Blood Glucose Control in Type 1DiabetesWith a Bihormonal BionicEndocrine PancreasSTEVEN J. RUSSELL, MD, PHD

1

FIRAS H. EL-KHATIB, PHD2

DAVID M. NATHAN, MD1

KENDRA L. MAGYAR, MSN, NP1

JOHN JIANG, BS2

EDWARD R. DAMIANO, PHD2

OBJECTIVEdTo test whether safe and effective glycemic control could be achieved in type 1diabetes using a bihormonal bionic endocrine pancreas driven by a continuous glucose monitorin experiments lasting more than two days and including six high-carbohydrate meals andexercise as challenges to glycemic control.

RESEARCH DESIGN AND METHODSdSix subjects with type 1 diabetes and no en-dogenous insulin secretion participated in two 51-h experiments. Blood glucose was managedwith a bionic endocrine pancreas controlling subcutaneous delivery of insulin and glucagon withinsulin pumps. A partial meal-priming bolus of insulin (0.035 units/kg/meal, then 0.05 units/kg/mealin repeat experiments) was administered at the beginning of each meal (on average 786 12 g ofcarbohydrates per meal were consumed). Plasma glucose (PG) control was evaluated with areference quality measurement on venous blood every 15 min.

RESULTSdThe overall mean PG was 158 mg/dL, with 68% of PG values in the range of 70–180 mg/dL. There were no significant differences in mean PG between larger and smaller meal-priming bolus experiments. Hypoglycemia (PG ,70 mg/dL) was rare, with eight incidentsduring 576 h of closed-loop control (0.7% of total time). During 192 h of nighttime control,mean PG was 123 mg/dL, with 93% of PG values in the range of 70–180 mg/dL and only oneepisode of mild hypoglycemia (minimum PG 62 mg/dL).

CONCLUSIONSdA bihormonal bionic endocrine pancreas achieved excellent glycemiccontrol with minimal hypoglycemia over the course of two days of continuous use despite high-carbohydrate meals and exercise. A trial testing a wearable version of the system under free-livingconditions is justified.

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Development of a fully or semiauto-mated device that achieves glycemiclevels demonstrated to reduce long-

term complications (1–4) while loweringthe risk for hypoglycemia (5) and reducingpatient burden has long been a goal in thetreatment of type 1 diabetes and would im-prove quality of life for people with type 1diabetes. We previously demonstrated thefeasibility of safe and effective bihormonaltherapy with subcutaneous insulin and

glucagon directed by a computer algorithmusing frequently sampled venous plasmaglucose (PG) in sedentary subjects overthe course of 27 h (6). In the same studywe also compared the accuracy and reliabil-ity of three commercially available contin-uous glucose monitors (CGMs) in eachsubject. Based on these results and preclin-ical studies in diabetic pigs, we hypothe-sized that glycemic control could beachieved in humans with type 1 diabetes

using glucose values from one of theseCGMs as the sole input to the controller.Here,we report the results of a study testingthis hypothesis in experiments more than2 days in length that included six high-carbohydratemeals and a period of exerciseas challenges to glycemic control. Subcuta-neous dosing of glucagon and insulin wascontrolled by an algorithm requiring onlythe subject weight for initialization.

RESEARCH DESIGNAND METHODS

SubjectsThe protocol was approved by the Mas-sachusetts General Hospital (MGH) andBoston University Human Research Com-mittees, and all participants gave writteninformed consent. At baseline, subjectswere required to be 18 years of age orolder, have had type 1 diabetes for at least1 year, and be treated with an insulinpump. In addition, glycated hemoglobinA1c (HbA1c) had to be#9.0%, BMI had tobe 20–35 kg/m2, daily insulin require-ment was #1 units/kg/day, and a peakstimulated C-peptide level had to be#0.1 nmol/L after a mixed meal tolerancetest. Other criteria are detailed in the Sup-plementary Data.

Closed-loop glucose control systemInsulin and glucagon were administeredunder closed-loop control (Supplemen-tary Fig. 1) using control algorithms sim-ilar to those previously described (6). Theonly input signal was data from the Free-style Navigator (Abbott Diabetes Care), aninterstitial fluid CGM approved by theU.S. Food and Drug Administration(FDA). Insulin dosing was controlledby a customized model predictive controlalgorithm incorporating a pharmacoki-netic model for insulin lispro thatassumed a tmax of 65 min. Glucagondosing was controlled by a customizedproportional derivative algorithm. In-sulin lispro and glucagon (Eli Lilly)were administered subcutaneously byOmniPod patch pumps (Insulet). Withthe exception of a weight-based partialmeal-priming bolus, delivered at the

c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c

From the 1Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard MedicalSchool, Boston,Massachusetts; and the 2Department of Biomedical Engineering, BostonUniversity, Boston,Massachusetts.

Corresponding author: Edward R. Damiano, edamiano@bu.edu.Received 12 January 2012 and accepted 12 June 2012.DOI: 10.2337/dc12-0071. Clinical trial reg. no. NCT01161862, clinicaltrials.gov.This article contains Supplementary Data online at http://care.diabetesjournals.org/lookup/suppl/doi:10

.2337/dc12-0071/-/DC1.S.J.R. and F.H.K. share equal responsibility for this work.© 2012 by the American Diabetes Association. Readers may use this article as long as the work is properly

cited, the use is educational and not for profit, and thework is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

See accompanying commentary, p. 2111.

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C l i n i c a l C a r e / E d u c a t i o n / N u t r i t i o n / P s y c h o s o c i a l R e s e a r c hO R I G I N A L A R T I C L E

Figure 1dMean (SD) of venous PG (A), CGMG (B), insulin and glucagon doses (C), plasma insulin levels (D), and glucagon levels (E) for allexperiments.A: The mean (SD) of venous PG levels with 15-min sampling is shown from all (N = 12) 48-h experiments in six subjects. Themaximumin the mean PG was 254 mg/dL at 9:15 A.M. after the first breakfast, and the mean nadir was 99 mg/dL at 12:30 P.M. at the start of the first lunch. Theoverall mean of all 48-h PG results (N = 193 measurements per experiment) was 1586 44 mg/dL. The overall mean PG during nighttime (11 P.M.–

7 A.M.) was 1236 13 mg/dL (N = 66 measurements per experiment). The six meals are indicated by the letter M. The exercise period is indicated byEx. B: The mean (SD) of CGMG levels, measured every 5 min, are shown. The mean peak CGMGwas 229 mg/dL, recorded at 9:30 A.M. after the firstbreakfast, and the mean nadir was 97 mg/dL at 12:30 P.M. at the start of the first lunch. The overall mean of all CGMGmeasurements was 1456 35mg/dL (N = 577 measurements per experiment). The overall mean CGMG overnight was 1166 9 mg/dL (N = 194 measurements per experiment).C: The means of all subcutaneous insulin doses (vertical lines below), including meal-priming insulin doses (indicated by downward arrows), and

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beginning of each meal, the system wasfully automated, and all of the compo-nents were worn by the subject exceptfor the CGM receiver and a computer,which were mounted on an intravenousline pole to allow freedom of movement.

The control system came online withthe first CGM glucose (CGMG) measure-ment. There was no device learning pe-riod, nor was there any information aboutthe subject’s usual insulin regimen pro-vided to the algorithm. The control systemreceived CGMG readings and commandeddosing of insulin or glucagon (or both) ev-ery 5 min. Because the lag between PG andCGMGhandicaps the system in controllingpostmeal glycemic excursions, a meal-priming insulin bolus based only on sub-ject weight (0.035 units/kg in the initialexperiments and 0.05 units/kg in repeatexperiments) was administered at the be-ginning of each meal. These meal-primingboluses were intended to provide less thanhalf the insulin required for each meal. Al-though the priming bolus was automati-cally delivered, this was possible onlybecause the timing of the six meals wasknown in advance. In normal use, thiswould not be the case, and the user wouldneed to indicate meal timing manually.

Closed-loop glucose controlexperimentsSubjects were admitted to the MGHClinical Research Center and receivedbasal insulin from their own pumps untilinitiation of closed-loop control. TheNavigator CGM, inserted the day before,was linked wirelessly to the system andcalibrated strictly according to the manu-facturer’s instructions, except that venousPG values were used for calibration. Ve-nous PG levels, from which the primaryoutcomeswere derived, weremeasured ev-ery 15 min with the GlucoScout (Interna-tional Biomedical) and were confirmedhourlywith a YSI 2300 STAT Plus Analyzer(YSI Life Sciences). Only CGMG valueswere available to the control system.

Two OmniPod pumps, one filledwith U-100 (100 units per mL) insulinlispro and the other filled with 1 mg/mLglucagon, were attached to the subject,activated, and linked wirelessly to the

system. The glucagon pump was replacedapproximately halfway through the 2-dayexperiment so that administered gluca-gon was never in solution for more than27 h (Supplementary Data). At 3:00 P.M.,closed-loop glucose control was initiated.Six meals (each consumed in 30 min)were provided over each of the 51-h ex-periments, with $50% of calories fromcarbohydrate. The mean carbohydrateconsumption was 78 6 12 g per meal(60–117 g per meal). Exercise on a sta-tionary bicycle began at 4:00 P.M. on thesecond day, regardless of the PG at thetime, and lasted ;30 min with a targetheart rate of 120–140 bpm, until a totalof 4,000 heartbeats were reached. The ex-periment ended at 6:00 P.M. on the thirdday, after 51 h of closed-loop control.

Hypoglycemia was defined as venousPG ,70 mg/dL. It was treated with fruitjuice if PG sampled every 15min remained,70mg/dL for three consecutive measure-ments, ,60 mg/dL for two consecutivemeasurements,,50mg/dL once, or if sub-jects had symptoms of hypoglycemiaconcurrent with PG ,70 mg/dL (Supple-mentary Data).

Laboratory and pharmacokineticanalysesSamples for insulin and glucagon mea-surements were obtained at 30-minintervals from 7:00 P.M. to 4:00 A.M. andat 60-min intervals otherwise. Immuno-assays were used to measure insulin (Ar-chitect insulin assay, Abbott Laboratories)and glucagon (glucagon radioimmuno-assay, Millipore). Blood obtained duringscreening for HbA1c measurement wasassayed by high-performance liquid chro-matography (7). C-peptide was measuredby a two-site immunometric assay usingelectrochemiluminescence detection(Roche). Plasma tmax for lispro was de-rived as previously described (6).

Statistical analysesThe prespecified primary outcomeswere: mean PG; percent of PG values,70 mg/dL, 70–120mg/dL, 70–180mg/dL,and.180 mg/dL; and number of total andcarbohydrate-treated hypoglycemicevents. Outcomes were calculated for the

last 48 h of each experiment to reduce theinfluence of pre-experimental conditions.The time from 11:00 P.M. to 7:00 A.M. wasdefined as night.

Three experiments were affected bytechnical failures associated with insulindelivery, but the results of these experi-ments were included in the analyses. Otherthan pump replacements when pump orinfusion cathetermalfunctionwas clinicallysuspected, no additional interventionsweremade; the control systemwas allowedto recover and manage the glycemic con-sequences autonomously.

Statistical analyses were performed inExcel (Microsoft). Comparisons betweengroups were performed with the pairedsample heteroskedastic Students t test.Because there were no significant differ-ences in the primary outcome measuresbetween the experiments using the twodifferent meal-priming boluses, the twogroups of experiments were combinedfor these analyses. Calculations of meanintrasubject and intersubject differencesare described in the Supplementary Data.

RESULTS

SubjectsSix subjects (three male, three female) eachparticipated in two 51-h closed-loop bloodglucose control experiments. Subjects were52 6 14 (33–72) years of age, had type 1diabetes for 326 14 (17– 50) years, had apeak stimulated C-peptide after a mixedmeal tolerance test that was below the assaylimit of detection (C-peptide ,0.033nmol/L) and had HbA1c at screening of7.4 6 0.7% (6.4–8.3%). Their averagebody mass was 72 6 10 kg (54–85 kg);they had a BMI of 25 6 3 kg/m2 (22–30kg/m2) and a total daily dose (TDD) of in-sulin of 0.45 6 0.09 units/kg (0.31–0.56units/kg) with their usual insulin regimen(Supplementary Table 1).

Glycemic controlThe aggregate results of all experimentsare shown in Fig. 1. The bionic endocrinepancreas achieved an aggregate mean PG of158 6 44 mg/dL (range 36–563 mg/dL)over 576 h of control (Table 1). Sixty-eightpercent of PGvalueswere in the target range

glucagon doses (vertical lines above), administered by the bionic endocrine pancreas, are indicated. The mean daily doses of insulin and glucagonadministered by the program were 0.5 units/kg/day and 3.64 mg/kg/day, respectively. D: The mean (SD) plasma insulin levels, measured every 30–60min, with mean over 48 h of 386 10mIU/mL. The tmax for insulin absorption ranged from 24–166min in all subjects and was 70min on average. E: Themean (SD) plasma glucagon levels, measured every 30–60min. Themean glucagon level over 48 hwas 836 28 pg/mL, with peakmean levels increasingonly transiently over the normal fasting range (indicated by shaded area) to 180 pg/mL at 5:00 P.M. after exercise. The peak level is consistent with theincreased glucagon dosing at the time of exercise seen in (C).

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of 70–180 mg/dL (8) (Fig. 2). The mean ofthe peak postprandial PG levels of all 72meals across the 12 subjects was 257 669 mg/dL (the means of the peak postpran-dial PG levels after the breakfast, lunch, anddinner meals were 269 6 64, 230 6 46,and 270 6 85 mg/dL, respectively). Themean PG during night hours was 123 613 mg/dL (range 62–406 mg/dL) over192 h of closed-loop control, with 93% ofPG values in the range of 70–180 mg/dL.

During structured exercise, whichoccurred in the late postprandial periodafter lunch, the mean rate of decrease inPG was more than four-fold greater thanin the same period on the day withoutexercise (1.8 6 1.2 vs. 0.4 6 0.4 mg/dL/min, P = 0.002) (Fig. 1A). Furthermore,the mean PG was markedly lower atthe PG check after exercise than it was24 h later (104 6 48 mg/dL vs. 159 632 mg/dL, P = 0.003). This nadir was fol-lowed by a rebound in mean PG to a max-imum of 120 6 38 mg/dL 45 min afterexercise, and was associated with a peakperiod in glucagon dosing. This patternwas absent on the day without exercise.However, there was no trend for reducedmean PG (1566 27 vs. 1616 22 mg/dL)between the first and second 24-h periodsor between the first and second nights(119 6 22 vs. 128 6 38 mg/dL). In fact,the mean PG tended to be higher duringthe night after exercise. However, with 12experiments, our study lacks sufficient

power to exclude a difference in glucosecontrol between the first and second 24-hperiods or between the nights before andafter exercise.

HypoglycemiaThere were eight episodes of hypoglyce-mia with nomore thanmild symptoms infive subjects, with 0.7% of all PG values,70 mg/dL. Six episodes occurred dur-ing the first 24-h period and two oc-curred during the second 24-h period(Fig. 3). During the night hours therewas a single episode of hypoglycemia(lowest PG 62 mg/dL) with 0.5% of nightPG values ,70 mg/dL. The longest epi-sode of PG ,70 mg/dL was 32 min, al-though the duration of episodes treatedwith carbohydrates likely would havebeen longer had carbohydrates not beenadministered. Two of the episodes re-solved without oral carbohydrate treat-ment (Fig. 3A, B), and one of theseappeared to be a measurement artifact(Fig. 3A). The six remaining hypoglyce-mic episodes, five in the late postprandialperiod and one nocturnal (1:15 A.M.) (Fig.3C–F), were treated with juice. Two ofthe episodes immediately followed theperiod of exercise (Fig. 3C, D). One epi-sode occurred when the meal-primingbolus was administered according toschedule, but the meal was presented tothe subject 20 min late (Fig. 3D, Supple-mentary Fig. 6).

Insulin and glucagon administrationand pharmacokineticsMean insulin usage was 0.5 6 0.1 units/kg/day (Table 1), with 0.49 6 0.1 and0.51 6 0.08 units/kg/day in the firstand second 24-h periods, respectively(P = 0.62), similar to the daily dose inthe home setting (Supplementary Table1). On average, less than half of the insu-lin for each meal was provided by themeal-priming bolus, with the remainderdelivered by the algorithm. In particular,for the 0.035 units/kg group, the primingbolus doses, controller bolus doses, andautomated basal doses accounted, on av-erage, for 22%, 27%, and 51% of the TDDof insulin, respectively. In the case of the0.05 units/kg group, this distributionwas 29%, 23%, and 48%. More of theprandial insulin (delivered in the 4 hafter presentation of the meal) camefrom the meal-priming bolus in the0.05 units/kg group than in the 0.035units/kg group (42 6 8% vs. 30 6 3%,P , 0.001). However, the mean intra-subject difference in insulin lispro TDDwas not significantly different from themean intersubject difference derived bycomparing each of the smaller prim-ing bolus experiments with all of thelarger priming bolus experiments, andvice versa (0.09 vs. 0.086 units/kg/day,P = 0.90).

As we found previously (6), therewere large intersubject and intrasubject

Table 1dSummary results of all 48-h closed-loop experiments

BM PG CGMG Percentage time Meal priming Carb Insulin Glucagon Insulin tmax

,70 70–180 .180 Bolus Consumptionkg mg/dL mg/dL mg/dL mg/dL mg/dL units g/kg/day units/kg/day mg/kg/day min

Aggregate† 73 6 10 158 6 44 145 6 35 0.7 68 31 3.1 6 0.7 3.2 0.50 6 0.26 3.6 6 5.1 70 6 40Experiment

203-S 76 169 165 0.0 59 41 2.7 2.9 0.54 2.5 51203-L 74 118 121 0.0 98 3 3.7 2.9 0.41 5.7 54211-S 76 152 144 0.0 71 29 2.7 3.0 0.49 1.5 39211-L 76 154 143 0.0 70 30 3.8 3.1 0.51 3.7 62212-S 54 142 134 2.6 76 21 1.9 3.7 0.43 4.1 49212-L 54 170 144 0.0 72 28 2.7 3.7 0.55 5.4 24214-S 76 141 141 0.0 76 24 2.7 3.2 0.48 2.7 49214-L 76 194 181 1.0 53 46 3.8 3 0.66 0.1 62221-S 72 172 149 0.0 59 41 2.5 3.1 0.51 2.9 69221-L 73 179 159 1.0 52 47 3.7 3.1 0.59 1.7 85236-S 85 160 133 1.0 65 34 3.0 3.0 0.43 7.4 166236-L 86 148 124 1.0 73 26 4.3 3.3 0.42 6.2 130

Mean‡ 73 158 145 0.6 69 31 3.1 3.2 0.50 3.6 70SD 10 20 17 0.8 12 12 0.7 0.3 0.08 2.2 40

†The aggregate mean values reported here and in the text are obtained after pooling the data from all 12 experiments into a single group. ‡The mean (SD) values areobtained from the mean values associated with each of the 12 experiments above. BM, body mass.

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variations in insulin lispro pharmacoki-netic parameters (Table 1) with tmax from24 to 166 min (mean 70 6 40 min). Themean intersubject difference in insulin lis-pro tmax was significantly larger than themean intrasubject difference (44.3 vs.19.3 min, P , 0.001). Three of the hypo-glycemic events treated with carbohydrateoccurred in a single subject (subject 236)who had markedly slow insulin lispro ab-sorption (Table 1); lispro tmax for this sub-ject was 133 min in one experiment (onecarbohydrate treatment; Fig. 3C) and 166min in another (two carbohydratetreatments; Fig. 3F).The mean glucagon usage was 3.8 mg/kg/day (0.01–0.63 mg/day), with thelargest single dose being 50 mg. Meanplasma glucagon levels were in the nor-mal range for the fasted state (50–150pg/mL), except during the period im-mediately after exercise when themean level was 180 6 107 pg/mL(compared with 40 6 12 pg/mL at thesame time on the day without exercise).The subject with the highest glucagonusage (7.4 and 6.2 mg/kg/day vs. 3.6 62.2 mg/kg/day for mean glucagon

usage) was the subject with the slowestinsulin lispro absorption (subject 236),consistent with our previous findingthat slow insulin absorption is associ-ated with increased glucagon usage (9).

Performance of the CGMA single Navigator sensor was usedthroughout each experiment and no extracalibrations were performed, so that thefinal 41–42 h of each experiment wereperformed without any Navigator calibra-tions. The Navigator CGM performedwell relative to PG measurements, with amean absolute relative difference of11.8% (10). However, the mean CGMG(145 6 35 mg/dL) was 13 mg/dL lowerthan themean PG, and CGMG tracked PGinto the hypoglycemic range during onlyone of the eight hypoglycemic episodes(Fig. 3). In six of the eight cases of hypo-glycemia, the rate of decline in PG was.1 mg/dL/min (Fig. 3A–E and Supple-mentary Figs. 6, 9, 11, and 13). In thesecases, the physiologic lag between PGand interstitial fluid glucose may havecontributed to the failure to prevent hy-poglycemia.

Technical failuresTwo experiments were affected by a fail-ure to deliver insulin. Both clinicallysuspected failures that prompted pumpreplacement were later confirmed withinsulin measurements (SupplementaryFigs. 7 and 9). Another experiment (Sup-plementary Fig. 2) was affected by a spu-rious report from the pump that a largedose of insulin (;61 units) had been de-livered. Because the algorithm considersinsulin-on-board when calculating dos-ing, it refrained from dosing insulin forseveral hours, and hyperglycemia ensued.This same experiment also was affectedby a 2-h period during which the systemwas offline and had to be restarted.

All data from each experiment areincluded in the outcome measures, in-cluding those affected by technical fail-ures. None of the failures resulted inhypoglycemia. The mean PG for thenine experiments without technical fail-ures was 151 6 45 mg/dL, with 71% ofPG measurements in the range of 70–180mg/dL; the mean PG at night for thesenine experiments was 115 6 7 mg/dL.

Adverse eventsThe subject with the slowest insulin ab-sorption and who received the most gluca-gon reported nausea on several occasions.However, the timing of these symptomsdid not correspond to periods of highplasma glucagon levels (SupplementaryFigs. 12 and 13). There were no other ad-verse events other than the nonsevere hy-poglycemic episodes described.

CONCLUSIONSdThese results dem-onstrate the feasibility of closed-loopblood glucose control with a bihormonalbionic endocrine pancreas utilizingCGMG measurements. The CGM anddrug delivery components of the systemare all off-the-shelf and approved by theFDA for diabetes management. The com-putational requirements of the algorithmare modest and easily could be met by amobile device with less processing powerthan a smart phone.

These studies differ from previouswork in closed-loop glucose control inseveral important respects. First, the con-trol algorithm requires no informationabout the subject’s usual insulin regimenor any other data other than the bodymass for initialization. The control algo-rithm and control parameters were iden-tical for all 12 experiments. The systemdoes not have to be customized for eachsubject as it does in other closed-loop

Figure 2dCumulative distributions for 48-h and night PG profiles. The solid curve shows thedistribution of all recorded venous PG levels (N = 2078) in the twelve 48-h closed-loop experi-ments. Venous PG was ,70 mg/dL 0.7% of total study time, within 70–120 mg/dL 38% of time,and within the American Diabetes Association target range of 70–180 mg/dL 68% of the time. Thedashed curve shows the distribution of all night (11:00 P.M.–7:00 A.M.) venous PG levels (676measurements over 2 nights for each experiment). Venous PG was,70 mg/dL 0.5% of total studytime, within 70– 120mg/dL 62% of the time, andwithin 70–180mg/dL 93% of the time. A previousmeta-analysis of closed-loop control studies limited to the night hours reported 76% of PG valuesin adults with type 1 diabetes were within their specified target range of 70–144 mg/dL (11). Inthis study, 84% of venous PG values were within this range despite one of the insulin deliveryfailures occurring during night hours (Supplementary Fig. 7).

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insulin delivery systems (12–15); it auto-matically adapts to the subject’s needs inreal time. Second, as in our previousstudy (6), we enrolled only subjects with-out stimulated C-peptide secretion, en-suring endogenous insulin secretion wasnot assisting closed-loop control. Third,

we report data for closed-loop controlover 48 continuous hours, which is lon-ger than has been reported previously,during which subjects consumed sixhigh-carbohydrate meals. Fourth, this isthe first published study to evaluateclosed-loop control during exercise, a

common cause of hypoglycemia in pa-tients with type 1 diabetes (16). Fifth,we monitored PG every 15 min, in con-trast to studies with up to 60-min inter-vals between measurements that mayhave missed episodes of hypoglycemia(12,13,15). Sixth, we used only a singleCGM sensor throughout the entire dura-tion of each experiment with no calibra-tions for the last 41–42 h (the longestperiod of closed-loop control withoutCGM calibration yet reported), allowingus to realistically assess the potential ofthe system to operate fully autonomously.Seventh, only our previous study and oneother study lasting more than 24 h (14)have tested systems using both insulinand glucagon. Finally, we have reportedall subject-level data for every experimentperformed with this control system, andwe included all data in calculating out-come measures, including experimentswith technical failures.

In addition to the delay in the absorp-tion of subcutaneously administered insu-lin, the lag in CGMG relative to PG furthercompounds the challenge of regulatinghyperglycemic excursions after meals.Therefore, we used a meal announcementto trigger automated delivery of a partialweight-based meal-priming insulin bolus.Interestingly, the glycemic control ach-ieved with two different meal-priming in-sulin doses did not differ; the controlalgorithm appeared to compensate fullyfor the smaller meal-priming bolus withmore reactive dosing of insulin. Additionalstudy is required to determine if furtherincreasing the meal-priming bolus can de-crease mean PG without increasing post-prandial hypoglycemia.

The period of exercise markedly in-creased glucose clearance and was asso-ciated with increased glucagon dosingand levels during and immediately afterthe exercise period. However, no differ-ence was observed in overall glycemiccontrol between the nights before andafter the period of exercise, in contrast to aprevious report that found a lower meanglucose and more hypoglycemia duringnights under closed-loop control after aperiod of structured exercise that tookplace prior to the start of closed-looptherapy (17). This apparent differencefrom our findings may be explained bythe lack of a counter-regulatory capabilityin the insulin-only system used in the pre-vious report. However, although our re-sults show a trend toward higher meanPG during the night after exercise, thestudy lacks sufficient power to exclude a

Figure 3dGlucose profiles and the corresponding insulin–glucagon doses that were adminis-tered by the control algorithm are shown for all eight hypoglycemic episodes. A–C: Hypoglycemicepisodes (defined as events with PG ,70 mg/dL) that occurred during experiments using 0.05units/kg meal-priming boluses (venous PG shown as stars and CGMG as circles). D–F: Hypo-glycemic episodes that occurred during experiments that used 0.035 units/kg meal-primingboluses. The isolated PG value of 69 mg/dL in (A) appears to be a measurement artifact. The firstPG value,70 mg/dL in each of the five hypoglycemic episodes shown in (B–E) were 28, 35, 47,48, and 89 mg/dL lower than their corresponding CGMG values, which were 94, 88, 107, 97, and148 mg/dL, respectively. Both interventions in (F) were at the request of the subject and, in bothcases, PGwas.60mg/dL and CGMGwas.80mg/dL. Note that in (D) themeal was presented tothe subject 20 min late, at 18:20, although the meal-priming bolus was administered on time, at18:00. (tmax for that experiment was 49 min.)

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reduced mean PG on the nights afterexercise.

Limitations of our study include tech-nical problems, primarily failures associ-ated with insulin delivery. Because wereport each complete study in the dataanalyses, including those with technicalfailures, the failures may have led us tounderestimate the potential of the controlapproach. However, the failures allowedus to observe that the control systemsucceeded in returning PG to target rangeafter each failure, thereby demonstratingthe robustness of the system. The occur-rence of such failures suggests that morereliable insulin delivery systems will im-prove system performance in the outpa-tient setting.

Another limitation is the lack of anopen-loop control group in which sub-jects manage their blood glucose levels inthe Clinical Research Center setting.Without data from such a study, wecannot assess how open-loop blood glu-cose control achieved by the subjectswould have compared with the perfor-mance of the bionic pancreas in theClinical Research Center with similarmeals and a similar level of activity. Inpilot open-loop control experiments, wefound that subjects tested their bloodglucose much more frequently than inusual care (as determined by self-reportedtesting frequency and meter downloads),achieved a much lower mean bloodglucose than in usual care (determinedby comparing mean blood glucose withestimated mean blood glucose based onHbA1c), and had more frequent hypogly-cemia requiring carbohydrate treatmentthan they reported during usual care (un-published observations). This problemcould be dealt with by a protocol that reg-ulates the number and timing of bloodglucose checks, but that approach alsowould alter subject behavior, limitingthe utility of the control group. We antic-ipate that control groups using sensor-augmented pump therapy will be lessvulnerable to confounding when trialscan be performed in less supervised envi-ronments with subjects involved in theirnormal daily activities.

Another limitation is the smallnumber of subjects in this trial. However,the total time under closed-loop controlin our study (576 h) was comparable withthe largest of previously published stud-ies comprising experiments lasting $24h (578 h) (13) and was greater than inother recent studies (6,12,14,15). Fur-thermore, within our cohort there

was a wide variation in age (33–72 years),body mass (54–86 kg), insulin lispro ab-sorption characteristics (tmax 24–166min), and TDD administered underclosed-loop control (0.41–0.66 units/kg/day). Interestingly, the mean differ-ence in insulin lispro TDD betweenexperiments in the same subject wasjust as large as the mean difference inTDD between subjects. Therefore, interms of interaction between the algo-rithm and the subject via insulin dosing,the six subjects participating in two ex-periments each were indistinguishablefrom 12 subjects each participating in asingle experiment.

A limitation of the bionic endocrinepancreas is the accuracy of the CGM.Although overall accuracy was good, theNavigator underestimated PG during hy-perglycemia, resulting in a lower overallmeanCGMGthan PG (145 vs. 158mg/dL).We also observed that CGMG failed todetect most of the hypoglycemic eventsthat were documented with PG measure-ments every 15 min. The errors in CGMGwe observed occurred despite our use ofreference-quality PG measurements forcalibrations. Calibration protocols thatdo not rely on reference-quality PG mea-surements must be explored for use in theoutpatient environment and sensors thatperform better in tracking PG would beexpected to improve mean PG and reducehypoglycemia.

Despite technical limitations of thepump and CGM components, we haveshown that a bihormonal bionic endo-crine pancreas is capable of achievinggood PG control with minimal hypogly-cemia during two continuous days in theface of high-carbohydrate meals and ex-ercise. Control was particularly good atnight, achieving mean PG values in thenormal rangewith no clinically significanthypoglycemia. The current study opensthe way for longer-term and more de-finitive studies of a wearable version ofthis system incorporating more robustpump technology. We anticipate thatthese studies will be at least five days inlength, with subjects following their nor-mal routines, eating when and what theychoose, and exercising at will on ourhospital campus. Such studies will leadthe way to testing a bionic endocrinepancreas in the outpatient setting.

AcknowledgmentsdThis study was sup-ported by grant R01-DK-085633 to E.R.D.,from the National Institutes of Health; grants

M01-RR-01066 and UL1-RR-025758, throughthe General Clinical Research Center and Clin-ical and Translational Science Center programsfrom the National Institutes of Health NationalCenter for Research Resources; Clinical Inves-tigations research grant 22-2009-798 to E.R.D.,from the Juvenile Diabetes Research Founda-tion; and a grant to D.M.N. from the CharltonFund for Innovative Research in Diabetes.F.H.K. and E.R.D. have one patent and one

pending patent on the closed-loop algorithm.No other potential conflicts of interest relevantto this article was reported.S.J.R., E.R.D., F.H.K., and D.M.N. designed

the study, performed the analysis, and in-terpreted the data. F.H.K. and E.R.D. designedand built the closed-loop control algorithm.F.H.K., J.J., and E.R.D. built the device. S.J.R.supervised the human studies. J.J. assisted in thedesign of several study procedures. K.L.M. as-sisted with the design of the human studies,coordinated subject enrollment, and was theprimary liaison to the Clinical Research Centernursing staff. S.J.R. wrote the first draft of themanuscript. S.J.R., E.R.D., D.M.N., F.H.K., andK.L.M. participated in revision of the manu-script for important intellectual content. E.R.D.had full access to the data and takes full re-sponsibility for this work as a whole, includingthe study design and the decision to submit andpublish the manuscript.Parts of this studywere presented in abstract

form at the 72nd Scientific Sessions of theAmerican Diabetes Association, Philadelphia,Pennsylvania, 8–12 June 2012.The authors thank the volunteers for their

time and enthusiasm; the diabetes care pro-viders who referred potential subjects forthe study; the nurses and laboratory staffof the MGH Clinical Research Center, espe-cially Kathy Hall and Kathy Grinke, and thestudy staff at the Diabetes Research Center,including Kerry Grennan, Richard Pompei,Cathy Beauharnais, and Laurel Macey, fortheir dedicated effort and careful execution ofthe experimental protocol; Mary Larkin,Camille Collings, and Nancy Kingori, Di-abetes Research Center, MGH, for organiza-tional and logistical support; Patrick Sluss,Reproductive Endocrine Laboratory, MGH,for performing insulin and glucagon assays;Timothy Goodnow, Marc Taub, and ErwinBudiman, Abbott Diabetes Care, for providingNavigator hardware and software support andtechnical advice; Robert Campbell and SteveGemmell, Insulet Corporation, for providingInsulet OmniPod hardware and softwaresupport and technical advice; John Segars andJennifer Isenberg, International Biomedical,for providing GlucoScout monitors and tech-nical assistance in their use; John Godine,Deborah Wexler, and Carl Rosow for servingon the data safety andmonitoring board for thestudy, the members of the Partners HumanResearch Committee and Boston UniversityMedical Campus Institutional Review Board fortheir oversight of the study; Charles Zimliki,Keith Marin, and Patricia Beaston, Office of

2154 DIABETES CARE, VOLUME 35, NOVEMBER 2012 care.diabetesjournals.org

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Device Evaluation, FDA, for their helpful sug-gestions during the process of obtaining theinvestigational device exemption for this study;and Toby Milgrome, Reliant Medical Group,for her valuable input and advice.

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