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Abstract

Background:

Continuous glucose monitoring (CGM) is becoming a standard diagnostic procedure in people with diabetes. However, little is known about how glucose oxidase-based interstitial sensors react to potentially interfering substances. We report on in vitro dynamic interference testing with Dexcom G6 sensors.

Methods:

Using pressure liquid chromatography (HPLC) pump-controlled substance gradients, we exposed sensors to varying concentrations of 69 substances in triplicate using YSI Stat 2300 Plus as glucose reference.

Results:

Interference was assumed if the device showed more than ±10% bias over baseline, and it was seen for Dexcom G6 with the following substances: acetaminophen, dithiothreitol, ethyl alcohol, galactose, gentisic acid, hydroxyurea, l-cysteine, l-dopa, mannose, N-acetyl-cysteine, and uric acid. Additionally, sensors could not subsequently be calibrated for use after exposure to dithiothreitol, gentisic acid, l-cysteine, and mesalazine (sensor fouling). Interference testing identified several nutritional, pharmacological, and endogenous substances that biased the Dexcom G6 signal in vitro.

Conclusions:

The in vitro test protocol cannot replace appropriate clinical testing, but if confirmed clinically, such interferences should be considered when making treatment decisions using Dexcom G6 readings. More research on the interference behavior of CGM sensors is strongly recommended before using them broadly in automated insulin delivery systems with insulin pumps.

Introduction

Continuous glucose monitoring (CGM) is an increasingly popular decision support tool for diabetes management for insulin-treated people with diabetes.¹ Use of CGM reduces frequency of hypoglycemia and hyperglycemia and improves overall glycemia management.^2–7 Many users report improvements in quality of life and treatment satisfaction.^8–10 However, no CGM device fulfills the ISO standards specifically designed for blood glucose measurement systems.^11,12 Clinically inexplicable deviations between sensor and blood glucose readings are often observed, with both factory-calibrated and user-calibrated sensors showing large—and sometimes clinically dangerous—differences to blood glucose, unexplained by well-characterized glucose dynamics between interstitial fluid and blood. A number of sensors also prematurely cease operating.^12,13 In response, health care providers and patients request efforts to understand the impact of food components, nutritional supplements, drugs, and endogenous substances on CGM sensor performance.^14,15

Without suitable in vitro test methods, expensive and resource-consuming clinical trials appeared to be the only way to investigate sensor performance. Thus, published literature regarding potential drug interference on, for example, Dexcom G6 (G6) (Dexcom, Inc., San Diego, CA, USA) sensors is sparse. Maahs et al. and Basu et al.^16,17 reported data from clinical studies investigating CGM systems (including Dexcom Seven Plus and Dexcom G4 Platinum) after oral acetaminophen in volunteers without diabetes. They observed clinically relevant interference with CGM glucose measurements that coincided with the appearance of acetaminophen in interstitial fluid. In a follow-up report, Basu and coworkers compared CGM glucose with plasma glucose after oral administration of one of several substances. They showed lisinopril, albuterol, acetaminophen, and even red wine appeared to interfere with CGM devices.¹⁸ In parallel, manufacturers worked to improve sensor performance. In 2018, Calhoun and coworkers reported that a G6 device iteration using a permselective membrane appeared to reduce acetaminophen interference.¹⁹ Accuracy of the G6 system was unaffected by acetaminophen up to a maximum dose of 1000 mg every 6 h. Nevertheless, taking higher doses, for example, 1000 mg every 4 h, may falsely raise CGM sensor readings.²⁰ In routine use, this interference increases the risk for hypoglycemia.^19–21 Additionally, individuals taking hydroxyurea should use an alternative glucose monitoring method, as this drug was recently shown to alter the G6 signal.^22–25

There is a need for cost-effective, large-scale, in vitro substance screening with CGM sensors for potential clinically relevant interfering substances and to effectively rule out noninterfering substances. We therefore developed an in vitro bench-based test system and protocol for dynamic interference evaluations in the laboratory. Our published experimental setup allows for sensor accuracy investigations at stable or dynamically changing glucose concentrations and for evaluating dynamic concentration changes of potentially interfering substances.²⁶ Here, we report results of an evaluation of G6 sensors with a panel of individual food components, nutritional supplements, drugs, and endogenous substances.

Methods

Our in vitro test setup allows detection of interfering substances by dynamic and continuous testing of CGM sensor devices. We employed our standard protocol²⁶ to identify substances that might influence readings of a G6 sensor. In brief, a 3D-printed test bench (PPE block, 15 × 15 × 4 cm) with an engraved channel (2 × 10 × 500 mm) was equipped with three G6 sensors. The channel was filled with a continuous flow of glucose at a concentration of 200 mg/dL in PBS buffer (pH 7.2) using an HPLC gradient pump (Waters 2695, Waters, Eschborn, Germany) at a flow rate of 1 mL/min. Another HPLC pump added varying concentrations of a substance for interference testing.

Samples for testing with a reference method (YSI Stat 2300 Plus, YSI, Yellow Springs, OH, USA) were taken at standardized time intervals during each experiment.

All experiments were run at fluid temperatures of 37°C and stable, ambient partial oxygen pressure (0.21 bar). After a conditioning period of 1 h (HPLC pump 1; buffer solution), the three G6 sensors were calibrated to the employed 200 mg/dL glucose concentration (added via HPLC pump 2). We followed a standard protocol, with the glucose level raised, then maintained at 200 mg/dL to generate baseline readings, followed after 30 min by an increasing concentration of the test substance. First, the test substance concentration was raised dynamically and linearly to the maximum concentration over 60 min and then was maintained at this level for 30 min. Second, the test substance concentration was decreased to 0 over 60 min and then maintained for another 30 min.

For analysis, the mean of the readings from the three sensors tested in serial order was calculated and compared with baseline. The dead volume of 10 mL between the HPLC pump and the sensors was corrected by employing a 10-min time shift. A time-shift correction for the YSI results (related to the middle sensor) was used to account for the delay of the reference readings induced by the experimental setup.²⁶ The percent bias over baseline (BOB) was calculated.²⁶ We have employed the U.S. Food and Drug Administration’s definition for interference for blood glucose test strips as our definition of interference for the CGM sensors (a deviation of >±10% between the sensor reading in the presence of the interfering substance versus baseline).²⁷ With our protocol, it is possible to identify an interference cutoff concentration (ICC) at which a substance exceeds ±10% BOB. Where information exists about the physiological or pharmacological concentrations of the substance in the interstitial fluid, it may be possible to estimate the clinical relevance of the observed findings.

Results

We investigated 69 individual substances with G6 sensors employing our setup and protocol. Substantial interference (BOB >±10%) was seen for G6 with the following 11 (11/69 substances: acetaminophen, dithiothreitol, ethyl alcohol, galactose, gentisic acid, hydroxyurea, l-cysteine, l-dopa, mannose, N-acetyl-cysteine, and uric acid), while no BOB interference was seen with the other tested substances. Additionally, mesalazine exposure affected subsequent sensor response (described below) while not leading to a significant BOB.

The results for each interfering substance, together with the ICC resulting in interference (>±10% BOB), and the results for noninterfering substances are provided in Table 1. Graphical presentations for an example noninterfering substance (rivaroxaban) and for the 11 identified BOB interfering substances are provided in Figures 1–3.

FIG. 1.

Glucose signal read-out results of the dynamic interference experiments. Sensors are exposed to dynamically and linearly increasing and decreasing concentrations of the substance (gradient) at a fixed glucose concentration (200 mg/dL). (A) shows a representative example (rivaroxaban) for the results observed with a noninterfering substance. (B–D) show the results obtained with the interfering substances acetaminophen, ethyl alcohol, and galactose, respectively.

FIG. 2.

Glucose signal read-out results of the dynamic interference experiments. Sensors are exposed to dynamically and linearly increasing and decreasing concentrations of the substance (gradient) at a fixed glucose concentration (200 mg/dL). (A–D) show the results obtained with the interfering substances hydroxyurea, mannose, N-acetyl-cysteine, and uric acid, respectively.

FIG. 3.

Glucose signal read-out results of the dynamic interference experiments. Sensors are exposed to dynamically and linearly increasing and decreasing concentrations of the substance (gradient) at a fixed glucose concentration (200 mg/dL). (A–C) show the results obtained with the interfering substances dithiothreitol, gentisic acid, and l-cysteine, respectively, that also resulted in subsequent sensor failure (sensor fouling). (D) shows the results obtained with the interfering substance l-dopa.

Table 1.

Results of Substances Identified as Interfering with Dexcom G6 in the Performed Dynamic Interference Testing Experiments and Results Obtained with the Noninterfering Substances

Substance	Maximumconcentration tested	BOB at maximumconcentration tested	ICC	Type of substance
Interfering substances
Acetaminophen	20 mg/dL	>100%	5.87 mg/dL	Drug
Dithiothreitol	6 mg/dL	−18%	n/a^a,b	Drug
Ethyl alcohol	316 mg/dL	+12%	300 mg/dL	Drug, nutrient
Galactose	300 mg/dL	+17%	212 mg/dL	Nutrient
Gentisic acid	1.5 mg/dL	+18%	1.0 mg/dL^a	Drug
Hydroxyurea	9.12 mg/dL	>100%	0.76 mg/dL	Drug
l-cysteine	5 mg/dL	−25%	2.5 mg/dL^a	Nutrient
l-dopa	0.75 mg/dL	+11%	0.5 mg/dL	Drug
Mannose	300 mg/dL	+20%	155 mg/dL	Endogenous, nutrient
Mesalazine	0.14 mg/dL	+0%	—^a,c	Drug
N-acetyl-cysteine	55.4 mg/dL	+18%	9.2 mg/dL	Drug
Uric acid	23.5 mg/dL	+33%	5.9 mg/dL	Endogeneous
Noninterfering substances
Ascorbic acid	6 mg/dL	+6%	—	Nutrient
Alpha-tocopherol	2 mg/dL	+4%	—	Nutrient
Amoxicillin	5 mg/dL	+2%	—	Drug
Atenolol	0.09 mg/dL	+3%	—	Drug
Atorvastatin	0.012 mg/dL	−1%	—	Drug
Bilirubin conjugated	50 mg/dL	+7%	—	Endogeneous
Bilirubin unconjugated	40 mg/dL	+2%	—	Endogeneous
Budensonide	230 ng/dL	+2%	—	Drug
Caffeine	5 mg/dL	+3%	—	Nutrient
Cholesterol	500 mg/dL	+2%	—	Endogeneous
Codeine	0.015 mg/dL	+1%	—	Drug
Creatinine	15 mg/dL	+1%	—	Endogeneous
D-Fructose	18 mg/dL	+2%	—	Nutrient
Dextromethorphan	5.1 µg/dL	+2%	—	Drug
Diclofenac	11.5 mg/dL	+2%	—	Drug
Dihydrolipoate	1 mg/dL	+3%	—	Drug
Diphenhydramine	0.011 mg/dL	+0%	—	Drug
Dopamine	0.09 mg/dL	+1%	—	Endogeneous
Doxylamine succinate	0.01 mg/dL	+3%	—	Drug
Dulaglutide	0.0115 mg/dL	+3%	—	Drug
EDTA	200 mg/dL	+0%	—	Drug
Empagliflozin	10 mg/dL	+1%	—	Drug
Enalapril	0.01 mg/dL	−2%	—	Drug
Ephedrine	12 mg/dL	+0%	—	Drug
Gemfibrozil	6.2 mg/dL	+1%	—	Drug
Glimepiride	0.055 mg/dL	+3%	—	Drug
Guaifenesin	0.15 mg/dL	−2%	—	Drug
Heparin	2.78 mg/dL	−1%	—	Drug
Hydrochlorothiazide	0.05 mg/dL	−1%	—	Drug
Ibuprofen	50 mg/dL	+2%	—	Drug
Icodextrin	224 mg/dL	−2%	—	Drug
Insulin	500 mg/dL	+2%	—	Endogeneous, drug
Isomalt	0.09 mg/dL	+2%	—	Nutrient
Lacitol	0.09 mg/dL	+4%	—	Sugar alcohol
Lactate	30 mg/dL	+2%	—	Endogeneous
Lactose	20 mg/dL	+1%	—	Nutrient
l-glutathione reduced	4.6 mg/dL	+1%	—	Endogeneous
Losartan	0.075 mg/dL	+1%	—	Drug
Mannitol	1800 mg/dL	−1%	—	Nutrient
Malitol	0.09 mg/dL	+2%	—	Sugar alcohol
Maltose	480 mg/dL	+9%	—	Nutrient
Metformin	0.5 mg/dL	+1%	—	Drug
Methyldopa	2 mg/dL	+7%	—	Drug
Methylprednisolone	0.05 mg/dL	+0%	—	Drug
Naproxen	7.8 mg/dL	−2%	—	Drug
Quinidine	6.2 mg/dL	+2%	—	Drug, nutrient
Phenylephedrine	0.065 mg/dL	+2%	—	Drug
Red wine	3.8 mL/dL	−5%	—	Nutrient
Resveratrol	0.08 mg/dL	−4%	—	Drug, nutrient
Rivaroxaban	0.03 mg/dL	+3%	—	Drug
Salicylic acid	60 mg/dL	+5%	—	Drug
Sitagliptin	0.0233 mg/dL	+2%	—	Drug
Sorbitol	0.09 mg/dL	+4%	—	Sugar alcohol
Tetracycline	0.5 mg/dL	+2%	—	Drug
Urea	24 mg/dL	+4%	—	Endogeneous
Xylitol	0.09 mg/dL	+2%	—	Sugar alcohol
Xylose	300 mg/dL	+7%	—	Nutrient

Substance that caused sensor fouling, such that after exposure to this substance, the sensors could not subsequently be calibrated for ongoing use.

It was not possible to determine which concentration induced sensor fouling.

Mesalazine did not cause interference but subsequently led to sensor failure.

BOB, percent bias over baseline; ICC, interference cut-off concentration = concentration at which >±10% BOB was reached during the experiment.

Exposure of the G6 sensors to four substances resulted in behavior that we attributed to sensor fouling. We observed a decreasing signal after exposure to higher concentrations of dithiothreitol, gentisic acid, l-cysteine, and mesalazine, such that the G6 sensors could not be calibrated for ongoing use or ceased showing results. After such exposure, the sensors needed to be discarded. In the case of mesalazine, while the fouling effect was observed on sensor performance, mesalazine itself exhibited a nonsignificant BOB effect. Thus, for clarity, Table 1 lists 12 substances having an interfering and/or fouling effect on the G6 sensor, 3 of which exhibited both a significant BOB interference and sensor fouling, and 1 of which exhibited only sensor fouling.

Discussion

To facilitate economic, large-scale screening for substances causing CGM sensor interference, we developed (26), and have now further investigated, a novel in vitro setup for dynamic CGM interference testing.

Our protocol uses a stable glucose concentration, while the sensors are exposed to increasing and decreasing concentrations of potentially interfering substances. Therefore, observed changes in the sensor signal are not related to changes in glucose concentration but instead to the influence of the tested substance. As shown in the figures 1 and 2, substances exhibiting an interfering effect caused an increasing BOB signal that resolved with 8 of the 11 identified interferents after the interfering substance concentration was reduced back to zero. In the case of four of the tested substances (dithiothreitol, gentistic acid, l-cysteine, and mesalazine), a substance-induced effect persisted, even after complete washout of the substance. It is reasonable to speculate that irreversible modifications to the sensing electrode were induced by these substances.

With this in vitro protocol, we have not only confirmed the previously published and clinically observed impacts of hydroxyurea and acetaminophen on G6 sensor performance,^16–25 but we have now also identified 10 additional substances that potentially interfere with the G6 sensor via BOB signal and/or sensor fouling that are not reported in product labeling. The four substances with an apparent fouling effect may conceivably induce irreversible damage to in situ sensors. Because of the continuous and dynamic nature of our protocol, we can determine the minimum concentration at which a substance exhibits an interfering effect (BOB >±10%). If information regarding the concentration of a substance in interstitial fluid is known or becomes available, it may be possible to predict potential clinical implications of our findings on patient care.

We recognize limitations within this study. In vitro assessments, while being of potential value for substance screening, can never fully substitute for in vivo testing due to complex pharmacokinetic factors, such as substance distribution between blood and interstitial fluid and the potential for metabolic conversion of interfering substances to noninterfering metabolites and vice versa. The CLSI EP37 supplemental tables²⁸ recommending interferent test levels were not necessarily followed, since these levels are based on representative values in blood, with equivalent interstitial fluid values being unavailable. However, the dynamic nature of our protocol means that interferent concentrations were not fixed, thus allowing us to identify the threshold concentration at which a substance interferes. Furthermore, our study design permitted CGM sensor reusage, requiring sensors to be recalibrated within the test bench. Thus, individual sensors may have been preexposed to different test substances, although repeat exposure of sensors to interferences may better reflect real-world sensor use and warrants future investigation. These recalibrations may also be the reason for the differences in variability between the three sensors in different experiments. We are reporting the results as they have been measured and documented and can only speculate that the calibration algorithms provided in the G6 software may not be optimized for an in vitro setup. The same is true for the finding that for some experiments, the mean sensor results do not match the 200 mg/dL glucose concentration after calibration. However, as long as the indicated glucose level was initially stable at a different level, this does not compromise the validity of our interference calculations.

An important issue to address in subsequent clinical studies is what concentrations of interfering substances identified by our screening protocol can be expected in interstitial fluid in vivo. If these concentrations reach or exceed the experimentally determined ICC, interference seems likely to occur clinically. The hydrodynamics of glucose transfer from blood to the interstitium are well known,²⁹ and it is widely understood that most molecular substances found in blood are also found in interstitial fluid, albeit at different concentrations to blood, as demonstrated by the in vivo microdialysis investigation by Basu et al.¹⁷

Disturbingly, there are clinical scenarios where an unknown or undetected interferent affecting the sensor reading may potentially result in patient harm. More obviously, an interferent causing a false elevation in the CGM glucose reading could result in an unnecessary insulin dose, which may result in hypoglycemia. More subtly, calibrating a sensor while an interferent that causes a falsely elevated glucose reading is present may lead to inaccurate lower glucose readings when the interferent is no longer present, potentially leading to subsequent prolonged periods of hyperglycemia if the user tries to raise glucose readings to target range. These persistent or repeated falsely lower CGM readings could cause the patient to skip insulin or other medication doses or ingest unnecessary carbs, potentially leading to an unexplained high HbA1c, hyperglycemia symptoms, undesired weight gain, or even an episode of diabetic ketoacidosis, a potentially fatal condition.

The impact of potential interfering substances on the accuracy of sensors for CGM is currently poorly understood and under-investigated. Given that hospitalized people often receive a number of medications, understanding the nature and degree of interference is a prerequisite to employing CGM sensors in critically ill patients despite what has been recently suggested.^25,30,31 The impact of potential interfering substances on a hybrid or a future fully autonomous closed-loop system must also be considered.^25,32

CGM sensors should be subjected to thorough investigations of substance interference. To eliminate potential endogenous interference (e.g., uric acid—if confirmed in clinical studies), we advise G6 users to always perform the optional calibration procedure and to not run the sensor without calibration. We consider the G6 calibration option an advantage in the still unclear interference situation. As already indicated in the device instructions for use, it is recommended to perform more than just the one recommended sensor calibration and compare sensor readings with blood glucose meter readings, especially when seeing unexpected or unexplainable interstitial glucose readings. This is important as unexpected CGM glucose readings might occur more frequently in the real world than is suggested by the literature, and, in everyday life, interferences may play a bigger role than many users and clinicians assume.^12,13,15

Conclusions

By employing our standardized dynamic interference testing protocol, we found 12 nutritional, endogenous, and pharmacological substances that influenced Dexcom G6 readings in vitro through bias over baseline interference and/or sensor fouling. If confirmed by clinical trials, such interferences should be considered when making treatment decisions using Dexcom G6 results in daily routine care.

Footnotes

Authors’ Contributions

N.T., H.J., G.S., and L.W. researched the data and contributed to the discussion. A.P. planned and supervised the study, researched the data, wrote the first draft of the article, and edited the article. E.H., M.G., and S.S. contributed to the discussion and reviewed and edited the article. All authors approved the final version of the article. A.P. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Author Disclosure Statement

The author(s) declare the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: In the context of this article, A.P. declares that he is a consultant or has received travel support and/or speaker fees from Sanofi/Germany, LifeScan/USA, Lifecare/Norway, and Abbott/Wiesbaden. S.S., M.G., and E.H. are employees of LifeScan Scotland or LifeScan Global Corporation. The other authors have no conflicts of interest to report.

Funding Information

This project received financial support for the research, authorship, and/or publication of this article from the European Union’s Horizon 2020 research and innovation program under grant agreement No 951933 (ForgetDiabetes) and an unrestricted grant from LifeScan Global Corporation, Malvern, PA, USA.

Abbreviations Used

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Dynamic Interference Testing Results with the Dexcom G6 Continuous Glucose Monitoring Device

Abstract

Background:

Methods:

Results:

Conclusions:

Introduction

Methods

Results

Discussion

Conclusions

Footnotes

Authors’ Contributions

Author Disclosure Statement

Funding Information

Abbreviations Used

References