabetes in the early stages is very important. Noninvasive in vivo diabetes-diagnosis procedures are very new and require thorough studies to be error-resistant and user-friendly. In this study, we compare two noninvasive procedures (two-wavelength- and three-wavelength-based methods) to estimate glycated hemoglobin (HbA1c) levels in different scenarios and evaluate them with error level calculations. The three-wavelength method, which has more model parameters, results in a more accurate estimation of HbA1c even when the blood oxygenation (SpO2) values change. The HbA1c-estimation error range of the two-wavelength model, due to change in SpO2, is found to be from −1.306% to 0.047%. On the other hand, the HbA1c estimation error for the three-wavelength model is found to be in the magnitude of 10−14% and independent of SpO2. The approximation of SpO2 from the two-wavelength model produces a lower error for the molar concentration based technique (−4% to −1.9% at 70% to 100% of reference SpO2) as compared to the molar absorption coefficient based technique. Additionally, the two-wavelength model is less susceptible to sensor noise levels (max SD of %error, 0.142%), as compared to the three-wavelength model (max SD of %error, 0.317%). Despite having a higher susceptibility to sensor noise, the three-wavelength model can estimate HbA1c values more accurately; this is because it takes the major components of blood into account and thus becomes a more realistic model.

Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform

Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s average blood glucose and blood oxygen levels. Digital volume pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood-vessel-only hypothesis, whereas the second model is based on a whole-finger model system. The two gray-box systems were validated on diabetic and nondiabetic patients. The mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO₂) were 0.375 and 1.676 for the blood-vessel model and 0.271 and 1.395 for the whole-finger model, respectively. The repeatability analysis indicated that these models resulted in a mean percent coefficient of variation (%CV) of 2.08% and 1.74% for %HbA1c and 0.54% and 0.49% for %SpO₂ in the respective models. Herein, both models exhibited similar performances (HbA1c estimation Pearson’s R values were 0.92 and 0.96, respectively), despite the model assumptions differing greatly. The bias values in the Bland–Altman analysis for both models were – 0.03 ± 0.458 and – 0.063 ± 0.326 for HbA1c estimation, and 0.178 ± 2.002 and – 0.246 ± 1.69 for SpO2 estimation, respectively. Both models have a very high potential for use in real-world scenarios. The whole-finger model with a lower standard deviation in bias and higher Pearson’s R value performs better in terms of higher precision and accuracy than the blood-vessel model.

Wearable sensors enable personalized predictions of clinical laboratory measurements

Vital signs, including heart rate and body temperature, are useful in detecting or monitoring medical conditions, but are typically measured in the clinic and require follow-up laboratory testing for more definitive diagnoses. Here we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic. Vital sign data collected from wearables can also predict several clinical laboratory measurements with lower prediction error than predictions made using clinically obtained vital sign measurements. The length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models. These results demonstrate the value of commercial wearable devices for continuous and longitudinal assessment of physiological measurements that today can be measured only with clinical laboratory tests.

Direct and Label-Free Determination of Human Glycated Hemoglobin Levels Using Bacteriorhodopsin as the Biosensor Transducer

Glycated hemoglobin (HbA1c) levels are an important index for the diagnosis and long-term control of diabetes. This study is the first to use a direct and label-free photoelectric biosensor to determine HbA1c using bacteriorhodopsin-embedded purple membranes (PM) as a transducer. A biotinylated PM (b-PM) coated electrode that is layered with protein A-oriented antibodies against hemoglobin (Hb) readily captures non-glycated Hb (HbA0) and generates less photocurrent. The spectra of bacteriorhodopsin and Hb overlap so the photocurrent is reduced because of the partial absorption of the incident light by the captured Hb molecules. Two HbA0 and HbA1c aptasensors that are prepared by conjugating specific aptamers on b-PM coated electrodes single-step detect HbA0 and HbA1c in 15 min, without cross reactivity, with detection limits of ≤0.1 μg/mL and a dynamic range of 0.1–100 μg/mL. Both aptasensors exhibit high selectivity and long-term stability. For the clinical samples, HbA0 concentrations and HbA1c levels that are measured with aptasensors correlate well with total Hb concentrations and the HbA1c levels that are determined using standard methods (correlation gradient = 0.915 ± 0.004 and 0.981 ± 0.001, respectively). The use of these aptasensors for diabetes care is demonstrated.

Analytical techniques for the detection of glycated haemoglobin underlining the sensors

The increase in concentrations of blood glucose results arise in the proportion of glycated haemoglobin. Therefore, the percentage of glycated haemoglobin in the blood could function as a biomarker for the average glucose level over the past three months and can be used to detect diabetes. The study of glycated haemoglobin tends to be complex as there are about three hundred distinct assay techniques available for evaluating glycated haemoglobin which contributes to some differences in the recorded values from the similar samples. This review outlines distinct analytical methods that have evolved in the recent past for precise recognition of the glycated - proteins.

Lossy Mode Resonance-Based Glucose Sensor with High-κ Dielectric Film

In the past, high-κ dielectrics gained much attention because of the constant demand for increasingly smaller semiconductors. At the same time, in the field of optical sensing, high-κ dielectrics are key materials. This study presents the experimental investigations on a lossy mode resonance-based optical planar waveguide (LMROPW) sensor coated with a high-κdielectric of an indium tin oxide (ITO) layer. Two types of sensing structures were fabricated by coating (i) only a single-layer ITO (or bared LMROPW) and (ii) an ITO layer with glucose probes onto the optical planar waveguide (or boronic LMROPW) to detect glucose molecules. The sensing characteristics of these two types of sensors toward the surrounding analyte were determined using different concentrations of glucose solutions. It was found that the bared LMROPW sensor is only suitable for a higher concentration of glucose; the boronic LMROPW sensor with glucose probes on ITO could be applied to a lower-concentration solution to monitor glucose adsorption onto the sensing surface. Furthermore, with the advantages of a simple structure, easy alignment, and suitable production, the LMROPW sensor with a high-κ dielectric surface could be applied in clinical testing and diagnostics.