Time in Range Analysis in Automated Insulin Delivery Era: Should Day and Nighttime Targets be the Same?

Abstract

Introduction:

Hybrid closed-loop systems (HCLS) use has shown that time in range (TIR) tends to improve more during the nighttime than during the day. This study aims to compare the conventional TIR, currently accepted as 70 to 180 mg/dL, with a proposed recalculated time in range (RTIR) considering a tighter glucose target of 70 to 140 mg/dL for the nighttime fasting period in T1DM patients under HCLS.

Methods:

We conducted a retrospective study that included adults patients receiving treatment with Tandem t:slim X2 Control-IQ. Daytime TIR was characterized as glucose values between 70 and 180 mg/dL during the 07:01 to 23:59 time frame. Nighttime fasting TIR was specified as glucose values from 70 to 140 mg/dL between 00:00 and 07:00. The combination of the daytime and nighttime fasting glucose targets results in an RTIR, which was compared with the conventional TIR for each patient. The 14 days Dexcom G6 CGM data were downloaded from Tidepool platform and analyzed.

Results:

We included 22 patients with a mean age of 49.7 years and diabetes duration of 24.7 years, who had been using automatic insulin delivery (AID) HCLS for a median of 305.3 days. We verified a mean conventional TIR of 68.7% vs a mean RTIR of 60.3%, with a mean percentage difference between these two metrics of −8.4%. A significant decrease in conventional TIR was verified when tighter glucose targets were considered during the nighttime period. No significant correlation was found between the percentage difference values and RTIR, even among the group of patients with the lowest conventional TIR.

Conclusions:

Currently, meeting the conventional TIR metrics may fall short of achieving an ideal level of glycemic control. An individualized strategy should be adopted until further data become available for a precise definition of optimal glucose targets.

Introduction

Type 1 diabetes mellitus (T1DM) is a chronic condition characterized by insulin deficiency and impaired counter-regulatory hormonal responses, significantly impacting the quality of life for individuals with diabetes.^1,2 Significant efforts have been devoted to improving diabetes therapy, with the goals of enhancing glycemic control, preventing end-organ complications, and reducing the overall disease burden, through the development of adjunct technologies designed to facilitate T1DM management, such as continuous interstitial glucose monitoring (CGM) sensors and automatic insulin delivery (AID) systems.

Automatic insulin delivery systems—characterized by the precise interaction between CGM, an insulin pump, and an algorithm—has shown the ability to increase the time spent within the target glucose range, with no increase or a reduction in hypoglycemia. ³ These benefits have been observed in people with T1DM across all age groups, gender, diabetes duration, prior insulin delivery methods, and baseline glycated hemoglobin (HbA_1c) levels.^3,4 These systems employ sophisticated controller algorithms that continuously adjust insulin delivery in response to real-time sensor glucose levels, residual insulin action, and other factors, including meal intake and exercise announcements. However, users are still required to manually report carbohydrate intake to ensure adequate postprandial insulin coverage.

Clinical data have indicated that even with the use of advanced AID hybrid closed-loop systems (HCLS), time in range (TIR) tends to improve more during the nighttime than during the day. ⁵ These differences can be attributed to distinct regulatory mechanisms governing postabsorptive (fasting) and postprandial glucose metabolism, which encompass both insulin-dependent and insulin-independent pathways. These variations prompt the question of whether a tighter glucose target should be considered during the nighttime fasting period.

This study aims to compare the standardized TIR, currently internationally accepted as 70 to 180 mg/dL, with a proposed recalculated time in range (RTIR) considering a tighter glucose target of 70 to 140 mg/dL for the nighttime fasting period in individuals with T1DM under closed-loop hybrid AID systems.

Methods

Patients Selection and Study Design

We conducted a retrospective observational study that included people with T1DM receiving treatment with HCLS for diabetes care at the endocrinology department of a tertiary center, Parc Tauli Hospital Universitari, in Sabadell, Barcelona, Spain. All patients underwent an evaluation by their health care providers to assess their ability to manage intensive insulin therapy, proficiency in mealtime insulin dosing, and their willingness to adopt a new device. Patients received specific training in AID system, which consisted of three sessions organized by the medical and nursing teams, with the participation of a specialized instructor from the respective commercial AID system provider—Tandem t:slim X2 Control-IQ. The AID settings were tailored to individualized glycemic targets, guided by recently acquired CGM metrics.

Eligible participants were adults with T1DM, aged 18 years or older, who had been on AID therapy for a minimum of two months in order to minimize potential interferences associated with the user’s limitations of the use of a new device, and with a minimum percentage of automatic mode use superior to 80%. Pregnant women and patients with predominantly nocturnal food intake patterns were excluded from the study. Regarding CGM metrics, data were obtained from Dexcom G6 interstitial glucose sensor and AID reports analyzed for a 14 day period. This study was approved by the local Ethics Committee, and written informed consent from each patient was obtained.

Data Collection

General characteristics and automatic insulin delivery report data

Data regarding participants’ age, sex, diabetes duration, anthropometric measures (weight and height), most recent HbA_1c levels, and AID commercial system on use were collected. The 14 days CGM glucose data were download from Tidepool platform and analyzed.

Daytime, nighttime fasting, and recalculated time in range definitions

For the analysis of CGM data, two time periods were defined, each with its own target glucose range definition. Daytime TIR was characterized as glucose values falling between 70 and 180 mg/dL (3.9 and 10.0 mmol/L), consistent with the current global TIR target conventional recommendation during the 07:01 to 23:59 time frame. Nighttime fasting TIR was specified as glucose values ranging from 70 to 140 mg/dL (3.9-7.8 mmol/L) between 00:00 and 07:00, a tight glucose target, considering the minimized effect of potential food ingestions and their repercussions on postprandial glycemic excursions.

The combination of the previous defined daytime and nighttime fasting glucose targets results in an RTIR, which was subsequently compared with the conventional TIR for each patient for a 14 day period—RTIR = Daytime TIR (70-180 mg/dL) + Nighttime fasting TIR (70-140 mg/dL)—Figure 1. The glycemic variation coefficient was calculated by dividing the standard deviation (SD) of glucose by the mean of daily glucose levels and multiplying by 100, resulting in a percentage.

Figure 1.

Daytime, nighttime fasting, and recalculated time in range (RTIR) definitions.

Statistical Analysis

Statistical analysis was conducted using IBM SPSS Statistics 25.0. Continuous variables were presented as mean ± SD or as median, percentile 25 to percentile 75, depending on the distribution normality or asymmetry determined through the Kolmogorov-Smirnov test. The paired t-test was employed to compare the RTIR with the conventional TIR and glycemic variation coefficient throughout the day and nighttime. Statistical significance was determined by accepting P values < .05.

Results

Our study included 22 patients diagnosed with T1DM who had been using the AID system for a median of 305.3 days (P25-P75 = 76.0-543.5). The majority of patients were female (59.1%, n = 13), with a mean age of 49.7 ± 9.6 years and a diabetes duration of 24.7 ± 9.8 years. Table 1 summarizes the general characteristics of the patients.

Table 1.

General Characteristics of Study Population.

Patients under AID HCLS	n = 22
AID HCLS treatment duration (days) b	305.5 [76.0-543.5]
Female (n, (%))	13 (59.1)
Age (years) a	49.7 ± 9.6
T1DM duration (years) a	24.7 ± 9.8
HbA1c (%) a	6.9 ± 0.9
Weight (kg) a	82.1 ± 16.0
Height (cm) a	168.9 ± 8.8
BMI (kg/m2) b	29.4 [24.6-34.5]

Abbreviations: AID, automatic insulin delivery; BMI, body mass index; CGM, continuous interstitial glucose monitoring; HbA_1c, glycated hemoglobin; n, number; %, percentage; T1DM, type 1 diabetes mellitus.

^aExpressed in mean ± standard deviation (SD).

^bExpressed as median P[25-75], percentile 25 to percentile 75.

Regarding CGM data evaluation, we observed a mean conventional TIR of 68.7 ± 13.5% (ranging from a minimum [min.] of 40.1 to a maximum [max.] of 91.3%) and a mean RTIR of 60.3 ± 14.3% (ranging from a min. of 30.0 to a max. of 88.4%). The mean percentage difference between these two metrics was −8.4 ± 2.9%. A significant statistical decrease in conventional TIR was verified when tighter glucose targets were considered during the nighttime fasting period by RTIR (P < .001). Table 2 provides detailed individual patient CGM metrics and illustrates the impact of assuming narrower glucose targets for nighttime fasting period through RTIR. A trend toward an inverse correlation was observed between the percentage difference values and RTIR, although it was not statistically significant according to the Pearson correlation test (r = −0.378, P = .083), even among the group of patients with the lowest conventional TIR (TIR < 70%). Glycemic variation was statistically higher during nighttime comparing to daytime (30.6 ± 0.2% at night and 26.6 ± 1.2% during the day, P = .00).

Table 2.

Comparison of Conventional TIR with RTIR After Considering a Narrower Glucose Target Range of 70 to 140 mg/dL for the Nighttime Fasting Period.

	Conventional TIR (%)	RTIR (%)	% Difference	P value a
Patient 1	59.43	47.02	-12.41	< .001
Patient 2	40.09	30.04	-10.05
Patient 3	52.71	43.22	-9.49
Patient 4	68.84	55.90	-12.94
Patient 5	63.43	58.83	-4.60
Patient 6	68.53	57.37	-11.16
Patient 7	84.92	72.36	-12.56
Patient 8	68.44	58.42	-10.02
Patient 9	58.64	52.87	-5.77
Patient 10	50.73	43.64	-7.09
Patient 11	82.88	75.15	-7.73
Patient 12	69.12	57.57	-11.55
Patient 13	65.26	57.67	-7.59
Patient 14	76.08	64.66	-11.42
Patient 15	54.17	48.62	-5.55
Patient 16	59.83	51.60	-8.23
Patient 17	91.32	88.37	-2.95
Patient 18	81.42	73.74	-7.68
Patient 19	82.74	78.04	-4.70
Patient 20	67.79	58.74	-9.05
Patient 21	75.37	68.41	-6.96
Patient 22	89.95	84.02	-5.93

Abbreviations: TIR, time in range; %, percentage.

^aThe paired t-test was employed to compare the RTIR with the conventional TIR. Statistical significance was determined by accepting a P < .05.

Discussion

Increasing use of advanced AID HCLS and their impact on glycemic control has prompted discussion regarding the optimal glucose targets definition and the potential beneficial effects of individual stricter targets in patients with T1DM. ⁶ These considerations stem from the current somewhat arbitrary recommendation of TIR being identified as 70 to 180 mg/dL and are grounded in the concepts of metabolic memory and the imperative of promptly achieving glycemic control to effectively reduce the risk of future complications.^3,7

When evaluating continuous glucose monitoring profiles in healthy individuals with negative islet antibodies and normal HbA_1c using CGM in blinded mode, Shah et al ⁷ reported an average mean glucose level ranging from 98 to 99 mg/dL (5.4-5.5 mmol/L) and a median time spent between 70 and 140 mg/dL (3.9-7.8 mmol/L) of 96% (interquartile range [IQR] = 93-98). The duration of time with glucose levels above 140 mg/dL (7.8 mmol/L) was 2.1% (equivalent to 30 minutes per day), whereas the time spent with glucose levels below 70 mg/dL (3.9 mmol/L) was 1.1% (equivalent to 15 minutes per day). Regarding the distribution of CGM glucose during daytime and nighttime, the overall median sensor glucose targeted between 70 and 140 mg/dL was 96% (IQR = 92-97) during the daytime and 99% (IQR = 95-100) during nighttime. Elevated glucose levels above 140 mg/dL were extremely rare during the night, with a mean percentage of 0% (IQR = 0.0-1.0). However, a prospective study conducted by Sofizadeh et al ⁸ in people without diabetes using CGM did not demonstrate a significant difference between daytime and nighttime periods spent between 70 and 144 mg/dL (3.9-8.0 mmol/L). The study also observed an overall increased percentage of time spent between 70 and 144 mg/dL (mean = 95.4%, 95% confidence interval [CI] = 93.5-96.5), with a mean percentage of time spent above 180 mg/dL (10 mmol/L) of only 1.44% (95% CI = 0.61-2.28). These data are of great importance when defining normoglycemia and to the accurate development of CGM reference metrics for the treatment of T1DM patients in advanced AID HCLS era.

In our study, upon adopting a narrower glucose target during the nighttime fasting period, we observed a statistically significant decrease in the percentage of the target glucose range, with a mean reduction of −8.4 ± 2.9% (P < .001). In fact, patients meeting the conventional TIR within internationally accepted recommendations might still be far from achieving ideal glycemic control. Although we observed a trend toward an inverse correlation between the percentage difference values and RTIR, we were unable to establish statistical significance, likely due to the limited sample size and reduced statistical power within this context. To our knowledge, this is the first study postulating the rational for possible different glucose targets during daytime postprandial and nighttime fasting periods and analyzing the glycemic control after assuming a stricter target in RTIR.

Daily food intake and it implications on postprandial glycemic excursions stands as one of the challenges in managing hyperglycemic fluctuations along with the need of exercise announcement for individuals using AID systems. ³ In a randomized controlled trial that included 112 patients with diabetes under the Tandem HCLS evaluating TIR improvements according baseline HbA_1c values, TIR improved in all groups, although with a greater reduction in percent time >180 mg/dL for those with baseline HbA_1c ≥8.5% of 21.9%, 19.2% during the day, and 30.4% overnight. ⁵ We believe that one potential explanation for the difference in improvements in TIR between daytime and overnight periods could be the heightened challenge in meeting current glucose targets caused by food-induced hyperglycemic spikes during the day. This may contribute to a relatively higher percentage of TIR achieved overnight. However, when discussing target glucose levels, it is crucial to carefully consider physiologic variances associated with distinct regulatory mechanisms governing postabsorptive (fasting) and postprandial glucose metabolism.

In the postprandial state, glucose’s role in regulating its own metabolism, independent of insulin, is pivotal for maintaining glycemic control.^9,10 Hyperglycemia exerts a dual impact on various tissues. It can augment glucose uptake in the liver and skeletal muscle through increased transporter activity, primarily as a mass action effect. However, acute elevations in glucose levels exceeding 350 mg/dL have been observed to downregulate GLUT4 transporters and reduce glucose uptake in skeletal muscle. ¹¹ This intriguing response suggests a dynamic interplay between hyperglycemia and glucose transport, potentially serving as a protective mechanism to avert glucotoxicity when circulating glucose surpasses physiological limits. On the contrary, in the liver, hyperglycemia leads to increased rates of glucose uptake while simultaneously suppressing glycogenolysis and hepatic glucose production. ¹²

Studies conducted in skeletal muscle using euglycemic-hyperinsulinemic clamps in combination with muscle biopsies have further unveiled that, under basal insulin concentrations and moderate hyperglycemia (~200 mg/dL), both glucose uptake and phosphorylation rates rise. ¹³ Therefore, at higher insulin concentrations, hyperglycemia may collaborate synergistically with insulin to maximize glycogen synthesis. This intricate interplay between glucose and insulin actions implies that, from a teleological perspective, the liver may allow a subtle increase in postprandial blood glucose levels (typically up to 140-160 mg/dL). ¹¹ Similarly, for individuals with type 1 diabetes, postprandial hyperglycemia is expected to play a significant role in postprandial excursions, involving both insulin-dependent and insulin-independent mechanisms. This may justify the acceptance of a more permissive target glucose range for daytime postprandial periods, comparing with a stricter definition of glucose target in nighttime fasting.

This retrospective study has, however, several limitations to be considered. On one hand, a small number of patients and a single commercial AID system were analyzed. Due to its retrospective, observational and uncontrolled nature, it was not possible to completely eliminate the potential impact of occasional food intake during the night or rebound hyperglycemia following the correction of hypoglycemia event within the same period. In addition, analysis of the potential for the dawn phenomenon, afternoon meal consumption, physical activity, or insulin administration leading to glycemic variability overnight was not possible to be performed. Pressure on the sensor during sleep may additionally result in inaccuracies of some CGM readings. Data on the health status or current therapy with hyperglycemic drugs (eg, corticosteroid therapy) were neither measured nor taken into account during the 14 day evaluation AID report period. On the contrary, our findings may not be generalized to patients with night shift work or those with an altered food intake pattern, which would require a redefinition of daytime and nighttime fasting TIR. Future prospective investigation is needed with a larger number of patients and with multiple AID systems in order to verify the external validation of our data.

Conclusions

Our study emphasizes the crucial role of advancing diabetes treatment and monitoring technologies in reshaping our comprehension of glycemic profiles and refining treatment target definitions in type 1 diabetes. As CGM devices become increasingly widespread and our knowledge expands, there is an opportunity to redefine glycemic metrics, potentially enhancing outcomes, especially for patients using advanced AID HCLS. This exploration cannot overlook the complexities of postprandial glucose metabolism and should highlight the significance of considering physiological differences between fasting and postprandial states. Currently, meeting the conventional TIR metrics may fall short of achieving an ideal level of glycemic control. An individualized strategy should be adopted until further data become available for a precise definition of optimal glucose targets.