Differences in Glucose Readings Between Right Arm and Left Arm Using a Continuous Glucose Monitor

Abstract

Objective:

Continuous glucose monitoring (CGM) devices are used for evaluating real-time glucose levels to optimize diabetes management. There is limited information, however, on whether readings differ when a device is placed on the right versus the left arm. This study evaluated the mean difference in glucose levels between the right and left arm and the effect of unilateral arm exercise on this difference. The effect of an intermittent fasting diet on body fat percentage was also evaluated.

Research Design and Methods:

In a prospective trial, 46 adult volunteers self-selected into the intermittent fasting (IF; N = 23) or free-living (FL; N = 23) diet group and were randomized into a unilateral arm exercise group. Volunteers had CGM sensors placed simultaneously on both arms for 12-14 days.

Results:

The mean glucose level in the right arm was significantly higher than the left arm by 3.7 mg/dL (P < .001), and this result was unaffected by diet or arm exercise. Glucose levels were in euglycemic range for 75.2% of the time in the right arm and 67.5% in the left arm (P < .001). The change from baseline in body fat percentage between the IF and FL diet groups was not significant.

Conclusions:

Measured glucose level and time in euglycemic range differ per placement of the CGM device, and the implications of this difference should be considered in clinical practice and research.

Introduction

Continuous glucose monitoring (CGM) is an emerging field for diabetes management. CGM devices allow providers to individualize therapy according to real-time glucose levels. By detecting changes in blood glucose, CGM also raises patient awareness of hypo- and hyperglycemic events. ¹ Common CGM devices can be placed on the patient’s arm or abdomen to monitor interstitial fluid glucose across several days. Interstitial fluid glucose is measured by inserting the device’s thin filament under the skin upon placement of the CGM sensor and is used to estimate blood glucose levels. There are multiple Food and Drug Administration (FDA) approved devices for CGM.

As the role of CGM devices in diabetes management grows, the importance of determining the accuracy and precision of these devices is increasing. According to FDA standards, a device is deemed to be accurate if 99% of blood glucose measurements are within 20% of lab results and if 95% of blood glucose measurements are within 15% of lab results. ² The FDA approved the first CGM system to make diabetes treatment decisions without needing confirmation with a traditional fingerstick test in 2016. ³ CGM data have been deemed accurate for self-use to adjust insulin dosage, for the detection of hypoglycemia, and for determining the clinical response to therapy. ⁴ Despite these improvements in accuracy, events of low glucose readings and false alarms have been reported. An analysis conducted on the reports to the FDA Manufacturer and User Facility Device Experience database since 2015 revealed over 25 000 complaints of CGM device inaccuracy. ⁵ Although CGM devices are researched extensively, it is apparent that there is an opportunity for ongoing improvement.

Abbott had previously reported simultaneously placing two FreeStyle Libre Pro Flash Glucose Monitor System sensors on patients with diabetes. One sensor was placed on the back of each arm. They compared the readings from time-matched glucose levels collected from the same subject and reported a percent absolute relative difference (PARD) of 8.6% with a coefficient of variation (%CV) of 6.1%. ⁶ Additional information on whether this result was attributed to consistently higher levels in one arm or whether external factors could account for the difference was not provided. In a previous analysis performed by our group on 10 subjects (5 right-handed, 5 left-handed), a statistically significant difference between glucose levels in the two arms was noted. Specifically, levels in the right arm were higher for 67% of the glucose readings (P < .001). ⁷ This difference was not explained by arm dominance, as there was no significant difference in inter-arm glucose difference between right- and left-handed subjects (3.7 vs 3.8 mg/dL, P = .54). ⁷ The key limitation of the previous study was the lack of patient diversity as the sample size was limited to 10 subjects.

Exercise has been well known to play an essential role in the management of type 2 diabetes. Exercise is one of the first interventions for newly diagnosed type 2 diabetes and is integral in type 2 diabetes prevention programs. ⁸ Resistance strength training has been shown to improve glycemic control, insulin resistance, and fat mass for those with type 2 diabetes. ⁹ Additionally, resistance exercise can help minimize the risk of exercise-induced hypoglycemia in type 1 diabetes. ¹⁰ However, physical activity may be another source of variability in CGM readings, as observed in one study that found CGM inaccuracies with glucose reporting during exercise when the device was placed on the abdomen. ¹¹

The current study will expand on our group’s previously conducted CGM glucose reading study by using a larger sample size and assessing factors such as diet which may influence blood glucose levels on the right and left sides of the body. This study will also explore whether unilateral resistance arm exercises using body weight can affect glucose readings and contribute to the differences in glucose readings between the right and left arm.

Research Design and Methods

Study Design

This was a prospective, open-label, single-center study conducted in the United States. An overview of the study design is illustrated in Figure 1. The study was done in full accordance with the guidelines for Good Clinical Practice and the Declaration of Helsinki. The protocol was approved by the Institutional Review Board at the University of the Pacific.

Figure 1.

Schematic of study design.

Study Population

Healthy adult volunteers 18-65 years of age were recruited in the study. Key exclusion criteria were critical illness, planned imaging by electromagnetic radiation (eg, magnetic resonance imaging, computed tomography scan, X-ray imaging), treatment with medications that have an increased risk of hypoglycemia (eg, sulfonylureas, insulin, thiazolidinediones), treatment with medications for obesity, medical conditions that can cause rapid weight loss (eg, hyperthyroidism, irritable bowel syndrome, cancer, Addison’s disease), participation in an active weight loss program or specialized diet in the past 2 weeks, and history of adverse events during prior fasting experiences.

Procedures

On day 1 of the study, each subject underwent insertion and simultaneous activation of two CGM sensors, with one sensor placed on the back of each upper arm. The FreeStyle Libre Pro Flash Glucose Monitoring system (Abbott Diabetes Care Inc., Alameda, CA, USA) was used to measure and record glucose levels by taking interstitial glucose readings every 15 minutes until the end of the study (days 12-14). If the CGM device fell out prior to the end of the study, the sensor was replaced and the subject was retained for statistical analysis. On the same day 1 study visit, a body fat analysis was performed for each subject using the Tanita MC-780U body composition monitor (Tanita Corp., Tokyo, Japan).

In the study, 23 subjects were enrolled into each of the intermittent fasting (IF) and free-living (FL) diet groups through self-selection. The IF diet arm subjects were instructed to limit their daily food consumption to an 8-hour period per day with no caloric restrictions during this window. Subjects enrolled in the FL diet arm were instructed to adhere to their routine diet throughout the duration of the study.

All subjects performed daily unilateral arm exercises which consisted of 20 minutes of rest followed by 20 minutes of subjects holding their arm at shoulder level for as long as possible during the exercise window. Subjects were permitted to take 60-second breaks of rest as needed during the period of exercise. On days 1-6, 11 subjects from the IF group and 12 subjects from the FL diet group were randomized to perform arm exercises on the left arm and the remaining subjects from both groups were randomized to perform arm exercises on the right arm. On days 7-14, all 46 subjects performed unilateral arm exercises with the opposite arm.

End-of-study visits occurred between days 12 and 14 for final CGM data download, safe sensor removal, post-study body fat composition analysis, and a brief oral survey recording subjects’ adherence to arm exercises and diet.

Assessments

The primary endpoint was the mean difference in time-matched glucose levels between the right arm and left arm. The co-primary endpoint was the change in total body fat percentage from baseline after 12-14 days of the designated diet. Secondary endpoints related to glucose levels included mean difference in glucose readings between the right arm and left arm before and during 20 minutes of unilateral arm exercise; percent of total glucose levels in hypoglycemic (<70 mg/dL), euglycemic (70-180 mg/dL), and hyperglycemic range (>180 mg/dL); and time spent with glucose levels in hypoglycemic, euglycemic, and hyperglycemic range.

Statistical Analysis

To detect a difference in percent body fat of 4.0% with a standard deviation of 4.2 (80% power and significance level of .05), 38 volunteers (19 per group) would be needed to assess the impact of IF between the groups (unpaired student’s t-test). Assuming a dropout of 20%, we planned to enroll 46 subjects for the entire study. The study enrolled 23 subjects in the open-label IF arm and 23 subjects in the FL diet arm.

All analyses were performed according to the intention-to-treat principle. CGM readings were time-matched between the right and left arms, with the first 12 hours of data eliminated from analysis to account for the CGM system’s calibration to the subjects’ body and to adhere to manufacturer recommendations. ¹² Readings between the right and left arms were considered time-matched if they occurred within 15 minutes of each other. Glucose levels between the right and left arm were analyzed with a paired Student’s t-test. The comparison of mean glucose levels before and during unilateral arm exercise was also evaluated using a paired Student’s t-test. Results were considered statistically significant if P < .05.

A 2-proportion z-test was used to compare the proportion of glucose levels in hypoglycemic, euglycemic, and hyperglycemic range between the right and left arms. The time spent with glucose levels in the hypoglycemic, euglycemic, and hyperglycemic range was derived from the percentage of glucose levels in each range.

All data were analyzed using R version 3.6.0 (Institute for Statistics and Mathematics, Vienna, Austria) and Microsoft Excel (Microsoft, Seattle, WA, USA).

Results

A total of 46 subjects were enrolled into the study between September 2019 and December 2019. Baseline characteristics were balanced between diet groups (Table 1). Among the subjects, 78% were female, and 83% were right-hand dominant. The median age was 22 years, and the median body mass index (BMI) was 24.4 kg/m². None of the enrolled subjects had diabetes. Of the enrolled subjects, 5 subjects discontinued prior to day 12-14 visits due to skin irritation from the CGM sensor (N = 2), anticipated contact with X-ray scanners (N = 2), and unspecified reasons (N = 1).

Table 1.

Baseline Subject Demographics.

	Intermittent fasting	Free living	Total
N	23	23	46
Age (years)	22 (18-48)	22 (19-39)	22 (18-48)
Gender	Male 17%	Male 26%	Male 22%
	Female 83%	Female 74%	Female 78%
Baseline BMI (kg/m2)	24.3 (19.6-39.6)	21.2 (16.7-31.4)	22.1 (16.7-39.6)
Baseline body fat percentage (%)	27.3 (10.2-48.1)	21.3 (8.5-38.0)	24.4 (8.5-48.1)
Race	Asian 65% White 30%	Asian 83% White 17%	Asian 74% White 24% Other 2%
	Other 4%
Dominant arm	Left 17%	Left 17%	Left 17%
	Right 83%	Right 83%	Right 83%

Median (range): age, baseline BMI, baseline body fat percentage.

BMI, body mass index.

In total, 41 288 paired glucose readings were available for analysis. The median difference in time-matched readings was 0 minutes, and ranged from 0 to 6 minutes. A significant difference was observed in mean glucose levels between the right arm and left arm. The mean glucose level in the right arm was higher than that of the left arm by 3.7 mg/dL (SD 10.6 mg/dL, P < .001), and this result was consistent in the IF (3.0 mg/dL, SD 11.5 mg/dL, P < .001) and FL (4.4 mg/dL, SD 9.5 mg/dL, P < .001) diet groups. The difference in glucose levels between the arms was not significantly different when comparing the difference before exercise (4.0 mg/dL, SD 10.2 mg/dL) and during exercise (4.3 mg/dL, SD 10.1 mg/dL, P = 0.77). The mean percent absolute relative difference (PARD) between the right arm and left arm was 9.8% with a %CV of 128%.

A total of 22.1% of glucose levels were in hypoglycemic range in the right arm compared to 29.8% in the left arm (P < .0001), 75.2% of glucose levels were in euglycemic range (70-180 mg/dL) in the right arm compared to 67.5% in the left arm (P < .001), and 2.8% of glucose levels were in hyperglycemic range in the right arm compared to 2.8% in the left arm (P = .83; Figure 2). This result was consistent between IF and FL diet subgroups (Table 2). Additional categorical analyses are presented in Supplementary Table 1. A subgroup analysis was performed in obese patients to assess if this glucose difference between arms was present in this subgroup (N = 4). On average, obese subjects had a glucose difference of 4.9 mg/dL (SD 14.6 mg/dL) between the right and left arm, which is comparable to that of the total study population.

Figure 2.

Continuous glucose monitoring right arm and left arm comparisons of time in target ranges.

TAR, time above range; TBR, time below range; TIR, time in range.

Table 2.

Percentage of Readings in Hypoglycemic, Euglycemic, and Hyperglycemic Range.

	TBR: <70 mg/dL (time/d)	TIR: 70-180 mg/dL (time/d)	TAR: >180 mg/dL (time/d)
All subjects
RA readings	22.1% (5 h 18 min)	75.2% (18 h 2 min)	2.8% (40 min)
LA readings	29.8% (7 h 9 min)	67.5% (16 h 11 min)	2.8% (40 min)
Intermittent fasting diet group
RA readings	20.1% (4 h 49 min)	74.8% (17 h 58 min)	5.1% (1 h 14 min)
LA readings	25.5% (6 h 8 min)	69.4% (16 h 39 min)	5.1% (1 h 5 min)
Free-living diet group
RA readings	24.4% (5 h 51 min)	75.6% (18 h 8 min)	0.1% (1 min)
LA readings	34.8% (8 h 21 min)	65.2% (15 h 39 min)	0.0% (1 min)

LA, left arm; RA, right arm; TAR, time above range; TBR, time below range; TIR, time in range.

For the co-primary endpoint evaluating the change from baseline in body fat percentage between the IF (−0.11%, SD 1.1) and FL (−0.68%, SD 1.4) diet groups, the difference between the body fat percentages in the diet groups was not found to be statistically significant (P = .13).

Discussion

In this prospective trial, we observed a statistically significant difference in time-matched CGM glucose levels between the right and left arm. This result was not influenced by unilateral arm exercise or diet groups. The clinical implications of this difference should be considered. The difference of 3.7 mg/dL between the right and left arm is not clinically significant; however, it may impact conclusions of whether a patient with diabetes is in hypoglycemic, euglycemic, or hyperglycemic state when glucose levels are near the boundaries of each category. A patient with diabetes may be euglycemic according to readings in the left arm, but hypoglycemic according to readings in the right arm, and therefore may pursue different interventions (eg, carbohydrate intake) depending on which arm the sensor is placed. While the current study evaluated the difference between the right and left arm in healthy volunteers, similar implications would apply if a similar difference was observed in patients with diabetes. Differences between two FreeStyle Libre Pro sensors worn at the same time has been previously described in patients with diabetes. ⁶ A PARD of 8.6% with a %CV of 6.1% was reported. By comparison, the mean PARD in healthy volunteers in the current study was 9.8% with a %CV of 128%. While the values for PARD are comparable, differences in %CV may potentially be attributed to differences in sample size and study population.

Per the international consensus report endorsed by the American Diabetes Association and other diabetes professional organizations, clinical CGM targets were established to guide the clinical interpretation of CGM data. ¹³ This panel selected time in range as a clinically relevant metric of glycemic control. The time in range metric consists of three measures, expressed as the percentage of CGM readings and amount of time per day below target glucose range (TBR), within target glucose range (TIR), and above target glucose range (TAR). CGM-based targets defined in this paper are TBR <4% or less than 58 minutes, TIR >70% or more than 16 hours 48 minutes, and TAR <25% or less than 6 hours. Using these CGM targets as reference in this study, the average TIR was 75.2% for the right arm and 67.5% for the left arm. As demonstrated in our sample of subjects, the placement of the CGM sensor in the right versus left arm may influence whether or not the CGM-based TIR target is met. The previously reported correlation of TIR with A1C levels and diabetes complications suggests that the identified discrepancy in TIR between the right and left arm may be clinically relevant in a population with diabetes.^14-17 While TIR, TBR, and TAR targets are intended for people living with diabetes instead of a healthy patient population and international guidelines recommend more than a 70% TIR for people with type 1 and type 2 diabetes, TIR is found to be typically around 40%-60% in the average person with diabetes according to studies and large real-world data sets.^14,17 In the current study population of healthy volunteers, the difference of 7.7% between average TIR in the right arm versus the left arm suggests that the subjects spent approximately 2 hours per day with glucose levels near the boundaries of euglycemia. It should be noted that the data from the current study are generated from the FreeStyle Libre Pro CGM system that records glucose levels every 15 minutes. Whether the results of this study can be generalized to other CGM devices with different glucose sampling rates is unclear.

The differences in glucose levels between the arms before and during exercise were not found to be statistically significant. Unilateral arm exercise was hypothesized to induce changes in arm perfusion, which could contribute to differences in glucose readings between the right and left arm. While these arm exercises did not affect the difference in glucose levels between the right and left arm, the influence of perfusion cannot be ruled out. The current study did not evaluate whether unilateral arm exercise is an adequate intervention to reliably induce significant change in arm perfusion. In addition, no biomarkers of baseline perfusion (eg, blood pressure) were evaluated to determine whether differences in perfusion existed between the right and left arm at rest. This should be explored in future studies, given the known asymmetries of volume distribution due to the location of the heart. Differences in baseline perfusion may potentially contribute to the observed difference in glucose readings between the right and left arm.

A potential limitation of this trial was that this study was performed in a largely healthy sample of subjects, whereas CGM devices are indicated for use in patients with diabetes. The current study was designed to include all-comers with the intention of exploring whether differences in glucose readings between the right and left arm could be observed and examining factors that may contribute to any observed difference. The difference between the arms was not expected to be attributed to disease status. Investigators planned to expand future studies to the diabetic population depending on the findings of the current study. A statistical difference in glucose readings identified between arms could potentially be extrapolated from this set of subjects to patients with diabetes. Although enrollment in this study was designed to include all-comers, the demographics showed that the average subject was a 22-year-old adult with a baseline BMI of 22.1 kg/m². Another potential limitation may be the use of the unilateral arm exercise. Subjects were asked to hold their designated arm parallel to the floor for 20 minutes each day to reproduce a resistance exercise using bodyweight, and it is unclear whether this reliably induced changes in arm perfusion. Because this intervention was exploratory in nature, biomarkers to measure perfusion (eg, blood pressure) were not evaluated prior to or within the study. This limitation may be addressed in future studies by incorporating the measurement of blood pressure before and during arm exercises.

In evaluating the change in body fat percentage between IF and FL diet groups, no statistically significant difference was seen and both diet groups showed a reduction of less than 1% of body fat percentage from baseline. The current study was powered to detect a 4% decrease in body fat percentage. A duration of 2 weeks for an IF diet did not appear to be adequate to allow for this 4% reduction in body fat percentage.

Conclusion

The current study found a statistically significant difference in glucose levels, unrelated to short-term diet differences or unilateral resistance arm exercises, between the right and left arm, as measured by CGM sensors. If a CGM user has glucose levels near normal glucose range cutoffs (ie, 70 mg/dL, 180 mg/dL), this discrepancy between arms could potentially contribute to an over- or underestimation or hypo- or hyperglycemic readings, which would subsequently influence the user’s diabetes management. In clinical practice, time in range is found to be an appropriate and useful CGM metric and has been correlated with various clinical outcomes measures.^14-17 The findings of the current study suggest that a subject could either meet or fail to reach the CGM-based target for TIR, based solely on which arm the CGM sensor is placed. These results suggest the potential need for research institutions and regulatory agencies to standardize methods of glucose measurement via CGM. In particular, practices such as consistently measuring glucose levels on the same side of the body should be considered. Ongoing research on medical devices should continue to consider potential variability introduced in the data due to differences in measurement associated with device placement on different areas of the body.