Interstitial fluid glucose time-lag correction for real-time continuous glucose monitoring

Microneedle Patches-Integrated Transdermal Bioelectronics for Minimally Invasive Disease Theranostics

Wearable epidermal electronics with non- or minimally-invasive characteristics can collect, transduce, communicate, and interact with accessible physicochemical health indicators on the skin. However, due to the stratum corneum layer, rich information about body health is buried under the skin stratum corneum layer, for example, in the skin interstitial fluid. Microneedle patches are typically designed with arrays of special microsized needles of length within 1000 µm. Such characteristics potentially enable the access and sample of biomolecules under the skin or give therapeutical treatment painlessly and transdermally. Integrating microneedle patches with various electronics allows highly efficient transdermal bioelectronics, showing their great promise for biomedical and healthcare applications. This comprehensive review summarizes and highlights the recent progress on integrated transdermal bioelectronics based on microneedle patches. The design criteria and state-of-the-art fabrication techniques for such devices are initially discussed. Next, devices with different functions, including but not limited to health monitoring, drug delivery, and therapeutical treatment, are highlighted in detail. Finally, key issues associated with current technologies and future opportunities are elaborated to sort out the state of recent research, point out potential bottlenecks, and provide future research directions.

Multifunctional Dopamine-Based Hydrogel Microneedle Electrode for Continuous Ketone Sensing

Diabetic ketoacidosis (DKA), a severe complication of type 1 diabetes (T1D), is triggered by production of large quantities of ketone bodies, requiring patients with T1D to constantly monitor their ketone levels. Here, a skin-compatible hydrogel microneedle (HMN)-continuous ketone monitoring (HMN-CKM) device is reported. The sensing mechanism relies on the catechol-quinone chemistry inherent to the dopamine (DA) molecules that are covalently linked to the polymer structure of the HMN patch. The DA serves the dual-purpose of acting as a redox mediator for measuring the byproduct of oxidation of 3-beta-hydroxybutyrate (β-HB), the primary ketone bodies; while, also facilitating the formation of a crosslinked HMN patch. A universal approach involving pre-oxidation and detection of the generated catechol compounds is introduced to correlate the sensor response to the β-HB concentrations. It is further shown that real-time tracking of a decrease in ketone levels of T1D rat model is possible using the HMN-CKM device, in conjunction with a data-driven machine learning model that considers potential time delays.

Continuous Ketone Monitoring via Wearable Microneedle Patch Platform

Ketone bodies (KBs), especially β-hydroxybutyrate (BHB), have gained tremendous attention as potential biomarkers as their presence in bodily fluids is closely associated with health and wellness. While a variety of blood fingerstick test strips are available for self-testing of BHB, there are major needs for wearable devices capable of continuously tracking changing BHB concentrations. To address these needs, we present here the first demonstration of a wearable microneedle-based continuous ketone monitoring (CKM) in human interstitial fluid (ISF) and illustrate its ability to closely follow the intake of ketone drinks. To ensure highly stable and selective continuous detection of ISF BHB, the new enzymatic microneedle BHB sensor relies on a gold-coated platinum working electrode modified with a reagent layer containing toluidine blue O (TBO) redox mediator, β-hydroxybutyrate dehydrogenase (HBD) enzyme, a nicotinamide adenine dinucleotide (NAD+) cofactor, along with carbon nanotubes (CNTs), chitosan (Chit), and a poly(vinyl chloride) (PVC) outer protective layer. The skin-worn microneedle sensing device operates with a miniaturized electrochemical analyzer connected wirelessly to a mobile electronic device for capturing, processing, and displaying the data. Cytotoxicity and skin penetration studies indicate the absence of potential harmful effects. A pilot study involving multiple human subjects evaluated continuous BHB monitoring in human ISF, against gold standard BHB meter measurements, revealing the close correlation between the two methods. Such microneedle-based CKM offers considerable promise for dynamic BHB tracking toward the management of diabetic ketoacidosis and personal nutrition and wellness.

Continuous Glucose Monitoring in the Management of Neonates With Persistent Hypoglycemia and Congenital Hyperinsulinism

BACKGROUND

Persistent hypoglycemia is common in the newborn and is associated with poor neurodevelopmental outcome. Adequate monitoring is critical in prevention, but is dependent on frequent, often hourly blood sampling. Continuous glucose monitoring (CGM) is increasingly being used in children with type 1 diabetes mellitus, but use in neonatology remains limited. We aimed to introduce real-time CGM to provide insights into patterns of dysglycemia and to support the management of persistent neonatal hypoglycemia.

METHODS

This is a single-center retrospective study of real-time CGM use over a 4-year period in babies with persistent hypoglycemia.

RESULTS

CGMs were inserted in 14 babies: 8 term and 6 preterm infants, 9 with evidence of congenital hyperinsulinism (CHI). A total of 224 days of data was collected demonstrating marked fluctuations in glucose levels in babies with CHI, with a higher sensor glucose SD (1.52 ± 0.79 mmol/L vs 0.77 ± 0.22 mmol/L) in infants with CHI compared with preterm infants. A total of 1254 paired glucose values (CGM and blood) were compared and gave a mean absolute relative difference of 11%.

CONCLUSION

CGM highlighted the challenges of preventing hypoglycemia in these babies when using intermittent blood glucose levels alone, and the potential application of CGM as an adjunct to clinical care.

Non-Invasive Sweat-Based Tracking of L-Dopa Pharmacokinetic Profiles Following an Oral Tablet Administration

Levodopa (L-Dopa) is the "gold-standard" medication for symptomatic therapy of Parkinson disease (PD). However, L-Dopa long-term use is associated with the development of motor and non-motor complications, primarily due to its fluctuating plasma levels in combination with the disease progression. Herein, we present the first example of individualized therapeutic drug monitoring for subjects upon intake of standard L-Dopa oral pill, centered on dynamic tracking of the drug concentration in naturally secreted fingertip sweat. The touch-based non-invasive detection method relies on instantaneous collection of fingertip sweat on a highly permeable hydrogel that transports the sweat to a biocatalytic tyrosinase-modified electrode, where sweat L-Dopa is measured by reduction of the dopaquinone enzymatic product. Personalized dose-response relationship is demonstrated within a group of human subjects, along with close pharmacokinetic correlation between the finger touch-based fingertip sweat and capillary blood samples.

Recent Developments of Flexible and Stretchable Electrochemical Biosensors

The skyrocketing popularity of health monitoring has spurred increasing interest in wearable electrochemical biosensors. Compared with the traditionally rigid and bulky electrochemical biosensors, flexible and stretchable devices render a unique capability to conform to the complex, hierarchically textured surfaces of the human body. With a recognition element (e.g., enzymes, antibodies, nucleic acids, ions) to selectively react with the target analyte, wearable electrochemical biosensors can convert the types and concentrations of chemical changes in the body into electrical signals for easy readout. Initial exploration of wearable electrochemical biosensors integrates electrodes on textile and flexible thin-film substrate materials. A stretchable property is needed for the thin-film device to form an intimate contact with the textured skin surface and to deform with various natural skin motions. Thus, stretchable materials and structures have been exploited to ensure the effective function of a wearable electrochemical biosensor. In this mini-review, we summarize the recent development of flexible and stretchable electrochemical biosensors, including their principles, representative application scenarios (e.g., saliva, tear, sweat, and interstitial fluid), and materials and structures. While great strides have been made in the wearable electrochemical biosensors, challenges still exist, which represents a small fraction of opportunities for the future development of this burgeoning field.

Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement

Non-invasive Glucose Measurement (NGM) technology makes great sense for the blood glucose management of patients with hyperglycemia or hypoglycemia. Individual Discrepancies (IDs), e.g., skin thickness and color, not only block the development of NGM, but also become the reason why NGM cannot be widely used. To solve this problem, our solution is designing an individual customized NGM model that can measure these discrepancies through multi-wavelength and tune parameters for glucose estimating. In this paper, an NGM prototype is designed, and a learning model for glucose estimating with automatically parameters tuning based on Independent Component Analysis (ICA) and Random Forest (RF) is presented. The clinic trial proves that the correlation coefficient between estimation and reference Blood Glucose Concentration (BGC) can reach 0.5 after merely 10 times of learning, and rise to 0.8 after about 60 times of learning.

Hyperglycemia-Induced Changes in Hyaluronan Contribute to Impaired Skin Wound Healing in Diabetes: Review and Perspective

Ulcers and chronic wounds are a particularly common problem in diabetics and are associated with hyperglycemia. In this targeted review, we summarize evidence suggesting that defective wound healing in diabetics is causally linked, at least in part, to hyperglycemia-induced changes in the status of hyaluronan (HA) that resides in the pericellular coat (glycocalyx) of endothelial cells of small cutaneous blood vessels. Potential mechanisms through which exposure to high glucose levels causes a loss of the glycocalyx on the endothelium and accelerates the recruitment of leukocytes, creating a proinflammatory environment, are discussed in detail. Hyperglycemia also affects other cells in the immediate perivascular area, including pericytes and smooth muscle cells, through exposure to increased cytokine levels and through glucose elevations in the interstitial fluid. Possible roles of newly recognized, cross-linked forms of HA, and interactions of a major HA receptor (CD44) with cytokine/growth factor receptors during hyperglycemia, are also discussed.

In vivo analytical performance of nitric oxide-releasing glucose biosensors

The in vivo analytical performance of percutaneously implanted nitric oxide (NO)-releasing amperometric glucose biosensors was evaluated in swine for 10 d. Needle-type glucose biosensors were functionalized with NO-releasing polyurethane coatings designed to release similar total amounts of NO (3.1 μmol cm(-2)) for rapid (16.0 ± 4.4 h) or slower (>74.6 ± 16.6 h) durations and remain functional as outer glucose sensor membranes. Relative to controls, NO-releasing sensors were characterized with improved numerical accuracy on days 1 and 3. Furthermore, the clinical accuracy and sensitivity of rapid NO-releasing sensors were superior to control and slower NO-releasing sensors at both 1 and 3 d implantation. In contrast, the slower, extended, NO-releasing sensors were characterized by shorter sensor lag times (<4.2 min) in response to intravenous glucose tolerance tests versus burst NO-releasing and control sensors (>5.8 min) at 3, 7, and 10 d. Collectively, these results highlight the potential for NO release to enhance the analytical utility of in vivo glucose biosensors. Initial results also suggest that this analytical performance benefit is dependent on the NO-release duration.

Comparison between one-point calibration and two-point calibration approaches in a continuous glucose monitoring algorithm

The purpose of this study was to investigate the effect of using a 1-point calibration approach instead of a 2-point calibration approach on the accuracy of a continuous glucose monitoring (CGM) algorithm. A previously published real-time CGM algorithm was compared with its updated version, which used a 1-point calibration instead of a 2-point calibration. In addition, the contribution of the corrective intercept (CI) to the calibration performance was assessed. Finally, the sensor background current was estimated real-time and retrospectively. The study was performed on 132 type 1 diabetes patients. Replacing the 2-point calibration with the 1-point calibration improved the CGM accuracy, with the greatest improvement achieved in hypoglycemia (18.4% median absolute relative differences [MARD] in hypoglycemia for the 2-point calibration, and 12.1% MARD in hypoglycemia for the 1-point calibration). Using 1-point calibration increased the percentage of sensor readings in zone A+B of the Clarke error grid analysis (EGA) in the full glycemic range, and also enhanced hypoglycemia sensitivity. Exclusion of CI from calibration reduced hypoglycemia accuracy, while slightly increased euglycemia accuracy. Both real-time and retrospective estimation of the sensor background current suggest that the background current can be considered zero in the calibration of the SCGM1 sensor. The sensor readings calibrated with the 1-point calibration approach indicated to have higher accuracy than those calibrated with the 2-point calibration approach.

Evaluation of an Algorithm for Retrospective Hypoglycemia Detection Using Professional Continuous Glucose Monitoring Data

BACKGROUND

People with type 1 diabetes (T1D) are unable to produce insulin and thus rely on exogenous supply to lower their blood glucose. Studies have shown that intensive insulin therapy reduces the risk of late-diabetic complications by lowering average blood glucose. However, the therapy leads to increased incidence of hypoglycemia. Although inaccurate, professional continuous glucose monitoring (PCGM) can be used to identify hypoglycemic events, which can be useful for adjusting glucose-regulating factors. New pattern classification approaches based on identifying hypoglycemic events through retrospective analysis of PCGM data have shown promising results. The aim of this study was to evaluate a new pattern classification approach by comparing the performance with a newly developed PCGM calibration algorithm.

METHODS

Ten male subjects with T1D were recruited and monitored with PCGM and self-monitoring blood glucose during insulin-induced hypoglycemia. A total of 19 hypoglycemic events occurred during the sessions.

RESULTS

The pattern classification algorithm detected 19/19 hypoglycemic events with 1 false positive, while the PCGM with the new calibration algorithm detected 17/19 events with 2 false positives.

CONCLUSIONS

We can conclude that even after the introduction of new calibration algorithms, the pattern classification approach is still a valuable addition for improving retrospective hypoglycemia detection using PCGM.

Continuous Glucose Monitoring Systems: A Review

There have been continuous advances in the field of glucose monitoring during the last four decades, which have led to the development of highly evolved blood glucose meters, non-invasive glucose monitoring (NGM) devices and continuous glucose monitoring systems (CGMS). Glucose monitoring is an integral part of diabetes management, and the maintenance of physiological blood glucose concentration is the only way for a diabetic to avoid life-threatening diabetic complications. CGMS have led to tremendous improvements in diabetic management, as shown by the significant lowering of glycated hemoglobin (HbA1c) in adults with type I diabetes. Most of the CGMS have been minimally-invasive, although the more recent ones are based on NGM techniques. This manuscript reviews the advances in CGMS for diabetes management along with the future prospects and the challenges involved.

A multistep algorithm for processing and calibration of microdialysis continuous glucose monitoring data

BACKGROUND

The deviation of continuous subcutaneous glucose monitoring (CGM) data from reference blood glucose measurements is substantial, and adequate signal processing is required to reduce the discrepancy between subcutaneous glucose and blood glucose values. The purpose of this study was to develop a multistep algorithm for the processing and calibration of continuous subcutaneous glucose monitoring data with high accuracy and short delay. Algorithm

PRESENTATION

The algorithm comprises three steps: rate-limiting filtering, selective smoothing, and robust calibration. Initially, the algorithm detects nonphysiological glucose rate-of-change and corrects it with a weighted local polynomial. Noisy signal parts that require smoothing are then detected based on zero crossing count of the sensor signal first-order differences, and an exponentially weighted moving average smooths the noisy parts of the signal afterward. Finally, calibration is performed using a first-order polynomial as the conversion function, with coefficients being estimated using robust regression with a bi-square weight function. ALGORITHM PERFORMANCE: The performance of the algorithm was evaluated on 16 patients with type 1 diabetes mellitus. To compare the algorithm with state-of-the-art CGM data denoising and calibration, the rate-limiting filter and selective smoothing were replaced with an adaptive Kalman filter, and the calibration method was replaced with the calibration algorithm presented in one of the Medtronic (Northridge, CA) CGM patents. The median (mean) of the absolute relative deviation (ARD) of the sensor glucose values processed by the newly developed algorithm from capillary reference blood glucose measurements was 14.8% (22.6%), 10.6% (14.6%), and 8.9% (11.7%) in hypoglycemia, euglycemia, and hyperglycemia, respectively, whereas for the alternative algorithm, the median (mean) was 22.2% (26.9%), 12.1% (15.9%), and 8.8 (11.3%), respectively. The median (mean) ARD in all ranges was 10.3% (14.7%) for the new algorithm and 11.5% (15.8%) for the alternative algorithm. The new algorithm had an average delay of 2.1 min across the patients, and the alternative algorithm had an average delay of 2.9 min.

CONCLUSIONS

The presented algorithm may increase the accuracy of CGM data.

Noninvasive blood glucose monitoring during oral intake of different sugars with optical coherence tomography in human subjects

The potential of OCT applied to noninvasive blood glucose monitoring has attracted significant efforts. In this work we investigated the feasibility of OCT in monitoring blood glucose during oral intake of different sugars in humans. Five groups of experiments were performed, in which different sugars were used. The OCT signal slope (OCTSS) changed with variation of blood glucose concentration (BGC). A good correlation between OCTSS and BGC was observed in these experiments. The averaged correlation coefficients R between OCTSS and BGC are 0.900, 0.836, 0.895 and 0.884, corresponding to oral administration of glucose, fructose, sucrose and mixed sugar, respectively. Our studies demonstrated the capability and accuracy of the OCT system in monitoring BGC noninvasively and it could become a powerful tool in daily blood glucose monitoring for patients.

Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes

BACKGROUND

Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection.

MATERIALS AND METHODS

Features such as time since last insulin injection and linear regression, kurtosis, and skewness of the CGM signal in different time intervals were extracted from data of 10 male subjects experiencing 17 insulin-induced hypoglycemic events in an experimental setting. Nondiscriminative features were eliminated with SEPCOR and forward selection. The feature combinations were used in a Support Vector Machine model and the performance assessed by sample-based sensitivity and specificity and event-based sensitivity and number of false-positives.

RESULTS

The best model was composed by using seven features and was able to detect 17 of 17 hypoglycemic events with one false-positive compared with 12 of 17 hypoglycemic events with zero false-positives for the CGM alone. Lead-time was 14 min and 0 min for the model and the CGM alone, respectively.

CONCLUSIONS

This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events.