Noninvasive glucose monitoring: increasing accuracy by combination of multi-technology and multi-sensors

BACKGROUND

The main concern in noninvasive (NI) glucose monitoring methods is to achieve high accuracy results despite the fact that no direct blood or interstitial fluid glucose measurement is performed. An alternative approach to increase the accuracy of NI glucose measurement was previously suggested through a combination of three NI methods: ultrasonic, electromagnetic, and thermal. This paper provides further explanation about the nature of the implemented technologies, and multi-sensors are presented, as well as a detailed elaboration on the novel algorithm for data analysis.

METHODS

Clinical trials were performed on two different days. During the first day, calibration and six subsequent measurements were performed. During the second day, a "full day" session of about 10 hours took place. During the trial, type 1 and 2 diabetes patients were calibrated and evaluated with GlucoTrack glucose monitor against HemoCue (Glucose 201+).

RESULTS

A total of 91 subjects were tested during the trial period. Clarke error grid (CEG) analysis shows 96% of the readings (on both days 1 and 2) fall in the clinically accepted A and B zones, of which 60% are within zone A. The absolute relative differences (ARDs) yield mean and median values of 22.4% and 15.9%, respectively. The CEG for day 2 of the trial shows 96% of the points in zones A and B, with 57% of the values in zone A. Mean and median ARD values for the readings on day 2 are 23.4% and 16.5%, respectively. The intervals between day 1 (calibration and measurements) and day 2 (measurements only) were 1-22 days, with a median of 6 days.

CONCLUSIONS

The presented methodology shows that increased accuracy was indeed achieved by combining multi-technology and multi-sensors. The approach of integration contributes to increasing the signal-to-noise ratio (glucose to other contributors). A combination of several technologies allows compensation of a possible aberration in one modality by the others, while multi-sensor implementation enables corrections for interference contributions. Furthermore, clinical trials indicate the ability of using the device for a wide range of demography, showing clearly that the calibration is valid for long term.

Blood glucose testing in the hospital: error sources and risk management

Glucose testing in the hospital with point-of-care devices presents multiple opportunities for error. Any device can fail under the right conditions. For glucose monitoring in the hospital, with thousands of operators, hundreds of devices, and dozens of locations involved, there is ample opportunity for errors that can impact the quality of test results. Errors can occur in any phase of the testing process: preanalytic, analytic, or postanalytic. Common sources of meter error include patient or methodology interferences, operator mistakes, environmental exposure, and device malfunction. Early models of glucose meters had few internal checks or capability to warn the operator of meter problems. The latest generation of glucose monitors has a number of internal checks and controls engineered into the testing process to prevent serious errors or warn the operator by suppressing test results. Some of these control processes are built into the software and data management system of the meters, others require the hospital to do something, such as regularly clean the meter or analyze control samples of known glucose concentration, to verify meter performance. Hospitals need to be aware of the potential for errors by understanding weaknesses in the testing process that could lead to erroneous results and take steps to prevent errors from occurring or to minimize the harm to patients when errors do occur. The reliability of a glucose result will depend on the balance of internal control features available from manufacturers in conjunction with the liquid control analysis and other control processes (operator training, device validation, and maintenance) utilized by the hospitals.

Glucose-Sensing Photonic Nanochain Probes with Color Change in Seconds

Glucose-sensing photonic crystals are promising for the significant advance of continuous glucose monitoring systems due to the naked-eye colorimetric readouts and noninvasive detection of diabetes, but the long response time hampers their practical applications. Here, for the first time probes of photonic nanochains (PNCs) are demonstrated that are capable of continuously and reversibly sensing glucose concentration ([glucose]) variation within seconds by color change without power consumption, much faster by 2-3 orders of magnitude than previous ones. They are comprised of 1D equidistant arrays of magnetic nanoparticles enveloped by tens-of-nanometer-thick phenylboronic acid-functionalized hydrogels, and fabricated by developing selective concentration polymerization of monomers in binary microheterogeneous solvents of dimethyl sulfoxide (DMSO) and H2 O. In this process, both 3-acrylamido phenylboronic acid (AAPBA) and N-2-hydroxyethyl acrylamide (HEAAm) are preferentially dissolved in the small volume of free DMSO concentrated in the vicinity of poly vinylpyrrolidone coated Fe3 O4 colloidal nanoparticles (Fe3 O4 @PVP), yielding Fe3 O4 @PVP@poly(AAPBA-co-HEAAm) PNCs after UV irradiation under magnetic field. The PNCs in phosphate buffered solution have a wavelength-shift range up to 130 nm when [glucose] changes from 0 to 20 × 10-3 m. The results can facilitate real-time glucose monitoring and provide an alternative to produce functional organic-inorganic nanostructures.

Comparing the performance of the optical glucose assay based on glucose binding protein with high-performance anion-exchange chromatography with pulsed electrochemical detection: efforts to design a low-cost point-of-care glucose sensor

BACKGROUND

The glucose binding protein (GBP) is one of many soluble binding proteins found in the periplasmic space of gram-negative bacteria. These proteins are responsible for chemotactic responses and active transport of chemical species across the membrane. Upon ligand binding, binding proteins undergo a large conformational change, which is the basis for converting these proteins into optical biosensors.

METHODS

The GBP biosensor was prepared by attaching a polarity-sensitive fluorescent probe to a single cysteine mutation at a site on the protein that is allosterically responsive to glucose binding. The fluorescence response of the resulting sensor was validated against high-performance anion-exchange chromatography (HPAEC) with pulsed electrochemical detection. Finally, a simple fluorescence reader was built using a lifetime-assisted ratiometric technique.

RESULTS

The GBP assay has a linear range of quantification of 0.100-2.00 microM and a sensitivity of 0.164 microM(-1) under the specified experimental conditions. The comparison between GBP and HPAEC readings for nine blind samples indicates that there is no statistical difference between the analytical results of the two methods at the 95% confidence level. Although the methods of fluorescence detection are based on different principles, the response of the homemade device to glucose concentrations was comparable to the response of the larger and more expensive tabletop fluorescence spectrophotometer.

CONCLUSIONS

A glucose binding protein labeled with a polarity-sensitive probe can be used for measuring micromolar amounts of glucose. Using a lifetime-assisted ratiometric technique, a low-cost GBP-based micromolar glucose monitor could be built.

Diet and Physical Activity as Determinants of Continuously Measured Glucose Levels in Persons at High Risk of Type 2 Diabetes

We examined how dietary and physical activity behaviors influence fluctuations in blood glucose levels over a seven-day period in people at high risk for diabetes. Twenty-eight participants underwent a mixed meal tolerance test to assess glucose homeostasis at baseline. Subsequently, they wore an accelerometer to assess movement behaviors, recorded their dietary intakes through a mobile phone application, and wore a flash glucose monitoring device that measured glucose levels every 15 min for seven days. Generalized estimating equation models were used to assess the associations of metabolic and lifestyle risk factors with glycemic variability. Higher BMI, amount of body fat, and selected markers of hyperglycemia and insulin resistance from the meal tolerance test were associated with higher mean glucose levels during the seven days. Moderate- to vigorous-intensity physical activity and polyunsaturated fat intake were independently associated with less variation in glucose levels (CV%). Higher protein and polyunsaturated fatty acid intakes were associated with more time-in-range. In contrast, higher carbohydrate intake was associated with less time-in-range. Our findings suggest that dietary composition (a higher intake of polyunsaturated fat and protein and lower intake of carbohydrates) and moderate-to-vigorous physical activity may reduce fluctuations in glucose levels in persons at high risk of diabetes.

Evaluation of postprandial glucose excursion using a novel minimally invasive glucose area-under-the-curve monitoring system

OBJECTIVE

To develop a minimally invasive interstitial fluid extraction technology (MIET) to monitor postprandial glucose area under the curve (AUC) without blood sampling, we evaluated the accuracy of glucose AUC measured by MIET and compared with that by blood sampling after food intake.

METHODS

Interstitial fluid glucose AUC (IG-AUC) following consumption of 6 different types of foods was measured by MIET. MIET consisted of stamping microneedle arrays, placing hydrogel patches on the areas, and calculating IG-AUC based on glucose levels in the hydrogels. Glycemic index (GI) was determined using IG-AUC and reference AUC measured by blood sampling.

RESULTS

IG-AUC strongly correlated with reference AUC (R = 0.91), and GI determined using IG-AUC showed good correlation with that determined by reference AUC (R = 0.88).

CONCLUSIONS

IG-AUC obtained by MIET can accurately predict the postprandial glucose excursion without blood sampling. In addition, feasibility of GI measurement by MIET was confirmed.

FreeStyle Mini blood glucose results are accurate and suitable for use in glycemic clamp protocols

OBJECTIVE

We assessed the accuracy of the FreeStyle Mini (FSM) meter for use in glycemic clamp and meal protocols in comparison with the HemoCue Glucose 201 DM Analyzer (HemoCue) and the YSI 2300 STAT Glucose Oxidase Analyzer (YSI).

METHODS

Seven volunteers with type 2 diabetes mellitus, 35-69 years old, underwent a frequently sampled meal test and a graded hyperglycemic test, on two separate days, with one of the volunteers undergoing each test twice. Samples for glucose measurements were obtained from arterialized venous blood. A total of 420 samples (with glucose levels ranging from 63 to 388 mg/dl) were available for comparison. On average, 10 measurements were available for every 5 mg/dl increment in glucose level in the range of 130-310 mg/dl. Blood glucose measurements were done on each sample with the FSM, HemoCue, and YSI.

RESULTS

FreeStyle Mini blood glucose values correlated closely with the YSI readings. Of the FSM measurements, 99.0% were within the Clarke error grid zone A; 51.3%, 84.7%, and 96.2% of the FSM readings were within 5%, 10% and 15% of the YSI values, respectively. The FSM was significantly more accurate than the HemoCue (84.7% vs 76.6% of results within 10% of the YSI results; p = .0038). The mean average relative difference of the FSM (5.8%) was also significantly lower than that of the HemoCue (6.8%; p = .0013)

CONCLUSIONS

The FSM provides accurate results and constitutes a suitable alternative for bedside blood glucose measurements in experimental procedures, helping to reduce sample size, turnaround time, and cost.

[Biomarkers in diabetes mellitus: contributions and discrepancies of new technologies. A case report]

Potential discrepancies between laboratory and estimated (from Continuous Glucose Monitoring (CGM)) glycated hemoglobin (HbA1c) have been reported by diabetologists. CGM devices produce an eA1c derived from average glucose and correlated with Time-in-Range (TIR, %) which is the relative time spent in a range of normal glycaemia. Through a case report, we studied the potential causes for these discrepancies. CGM devices estimate eA1c during the lifespan of the sensor, that is replaced every 14 days and HbA1c is a retrospective data of exposure to hyperglycemia over 8 to 12 weeks. In our case report, the patient had a poor glycemic control resulting in 9% eA1c compared to 7,4% HbA1c got by delocalized immune-assay (Siemens DCA-Vantage^®), confirmed at 7,7% by HPLC (Variant II Turbo). On top of the CGM data, an increased labile A1c (LA1c) fraction was found on the patient's HbA1c HPLC profile, both in favor of a recently altered glycemic control. Thus, recent and/or substantial variations in glycemic control will increase the gap between HbA1c and eA1c, being a potential source of therapeutic errors. The differences of those markers, particularly the time window during which it is estimated, make them hardly comparable. As the use of CGM is becoming widespread, it is important to understand and harness its data and biomarkers.

Photonic Nanochains for Continuous Glucose Monitoring in Physiological Environment

Diabetes is a common disease that seriously endangers human health. Continuous glucose monitoring (CGM) is important for the prevention and treatment of diabetes. Glucose-sensing photonic nanochains (PNCs) have the advantages of naked-eye colorimetric readouts, short response time and noninvasive detection of diabetes, showing immense potential in CGM systems. However, the developed PNCs cannot disperse in physiological environment at the pH of 7.4 because of their poor hydrophilicity. In this study, we report a new kind of PNCs that can continuously and reversibly detect the concentration of glucose (Cg) in physiological environment at the pH of 7.4. Polyacrylic acid (PAA) added to the preparation of PNCs forms hydrogen bonds with polyvinylpyrrolidone (PVP) in Fe3O4@PVP colloidal nanoparticles and the hydrophilic monomer N-2-hydroxyethyl acrylamide (HEAAm), which increases the content of PHEAAm in the polymer shell of prepared PNCs. Moreover, 4-(2-acrylamidoethylcarbamoyl)-3-fluorophenylboronic acid (AFPBA), with a relatively low pKa value, is used as the glucose-sensing monomer to further improve the hydrophilicity and glucose-sensing performances of PNCs. The obtained Fe3O4@(PVP-PAA)@poly(AFPBA-co-HEAAm) PNCs disperse in artificial serum and change color from yellow-green to red when Cg increases from 3.9 mM to 11.4 mM, showing application potential for straightforward CGM.

"Now I can see it works!" Perspectives on Using a Nutrition-Focused Approach When Initiating Continuous Glucose Monitoring in People with Type 2 Diabetes: Qualitative Interview Study

BACKGROUND

Food choices play a significant role in achieving glycemic goals and optimizing overall health for people with type 2 diabetes (T2D). Continuous glucose monitoring (CGM) can provide a comprehensive look at the impact of foods and other behaviors on glucose in real time and over the course of time. The impact of using a nutrition-focused approach (NFA) when initiating CGM in people with T2D is unknown.

OBJECTIVE

This study aims to understand the perspectives and behaviors of people with T2D who participated in an NFA during CGM initiation.

METHODS

Semistructured qualitative interviews were conducted with UNITE (Using Nutrition to Improve Time in Range) study participants. UNITE was a 2-session intervention designed to introduce and initiate CGM using an NFA in people with T2D who do not use insulin. The intervention included CGM initiation materials that emphasized the continuous glucose monitor as a tool to guide evidence-based food choices. The materials were designed to support conversation between the CGM user and diabetes care provider conducting the sessions. A rapid matrix analysis approach was designed to answer two main questions: (1) How do people who participate in an NFA during CGM initiation describe this experience? and (2) How do people who participate in an NFA during CGM initiation use CGM data to make food-related decisions, and what food-related changes do they make?

RESULTS

Overall, 15 people completed interviews after completion of the UNITE study intervention: 87% (n=13) identified as White, 60% (n=9) identified as male, mean age of 64 (SD 7.4) years, mean T2D duration of 7.5 (SD 3.8) years, and mean hemoglobin A1c level of 7.5% (SD 0.4%). Participants fluently discussed glycemic metrics such as time in range (percent time with glucose 70-180 mg/dL) and reported regularly using real-time and retrospective CGM data. Participants liked the simplicity of the intervention materials (eg, images and messaging), which demonstrated how to use CGM data to learn the glycemic impact of food choices and suggested how to adjust food choices for improved glycemia. Participants reported that CGM data impacted how they thought about food, and most participants made changes because of seeing these data. Many of the reported changes aligned with evidence-based guidance for a healthy lifestyle, including prioritizing nonstarchy vegetables, reducing foods with added sugar, or walking more; however, some people reported behavior changes, such as skipping or delaying meals to stay in the target glucose range. A few participants reported that the CGM amplified negative feelings about food or eating.

CONCLUSIONS

Participants agreed that pairing nutrition information with CGM initiation instructions was helpful for their diabetes care. In general, the NFA during CGM initiation was well received and led to positive changes in food choices and behaviors during a 2-month intervention.

Continuous Glucose Monitoring Use in Youth with Type 2 Diabetes: A Pilot Randomized Study

Objective: Continuous glucose monitoring (CGM) enhances diabetes self-management in insulin-treated individuals. However, the feasibility, acceptability, and benefits/burdens in youth-onset type 2 diabetes (Y-T2D) who are on infrequent self-monitoring of blood glucose (SMBG) regimens remain unclear. Research Design and Methods: In Y-T2D prescribed SMBG less than or equal to twice daily, we conducted a 12-week randomized 2:1 parallel pilot trial of CGM versus fingerstick monitoring (Control). Control participants had an optional 4-week extension period to use CGM (Control-CGM). Feasibility was defined as recruitment, study participation, and retention >60% of individuals. Acceptability was defined as an individual CGM wear time of ≥60% at the end of the study. Diabetes distress and the benefits/burdens of CGM scores, hemoglobin A1c (HbA1c), and CGM-derived glycemic variables were compared at baseline and at the end of the intervention. Results: The recruitment rate was 54% (52 screened eligible, 18 CGM, 10 Control; 82% female, 68% Black, 14.9 ± 3.8 years, body mass index: 36.2 ± 7.7 kg/m2, HbA1c: 7.4 ± 2.4% (mean ± standard deviation [SD]), and 8 entered the optional Control-CGM group. The most commonly cited reason for declining study participation was reluctance to wear the device (50%). The participation rate was 91% and 75%, and retention was 100% and 75% for CGM and Control-CGM, respectively. A majority of Y-T2D had ≥60% wear time at the end of the study (CGM: 56% and Control-CGM: 83%). Wear time declined during the study (1st month: 71 ± 31% vs. 2nd month: 55 ± 32% vs. 3rd month: 38 ± 34%, P = 0.003). There were no significant changes in glycemia, CGM burden/benefits, or diabetes distress scores (P > 0.05). Minor sensor adhesion adverse events were common (75%) causes of reduced wear time. Conclusion: CGM was a feasible and acceptable adjunct to diabetes self-care among >50% of Y-T2D prescribed infrequent SMBG monitoring. Unwillingness to wear a device and social stigma impeded device use. Additional research is needed to mitigate the high rates of skin adhesion-related adverse events in this population.