Higher Derived Time in Range With IDegLira Versus Insulin Glargine U100 in People With Type 2 Diabetes

Abstract

Background:

Derived time in range (dTIR), calculated from self-monitored blood glucose (SMBG-dTIR) profiles, has demonstrated correlation with risk of cardiovascular and microvascular complications. This post hoc analysis of the DUAL V and DUAL VIII trials aimed to compare dTIR with an insulin degludec/liraglutide fixed-ratio combination (IDegLira) versus insulin glargine 100 units/mL (glargine U100) in people with type 2 diabetes (T2D).

Materials and Methods:

Nine-point SMBG profiles were taken more than 24 hours at baseline and end of trial (EOT: 26 weeks [DUAL V] and 104 weeks [DUAL VIII]) and used to derive the percentage of readings within target range (70-180 mg/dL). Estimated treatment differences (ETDs, IDegLira–glargine U100) were analyzed using analysis of covariance, with treatment as fixed effects and baseline response as a covariate.

Results:

ETDs for change from baseline to EOT in dTIR were significantly greater with IDegLira versus glargine U100 in DUAL V (4.18%, P = .027) and DUAL VIII (5.17%, P = .001). The proportions of people achieving ≥70% dTIR at EOT with IDegLira and glargine U100, respectively, were 62% and 60% in DUAL V (P = .7541), and 50% and 26% in DUAL VIII (P < .0001). The proportion achieving a ≥5% increase in dTIR from baseline to EOT with IDegLira and glargine U100 was 63% in both groups in DUAL V (P = .9043), and 44% and 25%, respectively, in DUAL VIII (P < .0001).

Conclusions:

IDegLira was associated with significantly greater increases in dTIR versus basal insulin alone in people with T2D.

Trial ID(s):

ClinicalTrials.gov, NCT01952145 (DUAL V); ClinicalTrials.gov, NCT02501161 (DUAL VIII)

Keywords

IDegLira insulin self-monitored blood glucose time in range

Introduction

Derived time in range (dTIR) is a metric that can be used to collect information on a patient’s glycemic control to provide a picture of glycemic variability over a 24-hour period.¹ Derived time in range is calculated through using SMBG-dTIR measurements, which are obtained from the patient using a blood glucose (BG) meter at designated time points.¹ The amount of time spent in range (defined as glucose values of 70-180 mg/dL) can then be calculated from the available readings.¹ This metric could provide therapeutic and prognostic information in addition to hemoglobin A1c (HbA1c) alone, which shows only an average of blood sugar levels over several months.² Several studies have demonstrated correlations between time spent in range and outcomes such as albuminuria, diabetic retinopathy and peripheral neuropathy, and cardiovascular outcomes including all-cause and cardiovascular disease mortality.^3
-8 Using SMBG-dTIR may be advantageous when time in range data from continuous glucose monitoring (CGM) and the required technology are not available. However, as the BG readings are self-measured, nocturnal values are often not available. A limited number of studies have investigated the use of SMBG-dTIR, which supported its use as an endpoint of interest in clinical trials due to its strong association with the risk of microvascular complications and major adverse cardiovascular events.^9,10

The current post hoc analysis compared time within (dTIR), below (dTBR), or above (dTAR) target range with an IDegLira versus insulin glargine 100 units/mL (glargine U100), using data from the DUAL V and DUAL VIII clinical trials. DUAL V was a 26-week trial of adults with T2D uncontrolled on glargine U100, randomized to switch to IDegLira or up-titration of glargine U100, with continued background therapy of metformin (Supplementary Figure 1).¹¹ DUAL VIII was a 104-week durability trial of insulin-naive people with type 2 diabetes (T2D) inadequately controlled on oral glucose-lowering agents, randomized to either IDegLira or glargine U100 (Supplementary Figure 1).¹² In both trials, nine-point SMBG profiles were used, allowing the calculation of dTIR. DUAL V and DUAL VIII were selected for this comparison, as they both compared IDegLira with glargine U100 and collected data on nine-point SMBG. The difference in duration of the trials provided insights into the use of dTIR for patients after 26 and 104 weeks of treatment. Furthermore, DUAL V enrolled insulin-experienced individuals, while DUAL VIII enrolled insulin-naive individuals. In these analyses, we hypothesized greater improvements in dTIR profiles with combination glucagon-like peptide-1 receptor agonist (GLP-1RA) and insulin therapy (IDegLira) compared with basal insulin treatment alone (glargine U100) in T2D.

Materials and Methods

Data from DUAL V and DUAL VIII were evaluated post hoc. The DUAL V and DUAL VIII study designs have been previously described.^11,12 In both trials, SMBG profiles were taken over 1 day with time points at breakfast, 90 minutes after breakfast; lunch, 90 minutes after lunch; dinner, 90 minutes after dinner; bedtime, 04:00 hours and breakfast the next day, preferably within one week prior to site visits, which were carried out on weeks 0, 12, and 26 (end of trial [EOT]) (DUAL V), or weeks 0, 26, and 104 (EOT) (DUAL VIII). At EOT, SMBG values were only included for people who had not discontinued trial product before this point.

Outcomes

Data from nine-point SMBG profiles with ≥6 readings per visit were used to derive the percentage of available readings within (dTIR), below (dTBR), or above (dTAR) target range. Within range (dTIR) was defined as 70-180 mg/dL (3.9-10 mmol/L); below range (dTBR) was defined as <70 mg/mL (<3.9 mmol/L); and above range (dTAR) was defined as >180 mg/dL (>10 mmol/L). The rationale for using nine-point SMBG profiles was that each patient measured at least eight points within the same 24 hours, and using at least six-point measurements ensured that both pre- and post-meal values were captured and achieved data coverage for around two-thirds of nonmissing SMBG profile values. The proportion of patients who achieved a dTIR of ≥70% at EOT and the proportion of patients achieving a ≥5% increase in dTIR at EOT were also calculated. For each of the subgroups of patients with a dTIR of <70% and ≥70% at EOT, the proportion of patients who achieved a ≥5% increase in dTIR from baseline to EOT was also calculated. We also measured the correlation between baseline HbA1c and dTIR, and change from baseline to week 26 (DUAL V and DUAL VIII) and week 104 (DUAL VIII) in HbA1c and in dTIR.

Statistical Methods

Change from baseline to EOT was analyzed for people who had SMBG profiles available with ≥6-point measurements at both baseline and EOT. Estimated treatment differences (ETDs) between IDegLira and glargine U100 in change from baseline to EOT were analyzed using an analysis of covariance (ANCOVA) method with treatment as fixed effects and baseline response as a covariate. A test for two proportions was used to compare the proportion of people who had dTIR ≥70% or ≥5% increase in dTIR between the treatment arms at EOT. Time in range data were assumed to follow a normal distribution, and so a Pearson correlation was used to calculate the correlation between baseline HbA1c and baseline dTIR, and between change in HbA1c from baseline and change in dTIR from baseline.

Results

Participants

Of the 557 and 1012 people randomized to treatment in the DUAL V and DUAL VIII trials, respectively, dTIR data were not available or there were SMBG profiles with <6 readings at baseline for nine (1.6%) and 24 (2.4%) people in DUAL V and DUAL VIII, respectively; therefore, 1536 people were included in the current analysis at baseline (548 in DUAL V and 988 in DUAL VIII). Based on the design of DUAL VIII, EOT (week 104) SMBG readings were only collected in those who had not yet met the primary outcome criteria of inadequate control requiring treatment intensification.¹² Thus, at EOT, dTIR data with ≥6 readings were available and included in this analysis for 458 people (45.3%) in DUAL VIII. In DUAL V, the number of people with SMBG-dTIR readings at 04:00 hours in each treatment group was 268 (IDegLira) and 269 (glargine U100) at baseline, and 221 (IDegLira) and 245 (glargine U100) at EOT. In DUAL VIII, the number of people with SMBG-dTIR readings at 04:00 hours in each treatment group was 481 (IDegLira) and 487 (glargine U100) at baseline, and 284 (IDegLira) and 163 (glargine U100) at EOT. Tables 1 and 2 report the baseline demographics and characteristics of people in DUAL V and DUAL VIII, respectively, with available dTIR data at EOT.

Table 1.

Baseline Demographics and Characteristics of People in DUAL V With Available Data at EOT.

	IDegLira group(n = 233)	Glargine U100 group(n = 252)
Female, n (%)	110 (47.2)	130 (51.6)
Age, years	58.2 (9.7)	59.2 (9.4)
Race, n (%)
Asian	9 (3.9)	9 (3.6)
Black or African American	5 (2.1)	4 (1.6)
White	219 (94.0)	239 (94.8)
Ethnicity, n (%)
Hispanic or Latino	87 (37.3)	125 (49.6)
Not Hispanic or Latino	146 (62.7)	127 (50.4)
Body weight, kg	88.5 (17.1)	87.4 (15.5)
BMI, kg/m²	31.8 (4.4)	31.8 (4.5)
Duration of diabetes, years	11.5 (7.4)	11.1 (6.4)
HbA1c, %	8.4 (0.9)	8.2 (0.9)
HbA1c, mmol/mol	68.0 (9.9)	66.4 (9.5)
Fasting plasma glucose, mmol/L	8.9 (2.6)	8.8 (2.8)
Fasting plasma glucose, mg/dL	160.8 (46.1)	159.3 (50.9)
Metformin therapy at screening, n (%)	233 (100)	252 (100)

Data are mean (SD) unless otherwise stated.

Abbreviations: BMI, body mass index; EOT, end of trial (week 26); glargine U100, insulin glargine 100 units/mL; IDegLira, insulin degludec/liraglutide fixed-ratio combination; SD, standard deviation.

Table 2.

Baseline Demographics and Characteristics of People in DUAL VIII With Available Data at EOT.

	IDegLira group(n = 291)	Glargine U100 group(n = 167)
Female, n (%)	139 (47.8)	76 (45.5)
Age, years	57.1 (9.3)	57.8 (9.6)
Race, n (%)
Asian	25 (8.6)	19 (11.4)
Black or African American	9 (3.1)	7 (4.2)
White	257 (88.3)	141 (84.4)
Ethnicity, n (%)
Hispanic or Latino	67 (23.0)	36 (21.6)
Not Hispanic or Latino	224 (77.0)	131 (78.4)
Body weight, kg	90.1 (21.2)	88.0 (18.1)
BMI, kg/m²	32.1 (6.5)	31.4 (5.2)
Duration of diabetes, years	9.8 (5.7)	10.1 (5.5)
HbA1c, %	8.2 (0.9)	8.2 (0.9)
HbA1c, mmol/mol	65.9 (10.1)	66.3 (9.5)
Fasting plasma glucose, mmol/L	9.7 (2.7)	9.8 (2.4)
Fasting plasma glucose, mg/dL	174.8 (49.5)	176.1 (43.6)
Oral antidiabetic drugs at screening, n (%)
Metformin monotherapy	64 (22.0)	40 (24.0)
DPP-4i monotherapy	1 (0.3)	0 (0)
Sulfonylurea monotherapy	1 (0.3)	0 (0)
Thiazolidinedione monotherapy	1 (0.3)	1 (0.6)
Repaglinide monotherapy	0 (0)	1 (0.6)
Combination therapy^a	224 (77.0)	125 (74.9)

Data are mean (SD) unless otherwise stated.

Abbreviations: EOT, end of trial (week 104); IDegLira, insulin degludec/liraglutide fixed-ratio combination; glargine U100, insulin glargine 100 units/mL; BMI, body mass index; HbA1c, hemoglobin A1c; DPP-4i, dipeptidyl peptidase-4 inhibitor; SD, standard deviation.

Combination therapy consisted of two or more of the following: metformin, DPP-4i, sulfonylurea, thiazolidinedione, repaglinide.

Change in Derived Time in Range, Derived Time Above Range, and Derived Time Below Range Over Time

In both trials and treatment arms, the mean percentage of SMBG readings within the range of 70-180 mg/dL (dTIR) increased numerically from baseline (DUAL V: IDegLira: 55.9% and glargine U100: 54.0%; DUAL VIII: IDegLira: 54.1% and glargine U100: 48.1%) to EOT (DUAL V: IDegLira: 83.5% and glargine U100: 79.0%; DUAL VIII: IDegLira: 90.1% and glargine U100: 85.2%), while the percentage of SMBG readings >180 mg/dL (dTAR) decreased numerically from baseline to EOT (Figure 1a and b). The ETDs (IDegLira–glargine U100) for change from baseline to EOT in dTIR were significantly greater with IDegLira versus glargine U100 in both DUAL V (4.18% [95% confidence interval [CI]: 0.48, 7.89], P = .0269) (Figure 1a) and DUAL VIII (5.17% [95% CI: 2.07, 8.27], P = .0011) (Figure 1b). For dTAR, the ETDs (IDegLira–glargine U100) for the change from baseline to EOT were statistically significant in DUAL VIII (−5.12% [95% CI: −8.14, −2.10], P = .0009) (Figure 1b), but not DUAL V (−3.45% [95% CI: −6.96, 0.07], P = .0545) (Figure 1a). No significant differences were found between treatments for the change from baseline to EOT in dTBR in DUAL V (−0.77% [95% CI: −1.86, 0.31], P = .1618) or DUAL VIII (−0.10% [95% CI: −1.09, 0.90], P = .8485).

Figure 1.

dTIR, dTBR, and dTAR at baseline and at 12 and 26 weeks in DUAL V (a) and at baseline and at 12 and 26 weeks in DUAL VIII (b). Abbreviations: dTIR, derived time in range; dTBR, derived time below range; dTAR, derived time above range; ETD, estimated treatment difference; CI, confidence interval; IDegLira, degludec/liraglutide fixed-ratio combination; EOT, end of trial; glargine U100, insulin glargine 100 units/mL; ANCOVA, analysis of covariance; SMBG, self-monitored blood glucose.

Proportion of People Achieving ≥70% Derived Time in Range or a ≥5% Increase in Derived Time in Range at End of Trial

The proportions of people achieving ≥70% dTIR at EOT with IDegLira and glargine U100, respectively, were 62% and 60% in DUAL V, and 50% and 26% in DUAL VIII. The ETDs (IDegLira–glargine U100) for the proportion of people achieving ≥70% dTIR at EOT were 1.30% (95% CI: −6.81, 9.40), P = .7541 in DUAL V and 24.31% (95% CI: 18.51, 30.11), P < .0001 in DUAL VIII. The proportion achieving a ≥5% increase in dTIR from baseline to EOT with IDegLira and glargine U100 was 63% in both groups in DUAL V (ETD: −0.49% [95% CI: −8.50, 7.52], P = .9043), and 44% and 25%, respectively, in DUAL VIII (ETD: 18.97% [95% CI: 13.22, 24.72], P < .0001) (Figure 2). In the subgroup of people with <70% dTIR at EOT, the proportions who achieved a ≥5% increase in dTIR from baseline to EOT with IDegLira and glargine U100 were 11% and 16% in DUAL V, and 3% and 4% in DUAL VIII, respectively. In the subgroup of people who achieved ≥70% dTIR at EOT, 52% (IDegLira) and 47% (glargine U100) in DUAL V, and 41% (IDegLira) and 22% (glargine U100) in DUAL VIII, achieved a ≥5% increase in dTIR from baseline to EOT, respectively.

Figure 2.

The proportion of people who achieved ≥70% dTIR or a ≥5% increase in dTIR at EOT. Abbreviations: dTIR, derived time in range; EOT, end of trial; ETD, estimated treatment difference; CI, confidence interval; IDegLira, insulin degludec/liraglutide fixed-ratio combination; glargine U100, insulin glargine 100 units/mL; ANCOVA, analysis of covariance; SMBG, self-monitored blood glucose.

Histograms presenting the percentage of people within each percentile of dTIR (with each percentile representing the percentage of time individuals spent with SMBG readings within 70-180 mg/dL) are shown in Supplementary Figures 2 and 3.

Correlation Between Hemoglobin A1c and Derived Time in Range

A negative correlation was found between baseline HbA1c and baseline dTIR in DUAL V (Pearson correlation coefficient of −0.456, P < .0001) and DUAL VIII (Pearson correlation coefficient of −0.543, P < .0001). There was also a negative correlation between change in HbA1c from baseline to EOT and change in dTIR from baseline to EOT in DUAL V (Pearson correlation coefficient of −0.281, P < .0001) and DUAL VIII (Pearson correlation coefficient of −0.418, P < .0001), showing that, with increasing dTIR, HbA1c values decrease (Supplementary Figure 4).

Discussion

This post hoc analysis demonstrated that IDegLira is effective over 26 weeks in insulin-experienced people with T2D, with a significantly greater change in dTIR recorded in people treated with IDegLira compared with glargine U100 up-titration (DUAL V). A significantly greater increase in dTIR with IDegLira treatment versus glargine U100 was also shown longer-term, over 104 weeks, in insulin-naive people in DUAL VIII. The proportion of people achieving ≥70% dTIR at EOT, or a ≥5% increase in dTIR from baseline to EOT, was also statistically significantly higher with IDegLira than glargine U100, in DUAL VIII but not in DUAL V. These differing results are likely to be due to the population in DUAL VIII including insulin-naive people, who may show greater improvements in glycemic control following treatment initiation than insulin-experienced people. In both trials, decreases in dTAR values from baseline to EOT were greater for IDegLira versus glargine U100; however, this difference was only statistically significant in DUAL VIII. No statistically significant difference between the treatments was shown for change from baseline to EOT in dTBR values. This may be due to the low number of readings with a dTBR value at baseline and throughout the trials. The number of SMBG-dTIR profiles that included a reading at 04:00 hours was similar between treatment groups at baseline in both trials and at EOT in DUAL V, thus reducing the risk of bias when analyzing these dTBR data. The improved dTIR outcomes observed in the IDegLira treatment group versus glargine U100 are to be expected due to the synergistic effects of combined basal insulin and GLP-1RA on glycemic control. Basal insulin provides extended insulin release over time, resulting in sustained insulin levels throughout the day, and the glucose-dependent mode of action of GLP-1RAs addresses postprandial glucose levels.¹³ This is supported by the results of the primary analysis of DUAL V, in which reduction in HbA1c levels was found to be statistically superior with IDegLira versus glargine U100 (−1.81% vs −1.13%, respectively; ETD −0.59% [95% CI −0.74, −0.45]; P < .001).¹¹ Similarly, the primary analyses of DUAL VIII demonstrated significantly longer durability of glycemic control and significantly longer time until treatment intensification was needed with IDegLira compared with glargine U100.¹² In addition, IDegLira was associated with less weight gain compared with glargine, and a significantly lower mean daily insulin dose after 26 weeks (DUAL V: 41 units vs 66 units, respectively) and 104 weeks (DUAL VIII: 37 units vs 52 units, respectively) in both trials.^11,12

A strength of this post hoc analysis was that it demonstrated that IDegLira is an effective treatment option to improve dTIR in people with different treatment backgrounds, as DUAL V included people with uncontrolled diabetes being treated with insulin glargine (20-50 units) and metformin (≥1500 mg/day), and DUAL VIII included insulin-naive people. The analysis also demonstrated durability of the beneficial effect of IDegLira on dTIR up to 104 weeks.

An advantage of SMBG-dTIR is that it is a convenient metric to use in the clinic in the absence of CGM data and the required technology, where, if ≥6 SMBG measurements are available, it can provide a surrogate evaluation of daily glycemic control. This is supported by our analysis, which found a significant negative correlation between baseline HbA1c and baseline dTIR, as well as between change in HbA1c and change in dTIR from baseline. Previous findings have similarly validated the use of dTIR as an endpoint for clinical trials. An analysis using data from 1440 individuals with type 1 diabetes (T1D) across the longitudinal Diabetes Control and Complications Trial (DCCT) found a strong association between dTIR and the risk of microvascular complications.⁹ Another study, in which dTIR data from 5644 Chinese people with T2D were analyzed, demonstrated that dTIR reflected clinical BG regulation and predicted the risk of diabetic microvascular complications.¹⁴ Furthermore, dTIR provides a more complete picture of glycemic variability and hypoglycemic exposure through dTAR and dTBR results compared with HbA1c, which is only able to show an average of blood sugar levels. Glucose variability may be an independent risk factor for morbid outcomes in people with diabetes, including macrovascular and microvascular complications and mortality.^6,15
-17 Therefore, it is important to consider medications that not only improve HbA1c, but also TIR. The use of dTIR may help guide such treatment decisions, including the titration of insulin and assessment of treatment gaps. For example, a high dTAR might be caused by high postprandial glucose values, suggesting a need to change dietary habits or initiate therapies that lower postprandial glucose. Conversely, when dTBR is high, there may be a need to down-titrate basal insulin.

A limitation of SMBG-dTIR is that, unlike data from CGM, it typically does not provide data on nocturnal glucose values unless this time point is deliberately measured. In addition, SMBG-dTIR profiles usually consist of a large number of self-measurements per day (6-9), taken at prespecified time points, which people may find challenging to adhere to in a real-world setting. However, a previous study in a small cohort of people with T1D and T2D demonstrated that dTIR parameters derived from SMBG-dTIR profiles with ≥3 and ≥2 values per day significantly correlated with HbA1c.¹⁸ This suggests that <6 SMBG measurements may be sufficient in determining SMBG-dTIR parameters.

It is a limitation of this study that a high percentage of people from the primary analyses were not included, particularly for the analyses at EOT: 72 (12.9%) people in DUAL V and 554 (54.7%) people in DUAL VIII. This is largely due to the trial design, in which participants who met the primary durability outcome (inadequate glycemic control of HbA1c ≥7% on two consecutive visits from week 26 onwards) discontinued trial product by protocol in DUAL VIII before EOT at week 104; as such, the EOT SMBG profiles evaluated for DUAL VIII represent individuals who were still meeting treatment criteria at EOT. It is important to highlight that there was a difference in the discontinuation rate due to lack of treatment efficacy between the two treatment arms (125 of 506 patients [25%] in the IDegLira group vs 257 of 506 patients [51%] in the glargine U100 group).¹² A further limitation was the requirement to have ≥6 SMBG-dTIR profile data points at EOT. However, baseline demographics and characteristics of people in this post hoc study with available dTIR data at EOT were similar between treatment groups and were similar to the characteristics of the population reported in the primary analyses,^11,12 thus showing that the randomization of people between treatment arms in the primary analyses were largely preserved in the current post hoc study. Due to the post hoc nature of the analysis, the interpretation of data is limited. Nevertheless, sample sizes were large, and the observed trends were consistent.

Conclusions

This study showed that treatment with IDegLira was associated with significantly greater dTIR compared with glargine U100, in both insulin-naive and insulin-experienced people with T2D. The results of this post hoc analysis, together with the previously reported benefits of IDegLira on glycemic control, illustrate the effectiveness of IDegLira, a fixed-ratio combination of basal insulin with GLP-1RA, as a more effective option than basal insulin alone in targeting the multiple aspects of glycemic control in T2D.

Supplemental Material

sj-docx-1-dst-10.1177_19322968221149041 – Supplemental material for Higher Derived Time in Range With IDegLira Versus Insulin Glargine U100 in People With Type 2 Diabetes

Supplemental material, sj-docx-1-dst-10.1177_19322968221149041 for Higher Derived Time in Range With IDegLira Versus Insulin Glargine U100 in People With Type 2 Diabetes by Athena Philis-Tsimikas, Vanita R. Aroda, Christophe De Block, Liana K. Billings, Andreas Liebl, Ramsathish Sivarathinasami, John M. D’Cruz and Ildiko Lingvay in Journal of Diabetes Science and Technology

Footnotes

Acknowledgements

Medical writing and editorial support, under the guidance of the authors, were provided by Carrie Fielden, Beverly La Ferla, and Helen Marshall from Ashfield MedComms, an Inizio company, funded by Novo Nordisk.

Abbreviations

ANCOVA, analysis of covariance; BG, blood glucose; BMI, body mass index; CGM, continuous glucose monitoring; CI, confidence interval; DCCT, diabetes control and complications trial; DPP-4i, dipeptidyl peptidase-4 inhibitor; dTAR, derived time above range; dTBR, derived time below range; dTIR, derived time in range; EOT, end of trial; ETD, estimated treatment difference; glargine U100, insulin glargine 100 units/mL; GLP-1RA, glucagon-like peptide-1 receptor agonist; IDegLira, insulin degludec/liraglutide fixed-ratio combination; SD, standard deviation; SMBG, self-monitored blood glucose; T1D, type 1 diabetes; T2D, type 2 diabetes.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: AP-T provides consulting and research services to Novo Nordisk, Lilly, Bayer, Dexcom, and Medtronic on behalf of her employer but receives no direct or indirect reimbursement for the services. VRA reports consulting fees from Applied Therapeutics, Fractyl, Novo Nordisk, Pfizer, Sanofi, and institutional contracts for research from Applied Therapeutics/Medpace, Eli Lilly, Premier/Fractyl, Novo Nordisk, and Sanofi/Medpace. Her spouse is an employee of Janssen. CDB reports consulting fees and honoraria for speaking for Abbott, AstraZeneca, Boehringer-Ingelheim, Eli Lilly, Insulet, Medtronic, Novo Nordisk, and Roche. LKB reports honoraria for attending advisory boards or consulting services provided to Lilly, Novo Nordisk, Xeris, Bayer, and Sanofi. AL has received research support and honoraria for giving presentations and attending advisory boards: AstraZeneca, Bayer, Becton Dickinson, Boehringer-Ingelheim, Bristol Myers Squibb, Lilly, Medtronic, MSD, Novo Nordisk, Roche, and Sanofi. IL reports receiving advisory board fees and consulting fees from AstraZeneca, consulting fees from Bayer Healthcare Pharmaceuticals, Eli Lilly and Company, Intarcia, Intercept Pharmaceuticals, Janssen Global Services, MannKind Corporation, Target Pharma, Valeritas, and Zealand Pharma; advisory board fees from Boehringer-Ingelheim and Sanofi US Services; grant support, paid to UT Southwestern, from Merck; grant support, paid to her institution, from Mylan Pharmaceuticals and Pfizer; grant support, paid to UT Southwestern; and advisory board fees, consulting fees, and travel support from Novo Nordisk. RS and JMD’C are employees of Novo Nordisk A/S.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by Novo Nordisk A/S.

ORCID iD

Athena Philis-Tsimikas

Data Accessibility Statement

The data that support the findings of this study are available from co-author John M. D’Cruz, of Novo Nordisk A/S, upon reasonable request.

Supplemental Material

Supplemental material for this article is available online.

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