Noninvasive blood glucose monitoring with optical coherence tomography: a pilot study in human subjects

Effects of Skin Blood Flow Fluctuations on Non-Invasive Glucose Measurement and a Feasible Blood Flow Control Method

In non-invasive blood glucose measurement (NBGM) based on near-infrared spectroscopy, fluctuations in blood flow represent a primary source of interference. This paper proposes a local blood flow pre-stimulation method in which the local skin is heated to dilate blood vessels and increase blood flow. This approach aims to mitigate the impact of environmental temperature variations, emotional fluctuations, and insulin secretion on blood flow, thereby enhancing the accuracy of glucose measurement. To evaluate the effectiveness of this method, a blood flow interference experiment was conducted to compare the stability of the measured spectra with and without blood flow pre-stimulation. The results demonstrated that the pre-stimulation method presents good anti-interference capabilities. Furthermore, 45 volunteers underwent oral glucose tolerance tests (OGTTs) as a part of the validation experiments. In these tests, the forearm skin blood flow of 24 volunteers was pre-stimulated using elevated temperature, while the skin of the remaining 21 subjects was maintained at a natural temperature level without stimulation. The results indicate that compared to the non-stimulated condition, the correlation between the optical signal at 1550 nm and blood glucose levels was significantly enhanced under the pre-stimulation condition. Furthermore, the root mean square error (RMSE) of the linear prediction model was reduced to just 0.92 mmol/L. In summary, this paper presents a feasible blood flow control strategy that effectively stabilizes internal blood flow, thereby improving the accuracy of NBGM.

Evaluation and Validation on Sensitivity of Near-Infrared Diffuse Reflectance in Non-Invasive Human Blood Glucose Measurement

In non-invasive blood glucose measurement, the sensitivity of glucose-induced optical signals within human tissue is a crucial reference point. This study evaluates the sensitivity of glucose-induced diffuse reflectance in the 1000-1700 nm range. A key factor in understanding this sensitivity is the rate at which the scattering coefficient changes due to glucose, as it is significantly higher than in non-living media and predominantly influences the diffuse light signal level when blood glucose levels change. The study measured and calculated the changes in the scattering coefficient at 1314 nm, a wavelength chosen for its minimal interference from glucose absorption and other bodily constituents. Based on the Mie scattering theory and the results at 1314 nm, the changes in the scattering coefficient within the 1000-1700 nm range were estimated. Subsequently, the sensitivity of the glucose signal across this range was determined through Monte Carlo (MC) simulations. The findings from 25 human trials indicate that the measured sensitivities at five other typical wavelengths within this band generally align with the sensitivities calculated using the aforementioned method. This research can guide the identification of blood glucose signals and the selection of wavelengths for non-invasive blood glucose measurements.

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay. Our objective was to evaluate the effectiveness of these techniques in accurately predicting glucose levels and to determine which approach was most optimal. Our investigation involved the acquisition of spectral data from samples of glucose solutions using the three aforementioned spectroscopy techniques. The data was subjected to several preprocessing filtering methods, including convolutional moving average, Savitzky-Golay, multiplicative scatter correction, and normalisation. We then applied representative machine learning algorithms from three categories: linear modelling, traditional nonlinear modelling, and artificial neural networks. The evaluation results revealed that linear models exhibited higher predictive accuracy than nonlinear models, whereas artificial neural network models demonstrated comparable performance. Additionally, the comparative analysis of various filtering methods demonstrated that the convolutional moving average and Savitzky-Golay filters yielded the most precise outcomes overall. In conclusion, our study provides valuable insights into the efficacy of different machine learning techniques for glucose measurement and highlights the importance of applying appropriate filtering methods in enhancing predictive accuracy. These findings have important implications for the development of new and improved glucose quantification technologies.

Non-invasive paper-based sensors containing rare-earth-doped nanoparticles for the detection of D-glucose

Today, diabetes mellitus is one of the most common diseases that affects the population on a worldwide scale. Patients suffering from this disease are required to control their blood-glucose levels several times a day through invasive methods such as piercing their fingers. Our NaGdF4: 5% Er3+, 3% Nd3+ nanoparticles demonstrate a remarkable ability to detect D-glucose levels by analysing alterations in their red-to-green ratio, since this sensitivity arises from the interaction between the nanoparticles and the OH groups present in the D-glucose molecules, resulting in discernible changes in the emission of the green and red bands. These luminescent sensors were implemented and tested on paper substrates, offering a portable, low-cost and enzyme-free solution for D-glucose detection in aqueous solutions with a limit of detection of 22 mg/dL. With this, our study contributes to the development of non-invasive D-glucose sensors, holding promising implications for managing diabetes and improving overall patient well-being with possible future applications in D-glucose sensing through tear fluid.

Non-invasive glucose prediction and classification using NIR technology with machine learning

In this paper, a dual wavelength short near-infrared system is described for the detection of glucose levels. The system aims to improve the accuracy of blood glucose detection in a cost-effective and non-invasive way. The accuracy of the method is evaluated using real-time samples collected with the reference finger prick glucose device. A feed forward neural network (FFNN) regression method is employed to predict glucose levels based on the input data obtained from NIR technology. The system calculates glucose evaluation metrics and performs Surveillance error grid (SEG) analysis. The coefficient of determination R 2 and mean absolute error are observed 0.99 and 2.49 mg/dl, respectively. Additionally, the system determines the root mean square error (RMSE) as 3.02 mg/dl. It also shows that the mean absolute percentage error (MAPE) is 1.94% and mean squared error (MSE) is 9.16 ( m g / d l ) 2 for FFNN. The SEG analysis shows that the glucose values measured by the system fall within the clinically acceptable range when compared to the reference method. Finally, the system uses the multi-class classification method of the multilayer perceptron (MLP) and K-nearest neighbors (KNN) classifier to classify glucose levels with an accuracy of 99%.

High-accuracy noninvasive continuous glucose monitoring using OCT angiography-purified blood scattering signals in human skin

The accuracy of noninvasive continuous glucose monitoring (CGM) through near-infrared scattering is challenged by mixed scattering signals from different compartments, where glucose has a positive correlation with a blood scattering coefficient but a negative correlation with a tissue scattering coefficient. In this study, we developed a high-accuracy noninvasive CGM based on OCT angiography (OCTA)-purified blood scattering signals. The blood optical scattering coefficient (BOC) was initially extracted from the depth attenuation of backscattered light in OCT and then purified by eliminating the scattering signals from the surrounding tissues under the guidance of a 3D OCTA vascular map in human skin. The purified BOC was used to estimate the optical blood glucose concentration (BGC) through a linear calibration. The optical and reference BGC measurements were highly correlated (R = 0.94) without apparent time delay. The mean absolute relative difference was 6.09%. All optical BGC measurements were within the clinically acceptable Zones A + B, with 96.69% falling in Zone A on Parke's error grids. The blood glucose response during OGTT was mapped with a high spatiotemporal resolution of the single vessel and 5 seconds. This noninvasive OCTA-based CGM shows promising accuracy for clinical use. Future research will involve larger sample sizes and diabetic participants to confirm these preliminary findings.

Experimental and probabilistic model validation of ultrasonic MEMS transceiver for blood glucose sensing

In contrast to traditional laboratory glucose monitoring, recent developments have focused on blood glucose self-monitoring and providing patients with a self-monitoring device. This paper proposes a system based on ultrasound principles for quantifying glucose levels in blood by conducting an in-vitro experiment with goat blood before human blood. The ultrasonic transceiver is powered by a frequency generator that operates at 40 kHz and 1.6 V, and variations in glucose level affect the ultrasonic transceiver readings. The RVM probabilistic model is used to determine the variation in glucose levels in a blood sample. Blood glucose levels are measured simultaneously using a commercial glucose metre for confirmation. The experimental data values proposed are highly correlated with commercial glucose metre readings. The proposed ultrasonic MEMS-based blood glucometer measures a glucose level of [Formula: see text] mg/dl. In the near future, the miniature version of the experimental model may be useful to human society.

Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review

The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with machine learning methods are discussed. The review concludes with key points and insights for future development of PPG NIR-based blood glucose monitoring systems.

Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training

Personalized modeling has long been anticipated to approach precise noninvasive blood glucose measurements, but challenged by limited data for training personal model and its unavoidable outlier predictions. To overcome these long-standing problems, we largely enhanced the training efficiency with the limited personal data by an innovative Deduction Learning (DL), instead of the conventional Induction Learning (IL). The domain theory of our deductive method, DL, made use of accumulated comparison of paired inputs leading to corrections to preceded measured blood glucose to construct our deep neural network architecture. DL method involves the use of paired adjacent rounds of finger pulsation Photoplethysmography signal recordings as the input to a convolutional-neural-network (CNN) based deep learning model. Our study reveals that CNN filters of DL model generated extra and non-uniform feature patterns than that of IL models, which suggests DL is superior to IL in terms of learning efficiency under limited training data. Among 30 diabetic patients as our recruited volunteers, DL model achieved 80% of test prediction in zone A of Clarke Error Grid (CEG) for model training with 12 rounds of data, which was 20% improvement over IL method. Furthermore, we developed an automatic screening algorithm to delete low confidence outlier predictions. With only a dozen rounds of training data, DL with automatic screening achieved a correlation coefficient ([Formula: see text]) of 0.81, an accuracy score ([Formula: see text]) of 93.5, a root mean squared error of 13.93 mg/dl, a mean absolute error of 12.07 mg/dl, and 100% predictions in zone A of CEG. The nonparametric Wilcoxon paired test on [Formula: see text] for DL versus IL revealed near significant difference with p-value 0.06. These significant improvements indicate that a very simple and precise noninvasive measurement of blood glucose concentration is achievable.

Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring

The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the amount of sugar in a small sample of blood, usually drawn from pricking the fingertip, and placed on a disposable test strip. Therefore, there is a need for non-invasive continuous glucose monitoring, which is possible using a sweat sensor-based approach. As sweat sensors have garnered much interest in recent years, this study attempts to summarize recent developments in non-invasive continuous glucose monitoring using sweat sensors based on different approaches with an emphasis on the devices that can potentially be integrated into a wearable platform. Numerous research entities have been developing wearable sensors for continuous blood glucose monitoring, however, there are no commercially viable, non-invasive glucose monitors on the market at the moment. This review article provides the state-of-the-art in sweat glucose monitoring, particularly keeping in sight the prospect of its commercialization. The challenges relating to sweat collection, sweat sample degradation, person to person sweat amount variation, various detection methods, and their glucose detection sensitivity, and also the commercial viability are thoroughly covered.

Glucose biosensors in clinical practice: principles, limits and perspectives of currently used devices

The demand of glucose monitoring devices and even of updated guidelines for the management of diabetic patients is dramatically increasing due to the progressive rise in the prevalence of diabetes mellitus and the need to prevent its complications. Even though the introduction of the first glucose sensor occurred decades ago, important advances both from the technological and clinical point of view have contributed to a substantial improvement in quality healthcare. This review aims to bring together purely technological and clinical aspects of interest in the field of glucose devices by proposing a roadmap in glucose monitoring and management of patients with diabetes. Also, it prospects other biological fluids to be examined as further options in diabetes care, and suggests, throughout the technology innovation process, future directions to improve the follow-up, treatment, and clinical outcomes of patients.

90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c

Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the prediction accuracy (the ratio in zone A of Clarke's error grid) reduced to undesirable 60.6%. We suspect the low prediction accuracy induced by larger sample size might arise from the physiological diversity of subjects, and one possibility is that the diversity might originate from medication. Therefore, we split the subjects into two cohorts for deep learning: with and without medication (1682 and 856 recruited subjects, respectively). In comparison, the cohort training for subjects without any medication had approximately 30% higher prediction accuracy over the cohort training for those with medication. Furthermore, by adding quarterly (every 3 months) measured glycohemoglobin (HbA1c), we were able to significantly boost the prediction accuracy by approximately 10%. For subjects without medication, the best performing model with quarterly measured HbA1c achieved 94.3% prediction accuracy, RMSE of 12.4 mg/dL, MAE of 8.9 mg/dL, and MAPE of 0.08, which demonstrates a very promising solution for NIBG prediction via deep learning. Regarding subjects with medication, a personalized model could be a viable means of further investigation.

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Noninvasive OCT angiography-based blood attenuation measurements correlate with blood glucose level in the mouse retina

In this study, we investigated the correlation of the blood optical attenuation coefficient (OAC) and the blood glucose concentration (BGC). The blood OAC was measured in mouse retina in vivo by analyzing the depth attenuation of backscattered light under the guidance of OCT angiography (OCTA) vascular mapping, and then its correlation to the BGC was further investigated. The optical attenuation of the blood components presented a more reliable correlation to BGC than that of the background tissues. The arteries and veins presented a blood OAC change of ∼0.05-0.07 mm^-1 per 10 mg/dl and a significant (P < 0.001) elevation of blood OAC in diabetic mice was observed. Furthermore, different kinds of vessels also presented different performances. The veins had a higher correlation coefficient (R=0.86) between the measured blood OAC and BGC than that of the arteries (R=0.73). Besides, the blood OAC changes of the specific vessels occur without any obvious change in the vascular morphology in the retina. The blood OAC-BGC correlation suggests a concept of non-invasive OCTA-based glucometry, allowing a fast assessment of the blood glucose of specific vessels with superior motion immunity. A direct glucometry of the retina would be helpful for accurately monitoring the progression of diabetic retinopathy.

Clinic-on-a-Needle Array toward Future Minimally Invasive Wearable Artificial Pancreas Applications

In order to reduce medical facility overload due to the rise of the elderly population, modern lifestyle diseases, or pandemics, the medical industry is currently developing point-of-care and home medical device systems. Diabetes is an incurable and lifetime disease, accountable for a significant mortality and socio-economic public health burden. Thus, tight glucose control in diabetic patients, which can prevent the onset of its late complications, is of enormous importance. Despite recent advances, the current best achievable management of glucose control is still inadequate, due to several key limitations in the system components, mainly related to the reliability of sensing components, both temporally and chemically, and the integration of sensing and delivery components in a single wearable platform, which is yet to be achieved. Thus, advanced closed-loop artificial pancreas systems able to modulate insulin delivery according to the measured sensor glucose levels, independently of patient supervision, represent a key requirement of development efforts. Here, we demonstrate a minimally invasive, transdermal, multiplex, and versatile continuous metabolites monitoring system in the subcutaneous interstitial fluid space based on a chemically modified SiNW-FET nanosensor array on microneedle elements. Using this technology, ISF-borne metabolites require no extraction and are measured directly and continuously by the nanosensors. Due to their chemical sensing mechanism, the nanosensor response is only influenced by the specific metabolite of interest, and no response is observed in the presence of potential exogenous and endogenous interferents known to seriously affect the response of current electrochemical glucose detection approaches. The 2D architecture of this platform, using a single SOI substrate as a top-down multipurpose material, resulted in a standard fabricated chip with 3D functionality. After proving the ability of the system to act as a selective multimetabolites sensor, we have implemented our platform to reach our main goal for in vivo continuous glucose monitoring of healthy human subjects. Furthermore, minor adjustments to the fabrication technique allow the on-chip integration of microinjection needle elements, which can ideally be used as a drug delivery system. Preliminary experiments on a mice animal model successfully demonstrated the single-chip capability to both monitor glucose levels as well as deliver insulin. By that, we hope to provide in the future a cost-effective and reliable wearable personalized clinical tool for patients and a strong tool for research, which will be able to perform direct monitoring of clinical biomarkers in the ISF as well as synchronized transdermal drug delivery by this single-chip multifunctional platform.

Microparticle-Based Biochemical Sensing Using Optical Coherence Tomography and Deep Learning

Advancing continuous health monitoring beyond vital signs to biochemistry will revolutionize personalized medicine. Herein, we report a biosensing platform to achieve remote biochemical monitoring using microparticle-based biosensors and optical coherence tomography (OCT). Stimuli-responsive, polymeric microparticles were designed to serve as freely dispersible biorecognition units, wherein binding with a target biochemical induces volumetric changes of the microparticle. Analytical approaches to detect these submicron changes in 3D using OCT were devised by modeling the microparticle as an optical cavity, enabling estimations far below the resolution of the OCT system. As a proof of concept, we demonstrated the 3D spatiotemporal monitoring of glucose-responsive microparticles distributed throughout a tissue mimic in response to dynamically fluctuating levels of glucose. Deep learning was further implemented using 3D convolutional neural networks to automate the vast processing of the continuous stream of three-dimensional time series data, resulting in a robust end-to-end pipeline with immense potential for continuous in vivo biochemical monitoring.

Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission

We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

Measuring glucose concentration in a solution based on the indices of polarimetric purity

Polarization imaging is a powerful tool, which can be applied in biomedical diagnosis and many research fields. Here, we propose a new application of the indices of polarimetric purity (IPPs) composed of P1, P2, P3, to describe the glucose concentrations (GC) changes in the scattering system. The results suggest that P1 of the IPPs is a better indicator to GC in the solution than the degree of polarization (DoP) for the forward scattering detection. Meanwhile, the fitting relation among radius of scattering particle, GCs and P1 parameter has also been calculated, in which the error of inversion is no more than 4.73%. In the backscattering detection, the fitted frequency statistical histogram of the IPPs is used to measure the GCs, and their modes can represent changing trend of GCs.

Two-dimensional correlation (2D) method for improving the accuracy of OCT-based noninvasive blood glucose concentration (BGC) monitoring

The optical scattering coefficient (μ_s) in the dermis layer of human skin obtained with optical coherence tomography (OCT) has shown to have a strong correlation with the blood glucose concentration (BGC), which can be used for noninvasive BGC monitoring. Unfortunately, the nonhomogeneity in the skin may cause inaccuracies for the BGC analysis. In this paper, we propose a 2D correlation analysis method to identify 2D regions in the skin with μ_s sensitive to BGC variations and only use data in these regions to calculate μ_s for minimizing the inaccuracy induced by nonhomogeneity and therefore improving the accuracy of OCT-based BGC monitoring. We demonstrate the effectiveness of the 2D method with OCT data obtained with in vivo human forearm skins of nine different human subjects. In particular, we present a 3D OCT data set in a two-dimensional (2D) map of depth vs. a lateral dimension and calculate the correlation coefficient R between the μ_s and the BGC in each region of the 2D map with the BGC data measured with a glucose meter using finger blood. We filter out the μ_s data from regions with low R values and only keep the μ_s data with R values sufficiently high (R-filter). The filtered μ_s data in all the regions are then averaged to produce an average μ_s data. We define a term called overall relevancy (OR) to quantify the degree of correlation between the filtered/averaged μ_s data and the finger-blood BGC data to determine the optimal R value for such an R-filter with the highest obtained OR. We found that the optimal R for such an R-filter has an absolute value (|R|) of 0.6 or 0.65. We further show that the R-filter obtained with the 2D correlation method yields better OR between μ_s and the BGC than that obtained with the previously reported 1D correlation method. We believe that the method demonstrated in this paper is important for understanding the influence of BGC on μ_s in human skins and therefore for improving the accuracy of OCT-based noninvasive BGC monitoring, although further studies are required to validate its effectiveness.

Optical clearing of living brains with MAGICAL to extend i n vivo imaging

To understand brain functions, it is important to observe directly how multiple neural circuits are performing in living brains. However, due to tissue opaqueness, observable depth and spatiotemporal resolution are severely degraded in vivo. Here, we propose an optical brain clearing method for in vivo fluorescence microscopy, termed MAGICAL (magical additive glycerol improves clear alive luminance). MAGICAL enabled two-photon microscopy to capture vivid images with fast speed, at cortical layer V and hippocampal CA1 in vivo. Moreover, MAGICAL promoted conventional confocal microscopy to visualize finer neuronal structures including synaptic boutons and spines in unprecedented deep regions, without intensive illumination leading to phototoxic effects. Fluorescence emission spectrum transmissive analysis showed that MAGICAL improved in vivo transmittance of shorter wavelength light, which is vulnerable to optical scattering, thus unsuited for in vivo microscopy. These results suggest that MAGICAL would transparentize living brains via scattering reduction.

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Oral glycerol administration (MAGICAL) enhances fluorescent signals in living brains

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MAGICAL achieves in vivo optical clearing for living brains via scattering reduction

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MAGICAL enables in vivo microscopy to observe brains faster, deeper, and more finely

Improving the characterization of ex vivo human brain optical properties using high numerical aperture optical coherence tomography by spatially constraining the confocal parameters

Significance: The optical properties of biological samples provide information about the structural characteristics of the tissue and any changes arising from pathological conditions. Optical coherence tomography (OCT) has proven to be capable of extracting tissue's optical properties using a model that combines the exponential decay due to tissue scattering and the axial point spread function that arises from the confocal nature of the detection system, particularly for higher numerical aperture (NA) measurements. A weakness in estimating the optical properties is the inter-parameter cross-talk between tissue scattering and the confocal parameters defined by the Rayleigh range and the focus depth. Aim: In this study, we develop a systematic method to improve the characterization of optical properties with high-NA OCT. Approach: We developed a method that spatially parameterizes the confocal parameters in a previously established model for estimating the optical properties from the depth profiles of high-NA OCT. Results: The proposed parametrization model was first evaluated on a set of intralipid phantoms and then validated using a low-NA objective in which cross-talk from the confocal parameters is negligible. We then utilize our spatially parameterized model to characterize optical property changes introduced by a tissue index matching process using a simple immersion agent, 2,2'-thiodiethonal. Conclusions: Our approach improves the confidence of parameter estimation by reducing the degrees of freedom in the non-linear fitting model.

Biophotonics methods for functional monitoring of complications of diabetes mellitus

The prevalence of diabetes complications is a significant public health problem with a considerable economic cost. Thus, the timely diagnosis of complications and prevention of their development will contribute to increasing the length and quality of patient life, and reducing the economic costs of their treatment. This article aims to review the current state-of-the-art biophotonics technologies used to identify the complications of diabetes mellitus and assess the quality of their treatment. Additionally, these technologies assess the structural and functional properties of biological tissues, and they include capillaroscopy, laser Doppler flowmetry and hyperspectral imaging, laser speckle contrast imaging, diffuse reflectance spectroscopy and imaging, fluorescence spectroscopy and imaging, optical coherence tomography, optoacoustic imaging and confocal microscopy. Recent advances in the field of optical noninvasive diagnosis suggest a wider introduction of biophotonics technologies into clinical practice and, in particular, in diabetes care units.

Ultrasound-modulated optical glucose sensing using a 1645 nm laser

Regular and frequent blood glucose monitoring is vital in managing diabetes treatment plans and preventing severe complications. Because current invasive techniques impede patient compliance and are not infection-free, many noninvasive methods have been proposed. Among them, optical methods have drawn much attention for their rich optical contrast, but their resolution is degraded in deep tissue. Here, we present an ultrasound-modulated optical sensing (UOS) technique to noninvasively monitor glucose that uses an infrared laser (1645 nm) and a single-element focused ultrasound transducer. Focused ultrasound waves can acoustically localize diffused photons in scattering media, and thus optical contrast can be represented with much enhanced spatial resolution. To maximize the signal-to-noise ratio, we compared the modulation depths of UOS signals in both continuous and burst ultrasound transmission modes. Finally, UOS measurements of various glucose concentrations are presented and compared with those acquired in phantoms with a conventional diffuse optical sensing method. The UOS measurements in a 20 mm thick tissue-mimicking phantom show 26.6% accuracy in terms of mean absolute relative difference (MARD), which indicates the great potential of the proposed technique as a noninvasive glucose sensor.

Nonenzymatic Glucose Sensing Using Ni60Nb40 Nanoglass

Despite being researched for nearly five decades, chemical application of metallic glass is scarcely explored. Here we show electrochemical nonenzymatic glucose-sensing ability of nickel-niobium (Ni₆₀Nb₄₀) amorphous alloys in alkaline medium. Three different Ni₆₀Nb₄₀ systems with the same elemental composition, but varying microstructures are created following different synthetic routes and tested for their glucose-sensing performance. Among melt-spun ribbon, nanoglass, and amorphous-crystalline nanocomposite materials, nanoglass showed the best performance in terms of high anodic current density, sensitivity (20 mA cm^-2 mM^-1), limit of detection (100 nM glucose), stability, reproducibility (above 5000 cycles), and sensing accuracy among nonenzymatic glucose sensors involving amorphous alloys. When annealed under vacuum, only the heat-treated nanoglass retained a similar electrochemical-sensing property, while the other materials failed to yield desired results. In nanoglass, a network of glassy interfaces, compared to melt-spun ribbon, is plausibly responsible for the enhanced sensitivity.

Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone

Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy.

Comparison between the QRMA Measurement with the Anamnesis and the Capillary Blood Glucose Test

ABSTRACTIntroduction: The examination of patients with diabetes mellitus (DM) can be done by reviewing their complaints and through a capillary blood glucose level test to determine the value of their Random Blood Glucose Level. QRMA (Quantum Resonance Magnetic Analyzer) is claimed to be able to check the patient’s bodily condition (including blood glucose) with an accuracy of 85%. The purpose of this study was to verify the validity of the QRMA tool and its accuracy by comparing the results of the anamnesis and the examination conducted using the capillary blood glucose test method.Methods: The research method used was a cross-sectional design. The total sample consisted of 44 respondents in the working area of the Community Health Centers in Yogyakarta with the risk factor being blood sugar level instability. The sampling technique used was purposive sampling. The main variable in this study was the value of the blood sugar level measured based on the coefficient value of the QRMA tool and the value of Random Blood Glucose obtained through the capillary blood glucose test.Results: The blood glucose value was not correlated significantly with the coefficient value of QRMA. The value of blood glucose when examined alongside the result of the respondent's anamnesis showed there to be a significant difference. The value of the QRMA coefficient when examined against the results from the history of the respondents showed no significant difference. Linear regression showed that the variables of height, body weight, and IMT had a correlation with the QRMA coefficient value.Conclusion: The QRMA tool was not able to provide a picture of the actual condition of the blood glucose level of the respondents when compared with the results of the anamnesis and the blood glucose value from the capillary blood glucose test. Non-invasive health measurement devices such as QRMA are not used by nurses as a standard for determining the health status of DM patients.

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.

Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device

Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes.

The Progress of Glucose Monitoring-A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors

Current glucose monitoring methods for the ever-increasing number of diabetic people around the world are invasive, painful, time-consuming, and a constant burden for the household budget. The non-invasive glucose monitoring technology overcomes these limitations, for which this topic is significantly being researched and represents an exciting and highly sought after market for many companies. This review aims to offer an up-to-date report on the leading technologies for non-invasive (NI) and minimally-invasive (MI) glucose monitoring sensors, devices currently available in the market, regulatory framework for accuracy assessment, new approaches currently under study by representative groups and developers, and algorithm types for signal enhancement and value prediction. The review also discusses the future trend of glucose detection by analyzing the usage of the different bands in the electromagnetic spectrum. The review concludes that the adoption and use of new technologies for glucose detection is unavoidable and closer to become a reality.

Determination of confocal profile and curved focal plane for OCT mapping of the attenuation coefficient

The attenuation coefficient has proven to be a useful tool in numerous biological applications, but accurate calculation is dependent on the characterization of the confocal effect. This study presents a method to precisely determine the confocal effect and its focal plane within a sample by examining the ratio of two optical coherence tomography (OCT) images. The method can be employed to produce a single-value estimate, or a 2D map of the focal plane accounting for the curvature or tilt within the sample. Furthermore, this method is applicable to data obtained with both high numerical aperture (NA) and low-NA lenses, thereby furthering the applicability of the attenuation coefficient to high-NA OCT data. We test and validate this method using standard samples of Intralipid 20% and 5%, improving the accuracy to 99% from 65% compared to the traditional method and preliminarily show applicability to real biological data of glioblastoma acquired in vivo in a murine model.

First Experiences With a Wearable Multisensor in an Outpatient Glucose Monitoring Study, Part I: The Users' View

BACKGROUND

Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions.

METHODS

To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient.

RESULTS

Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase.

CONCLUSIONS

The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.

First Experiences With a Wearable Multisensor Device in a Noninvasive Continuous Glucose Monitoring Study at Home, Part II: The Investigators' View

BACKGROUND

Extensive past work showed that noninvasive continuous glucose monitoring with a wearable multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions.

METHOD

To test previous findings also in uncontrolled conditions, a long term at home study has been organized to collect multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking multisensor data to glucose applied to estimate glucose levels noninvasively.

RESULTS

The algorithm used here calculates glucose values from sensor data and adds a constant obtained by a daily calibration. It provides point inaccuracy measured by a MARD of 35.4 mg/dL on test data. This is higher than current state-of-the-art minimally invasive devices, but still 86.9% of glucose rate points fall within the zone AR+BR.

CONCLUSIONS

The multisensor device and the algorithmic routine used earlier in controlled conditions tracks glucose changes also in uncontrolled conditions, although with lower accuracy. The examination of learning curves suggests that obtaining more data would not improve the results. Therefore, further efforts would focus on the development of more complex algorithmic routines able to compensate for environmental and physiological confounders better.

Noninvasive measurement of glucose concentration on human fingertip by optical coherence tomography

A method is proposed for determining the glucose concentration on the human fingertip by extracting two optical parameters, namely the optical rotation angle and the depolarization index, using a Mueller optical coherence tomography technique and a genetic algorithm. The feasibility of the proposed method is demonstrated by measuring the optical rotation angle and depolarization index of aqueous glucose solutions with low and high scattering, respectively. It is shown that for both solutions, the optical rotation angle and depolarization index vary approximately linearly with the glucose concentration. As a result, the ability of the proposed method to obtain the glucose concentration by means of just two optical parameters is confirmed. The practical applicability of the proposed technique is demonstrated by measuring the optical rotation angle and depolarization index on the human fingertip of healthy volunteers under various glucose conditions.

Monitoring of temperature-mediated phase transitions of adipose tissue by combined optical coherence tomography and Abbe refractometry

Observation of temperature-mediated phase transitions between lipid components of the adipose tissues has been performed by combined use of the Abbe refractometry and optical coherence tomography. The phase transitions of the lipid components were clearly observed in the range of temperatures from 24°C to 60°C, and assessed by quantitatively monitoring the changes of the refractive index of 1- to 2-mm-thick porcine fat tissue slices. The developed approach has a great potential as an alternative method for obtaining accurate information on the processes occurring during thermal lipolysis.

Characterizing the optical properties of human brain tissue with high numerical aperture optical coherence tomography

Quantification of tissue optical properties with optical coherence tomography (OCT) has proven to be useful in evaluating structural characteristics and pathological changes. Previous studies primarily used an exponential model to analyze low numerical aperture (NA) OCT measurements and obtain the total attenuation coefficient for biological tissue. In this study, we develop a systematic method that includes the confocal parameter for modeling the depth profiles of high NA OCT, when the confocal parameter cannot be ignored. This approach enables us to quantify tissue optical properties with higher lateral resolution. The model parameter predictions for the scattering coefficients were tested with calibrated microsphere phantoms. The application of the model to human brain tissue demonstrates that the scattering and back-scattering coefficients each provide unique information, allowing us to differentially identify laminar structures in primary visual cortex and distinguish various nuclei in the midbrain. The combination of the two optical properties greatly enhances the power of OCT to distinguish intricate structures in the human brain beyond what is achievable with measured OCT intensity information alone, and therefore has the potential to enable objective evaluation of normal brain structure as well as pathological conditions in brain diseases. These results represent a promising step for enabling the quantification of tissue optical properties from high NA OCT.

Skin-like biosensor system via electrochemical channels for noninvasive blood glucose monitoring

Currently, noninvasive glucose monitoring is not widely appreciated because of its uncertain measurement accuracy, weak blood glucose correlation, and inability to detect hyperglycemia/hypoglycemia during sleep. We present a strategy to design and fabricate a skin-like biosensor system for noninvasive, in situ, and highly accurate intravascular blood glucose monitoring. The system integrates an ultrathin skin-like biosensor with paper battery-powered electrochemical twin channels (ETCs). The designed subcutaneous ETCs drive intravascular blood glucose out of the vessel and transport it to the skin surface. The ultrathin (~3 μm) nanostructured biosensor, with high sensitivity (130.4 μA/mM), fully absorbs and measures the glucose, owing to its extreme conformability. We conducted in vivo human clinical trials. The noninvasive measurement results for intravascular blood glucose showed a high correlation (>0.9) with clinically measured blood glucose levels. The system opens up new prospects for clinical-grade noninvasive continuous glucose monitoring.

Glucose sensing in oral mucosa simulating phantom using differential absorption based frequency domain low-coherence interferometry

The superluminescent diode based differential absorption frequency domain low-coherence interferometry (FD-DALCI) technique is proposed and demonstrated for sensing physiological concentrations of glucose (0-250 mg/dl) in oral mucosa simulating phantoms (intralipid of concentrations 0.25-0.50%) with wavelengths at 1589 and 1310 nm. The proposed technique allows simultaneous measurements of refractive index based spectral shift and estimation of physiological concentration of glucose in intralipid with scattering characteristics using the differential absorption approach. The sensitivity of the glucose concentration obtained by spectral shift measurement was ≈0.016  nm/(mg/dl), irrespective of the intralipid concentration. The resolution of the glucose level was estimated to be ≈15  mg/dl in 0.25% intralipid and ≈19  mg/dl in 0.5% intralipid using the FD-DALCI technique.

Accurate viscosity measurements of flowing aqueous glucose solutions with suspended scatterers using a dynamic light scattering approach with optical coherence tomography

The viscosity of turbid colloidal glucose solutions has been accurately determined from spectral domain optical coherence tomography (OCT) M-mode measurements and our recently developed OCT dynamic light scattering model. Results for various glucose concentrations, flow speeds, and flow angles are reported. The relative "combined standard uncertainty" uc(η) on the viscosity measurements was ±1% for the no-flow case and ±5% for the flow cases, a significant improvement in measurement robustness over previously published reports. The available literature data for the viscosity of pure water and our measurements differ by 1% (stagnant case) and 1.5% (flow cases), demonstrating good accuracy; similar agreement is seen across the measured glucose concentration range when compared to interpolated literature values. The developed technique may contribute toward eventual noninvasive glucose measurements in medicine.

Retroreflective-type Janus microspheres as a novel contrast agent for enhanced optical coherence tomography

Optical coherence tomography (OCT) is a well-developed technology that utilizes near-infrared light to reconstruct three-dimensional images of biological tissues with micrometer resolution. Improvements of the imaging contrast of the OCT technique are able to further widen its extensive biomedical applications. In this study, Janus microspheres were developed and used as a positive contrast agent for enhanced OCT imaging. Phantom and ex vivo liver tissue experiments as well as in vivo animal tests were conducted, which validated that Janus microspheres, as a novel type of OCT tracer, were very effective in improving the OCT imaging contrast. Working principle and SEM image of Janus microsphere (top). Enhanced OCT imaging (bottom) of Janus microspheres in zebrafish stomach (blue dash line) and sinusoids (green arrows) of nude liver.

A technology roadmap of smart biosensors from conventional glucose monitoring systems

The objective of this review article is to focus on technology roadmap of smart biosensors from a conventional glucose monitoring system. The estimation of glucose with commercially available devices involves analysis of blood samples that are obtained by pricking finger or extracting blood from the forearm. Since pain and discomfort are associated with invasive methods, the non-invasive measurement techniques have been investigated. The non-invasive methods show advantages like non-exposure to sharp objects such as needles and syringes, due to which there is an increase in testing frequency, improved control of glucose concentration and absence of pain and biohazard materials. This review study is aimed to describe recent invasive techniques and major noninvasive techniques, viz. biosensors, optical techniques and sensor-embedded contact lenses for glucose estimation.

The dynamics of invasive and noninvasive blood glucose monitoring methods: Recent trends

Improved prognoses of patients with type 2 diabetes are primarily determined by the extent of blood glucose control (correction of both hyper- and hypoglycemia and normalization of blood glucose levels). The proper identification and timely correction of abnormal blood glucose levels require frequent blood glucose monitoring by the patient. Currently used methods for the self-monitoring of blood glucose have significant drawbacks that limit their use. The most significant problems with these methods include insufficient accuracy, invasiveness and high cost, leading to noncompliance and difficult assessment of disease status. Such factors underscore the need for a noninvasive, cost-effective and highly accurate method to measure blood glucose levels. There are several different approaches for the noninvasive measurement of blood glucose levels, including optical analysis, ultrasound and bioimpedance. The concept of a noninvasive glucometer was launched more than 30 years ago. Nevertheless, most noninvasive technologies are still in early stages of development and are not used in clinical practice. This review describers the most promising developments in this area.

Influence of Osmolytes on In Vivo Glucose Monitoring Using Optical Coherence Tomography

Diabetes mellitus and its complications are the third leading cause of death in the world, exceeded only by cardiovascular disease and cancer. Tighter monitoring and control of blood glucose could minimize complications associated with diabetes. Recently, optical coherence tomography (OCT) for noninvasive glucose monitoring was proposed and tested in vivo. The aim of this work was to investigate the influence of changes in blood glucose concentration ([glu]) and sodium concentration ([Na+]) on the OCT signal. We also investigated the influence of other important analytes on the sensitivity of glucose monitoring with OCT. The experiments were carried out in anesthetized female pigs. The OCT images were acquired continuously from skin, while [glu] and [Na+] were experimentally varied within their physiological ranges. Correlations of the OCT signal slope with [glu] and [Na+] were studied at different tissue depths. The tissue area probed with OCT was marked and cut for histological examination. The correlation of blood [glu] and [Na+] with the OCT signal slope was observed in separate tissue layers. On average, equimolar changes in [glu] produced 2.26 ± 1.15 greater alterations of the OCT signal slope than changes in [Na+]. Variation of concentrations of other analytes did not Influence the OCT signal slope. The influence of [Na+] on relative changes in the OCT signal slope was generally less than [glu]-induced changes. OCT is a promising method for noninvasive glucose monitoring because of its ability to track the influence of changing [glu] on individual tissue layers.

In Vivo Detection of the Effect of Electroacupuncture on "Zusanli" Acupoint in Rats with Adjuvant-Induced Arthritis through Optical Coherence Tomography

This study aimed to investigate the effect of electroacupuncture (EA) treatment through optical coherence tomography (OCT) in vivo on rats with adjuvant-induced arthritis. OCT images were obtained from the ankle of the right hind paws of the rats in control, model, and EA groups before modelling and 1 day, 8 days, 15 days, 22 days, and 29 days after modelling. Results demonstrated that the OCT signal of the ankle of the right hind paws of the rats was indistinct compared to 1 day after modelling and before modelling in the EA group. In the EA group, the light averaged attenuation coefficients of the ankle tissues decreased as treatment duration was prolonged after EA was administered (3.43, 2.96, 2.61, 2.42, and 2.29 mm⁻¹, resp.). There was a significant difference in attenuation coefficient decrease between the 29th d and the 1st d for EA group compared with control group (P < 0.01). This condition indicated that the light absorption of the ankle of the treated rats in the EA group decreased. Therefore, OCT can be used to monitor the effect of treatment on rats with arthritis in vivo.

Dual-modulation, dual-wavelength, optical polarimetry system for glucose monitoring

A dual modulation optical polarimetry system utilizing both laser intensity and polarization modulation was designed, built, and tested. The system was designed to reduce complexity and enhance the speed in order to facilitate the reduction of motion-induced time-varying birefringence, which is one of the major limitations to the realization of polarimetry for glucose monitoring in the eye. The high-speed less complex technique was tested using in vitro phantom studies with and without motion artifact introduced. The glucose concentration ranged from 0 to 600  mg/dl and the glucose measurements demonstrated a standard error of prediction to within 8.1  mg/dl without motion and to within 13.9  mg/dl with motion. Our feedback control systems took less than 10 ms to reach stabilization, which is adequately fast to eliminate the effect of time-varying birefringence. The results indicate that this new optical polarimetric approach has improved the speed and reduced the complexity, showing the potential for it to be used for noninvasive glucose measurements.

Self-monitoring Blood Glucose (SMBG): Now and the Future

In 2002, the cost of diabetes in the United States reached $132 billion. There is a well-established relationship between blood glucose control and the risk of diabetes-related complications. Tight blood glucose control, through intensive diabetes therapy, reduces the risk and delays the onset of diabetesrelated microvascular complications. Regular and consistent self-monitoring of blood glucose (SMBG) is and should be a part of all diabetes disease state management programs. Pharmacists can truly increase the numbers of patients who use SMBG by being aware and familiar with the monitoring devices available to patients and identifying the physical and psychological issues surrounding SMBG. Results from SMBG and hemoglobin A1C are the basis formost of the medical decisions made for patients with diabetes. This review discusses the best time for patients to test their blood glucose, information regarding blood glucose monitoring devices, alternative site testing, and the newest technology available in glucose monitoring.

Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review

Diabetic individuals need to tightly control their blood glucose concentration. Several methods have been developed for this purpose, such as the finger-prick or continuous glucose monitoring systems (CGMs). However, these methods present the disadvantage of being invasive. Moreover, CGMs have limited accuracy, notably to detect hypoglycemia. It is also known that physical exercise, and even daily activity, disrupt glucose dynamics and can generate problems with blood glucose regulation during and after exercise. In order to deal with these challenges, devices for monitoring patients’ physical activity are currently under development. This review focuses on non-invasive sensors using physiological parameters related to physical exercise that were used to improve glucose monitoring in type 1 diabetes (T1DM) patients. These devices are promising for diabetes management. Indeed they permit to estimate glucose concentration either based solely on physical activity parameters or in conjunction with CGM or non-invasive CGM (NI-CGM) systems. In these last cases, the vital signals are used to modulate glucose estimations provided by the CGM and NI-CGM devices. Finally, this review indicates possible limitations of these new biosensors and outlines directions for future technologic developments.

Disposable Non-Enzymatic Glucose Sensors Using Screen-Printed Nickel/Carbon Composites on Indium Tin Oxide Electrodes

Disposable screen-printed nickel/carbon composites on indium tin oxide (ITO) electrodes (DSPNCE) were developed for the detection of glucose without enzymes. The DSPNCE were prepared by screen-printing the ITO substrate with a 50 wt% nickel/carbon composite, followed by curing at 400 °C for 30 min. The redox couple of Ni(OH)₂/NiOOH was deposited on the surface of the electrodes via cyclic voltammetry (CV), scanning from 0–1.5 V for 30 cycles in 0.1 M NaOH solution. The DSPNCE were characterized by field-emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), and electrochemical methods. The resulting electrical currents, measured by CV and chronoamperometry at 0.65 V vs. Ag/AgCl, showed a good linear response with glucose concentrations from 1.0–10 mM. Also, the prepared electrodes showed no interference from common physiologic interferents such as uric acid (UA) or ascorbic acid (AA). Therefore, this approach allowed the development of a simple, disposable glucose biosensor.

Accuracy of Noninvasive Glucose Sensing Based on Near-Infrared Spectroscopy

The noninvasive sensing of the blood glucose concentration is usually based on optical, electrical, or acoustical signals induced by blood glucose; these signals are extremely weak and subject to fluctuations caused by the variation in the body or surroundings. Therefore, it is challenging to detect blood glucose noninvasively with high accuracy, and no successful accurate and noninvasive clinical application has been reported. We found that there are two key measurement issues to be addressed: systematic errors, such as the errors induced by the drifts of devices or by variations in body temperature, among others, are too large to guarantee the trueness of measurement at present; and random disturbances in repeated tests, such as disturbances associated with variations in the human-machine interface, pulses, and the thermal noise of the devices, cause larger repeated measurement errors and compromise precision. Recent novel reference measurements based on differential near-infrared (NIR) spectroscopy are considered promising for solving the systematic error issue by establishing matched references, collected at another detection site or at another time, and subsequently differencing to remove the common systematic errors. However, differencing weakens the signal of interest itself and enlarges the effects of the second issue, random disturbances affecting the precision. It is understood that only reference measurements that can meet the precision requirement will be promising for future applications. Therefore, this study quantitatively evaluates the precision of the main differential NIR spectroscopy measurements considering similar conditions and minimized random disturbances. The precision of the measurements under these conditions should represent their optimal precision levels. After the evaluation, noninvasive glucose-sensing methods that hold promise for future clinical application are proposed. Finally, the evaluation criteria could be a reference for the noninvasive detection of other physiological components.

The Effect of a Global, Subject, and Device-Specific Model on a Noninvasive Glucose Monitoring Multisensor System

BACKGROUND

We study here the influence of different patients and the influence of different devices with the same patients on the signals and modeling of data from measurements from a noninvasive Multisensor glucose monitoring system in patients with type 1 diabetes. The Multisensor includes several sensors for biophysical monitoring of skin and underlying tissue integrated on a single substrate.

METHOD

Two Multisensors were worn simultaneously, 1 on the upper left and 1 on the upper right arm by 4 patients during 16 study visits. Glucose was administered orally to induce 2 consecutive hyperglycemic excursions. For the analysis, global (valid for a population of patients), personal (tailored to a specific patient), and device-specific multiple linear regression models were derived.

RESULTS

We find that adjustments of the model to the patients improves the performance of the glucose estimation with an MARD of 17.8% for personalized model versus a MARD of 21.1% for the global model. At the same time the effect of the measurement side is negligible. The device can equally well measure on the left or right arm. We also see that devices are equal in the linear modeling. Thus hardware calibration of the sensors is seen to be sufficient to eliminate interdevice differences in the measured signals.

CONCLUSIONS

We demonstrate that the hardware of the 2 devices worn on the left and right arms are consistent yielding similar measured signals and thus glucose estimation results with a global model. The 2 devices also return similar values of glucose errors. These errors are mainly due to nonstationarities in the measured signals that are not solved by the linear model, thus suggesting for more sophisticated modeling approaches.