Continuous Glucose Monitoring Sensor Research Articles

648-P: Lag Times in a Seventh-Generation Continuous Glucose Monitoring System

Background: Glucose transport from blood to interstitial fluid impacts performance of interstitially-placed continuous glucose monitoring (CGM) sensors. We examined time series offsets (“lag times”) of the G7 CGM system (Dexcom) . Methods: In a pivotal accuracy study conducted in the US, 316 adults with diabetes wearing 6sensors participated in clinic sessions that included glucose manipulations and frequent venous plasma glucose (YSI) determinations. G7 values were obtained every 5 min and interpolated to provide values at 1-min intervals. Each YSI value was associated with 21 G7 values obtained from min before to min after. The absolute relative difference (ARD) was calculated for each YSI:G7 pair and each sensor’s lag was estimated as the time with the lowest mean ARD. Results: A lag time of 2 minutes provided the lowest mean ARD of 8.6% and the highest %20/20 agreement rate of 94.4%. The Figure shows summary statistics for the distribution of sensor-specific lag times. Lag times of ≤0 min (where interstitial glucose leads blood glucose) were observed for 2 (33.0%) of the sensors. Conclusions: The distribution of G7 time series offsets suggests that CGM values may most accurately reflect previous or upcoming blood glucose values. This finding is likely due to technological innovations in G7’s hardware and algorithm. The minimal lag time observed should provide users with confidence in using CGM to manage their diabetes. Disclosure X.Zhang: Employee; Dexcom, Inc. J.L.Reid: Employee; Dexcom, Inc., GenMark Diagnostics. T.C.Walker: Employee; Dexcom, Inc., Stock/Shareholder; Dexcom, Inc. J.Welsh: Employee; Dexcom, Inc. A.Balo: Employee; Dexcom, Inc. Funding Dexcom, Inc.

253-OR: Maternal Hyperglycemia Is Associated with Offspring Adiposity through Altered Lipid Metabolism in Type 1 Diabetes Pregnancy

Objective: Type 1 diabetes in pregnancy is associated with increased birthweight and adiposity in offspring, which may contribute to increased future cardiometabolic risk. We assessed if the effects of maternal hyperglycemia on adiposity were mediated by altered lipid metabolism in pregnancy. Methods and Outcomes: 200 women in the Continuous Glucose Monitoring (CGM) in Pregnant women with Type 1 diabetes Trial (CONCEPTT) had a livebirth. Liquid chromatography-mass spectrometry of maternal serum (12, 24, 34 wks; n=174) and cord blood (n=93) measured 21metabolites. Anthropometry was performed at birth by trained staff. Statistical Analysis: Linear regression of CGM time above range 12, 24, 34 wks (>140 mg/dl; >7.8 mmol/l) with adjustment for maternal age, BMI, parity, ethnicity, education and intervention (birth gestation for offspring) . Significance limit was p=0.0for unselected analysis of all metabolites, p=0.for subset analysis with skinfolds. Results: Maternal hyperglycemia at 24 and 34 weeks was associated with increased triglycerides containing FA (16:0) , (16:1) , (18:0) and (18:1) in maternal serum, considered biomarkers of de novo lipogenesis. Maternal hyperglycemia was associated with abundant carnitines in cord blood, suggesting increased fetal beta oxidation. Lipogenesis-associated species (maternal serum) and carnitines (cord blood) were associated with offspring skinfold sum, independently of maternal hyperglycemia. However, maternal hyperglycemia retained a strong independent association with skinfold sum despite adjustment for potential lipid mediators. Conclusions: Maternal hyperglycemia is associated with features of de novo lipogenesis in the maternal metabolism and beta oxidation in the offspring. Altered lipid metabolism significantly contributes to offspring adiposity but does not appear to solely mediate the relationship between hyperglycemia and body composition in type 1 diabetes pregnancy. Disclosure C.L.Meek: Research Support; Dexcom, Inc. Z.A.Stewart: None. D.Feig: Advisory Panel; Novo Nordisk, Research Support; Apotex. A.Koulman: None. H.R.Murphy: Advisory Panel; Medtronic. Funding This project was funded by Diabetes UK (PG 2017/2278) . The CONCEPTT trial was funded by Juvenile Diabetes Research Foundation (JDRF) grants #17-2011-533, and grants under the JDRF Canadian Clinical Trial Network, a public-private partnership including JDRF and FedDev Ontario and supported by JDRF #80-2010-585. Medtronic supplied the CGM sensors and CGM systems at reduced cost. The study sponsor/funders were not involved in the design of the study; the collection, analysis, and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report.CLM is supported by the Diabetes UK Harry Keen Intermediate Clinical Fellowship (DUK-HKF 17/0005712) and the European Foundation for the Study of Diabetes – Novo Nordisk Foundation Future Leaders’ Award (NNF19SA058974) . HRM conducts independent research supported by the National Institute for Health Research (Career Development Fellowship, CDF-2013-06-035) , and is supported by Tommy’s charity. SF and AK acknowledge funding from the BBSRC (BB/M027252/1) . DSF conducts independent research supported by the Canadian Institute for Health Research.

684-P: Tracking Type 2 Diabetes Progression Using CGM Systems

Disposition index (DI) is an index quantifying the quality of glucose control, usable to track disease progression. It is usually calculated as the product of beta-cell function and insulin sensitivity, assessed, in hospitalized setting, either from clamp data, surrogate indices or model-based methods, like the Oral Minimal Model (OMM) . The increasing use of continuous glucose monitoring (CGM) systems, even in individuals with type 2 diabetes (T2D) , opens the door to the use of such devices to monitor glucose control in outpatients. Here, we propose a method to assess DI from CGM data, validated it against OMM and proved its usability to track disease progression. We studied 100 early stage (ES) and 100 advanced stage (AS) subjects with T2D of the Padova T2D simulator, matched for age (54 [42,75] y) and different BMI (33 [29,37] vs. 31 [28,34] kg/m2; p=0.016) , which underwent a mixed meal challenge (75g of carbohydrates) while wearing CGM. Plasma glucose, insulin and C-peptide concentrations were measured for 6 hours after the meal and used for the estimation of the DI with the OMM (DIMM) , while CGM sensor data were used to calculate DI with the sensor-based method (DISB) . DISB well correlated with DIMM (ρES = 0.81 and ρAS = 0.77; p<0.001) and both were significantly lower in AD than ES (p<0.001, Fig. 1) . We conclude that DISB is usable to assess DI in subjects with different stage of T2D wearing CGM and thus, potentially, to track disease progression in noninvasive way. Disclosure M.Schiavon: None. C.Dalla man: Research Support; Becton, Dickinson and Company, Sanofi-Aventis Deutschland GmbH. Funding MIUR (Italian Minister for Education) under the initiative "Departments of Excellence" (Law 232/2016)

1137-P: Continuous Glucose Monitoring (CGM) Use and Its Impact on Glycemic Control and Acute Complications among Older-Onset Type 1 Diabetes Patients within the Veterans Health Administration (VHA)

There is increasing recognition that older onset type 1 diabetes (T1D) is not uncommon. Although CGM use is rising dramatically among those with T1D, little is known regarding its use and benefits within this older T1D population. To address these questions, we identified new CGM users (new CGM sensor prescriptions for T1D developing after military enrollment) and non-users (T1D receiving care and glucose strips from the VHA) during the years 2015-2020 and used national VHA healthcare data and ICD-9/ICD-diagnostic codes from visit encounters to compare baseline characteristics, change in glucose control and new emergency (ER) or hospital admissions. Linear mixed models and Cox proportional models (with propensity score overlap weighting) , were used to compare pre/post changes in HbA1c, and time to acute admissions, respectively. Compared with 4732 nonCGM users, CGM users (n=5,143) had similar HbA1c levels but differed in many ways at initiation of CGM, including: being significantly younger (59 vs. 65 years) , higher proportion White (83 vs. 75%) , and more frequently using glucagon (36 vs. 12 %) , short-acting insulin (99 vs. 86%) and insulin pumps (54 vs.7%) . After propensity overlap weighting, all characteristics were nearly identical between groups. Initiation of CGM was associated with reduced HbA1c levels (- 0.2 and -0.26% at 6 and 12 months (p’s< 0.001) compared with nonCGM users. Similarly, CGM users also had greater declines in risk for total hospitalizations (HR = .88; CI 0.79-0.98) and hypoglycemic (HR = .69; CI 0.53-0.90) and hyperglycemic (HR = .77; CI 0.64-0.94) related ER visits/hospitalizations (all p’s < 0.02) . In this large national study of CGM initiation in older T1D patients, CGM use was linked with improved glucose control as well as reduced ER and/or hospital admissions for hypoglycemia, hyperglycemia or any cause. Disclosure J.Zhou: Research Support; Dexcom, Inc. M.Newell: Research Support; Dexcom, Inc. S.Rivas: None. X.Zhou: None. G.J.Norman: Employee; Dexcom, Inc. P.Reaven: Research Support; AstraZeneca, Dexcom, Inc. Funding DexCom, Inc

1138-P: Impact of Continuous Glucose Monitoring (CGM) on Glycemic Control and Acute Complications in Type 2 Diabetes (T2D) in the Veterans Health Administration (VHA)

Use of CGM in T2D is rising rapidly, yet little is known regarding its effects on glucose management in the VHA. We thus identified new CGM users (new CGM sensor prescriptions for T2D on insulin) and non-users (T2D receiving glucose strips) during the years 2015-2020 and used national VHA healthcare data from visit encounters to compare change over 12 months in glucose control and new emergency room (ER) or hospital admissions. Changes in HbA1c and acute admissions were compared by linear mixed models and generalized linear models, respectively. Compared with 36,082 non-users, CGM users (n=16,013) differed in many ways but were well-balanced with overlap weighting from propensity score modeling. CGM users had a greater reduction in HbA1c over 12 months (-0.25%; 95% CI -0.21-0.29, p<0.0001) and a decline in hypoglycemia related ER visits/hospitalizations (OR = 0.78; CI 0.62-0.97) . Among CGM users, those < 65 years old or with initial HbA1c > 8. 4% (median) had greater reductions in HbA1c, while those at higher hypoglycemia risk had reduced hypoglycemia and hyperglycemia related ER visits/hospitalizations and all-cause hospitalizations (Table) . In this large national study in T2D, CGM initiation was linked with better glucose control and reduced ER and/or hospital admissions for hypoglycemia and showed even greater benefits in subsets of CGM users. Disclosure P.Reaven: Research Support; AstraZeneca, Dexcom, Inc. M.Newell: Research Support; Dexcom, Inc. S.Rivas: None. X.Zhou: None. G.J.Norman: Employee; Dexcom, Inc. J.Zhou: Research Support; Dexcom, Inc. Funding DexCom, Inc

Viscoelastic and Viscoplastic Glucose Theory (VGT #20): Applying Theories of Viscoelasticity and Viscoplasticity from Engineering and Energy Theory from Physics for Two eAG Cases to Compare the Energy Loss in the LoadingUnloading Process Through Two Hysteresis Loop Areas Along with Comparing Two eAG Associated Energies from Time-Domain and Frequency-Domain Based on the GH-Method: Math-Physical Medicine (No. 599)

Recently, the author applied theories of elasticity, plasticity, viscoelasticity, and viscoplasticity from engineering along with the theories of wave and energy from physics to conduct his research on output biomarkers (symptoms or behaviors) resulting from a suspected or identified input biomarkers (causes or stressors). As a result, he has written a series of 19 different articles. In this research, he studies two related topics of glucose energy utilizing a continuous glucose monitoring (CGM) sensor device that collected the estimated daily glucose (eAG) from 1,374 days for a period of 5/5/2018 to 2/7/2022. The first one is the energy loss of hysteresis loops via VGT analysis. The second one is the eAG associated relative energy level in time-domain (TD) and frequency-domain(FD) through wave theory and energy theory of physics. He then compares his two respective energies corresponding to 2 eAG cases which are divided by 2 different PPG ranges: PPG < 150 mg/dL, and PPG >150 mg/dL.

Viscoelastic and Viscoplastic Glucose Theory (VGT #19): Applying Theories of Viscoelasticity and Viscoplasticity from Engineering and Energy Theory from Physics for Three PPG Cases to Compare the Energy Loss in Loading-Unloading Process via Three Hysteresis Loop Areas Along with Comparing Three PPG Associated Energies via Time-Domain and Frequency-Domain Based on the GH-Method: Math-Physical Medicine (No. 598)

Recently, the author applied theories of elasticity, plasticity, viscoelasticity, and viscoplasticity from engineering along with the theories of wave and energy from physics to conduct his research on output biomarkers (symptoms or behaviors) resulting from a suspected or identified input biomarkers (causes or stressors). As a result, he has written a series of 18 different articles. In this research, he studies two related topics of glucose energy utilizing a continuous glucose monitoring (CGM) sensor device that collected postprandial plasma glucose (PPG) from 4,168 meals during the period from 5/5/2018 to 2/6/2022. The first one is the energy loss of hysteresis loops via VGT analysis. The second one is the PPG associated relative energy level in time-domain (TD) and frequency-domain(FD) through wave theory and energy theory of physics. He then compares his 3 respective energy results corresponding to 3 PPG cases: PPG between 0 mg/dL to 400 mg/dL, PPG < 120 mg/dL, and PPG >160 mg/dL.

Laboratory Protocol and Pilot Results for Dynamic Interference Testing of Continuous Glucose Monitoring Sensors.

Testing the potential influence of interfering substances on the measurement performance of needle sensors for continuous glucose monitoring (CGM) is a challenging task. For proper function, the sensors need an almost stable fluidic environment. Previously published in vitro interference experiments were measuring under static concentration conditons. Our experimental setup allows for interference testing with dynamic changes of the interferent concentrations. We designed a macrofluidic test stand that is fueled by several high-pressure liquid chromatography (HPLC) pumps generating programmable glucose and/or interferent gradients in phosphate-buffered saline (PBS). After optimizing experimental parameters (channel dimensions, temperature, flow rates, gradient slopes, buffer, pH etc.), we validated the setup using Dexcom G6 (G6) and Freestyle Libre 2 (L2) sensors with/without interferents, and using YSI 2300 Stat plus as the reference glucose device at room temperature. Both sensors tracked the programmed glucose changes. After calibration, G6 results closely matched glucose reference readings, while L2 routinely showed ~50% to 60% lower readings, most likely because of the factory-based calibration and temperature compensation. Gradients of maltose, acetaminophen, and xylose were employed to further validate the setup. As expected, both sensors were not affected by maltose. We confirmed previous findings regarding susceptibility of G6 readings to acetaminophen and L2 readings to xylose. Signals from both sensors are influenced by temperature in a linear fashion. Our experimental in vitro setup and protocol may provide a useful method to dynamically test CGM sensors for interfering substances. This may help to improve the accuracy of future CGM sensor generations.

Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach

Background and objectiveIn type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) notably facilitate the design/testing of new therapies. Published simulation tools embed mathematical models of blood glucose (BG) and insulin dynamics, continuous glucose monitoring (CGM) sensors, and insulin treatments, but lack a realistic description of some aspects of patient lifestyle impacting on glucose control. Specifically, to effectively simulate insulin correction boluses, required to treat post-meal hyperglycemia (BG > 180 mg/dL), the timing of the bolus may be influenced by subjects’ behavioral attitudes. In this work, we develop an easily interpretable model of the variability of correction bolus timing observed in real data, and embed it into a popular simulation tool for ISCTs. MethodsUsing data collected in 196 adults with T1D monitored in free-living conditions, we trained a decision tree (DT) model to classify whether a correction bolus is injected in a future time window, based on predictors collected back in time, related to CGM data, previous insulin boluses and subject's characteristics. The performance was compared to that of a logistic regression classifier with LASSO regularization (LC), trained on the same dataset. After validation, the DT was embedded within a popular T1D simulation tool and an ISCT was performed to compare the simulated correction boluses against those observed in a subset of data not used for model training. ResultsThe DT provided better classification performance (accuracy: 0.792, sensitivity: 0.430, specificity: 0.878, precision: 0.455) than the LC and presented good interpretability. The most predictive features were related to CGM (and its temporal variations), time since the last insulin bolus, and time of the day. The correction boluses simulated by the DT, after implementation in the simulation tool, showed a good agreement with real-world data. ConclusionsThe DT developed in this work represents a simple set of rules to mimic the same timing of correction boluses observed on real data. The inclusion of the model in simulation tools allows investigators to perform ISCTs that more realistically represent the patient behavior in taking correction boluses and the post-prandial BG response. In the future, more complex models can be investigated.

In Vitro Continuous 3 Months Operation of Direct Electron Transfer Type Open Circuit Potential Based Glucose Sensor: Heralding the Next CGM Sensor.

While continuous glucose monitoring (CGM) systems allow precise and real-time blood glucose control, current electrochemicalbased CGM technologies inherently harbor enzyme instability issues. The direct electron transfer (DET) type open circuit potential (OCP) based enzyme sensing principle can minimize the catalytic turnover of the enzyme reaction, thereby providing longer-term operational stability in future CGM glucose sensors. DET-type OCP based glucose sensors were constructed using gold disk electrodes with glucose dehydrogenase capable of DET which was immobilized using a self-assembled monolayer (SAM). The single enzyme layer prepared on the gold electrode was operated in the presence of glucose, using in vitro buffer solution, continuously for over 3 months with the OCP sensor signal monitored every 10 seconds at 25°C. The DET-type OCP glucose sensor was continuously operated for more than 3 months without a significant decrease of the sensor signal and sensitivity (slope). These results suggest that the DET-type OCP glucose sensor is far more stable than the sensor constructed based on the amperometric principle. The long-term stability of DET-type OCP glucose sensor is attributed to the enzyme's minimized catalytic reaction during the operation, thereby extending the lifetime of enzyme. The DET-type OCP glucose sensor can be continuously operated for more than 3 months at 25 °C, in vitro without significant decreases in sensor signal and sensitivity. While the further investigation will be required for in vivo validation, the DET-type OCP glucose sensor is ideal for next generation CGM's, especially in long duration implantable use cases.

Sensitive Electrochemical Non-Enzymatic Detection of Glucose Based on Wireless Data Transmission.

Miniaturization and wireless continuous glucose monitoring are key factors for the successful management of diabetes. Electrochemical sensors are very versatile and can be easily miniaturized for wireless glucose monitoring. The authors report a microneedle-based enzyme-free electrochemical wireless sensor for painless and continuous glucose monitoring. The microneedles (MNs) fabricated consist of a 3 × 5 sharp and stainless-steel electrode array configuration. Each MN in the 3 × 5 array has 575 µm × 150 µm in height and width, respectively. A glucose-catalyzing layer, porous platinum black, was electrochemically deposited on the tips of the MNs by applying a fixed cathodic current of 2.5 mA cm−2 for a period of 200 s. For the non-interference glucose sensing, the platinum (Pt)-black-coated MN was carefully packaged into a biocompatible ionomer, nafion. The surface morphologies of the bare and modified MNs were studied using field-emission scanning electron microscopy (FESEM) and energy-dispersive X-ray analysis (EDX). The wireless glucose sensor displayed a broad linear range of glucose (1→30 mM), a good sensitivity and higher detection limit of 145.33 μA mM−1 cm−2 and 480 μM, respectively, with bare AuMN as a counter electrode. However, the wireless device showed an improved sensitivity and enhanced detection limit of 445.75, 165.83 μA mM−1 cm−2 and 268 μM, respectively, with the Pt-black-modified MN as a counter electrode. The sensor also exhibited a very good response time (2 s) and a limited interference effect on the detection of glucose in the presence of other electroactive oxidizing species, indicating a very fast and interference-free chronoamperometric response.

IDF21-0095 Continuous glucose monitoring calibrated with point of care devices or glucometers for self monitoring of blood glucose

Background: Iatrogenic hypoglycemia is an important complication for patients with diabetes. To detect a risk of hypoglycemia accurately during hospitalization, increasing accuracy of continuous glucose monitor (CGM) is necessary. Aim: We examined differences in calibration accuracy between point of care devices and “glucometers that were compliant with ISO15197:2013” (ISO compliant glucometers) when using personal CGM during hospitalization. Method: This was a prospective study where personal-CGM (GUARDIAN CONNECT) was worn for 6 days (CGM attachment: day 1) and blood glucose levels used to calibrate were obtained using point of care devices (StatStrip Xpress Glucose/Ketone) or ISO compliant glucometers (ACCU-CHEK Guide) using a randomized crossover design. Sixteen inpatients with type 2 diabetes were equally allocated to 2 groups. Point of care devices provided 11 blood glucose levels used to calibrate during days 2-3 and ISO compliant glucometers provided 11 blood glucose levels used to calibrate during days 4-5 in group 1. ISO compliant glucometers provided 11 blood glucose levels used to calibrate during days 2-3 and point of care devices provided 11 blood glucose levels used to calibrate during days 4-5 in group 2. In each group 1 and group 2, point of care devices and ISO compliant glucometers were allocated from “fixed 2 point of care devices” and “fixed 2 ISO compliant glucometers” using a randomized quadruple (2 × 2) crossover design. Mean absolute relative difference (MARD) was calculated for every calibration. Results: Blood glucose levels used to calibrate obtained using point of care devices was significantly higher than blood glucose levels used to calibrate obtained using ISO compliant glucometers (173.6 ± 69.9 mg/dL vs. 158.2 ± 47.9 mg/dL, p = 0.02: unpaired t-test). MARD was significantly higher on calibration using point of care devices than on calibration using ISO compliant glucometers (7.6 ± 7.5% vs. 5.8 ± 5.8%, p = 0.01: unpaired t-test). The study patients correlated to distributions of MARD on calibration using point of care devices (η2 = 0.19, p = 0.003: correlation ratio). The study patients also correlated to distributions of MARD on calibration using ISO compliant glucometers (η2 = 0.23, p < 0.001: correlation ratio). Discussion: StatStrip Xpress Glucose/Ketone is cheaper than other point of care devices in Japan, therefore, StatStrip Xpress Glucose/Ketone should be thought to be superior in terms of being able to measure the wide range of glucose levels (10–900 mg/dL) cheaply. ISO compliant glucometers may calibrate personal CGM more accurate than StatStrip Xpress Glucose/Ketone during hospitalization. The correlation between study patients and distributions of MARD may suggest the individual differences in CGM sensor accuracy between patients.

Viscoelastic and Viscoplastic Glucose Theory (VGT #25): Applying the Concepts of Viscoelasticity, Viscoplasticity and Perturbation Theories to Predict PPG Dataset and Waveforms for the Pre-Virus and Virus Periods Using the Total PPG Values as a Baseline Along with the Estimated PPG Relative Energy Levels in Both Time-Domain and Frequency-Domain Based on the GH-Method: MathPhysical Medicine (No. 605)

Recently, the author applied theories of viscoelasticity and viscoplasticity from engineering and perturbation theory from quantum mechanics along with the wave theory and energy theory from physics to conduct his biomedical research on the output biomarkers of postprandial plasma glucose (PPG), a symptom of diabetes, resulting from the input biomarker of carbs/sugar amount (a cause of high glucose). In this article, he calculated two additional PPG datasets and curves, which are the predicted PPG for the pre-virus period from 5/1/2018 to 1/18/2020 and the virus period from 1/19/2020 to 2/12/2022. He used the total period from 5/1/2018 to 2/12/2022 PPG values as the dataset baseline. All PPG data from the three periods are collected via a continuous glucose monitoring (CGM) sensor device at 15-minute time-interval over a period of 1,379 days. For this article, there are two primary objectives to check the different PPG behaviors between two time periods with different lifestyles due to the COVID-19 virus quarantine. First, he applies the visco-perturbation model to predict these two different PPG behaviors and verify their associated correlation coefficients along with the prediction accuracy percentages. Second, he estimates and compares the three relative energies associated with PPG in both time-domain (TD) and frequency-domain (FD) for these three periods, including the total period of both pre-virus and virus periods.

Viscoelastic and Viscoplastic Glucose Theory (VGT #19): Applying Theories of Viscoelasticity and Viscoplasticity from Engineering and Energy Theory from Physics for Three PPG Cases to Compare the Energy Loss in Loading-Unloading Process via Three Hysteresis Loop Areas Along with Comparing Three PPG Associated Energies via Time-Domain and Frequency-Domain Based on the GH-Method: Math-Physical Medicine (No. 598)

Recently, the author applied theories of elasticity, plasticity, viscoelasticity, and viscoplasticity from engineering along with the theories of wave and energy from physics to conduct his research on output biomarkers (symptoms or behaviors) resulting from a suspected or identified input biomarkers (causes or stressors). As a result, he has written a series of 18 different articles. In this research, he studies two related topics of glucose energy utilizing a continuous glucose monitoring (CGM) sensor device that collected postprandial plasma glucose (PPG) from 4,168 meals during the period from 5/5/2018 to 2/6/2022. The first one is the energy loss of hysteresis loops via VGT analysis. The second one is the PPG associated relative energy level in time-domain (TD) and frequency-domain(FD) through wave theory and energy theory of physics. He then compares his 3 respective energy results corresponding to 3 PPG cases: PPG between 0 mg/dL to 400 mg/dL, PPG &lt; 120 mg/dL, and PPG &gt;160 mg/dL.

Viscoelastic and Viscoplastic Glucose Theory (VGT #20): Applying Theories of Viscoelasticity and Viscoplasticity from Engineering and Energy Theory from Physics for Two eAG Cases to Compare the Energy Loss in the LoadingUnloading Process through Two Hysteresis Loop Areas along with Comparing Two eAG Associated Energies from Time-Domain and Frequency-Domain Based on the GH-Method: Math-Physical Medicine (No. 599)

Recently, the author applied theories of elasticity, plasticity, viscoelasticity, and viscoplasticity from engineering along with the theories of wave and energy from physics to conduct his research on output biomarkers (symptoms or behaviors) resulting from a suspected or identified input biomarkers (causes or stressors). As a result, he has written a series of 19 different articles. In this research, he studies two related topics of glucose energy utilizing a continuous glucose monitoring (CGM) sensor device that collected the estimated daily glucose (eAG) from 1,374 days for a period of 5/5/2018 to 2/7/2022. The first one is the energy loss of hysteresis loops via VGT analysis. The second one is the eAG associated relative energy level in time-domain (TD) and frequency-domain(FD) through wave theory and energy theory of physics. He then compares his two respective energies corresponding to 2 eAG cases which are divided by 2 different PPG ranges: PPG &lt; 150 mg/dL, and PPG &gt;150 mg/dL.

Viscoelastic Glucose Theory (VGT #10): Applying Elasticity Theory, Viscoelasticity Theory, and Energy Theory to Conduct a Comparison Study Between the Author’s Collected 101,595 Postprandial Plasma Glucose (PPG) Data and Synthesized Waveform During the Past 6.5 Years and Two Hypothetical Cases of PPG Data and Waveforms to Illustrate Viscoelastic PPG Behavior and Associated Relative Energy of PPG Based on GH-Method: Math-Physical

The author utilized a continuous glucose monitoring (CGM) sensor device to collect 96 glucoses daily along with 13 postprandial plasma glucose (PPG) data for each meal. To date, he has collected 7,815 meals PPG values (a total of 101,595 data) with an average carbs/sugar intake amount of 14.1 grams and an average post-meal walking of 4.325 k-steps. Recently, he has applied viscoelastic and viscoplastic glucose theory (VGT) to conduct his research on various biomarkers behaviors since almost all known biomarkers are time-dependent variables. Initially, he completed the PPG behavior analyses using 3 known methods: time-domain (TD), space-domain (SD), and frequency-domain (FD). They are used to study the PPG wave characteristics, viscoelastic stress-strain relationship, and PPG’s associated energy from TD and FD. Out of curiosity regarding the applicability of research findings of using his PPG data, he decided to construct two hypothetical cases. The first case was to construct one PPG waveform based on linear elastic glucose theory (LEGT) which is named as the “triangle case” due to its PPG waveform shape. The second case was to construct another PPG waveform which climbed from 0-minute to 60-minutes before remaining flat after 60-minutes; therefore, it is named as the “flat case” due to its PPG waveform shape after 60-minutes. Once the two PPG curves were constructed in TD, he followed the same procedures to build their respective spatial-diagram of stress-strain relationship for the viscoelastic validation and FD using fast Fourier Transform (FFT) for energy estimation.

Viscoelastic Glucose Theory (VGT #3): Similarity and Difference of Two StressStrain Diagrams for Viscoelastic Glucose Behaviors from Continuous Glucose Monitor Sensor Measured and Perturbation Theory Produced Postprandial Plasma Glucoses with “Time-Dependent” Characteristics of a Synthesized Postprandial Plasma Glucose Waveform Generated from 4,115 Meals over a 3.7-Year Period from 5/8/2018 to 1/18/2022 based on GH-Method: MathPhysical Medicine (No. 580)

The author has studied strength of materials and theory of elasticity through his undergraduate courses at the University of Iowa. He also conducted research work to earn a master’s degree in Biomechanics under Professor James Andrews. He remembers using the spring and dashpot models to simulate the behaviors of human joints, bones, muscles, and tendons in order to investigate the human-weapon interactions. Later, he went to MIT to pursue his PhD study under Professor Norman Jones, who taught him theory of plasticity and dynamic plastic behaviors of various structure elements. He also took additional graduate courses in the field of fluid dynamics and thermodynamics. Since then, many advancements have been made in a few application areas of biomechanics, especially tissues in the human body which possess viscoelastic characteristics, such as bones, muscles, cartilages, tendons (connect bone to muscle), ligaments (connect bone to bone), fascia, and skin. For example, the author suffered plantar fasciitis for many years. He understood that the night splint dorsiflexes forefoot, at the back of the foot, increases plantar fascia tension to offer stress-relaxation for the pain. This model of muscles and tendons connecting the lower leg and foot is a form of viscoelastic problem. However, when dealing with the human internal organs, it is not easy to conduct live experiments to obtain accurate measurements for the biomedical material properties. Although blood itself is a viscous (time-dependent) material, the viscosity factor may fall between water, honey, syrup, or gel. However, the author’s research subject is “glucose”, the carbohydrates and sugar amount in blood produced by the liver and carried by red blood cells, not the blood itself. It is nearly impossible to measure material geometry or certain engineering properties of glucose, for example, to determine the viscosity of “glucose”. Therefore, the best he could do is to apply the “concept of viscoelasticity and/or viscoplasticity” to construct an analogy model of time-dependent glucose behaviors.

A machine learning-based on-demand sweat glucose reporting platform

Diabetes is a chronic endocrine disease that occurs due to an imbalance in glucose levels and altering carbohydrate metabolism. It is a leading cause of morbidity, resulting in a reduced quality of life even in developed societies, primarily affected by a sedentary lifestyle and often leading to mortality. Keeping track of blood glucose levels noninvasively has been made possible due to diverse breakthroughs in wearable sensor technology coupled with holistic digital healthcare. Efficient glucose management has been revolutionized by the development of continuous glucose monitoring sensors and wearable, non/minimally invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. This paper presents a highly novel and completely non-invasive sweat sensor platform technology that can measure and report glucose concentrations from passively expressed human eccrine sweat using electrochemical impedance spectroscopy and affinity capture probe functionalized sensor surfaces. The sensor samples 1–5 µL of sweat from the wearer every 1–5 min and reports sweat glucose from a machine learning algorithm that samples the analytical reference values from the electrochemical sweat sensor. These values are then converted to continuous time-varying signals using the interpolation methodology. Supervised machine learning, the decision tree regression algorithm, shows the goodness of fit R2 of 0.94 was achieved with an RMSE value of 0.1 mg/dL. The output of the model was tested on three human subject datasets. The results were able to capture the glucose progression trend correctly. Sweet sensor platform technology demonstrates a dynamic response over the physiological sweat glucose range of 1–4 mg/dL measured from 3 human subjects. The technology described in the manuscript shows promise for real-time biomarkers such as glucose reporting from passively expressed human eccrine sweat.

External validation of a classifier of daily continuous glucose monitoring (CGM) profiles

As continuous glucose monitoring (CGM) sensors generate ever increasing amounts of CGM data, the need for methods to simplify the storage and analysis of this data becomes increasingly important. Lobo et al. developed a classifier of daily CGM profiles as an initial step in addressing this need. The classifier has several important applications including, but not limited to, data compression, data encryption, and indexing of databases. While the classifier has already successfully classified 99.0% of the 42,595 daily CGM profiles in a Test Set, this work presents an external validation using an external validation set (EVal Set) derived from 8 publicly available data sets. The Test Set and the EVal Set differ in terms of (but not limited to) demographics, data collection time periods, and data collection geographies. The classifier successfully classified 98.2% of the 137,030 daily CGM profiles in the EVal Set. Furthermore, each of the 483 distinct groups of classified daily CGM profiles from the EVal Set retains the same clinical characteristics as the corresponding group from the Test Set, as desired. Finally, the set of unclassified daily CGM profiles from the EVal Set retains the same statistical characteristics as the set of unclassified daily CGM profiles from the Test Set, as desired. These results establish the robustness and generalizability of the classifier: the performance of the classifier is unchanged despite the marked differences between the Test Set and the EVal Set.

Accuracy of Continuous Glucose Monitors for Inpatient Diabetes Management.

In hospitalized patients, continuous glucose monitoring (CGM) may improve glycemic control, prevent hypoglycemic events, and reduce staff workload compared with point-of-care (POC) capillary glucose monitoring. To evaluate CGM accuracy and safety of use in the inpatient setting, two versions of CGM sensors were placed on 43 and 34 adult patients with diabetes admitted to non-intensive care unit (ICU) medical wards, respectively. CGM accuracy relative to POC and safety of use were measured by calculating mean absolute relative difference (MARD) and by Clarke Error Grid (CEG) analysis. CGM version 2 had improved accuracy compared with CGM version 1 with MARD 17.7 compared with 21.4%. CGM accuracy did not change with POC value or with time of sensor wear. On CEG, 98.8% of paired values fell within acceptable zones A and B. Despite reduced accuracy compared with the outpatient setting, both versions of CGMs had acceptable safety profiles in the inpatient setting.