Continuous Glucose Monitoring Sensor Research Articles

A Double-Needle Gold-Silver Electrodes Continuous Glucose Monitoring Device

ObjectivesDiabetes is a serious, long-term disease and the use of continuous glucose monitoring sensors can reduce reliance on other painful invasive blood testing methods such as the finger blood glucose test. According to our work, a low-cost continuous glucose sensor has been developed based on electrochemical measurement techniques. MaterialsThe sensor is based on a two needles system; a gold and a silver electrode are integrated into a circular shaped electronic printed circuit board (PCB). The sensing part is based on biological electrochemical measurements. Glucose oxidase (Gox) was used as the active sensing element and ferrocene (Fc) as a mediator. Simple and low-cost coating methods were used; these methods are self-assembled monolayers and deep coating. This will reduce the final cost of the sensor as no expensive technique was used. The electrical subsystem contains a low-noise and low-power trans-impedance front-end as well as a single-chip low-power Bluetooth microcontroller with a 12-bit Analog-to-Digital Converter (ADC). ResultsThe sensor was tested in various concentrations of glucose. As a result of initial in vitro experiments, detailed analytical performance metrics are presented. The device has consistently shown a sensitivity of 3.059 mV/(mg/dl) reading with a linear range of 0-400 mg/dl. ConclusionThe proposed study shows promising results for glucose detection. Thus, this type of sensor can be used for different analyzes targeting biological applications after further investigations and analysis.

Ambulatory Glucose Profile of Type 2 Diabetes Patients with End-Stage Renal Disease Undergoing Hemodialysis

Background: Management of glucose levels in patients with diabetes mellitus and end-stage renal disease (ESRD) is challenging. There are various factors contributing to glycemic variability (GV), including altered metabolism of glucose and insulin in the ESRD stage. Continuous glucose monitoring (CGM) system measures glucose levels continuously and helps to study the glucose profile of patients undergoing hemodialysis (HD). This pilot study aimed to analyze the ambulatory glucose profile (AGP) of type 2 diabetes patients with ESRD undergoing HD and study the GV when patients were on HD (referred to as ON days) versus when patients were not on HD (referred as OFF days). Materials and Methods: We enrolled 10 patients with type 2 diabetes mellitus and ESRD undergoing maintenance HD in the study. Data regarding patient characteristics, including age, gender, duration of diabetes mellitus, HbA1c, and serum fructosamine, were collected. The AGP data obtained by the CGM sensor on the HD ON days was compared to AGP data on HD OFF days. Results: There was a significant linear correlation of CGM-derived average blood glucose with HbA1c value; however, the association was not significant with fructosamine level. The CGM-derived average glucose level was significantly lower during HD ON days compared to HD OFF days, difference being 29.9 mg/dl (−33.7, −26.0; P < 0.001), whereas GV was significantly higher during HD OFF days compared to ON days. Conclusion: The variation in the glucose levels was higher during the HD OFF days compared to HD ON days. Future AGP studies recruiting more number of patients on HD will help better understand the clinical impact of glucose profile and GV in ESRD patients.

Continuous Glucose Monitoring for Patients with COVID-19 Pneumonia: Initial Experience at a Tertiary Care Center

ObjectivePatients hospitalized with COVID-19 and hyperglycemia require frequent glucose monitoring, usually performed with glucometers. Continuous glucose monitors (CGMs) are common in the outpatient setting but not yet approved for hospital use. We evaluated CGM accuracy, safety for insulin dosing, and CGM clinical reliability in 20 adult patients hospitalized with COVID-19 and hyperglycemia. MethodsStudy patients were fitted with a remotely monitored CGM. CGM values were evaluated against glucometer readings. The CGM sensor calibration was performed if necessary. CGM values were used to dose insulin, without glucometer confirmation. ResultsCGM accuracy against glucometer, expressed as mean absolute relative difference (MARD), was calculated using 812 paired glucometer-CGM values. The aggregate MARD was 10.4%. For time in range and grades 1 and 2 hyperglycemia, MARD was 11.4%, 9.4%, and 9.1%, respectively, with a small variation between medical floors and intensive care units. There was no MARD correlation with mean arterial blood pressure levels, oxygen saturation, daily hemoglobin levels, and glomerular filtration rates. CGM clinical reliability was high, with 99.7% of the CGM values falling within the “safe” zones of Clarke error grid. After CGM placement, the frequency of glucometer measurements decreased from 5 to 3 and then 2 per day, reducing nurse presence in patient rooms and limiting viral exposure. ConclusionWith twice daily, on-demand calibration, the inpatient CGM use was safe for insulin dosing, decreasing the frequency of glucometer fingersticks. For glucose levels >70 mg/dL, CGMs showed adequate accuracy, without interference from vital and laboratory values.

Continuous glucose monitoring during therapeutic hypothermia for hypoxic ischaemic encephalopathy: a feasibility study

ObjectivesGlucose dysregulation is common in infants with hypoxic ischaemic encephalopathy (HIE) and is likely to exacerbate cerebral injury. Infrequent measurement of glucose concentrations makes both identification and prevention of this...

PSUN229 Transitions of Care and Readmissions in Patients with Diabetes Receiving CGM at Hospital Discharge

Abstract Background Use of inpatient continuous glucose monitoring (CGM) has increased during the COVID-19 pandemic. The objective of this study was to evaluate outpatient follow-up patterns and readmission rates for patients discharged from the hospital with CGM. Research Design and Methods We retrospectively identified patients with diabetes (DM) aged >18 years who were admitted between 3/27/20 and 5/13/21 and received Freestyle Libre or Freestyle Libre 2 14-day sensor at the time of discharge from the hospital. The sensor was placed in the hospital by our diabetic educator. Only the first admission was included. The medical record was reviewed to determine attendance at outpatient follow-up visit, CGM downloading and prescribing at that visit, and readmission within 30 days. Results 87 patient charts were analyzed (31% type 1 DM, 66% type 2 DM, 3% other). The most common reasons for CGM placement were patients new to insulin (40%), DKA (34%), new diabetes diagnosis (30%), or HHS (5%). Follow-up information was not available for 25 patients (29%). The remaining 62 patients had scheduled follow-up within our system. 42 of those patients (68%) attended their follow-up appointment while 14 patients (33%) attended follow-up in an endocrine clinic. Among the 42 patients who attended follow-up, 8 patients (19%) had CGM data downloaded and 18 patients (43%) were prescribed CGM. Of the patients who attended endocrine follow-up, 50% had CGM downloads, compared to only 4% of patients seen in non-endocrine clinics (p = 0.001). Similarly, 79% of patients with endocrine follow-up were prescribed CGM sensors, while 25% of patients seen in non-endocrine clinics received a CGM prescription (p = 0.001). For patients newly diagnosed with DM and available follow-up data, 89% (N=16) attended their appointment. Overall, 16% of patients were readmitted within 30 days. Of the patients readmitted, only 17% attended outpatient follow-up and none had CGM downloaded or prescribed. Backward binary logistic regression was used to assess the relationship between outcomes (follow-up attendance and readmission) and variables (including diagnosis and follow-up location) in the 62 patients with follow-up data. In the final models, a new diagnosis of DM was associated with follow-up attendance, and type 1 DM and follow-up nonattendance were associated with 30-day readmission. Results were verified using exact logistic regression. Conclusions Downloading and prescribing of CGM after hospital discharge occurred in less than half of patients, but occurred more frequently in endocrine clinics. A specialized transition of care clinic may benefit patients discharged with CGM. Follow-up attendance among readmitted patients was poor. Patients with a new diagnosis of DM had good follow-up attendance and may be good candidates for CGM at hospital discharge, which can be examined in future prospective studies. Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Lower Glucose Effectiveness Is Associated with Postprandial Hyperglycemia in Obese/Overweight Men, Independently of Insulin Secretion.

The role of glucose effectiveness on postprandial hyperglycemia in daily life is not fully studied. Here, we examined the association between SgIo, an index of glucose effectiveness calculated from a 75 g oral glucose tolerance test, and the indices of hyperglycemia in obese/overweight men. SgIo was significantly associated with 1,5-anhydroglycitol, a biochemical marker for postprandial hyperglycemia. The receiver operating characteristic analyses of SgIo and oral disposition index for detecting the subjects with 1,5-anhydroglycitol < 14 μg/mL revealed that the areas under the curves were 0.77 and 0.76, while the cutoff points (sensitivity, selectivity) were 2.53 (0.9, 0.7) and 2.06 (0.36, 0.79), respectively. Both the SgIo < 2.53 category and the disposition index < 2.06 category were significantly associated with the percentages of meals with postprandial glucose levels ≥ 200 mg/dL, and the percentages of time when continuous glucose monitoring sensor readings were ≥200 mg/dL. After adjustment with disposition index, 45.5% of the subjects with the SgIo < 2.53 category had their 1,5-anhydroglycitol < 14 μg/mL, while, in the SgIo ≥ 2.53 category, 3.6% of the subjects had the hyperglycemia (p < 0.001). In addition, there were tendencies toward higher and lower SgIo quartile categories in subjects with walking (≥8000 steps) ≥60% of days and with noodle ingestion ≥20% of meals, respectively (p for trend, 0.008 and 0.038). In conclusion, lower glucose effectiveness is associated with postprandial hyperglycemia in the daily life of obese/overweight men, independently of insulin secretion. Lifestyles such as habits of walking and noodle ingestion are significantly associated with higher and lower glucose effectiveness, respectively.

High-Linearity Hydrogel-Based Capacitive Sensor Based on Con A-Sugar Affinity and Low-Melting-Point Metal.

Continuous glucose monitoring (CGM) plays an important role in the treatment of diabetes. Affinity sensing based on the principle of reversible binding to glucose does not produce intermediates, and the specificity of concanavalin A (Con A) to glucose molecules helps to improve the anti-interference performance and long-term stability of CGM sensors. However, these affinity glucose sensors have some limitations in their linearity with a large detection range, and stable attachment of hydrogels to sensor electrodes is also challenging. In this study, a capacitive glucose sensor with high linearity and a wide detection range was proposed based on a glucose-responsive DexG–Con A hydrogel and a serpentine coplanar electrode made from a low-melting-point metal. The results show that within the glucose concentration range of 0–20 mM, the sensor can achieve high linearity (R2 = 0.94), with a sensitivity of 33.3 pF mM−1, and even with the larger glucose concentration range of 0–30 mM the sensor can achieve good linearity (R2 = 0.84). The sensor also shows resistance to disturbances of small molecules, good reversibility, and long-term stability. Due to its low cost, wide detection range, high linearity, good sensitivity, and biocompatibility, the sensor is expected to be used in the field of continuous monitoring of blood glucose.

Accuracy of Three HbA1C Equations and their Predicted Results in Comparison with the Lab-Tested A1C on 7/22/2021 Based on GH-Method: Math-Physical Medicine (No. 485)

Since 7/1/2015, the author has utilized his collected data of finger pierced glucose readings 4 times daily, carbs/sugar intake amount, and post-meal walking steps for each meal to calculate the predicted daily HbA1C values (the “daily finger A1C”). Over the past 5.5 years, the predicted HbA1C values were calculated 12 times within the same timeframe of the12 different lab-tested dates. During the 12 continuous 5-month time periods, he achieved a 100% prediction accuracy using his daily finger A1C model. Starting from 5/5/2018, along with finger glucose levels, he has been collecting 96 glucose data each day using a continuous glucose monitoring (CGM) sensor device until present day. Based on the collected CGM sensor glucoses, he further developed two extra HbA1C prediction models, the “sensor-1” A1C model using the combination of both average sensor glucoses and daily glucose fluctuations, and the “sensor-2” A1C model using the average sensor glucoses (eAG). On 7/22/2021, he performed his HbA1C test at a medical laboratory and received its latest quarterly HbA1C result of 6.3%. In conclusion, all three HbA1C prediction models (finger, sensor-1, and sensor-2) have yielded the same predicted HbA1C values of 6.3% which is identical to his lab-tested HbA1C value on 7/22/2021.

Quantification of the CVD/Stroke Risk Probability Due to the Extra Input of an Adjustment Factor of Glycemic Variability or Glucose Fluctuation Using Three Years of the Continuous Glucose Monitoring Sensor Device Collected Data Based on GH-Method: Math-Physical Medicine (No. 456)

Since 2017, the author utilized his collected data of finger pierced glucoses 4x per day, along with data of 10 metabolism index (MI) categories including 4 medical conditions and 6 lifestyle details over the period of 9.5 years, from 2012 to 2021. This is to estimate his annual risk probabilities of having a stroke, cardiovascular disease, diabetic kidney disease, diabetic retinopathy, Alzheimer’s disease, and certain cancers. Most of his research articles using the MI model have been published in various medical journals. The purpose of his previous risk assessment studies were aimed at determining his own risk reduction rates from improvements achieved to overall health conditions and MI, since he is a severe diabetes patient. Starting on 5/5/2018, along with the finger glucoses, he collects 96 data of glucose values per day for 1,095 days using a continuous glucose monitor (CGM) sensor device for a total of ~105,120 glucose data. Currently, he has accumulated 3+ years of sensor glucose data; therefore, he has enhanced his research work using his collected sensor glucose data.

Comparison of Waves and Energies between the 3-Hours Versus 24-Hours of Glucose Fluctuation using 3+ Years of Continuous Glucose Monitoring Sensor Device Collected Data Based on GH-Method: Math-Physical Medicine (No. 457)

Since 2017, the author utilized his collected data of finger pierced glucoses 4x per day, along with the data of 10 metabolism index (MI) categories including 4 medical conditions and 6 lifestyle details over a 9.5-year period, from 2012 to 2021, to estimate his risk probabilities of having diabetic complications. They include macro-vascular and micro-vascular diseases such as cardiovascular disease, stroke, diabetic kidney disease, diabetic retinopathy, foot ulcer, Alzheimer’s disease, and certain cancers. In addition to the mean value of glucoses, namely the average glucose such as HbA1C, the actual glucose excursion or glucose fluctuation (GF) has noticeable influences on these diabetic complications. Starting from 5/5/2018, along with the finger glucoses, he collected 96 data of glucose values per day for 1,120 days using a continuous glucose monitoring (CGM) sensor device for a total of 107,520 glucose data. Thus far, he has accumulated 3+ years of sensor glucose data; therefore, he would like to enhance his medical research work by using them. Especially with 96 glucose data collected per day, he is now able to easily study the phenomenon of glucose excursion, glucose wave vibration, or glucose data oscillation. The medical community has used the term “glycemic variability (GV)” to describe the glucose excursion which involves several defined GV equations with some inconclusive findings. The author believes that the word “variability” could mean many things; therefore, he decided to apply the same basic concept of glucose excursion or GF without using the other defined GV equations in order to deeply understand and precisely describe the basic biophysical phenomenon of “glucose excursion”. The author has been utilizing glucose fluctuation known as “Daily GF or 24-hour GF” over a 24-hour period in his research work each day. The definition of GF is the maximum glucose (usually around 60-minutes after a meal) minus the minimum glucose (usually around 3am to 4am during sleep) within 24-hours or another selected time period. Recently, he noticed the extremely high and extremely low glucoses frequently occurring within a shorter duration of 3 hours. Therefore, he has inserted a new algorithm of computation into his software program to dynamically calculate the difference between the maximum glucose and the minimum glucose within the moving duration of 3 hours, at15-minute increments throughout the day. By the end of a day, the largest number of GF, which is defined as 3-hour GF, is selected and stored on the cloud server

A Clinical Case Study for Weight, Glucose, and Glucose Fluctuations Using 138 Days of Sixteen-Hour Intermittent Fasting Data Based on GH-Method: Math-Physical Medicine (No. 452)

The author uses a continuous glucose monitoring sensor (CGMS) device to collect 96 sensor glucose values per day since 5/5/2018. He selects two distinctive time periods with 138 meals each. The first period, from 11/8/2020 to 5/17/2021, consists of 138 tea only meals (0.02 grams of carbohydrates each), where it combined with 16-hours of fasting, from 20:00 previous day through 12:00 next day. He names it “the intermittent fasting (IF) period”. The second period, from 6/22/2020 to 11/7/2020, comprises of 138 light breakfasts which contains egg, coffee, and an occasional half-toast (average at 4.5 grams of sugar and carbohydrates amount each, also known as “low-carbs” meals). He names it “the Non-IF period”.

Comparison of High Range and Low Range of Sensor eAG and 24-Hour Daily GF Using 3+ years of Continuous Glucose Monitoring Sensor Device Collected Data Based on GH-Method: Math-Physical Medicine (No. 458)

Since 5/5/2018, the author utilized a continuous glucose monitoring (CGM) sensor device to collect his glucoses 96 times each day. He then calculates his average daily sensor glucoses (eAG) and sensor glucose fluctuation (GF) within a 24-hour period each. His GF is defined as the maximum glucose value minus the minimum glucose value within a day. The definition of “eAG” is the mean value of glucose data that is similar to HbA1C which is useful in diabetes control. Moreover, the glucose excursion or GF has noticeable influences on various diabetes complications. During the period of 1,218 days from 5/5/2018 through 5/31/2021, he has collected a total of 116,928 glucose data. With this big data accumulated for over 3+ years and stored on a cloud server, it is easily for him to study and observe the overall glucose changes from day to day along with the phenomenon of his daily GF changes. During the past decade, the medical community has used the term “glycemic variability (GV)” to describe the glucose excursion which involves some questionable definitions of mathematical equations with less-quantitative and somewhat inconclusive findings. It is the author’s belief that the word “variability” could mean many things to different people; therefore, he decides to apply the basic concept of glucose excursion (fluctuation) without using the defined GV equation. This will allow him to have a better understanding and achieve a deeper appreciation for the important biophysical phenomenon of “glucose fluctuation”. Many research publications have covered the importance and impact of GV or GF on diabetic macro-vascular and microvascular complications (References 16 and 17). In those publications, it has defined and “qualitatively proven” that GF does impact the macro-vascular system, including the heart and brain, as well as the micro-vascular system such as kidneys, feet, eyes, nerves, etc. This particular report adopts the author’s developed GH-Method: math-physical medicine to seek more quantitatively described results. Hopefully, it can provide a different but still accurate enough description to complement those using biochemical medicine interpretations of glucose and glucose fluctuations.

Continuous Glucose Monitoring Sensor Glucose Data Analysis of Time in Range, Time Above Range, Time Below Range and Waveform Comparison Study of Time in Range Curve Against both Average Daily Glucose Curve and Glucose Fluctuation Curve for a 3.3 Year Period based on GH-Method: Math-Physical Medicine (No. 500)

The author applies quantitative pattern and trend analysis tools using his collected continuous glucose monitoring (CGM) sensor data during a 3.3 year period from 5/8/2018 to 8/25/2021. Special attention has been placed on applying the American Diabetes Association (ADA) 2020 Guidelines for Time In Range (TIR) % with TIR average glucose values, Time Above Range (TAR) % with TAR average glucose value, and Time Below Range (TBR) % with TBR average glucose value. The purpose of this article is to study the correlations between the TIR curve and both average daily glucose curve (eAG) and glucose fluctuation curve (GF). GF is defined as the difference between the maximum glucose and the minimum glucose within one day or 24 hours duration. This GF term expresses the same meaning as the glycemic variability (GV) used by some diabetes research scientists. However, the author prefers GF over GV since GF describes the amplitude of glucose excursion in an exact and most direct way. Depending on the object of his research project, he sometimes selects the GF value within a meal’s PPG waveform of a 3-hours duration. Furthermore, the “primary range” of TIR is defined between 70 mg/dL and 180 mg/dL while a “secondary range” of TIR is defined between 70 mg/dL and 140 mg/dL. Of course, both the TIR’s and TAR’s percentages and their averaged values of secondary range (difference of 70 mg/dL) are smaller than the primary range results due to the secondary range’s narrower glucose range of 40 mg/dL (110-70 or 180-140). In summary, there are five noticeable findings from this study: (1) TIR: His TIR percentage (89%) and average TIR value (121 mg/dL) of the primary range, along with the TIR percentage (72%) and average TBR value (114 mg/dL) of the secondary range represent the majority of the data. This means that his type 2 diabetes (T2D) is quite well under control. (2) TBR: His TBR percentage (5%) and average TBR value (65 mg/dL) of the primary range, along with the same TBR percentage (5%) and the same average TBR value (65 mg/dL) of the secondary range contain rather small amounts. This means that his risk of having hypoglycemia (insulin shock) is relatively low.

A Comparison of CVD/Stroke Risk Using Metabolism Index and Continuous Glucose Monitor Sensor Collected Glucose Fluctuations over Six and Twelve Periods Based on GH-Method: Math-Physical Medicine (No. 446)

In this article, the author investigates the risk differences of having cardiovascular disease (CVD) or stroke over six equallength periods (~180 days for each period) and twelve equal-length periods (~90 days for each period) using both metabolism index (MI) and glucose fluctuations (GF). Through the introduction of GF influential factor into his developed risk assessment method, physical and engineering modeling, and mathematical equations, he is able to calculate the risk probability of having severe diabetes related complications, especially for macro-vascular (arteries) diseases, such as CVD and stroke. The author included a few excepts from other research papers regarding the subject of glycemic variability (GV) and the reason he adopts GF instead of using GV in his research work due to GF’s non-ambiguity of definition and ease-of-collection of data. Of course, the advantages of using GF to replace GV has become easier due to the wide acceptance of the continuous glucose monitor (CGM) sensor device for diabetes patients.

Risk Probability of Having Cardiovascular Disease Resulted from the Combination of Metabolism Index and Glucose Fluctuation using a Continuous Glucose Monitoring Sensor Collected Daily Glucose Data over 1,170 Days Based on GH-Method: Math-Physical Medicine (No. 483)

Recently, the author read an article published in the medical journal of Cardiovascular Diabetologist on July 4, 2020, and selected the following excerpt: “Although Glycemic variability (GV) remains yet no consensus, accumulating evidence has suggested that GV, representing either short-term (with-day and between-day variability) or long-term GV, was associated with an increased risk of diabetic macro-vascular and microvascular complications, hypoglycemia, mortality rates and other adverse clinical outcomes.” The author has selected a few excepts from other research papers regarding the subject of glycemic variability (GV). Of course, the advantages of using glucose fluctuations (GF) has become easier due to the wide acceptance of the continuous glucose monitor (CGM) sensor device for diabetes patients in recent years.

Three Predicted HbA1C Equations and Results in Comparison with Lab-Tested A1C from 12 Discrete Lab-Tested Dates over a 3-Year Period Based on GHMethod: Math-Physical Medicine (No. 467)

The author has utilized his collected data of finger pierced glucose readings 4x daily, carbs-sugar intake amount and postmeal walking steps for each meal over a 4-year period, from 2017 to 2020, to calculate the predicted HbA1C values (daily finger A1C). His previously predicted A1C values were conducted 10x close to 10 different lab-tested dates over 5 months for each period. During the 10 continuous 5-month periods, he achieved a 100% prediction accuracy using the daily finger A1C model. Starting from 5/5/2018, along with the finger glucoses, he collected 96 glucose data per day for 1,127 days using a continuous glucose monitoring (CGM) sensor device for a total of ~105,120 glucose data. He noticed that from 5/5/2018 through 6/5/2019, his average daily sensor glucose (123.96 mg/dL) is 12% higher than his average daily finger glucose (110.72 mg/ dL); therefore, if he uses the same formula for predicting HbA1C, it will result in a 12% higher Sensor A1C (7.39%) than his finger A1C (6.60%). In order to match his predicted sensor A1C with the lab A1C, he must multiply the average sensor glucose with a conversion factor to obtain the HbA1C value. In this article, he uses the 90-days moving average daily glucose data, eAG as his calculation base, which applies to the following three different equations as his predicted HbA1C formula with a conversion factor (CF): (a) Daily A1C = (finger eAG) / 16.84 (b) New A1C-1 = (29% * sensor eAG +71% * GF) / 15.75 (c) New A1C-2 = (sensor eAG) / 18.86 The 3 conversion factors, 16.84, 15,75, 18.86, are the best-fitted CF values via a trial-and-error approach in order to make his predicted-A1C as close to the lab-A1C as possible. It should be noted that the New A1C-1 includes the influences from the glucose fluctuation (GF) factor. The GF influenced the outcomes of diabetes complications such as Stroke, Atherosclerosis, and cardiovascular disease. Furthermore, by choosing a high weighting factor of 71% for GF, it would modify the basic characteristics of the traditionally defined HbA1C. For example, the New A1C-1 has a different waveform shape from the daily finger A1C and New A1C-2 (daily sensor A1C) under the influences of eAG only.

A Study of Postprandial Plasma Glucose Changes, Including Glucose Fluctuations, among three Time Periods Using GH-Method: Math-Physical Medicine (No. 439)

The author uses a continuous glucose monitoring sensor device (CGMS) to collect his glucose data at 15-minute time intervals from 5/5/2018 to 4/21/2021 which contains 1,074 breakfasts. He selects three similar time periods with equal number of breakfasts of 164 for each period. The first period is Case A from 11/8/2018 to 4/20/2019, and without any fasting days. The second period is Case B from 11/8/2019 to 4/20/2020, and also without any fasting days. The third period is Case C from 11/8/2020 to 4/20/2021, but with 115 fasting days (70% of intermittent fasting, drinking tea only as his breakfast). The first two non-fasting periods, Case A and Case B have normal breakfasts. This specific study includes two investigations. The first part examines the changes on his postprandial plasma glucoses (PPG). The second part analyzes PPG wave fluctuations i.e., glycemic variability (GV) or glucose fluctuations (GF). The objective of this study is to explore any significant differences on his PPG magnitude, and his GF between Case C versus both Case A and Case B. The key conclusion is that the Case C (with 70% of IF influences) has indeed shown significant differences versus those two selected non-IF Case A and Case B in regard to breakfast PPG, daily PPG, daily glucose (eAG), and body weight. Even the averaged breakfast glucose fluctuation of Case C (11 mg/dL) is significantly less than those two Non-IF cases (19 mg/dL for Case B and 27 mg/dL for Case A). However, in terms of Case C’s daily glucose GF (~95 mg/dL) and PPG GF (~44 mg/ dL), the Case C has demonstrated comparable GF values as both Case A and Case B. His previous conclusion from his paper No. 438 (Reference 33) was that the IF case did not show any significant differences from the two selected non-IF cases in regard to PPG, daily glucose, and body weight. But now inserting above findings from this particular study’s paper No. 439, his combined and modified conclusions are that the If effect on our body, both Glucose, Weight, and internal organ impact, is a “longer-term” concerning factor. We ought to be ultra-careful about drawing any conclusion quickly from a short time period with a limited amount of data. In addition, the overall lifestyles has a direct impact on medical conditions which out to be included in the IF study as well. For example, in comparison, Case A has the worst outcomes and Case C has the best outcomes while Case B is in the middle. This observation can be verified through his carbs/sugar intake amounts and body weights.

Open-Source-Systeme zur automatisierten Insulinabgabe

New developments and technological advancements in diabetes therapies are expected not only to improve metabolic outcomes of people with diabetes, but also to reduce the complexity of diabetes management and consequently their psychosocial burden. Automated insulin delivery (AID) systems—in which continuous glucose monitoring sensors and insulin pumps are linked to control algorithms that automate numerous dose decisions, mimicking the insulin secretion of an islet cell—represent an important new therapeutic approach. Because the development and approval of new therapies is costly and time-consuming, people with diabetes and their loved ones have taken action themselves. Behind the hashtag #WeAreNotWaiting is a community developing open-source AID systems for use with currently available medical devices; the source code and instructions for use are made freely available worldwide. More than 10,000 children and adults are already using open-source AID systems, and studies have demonstrated their safety and efficacy, as well as their positive impact on the quality of life of users. Due to the unclear legal situation regarding the use of unapproved therapy methods at one’s own risk, an international consensus paper has been developed by the EU-funded OPEN project, which provides supportive information for health professionals in the therapy support of “loopers”. In addition to the current state of studies, this article discusses what can be learned from the #WeAreNotWaiting movement and how we can achieve the common goal of making healthcare innovations accessible to as many patients as possible in the future.

Feasibility of Using a Factory-Calibrated Continuous Glucose Monitoring System to Diagnose Type 2 Diabetes.

Context: Plasma glucose or A1C criteria can be used to establish the diagnosis of type 2 diabetes (T2D). Objective: We examined whether continuous glucose monitoring (CGM) data from a single 10-day wear period could form the basis of an alternative diagnostic test for T2D. Design: We developed a binary classification diagnostic CGM (dCGM) algorithm using a dataset of 716 individual CGM sensor sessions from 563 participants with associated A1C measurements from seven clinical trials. Data from 470 participants were used for training and 93 participants for testing (49 normoglycemic [A1C <5.7%], 27 prediabetes, and 17 T2D [A1C ≥6.5%] not using pharmacotherapy). dCGM performance was evaluated against the accompanying A1C measurement, which was assumed to provide the correct diagnosis. Results: The dCGM algorithm's overall sensitivity, specificity, positive predictive value, and negative predictive value were 71%, 93%, 71%, and 93%, respectively. At other clinically relevant A1C thresholds, dCGM specificity among normoglycemic participants was 98% (48/49 correctly classified), and for participants with suboptimally controlled diabetes (A1C ≥7%, above the American Diabetes Association recommended A1C goal) the sensitivity was 100% (8/8 participants correctly diagnosed with T2D). Conclusions: Classifications based on the dCGM algorithm were in good agreement with traditional methods based on A1C. The dCGM algorithm may provide an alternative method for screening and diagnosing T2D, and warrants further investigation.

A stable glucose sensor with direct electron transfer, based on glucose dehydrogenase and chitosan hydro bonded multi-walled carbon nanotubes

Direct electron transfer (DET) glucose sensors are third-generation biosensors, which allow the enzyme to directly transfer electrons by glucose oxidation without a mediator. In previous studies, many researchers have focused on the glucose oxidase (GOx)-based DET glucose sensor; however, an efficient DET glucose sensor using flavin adenine dinucleotide (FAD)-glucose dehydrogenase (GDH) has not been developed. In the present study, we bound FAD-GDH and multi-walled carbon nanotubes (MWCNT), using chitosan (CS) compounds that support hydrogen bonding, van der Waals forces, and 3D structural adsorption. The GDH/CS-MWCNT-5 composite, which had a GDH concentration of 75 wt%, was physically adsorbed on screen-printed carbon electrodes (SPCEs), and cyclic voltammetry indicated that its oxidation and reduction peaks were at − 0.422 V and − 0.543 V (vs Ag/AgCl), respectively. In addition, the electrochemical results confirmed that the prepared GDH/CS-MWCNT/SPCEs were not affected by other interfering substances or oxygen at pH of 7. The GDH/CS-MWCNT/SPCEs displayed oxidation catalytic currents, which increased according to glucose concentrations across a range of 0–5.5 mM. Finally, the short-term stability of glucose, assessed for 10 days, was maintained at 80% of the GDH enzyme activity for 6 days, and it reduced to 50% of the initial activity for the remaining 4 days. Here, we illustrate the potential utility of the FAD-GDH-based DET method in continuous glucose monitoring sensors.