“The Artificial Pancreas: Current progress and future outlook in
the treatment of Type 1 Diabetes”
Running head: The Artificial Pancreas: current progress and
future outlook
Authors: Rozana Ramli, Monika Reddy, Nick Oliver.
Division of Diabetes, Endocrinology and Metabolism, Faculty of
Medicine, Imperial College, London
Corresponding author:
Professor Nick Oliver
7S7a, Commonwealth Building
Hammersmith Campus
Du Cane Road
London
W12 0HS
[email protected]
(5822 words excluding abstract and references)
Acknowledgements
Infrastructure support is provided by the NIHR Imperial
Biomedical Research Centre and the NIHR Imperial Clinical Research
Facility. The views expressed are those of the authors and not
necessarily those of the NHS, the NIHR or the Department of Health
and Social Care.
Abstract (153 words):
Type 1 diabetes (T1D) is characterised by insulin deficiency
caused by autoimmune destruction of the pancreatic beta cells. The
treatment of T1D is exogenous insulin in the form of multiple daily
injections or continuous subcutaneous insulin infusion. Advances in
diabetes technology has been exponential in the past few decades,
culminating in studies to develop an automated artificial pancreas
(AP), also known as the closed-loop system. This has recently led
to a commercially available hybrid AP in the United States and
Europe. This review article aims to provide an overview of the
rationale for an AP system and an update of the current state of AP
development. We explore the different types of AP systems being
studied, including the use of adjunctive therapy, and the use of
these systems in different groups of users. In addition, we discuss
the potential psychosocial impact and the challenges and
limitations of implementing AP use into clinical practice.
Key Points
Type 1 diabetes is treated with exogenous insulin, and recent
advances in diabetes technology has culminated in the development
of a commercially available hybrid artificial pancreas.
Adjunctive therapy to insulin-only AP including glucagon, amylin
analogues and glucagon-like peptide 1 receptor agonists have shown
promise.
AP has also been investigated in various cohorts including in
pregnancy, type 2 diabetes and in critically ill individuals, but
larger studies with evaluation of psychosocial impact of AP in
these different cohorts are warranted.
1 Introduction
Type 1 diabetes mellitus (T1D) is characterised by insulin
deficiency caused by autoimmune destruction of the pancreatic islet
beta cells. 425 million people worldwide have diabetes and T1D
accounts for 5-10% of all cases (1). The treatment for T1D is
exogenous insulin, delivered in the form of either multiple daily
injections of insulin (MDI) or continuous subcutaneous insulin
infusion (CSII), also known as an insulin pump. Insulin
requirements throughout the day vary depending on time, activity,
carbohydrate contents of meals, menstruation, stress and illness.
Achieving optimal glycaemic control in this context can be
physically and psychologically challenging.
The landmark Diabetes Control and Complications trial (DCCT)
showed that intensive insulin therapy results in a lower HbA1c
compared to conventional treatment. It is also associated with
lower risk of diabetic microvascular complications (2). The 30-year
follow up on DCCT study participants also showed that intensive
insulin therapy has long-term beneficial effects on the incidence
of cardiovascular disease in T1D (3). However, in achieving
glycaemic control closer to target, and limiting future
complications with intensive insulin therapy, the risk for
hypoglycaemia is increased (4) with potential acute complications
including seizures, unconsciousness and death (4, 5).
Technology aiding self-management of T1D has advanced
exponentially over the past few decades. Insulin pumps are becoming
increasingly accessible worldwide for people with T1D, enabling
flexibility that fits around the individual’s lifestyle.
Meta-analyses of CSII in T1D have shown that CSII improves
glycaemic control (6, 7), whilst at the same time is associated
with significantly reduced rates of severe hypoglycaemia (6) and
improved quality of life (8) compared with MDI.
Similar advances have been observed in glucose monitoring
devices since the development of blood glucose meters in the 1970s
to the emergence of continuous glucose monitoring (CGM) in the
1990s. CGM provides continuous access to real-time glucose data,
information on the direction and rate of change of glucose. Some
CGM devices may be used without the need for calibration or
confirmatory capillary blood glucose for decision-making. The
accuracy of CGMs in the recent years have improved compared to
previous iterations (9). Factors contributing to CGM accuracy
includes its calibration and software algorithm which improve the
MARD (mean absolute relative difference) by filtering and smoothing
the signal (10). A lower % MARD is corresponds to better sensor
performance, with a MARD of under 10% representing sufficient
accuracy for CGM data to make insulin dosing decision (11). The
MARDs of commercially available subcutaneous CGM systems at present
range between 9.0 – 13.6 % (12).
CGM devices are also equipped with real-time alerts and alarm
for impending hypo-and hyperglycaemia and can therefore be
beneficial in individuals with T1D at high risk of hypoglycaemia,
such as those with recurrent severe hypoglycaemia and hypoglycaemia
unawareness (13).
With the development of continuous glucose sensors, CSII
technology has evolved. Sensor-augmented pump therapy (SAP), which
combines the technology of an insulin pump with a continuous
glucose monitoring sensor, was developed, and subsequently the
ability to suspend insulin delivery when glucose is low (low
glucose suspend or LGS) or when glucose is predicted to become low
(predicted low glucose suspend or PLGS) was added.
The next evolutionary step from SAP with PLGS is the artificial
pancreas (AP), also known as a closed-loop system or automated
insulin delivery, which aims to mimic the endocrine function of a
healthy pancreas for glucose homeostasis. The AP system
incorporates a sensor for continuous glucose monitoring, an insulin
pump to deliver insulin and an algorithm connecting the two
devices, which directs the pump to deliver insulin based on the
real-time glucose readings from the sensor (Figure 1). Several AP
systems are currently in development at different stages (14).
Various aspects of the AP have been studied in clinical trials in
the last decade, including its use in the outpatient and home
setting, single versus dual-hormone systems and its use in
different cohorts. Recently, the FDA approved the first hybrid
artificial pancreas system, the Medtronic 670G (Medtronic,
Northridge, CA), for use by people with T1D over 14 years of age
(15). It is also CE Mark approved for use in people with T1D over 7
years of age within Europe. The hybrid system is able to deliver
and adjust basal insulin automatically without user input when used
in the Auto Mode. However, the user must still manually deliver
bolus insulin during meals.
This review article aims to explore the rationale behind AP,
discuss the development status of various closed-loop systems,
including their benefits and limitations, report on their efficacy
and tolerability in different cohorts and explore the future
outlook in the treatment of T1D.
The search terms used to identify publications on PubMed
included “artificial pancreas”, “closed-loop insulin” and
combinations of these with “bi-hormonal”, “glucagon”,
“pramlintide”, “GLP-1 agonist”, “pregnancy”, “type 2 diabetes”,
“critically ill” and “psychosocial”.
2 Artificial Pancreas: How does it mimic a pancreas?
The main hormones involved in the regulation of glucose
homeostasis are insulin and glucagon. Insulin is synthesised and
secreted by the pancreatic beta cells in response to rising glucose
levels, and increases glucose uptake into skeletal muscle and fat,
inhibiting gluconeogenesis, and stimulating glycogen synthesis. In
contrast, glucagon is produced by the alpha cells of the islets of
Langerhans and when secreted in response to low blood glucose
levels, stimulates hepatic glycogenesis and activates
gluconeogenesis. As with most hormonal feedback loops, the
interaction between these hormones is constantly in motion and is
tightly regulated.
The ideal treatment for T1D would be an intervention that can
mimic the glucose regulating function of the pancreas. The
development of AP can be traced back to the 1960s when the
possibility for external blood glucose regulation was established
in studies in people with T1D using intravenous glucose measurement
and infusion of insulin and glucose (16). Multiple studies have
since been conducted to develop a system that is portable, safe and
efficient to use in the outpatient setting.
3 Control Algorithm
The control algorithm within an AP is arguably the most
important piece of the system. Several control algorithms have been
developed and studied, including model predictive control (MPC),
proportional-integral-derivative control (PID) and fuzzy logic
control (FL). The former two approaches are more commonly used in
clinical studies and development of AP (17).
The basic principles of the MPC approach involve a model which
is used to predict the outcome of control moves (insulin infusion)
on future outputs (glucose) over a defined prediction horizon (18).
MPC is a general control paradigm and is flexible, allowing it to
be used in dual-hormone AP (19).
The PID control algorithm was initially modelled on the
pancreatic beta cell response and is also referred to as
physiologic insulin delivery (20). It calculates insulin delivery
based on three set-points: 1. Proportional (P) - insulin delivery
is adjusted in response to current measured glucose, 2. Integral
(I) - insulin delivery adjusted corresponding to the area under the
curve between measured and target glucose levels, and 3. Derivative
(D) - insulin is delivered based on the rate of change of glucose
over time.
A randomised crossover study comparing personalised MPC and PID
control algorithms in 30 participants was conducted for 27.5 hours
with an unannounced 65g meal in a supervised outpatient suite. The
results showed good overall performance in both groups. MPC showed
significantly greater improvement in glucose control with greater
mean time in range 3.9- 10.0 mmol/L (74.4 vs. 63.7%, P = 0.020),
lower mean glucose during entire trial duration (7.7 vs. 8.9
mmol/L, P = 0.012) and 5 hours after the unannounced 65-g meal
(10.1 vs. 12.2 mmol/L, P = 0.019). Percentage of time in
hypoglycaemia (< 3.9 mmol/L) were minimal in both MPC (4.6%) and
PID (2.9%) with no significant differences between the groups
(19).
Other types of control algorithm used in studies of AP include
fuzzy logic control (FL) and bio-inspired control algorithms.
Although FL is not used as frequently as MPC or PID, its use has
increased in recent years. The fuzzy logic algorithm in an AP
system modulates insulin delivery based on rules that attempt to
replicate diabetes clinical practitioners (21). A bio-inspired
control algorithm is based on a mathematical model of pancreatic
beta cell physiology (22) but its use in AP studies has been
limited thus far (23).
Most control algorithms include safety modules to constrain
insulin delivery, limiting the amount of insulin on board or the
maximum rate of insulin delivery, and suspending insulin delivery
when glucose levels are low or decreasing (24).
Individual parameters that guide insulin delivery (such as basal
rates of insulin, Insulin: carbohydrate ratios (ICR) and insulin
sensitivity factors (ISF)) are not fixed, but change over time in
people with T1D, some AP algorithms have been developed to
incorporate adaptive features that enable automatic adjustment of
basal insulin delivery and ICR/ISF in response to changes seen in
insulin sensitivity and post-prandial glucose responses. Different
approaches to AP adaptation have been explored including the
run-to-run approach (25).
4 Types of Closed-loop Systems
The Artificial Pancreas Project launched by JDRF in 2006
developed a 6-stage pathway that defines the different stages of
development and types of artificial pancreas, based on automation
of insulin with or without glucagon delivery (Figure 2).
4.1 Sensor augmented pump with automated insulin suspension
A sensor-augmented insulin pump combines the technology of CSII
with a CGM sensor which transmits the glucose readings to the
insulin pump. This comprises Step 1 to 3 of the JDRF 6-stages
artificial pancreas pathway, and includes low glucose suspend and
predictive low glucose suspend systems.
A low glucose suspend system interrupts insulin delivery when
the glucose level reaches a predefined threshold value (e.g. 4.0
mmol/L). Insulin delivery is suspended for 2 hours if the user does
not respond to the low glucose alarm and resumes automatically
after 2 hours irrespective of the glucose level, although it can be
restarted beforehand by the user. An example of this is the
Medtronic MiniMed Paradigm VEO. In a study of 247 people with T1D
comparing the use of SAP with threshold suspend feature against
standard sensor-augmented insulin-pump therapy over 3 months, the
results showed reduction in nocturnal hypoglycaemic events (by
31.8%) in the threshold-suspend group (1.5±1.0 vs. 2.2±1.3 per
patient-week, P <0.001) without an increase in the HbA1c
(26).
Subsequently insulin pumps are available with a PLGS function
which reduces or suspends insulin delivery when the glucose reading
is predicted to be low. As well as reducing or suspending insulin
delivery, PLGS also automatically resumes basal insulin infusion
after up to 2 hours in the absence of intervention. Insulin
delivery can automatically restart after 30 minutes if the glucose
level rises above a predefined threshold value. Randomised studies
using PLGS in children, adolescents and adults compared to SAP have
demonstrated a reduction in hypoglycaemia without an increase in
hyperglycaemia (27, 28) and no difference in HbA1c at 6 months
(29).
Later generation systems in Step 4 and 5 comprise automated
insulin-only delivery systems. Step 6 is fully automated
multi-hormone closed loop systems, using glucagon in addition to
insulin.
4.2 Insulin-only artificial pancreas
An insulin-only system controls glucose level by increasing or
decreasing the amount of insulin infused based on CGM data. This
includes hybrid systems that automatically adjust basal insulin
with manual bolus insulin delivery at mealtimes managed by the
user, and fully closed-loop systems which automatically adjusts
basal and prandial insulin.
Most of the AP systems being developed and investigated use a
single hormone (insulin-only) system. Insulin-only AP has been
trialled in various settings including research facilities,
diabetes camps, and home conditions across different age
populations from children to adults with type 1 diabetes.
Meta-analyses and systematic reviews on the use of AP in the
adult and paediatric populations have shown a better mean glucose
concentration (30), a higher percentage of time in target range
(31, 32) and reduced time in hypoglycaemia and hyperglycaemia
(31).
A meta-analysis and systematic review on the use of artificial
pancreas in 2018 included 8 studies and 354 participants of which 7
studies (318 participants) were based in the home setting. AP
significantly maintained a better mean glucose concentration
(weighted mean difference (WMD) -1.03, 95% CI -1.32 to -0.75; P =
0.00001) compared to the control group over 24 hours. Time spent in
the hypoglycaemia was also significantly lower (WMD -1.23, 95% CI
-1.56 to -0.91; P = 0.00001) (30).
These findings were supported by another meta-analysis on AP
treatment for outpatients with T1D (31). 40 randomised controlled
trials of any AP systems (single and bi-hormonal) compared to any
manual insulin treatment in non-pregnant outpatients with T1D were
included. The use of both single and bi-hormonal artificial
pancreas is associated with a modest but significantly higher
proportion of time in the target range (3.9-10.0 mmol/L) over 24
hours. In particular, the single-hormone system favoured a higher
percentage of time in target both overnight and over 24 hours (WMD
12.77, 95% CI 9.82 to 15.71 and WMD 8.53, 95% CI 6.34 to 10.72
respectively). Time in hypoglycaemia (glucose <3.9mmo/L, WMD
-1.28, 95% CI -1.65 to -0.92) and hyperglycaemia (glucose
>10.0mmol/L, WMD -7.52, 95% CI -10.38 to -4.66) are also
reduced.
In the paediatric population, a recent systematic review and
meta-analysis examined 25 studies comparing AP and open-loop
interventions for children with T1D. 21 out of 25 studies were
conducted in the outpatient setting. In a total of 305 paediatric
participants with T1D, the percentage time in target range was
increased by approximately 12% in the AP group compared to
sensor-augmented pump therapy (32). The findings also showed that
the closed loop system was associated with significantly reduced
percentage times in the hypoglycaemic and hyperglycaemic range
(-0.67% and -3.01% respectively).
4.2.1 Approved hybrid artificial pancreas for clinical use
In 2017, the U.S. Food and Drug Administration (FDA) approved
the use of the Medtronic MiniMed 670G system (Medtronic,
Northridge, CA), the first commercially available hybrid AP system
in people aged 14 and above with T1D. This approval was expanded in
August 2018 to be used within an older paediatric group aged 7 to
13 with T1D. More recently, it received CE Mark approval for use
within the same age group in Europe. A hybrid AP is partially
automated in that it only delivers basal insulin automatically and
requires the user to manually input carbohydrate content into the
bolus calculator for the insulin pump to deliver insulin at
mealtimes.
The pivotal study evaluating the safety of hybrid AP system in
T1D included 123 people aged 14 to 75 years old in 10
investigational sites (15). Each subject wore the system for 3.5
months in three study phases. Although there were no statistically
powered endpoints in the study, it did demonstrate a reduction in
mean HbA1c from 7.4% ± 0.9 to 6.9% ± 0.6 with an increase in mean
percentage of time in range (3.9- 10.0 mmol/L) from 66.7% ± 12.2 to
72.2% ± 8.8. There were 24 severe hyperglycaemic events reported
(defined in protocol as a glucose concentration of >16.7mmol/L
with blood ketones >0.6mmol/L or accompanied by symptoms of
nausea, vomiting or abdominal pain). However, there were no reports
of diabetic ketoacidosis or severe hypoglycaemic events.
The latest published study evaluating the safety of the
Medtronic MiniMed 670G system in 105 children (ages 7-13 years)
with T1D over 3 months showed that in-home use of this system was
safe and associated with reduced HbA1c levels (from 7.9% ± 0.8% to
7.5% ± 0.6%, P < 0.001) and increased time in target glucose
range (from 56.2% ± 11.4% to 65.0% ± 7.7%, P < 0.001) compared
with baseline (33).
Although hybrid AP systems are a significant advance in the
development of artificial pancreas, they are not a fully
closed-loop system. Barriers remaining to full automation include
the slow pharmacokinetics of subcutaneous insulin, sensor accuracy
and the impact of other factors such as activity. Further
developments including the addition of glucagon, better accuracy of
CGM and availability of insulins with more rapid onset of action
has the potential to improve current AP systems (34).
4.3 Dual-hormone artificial pancreas (Glucagon)
Glucagon is secreted from alpha cells of a healthy pancreas, and
acts as a counter-regulatory hormone to insulin by elevating
glucose levels through promotion of gluconeogenesis and
glycogenolysis. Alpha cell function and glucagon secretion are
impaired in longstanding T1D. The use of glucagon in the AP is
therefore logical with the aim to reduce risk of hypoglycaemia.
Dual-hormone closed-loop systems have been extensively
investigated in trials and have been shown to have better outcomes
than SAP, but have not clearly been demonstrated to be superior to
an insulin-alone system.
A randomised crossover trial involving 39 participants aged 18
years and above, assigned to glycaemic regulation with a
bi-hormonal bionic pancreas or usual care (conventional or
sensor-augmented insulin pump therapy) showed that bi-hormonal AP
can be safely used at home. It also demonstrated significantly
lower mean CGM glucose concentration in the AP period (7.8 mmol/L,
SD = 0.6) compared to usual care period (9.0 mmol/L, SD = 1.6)
(difference 1.3mmol/L, 95% CI 0.8-1.8; P <0.0001) (35).
Additionally, as meal announcement was optional, therefore not
requiring carbohydrate counting, this bi-hormonal AP could reduce
part of the user burden associated with management of diabetes.
A randomised crossover study in 19 children aged 6 to 11
investigated the use of a bi-hormonal closed loop system versus
conventional insulin pump therapy in a diabetes camp setting. The
study showed better mean CGM-measured glucose concentration (7.6
mmol/L (SD 0.6) vs. 9.3 mmol/L (SD 1.7), P = 0.00037) and lower
proportion of time with a CGM-measured hypoglycaemia (1.2% (SD 1.1)
vs. 2.8% (SD 1.2), P <0.0001) with the bi-hormonal AP system
relative to insulin pump therapy (36). In a recent randomised
crossover study, better glucose control (mean % time spent in
plasma glucose target range over 24 hours 63% (SD 18) vs. 62% (SD
18) in single-hormone AP vs. 51% (SD 19) in CSII) and significant
reduction of time spent in hypoglycaemia (episodes of hypoglycaemic
events 9 vs 13 vs. 52 respectively) were observed with dual-hormone
closed loop system compared to single-hormone closed loop system
and CSII (37). The clinical significance of the time in range
difference is small and is achieved with greater insulin
infusion.
The use of single- and dual-hormone AP in exercise has also been
investigated. Using wearable sensors to detect exercise,
dual-hormone AP was shown to have a lower mean time in
hypoglycaemia during the exercise period (3.4% (SD 4.5)) compared
to single-hormone AP (8.3% (SD 12.6); P = 0.009) and PLGS (7.6% (SD
8.0); P< 0.001) (38).
Including glucagon within the AP system is aimed to
theoretically reduce the incidence of hypoglycaemia. This is
especially important in high-risk groups such as individuals with
impaired awareness of hypoglycaemia, those who exercise frequently
and very young children However, it comes with added complexity and
with an additional cannula site. Currently available formulations
of glucagon are also unstable and further development of a stable
glucagon analogue is required.
Table 1 summarises 24-hour (day and night) AP studies performed
in the home setting from 2016 onwards.
4.4 Intraperitoneal delivery of insulin in AI
To overcome the challenges associated with the pharmacokinetics
of insulin delivered in the interstitial space, as well as the
constraints of wearing an external device, an implantable AP system
(based on intraperitoneal insulin delivery, PID controller and a
venous glucose sensor) was developed by Renard et al. in 2006 (39).
In 2010 the same study group conducted a 2-day semi-automatic
(pre-meal boluses of insulin given) closed-loop trial (n=8) using a
simpler system with a subcutaneous sensor, intraperitoneal insulin
delivery and a PID algorithm which showed that a higher percentage
of time was spent in the study glucose target (4.4-6.6mmol/L)
during closed-loop vs. open-loop (39.1% vs. 27.7%, p=0.05) (40).
More recently, a non-randomized 24-hour sequential AP study
(n=10) comparing a subcutaneous AP system (using a fast-acting
insulin analogue) versus an intraperitoneal AP system (using
regular insulin via the Diaport system) using an MPC algorithm was
conducted. Percentage time spent within the primary endpoint
glucose target range (4.4 – 7.8mmol/l) was significantly higher for
intraperitoneal delivery than for subcutaneous delivery: 39.8 ± 7.6
vs 25.6 ± 13.1 (P = 0.03) (41). The evaluation of first AP system
integrating the Eversense implantable subcutaneous glucose sensor
is planned as part of the International Diabetes Closed Loop (IDCL)
trial, but outcome data have not yet been published.
5 Adjunctive therapy in artificial pancreas
Postprandial hyperglycaemia following an unannounced meal
remains an issue with single-hormone AP. This was demonstrated in a
study involving 10 adults and adolescents, investigating the safety
and performance of an AP system that uses a probabilistic
estimation of meals to allow for automated meal detection (42).
Participants were given daily exercise and meal challenges
(announced and unannounced meals). The results showed that
postprandial hyperglycaemia was significantly more pronounced for
unannounced meals compared to announced meals (4-hour post-meal CGM
11.0 mmol/L ± 2.5 mmol/L vs. 7.8 mmol/L ± 1.9 mmol/L, P <
0.001). This difference in post-prandial glucose with unannounced
meals arises from the delay in subcutaneous insulin administration
with a meal that is not announced to the controller, and the
pharmacokinetics of subcutaneous insulin which may not be well
matched to the absorption of macronutrients.
5.1 Amylin analogues
Amylin is a peptide produced in pancreatic beta cells and
co-secreted with insulin. It affects glucose control by slowing
gastric emptying, regulating postprandial glucagon and reducing
food intake (43) and is deficient in T1D (44). Pramlintide is an
amylin analogue that can be administered subcutaneously at
mealtimes.
In a 52-week randomised study evaluating the use of pramlintide
versus placebo as an adjunct to insulin therapy in T1D, treatment
with pramlintide led to a mean significant reduction in HbA1c from
baseline to week 13 (0.67% vs. 0.16%, P < 0.001) without
inducing weight gain or increasing overall risk of severe
hypoglycaemia (45).
Due to its effect in lowering post-prandial glucose excursions,
and potentially eliminating the need for meal announcement, the use
of pramlintide has been evaluated in AP clinical trials. One of the
earlier studies showed that the use of pramlintide (30mcg pre-meal
injections) in addition to AP was associated with overall delayed
time to peak blood glucose and reduced magnitude of glycaemic
excursion compared to closed-loop system only (46).
A more recent clinical trial investigated the effects of
adjunctive pramlintide with AP in 10 participants over a 24-hour
period (47). Pramlintide was shown to delay the time to peak plasma
glucose excursion (AP 1.6 ± 0.5 hour vs. AP + Pramlintide 2.6 ± 0.9
hour, P < 0.001). Pramlintide with AP was also associated with
blunting of peak postprandial increments in plasma glucose (P <
0.001) and reductions in post-meal incremental plasma glucose are
under the curve (AUC) (P = 0.0002).
Even though pramlintide is associated with side effects such as
nausea, vomiting and abdominal bloating, the major barrier to its
use with AP is the requirement for manual subcutaneous
administration at mealtimes which may increase the treatment burden
of the AP. The effect of co-administration of insulin and
pramlintide within an AP in T1D is currently being
investigated.
5.2 Glucagon-like Peptide (GLP) -1 receptor agonists
GLP-1 is an endogenous hormone that regulates secretion of
insulin and glucagon in response to meals. It also slows gastric
emptying, inhibits inappropriate post-meal glucagon release and
reduces food intake. GLP-1 receptor agonists are established
treatment options in the type 2 diabetes management pathway (48).
Evidence for its use in T1D is limited. In general, HbA1c lowering
with GLP-1 receptor agonists in T1D has been modest, with a
relative decrease in HbA1c of 0.1% to 0.2% when tested against a
control group (49). It is however, associated with weight loss in
all trials and may be considered in people with T1D who are
overweight or obese.
The use of GLP-1 receptor agonists in AP has been investigated.
A randomised crossover trial comparing insulin monotherapy versus
adjuvant subcutaneous liraglutide 1.2mg and insulin in an AP system
was conducted in 15 participants. The liraglutide arm was
associated with an overall significantly decreased mean blood
glucose levels and better two-hour post breakfast and lunch glucose
profiles (50). There was no difference in hypoglycaemic episodes
between the groups.
Similar results were seen in another study evaluating the use of
adjunctive liraglutide in AP (47). Liraglutide with AP was
associated with marginal reductions in peak glucose excursions (P =
0.05) and incremental peak glucose AUC (P = 0.004). There was also
a 26% reduction in total daily insulin dose (P = 0.05) and weight
loss of 3.2 ± 1.8kg (P = 0.003) in the liraglutide arm.
6 Artificial pancreas use in specific groups
6.1 Pregnancy
T1D in pregnancy is associated with increased risk of fetal and
maternal adverse outcomes including congenital malformations,
miscarriage, preterm delivery, preeclampsia, macrosomia and
perinatal mortality (51). Maintaining tight glycaemic control
during pregnancy minimises risk but can be challenging as insulin
requirements increase during the later trimesters (52).
Hypoglycaemia can also occur more frequently during pregnancy
(53).
In a randomised crossover study comparing overnight AP to SAP,
16 pregnant women with T1D were recruited. This was followed by a
continuation phase in which AP was used day and night. Overnight AP
was shown to improve glucose control compared to SAP (74.7% vs.
59.5% in target range; P = 0.002). There were no significant
differences in time in hypoglycaemia, insulin doses or in
adverse-event rates (54). 14 out of the 16 women also chose to
continue using the AP up to an additional 14.6 weeks, including
time during labour and delivery. During this period, glucose levels
were in target range 68.7% of the time with the mean glucose level
of 7.0 mmol/L (54).
A more recent study by the same group looked at longer-term
feasibility of day-and-night AP use. In this randomised crossover
trial, 16 pregnant women completed 28 days of AP and SAP insulin
delivery and were given the option to continue using the AP up to 6
weeks post-partum. AP was associated with comparable glucose
control and fewer hypoglycaemic episodes than SAP therapy (median
8.0 (range 1-17) vs. 12.5 (1-53) over 28 days, P = 0.04) (55).
6.2 Critical care
Hyperglycaemia and insulin resistance are common in critically
ill people, with or without diabetes. Intensive insulin therapy
(glucose maintenance of between 4.4 to 6.1 mmol/L) in critical
illness, even without previous diabetes, may reduce mortality
during intensive care compared to conventional treatment (56).
However, subsequent studies have shown conflicting outcomes with
intensive insulin therapy in the critical care setting, mostly
reflecting an increased risk of hypoglycaemia (57). The use of AP
to optimise glucose without hypoglycaemia has therefore been
explored.
The effect of an AP device (STG-22; NIKKISO, Tokyo) on
maintenance of blood glucose levels was investigated in 280
intensive care participants. The STG-22 system, which monitors
blood glucose levels using a dual-lumen intravenous catheter and
delivers insulin intravenously, was associated with maintenance of
blood glucose between 3.9- 10 mmol/L for 87.9% of the study period
(33.9 ± 42.4 hours), with no hypoglycaemic events (58).
The use of AP, the majority of which uses subcutaneous insulin
delivery and interstitial glucose sampling, is currently limited in
the critical care setting. This is due to various limitations that
may cause inaccuracy in glucose readings and affects effectiveness
of insulin delivery in this cohort (e.g. oedema, vasoconstriction)
(59).
The feasibility of an automated closed-loop therapy based on
subcutaneous continuous glucose-monitoring (CGM) system compared to
an intravenous sliding-scale insulin in critically ill adults was
evaluated (60). The authors concluded that the AP system is safe
and efficacious, and may improve glucose levels without increasing
the risk of hypoglycaemia in this cohort.
6.3 Type 2 diabetes
The efficacy and safety of automated AP without meal-time
boluses compared with conventional subcutaneous insulin therapy was
assessed in 40 participants with type 2 diabetes in a non-critical
care inpatient setting for a maximum of 72 hours. The use of AP in
this setting was associated with a larger proportion of time spent
in the target glucose range compared to control (59.8% vs. 38.1%, P
= 0.0004), with no episodes of severe hypoglycaemia or
hyperglycaemia in either group (61).
Another study evaluated the feasibility of AP in insulin-naïve
people with type 2 diabetes compared with conventional therapy with
oral hypoglycaemic agents (62). Their results showed greater time
in target glucose 3.9- 8.0 mmol/L (median 78 vs. 35%; P = 0.041)
and less time in hyperglycaemia (22 vs. 65%; P = 0.041)
overnight.
This outcome was replicated in a recent two-centre randomised
study which investigated the use of AP versus conventional
subcutaneous insulin therapy in 136 adults with type 2 diabetes on
general inpatient wards. AP was shown to result in significantly
better glycaemic control than conventional subcutaneous insulin
therapy, without a higher risk of hypoglycaemia (63), clearly
demonstrating the feasibility and potential effectiveness of AP in
in-patients with diabetes. However, education and resource
allocation need to be overcome to implement AP in this environment.
The impact on clinical outcomes in this cohort has also not been
demonstrated.
7 Psychosocial aspects of artificial pancreas
It is recognised that psychosocial factors, encompassing
environmental, social, behavioural and emotional factors, may
affect people with diabetes and their outcomes. The American
Diabetes Association recommends that psychosocial care should be
integrated in patient-centred care and provided to all people with
diabetes to optimise health outcomes and health-related quality of
life (64).
As technology implementation in diabetes care continues to grow,
so should assessments on the impact of these technologies on the
psychosocial aspect of these individuals. A review reported that
despite its association with body image and self-consciousness,
insulin pump therapy is also associated with a high level of
satisfaction, improved or similar levels of depression, reduced
anxiety, improved self-efficacy, family functioning and quality of
life (65). The use of CGM shows generally high levels of
satisfaction and reduced fear of hypoglycaemia amongst its users
(66). However, poorer sleep and increased anxiety have also been
reported in parents of children with T1D using CGM (67).
Users’ perception of AP is generally positive, with perceived
advantages of stable glucose regulation, less need for
self-monitoring, relief of daily concerns and time saving (68). In
a study of overnight AP use within the home setting, adolescents
with T1D reported positive impact on their sleep, improved blood
glucose control, and reduced parental fear of hypoglycaemia and
anxiety. There are also negatives associated with AP - these
include practical difficulties with carrying and using several
devices and feeling that the devices control one’s life (69). Alarm
fatigue has been shown to be an important negative factor that
decreases adherence to AP systems (70). Levels of reported trust in
the AP also vary across studies (68). This may in turn affect the
level of anxiety of the user. Future longer-term research exploring
the use of AP and its psychosocial impacts in different cohorts
should be considered.
8 Challenges, limitations and the future of artificial
pancreas
The artificial pancreas is regarded as cutting-edge technology
in the management of T1D. Although the development of the AP system
is progressing, there are challenges and limitations to current
systems that need to be overcome before a fully automated AP can be
achieved.
A long-standing concern in the development of AP has been sensor
performance. CGM works by measuring glucose level in the
interstitial fluid within the subcutaneous tissue. There is a
physiological lag of glucose transport from the intravascular to
interstitial fluid compartments, and therefore in CGM measurements.
The lag time is at least 6-7 mins but may be up to 10 mins in
people with type 1 diabetes (71, 72).
The pharmacokinetics of currently available rapid acting insulin
analogues are relatively slow with onset within 10-15 minutes, and
a prolonged duration of action, with time to maximal glucose
excursion of 40-60 minutes and duration of action of 4-6 hours
(73). This may limit control of rising glucose and avoidance of
hypoglycaemia at times of rapidly changing glucose.
Faster acting insulin Aspart (Fiasp, Novo Nordisk) is a newer
formulation of insulin aspart with two additional formulation
excipients, L-arginine and niacinamide (74). Pharmacokinetic and
pharmacodynamic studies comparing Fiasp and insulin aspart have
shown a five minute earlier onset of first appearance of insulin (4
vs. 9 min), approximately two times higher early insulin exposure,
and a 74% greater early glucose-lowering effect (75). Data
assessing Fiasp, compared to insulin Aspart in the closed-loop
system (e.g. NCT03554486, NCT03579615, NCT03212950) are
pending.
Newer oral anti-glycaemic agents such as SGLT2 inhibitors, that
act by increasing renal glucose excretion, may have a potential
adjunctive role in future AP trials aiming for improved
post-prandial glucose control.
The practicality of wearing and carrying several devices of a
closed-loop system may be a hindrance to future use of the system.
Dual-chamber pumps for the use of dual-hormone closed-loop system
are currently in development and may reduce the burden of wear for
the users.
A recent critical review investigated potential ethically
problematic situations arising through artificial pancreas use.
These include confidentiality and data safety, cost coverage and
insurability of care, patient selection, patient coaching and
support, and personal identity and agency (76). More consideration
is needed to validate the ethical issues raised to improve our
understanding of the implementation of the technology.
Despite these challenges, the future of artificial pancreas
seems promising. Iterations of the hybrid closed-loop system are
commercially available and are being improved. A randomised study
using an enhanced performance version of the Medtronic hybrid
algorithm including insulin bolus correction and improved automode
parameters (77) reported improved time in target glucose sensor
range (3.9-10mmol/L) with intervention compared to baseline values
(74.32% ± 8.41% during study vs 52.15% ± 9.55% at baseline,
relative change 42%) and other systems are imminently available
(Tandem Control-IQ).
Conclusions
In the future, further evaluation of faster acting insulin in
AP, increased accuracy and reduced lag-time of CGM as well as
self-learning adapting algorithms will improve the level of
automation and effectiveness. Longer-term home-setting studies
using AP, single or dual-hormone, need to be conducted and extended
into more targeted groups of people with T1D for us to understand
its overall benefits and, importantly, cost-effectiveness in the
general population.
Compliance with Ethical Standards
No sources of funding were used to assist in the preparation of
this review.
RR has no conflicts of interest that are directly relevant to
the content of this study. MR has received research funding towards
an investigator initiated study from Dexcom, and has participated
in advisory boards for Roche Diabetes. NO has received research
funding towards investigator initiated studies from Dexcom, and has
participated in advisory boards for Roche Diabetes, Dexcom and
Medtronic Diabetes.
References:
1.Federation ID. Diabetes facts & figures 2017 [Available
from:
https://www.idf.org/aboutdiabetes/what-is-diabetes/facts-figures.html.
2.Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M,
et al. The effect of intensive treatment of diabetes on the
development and progression of long-term complications in
insulin-dependent diabetes mellitus. The New England journal of
medicine. 1993;329(14):977-86.
3.Diabetes Control and Complications Trial (DCCT)/Epidemiology
of Diabetes Interventions and Complications (EDIC) Study Research
Group. Intensive Diabetes Treatment and Cardiovascular Outcomes in
Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up. Diabetes
care. 2016;39(5):686-93.
4.The Diabetes Control and Complications Trial Research Group.
Hypoglycemia in the Diabetes Control and Complications Trial. The
Diabetes Control and Complications Trial Research Group. Diabetes.
1997;46(2):271-86.
5.Cryer PE. Hypoglycaemia: Pathophysiology, diagnosis and
treatment: Oxford University Press; 1997.
6.Pickup JC, Sutton AJ. Severe hypoglycaemia and glycaemic
control in Type 1 diabetes: meta-analysis of multiple daily insulin
injections compared with continuous subcutaneous insulin infusion.
Diabetic medicine : a journal of the British Diabetic Association.
2008;25(7):765-74.
7.Jeitler K, Horvath K, Berghold A, Gratzer TW, Neeser K, Pieber
TR, et al. Continuous subcutaneous insulin infusion versus multiple
daily insulin injections in patients with diabetes mellitus:
systematic review and meta-analysis. Diabetologia.
2008;51(6):941-51.
8.Group RS. Relative effectiveness of insulin pump treatment
over multiple daily injections and structured education during
flexible intensive insulin treatment for type 1 diabetes: cluster
randomised trial (REPOSE). BMJ (Clinical research ed).
2017;356:j1285.
9.Facchinetti A. Continuous Glucose Monitoring Sensors: Past,
Present and Future Algorithmic Challenges. Sensors (Basel,
Switzerland). 2016;16(12).
10.Bailey TS. Clinical Implications of Accuracy Measurements of
Continuous Glucose Sensors. Diabetes technology & therapeutics.
2017;19(S2):S51-s4.
11.Kovatchev BP, Patek SD, Ortiz EA, Breton MD. Assessing sensor
accuracy for non-adjunct use of continuous glucose monitoring.
Diabetes technology & therapeutics. 2015;17(3):177-86.
12.Avari P, Reddy M, Oliver N. Is it possible to constantly and
accurately monitor blood sugar levels, in people with Type 1
diabetes, with a discrete device (non-invasive or invasive)?
Diabetic medicine : a journal of the British Diabetic Association.
2019.
13.van Beers CA, DeVries JH, Kleijer SJ, Smits MM,
Geelhoed-Duijvestijn PH, Kramer MH, et al. Continuous glucose
monitoring for patients with type 1 diabetes and impaired awareness
of hypoglycaemia (IN CONTROL): a randomised, open-label, crossover
trial. The lancet Diabetes & endocrinology.
2016;4(11):893-902.
14.Trevitt S, Simpson S, Wood A. Artificial Pancreas Device
Systems for the Closed-Loop Control of Type 1 Diabetes: What
Systems Are in Development? Journal of diabetes science and
technology. 2016;10(3):714-23.
15.Administration USFD. Summary of safety and effectiveness data
(SSED) of the Medtronic MiniMed 670G System 2016 [Available from:
https://www.accessdata.fda.gov/cdrh_docs/pdf16/P160017b.pdf.
16.Cobelli C, Renard E, Kovatchev B. Artificial pancreas: past,
present, future. Diabetes. 2011;60(11):2672-82.
17.Doyle FJ, 3rd, Huyett LM, Lee JB, Zisser HC, Dassau E.
Closed-loop artificial pancreas systems: engineering the
algorithms. Diabetes care. 2014;37(5):1191-7.
18.Bequette BW. Algorithms for a closed-loop artificial
pancreas: the case for model predictive control. Journal of
diabetes science and technology. 2013;7(6):1632-43.
19.Pinsker JE, Lee JB, Dassau E, Seborg DE, Bradley PK,
Gondhalekar R, et al. Randomized Crossover Comparison of
Personalized MPC and PID Control Algorithms for the Artificial
Pancreas. Diabetes care. 2016;39(7):1135-42.
20.Steil GM, Panteleon AE, Rebrin K. Closed-loop insulin
delivery-the path to physiological glucose control. Advanced drug
delivery reviews. 2004;56(2):125-44.
21.Nimri R, Bratina N, Kordonouri O, Avbelj Stefanija M, Fath M,
Biester T, et al. MD-Logic overnight type 1 diabetes control in
home settings: A multicentre, multinational, single blind
randomized trial. Diabetes, obesity & metabolism.
2017;19(4):553-61.
22.Herrero P, Georgiou P, Oliver N, Johnston DG, Toumazou C. A
bio-inspired glucose controller based on pancreatic beta-cell
physiology. Journal of diabetes science and technology.
2012;6(3):606-16.
23.Reddy M, Herrero P, Sharkawy ME, Pesl P, Jugnee N, Pavitt D,
et al. Metabolic Control With the Bio-inspired Artificial Pancreas
in Adults With Type 1 Diabetes: A 24-Hour Randomized Controlled
Crossover Study. Journal of diabetes science and technology.
2015;10(2):405-13.
24.Bally L, Thabit H, Hovorka R. Glucose-responsive insulin
delivery for type 1 diabetes: The artificial pancreas story.
International journal of pharmaceutics. 2018;544(2):309-18.
25.Toffanin C, Visentin R, Messori M, Palma FD, Magni L, Cobelli
C. Toward a Run-to-Run Adaptive Artificial Pancreas: In Silico
Results. IEEE transactions on bio-medical engineering.
2018;65(3):479-88.
26.Bergenstal RM, Klonoff DC, Garg SK, Bode BW, Meredith M,
Slover RH, et al. Threshold-based insulin-pump interruption for
reduction of hypoglycemia. The New England journal of medicine.
2013;369(3):224-32.
27.Forlenza GP, Li Z, Buckingham BA, Pinsker JE, Cengiz E, Wadwa
RP, et al. Predictive Low-Glucose Suspend Reduces Hypoglycemia in
Adults, Adolescents, and Children With Type 1 Diabetes in an
At-Home Randomized Crossover Study: Results of the PROLOG Trial.
Diabetes care. 2018;41(10):2155-61.
28.Calhoun PM, Buckingham BA, Maahs DM, Hramiak I, Wilson DM,
Aye T, et al. Efficacy of an Overnight Predictive Low-Glucose
Suspend System in Relation to Hypoglycemia Risk Factors in Youth
and Adults With Type 1 Diabetes. Journal of diabetes science and
technology. 2016;10(6):1216-21.
29.Abraham MB, Nicholas JA, Smith GJ, Fairchild JM, King BR,
Ambler GR, et al. Reduction in Hypoglycemia With the Predictive
Low-Glucose Management System: A Long-term Randomized Controlled
Trial in Adolescents With Type 1 Diabetes. Diabetes care.
2018;41(2):303-10.
30.Dai X, Luo ZC, Zhai L, Zhao WP, Huang F. Artificial Pancreas
as an Effective and Safe Alternative in Patients with Type 1
Diabetes Mellitus: A Systematic Review and Meta-Analysis. Diabetes
therapy : research, treatment and education of diabetes and related
disorders. 2018;9(3):1269-77.
31.Bekiari E, Kitsios K, Thabit H, Tauschmann M, Athanasiadou E,
Karagiannis T, et al. Artificial pancreas treatment for outpatients
with type 1 diabetes: systematic review and meta-analysis. BMJ
(Clinical research ed). 2018;361:k1310.
32.Karageorgiou V, Papaioannou TG, Bellos I, Alexandraki K,
Tentolouris N, Stefanadis C, et al. Effectiveness of artificial
pancreas in the non-adult population: A systematic review and
network meta-analysis. Metabolism: clinical and experimental.
2019;90:20-30.
33.Forlenza GP, Pinhas-Hamiel O, Liljenquist DR, Shulman DI,
Bailey TS, Bode BW, et al. Safety Evaluation of the MiniMed 670G
System in Children 7-13 Years of Age with Type 1 Diabetes. Diabetes
technology & therapeutics. 2019;21(1):11-9.
34.Stone JY, Haviland N, Bailey TS. Review of a commercially
available hybrid closed-loop insulin-delivery system in the
treatment of Type 1 diabetes. Therapeutic delivery.
2018;9(2):77-87.
35.El-Khatib FH, Balliro C, Hillard MA, Magyar KL, Ekhlaspour L,
Sinha M, et al. Home use of a bihormonal bionic pancreas versus
insulin pump therapy in adults with type 1 diabetes: a multicentre
randomised crossover trial. Lancet (London, England).
2017;389(10067):369-80.
36.Russell SJ, Hillard MA, Balliro C, Magyar KL, Selagamsetty R,
Sinha M, et al. Day and night glycaemic control with a bionic
pancreas versus conventional insulin pump therapy in preadolescent
children with type 1 diabetes: a randomised crossover trial. The
lancet Diabetes & endocrinology. 2016;4(3):233-43.
37.Haidar A, Legault L, Matteau-Pelletier L, Messier V, Dallaire
M, Ladouceur M, et al. Outpatient overnight glucose control with
dual-hormone artificial pancreas, single-hormone artificial
pancreas, or conventional insulin pump therapy in children and
adolescents with type 1 diabetes: an open-label, randomised
controlled trial. The lancet Diabetes & endocrinology.
2015;3(8):595-604.
38.Castle JR, El Youssef J, Wilson LM, Reddy R, Resalat N,
Branigan D, et al. Randomized Outpatient Trial of Single- and
Dual-Hormone Closed-Loop Systems That Adapt to Exercise Using
Wearable Sensors. Diabetes care. 2018;41(7):1471-7.
39.Renard E, Costalat G, Chevassus H, Bringer J. Closed loop
insulin delivery using implanted insulin pumps and sensors in type
1 diabetic patients. Diabetes Research and Clinical Practice.
2006;74:S173-S7.
40.Renard E, Place J, Cantwell M, Chevassus H, Palerm CC.
Closed-loop insulin delivery using a subcutaneous glucose sensor
and intraperitoneal insulin delivery: feasibility study testing a
new model for the artificial pancreas. Diabetes care.
2010;33(1):121-7.
41.Dassau E, Renard E, Place J, Farret A, Pelletier MJ, Lee J,
et al. Intraperitoneal insulin delivery provides superior glycaemic
regulation to subcutaneous insulin delivery in model predictive
control-based fully-automated artificial pancreas in patients with
type 1 diabetes: a pilot study. Diabetes, obesity & metabolism.
2017;19(12):1698-705.
42.Forlenza GP, Cameron FM, Ly TT, Lam D, Howsmon DP, Baysal N,
et al. Fully Closed-Loop Multiple Model Probabilistic Predictive
Controller Artificial Pancreas Performance in Adolescents and
Adults in a Supervised Hotel Setting. Diabetes technology &
therapeutics. 2018;20(5):335-43.
43.Hay DL, Chen S, Lutz TA, Parkes DG, Roth JD. Amylin:
Pharmacology, Physiology, and Clinical Potential. Pharmacological
reviews. 2015;67(3):564-600.
44.Hieronymus L, Griffin S. Role of Amylin in Type 1 and Type 2
Diabetes. The Diabetes educator. 2015;41(1 Suppl):47s-56s.
45.Whitehouse F, Kruger DF, Fineman M, Shen L, Ruggles JA, Maggs
DG, et al. A randomized study and open-label extension evaluating
the long-term efficacy of pramlintide as an adjunct to insulin
therapy in type 1 diabetes. Diabetes care. 2002;25(4):724-30.
46.Weinzimer SA, Sherr JL, Cengiz E, Kim G, Ruiz JL, Carria L,
et al. Effect of pramlintide on prandial glycemic excursions during
closed-loop control in adolescents and young adults with type 1
diabetes. Diabetes care. 2012;35(10):1994-9.
47.Sherr JL, Patel NS, Michaud CI, Palau-Collazo MM, Van Name
MA, Tamborlane WV, et al. Mitigating Meal-Related Glycemic
Excursions in an Insulin-Sparing Manner During Closed-Loop Insulin
Delivery: The Beneficial Effects of Adjunctive Pramlintide and
Liraglutide. Diabetes care. 2016;39(7):1127-34.
48.Shyangdan DS, Royle P, Clar C, Sharma P, Waugh N, Snaith A.
Glucagon-like peptide analogues for type 2 diabetes mellitus. The
Cochrane database of systematic reviews. 2011(10):Cd006423.
49.Janzen KM, Steuber TD, Nisly SA. GLP-1 Agonists in Type 1
Diabetes Mellitus. The Annals of pharmacotherapy.
2016;50(8):656-65.
50.Ilkowitz JT, Katikaneni R, Cantwell M, Ramchandani N,
Heptulla RA. Adjuvant Liraglutide and Insulin Versus Insulin
Monotherapy in the Closed-Loop System in Type 1 Diabetes: A
Randomized Open-Labeled Crossover Design Trial. Journal of diabetes
science and technology. 2016;10(5):1108-14.
51.Casson IF, Clarke CA, Howard CV, McKendrick O, Pennycook S,
Pharoah PO, et al. Outcomes of pregnancy in insulin dependent
diabetic women: results of a five year population cohort study. BMJ
(Clinical research ed). 1997;315(7103):275-8.
52.Garcia-Patterson A, Gich I, Amini SB, Catalano PM, de Leiva
A, Corcoy R. Insulin requirements throughout pregnancy in women
with type 1 diabetes mellitus: three changes of direction.
Diabetologia. 2010;53(3):446-51.
53.Ringholm L, Pedersen-Bjergaard U, Thorsteinsson B, Damm P,
Mathiesen ER. Hypoglycaemia during pregnancy in women with Type 1
diabetes. Diabetic medicine : a journal of the British Diabetic
Association. 2012;29(5):558-66.
54.Stewart ZA, Wilinska ME, Hartnell S, Temple RC, Rayman G,
Stanley KP, et al. Closed-Loop Insulin Delivery during Pregnancy in
Women with Type 1 Diabetes. The New England journal of medicine.
2016;375(7):644-54.
55.Stewart ZA, Wilinska ME, Hartnell S, O'Neil LK, Rayman G,
Scott EM, et al. Day-and-Night Closed-Loop Insulin Delivery in a
Broad Population of Pregnant Women With Type 1 Diabetes: A
Randomized Controlled Crossover Trial. Diabetes care.
2018;41(7):1391-9.
56.van den Berghe G, Wouters P, Weekers F, Verwaest C,
Bruyninckx F, Schetz M, et al. Intensive insulin therapy in
critically ill patients. The New England journal of medicine.
2001;345(19):1359-67.
57.Wiener RS, Wiener DC, Larson RJ. Benefits and risks of tight
glucose control in critically ill adults: a meta-analysis. Jama.
2008;300(8):933-44.
58.Yatabe T, Yamazaki R, Kitagawa H, Okabayashi T, Yamashita K,
Hanazaki K, et al. The evaluation of the ability of closed-loop
glycemic control device to maintain the blood glucose concentration
in intensive care unit patients. Critical care medicine.
2011;39(3):575-8.
59.Salinas PD, Mendez CE. Glucose Management Technologies for
the Critically Ill. Journal of diabetes science and technology.
2019:1932296818822838.
60.Leelarathna L, English SW, Thabit H, Caldwell K, Allen JM,
Kumareswaran K, et al. Feasibility of fully automated closed-loop
glucose control using continuous subcutaneous glucose measurements
in critical illness: a randomized controlled trial. Critical care
(London, England). 2013;17(4):R159.
61.Thabit H, Hartnell S, Allen JM, Lake A, Wilinska ME, Ruan Y,
et al. Closed-loop insulin delivery in inpatients with type 2
diabetes: a randomised, parallel-group trial. The lancet Diabetes
& endocrinology. 2017;5(2):117-24.
62.Kumareswaran K, Thabit H, Leelarathna L, Caldwell K, Elleri
D, Allen JM, et al. Feasibility of closed-loop insulin delivery in
type 2 diabetes: a randomized controlled study. Diabetes care.
2014;37(5):1198-203.
63.Bally L, Thabit H, Hartnell S, Andereggen E, Ruan Y, Wilinska
ME, et al. Closed-Loop Insulin Delivery for Glycemic Control in
Noncritical Care. The New England journal of medicine.
2018;379(6):547-56.
64.Young-Hyman D, de Groot M, Hill-Briggs F, Gonzalez JS, Hood
K, Peyrot M. Psychosocial Care for People With Diabetes: A Position
Statement of the American Diabetes Association. Diabetes care.
2016;39(12):2126-40.
65.Franklin V. Influences on Technology Use and Efficacy in Type
1 Diabetes. Journal of diabetes science and technology.
2016;10(3):647-55.
66.Farrington C. Psychosocial impacts of hybrid closed-loop
systems in the management of diabetes: a review. Diabetic medicine
: a journal of the British Diabetic Association.
2018;35(4):436-49.
67.Patton SR, Clements MA. Psychological Reactions Associated
With Continuous Glucose Monitoring in Youth. Journal of diabetes
science and technology. 2016;10(3):656-61.
68.van Bon AC, Kohinor MJ, Hoekstra JB, von Basum G, deVries JH.
Patients' perception and future acceptance of an artificial
pancreas. Journal of diabetes science and technology.
2010;4(3):596-602.
69.Barnard KD, Wysocki T, Allen JM, Elleri D, Thabit H,
Leelarathna L, et al. Closing the loop overnight at home setting:
psychosocial impact for adolescents with type 1 diabetes and their
parents. BMJ open diabetes research & care.
2014;2(1):e000025.
70.Barnard KD, Wysocki T, Thabit H, Evans ML, Amiel S, Heller S,
et al. Psychosocial aspects of closed- and open-loop insulin
delivery: closing the loop in adults with Type 1 diabetes in the
home setting. Diabetic medicine : a journal of the British Diabetic
Association. 2015;32(5):601-8.
71.Christiansen SC, Fougner AL, Stavdahl O, Kolle K, Ellingsen
R, Carlsen SM. A Review of the Current Challenges Associated with
the Development of an Artificial Pancreas by a Double Subcutaneous
Approach. Diabetes therapy : research, treatment and education of
diabetes and related disorders. 2017;8(3):489-506.
72.Basu A, Dube S, Veettil S, Slama M, Kudva YC, Peyser T, et
al. Time lag of glucose from intravascular to interstitial
compartment in type 1 diabetes. Journal of diabetes science and
technology. 2015;9(1):63-8.
73.Home PD. The pharmacokinetics and pharmacodynamics of
rapid-acting insulin analogues and their clinical consequences.
Diabetes, obesity & metabolism. 2012;14(9):780-8.
74.Akturk HK, Rewers A, Joseph H, Schneider N, Garg SK. Possible
Ways to Improve Postprandial Glucose Control in Type 1 Diabetes.
Diabetes technology & therapeutics. 2018;20(S2):S224-s32.
75.Heise T, Pieber TR, Danne T, Erichsen L, Haahr H. A Pooled
Analysis of Clinical Pharmacology Trials Investigating the
Pharmacokinetic and Pharmacodynamic Characteristics of Fast-Acting
Insulin Aspart in Adults with Type 1 Diabetes. Clinical
pharmacokinetics. 2017;56(5):551-9.
76.Quintal A, Messier V, Rabasa-Lhoret R, Racine E. A critical
review and analysis of ethical issues associated with the
artificial pancreas. Diabetes & metabolism.
2019;45(1):1-10.
77.de Bock M, Dart J, Hancock M, Smith G, Davis EA, Jones TW.
Performance of Medtronic Hybrid Closed-Loop Iterations: Results
from a Randomized Trial in Adolescents with Type 1 Diabetes.
Diabetes technology & therapeutics. 2018;20(10):693-7.
78.Tauschmann M, Allen JM, Nagl K, Fritsch M, Yong J, Metcalfe
E, et al. Home Use of Day-and-Night Hybrid Closed-Loop Insulin
Delivery in Very Young Children: A Multicenter, 3-Week, Randomized
Trial. Diabetes care. 2019;42(4):594-600.
79.Deshpande S, Pinsker JE, Zavitsanou S, Shi D, Tompot R,
Church MM, et al. Design and Clinical Evaluation of the
Interoperable Artificial Pancreas System (iAPS) Smartphone App:
Interoperable Components with Modular Design for Progressive
Artificial Pancreas Research and Development. Diabetes technology
& therapeutics. 2019;21(1):35-43.
80.Tauschmann M, Thabit H, Bally L, Allen JM, Hartnell S,
Wilinska ME, et al. Closed-loop insulin delivery in suboptimally
controlled type 1 diabetes: a multicentre, 12-week randomised
trial. Lancet (London, England). 2018;392(10155):1321-9.
81.Benhamou PY, Huneker E, Franc S, Doron M, Charpentier G.
Customization of home closed-loop insulin delivery in adult
patients with type 1 diabetes, assisted with structured remote
monitoring: the pilot WP7 Diabeloop study. Acta diabetologica.
2018;55(6):549-56.
82.Biester T, Nir J, Remus K, Farfel A, Muller I, Biester S, et
al. DREAM5: An open-label, randomized, cross-over study to evaluate
the safety and efficacy of day and night closed-loop control by
comparing the MD-Logic automated insulin delivery system to sensor
augmented pump therapy in patients with type 1 diabetes at home.
Diabetes, obesity & metabolism. 2018.
83.Bally L, Thabit H, Kojzar H, Mader JK, Qerimi-Hyseni J,
Hartnell S, et al. Day-and-night glycaemic control with closed-loop
insulin delivery versus conventional insulin pump therapy in
free-living adults with well controlled type 1 diabetes: an
open-label, randomised, crossover study. The lancet Diabetes &
endocrinology. 2017;5(4):261-70.
84.Forlenza GP, Deshpande S, Ly TT, Howsmon DP, Cameron F,
Baysal N, et al. Application of Zone Model Predictive Control
Artificial Pancreas During Extended Use of Infusion Set and Sensor:
A Randomized Crossover-Controlled Home-Use Trial. Diabetes care.
2017;40(8):1096-102.
85.DeBoer MD, Breton MD, Wakeman C, Schertz EM, Emory EG, Robic
JL, et al. Performance of an Artificial Pancreas System for Young
Children with Type 1 Diabetes. Diabetes technology &
therapeutics. 2017;19(5):293-8.
86.Garg SK, Weinzimer SA, Tamborlane WV, Buckingham BA, Bode BW,
Bailey TS, et al. Glucose Outcomes with the In-Home Use of a Hybrid
Closed-Loop Insulin Delivery System in Adolescents and Adults with
Type 1 Diabetes. Diabetes technology & therapeutics.
2017;19(3):155-63.
87.Haidar A, Messier V, Legault L, Ladouceur M, Rabasa-Lhoret R.
Outpatient 60-hour day-and-night glucose control with dual-hormone
artificial pancreas, single-hormone artificial pancreas, or
sensor-augmented pump therapy in adults with type 1 diabetes: An
open-label, randomised, crossover, controlled trial. Diabetes,
obesity & metabolism. 2017;19(5):713-20.
88.Tauschmann M, Allen JM, Wilinska ME, Thabit H, Stewart Z,
Cheng P, et al. Day-and-Night Hybrid Closed-Loop Insulin Delivery
in Adolescents With Type 1 Diabetes: A Free-Living, Randomized
Clinical Trial. Diabetes care. 2016;39(7):1168-74.
89.Tauschmann M, Allen JM, Wilinska ME, Thabit H, Acerini CL,
Dunger DB, et al. Home Use of Day-and-Night Hybrid Closed-Loop
Insulin Delivery in Suboptimally Controlled Adolescents With Type 1
Diabetes: A 3-Week, Free-Living, Randomized Crossover Trial.
Diabetes care. 2016;39(11):2019-25.
90.Renard E, Farret A, Kropff J, Bruttomesso D, Messori M, Place
J, et al. Day-and-Night Closed-Loop Glucose Control in Patients
With Type 1 Diabetes Under Free-Living Conditions: Results of a
Single-Arm 1-Month Experience Compared With a Previously Reported
Feasibility Study of Evening and Night at Home. Diabetes care.
2016;39(7):1151-60.
1
Study
Setting
Participants
N
Artificial pancreas
(single/dual hormone)
Comparator
Length of study
Outcomes (vs. comparator)
Tauschmann
2019
(78)
Home
Children
(1-7y)
21
Single
(hybrid with insulin U20)
Hybrid U100
21 days
No difference in % time within target glucose range (72 ± 8% vs.
70 ± 7%; P = 0.16)
No difference in % time with glucose <2.9mmol/L (4.5 ± 1.7%
vs. 4.7 ±1.4%, P =0.47)
Forlenza
2019
(33)
Home
Children
(7-13y)
105
Single
MiniMed 670G
-
3 month
From baseline to end of study:
Overall sensor glucose reduced by 6.9 ± 17.2mg/dL (P <
0.001)
HbA1c decreased from 7.9% ± 0.8% to 7.5% ± 0.6% (P <
0.001)
% time in target glucose range increased from 56.2% ± 11.4% to
65.0% ± 7.7% (P < 0.001)
Deshpande 2019 (79)
Home
Adults
6
Single (using iAPS)
SAP
8 days
Improved time in target range (78.8% vs. 83.1%; P = 0.31)
Reduced % time < 3.9mmol/L (6.1% vs. 2.2%, P = 0.03)
Tauschmann 2018 (80)
Home
Children
Adults
86
Single
SAP
12 weeks
Higher % time in target glucose range (65% ± 8% vs. 54% ± 9%, P
< 0.0001)
Greater reduction in HbA1c (mean difference in change 0.36%, 95%
CI 0.19-0.53, P <0.0001)
Benhamou
2018 (81)
Home
Adults
8
Single
Diabeloop
-
3 weeks
% time in target glucose range 70.2%
Time in hypoglycaemia 2.9%
Biester
2018 (82)
Home
Adolescents
Adults
48
Single
SAP
60 hours
Increase in % time within target glucose range (66.6% vs. 59.9%,
P = 0.002)
No difference in % of time below 70mg/dL (2.3% vs. 1.5%, P =
0.369)
Bally
2017
(83)
Home
Adults
29
Single
SAP
4 weeks
Higher % of time in target glucose range (10.5% percentage
points higher, 95% CI 7.6-13.4; P <0.0001)
Reduced % time < 3.5mmol/L (by 65%, 95% CI 53-74; P <
0.0001)
Reduced % time < 2.8mmol/L (by 76%, 95% CI 59-86, P <
0.0001)
Forlenza
2017 (84)
Home
Adults
19
Single
SAP
2 weeks
Higher % time in target glucose range (71.6 vs. 65.2%; P =
0.008)
Decrease % time <3.9mmol/L (1.3 vs. 2.7%, P = 0.001)
DeBoer
2017
(85)
Home
Outpatient admission
Children
(5-8y)
12
Single
CSII
3 days
Outpatient admission vs home care:
Increased time in target glucose range (73% vs. 47%, P <
0.001)
Lower mean blood glucose (152mg/dL vs. 190mg/dL, P <
0.001)
Garg
2017
(86)
Home
Hotel
Adolescents
Adults
30
94
Single
MiniMed 670G
-
3 months
From baseline to end of study:
Decreased HbA1c in adolescents (7.7% ± 0.8% to 7.1% ± 0.6% and
adults (7.3% ± 0.9% to 6.8% ± 0.6%) P <0.001
Increased % time within target glucose range in adolescents
(60.4% ± 10.9% to 67.2% ± 8.2% and adults (68.8% ± 11.9% to
73.8% ± 8.4%) P < 0.001
Haidar
2017
(87)
Home
Adults
23
Single
Dual
SAP
60 hours
Reduced % time < 4.0mmol/L 3.9% vs. 7.9%, P = 0.001
Reduced % time < 4.0mmol/L 3.6% vs. 7.9%, P < 0.002
Tauschmann
2016 (88)
Home
Adolescents
12
Single
(hybrid)
SAP
7 days
Higher % time in target glucose range (72 vs. 53%, P <
0.001)
Lower mean glucose concentration (8.7 vs. 10.1mmol/L, P =
0.028)
Tauschmann
2016 (89)
Home
Adolescents
12
Single
(hybrid)
SAP
21 days
Increased in % time within target glucose range by 18.8 ± 9.8%
point, P <0.001
Mean sensor glucose level reduced by 1.8 ± 1.3 mmol/L (P =
0.001)
Time spend above target reduced by 19.3 ± 11.3 % points (P <
0.001)
El-Khatib
2016 (35)
Home
Adults
39
Dual
SAP
11 days
Lower mean glucose concentration (7.8 ± 0.6 vs. 9.0 ± 1.6
mmol/L, P < 0.0001)
Lower mean time with glucose < 3.3mmol/L (0.6% vs. 1.7%;
difference of 1.3%, 95% CI 0.8-1.8, P < 0.0001)
Renard
2016 (90)
Home
Adults
20
Single
SAP
1 month
Higher % time in target glucose range (64.7% ± 7.6% vs. 59.7% ±
9.6%; P = 0.01)
Reduced time <3.9mmol/L (P < 0.001)
Table 1. Summary of 24-hour (day-and-night) artificial pancreas
studies in the home setting (from 2016 onwards)
SAP = Sensor-augmented pump; CI = Confidence interval
Continuous glucose sensor
Continuous subcutaneous insulin infusion
Control algorithm
Insulin delivery
Glucose
Figure 1. A model of an artificial pancreas, comprises of a
continuous glucose sensor and a continuous subcutaneous insulin
infusion which are connected by a control algorithm. Insulin
delivery is dependent on the glucose level and the algorithm, which
in turn affect the final glucose levels.
Figure 2. JDRF’s 6-step Artificial Pancreas Project (APP)
development pathway.