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Title
AUTONOMY: The first randomized trial comparing two patient-driven approaches to initiate
and titrate prandial insulin lispro in type 2 diabetes
Authors
Steve V. Edelman, MD,1 Rong Liu, PhD,2 Jennal Johnson, MSN,2 Leonard C. Glass, MD2
1. University of California, San Diego Department of Medicine, 9500 Gilman Drive #9111G, La Jolla, CA 92093-9111, (858) 552-8585 ext. 7361, fax: (858) 642-6242,
[email protected]
2. Eli Lilly and Company, 307 E. Merril St., Indianapolis, Indiana 46225, USA
Rong Liu: phone: [email protected]
Jennal Johnson: phone: [email protected]
Leonard C. Glass: phone: [email protected]
Running Title: Self-titrating lispro in type 2 diabetes
Word Count: 4,166
Tables: 2
Figures: 2
Clinical Trial Registry Number: The trial was registered with ClinicalTrials.gov. The trial
number is NCT01215955 and the name is “Study of Insulin Lispro in Patients With Inadequately
Controlled Type 2 Diabetes (AUTONOMY).”
.
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Diabetes Care
ABSTRACT
Objective—To compare 2 self-titration algorithms for initiating and escalating prandial insulin
lispro in patients with type 2 diabetes inadequately controlled on basal insulin.
Research design and methods—The trial was designed as 2 independent, multinational,
parallel, open-label studies (A and B), identical in design, to provide substantial evidence of
efficacy and safety in endocrine and generalist settings. Subjects were 18–85 years old (Study
A:N=528); (Study B:N=578), on basal insulin plus oral antidiabetic drugs for ≥3 months, and
with HbA1c >7.0% to ≤12.0% (>53.0 to ≤107.7 mmol/mol). Once optimized on insulin glargine,
subjects were randomized to one of 2 self-titration algorithms groups adjusting lispro either
every day (Q1D) or every 3 days (Q3D) for 24 weeks. The primary outcome was the change in
HbA1c from baseline. The primary and secondary objectives were evaluated for the overall
population and subjects ≥65 years old.
Results—Baseline HbA1c were similar (Study A: Q1D: 8.3% (67.2 mmol/mol) vs. Q3D: 8.4%
(68.3 mmol/mol), P=0.453; Study B Q1D: 8.3% (67.2 mmol/mol) vs. Q3D: 8.4% (68.3
mmol/mol), P=0.162). Both algorithms had significant and equivalent reductions in HbA1c from
baseline (Study A: Q3D:–0.96% (–10.49 mmol/mol), Q1D:–1.00% (–10.93 mmol/mol), Q3D–
Q1D: 0.04% (0.44 mmol/mol) [95%CI:–0.15% (–1.64 mmol/mol), 0.22% (2.40 mmol/mol)];
Study B: Q3D:–0.92% (–10.06 mmol/mol), Q1D:–0.98% (–10.71 mmol/mol), Q3D–Q1D:
0.06% (0.66 mmol/mol) [95%CI:–0.12% (–1.31 mmol/mol), 0.24% (2.62 mmol/mol)]. The
incidence and rate of hypoglycemia were similar for Q3D and Q1D in both studies. In general,
no clinically relevant differences were found between the 2 algorithms in subjects ≥65 years old
in either study.
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Conclusions—Prandial insulin lispro can effectively and safely be initiated, by either of 2 selftitrated algorithms, in a variety of practice settings.
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The management of patients with type 2 diabetes generally requires stepwise
intensification of therapy beginning with lifestyle changes and oral antidiabetic drugs (OADs)
progressing to noninsulin injectable antidiabetic agents and, given the progressive deterioration
in β-cell function , to the addition of exogenous insulin (1, 2, 3, 4). The results of the United
Kingdom Prospective Diabetes Study (UKPDS) supports the need for treatment intensification
with exogenous insulin in combination with OADs in a significant percentage of patients to
achieve and maintain metabolic control (5).
The Treat-to-Target trial investigated the efficacy and safety of adding basal insulin
glargine (GLA) or NPH insulin in patients on OADs with poorly controlled type 2 diabetes, in
North America, and it established a clinical standard for basal insulin treatment trials:
approximately 60% of patients achieved the HbA1c target of <7.0% (53.0 mmol/mol)
recommended by the American Diabetes Association using both GLA and NPH insulins;
however, GLA resulted in lower rates of mild nocturnal hypoglycemia (3, 6, 7). The prevention
of hypoglycemic episodes in the management of type 2 diabetes is critical because hypoglycemia
may limit the adoption of further insulin intensification and has been shown to increase the risk
for cardiovascular disease and other adverse events, particularly in older adults (8, 9, 10).
Additionally, hypoglycemia is a primary source of fear that negatively impacts patients’
adherence to treatment regimens and quality of life (10, 11). In contrast to the Treat-to-Target
trial, only 32-43% of patients in a European trial which investigated the efficacy of initiating
once a day GLA versus NPH insulin at bedtime, in combination with glimepiride, reached goal
HbA1c of <7.5% (58.5 mmol/mol) and the mean endpoint HbA1c in all groups was higher than
in Treat-to-Target (6, 12).
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The Treating to Target in Type 2 Diabetes (4-T) Trial investigated the efficacy of
adding a basal, prandial, or biphasic insulin regimen to OAD therapy (13). The 3–year results
from this study demonstrated that a greater proportion of patients on basal and prandial
interventions reached a HbA1c of ≤6.5% (≤47.5 mmol/mol) than those treated with biphasic
premixed insulin (13). Given limited data and myriad treatment approaches, there is currently no
global clinical consensus for the approach to treatment intensification with insulin therapy. A
meta-analysis and a systemic review of randomized controlled trials suggests that the most
effective use of insulin is achieved using a basal-bolus regimen (14, 15). Basal-bolus therapy
allows for more effective control of postprandial glucose excursions than basal insulin alone and
provides greater flexibility for mealtime insulin timing and titration than premixed biphasic
insulin therapies.
There is a need, particularly in the generalist setting, for evidence to support the
implementation of simple approaches to prandial insulin therapy that empower the patient and
promotes individualized treatment. No randomized, controlled studies in patients with type 2
diabetes have investigated treatment escalation with prandial (bolus) insulin using patient-driven
treatment intensification. The AUTONOMY trial was designed to compare the efficacy and
safety of 2 patient-based self-titration algorithms for initiation and titration of prandial insulin
lispro therapy in patients with type 2 diabetes who could not achieve adequate glycemic control
on basal insulin plus OADs. The study provides the first comparison of 2 self-titration insulin
algorithms for the escalation of prandial insulin therapy in a large, multicountry, randomized,
controlled trial.
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RESEARCH DESIGN AND METHODS
AUTONOMY was a 14-country/1-territory (Argentina, Austria, Brazil, Canada, Croatia,
Denmark, France, Lithuania, Mexico, Poland, Romania, Russian Federation, South Africa, and
United States/Puerto Rico), multicenter, randomized, open-label, parallel trial in subjects with
type 2 diabetes who had inadequate glycemic control on basal insulin plus OADs. The trial was
designed as 2 independent studies (Study A N=528 and Study B N=578) utilizing a single
protocol to corroborate substantial evidence of reproducibility. The data for each study were
analyzed separately and independently. To maintain integrity, each investigator was assigned to
one of the 2 studies according to an allocation plan specified before initiation of the trial.
Stratification variables included baseline HbA1c (≤8.0% and >8.0% [≤63.9 and >63.9
mmol/mol]), country, and sulfonylurea/ meglitinide use. Approximately, 44% of trial sites were
in primary care (nonspecialist) settings. The trial enrolled subjects with type 2 diabetes (16), 18
to 85 years of age, a BMI <45kg/m2 and HbA1c >7.0% (53.0 mmol/mol) and ≤12.0% (107.7
mmol/mol); treated with at least 20 U/day of insulin GLA, NPH, lispro protamine suspension
(NPL), or detemir; and had been using metformin, meglitinide, sulfonylurea, pioglitazone,
sitagliptin, or a combination of these for ≥3 months. The exclusion criteria included prior rapidor short-acting insulin therapy, excessive insulin resistance (>2 U/kg), morbid obesity (BMI ≥45
kg/m2), pregnancy or planned pregnancy, cancer, recent history of severe hypoglycemia, and
moderate to severe cardiovascular/renal/hepatic/hematologic disease. Patients were excluded
from the study if they were taking the following medications: glucagon-like peptide-1 receptor
agonists, α-glucosidase inhibitors, dipeptidyl peptidase-4 inhibitors (except sitagliptin), and
rosiglitazone within 3-months or glucocorticoids within 2-weeks of screening.
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All subjects provided informed consent, and the trial was conducted in compliance with
the International Conference on Harmonization Guidelines for Good Clinical Practice and the
Declaration of Helsinki (17). The trial was registered with ClinicalTrials.gov (NCT01215955).
Study protocol and treatment
Subjects treated with GLA (Lantus; Sanofi-Aventis) at entry who had HbA1c >7.0%
(53.0 mmol/mol) and fasting blood glucose (FBG) ≤120 mg/dL did not require a lead-in period
and were randomized to one of the 2 treatment arms. Those who required conversion to, or
optimization of, GLA underwent a 6-week lead-in period during which the dose was adjusted by
investigators every 3 to 7 days based on the treat-to-target algorithm (6). As GLA is widely used
in clinical practice, with prior studies supporting comparable efficacy and safety to other basal
insulins, the protocol was designed for use of this single basal insulin. After randomization,
bedtime doses of GLA were only adjusted based upon clinical judgment of the investigator.
Subjects were randomized 1:1 at baseline (randomization) to begin insulin lispro (Humalog; Eli
Lilly and Company) therapy with either the Q1D or Q3D self-titration algorithm (Appendix 1).
Assignment to treatment groups was designated by a computer-generated random sequence using
an interactive voice response system. Subjects continued the use of OADs at prestudy dose, and
those on sulfonylurea or meglitinide discontinued that drug at randomization and increased GLA
dose by 10% of their total daily total dose (TDD). A 24-week intervention duration after
optimization and randomization was selected to allow sufficient time for prandial insulin therapy
intensification and to stabilize glycemic control as measured by HbA1c. Primary and secondary
outcome measures were mainly recorded at baseline and 7, 12, and 24 weeks.
Safety was monitored throughout the study, and the occurrence and nature of all adverse
events were recorded. Hypoglycemia was considered an adverse event, and a severe
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hypoglycemic event was recorded as a serious adverse event. Hypoglycemia was defined as
anytime the subject experienced a sign or symptom associated with hypoglycemia or a blood
glucose reading ≤70 mg/dL even if it was not associated with signs or symptoms.
Treatment algorithms
Subjects were assigned to either the Q1D algorithm or Q3D algorithm, which were
developed based on the pharmacokinetic/pharmacodynamics (PK/PD) properties of lispro, where
the first dose of insulin lispro was administered before the subject’s first meal of the day
(prebreakfast). If the patient did not eat breakfast (he/she consumed only water, black coffee with
no sugar or cream, or noncaloric drink) the individual began with the prelunch dose. The lispro
dose started at 10% of the total daily GLA dose. If necessary, the investigators sequentially
added bolus lispro injections at subsequent meals (prelunch followed by predinner) for a
maximum of 3 mealtime injections per day. Subjects followed the algorithms using especially
created logbooks. The algorithms were designed to titrate independent of the subjects’ food
intake or carbohydrate counting to simplify the dosing of mealtime insulin.
The Q1D algorithm was self-titrated every day based on premeal glucose readings from
the previous day, for example, when adjusting the prebreakfast dose, subjects used their prelunch
readings from the previous day. The premeal target blood glucose was 85–114 mg/dL. If this
target was not achieved, the subject increased the dose by 1 U/day until the target was reached. If
the subject had a blood glucose reading of 56–84 mg/dL, the dose was decreased by 1 U, and if
the subject had a reading of <56 mg/dL, the dose was decreased by 2 U.
The Q3D algorithm was self-titrated every 3 days based on the median blood glucose
readings from the 3 days before: to adjust the prebreakfast dose, the subject used the median
prelunch blood glucose reading from the past 3 days. If the median reading was 85–114 mg/dL,
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there was no change in insulin lispro dose; if the median was 115–144 mg/dL, the subject
increased the dose by 2 U; if the median was ≥145 mg/dL, the dose was increased by 4 U; if the
median was 56–84 mg/dL, the dose was decreased by 2 U; if the median was <56 mg/dL, the
dose was decreased by 4 U.
Outcome measures
The primary efficacy measure, the HbA1c change from baseline to the end of the study
(week 24 after randomization), was compared between Q3D and Q1D algorithms. Secondary
outcome measures included incidence and annualized rate of self-reported total, severe, and
nocturnal hypoglycemia. Additional secondary outcome measures included proportion of
subjects achieving the target HbA1c of ≤7.0% (53.0 mmol/mol), change in fasting blood glucose
(FBG), 7-point self-monitored blood glucose (SMBG) profile, weight change from baseline, and
dose of basal (GLA), prandial (lispro) insulin at the end of the study, and change in 1,5anhydroglucitol (1,5-AG), a marker of hyperglycemia, particularly in the postprandial state, and
is useful in assessing glycemic control (18). In addition, change in HbA1c, hypoglycemia
(incidence and rate), FBG, and proportion of subjects achieving target were compared between
the two algorithms in the subjects ≥65 years of age.
Statistical analyses
The sample size calculation was based on the primary outcome: change in HbA1c from
baseline to week 24. It was estimated that 640 completers would provide approximately 98%
probability of reaching a conclusive outcome using the classification method; assuming a
standard deviation of 1.1% (12.0 mmol/mol), no treatment difference, and a non-inferiority
margin of 0.4% (4.7 mmol/mol) (19). The early drop-out rate was monitored, and to be
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conservative, the enrollment was continued to enroll 1096 subjects (548 in each study) to reach
the target number of completers.
Because there was no prior projection, preference, or historical evidence on which selftitration algorithm performs better, a classification method was applied to the analysis of the
primary efficacy measure (19).
All safety outcomes were assessed in the entire randomized population (all subjects who
entered the study, completed the GLA optimization lead-in period [if applicable], and were
randomized to one of the 2 treatment arms). All efficacy analyses were based upon the full
analysis set (subjects in the all randomized population who took at least one dose of insulin
lispro). A sensitivity analysis was conducted for the primary efficacy measure based upon the all
completer population.
All efficacy and safety analyses were conducted at an α-level of 0.05. All CIs were
computed as 2-tailed using a 95% significance level. Continuous efficacy and safety variables
measured repeatedly were evaluated using a mixed model, repeated measure (MMRM) approach
using the restricted maximum likelihood method, including the following independent variables:
fixed effects for treatment algorithm, all stratification variables, visit, treatment by visit
interaction, and baseline outcome variable as the covariate (20). Treatment-by-age group (≥65
years, <65 years) interaction for the change in HbA1c was tested using another MMRM model
with additional items including subgroup and subgroup by treatment algorithm interaction. For
categorical measures, including adverse events and hypoglycemia incidence, Fisher’s exact test
or Pearson’s chi-square test was used. The hypoglycemia incidence was also analyzed with a
logistic model with terms for treatment algorithm and all stratification variables as sensitivity
analysis. The rate of total, nocturnal, and severe hypoglycemia per year during the treatment
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phase was analyzed using last observation carried forward (LOCF) applying a negative binomial
model with terms for treatment algorithm, HbA1c stratum, sulfonylurea/meglitinide use, and the
logarithm of the exposure time (in days) as an offset variable and compound symmetry as
variance-covariance structure (21, 22). A Wilcoxon rank-sum test was conducted as a sensitivity
analysis. The percentages of subjects achieving HbA1c targets at the end of the study (LOCF)
were analyzed using a logistic regression model with terms for treatment algorithm and strata.
All data are expressed as least square mean (LSM) ± standard error (SE) unless otherwise stated,
and a P-value of <0.05 was considered significant.
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RESULTS
Patient disposition and baseline characteristics
The patient disposition was based on all randomized subjects. After screening and the
lead-in period 1112 subjects were allocated separately into Study A (531 subjects) and Study B
(581 subjects) (Appendix 2). Percentages of discontinued subjects and reasons for withdrawal
were similar comparing treatment algorithms in both studies (Study A: Q1D: 16.8%, Q3D:
20.2%; Study B: Q1D: 15.6%, Q3D: 17.5%). The percentages of subjects on specific OAD
regimens were equivalent; approximately 89% subjects were taking biguanides, and
approximately 43% were taking 2 or more OADs. There were no statistical differences between
the treatment algorithms in either Study A or B regarding baseline demographics (Table 1).
Glycemic control, insulin dose, and body weight
At weeks 7, 12, and 24 there were significant decreases in HbA1c from baseline for both
Q1D and Q3D algorithms in Study A and B (Figure 1A and B). The 95% CIs for the LSM
difference from both studies were within the interval (–0.4% to 0.4% [–4.4 mmol/mol to 4.4
mmol/mol) and contain 0% ([0 mmol/mol] i.e., Q3D was noninferior to Q1D, Q1D was
noninferior to Q3D, and neither was superior to the other), indicating that Q1D and Q3D were
clinically equivalent (Figure 1A and B). The all-completer population concluded the same
outcome. No statistically significant 2-way interaction (treatment by age group) was evident for
the change in HbA1c (Study A: p=0.656; Study B: p=0.364). There was no difference in
treatment effect among those taking sulfonylureas or meglitinides, prior to randomization, and
those not taking these medications.
The overall percentage of subjects reaching the goal HbA1c of ≤7.0% (53.0 mmol/mol)
at the end of the study (LOCF) was not statistically significantly different between the Q3D
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(Study A= 42.5%; Study B= 42.4%) and the Q1D adjustment (Study A= 49.8%; Study B=
49.3%) for Study A (odds ratio 0.75; 95% CI 0.52 to 1.09; P=0.128) and Study B (odds ratio
0.77; 95% CI 0.53 to 1.11; P=0.162). Similarly, there was no statistical difference in the
percentage of subjects ≥65 years of age reaching target in Study A between Q3D (58.0%) and
Q1D (58.5%; odds ratio 1.17; 95% CI 0.52 to 2.67; P=0.701). The percentage of subjects ≥65
years of age reaching target in Study B was significantly lower for those randomized to Q3D
algorithm (46.2%) than to the Q1D algorithm (67.9%; odds ratio 0.32; 95% CI 0.13 to 0.80;
P=0.015); however, it is notable that 4 subjects started with an HbA1c ≤7.0% (53.0 mmol/mol)
in the Q1D group compared with none in the Q3D group.
There was a significant decrease from baseline to week 24 in the 7-point SMBG profile at
all time points except for morning premeal values in both Studies A and B for both algorithms
(Figure 2). There was no statistical difference in the change from baseline in 7-point SMBG
between Q3D and Q1D in Study A. In Study B, there was a significantly greater decrease from
baseline to week 24 in blood glucose concentrations in subjects using Q1D than in those using
Q3D at midday premeal (LSM Q3D–Q1D 95% CI 0.1 to 12.3; P=0.045), bed time (LSM Q3D–
Q1D 95% CI 1.7 to 20.6; P=0.020), and 0300 hours (LSM Q3D–Q1D 95% CI 0.5 to 17.5;
P=0.037) (Figure 2). The change in FBG at week 24 was not significant in Study A: For Q1D,
the LSM change from baseline was 1.4 ± 4.0 mg/dL, and for Q3D, it was 6.6 ± 4.1 mg/dL, with
no difference between algorithms (P=0.238). There was a significant difference in the change
from baseline to week 24 in FBG between Q3D (8.0 ± 3.7 mg/dL) and Q1D (-6.5 ± 3.8 mg/dL)
in Study B (P=0.002). The change in FBG from baseline in subjects ≥65 years of age was not
statistically different (P=0.242) between Q3D (18.1 ± 8.2 mg/dL) and Q1D (7.5 ± 7.9 mg/dL) in
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Study A. The change in FBG from baseline in subjects ≥65 years of age was not statistically
different (P=0.082) between Q3D (17.2 ± 8.2 mg/dL) and Q1D (-3.1 ± 9.1 mg/dL) in Study B.
There was a significant increase at week 24 from baseline in 1,5-AG levels (µg/mL) in
both Q1D and Q3D in Study A, as well as for Study B. In addition, there was no statistical
difference in the change from baseline in 1,5-AG levels in both Study A and B between Q3D and
Q1D (Figure 1C and D).
There was no difference in baseline body weight in either Study A or Study B between
Q3D and Q1D treatment algorithms (Table 1). In both studies, subjects gained weight from
baseline regardless of titration algorithm. Subjects in Study A using the Q3D algorithm gained
more weight from baseline than subjects using the Q1D algorithm (3.0 ± 0.3 vs. 2.2 ± 0.3 kg;
P=0.014), while there was no difference in weight gain between Q3D (2.0 ± 0.2 kg) and Q1D
approaches (2.5 ± 0.2 kg) in Study B (P=0.108).
In Study A and Study B, GLA doses at week 24 were not statistically different between
Q3D and Q1D algorithms and the GLA doses were stable throughout the 24 week treatment.
There was no significant difference between treatment algorithms in insulin lispro dose at week
24 of either study. The percentages of basal and bolus doses for the TDD in Study A were: Q1D:
basal–58.2%, bolus–41.8%; Q3D: basal–53.8%, bolus–46.2%. The percentages of basal and
bolus doses for the TDD in Study B were: Q1D: basal–57.4%, bolus–42.6%; Q3D: basal–57.2%,
bolus–42.8%. Approximately 61% of subjects required ≤2 injections (Table 1).
Hypoglycemia
In the overall subject population and those ≥65 years of age, the incidences and
annualized rates of overall, nocturnal, and severe hypoglycemic episodes during the treatment
phase (LOCF) were not statistically different between treatment algorithms Q3D and Q1D in
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either study (Table 2). There was no difference in the rate of hypoglycemia in those subjects
taking sulfonylureas or meglitinides prior to randomization, when compared to those not taking
these medications.
Safety
The incidence of serious adverse events in Study A was similar between Q1D (n=18
[6.7%]) and Q3D (n=12 [4.6%]). The incidences of serious adverse events in Study B were
similar in Q1D (n=21 [7.3%]) and Q3D (n=25 [8.6%]) (Appendix 2).
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CONCLUSIONS
AUTONOMY uniquely demonstrates the comparable effectiveness of 2 unique
self-titration algorithms when mealtime insulin lispro is added to appropriately optimized GLA.
The AUTONOMY trial addressed the need for approaches to escalate prandial insulin treatment
in patients with type 2 diabetes in a real-world setting. Both patient-driven algorithms (Q1D and
Q3D) demonstrated statistically significant and clinically equivalent reductions in HbA1c,
significant increases in 1,5-AG, and improved 7-point SMBG profiles in Studies A and B. By
implementing either algorithm, approximately 50% of subjects, who had previously failed to
reach goal HbA1c of ≤7.0% (53.0 mmol/mol) with basal insulin optimization plus OADs,
achieved the ADA goals for glycemic control with less glucose variability. Moreover, the
sequential addition of prandial insulin lispro injections resulted in approximately 61% of subjects
only requiring 2 or fewer doses rather than a full basal-bolus regimen, which simplifies treatment
and could enhance therapy compliance.
The improved metabolic control with the initiation and escalation of lispro, regardless of
titration algorithm, was accomplished with low incidences and rates of nocturnal and severe
hypoglycemia in both the overall study population and the elderly subgroup (≥65 years of age).
The efficacy and safety of treatment intensification in the elderly is critical because of the aging
population and higher prevalence of type 2 diabetes in this group (23). These findings are
consistent with those of the A1chieve study and show that a basal-bolus therapy can be initiated
in the elderly without increased risk of hypoglycemia (24). Moreover, in a pooled analysis, Lee
et al. described that adding insulin GLA in an elderly patient population had low rates of
hypoglycemia with decreases in HbA1c similar to those in younger patients (25); however,
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AUTONOMY expanded on these results to demonstrate that therapy intensification and selftitration, starting with a single prandial dose of lispro, can safely occur in the elderly.
In support of the current study, results from the START and FullSTEP trials
demonstrated similar decreases in HbA1c when a basal-bolus regimen was initiated in patients
inadequately controlled on basal insulin plus OADs (26, 27). The START Study showed similar
glycemic control can be achieved by patients using a breakfast preprandial insulin titration
approach compared to a physician managed strategy (26). The FullSTEP study demonstrated that
a stepwise insulin approach resulted in greater patient treatment satisfaction with fewer
hypoglycemic events than a full basal-bolus regimen (27). However, the study initiated the first
prandial insulin dose at the largest meal, and the prandial dose was adjusted by the study
investigators, and both AUTONOMY and START began the prandial therapy at the first meal of
the day. Nevertheless, AUTONOMY utilized 2 self-titration algorithms, initiated the prandial
dose based on a percentage of the total basal dose, and added other mealtime doses as necessary.
The majority of diabetes management is performed in a generalist setting, in which
substantial clinical inertia—the failure to intensify treatment—exists (28, 29, 30). Two
retrospective studies in the U.K. determined that patients with suboptimal glycemic control
remained poorly controlled for more than 7 years before insulin treatment initiation, with a mean
HbA1c of approximately 9.0-10.0% (74.9-85.8 mmol/mol) (31, 32). In AUTONOMY the
average duration of diabetes at entry was 12 years, further supporting that this inertia prevents
early glycemic control and timely treatment intensification with exogenous insulin. A 10-year
follow-up of the UKPDS showed that a legacy effect exists from early intensive glycemic
control, reducing the long-term risk for cardiovascular complications associated with type 2
diabetes (33). The complexity of therapy with multiple medications, the fear of hypoglycemia,
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and weight gain are major barriers to treatment intensification, especially with insulin (34, 35,
36, 37). AUTONOMY demonstrated that prandial insulin can be initiated in an adult population,
including the elderly, to lower HbA1c and limit mealtime glucose excursions safely, with either
patient-driven algorithm, in the endocrinology and generalist setting. Utilizing Q1D and Q3D
algorithms simplified insulin therapy by not requiring patient training on carbohydrate counting
or insulin correction factor and reduced the number of OADs in those treated with sulfonylurea
or meglitinide. In 2009, Oyer et al. reported on the self-titration of twice-daily biphasic insulin in
insulin naïve patients with type 2 diabetes (38). While AUTONOMY further supports the
concept of self-titration it does this, in those already optimized on basal insulin, with a basalbolus algorithm traditionally considered more complex. Subjects gained 2–3kg of weight,
regardless of treatment algorithm, with the initiation of prandial insulin; however, a previous
study determined that treatment satisfaction increased and regimen-related distress decreased
with the addition of rapid-acting insulin analogs to basal insulin despite any weight gain as a side
effect (37). This simple patient-centric approach has the potential to empower patients and to
limit barriers to achieve glycemic goals while improving treatment satisfaction.
A limitation of this study was the exclusion of subjects with BMIs ≥45 kg/m2, which,
with the growing health care burden associated with obesity, could be an important study
population. Future research needs to address whether the safety and efficacy proven with
applying either of the self-titration algorithms is applicable to Asian populations, because no
Asian countries were included; however, a large and multinational sample population was
studied. Although numerical differences were observed, which seem to benefit the use of Q1D
vs. Q3D, these were not statistically significant and only should be considered hypothesis
generating. Furthermore, the AUTONOMY algorithms were based on PK/PD modeling of GLA
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and lispro insulins; there may not be substantial differences with the use of other short acting
prandial insulin analogues. As in other insulin trials, the risk of hypoglycemia increased when
subjects were initiated on a prandial insulin regimen (39, 40). Although this trial investigated the
use of basal-bolus as an approach to controlling postprandial glucose excursions other options,
such as the combination of GLP-1 receptor agonists with insulin, may be considered (41).
In summary, the AUTONOMY trial provides novel data and the basis for the initiation
and escalation of lispro therapy using 2 simple, self-titration regimens in patients with type 2
diabetes who failed to achieve adequate glycemic control on appropriately titrated basal insulin
plus OADs. The trial demonstrated that a basal-bolus regimen can effectively and safely be
initiated in the endocrinology and generalist settings, by empowering patients to self-titrate their
bolus insulin in order to achieve glycemic goals with less glucose variability and low rates of
nocturnal and severe hypoglycemia.
19
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ACKNOWLEDGMENTS
S.E. serves on an advisory board for Eli Lilly and is on a Lilly speakers’ board. S.E. has
advised for Tandem, Merck, BI, BMS, Dexcom, NovoNordisk, Sanofi, and Abbott.
The research was supported by Eli Lilly and Company, Indianapolis, Indiana. R.L., J.J.,
and L.C.G. are full-time employees of Eli Lilly and Company and are also minor stock owners as
part of an employee offering program. No other potential of conflicts of interest were reported.
R.L., J.J., and L.C.G. contributed to the design of the study, analyzed and interpreted
data, reviewed and edited the manuscript, contributed to the discussion, and confirmed final
approval. S.E. analyzed and interpreted data, reviewed and edited the manuscript, contributed to
the discussion, and confirmed final approval. L.C.G. is the guarantor of this work and had full
access to the data and takes responsibility for the integrity of the data and accuracy of data
analysis.
Parts of this study were presented at the 73rd Scientific Sessions of the American
Diabetes Association, Chicago, Illinois, 21–25 June 2013 and at the 49th European Association
for the Study of Diabetes, Barcelona, Spain, 23–27 September 2013.
The authors thank Deborah Wimberley (Eli Lilly and Company) for the management of
the trial and William Huster (Eli Lilly and Company), Yongming Qu (Eli Lilly and Company),
Rong Qi (Eli Lilly and Company), Chunxue Shi (inVentiv Health), and Cheng Shao (inVentiv
Health) for statistical support. Additionally, the authors thank Jeff Bonner (Eli Lilly and
Company) for support and assistance in writing and preparing the manuscript.
20
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HC. Does a patient-managed insulin intensification strategy with insulin glargine and
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with type 2 diabetes: a retrospective cohort study of more than 80,000 people. Diabetes
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oral hypoglycaemic agents or insulin in primary care: retrospective cohort study. Br J
Gen Pract 2007;57:455–460
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Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of
intensive glucose control in type 2 diabetes. N Engl J Med 2008;359:1577–1589
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Miccoli R, Penno G, Del Prato S. Multidrug treatment of type 2 diabetes: a challenge for
compliance. Diabetes Care 2011;34:S231–S235
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Kleinebreil L; International Dawn Advisory Panel. Resistance to insulin therapy among
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JV. Why don't diabetes patients achieve recommended risk factor targets? Poor adherence
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Peyrot M, Rubin RR, Polonsky WH, Best JH. Patient reported outcomes in adults with
type 2 diabetes on basal insulin randomized to addition of mealtime pramlintide or rapidacting insulin analogs. Curr Med Res Opin 2010;26:1047–1054
38.
Oyer DS, Shepherd MD, Coulter FC, Bhargava A, Brett J, Chu PL, Trippe BS;
INITIATEplus Study Group. A1c control in a primary care settting: self-titrating an
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39.
Rosenstock J, Ahmann AJ, Colon G, Scism-Bacon J, Jiang H, Martin S. Advancing
insulin therapy in type 2 diabetes previously treated with glargine plus oral agents:
prandial premixed (insulin lispro protamine suspension/lispro) versus basal/bolus
(glargine/lispro) therapy. Diabetes Care 2008;31:20-25
40.
Rodbard HW, Visco VE, Andersen H, Hiort LC, Shu DHW. Treatment intensification
with stepwise addition of prandial insulin aspart boluses compared with full basal-bolus
therapy (FullSTEP Study): a randomised, treat-to-target clinical trial. Lancet Diabetes
Endocrinol 2014;2:30-37
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Buse JB, Bergenstal RM, Glass LC, Heilmann CR, Lewis MS, Kwan AY, Hoogwerf BJ,
Rosenstock J. Use of twice-daily exenatide in basal insulin—treated patients with type 2
diabetes. Ann Intern Med 2011;154:103—112
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Diabetes Care
Table 1. Baseline demographics, insulin dose, insulin injections, and concomitant
medications
Study A
Study B
Q1D
(N=267)
Q3D
(N=261)
Q3D vs.
Q1D
*P-value
Q1D
(N=288)
Q3D
(N=290)
Q3D vs.
Q1D
*P-value
57.9 ± 10.3
24.3%
58.8 ± 9.5
26.4%
0.278
0.618
57.7 ± 9.7
19.4%
57.0 ± 10.6
22.4%
0.412
0.414
Race: white
82.3%
83.5%
0.781
79.7%
83.3%
0.308
Sex: female
49.8%
52.9%
0.487
53.8%
53.4%
0.934
BMI, kg/m
2
BMI ≥30 kg/m
33.3 ± 5.3
73.4%
33.4 ± 5.5
69.7%
0.793
0.385
32.6 ± 5.2
66.3%
33.2 ± 5.7
68.6%
0.174
0.594
Body weight, kg
94.6 ± 20.2
92.4 ± 17.7
0.188
90.8 ± 18.3
93.5 ± 21.2
0.112
Duration of
diabetes, years
Subjects >10 years
11.7 ± 6.3
54.3%
12.6 ± 7.9
60.2%
0.129
0.188
11.6 ± 6.5
54.5%
11.9 ± 7.1
53.8%
0.645
0.868
8.3 ± 0.9
67.2 ± 9.8
8.4 ± 1.0
68.3 ± 10.9
0.453
8.3 ± 1.0
67.2 ± 10.9
8.4 ± 1.0
68.3 ± 10.9
0.162
56.6%
58.2%
0.725
53.5%
57.6%
0.357
Biguanides
85.4%
89.3%
–
93.8%
89.3%
–
Sulfonylurea/
meglitinide
49.4%
52.5%
–
34.7%
40.3%
–
DPP-4 inhibitors
9.7%
10.0%
–
8.0%
7.2%
–
Thiazolidinediones
5.2%
7.3%
–
3.8%
6.6%
–
OAD class ≥2
44.9%
51.0%
–
36.1%
39.3%
–
GLA at entry
(n=180)
46.8 ± 32.4
(n=177)
48.6 ± 27.8
–
(n=163)
46.8 ± 29.2
(n=163)
45.0 ± 30.0
–
NPH at entry
(n=50)
50.4 ± 26.7
(n=48)
45.0 ± 26.4
–
(n=75)
46.1 ± 32.7
(n=78)
47.6 ± 23.2
–
Detemir at entry
(n=37)
58.9 ± 42.6
(n=35)
46.2 ± 30.8
–
(n=49)
52.4 ± 32.0
(n=47)
60.8 ± 44.1
–
(n=0)
NA
(n=0)
NA
–
(n=0)
NA
(n=1)
34.0 ± NA
–
Baseline
Demographics
Age, years
Subjects ≥65 years
2
HbA1c, %
mmol/mol
HbA1c >8.0%
(>63.93 mmol/mol)
Concomitant
Medications
(% subjects)
Insulin Dose (U/d)
NPL at entry
25
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Diabetes Care
Basal (GLA) at
randomization
Page 26 of 33
62.8 ± 33.9
60.3 ± 32.1
0.335
57.3 ± 32.5
60.0 ± 33.0
0.236
Basal (GLA) at
week 24
66.4 ± 35.1
63.5 ± 34.6
0.543
59.9 ± 33.4
65.2 ± 42.5
0.497
Bolus (Lispro) at
week 24
47.7 ± 41.1
54.6 ± 46.7
0.095
44.5 ± 36.8
48.8 ± 51.0
0.156
1 Injection
(n=84)
31.5%
(n=81)
31.0%
–
(n=102)
35.4%
(n=100)
34.5%
–
2 Injections
(n=69)
25.8%
(n=66)
25.3%
–
(n=85)
29.5%
(n=89)
30.7%
–
3 Injections
(n=114)
42.7%
(n=114)
43.7%
–
(n=101)
35.1%
(n=101)
34.8%
–
Bolus Injections
(LOCF %subjects)
Data are mean ± standard deviation (SD) or % subjects. *P-values for continuous measures were
based on an analysis of variance and categorical measures were based on Fisher’s exact test for
treatment algorithm Q3D vs. Q1D. – indicates that P-values were not calculated. Abbreviations:
NPL= insulin lispro protamine suspension; DPP-4= dipeptidyl peptidase-4; OAD= oral
antidiabetic drug; LOCF= last observation carried forward; NA= not applicable
26
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Diabetes Care
Table 2. Total, nocturnal, and severe hypoglycemia incidences and rates per 1 year
Overall
Hypoglycemia
Q1D
(N=268)
Q3D
(N=263)
≥ 65 years old
Q3D vs.
Q1D
*P-value
Q1D
(N=66)
Q3D
(N=69)
Q3D/Q1D
Rate Ratio
(95% CI)
Q3D vs.
Q1D
*P-value
Q3D/Q1D
Rate Ratio
(95% CI)
Study A
Total
Incidence (n [%])
Rate per 1 year
(NBM ± SE)
Nocturnal
Incidence (n [%])
Rate per 1 year
(NBM ± SE)
Severe
Incidence (n [%])
Rate per 1 year
(Mean ± SD)
231 (86.2)
218 (83.2)
0.435
38.32±2.80
40.58±3.06
169 (63.1)
167 (63.7)
8.59±0.80
9.60±0.93
5 (1.9)
2 (0.8)
0.258
0.04±0.31
0.03±0.41
0.271
0.586
1.06
(0.86-1.30)
0.870
0.404
1.12
(0.86-1.45)
60 (90.9)
61 (88.4)
41.62±5.42
48.84±6.21
45 (68.2)
49 (71.0)
8.71±1.48
11.60±1.92
3 (4.5)
1 (1.4)
0.296
0.03±0.25
0.294
0.10±0.49
0.802
0.383
1.17
(0.82-1.68)
0.763
0.229
1.33
(0.84-2.12)
Study B
Total
Incidence (n [%])
Rate per 1 year
(NBM ± SE)
Nocturnal
Incidence (n [%])
Rate per 1 year
(NBM ± SE)
Severe
Incidence (n [%])
Q1D
(N=289)
Q3D
(N=292)
238 (82.4)
231 (79.1)
38.76±3.14
40.54±3.29
156 (54.0)
149 (51.0)
7.14±0.80
8.23±0.91
7 (2.4)
8 (2.7)
Q3D vs.
Q1D
*P-value
0.351
0.689
1.05
(0.84-1.30)
0.470
0.358
1.15
(0.85-1.56)
0.856
Q1D
(N=56)
Q3D
(N=65)
51 (91.1)
53 (81.5)
51.38±8.26
42.88±6.35
42 (75.0)
43 (66.2)
12.01±2.40
10.69±2.03
1 (1.8)
2 (3.1)
27
CONFIDENTIAL-For Peer Review Only
Q3D vs.
Q1D
*P-value
0.205
0.404
0.83
(0.55-1.28)
0.383
0.671
0.89
(0.52-1.52)
0.797
Diabetes Care
Rate per 1 year
(Mean± SD)
0.11±1.09
0.06±0.36
0.816
Page 28 of 33
0.05±0.37
0.07±0.38
0.657
Data are n (%), negative binomial mean (NBM) ± standard error (SE), or mean ± standard
deviation (SD) for severe rates. Incidence is reported as the number of subjects with at least one
hypoglycemic episode. Hypoglycemia was defined as anytime the subject experienced a sign or
symptom associated with hypoglycemia or a blood glucose reading ≤70 mg/dL even if it was not
associated with signs or symptoms. Severe hypoglycemia was defined as an event requiring
assistance of another person to actively administer carbohydrates, glucagon, or other
resuscitative actions.*P-values for the incidences of each category were based on a logistic
regression model for Q3D vs. Q1D. *P-values for rate adjusted per 1 year were based on NBM
regression for Q3D vs. Q1D.Wilcoxon test values were not presented but confirmed no
significance. Due to low occurrence of severe hypoglycemia, mean ± SD and only Wilcoxon test
P-values are presented.
28
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Diabetes Care
Figure 1—Data for the change from baseline in HbA1c (%) in Study A (A) and Study B (B) and
1,5-anhydroglucitol (AG) in Study A (C) and Study B (D) are least square mean (LSM) ± SE.
The 95% CI is the LSM difference between Q3D-Q1D. *Indicates a significant change from
baseline based on 95% confidence intervals from a mixed, model repeated measure approach
using restricted maximum likelihood method for both Q1D and Q3D. In the sensitivity analysis
of change in HbA1c from baseline, Study A all completer population (LSM) was Q1D –1.08%
(95% CI –1.25% to –0.92%) and Q3D –1.04 (95% CI –1.2%1 to –0.87%); Study B all completer
population for change in HbA1c from baseline (LSM) was Q1D –1.01% (95% CI –1.15% to –
0.87%) and Q3D –0.98 (95% CI –1.12% to –0.84%). black circles = Study A Q1D; white circles
= Study A Q3D; black squares = Study B Q1D; white squares = Study B Q3D.
Figure 2—Data for 7-point self-monitored blood glucose (SMBG) profiles at baseline and week
24 in Study A (A) and Study B (B) are mean ± SD. *Represents significant change in SMBG
from baseline based on 95% CIs from a mixed, model repeated measure (MMRM) approach
using restricted maximum likelihood method (REML) for both Q1D and Q3D. †Represents
significant difference between Q3D and Q1D at week 24 from a MMRM model using REML in
Study B only. black circles = Q1D at baseline and week 24; white squares = Q3D at baseline and
week 24.
29
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Figure 1
Diabetes Care
Study A (N= 528)
B
Study B (N= 578)
0.0
-0.2
-0.2
-0.4
Q1D
Q3D
-0.6
*
-0.8
-1.0
*
Baseline
Q3D-Q1D LSM:
95% CI:
Week 12
Q1D
Q3D
-0.6
*
-0.8
*
Week 24
Baseline
Week 7
Week 12
Week 24
Q3D-Q1D LSM:
-0.01
-0.02
0.06
95% CI:
(-0.11, 0.09) (-0.15, 0.11) (-0.12, 0.24)
0.07
0.08
0.04
(-0.04, 0.17) (-0.06, 0.22)(-0.15, 0.22)
Study B (N= 578)
D
3
*
2
*
Q1D
Q3D
1
*
-1.2
Study A (N= 528)
4
1,5-AG Change From Baseline
(g/mL; LSM ± SE)
Week 7
-0.4
-1.0
*
-1.2
C
HbA1c Change From Baseline
(%; LSM ± SE)
0.0
4
1,5-AG Change From Baseline
(g/mL; LSM ± SE)
HbA1c Change From Baseline
(%; LSM ± SE)
A
Page 30 of 33
3
*
2
*
*
*
0
Q1D
Q3D
1
0
Baseline
Q3D-Q1D LSM:
95% CI:
Week 7
Week 12
Week 24
-0.08
-0.29
-0.15
(-0.54, 0.38)(-0.88, 0.30) (-0.95, 0.66)
Baseline
Q3D-Q1D LSM:
95% CI:
CONFIDENTIAL-For Peer Review Only
Week 7
Week 12
Week 24
0.10
0.09
-0.28
(-0.33, 0.53) (-0.51, 0.70) (-1.08, 0.52)
00
H
ou
rs
Ti
m
e
*
03
*
B
ed
l
PP
l
em
ea
Pr
2h
*
†
g
in
y
0
da
*
Ev
en
l
PP
em
ea
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50
M
id
y
da
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Figure 2
M
id
0
*
em
ea
*
ni
n
100
Pr
150
g
250
M
or
200
7-Point Self-Monitored
Blood Glucose (mg/dL)
300
Q1D Baseline
Q1D Week 24
Q3D Baseline
Q3D Week 24
ni
n
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Ho
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s
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e
Ti
*
Be
d
Study A (N= 528)
M
or
PP
l
em
ea
l
Pr
2h
*
03
00
g
in
y
da
PP
l
*
Ev
en
2h
50
em
ea
Pr
g
A
M
id
y
da
M
id
em
ea
Pr
ni
n
M
or
ni
ng
M
or
7-Point Self-Monitored
Blood Glucose (mg/dL)
Page 31 of 33
Diabetes Care
B
300
Study B (N= 578)
250
Q1D Baseline
Q1D Week 24
Q3D Baseline
Q3D Week 24
200
150
100
*
†
*
†
CONFIDENTIAL-For Peer Review Only
Diabetes Care
Page 32 of 33
Appendix 1
Add insulin lispro 1-2-3 with adjustments
every 1 day (Q1D)
Randomize
Enrollment
•Type 2 diabetes
•18 to 85 years of age
•BMI <45 kg/m2
•HbA1c >7% and ≤12% (>53
mmol/mol and ≤108 mmol/mol)
•On insulin glargine, NPH, NPL,
or detemir for ≥3 months and a
dose of ≥20 U/d at screening
•May be on metformin,
meglitinide, sulfonylurea,
pioglitazone, DPP-4 inhibitor, or
a combination for ≥3 months in
addition to basal insulin glargine,
NPH, NPL or detemir
No
Add insulin lispro 1-2-3 with adjustments
every 3 days (Q3D)
HbA1c ≤7.0%
(≤53.0 mmol/mol)
GLA optimization lead-in
(optional)
Yes
Discontinue
6 WEEKS
Visit:
Week:
1 (screening)
-1
2a
0
3a 4a,b 5a,b
1 2
4
24 WEEKS
6a
6
7a 8b 9b 10 11b
7 8 9 10 12
12
14
13b
16
14b
18
15
19
16b
23
17
27
18b 19
29 31
Abbreviations: BMI = body mass index; DPP-4 = dipeptidyl peptidase-4; NPH = neutral protamine Hagedorn; NPL = neutral protamine
lispro; HbA1c = glycated hemoglobin; Q1D = every day; Q3D = every 3 days. aThe 6-week glargine optimization lead-in period was only
required for those subjects who had to be converted to glargine from insulin NPH, NPL, or detemir; require conversion from glargine
twice a day to glargine once daily; or who were on once daily glargine at study entry with HbA1c >7.0% (53 mmol/mol) and fasting blood
glucose >120 mg/dL (>6.7 mmol/L). Subjects who CONFIDENTIAL-For
did not require glargine
optimization were randomized at visit 2, forewent visits 3 to 7
Peer Review Only
and instead proceeded to the randomized treatment period beginning with visit 8 activities, 1 week after visit 2. bTelephone visits.
Page 33 of 33
Diabetes Care
Appendix 2
Study A
Randomized (N = 531)
Q1D (N = 268)a
Completed, n = 223 (83.2%)
Early terminated, n (%), 45 (16.8)
Reasons:
Adverse event
1 (0.40
Death
2 (0.7)
Entry criteria not met
1 (0.4)
Lack of efficacy
1 (0.4)
Lost to follow-up
4 (1.5)
Physician decision
4 (1.5)
Protocol violation
17 (6.3)
Sponsor decision
1 (0.4)
Subject decision
14 (5.2)
Study B
Randomized (N = 581)
Q3D (N = 263)a
Q1D (N = 289)a
Completed, n = 210 (79.8%)
Completed, n = 244 (84.4%)
Early terminated, n (%), 53 (20.2)
Reasons:
Adverse event
4 (1.5)
Death
0 (0.0)
Entry criteria not met
3 (1.1)
Lack of efficacy
2 (0.8)
Lost to follow-up
4 (1.5)
Physician decision
10 (3.8)
Protocol violation
13 (4.9)
Sponsor decision
2 (0.8)
Subject decision
15 (5.7)
Early terminated, n (%), 45 (15.6)
Reasons:
Adverse event
2 (0.7)
Death
1 (0.3)
Entry criteria not met
4 (1.4)
Lack of efficacy
1 (0.3)
Lost to follow-up
8 (2.8)
Physician decision
8 (2.8)
Protocol violation
8 (2.8)
Sponsor decision
0 (0.0)
Subject decision
13 (4.5)
Q3D (N = 292)a
Completed, n = 241 (82.5%)
Early terminated, n (%), 51 (17.5)
Reasons:
Adverse event
3 (1.0)
Death
3 (1.0)
Entry criteria not met
7 (2.4)
Lack of efficacy
0 (0.0)
Lost to follow-up
9 (3.1)
Physician decision
11 (3.8)
Protocol violation
8 (2.7)
Sponsor decision
1 (0.3)
Subject decision
9 (3.1)
Patient disposition was based on all randomized subjects. There was no significant difference between the percentages of subjects who
discontinued from Q1D or Q3D for any reason of early termination. Deaths were not attributed to the treatment. aSix subjects were randomized
but did not receive at least one dose of lispro--Study A Q1D: n=1 and Q3D: n=2; Study B Q1D: n=1 and Q3D: n=2. These subjects (ie, subjects
not exposed to lispro) were not included in the full analysis set.
CONFIDENTIAL-For Peer Review Only