Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy

Continuous glucose monitoring: Minimally and non-invasive technologies

This paper highlights technological advancements in non-invasive blood glucose monitoring against the backdrop of increasing global prevalence of diabetes. Traditional monitoring methods, primarily invasive methods face limitations in providing continuous glucose level data, which is essential for effective and timely diagnosis of disease stage and for determining the optimal therapeutic strategy. Recent non-invasive technologies encompass optical, acoustic, electromagnetic, and chemical approaches. These technologies exploit the intrinsic properties of glucose, such as its optical absorption coefficients, to offer promising avenues for less intrusive blood glucose monitoring. Despite these advancements, challenges in achieving high accuracy persist due to interference from substances like water and other blood components. This underlines the need for sophisticated algorithms and sensor designs for accurate glucose estimation. Further research is required to integrate various sensing techniques and advanced data processing to enhance accuracy and user-friendliness. In conclusion, while significant progress has been made, developing a reliable, convenient, and accessible method for non-invasive glucose monitoring is crucial for transforming diabetes management and improving patients' quality of life.

Calibration and performance of a Raman-based device for non-invasive glucose monitoring in type 2 diabetes

Raman spectroscopy has been demonstrated as a viable technique for non-invasive glucose monitoring (NIGM). However, its clinical utility is limited by an extended calibration period lasting several weeks. In this study, we address this limitation by employing a pre-trained calibration model, which is individualized through a brief calibration phase consisting of 10 measurements. The performance of the Raman-based NIGM device was evaluated in a clinical trial involving 50 individuals with type 2 diabetes over a 2-day study period. The protocol included a 4-h calibration phase on the first day, followed by validation phases of 4 h and 8 h on days 1 and 2, respectively. NIGM glucose readings were compared with capillary blood glucose measurements, with glucose fluctuations induced by standardized meal challenges. The numerical and clinical accuracy of the NIGM device was evaluated on 1918 paired points and expressed by mean absolute relative difference of 12.8% (95% CI 12.4, 13.2) and consensus error grid analysis showing 100% of NIGM readings in zones A and B. These results highlight the ability to reliably track blood glucose levels in people with type 2 diabetes. The successful introduction of a practical calibration scheme underlines Raman spectroscopy as a promising technology for NIGM and constitutes an important step towards factory calibration.

In vivo Raman spectroscopy for non-invasive transcutaneous glucose monitoring on animal models and human subjects

Non-invasive glucose monitoring represents a significant advancement in diabetes management and treatment as non-painful alternatives than finger-sticks tests. After developing an integrated Raman spectral system with a 785 nm laser, this study systematically explores the application of in vivo Raman spectroscopy for quantitative, noninvasive glucose monitoring. In addition to observing characteristic glucose spectral information from a mouse model, a strong spectral correlation was also recognized with the blood glucose concentration. The glucose fingerprint information detected from the nailfolds of 30 human volunteers exhibited concentration dependent changes, especially when the intraspectrum intensity ratio was calculated between 1125 cm-1 and 1445 cm-1 to monitor normalized differences in the glucose Raman band. Furthermore, by accounting for all intersubject variations observed in the acquired spectral features, a particle swarm optimization-backpropagation artificial neural network (PSO-BP-ANN) model was proposed for linking measured Raman information with actual glucose concentrations quantitatively. Following model training and testing, the prediction accuracy of the PSO-BP-ANN model was evaluated using 12 spectra acquired from an additional three volunteers. Statistical evaluations indicated that the proposed methodology may have a good application potential for in vivo transcutaneous spectral glucose monitoring.

Clinical evaluation of a polarization-based optical noninvasive glucose sensing system

Diabetes affects millions in the US, causing elevated blood glucose levels that could lead to complications like kidney failure and heart disease. Recent development of continuous glucose monitors has enabled a minimally invasive option, but the discomfort and social factors highlight the need for noninvasive alternatives in diabetes management. We propose a portable noninvasive glucose sensing system based on the glucose's optical activity property which rotates linearly polarized light depending on its concentration level. To enable a portable form factor, a light trap mechanism is used to capture unwanted specular reflection from the palm and the enclosure itself. We fabricate four sensing prototypes and conduct a 363-day multi-session clinical evaluation in real-world settings. 30 participants are provided with a prototype for a 5-day home monitoring study, collecting on average 8 data points per day. We identify the error caused by differences between the sensing boxes and the participants' improper usage. We utilize a machine learning pipeline together with Bayesian Ridge Regressor models and multiple-step data processing techniques to deal with the noisy data. Over 95% of the predictions fall within Zone A (clinically accurate) or B (clinically acceptable) of the Consensus Error Grid with a 0.24 mean absolute relative differences.

Subcutaneous depth-selective spectral imaging with mμSORS enables noninvasive glucose monitoring

Noninvasive blood glucose monitoring offers substantial advantages for patients, but current technologies are often not sufficiently accurate for clinical applications or require personalized calibration. Here we report multiple μ-spatially offset Raman spectroscopy, which captures Raman signals at varying skin depths, and show that it accurately detects blood glucose levels in humans. In 35 individuals with or without type 2 diabetes, we first determine the optimal depth for glucose detection to be at or below the capillary-rich dermal-epidermal junction, where we observe a strong correlation between specific Raman bands and venous plasma glucose concentrations. In a second study, comprising 230 participants, we then improve accuracy of our regression model to reach a mean absolute relative difference of 14.6%, without personalized calibration, whereby 99.4% of calculated glucose values fall into clinically acceptable zones of the consensus error grid (zones A and B). These findings highlight the ability and robustness of multiple μ-spatially offset Raman spectroscopy for noninvasive blood glucose measurement in a clinical setting.

Flexible Non-Enzymatic Glucose Sensors: One-Step Green Synthesis of NiO Nanoporous Films via an Electro-Exploding Wire Technique

In this study, we successfully synthesized nickel oxide (NiO) nanoparticles (NPs), i.e., samples NiO 24V, NiO 36V, and NiO 48V, via an environmentally friendly one-step electro-exploding wire technique by employing three distinct voltage levels of 24, 36, and 48 V, respectively. Sample NiO 48V showed the most rugged surface and smallest particle size, which helped to enhance electrocatalytic properties. The highest Ni3+ content of sample NiO 48V contributed to the increasing redox current and rendering highly enhanced chemical reactions and thereby improving their electrochemical properties and electrocatalytic performance in the glucose oxidation processes in alkaline (0.1 M NaOH, pH = 13) media. The NiO 48V electrode showcased an excellent linear detection range spanning from 0.1 to 1 mM, featuring a remarkable sensitivity of 1202 μA mM-1 cm-2 and an exceptionally low limit of detection (LOD) value of 0.25 μM. Remarkably, NiO NPs exhibited exceptional long-term stability, commendable reproducibility, favorable repeatability, and outstanding selectivity. This study also highlights the excellent operational performance of the NiO 48V electrode in real-world samples, such as commercially available beverages and human urine, highlighting the practical nature of these nonenzymatic sensors in real-life scenarios for the food industries, clinical diagnostics, and biotechnology applications.

Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques

Accurate non-invasive monitoring of blood glucose (BG) is a challenging issue in the therapy of diabetes. Here near-infrared (NIR) photoplethysmography (PPG) sensor based on a vapor-deposited mixed tin-lead hybrid perovskite photodetector is developed. The device shows a high detectivity of 5.32 × 1012 Jones and a large linear dynamic range (LDR) of 204 dB under NIR light, guaranteeing accurate extraction of eleven features from the PPG signal. By a combination of machine learning, accurate prediction of blood glucose level with mean absolute relative difference (MARD) as small as 2.48% is realized. The self-powered PPG sensor also works for real-time outdoor healthcare monitors using sunlight as a light source. The potential for early diabetes diagnoses by the perovskite PPG sensor is demonstrated.

Accuracy enhancement of metabolic index-based blood glucose estimation with a screening process for low-quality data

Significance

Many researchers have proposed various non-invasive glucose monitoring (NIGM) approaches using wearable or portable devices. However, due to the limited capacity of detectors for such compact devices and the movement of the body during measurement, the precision of the acquired data frequently diminishes, which can cause problems during actual use in daily life. In addition, intensive smoothing is often used in post-processing to mitigate the effects of erroneous values. However, this requires a considerable amount of data and results in a delay in the response to the actual blood glucose level (BGL).

Aim

Instead of just applying data smoothing in the post-process of the data acquisition, we propose an active low-quality data screening method in the pre-process. In the proposal phase of the screening process, we employ an analytical approach to examine and formulate factors that might affect the BGL estimation accuracy.

Approach

A signal quality index inspired by the standard deviation concept is introduced to detect visually apparent noise on signals. Furthermore, the total estimation error in the metabolic index (MI) is calculated based on potential perturbations defined by the signal-to-noise ratio (SNR) and the uncertainty due to discrete sampling. Thereafter, the acquired data were screened by these quality indices.

Results

By applying the proposed data screening process to the data obtained from a commercially available smartwatch device in the pre-process, the estimation accuracy of the MI-based BGL was improved significantly.

Conclusions

Adopting the proposed screen process improves BGL estimation accuracy in the smartwatch-based prototype. Applying the proposed screen process will facilitate the integration of wearable and continuous BGL monitoring into size- and SNR-limited devices such as smartwatches and smart rings.

Diabetes Technology Meeting 2023

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Engineering vascularized skin-mimetic phantom for non-invasive Raman spectroscopy

Recent advances in Raman spectroscopy have shown great potential for non-invasive analyte sensing, but the lack of a standardized optical phantom for these measurements has hindered further progress. While many research groups have developed optical phantoms that mimic bulk optical absorption and scattering, these materials typically have strong Raman scattering, making it difficult to distinguish metabolite signals. As a result, solid tissue phantoms for spectroscopy have been limited to highly scattering tissues such as bones and calcifications, and metabolite sensing has been primarily performed using liquid tissue phantoms. To address this issue, we have developed a layered skin-mimetic phantom that can support metabolite sensing through Raman spectroscopy. Our approach incorporates millifluidic vasculature that mimics blood vessels to allow for diffusion akin to metabolite diffusion in the skin. Furthermore, our skin phantoms are mechanically mimetic, providing an ideal model for development of minimally invasive optical techniques. By providing a standardized platform for measuring metabolites, our approach has the potential to facilitate critical developments in spectroscopic techniques and improve our understanding of metabolite dynamics in vivo.

Blood glucose monitoring devices for type 1 diabetes: a journey from the food and drug administration approval to market availability

Blood glucose monitoring constitutes a pivotal element in the clinical management of Type 1 diabetes (T1D), a globally escalating metabolic disorder. Continuous glucose monitoring (CGM) devices have demonstrated efficacy in optimizing glycemic control, mitigating adverse health outcomes, and augmenting the overall quality of life for individuals afflicted with T1D. Recent progress in the field encompasses the refinement of electrochemical sensors, which enhances the effectiveness of blood glucose monitoring. This progress empowers patients to assume greater control over their health, alleviating the burdens associated with their condition, and contributing to the overall alleviation of the healthcare system. The introduction of novel medical devices, whether derived from existing prototypes or originating as innovative creations, necessitates adherence to a rigorous approval process regulated by the Food and Drug Administration (FDA). Diverse device classifications, stratified by their associated risks, dictate distinct approval pathways, each characterized by varying timelines. This review underscores recent advancements in blood glucose monitoring devices primarily based on electrochemical sensors and elucidates their regulatory journey towards FDA approval. The advent of innovative, non-invasive blood glucose monitoring devices holds promise for maintaining stringent glycemic control, thereby preventing T1D-associated comorbidities, and extending the life expectancy of affected individuals.

Non-invasive blood glucose estimation method based on the phase delay between oxy- and deoxyhemoglobin using visible and near-infrared spectroscopy

Significance

Many researchers have attempted to estimate blood glucose levels (BGLs) noninvasively using near-infrared (NIR) spectroscopy. However, the optical absorption change induced by blood glucose is weak in the NIR region and often masked by interference from other components such as water and hemoglobin.

Aim

Instead of using direct optical absorption by glucose, this study proposes an index calculated from oxy- and deoxyhemoglobin signals that shows a good correlation with BGLs while using conventional visible and NIR spectroscopy.

Approach

The metabolic index, which is based on tissue oxygen consumption, was derived through analytical methods and further verified and reproduced in a series of glucose challenge experiments. Blood glucose estimation units were prototyped by utilizing commercially available smart devices.

Results

Our experimental results showed that the phase delay between the oxy- and deoxyhemoglobin signals in near-infrared spectroscopy correlates with BGL measured by a conventional continuous glucose monitor. The proposed method was also confirmed to work well with visible spectroscopy systems based on smartphone cameras. The proposed method also demonstrated excellent repeatability in results from a total of 19 oral challenge tests.

Conclusions

This study demonstrated the feasibility of non-invasive glucose monitoring using existing photoplethysmography sensors for pulse oximeters and smartwatches. Evaluating the proposed method in diabetic or unhealthy individuals may serve to further increase its practicality.

A Novel Biosensor for the Detection of Glucose Concentration Using the Dual-Peak Long Period Grating in the Near- to Mid-Infrared

In this article, we demonstrate an improved efficient fibre sensor with a high sensitivity to measure glucose concentrations in the physiological range of human beings, operating in a broad spectral bandwidth from the near- to mid-infrared. The sensor consists of a dual-peak long period grating (DPLPG) with a period of 150 μm inscribed in an optical fibre with a diameter of 80 μm. The investigation of sensing for refractive index results in a sensitivity of ~-885.7 nm/refractive index unit (RIU) and ~2008.6 nm/RIU in the range of 1.30-1.44. The glucose measurement is achieved by the immobilisation of a layer of enzyme of glucose oxidase (GOD) onto the fibre surface for the selective enhancement of sensitivity for glucose. The sensor can measure glucose concentrations with a maximum sensitivity of -36.25 nm/(mg/mL) in the range of 0.1-3.0 mg/mL. To the best of our knowledge, this is the highest sensitivity ever achieved for a measurement of glucose with a long period grating-based sensor, indicating its potential for many applications including pharmaceutical, biomedical and food industries.

Non-contact monitoring of glucose concentration and pH by integration of wearable and implantable hydrogel sensors with optical coherence tomography

Optical coherence tomography (OCT) is a noninvasive imaging technique with large penetration depth into the tissue, but limited chemical specificity. By incorporating functional co-monomers, hydrogels can be designed to respond to specific molecules and undergo reversible volume changes. In this study, we present implantable and wearable biocompatible hydrogel sensors combined with OCT to monitor their thickness change as a tool for continuous and real-time monitoring of glucose concentration and pH. The results demonstrate the potential of combining hydrogel biosensors with OCT for non-contact continuous in-vivo monitoring of physiological parameters.

High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm

Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human blood using Raman spectroscopy. This paper researches a novel integrated machine learning algorithm called Bagging-ABC-ELM. The optimal input weights and biases of extreme learning machine (ELM) model are obtained by artificial bee colony (ABC) algorithm. The bagging algorithm is used to obtain a better the stability of the model and higher performance than ELM algorithm. The results show that the mean value of coefficient of determination is 0.9928, and root mean square error is 0.1928. Compared to other regression models, the Bagging-ABC-ELM model exhibited superior prediction accuracy, robustness, and generalization capability. The Bagging-ABC-ELM model presents a promising alternative for analyzing blood glucose levels in clinical and research settings.

Electrospun Manganese-Based Metal-Organic Frameworks for MnOx Nanostructures Embedded in Carbon Nanofibers as a High-Performance Nonenzymatic Glucose Sensor

Material-specific electrocatalytic activity and electrode design are essential factors in evaluating the performance of electrochemical sensors. Herein, the technique described involves electrospinning manganese-based metal-organic frameworks (Mn-MOFs) to develop MnOx nanostructures embedded in carbon nanofibers. The resulting structure features an electrocatalytic material for an enzyme-free glucose sensor. The elemental composition, morphology, and microstructure of the fabricated electrodes materials were characterized by using energy-dispersive X-ray spectroscopy (EDX), field-emission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM). Cyclic voltammetry (CV) and amperometric i-t (current-time) techniques are characteristically employed to assess the electrochemical performance of materials. The MOF MnOx-CNFs nanostructures significantly improve detection performance for nonenzymatic amperometric glucose sensors, including a broad linear range (0 mM to 9.1 mM), high sensitivity (4080.6 μA mM-1 cm-2), a low detection limit (0.3 μM, S/N = 3), acceptable selectivity, outstanding reproducibility, and stability. The strategy of metal and metal oxide-integrated CNF nanostructures based on MOFs opens interesting possibilities for the development of high-performance electrochemical sensors.

Label-Free Human Disease Characterization through Circulating Cell-Free DNA Analysis Using Raman Spectroscopy

Circulating cell-free DNA (ccfDNA) is a liquid biopsy biomaterial attracting significant attention for the implementation of precision medicine diagnostics. Deeper knowledge related to its structure and biology would enable the development of such applications. In this study, we employed Raman spectroscopy to unravel the biomolecular profile of human ccfDNA in health and disease. We established reference Raman spectra of ccfDNA samples from healthy males and females with different conditions, including cancer and diabetes, extracting information about their chemical composition. Comparative observations showed a distinct spectral pattern in ccfDNA from breast cancer patients taking neoadjuvant therapy. Raman analysis of ccfDNA from healthy, prediabetic, and diabetic males uncovered some differences in their biomolecular fingerprints. We also studied ccfDNA released from human benign and cancer cell lines and compared it to their respective gDNA, confirming it mirrors its cellular origin. Overall, we explored for the first time Raman spectroscopy in the study of ccfDNA and provided spectra of samples from different sources. Our findings introduce Raman spectroscopy as a new approach to implementing liquid biopsy diagnostics worthy of further elaboration.