A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management

A Prospective Pilot Study Demonstrating Noninvasive Calibration-Free Glucose Measurement

BACKGROUND

Glucose is an essential molecule in energy metabolism. Dysregulated glucose metabolism, the defining feature of diabetes, requires active monitoring and treatment to prevent significant morbidity and mortality. Current technologies for intermittent and continuous glucose measurement are invasive. Noninvasive glucose measurement would eliminate this barrier toward making glucose monitoring more accessible, extending the benefits from people living with diabetes to prediabetes and the healthy.

METHODS

A novel spectroscopy-based system for measuring glucose noninvasively was used in an exploratory, prospective, single-center clinical study (NCT06272136) to develop and test a machine learning-based computational model for continuous glucose monitoring without per-subject calibration. The study design blinded the development investigators to the validation analyses.

RESULTS

Twenty subjects were enrolled. Fifteen were used for the development set, and five in the validation set. All study participants were adults with insulin-treated diabetes and median glycated hemoglobin (HbA1c) of 7.3% (interquartile range [IQR] = 6.7-7.7). The computational model resulted in a mean absolute relative difference (MARD) of 14.5% and 96.5% of the paired glucose data points in the A plus B zones of the Diabetes Technology Society (DTS) error grid. The correlation between the average model sensitivity by wavelength and the spectrum of glucose was 0.45 (P < .001).

CONCLUSIONS

Our findings suggest that Raman spectroscopy coupled with advanced computational methods can enable continuous, noninvasive glucose measurement without per-subject invasive calibration.

Edge-AI Enabled Wearable Device for Non-Invasive Type 1 Diabetes Detection Using ECG Signals

Diabetes is a chronic condition, and traditional monitoring methods are invasive, significantly reducing the quality of life of the patients. This study proposes the design of an innovative system based on a microcontroller that performs real-time ECG acquisition and evaluates the presence of diabetes using an Edge-AI solution. A spectrogram-based preprocessing method is combined with a 1-Dimensional Convolutional Neural Network (1D-CNN) to analyze the ECG signals directly on the device. By applying quantization as an optimization technique, the model effectively balances memory usage and accuracy, achieving an accuracy of 89.52% with an average precision and recall of 0.91 and 0.90, respectively. These results were obtained with a minimal memory footprint of 347 kB flash and 23 kB RAM, showcasing the system's suitability for wearable embedded devices. Furthermore, a custom PCB was developed to validate the system in a real-world scenario. The hardware integrates high-performance electronics with low power consumption, demonstrating the feasibility of deploying Edge-AI for non-invasive, real-time diabetes detection in resource-constrained environments. This design represents a significant step forward in improving the accessibility and practicality of diabetes monitoring.

Digital continuous glucose monitoring systems for patients with HIV-diabetes comorbidity in Ethiopia: a situational analysis

In patients with HIV-diabetes mellitus (DM) comorbidity, invasive blood glucose testing can increase the risk of HIV-related blood contamination and discourage regular glucose monitoring. Digital continuous glucose monitoring (CGM) systems may allow real-time glucose monitoring without the need for blood specimens. However, in high-burden HIV-DM countries, current glucose monitoring practices and their challenges are insufficiently explored to guide digital CGM research and developments. This study sought to explore the lived experiences of patients with HIV-DM comorbidity and their healthcare providers regarding glucose monitoring practices, and their openness to CGM and other digital technologies, to provide formative insights for a planned implementation trial of digital CGM in Ethiopia. A phenomenological qualitative study was conducted among patients with HIV-DM and their providers at the two largest public hospitals in Ethiopia. Both groups were interviewed face-to-face about DM clinic workflows, blood glucose monitoring and self-testing practices, and potential benefits and limitations of digital CGM systems. Interviews were audio-recorded, transcribed verbatim, and analyzed thematically. A total of 37 participants were interviewed, consisting of 18 patients with HIV-DM comorbidity and 19 healthcare providers. Patients had an average (min-max) duration of living with HIV and DM of 14 (8-31) and 6.6 (1-16) years, respectively, with 61% taking insulin-33% alone and 28% alongside oral hypoglycemic agents-and 79% having comorbid hypertension. The thematic analysis identified five main themes: "Diabetes routine clinical care and follow-up", "Blood glucose monitoring practices", "Perceptions about digital CGMs", "Technology adoption", and "Financial coverage". Home self-testing was deemed beneficial, but the need for regular follow-ups, result cross-referencing, and glucometer reliability were emphasized. Patients performed fingerstick themselves or with family members, expressing concerns about waste disposal and the risk of HIV transmission. They rely mainly on health insurance for DM care. Patients and providers are happy with the quality of DM services but note a lack of integrated HIV-DM care. Very few providers and patients possessed background information about digital CGMs, and all have not yet utilized them in practice, but expressed keen interest in trying them, representing an important step for upcoming CGM clinical trials in these settings. Given the crucial role of regular glucose testing in managing HIV-DM comorbidity, it is essential to explore testing options that align with patient preferences and minimize the risk of HIV transmission.

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.

Utilizing machine learning algorithms for precise discrimination of glycosuria in fluorescence spectroscopic data

Fluorescence spectroscopy coupled with a random forest machine learning algorithm offers a promising non-invasive approach for diagnosing glycosuria, a condition characterized by excess sugar in the urine of diabetic patients. This study investigated the ability of this method to differentiate between diabetic and healthy control urine samples. Fluorescent spectra were captured from urine samples using a Xenon arc lamp emitting light within the 200 to 950 nm wavelength range, with consistent fluorescence emission observed at 450 nm under an excitation wavelength of 370 nm. Healthy control samples were also analyzed within the same spectral range for comparison. To distinguish spectral differences between healthy and infected samples, the random forest (RF) and K-Nearest Neighbors (KNN) machine learning algorithms have been employed. These algorithms automatically recognize spectral patterns associated with diabetes, enabling the prediction of unknown classifications based on established samples. Principal component analysis (PCA) was utilized for dimensionality reduction before feeding the data to RF and KNN for classification. The model's classification performance was evaluated using 10-fold cross-validation, resulting in the proposed RF-based model achieving accuracy of 96 %, specificity of 100 %, sensitivity of 93 %, and precision of 100 %. These results suggest that the proposed method holds promise for a more convenient and potentially more accurate method for diagnosing glycosuria in diabetic patients.

Recent advances in gold nanostructure-based biosensors in detecting diabetes biomarkers

Diabetes mellitus (DM) is a prevalent disorder with an urgent need for continuous, precise, and on-site biomarker monitoring devices. The continuous monitoring of DM biomarkers from different biological matrices will become routine in the future, thanks to the promising biosensor design. Lately, employing different nanomaterials in biosensor receptor parts has had a great impact on smart DM monitoring. Among them, gold nanostructures (AuNSs) have arisen as highly potential materials in fabricating precise DM biosensors due to their unique properties. The present study provides an update on the applications of AuNSs in biosensors for detecting glucose as well as other DM biomarkers, such as glycated hemoglobin (HbA1c), glycated albumin (GA), insulin, insulin antibodies, uric acid, lactate, and glutamic acid decarboxylase antibodies (GADA), with a focus on the most important factors in biosensor performance such as sensitivity, selectivity, response time, and stability. Specified values of limit of detection (LOD), linear concentrations, reproducibility%, recovery%, and assay time were used to compare studies. In conclusion, AuNSs, owing to the wide electrochemical potential window and low electrical resistivity, are valuable tools in biosensor design, alongside other biological reagents and/or nanomaterials.

Development of sensor system and data analytic framework for non-invasive blood glucose prediction

Periodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900-1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed 'niGLUC-2.0v'. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland-Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.

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.

Non-invasive blood glucose monitoring technology in diabetes management: review

Diabetes is one of the leading non-communicable diseases globally, adversely impacting an individual's quality of life and adding a considerable burden to the healthcare systems. The necessity for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG, such as pain and discomfort, has motivated the development of non-invasive BG approaches. However, the current research progress is slow, and only a few BG self-monitoring devices have made considerable progress. Hence, we evaluate the available non-invasive glucose monitoring technologies validated against BG recordings to provide future research direction to design, develop, and deploy self-monitoring of BG with integrated emerging technologies. We searched five databases, Embase, MEDLINE, Proquest, Scopus, and Web of Science, to assess the non-invasive technology's scope in the diabetes management paradigm published from 2000 to 2020. A total of three approaches to non-invasive screening, including saliva, skin, and breath, were identified and discussed. We observed a statistical relationship between BG measurements obtained from non-invasive methods and standard clinical measures. Opportunities exist for future research to advance research progress and facilitate early technology adoption for healthcare practice. The results promise clinical validity; however, formulating regulatory guidelines could foresee the deployment of approved non-invasive BG monitoring technologies in healthcare practice. Further, research prospects are there to design, develop, and deploy integrated diabetes management systems with mobile technologies, data analytics, and the internet of things (IoT) to deliver a personalised monitoring system.

Non-Invasive Wearable Devices for Monitoring Vital Signs in Patients with Type 2 Diabetes Mellitus: A Systematic Review

Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the need to explore non-invasive alternatives for monitoring vital signs in patients with T2D. Objective: This systematic review is the first that explores the current literature and critically evaluates the use and reporting of non-invasive wearable devices for monitoring vital signs in patients with T2D. Methods: Employing the PRISMA and PICOS guidelines, we conducted a comprehensive search to incorporate evidence from relevant studies, focusing on randomized controlled trials (RCTs), systematic reviews, and meta-analyses published since 2017. Of the 437 publications identified, seven were selected based on predetermined criteria. Results: The seven studies included in this review used various sensing technologies, such as heart rate monitors, accelerometers, and other wearable devices. Primary health outcomes included blood pressure measurements, heart rate, body fat percentage, and cardiorespiratory endurance. Non-invasive wearable devices demonstrated potential for aiding T2D management, albeit with variations in efficacy across studies. Conclusions: Based on the low number of studies with higher evidence levels (i.e., RCTs) that we were able to find and the significant differences in design between these studies, we conclude that further evidence is required to validate the application, efficacy, and real-world impact of these wearable devices. Emphasizing transparency in bias reporting and conducting in-depth research is crucial for fully understanding the implications and benefits of wearable devices in T2D management.

Roadmap to the Effective Use of Continuous Glucose Monitoring: Innovation, Investigation, and Implementation

For 25 years, continuous glucose monitoring (CGM) has been evolving into what it is now: a key tool to both measure individuals' glycemic status and to help guide their day-to-day management of diabetes. Through a series of engineering innovations, clinical investigations, and efforts to optimize workflow implementation, the use of CGM is helping to transform diabetes care. This article presents a roadmap to the effective use of CGM that outlines past, present, and possible future advances in harnessing the potential of CGM to improve the lives of many people with diabetes, with an emphasis on ensuring that CGM technology is available to all who could benefit from its use.

Review of Noninvasive Continuous Glucose Monitoring in Diabetics

For diabetics, taking regular blood glucose measurements is crucial. However, traditional blood glucose monitoring methods are invasive and unfriendly to diabetics. Recent studies have proposed a biofluid-based glucose sensing technique that creatively combines wearable devices with noninvasive glucose monitoring technology to enhance diabetes management. This is a revolutionary advance in the diagnosis and management of diabetes, reflects the thoughtful modernization of medicine, and promotes the development of digital medicine. This paper reviews the research progress of noninvasive continuous blood glucose monitoring (CGM), with a focus on the biological liquids that replace blood in monitoring systems, the technical principles of continuous noninvasive glucose detection, and the output and calibration of sensor signals. In addition, the existing limits of noninvasive CGM systems and prospects for the future are discussed. This work serves as a resource for further promoting the development of noninvasive CGM systems.

Recent advances in zinc ferrite (ZnFe2O4) based nanostructures for magnetic hyperthermia applications

Spinel ferrite-based magnetic nanomaterials have been investigated for numerous biomedical applications, including targeted drug delivery, magnetic hyperthermia therapy (MHT), magnetic resonance imaging (MRI), and biosensors, among others. Recent studies have found that zinc ferrite-based nanomaterials are favorable candidates for cancer theranostics, particularly for magnetic hyperthermia applications. Zinc ferrite exhibits excellent biocompatibility, minimal toxicity, and more importantly, exciting magnetic properties. In addition, these materials demonstrate a Curie temperature much lower than other transition metal ferrites. By regulating synthesis protocols and/or introducing suitable dopants, the Curie temperature of zinc ferrite-based nanosystems can be tailored to the MHT therapeutic window, i.e., 43-46 °C, a range which is highly beneficial for clinical hyperthermia applications. Furthermore, zinc ferrite-based nanostructures have been extensively used in successful pre-clinical trials on mice models focusing on the synergistic killing of cancer cells involving magnetic hyperthermia and chemotherapy. This review provides a systematic and comprehensive understanding of the recent developments of zinc ferrite-based nanomaterials, including doped particles, shape-modified structures, and composites for magnetic hyperthermia applications. In addition, future research prospects involving pure ZnFe2O4 and its derivative nanostructures have also been proposed.

Monitoring saliva compositions for non-invasive detection of diabetes using a colorimetric-based multiple sensor

The increasing population of diabetic patients, especially in developing countries, has posed a serious risk to the health sector, so that the lack of timely diagnosis and treatment process of diabetes can lead to threatening complications for the human lifestyle. Here, a multiple sensor was fabricated on a paper substrate for rapid detection and controlling the progress of the diabetes disease. The proposed sensor utilized the sensing ability of porphyrazines, pH-sensitive dyes and silver nanoparticles in order to detect the differences in saliva composition of diabetic and non-diabetic patients. A unique color map (sensor response) was obtained for each studied group, which can be monitored by a scanner. Moreover, a good correlation was observed between the colorimetric response resulting from the analysis of salivary composition and the fasting blood glucose (FBG) value measured by standard laboratory instruments. It was also possible to classify participants into two groups, including patients caused by diabetes and those were non-diabetic persons with a total accuracy of 88.9%. Statistical evaluations show that the multiple sensor can be employed as an effective and non-invasive device for continuous monitoring of diabetes, substantially in the elderly.

Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection

The prevalence of diabetes is rapidly increasing worldwide and can lead to a range of severe health complications that have the potential to be life-threatening. Patients need to monitor and control blood glucose levels as it has no cure. The development of non-invasive techniques for the measurement of blood glucose based on photoacoustic spectroscopy (PAS) has advanced tremendously in the last couple of years. Among them, PAS in the mid-infrared (MIR) region shows great promise as it shows the distinct fingerprint region for glucose. However, two problems are generally encountered when it is applied to monitor real samples for in vivo measurements in this MIR spectral range: (i) low penetration depth of MIR light into the human skin, and (ii) the effect of other interfering components in blood, which affects the selectivity of the detection system. This review paper systematically describes the basics of PAS in the MIR region, along with recent developments, technical challenges, and data analysis strategies, and proposes improvements for the detection sensitivity of glucose concentration in human bodies. It also highlights the recent trends of incorporating machine learning (ML) to enhance the detection sensitivity of the overall system. With further optimization of the experimental setup and incorporation of ML, this PAS in the MIR spectral region could be a viable solution for the non-invasive measurement of blood glucose in the near future.

Advances in the Design of Phenylboronic Acid-Based Glucose-Sensitive Hydrogels

Diabetes, characterized by an uncontrolled blood glucose level, is the main cause of blindness, heart attack, stroke, and lower limb amputation. Glucose-sensitive hydrogels able to release hypoglycemic drugs (such as insulin) as a response to the increase of the glucose level are of interest for researchers, considering the large number of diabetes patients in the world (537 million in 2021, reported by the International Diabetes Federation). Considering the current growth, it is estimated that, up to 2045, the number of people with diabetes will increase to 783 million. The present work reviews the recent developments on the hydrogels based on phenylboronic acid and its derivatives, with sensitivity to glucose, which can be suitable candidates for the design of insulin delivery systems. After a brief presentation of the dynamic covalent bonds, the design of glucose-responsive hydrogels, the mechanism by which the hypoglycemic drug release is achieved, and their self-healing capacity are presented and discussed. Finally, the conclusions and the main aspects that should be addressed in future research are shown.