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Aims: To explore healthcare professionals’ views about the training and support needed to rollout closed-loop technology to pregnant women with type 1 diabetes.Methods: We interviewed (n=19) healthcare professionals who supported pregnant women using CamAPS FX closed-loop during the AiDAPT trial. Data were analysed descriptively. An online workshop involving (n=15) trial team members was used to inform recommendations.Results: Interviewees expressed enthusiasm for a national rollout of closed-loop, but anticipated various challenges, some specific to use during pregnancy. These included variations in insulin pump and continuous glucose monitoring expertise and difficulties embedding and retaining key skills, due to the relatively small numbers of pregnant women using closed-loop. Inexperienced staff also highlighted difficulties interpreting data downloads. To support rollout, interviewees recommended providing expert initial advice training, delivered by device manufacturers together with online training resources and specific checklists for different systems. They also highlighted a need for 24-hour technical support, especially when supporting technology naïve women after first transitioning onto closed-loop in early pregnancy. They further recommended providing case-based meetings and mentorship for inexperienced colleagues, including support interpreting data downloads. Interviewees were optimistic that if healthcare professionals received training and support, their longer-term workloads could be reduced because closed-loop lessened women’s need for glycaemic management input, especially in later pregnancy.Conclusions: Interviewees identified challenges and opportunities to rolling-out closed-loop and provided practical suggestions to upskill inexperienced staff supporting pregnant women using closed-loop. A key priority will be to determine how best to develop mentorship services to support inexperienced staff delivering closed-loop.

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Funder: Efficacy and Mechanism Evaluation (EME) Programme; Id: http://dx.doi.org/10.13039/501100001922, Funder: National Institute for Health Research Cambridge Biomedical Research Centre; Id: http://dx.doi.org/10.13039/501100018956, Funder: Juvenile Diabetes Research Foundation International; Id: http://dx.doi.org/10.13039/100000901, BACKGROUND: Interest is growing in how closed-loop systems can support attainment of within-target glucose levels amongst pregnant women with type 1 diabetes. We explored healthcare professionals' views about how, and why, pregnant women benefitted from using the CamAPS FX system during the AiDAPT trial. METHODS: We interviewed 19 healthcare professionals who supported women using closed-loop during the trial. Our analysis focused on identifying descriptive and analytical themes relevant to clinical practice. RESULTS: Healthcare professionals highlighted clinical and quality-of-life benefits to using closed-loop in pregnancy; albeit, they attributed some of these to the continuous glucose monitoring component. They emphasised that the closed-loop was not a panacea and that, to gain maximum benefit, an effective collaboration between themselves, the woman and the closed-loop was needed. Optimal performance of the technology, as they further noted, also required women to interact with the system sufficiently, but not excessively; a requirement that they felt some women had found challenging. Even where healthcare professionals felt that this balance was not achieved, they suggested that women had still benefitted from using the system. Healthcare professionals reported difficulties predicting how specific women would engage with the technology. In light of their trial experiences, healthcare professionals favoured an inclusive approach to closed-loop rollout in routine clinical care. CONCLUSIONS: Healthcare professionals recommended that closed-loop systems be offered to all pregnant women with type 1 diabetes in the future. Presenting closed-loop systems to pregnant women and healthcare teams as one pillar of a three-party collaboration may help promote optimal use.

Background Pregnant women with type 1 diabetes strive for tight glucose targets (3.5-7.8 mmol/L) to minimise the risks of obstetric and neonatal complications. Despite using diabetes technologies including continuous glucose monitoring (CGM), insulin pumps and contemporary insulin analogues, most women struggle to achieve and maintain the recommended pregnancy glucose targets. This study aims to evaluate whether the use of automated closed-loop insulin delivery improves antenatal glucose levels in pregnant women with type 1 diabetes. Methods/design A multicentre, open label, randomized, controlled trial of pregnant women with type 1 diabetes and a HbA1c of ≥48 mmol/mol (6.5%) at pregnancy confirmation and ≤ 86 mmol/mol (10%) at randomization. Participants who provide written informed consent before 13 weeks 6 days gestation will be entered into a run-in phase to collect 96 h (24 h overnight) of CGM glucose values. Eligible participants will be randomized on a 1:1 basis to CGM (Dexcom G6) with usual insulin delivery (control) or closed-loop (intervention). The closed-loop system includes a model predictive control algorithm (CamAPS FX application), hosted on an android smartphone that communicates wirelessly with the insulin pump (Dana Diabecare RS) and CGM transmitter. Research visits and device training will be provided virtually or face-to-face in conjunction with 4-weekly antenatal clinic visits where possible. Randomization will stratify for clinic site. One hundred twenty-four participants will be recruited. This takes into account 10% attrition and 10% who experience miscarriage or pregnancy loss. Analyses will be performed according to intention to treat. The primary analysis will evaluate the change in the time spent in the target glucose range (3.5-7.8 mmol/l) between the intervention and control group from 16 weeks gestation until delivery. Secondary outcomes include overnight time in target, time above target (> 7.8 mmol/l), standard CGM metrics, HbA1c and psychosocial functioning and health economic measures. Safety outcomes include the number and severity of ketoacidosis, severe hypoglycaemia and adverse device events. Discussion This will be the largest randomized controlled trial to evaluate the impact of closed-loop insulin delivery during type 1 diabetes pregnancy. Trial registration ISRCTN 56898625 Registration Date: 10 April, 2018.

Funder: Juvenile Diabetes Research Foundation International; Id: http://dx.doi.org/10.13039/100000901, Funder: Juvenile Diabetes Research Foundation Canada; Id: http://dx.doi.org/10.13039/100009881, AIMS: To examine maternal fear of hypoglycaemia, glycaemia and pregnancy outcomes in women with impaired and normal awareness of hypoglycaemia. METHODS: A pre-planned sub-study of 214 pregnant women with type 1 diabetes who participated in the CONCEPTT trial. Participants completed hypoglycaemia fear surveys (HFS-II) at baseline. Logistic regression and Poisson regression analyses were used to obtain an adjusted estimate for the rate ratio relating awareness to the number of severe hypoglycaemic episodes, and for several neonatal outcomes in relation to the total HFS-II score. The role of continuous glucose monitoring (CGM) use was examined. RESULTS: Overall, 30% of participants reported impaired awareness of hypoglycaemia (n = 64). Women with impaired awareness of hypoglycaemia had more episodes of severe hypoglycaemia (mean 0.44 vs. 0.08, p < 0.001) (12-34 weeks gestation) and scored higher on the HFS-II scale (43.7 vs. 36.0, p 0.008), indicating more fear of hypoglycaemia. They spent more time below range (CGM

Objective: The optimal method of monitoring glycemia in pregnant women with type 1 diabetes remains controversial. This study aimed to assess the predictive performance of HbA1c, continuous glucose monitoring (CGM) metrics, and alternative biochemical markers of glycemia to predict obstetric and neonatal outcomes. Methods: 157 women from the CGM in pregnant women with type 1 diabetes trial (CONCEPTT) were included in this pre-specified secondary analysis. HbA1c, CGM data, and alternative biochemical markers (glycated CD59, 1,5 anhydroglucitol, fructosamine and glycated albumin) were compared at approximately 12, 24 and 34 weeks gestation using logistic regression and ROC curves to predict pregnancy complications (pre-eclampsia, preterm delivery, large-for-gestational-age, neonatal hypoglycemia, admission to neonatal intensive care unit). Results: HbA1c, CGM metrics, and alternative laboratory markers were all significantly associated with obstetric and neonatal outcomes at 24 weeks gestation. More outcomes were associated with CGM metrics during the 1st trimester and with laboratory markers (area under ROC generally 140 mg/dl; >7.8 mmol/l) were the most consistently predictive CGM metrics. HbA1c was also a consistent predictor of suboptimal pregnancy outcomes. Some alternative laboratory markers showed promise, but overall, they had lower predictive ability than HbA1c. Conclusions: HbA1c is still an important biomarker for obstetric and neonatal outcomes in type 1 diabetes pregnancy. Alternative biochemical markers of glycemia and other CGM metrics did not substantially increase the prediction of pregnancy outcomes compared to widely available HbA1c and increasingly available CGM metrics (TIR and TAR).

The CONCEPTT trial compared real-time Continuous Glucose Monitoring (RT-CGM) to capillary glucose monitoring in pregnant women with type 1 diabetes. We analyzed CGM and glycated hemoglobin (HbA1c) measures in first (n = 221), second (n = 197), and third (n = 172) trimesters, aiming to examine target glucose attainment and associations with pregnancy outcomes. CGM targets were Time-in-range (TIR) > 70%, Time-above-range (TAR)

CamAPS FX (CamDiab, Cambridge, UK) is a hybrid closed-loop app hosting the Cambridge closed-loop algorithm on an Android smartphone, and is approved in the EU for use in children ≥1 year, and adults (including during pregnancy) with type 1 diabetes (T1D). The interoperable CamAPS FX app receives glucose data from a compatible CGM system (Dexcom G6; Dexcom, San Diego, CA), connects to a compatible insulin pump (Dana Diabecare RS and DANA-i; Sooil, Seoul, South Korea) to direct glucose-responsive insulin delivery every 8-12 minutes, includes a bolus calculator allowing discrete bolusing via the app, and streams data in real-time to cloud-based diabetes data repositories (Diasend/Glooko, Gothenburg, Sweden).

Objective: We assessed longitudinal patterns of maternal C-peptide concentration to examine the hypothesis of beta-cell regeneration in type 1 diabetes pregnancy. Research Design & Methods: C-peptide was measured on maternal serum samples from 127 participants (12, 24, 34 weeks) and cord blood during the continuous glucose monitoring in type 1 diabetes pregnancy trial (CONCEPTT). C-peptide was measured using a highly sensitive direct and solid-phase competitive electrochemiluminescent immunoassay. Results: Three discrete patterns of maternal C-peptide trajectory were identified: Pattern 1 undetectable throughout pregnancy, n=74 (58%, maternal C-peptide Conclusion: First maternal C-peptide appearance at 34 weeks was associated with mid-trimester hyperglycemia, elevated cord blood C-peptide and high rates of neonatal complications. This suggests transfer of C-peptide from fetal to maternal serum and is inconsistent with pregnancy-related beta-cell regeneration.

Background: Diabetes in pregnancy is associated with preterm delivery, birthweight extremes, and increased rates of congenital anomaly, stillbirth, and neonatal death. We aimed to identify and compare modifiable risk factors associated with adverse pregnancy outcomes in women with type 1 diabetes and those with type 2 diabetes and to identify effective maternity clinics. Methods: In this national population-based cohort study, we used data for pregnancies among women with type 1 or type 2 diabetes collected in the first 5 years of the National Pregnancy in Diabetes audit across 172 maternity clinics in England, Wales, and the Isle of Man, UK. Data for obstetric complications (eg, preterm delivery [90th percentile]) and adverse pregnancy outcomes (congenital anomaly, stillbirth, neonatal death) were obtained for pregnancies completed between Jan 1, 2014, and Dec 31, 2018. We assessed associations between modifiable (eg, HbA 1c, BMI, pre-pregnancy care, maternity clinic) and non-modifiable risk factors (eg, age, ethnicity, deprivation, duration of type 1 diabetes) with pregnancy outcomes in women with type 1 diabetes compared with those with type 2 diabetes. We calculated associations between maternal factors and perinatal deaths using a regression model, including diabetes type and duration, maternal age, BMI, deprivation quintile, first trimester HbA 1c, preconception folic acid, potentially harmful medications, and third trimester HbA 1c. Findings: Our dataset included 17 375 pregnancy outcomes in 15 290 pregnant women. 8690 (50·0%) of 17 375 pregnancies were in women with type 1 diabetes (median age at delivery 30 years [10–90th percentile 22–37], median duration of diabetes 13 years [3–25]) and 8685 (50·0%) were in women with type 2 diabetes (median age at delivery 34 years [27–41], median duration of diabetes 3 years [0–10]). The rates of preterm delivery (3325 [42·5%] of 7825 pregnancies among women with type 1 diabetes, 1825 [23·4%] of 7815 with type 2 diabetes; p

Background: Offspring of women with type 1 diabetes are at increased risk of fetal growth patterns which are associated with perinatal morbidity. Our aim was to compare rates of large- and small-for-gestational age (LGA; SGA) defined according to different criteria, using data from the Continuous Glucose Monitoring in Type 1 Diabetes Pregnancy Trial (CONCEPTT). Methods: This was a pre-specified analysis of CONCEPTT involving 225 pregnant women and liveborn infants from 31 international centres (ClinicalTrials.gov NCT01788527; registered 11/2/2013). Infants were weighed immediately at birth and GROW, INTERGROWTH and WHO centiles were calculated. Relative risk ratios, sensitivity and specificity were used to assess the different growth standards with respect to perinatal outcomes, including neonatal hypoglycaemia, hyperbilirubinaemia, respiratory distress, neonatal intensive care unit (NICU) admission and a composite neonatal outcome. Results: Accelerated fetal growth was common, with mean birthweight percentiles of 82.1, 85.7 and 63.9 and LGA rates of 62, 67 and 30% using GROW, INTERGROWTH and WHO standards respectively. Corresponding rates of SGA were 2.2, 1.3 and 8.9% respectively. LGA defined according to GROW centiles showed stronger associations with preterm delivery, neonatal hypoglycaemia, hyperbilirubinaemia and NICU admission. Infants born > 97.7th centile were at highest risk of complications. SGA defined according to INTERGROWTH centiles showed slightly stronger associations with perinatal outcomes. Conclusions: GROW and INTERGROWTH standards performed similarly and identified similar numbers of neonates with LGA and SGA. GROW-defined LGA and INTERGROWTH-defined SGA had slightly stronger associations with neonatal complications. WHO standards underestimated size in preterm infants and are less applicable for use in type 1 diabetes. Trial registration: This trial is registered with ClinicalTrials.gov. number NCT01788527. Trial registered 11/2/2013.

Additional file 1: Supplemental Figure 1. The effect of RT-CGM on levels of circulating EVs. Shown are values of circulating levels of EVs at 34 weeks gestation in individuals utilizing real time continuous glucose monitoring (CGM) compared with conventional self-monitoring of blood glucose (SMBG). (A) Platelet-derived EV; (B). Endothelial-derived EV; (C). Leukocyte-derived EV and (D). Total annexin V levels. The number of participants for CGM and SMBG are n = 72 and n = 77 respectively. No significant differences were observed for platelet-derived EVs (0.74), endothelial-derived EV (0.88), leukocyte-derived EVs (0.31), or total annexin V+ EVs (0.17).

Additional file 2. List of members of the CONCEPTT Collaborative Group

Additional file 1.

OBJECTIVE: We evaluated the safety and efficacy of closed-loop basal insulin delivery during sleep and after regular meals and unannounced periods of exercise. RESEARCH DESIGN AND METHODS: Twelve adolescents with type 1 diabetes (five males; mean age 15.0 [SD 1.4] years; HbA1c 7.9 [0.7]%; BMI 21.4 [2.6] kg/m(2)) were studied at a clinical research facility on two occasions and received, in random order, either closed-loop basal insulin delivery or conventional pump therapy for 36 h. During closed-loop insulin delivery, pump basal rates were adjusted every 15 min according to a model predictive control algorithm informed by subcutaneous sensor glucose levels. During control visits, subjects' standard infusion rates were applied. Prandial insulin boluses were given before main meals (50-80 g carbohydrates) but not before snacks (15-30 g carbohydrates). Subjects undertook moderate-intensity exercise, not announced to the algorithm, on a stationary bicycle at a 140 bpm heart rate in the morning (40 min) and afternoon (20 min). Primary outcome was time when plasma glucose was in the target range (71-180 mg/dL). RESULTS: Closed-loop basal insulin delivery increased percentage time when glucose was in the target range (median 84% [interquartile range 78-88%] vs. 49% [26-79%], P = 0.02) and reduced mean plasma glucose levels (128 [19] vs. 165 [55] mg/dL, P = 0.02). Plasma glucose levels were in the target range 100% of the time on 17 of 24 nights during closed-loop insulin delivery. Hypoglycemia occurred on 10 occasions during control visits and 9 occasions during closed-loop delivery (5 episodes were exercise related, and 4 occurred within 2.5 h of prandial bolus). CONCLUSIONS: Day-and-night closed-loop basal insulin delivery can improve glucose control in adolescents. However, unannounced moderate-intensity exercise and excessive prandial boluses pose challenges to hypoglycemia-free closed-loop basal insulin delivery.

OBJECTIVE: Despite advances in technology, optimal glucose control remains elusive and neonatal complications remain ubiquitous in type 1 diabetes (T1D) pregnancy. Our aim was to examine the safety, efficacy, and longer-term feasibility of day-and-night closed-loop insulin delivery. RESEARCH DESIGN AND METHODS: We recruited 16 pregnant women (mean [SD]: age 32.8 [5.0] years, T1D duration 19.4 [10.2] years, HbA1c 8.0% [1.1], and BMI 26.6 [4.4] kg/m2) to an open-label, randomized, crossover trial. Participants completed 28 days of closed-loop and sensor-augmented pump (SAP) insulin delivery separated by a washout period. Afterward, participants could continue to use the closed-loop system up to 6 weeks postpartum. The primary end point was the proportion of time with glucose levels within the target range (63-140 mg/dL). RESULTS: The proportion of time with glucose levels within target was comparable during closed-loop and SAP insulin delivery (62.3 vs. 60.1% [95% CI -4.1 to 8.3]; P = 0.47). Mean glucose and time spent hyperglycemic >140 mg/dL also did not differ (131.4 vs. 131.4 mg/dL [P = 0.85] and 36.6 vs. 36.1% [P = 0.86], respectively). During closed-loop, fewer hypoglycemic episodes occurred (median 8 [range 1-17] vs. 12.5 [1-53] over 28 days; P = 0.04) and less time at

BACKGROUND: Pregnant women with type 1 diabetes are a high-risk population who are recommended to strive for optimal glucose control, but neonatal outcomes attributed to maternal hyperglycaemia remain suboptimal. Our aim was to examine the effectiveness of continuous glucose monitoring (CGM) on maternal glucose control and obstetric and neonatal health outcomes. METHODS: In this multicentre, open-label, randomised controlled trial, we recruited women aged 18-40 years with type 1 diabetes for a minimum of 12 months who were receiving intensive insulin therapy. Participants were pregnant (≤13 weeks and 6 days' gestation) or planning pregnancy from 31 hospitals in Canada, England, Scotland, Spain, Italy, Ireland, and the USA. We ran two trials in parallel for pregnant participants and for participants planning pregnancy. In both trials, participants were randomly assigned to either CGM in addition to capillary glucose monitoring or capillary glucose monitoring alone. Randomisation was stratified by insulin delivery (pump or injections) and baseline glycated haemoglobin (HbA1c). The primary outcome was change in HbA1c from randomisation to 34 weeks' gestation in pregnant women and to 24 weeks or conception in women planning pregnancy, and was assessed in all randomised participants with baseline assessments. Secondary outcomes included obstetric and neonatal health outcomes, assessed with all available data without imputation. This trial is registered with ClinicalTrials.gov, number NCT01788527. FINDINGS: Between March 25, 2013, and March 22, 2016, we randomly assigned 325 women (215 pregnant, 110 planning pregnancy) to capillary glucose monitoring with CGM (108 pregnant and 53 planning pregnancy) or without (107 pregnant and 57 planning pregnancy). We found a small difference in HbA1c in pregnant women using CGM (mean difference -0·19%; 95% CI -0·34 to -0·03; p=0·0207). Pregnant CGM users spent more time in target (68% vs 61%; p=0·0034) and less time hyperglycaemic (27% vs 32%; p=0·0279) than did pregnant control participants, with comparable severe hypoglycaemia episodes (18 CGM and 21 control) and time spent hypoglycaemic (3% vs 4%; p=0·10). Neonatal health outcomes were significantly improved, with lower incidence of large for gestational age (odds ratio 0·51, 95% CI 0·28 to 0·90; p=0·0210), fewer neonatal intensive care admissions lasting more than 24 h (0·48; 0·26 to 0·86; p=0·0157), fewer incidences of neonatal hypoglycaemia (0·45; 0·22 to 0·89; p=0·0250), and 1-day shorter length of hospital stay (p=0·0091). We found no apparent benefit of CGM in women planning pregnancy. Adverse events occurred in 51 (48%) of CGM participants and 43 (40%) of control participants in the pregnancy trial, and in 12 (27%) of CGM participants and 21 (37%) of control participants in the planning pregnancy trial. Serious adverse events occurred in 13 (6%) participants in the pregnancy trial (eight [7%] CGM, five [5%] control) and in three (3%) participants in the planning pregnancy trial (two [4%] CGM and one [2%] control). The most common adverse events were skin reactions occurring in 49 (48%) of 103 CGM participants and eight (8%) of 104 control participants during pregnancy and in 23 (44%) of 52 CGM participants and five (9%) of 57 control participants in the planning pregnancy trial. The most common serious adverse events were gastrointestinal (nausea and vomiting in four participants during pregnancy and three participants planning pregnancy). INTERPRETATION: Use of CGM during pregnancy in patients with type 1 diabetes is associated with improved neonatal outcomes, which are likely to be attributed to reduced exposure to maternal hyperglycaemia. CGM should be offered to all pregnant women with type 1 diabetes using intensive insulin therapy. This study is the first to indicate potential for improvements in non-glycaemic health outcomes from CGM use. FUNDING: Juvenile Diabetes Research Foundation, Canadian Clinical Trials Network, and National Institute for Health Research.

OBJECTIVE Despite advances in technology, optimal glucose control remains elusive and neonatal complications ubiquitous in type 1 diabetes (T1D) pregnancy. Our aim was to examine the safety, efficacy, and longer-term feasibility of day-and-night closed-loop insulin delivery. RESEARCH DESIGN AND METHODS We recruited 16 pregnant women (mean [SD]: age 32.8 [5.0] years, T1D duration 19.4 [10.2] years, HbA1c 8.0% [1.1%], BMI 26.6 [4.4] kg/m2) to an open-label, randomized, crossover trial. Participants completed 28 days of closed-loop and sensor-augmented pump (SAP) insulin delivery separated by a washout period. Afterward, participants could continue to use the closed-loop system up to 6 weeks postpartum. The primary end point was the proportion of time with glucose levels within the target range (63–140 mg/dL). RESULTS The proportion of time with glucose levels within target was comparable during closed-loop and SAP insulin delivery (62.3 vs. 60.1% [95% CI −4.1 to 8.3%]; P = 0.47). Mean glucose and time spent hyperglycemic >140 mg/dL also did not differ (131.4 vs. 131.4 mg/dL [P = 0.85] and 36.6 vs. 36.1% [P = 0.86], respectively). During closed-loop, fewer hypoglycemic episodes occurred (median [range] 8 [1–17] vs. 12.5 [1–53] over 28 days; P = 0.04) and less time at

Background: \ud Pregnant women with type 1 diabetes are a high-risk population who are recommended to strive for optimal glucose control, but neonatal outcomes attributed to maternal hyperglycaemia remain suboptimal. Our aim was to examine the effectiveness of continuous glucose monitoring (CGM) on maternal glucose control and obstetric and neonatal health outcomes.\ud \ud Methods: \ud In this multicentre, open-label, randomised controlled trial, we recruited women aged 18–40 years with type 1 diabetes for a minimum of 12 months who were receiving intensive insulin therapy. Participants were pregnant (≤13 weeks and 6 days' gestation) or planning pregnancy from 31 hospitals in Canada, England, Scotland, Spain, Italy, Ireland, and the USA. We ran two trials in parallel for pregnant participants and for participants planning pregnancy. In both trials, participants were randomly assigned to either CGM in addition to capillary glucose monitoring or capillary glucose monitoring alone. Randomisation was stratified by insulin delivery (pump or injections) and baseline glycated haemoglobin (HbA1c). The primary outcome was change in HbA1c from randomisation to 34 weeks' gestation in pregnant women and to 24 weeks or conception in women planning pregnancy, and was assessed in all randomised participants with baseline assessments. Secondary outcomes included obstetric and neonatal health outcomes, assessed with all available data without imputation. This trial is registered with ClinicalTrials.gov, number NCT01788527.\ud \ud Findings: \ud Between March 25, 2013, and March 22, 2016, we randomly assigned 325 women (215 pregnant, 110 planning pregnancy) to capillary glucose monitoring with CGM (108 pregnant and 53 planning pregnancy) or without (107 pregnant and 57 planning pregnancy). We found a small difference in HbA1c in pregnant women using CGM (mean difference −0·19%; 95% CI −0·34 to −0·03; p=0·0207). Pregnant CGM users spent more time in target (68% vs 61%; p=0·0034) and less time hyperglycaemic (27% vs 32%; p=0·0279) than did pregnant control participants, with comparable severe hypoglycaemia episodes (18 CGM and 21 control) and time spent hypoglycaemic (3% vs 4%; p=0·10). Neonatal health outcomes were significantly improved, with lower incidence of large for gestational age (odds ratio 0·51, 95% CI 0·28 to 0·90; p=0·0210), fewer neonatal intensive care admissions lasting more than 24 h (0·48; 0·26 to 0·86; p=0·0157), fewer incidences of neonatal hypoglycaemia (0·45; 0·22 to 0·89; p=0·0250), and 1-day shorter length of hospital stay (p=0·0091). We found no apparent benefit of CGM in women planning pregnancy. Adverse events occurred in 51 (48%) of CGM participants and 43 (40%) of control participants in the pregnancy trial, and in 12 (27%) of CGM participants and 21 (37%) of control participants in the planning pregnancy trial. Serious adverse events occurred in 13 (6%) participants in the pregnancy trial (eight [7%] CGM, five [5%] control) and in three (3%) participants in the planning pregnancy trial (two [4%] CGM and one [2%] control). The most common adverse events were skin reactions occurring in 49 (48%) of 103 CGM participants and eight (8%) of 104 control participants during pregnancy and in 23 (44%) of 52 CGM participants and five (9%) of 57 control participants in the planning pregnancy trial. The most common serious adverse events were gastrointestinal (nausea and vomiting in four participants during pregnancy and three participants planning pregnancy).\ud \ud Interpretation: \ud Use of CGM during pregnancy in patients with type 1 diabetes is associated with improved neonatal outcomes, which are likely to be attributed to reduced exposure to maternal hyperglycaemia. CGM should be offered to all pregnant women with type 1 diabetes using intensive insulin therapy. This study is the first to indicate potential for improvements in non-glycaemic health outcomes from CGM use.\ud \ud Funding: \ud Juvenile Diabetes Research Foundation, Canadian Clinical Trials Network, and National Institute for Health Research.

Background Women with type 1 diabetes strive for optimal glycemic control before and during pregnancy to avoid adverse obstetric and perinatal outcomes. For most women, optimal glycemic control is challenging to achieve and maintain. The aim of this study is to determine whether the use of real-time continuous glucose monitoring (RT-CGM) will improve glycemic control in women with type 1 diabetes who are pregnant or planning pregnancy. Methods/design A multi-center, open label, randomized, controlled trial of women with type 1 diabetes who are either planning pregnancy with an HbA1c of 7.0 % to ≤10.0 % (53 to ≤ 86 mmol/mol) or are in early pregnancy (

Background: Outside pregnancy, closed-loop (automated) insulin delivery can improve glycemic control, but its efficacy, safety and feasibility in pregnancy have not been studied. Methods: We performed an open-label, randomized, crossover study comparing overnight closed-loop with sensor-augmented pump therapy, followed by a day-and-night closed-loop continuation phase. 16 participants completed four weeks of closed-loop (intervention) and sensor-augmented pump therapy (control) in random order. During the feasibility phase, 14 participants continued day-and-night closed-loop until delivery. The primary outcome was the proportion of time with overnight glucose level within target range (63-140mg/dL). Results: The proportion of time with target overnight glucose levels was higher during closed-loop therapy than control (74.7% vs 59.5%; absolute difference (95% CI) 15.2 percentage points (6.1 to 24.2); p=0.002). The overnight mean glucose level was lower during closed-loop (119 vs 133 mg/dL; p=0.009). There were no significant differences between closed-loop and sensor-augmented pump therapy phases in overnight time spent hypoglycemic (1.9% vs 1.3%; p=0.28), insulin doses or adverse event rates. During the continuation phase (up to 14.6 additional weeks including acute antenatal hospitalizations, labor and delivery), the proportion of time spent in target range was 68.7%; mean glucose level was 126mg/dL. No episodes of severe hypoglycemia requiring third-party assistance occurred during either phase. Conclusions: Overnight closed-loop therapy resulted in better glucose control than sensor-augmented pump therapy in pregnant women with type 1 diabetes. Day-and-night closed-loop maintained glycemic control a high proportion of the time during antenatal hospital admissions, labor and delivery. (National Institute for Health Research, Diabetes UK; Controlled-Trials.com number, ISRCTN71510001)

AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) is associated with increased risks to mother and child, but globally agreed diagnostic criteria remain elusive. Identification of women with GDM is important, as treatment reduces adverse outcomes such as perinatal death, shoulder dystocia and neonatal hypoglycaemia. Recently, the UK's National Institute for Health and Care Excellence (NICE) recommended new diagnostic thresholds for GDM which are different from the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria endorsed by the WHO. The study aim was to assess neonatal and obstetric outcomes among women who would test positive for the IADPSG criteria but negative for the NICE 2015 criteria. METHODS: Data from 25,543 consecutive singleton live births (2004-2008) were obtained retrospectively from hospital records. Women were screened with a random plasma glucose (RPG; 12-16 weeks) and a 50 g glucose challenge test (GCT; 26-28 weeks). If RPG >7.0 mmol/l, GCT >7.7 mmol/l or symptoms were present, a 75 g OGTT was offered (n = 3,848). RESULTS: In this study, GDM prevalence was 4.13% (NICE 2015) and 4.62% (IADPSG). Women who 'fell through the net', testing NICE-negative but IADPSG-positive (n = 387), had a higher risk of having a large-for-gestational-age (LGA) infant (birthweight >90th percentile for gestational age; adjusted OR [95% CI] 3.12 [2.44, 3.98]), Caesarean delivery (1.44 [1.15, 1.81]) and polyhydramnios (6.90 [3.94, 12.08]) compared with women with negative screening results and no OGTT (n = 21,695). LGA risk was highest among women with fasting plasma glucose 5.1-5.5 mmol/l (n = 167): the mean birthweight was 350 g above that of the reference population and 37.7% of infants were LGA. CONCLUSIONS/INTERPRETATION: The IADPSG criteria identify women at substantial risk of complications who would not be identified by the NICE 2015 criteria.

AIMS/HYPOTHESIS: Women with diabetes remain at increased risk of adverse pregnancy outcomes associated with poor pregnancy preparation. However, women with type 2 diabetes are less aware of and less likely to access pre-pregnancy care (PPC) compared with women with type 1 diabetes. We developed and evaluated a community-based PPC programme with the aim of improving pregnancy preparation in all women with pregestational diabetes. METHODS: This was a prospective cohort study comparing pregnancy preparation measures before and during/after the PPC intervention in women with pre-existing diabetes from 1 June 2013 to 28 February 2017. The setting was 422 primary care practices and ten National Health Service specialist antenatal diabetes clinics. A multifaceted approach was taken to engage women with diabetes and community healthcare teams. This included identifying and sending PPC information leaflets to all eligible women, electronic preconception care templates, online education modules and resources, and regional meetings and educational events. Key outcomes were preconception folic acid supplementation, maternal HbA1c level, use of potentially harmful medications at conception and gestational age at first presentation, before and during/after the PPC programme. RESULTS: A total of 306 (73%) primary care practices actively participated in the PPC programme. Primary care databases were used to identify 5075 women with diabetes aged 18-45 years. PPC leaflets were provided to 4558 (89.8%) eligible women. There were 842 consecutive pregnancies in women with diabetes: 502 before and 340 during/after the PPC intervention. During/after the PPC intervention, pregnant women with type 2 diabetes were more likely to achieve target HbA1c levels ≤48 mmol/mol (6.5%) (44.4% of women before vs 58.5% of women during/after PPC intervention; p = 0.016) and to take 5 mg folic acid daily (23.5% and 41.8%; p = 0.001). There was an almost threefold improvement in 'optimal' pregnancy preparation in women with type 2 diabetes (5.8% and 15.1%; p = 0.021). Women with type 1 diabetes presented for earlier antenatal care during/after PPC (54.0% vs 67.3% before 8 weeks' gestation; p = 0.003) with no other changes. CONCLUSIONS/INTERPRETATION: A pragmatic community-based PPC programme was associated with clinically relevant improvements in pregnancy preparation in women with type 2 diabetes. To our knowledge, this is the first community-based PPC intervention to improve pregnancy preparation for women with type 2 diabetes. DATA AVAILABILITY: Further details of the data collection methodology, individual clinic data and the full audit reports for healthcare professionals and service users are available from https://digital.nhs.uk/data-and-information/clinical-audits-and-registries/our-clinical-audits-and-registries/national-pregnancy-in-diabetes-audit .

OBJECTIVE: This study evaluated closed-loop insulin delivery with a model predictive control (MPC) algorithm during early (12-16 weeks) and late gestation (28-32 weeks) in pregnant women with type 1 diabetes. RESEARCH DESIGN AND METHODS: Ten women with type 1 diabetes (age 31 years, diabetes duration 19 years, BMI 24.1 kg/m(2), booking A1C 6.9%) were studied over 24 h during early (14.8 weeks) and late pregnancy (28.0 weeks). A nurse adjusted the basal insulin infusion rate from continuous glucose measurements (CGM), fed into the MPC algorithm every 15 min. Mean glucose and time spent in target (63-140 mg/dL), hyperglycemic (>140 to ≥ 180 mg/dL), and hypoglycemic (140 mg/dL) was 7% (0-40%) in early and 0% (0-6%) in late pregnancy (P = 0.25) and hypoglycemic (

We consider situations, which are common in medical statistics, where we have a number of sets of response data, from different individuals, say, potentially under different conditions. A parametric model is defined for each set of data, giving rise to a set of random effects. Our goal here is to efficiently explore a range of possible 'population' models for the random effects, to select the most appropriate model. The range of possible models is potentially vast, because the random effects may depend on observed covariates, and there may be multiple credible ways of partitioning their variability. Here, we consider pharmacokinetic (PK) data on insulin aspart, a fast acting insulin analogue used in the treatment of diabetes. PK models are typically nonlinear (in their parameters), often complex and sometimes only available as a set of differential equations, with no closed-form solution. Fitting such a model for just a single individual can be a challenging task. Fitting a joint model for all individuals can be even harder, even without the complication of an overarching model selection objective. We describe a two-stage approach that decouples the population model for the random effects from the PK model applied to the response data but nevertheless fits the full, joint, hierarchical model, accounting fully for uncertainty. This allows us to repeatedly reuse results from a single analysis of the response data to explore various population models for the random effects. This greatly expedites not only model exploration but also cross-validation for the purposes of model criticism. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Measurement of glycated hemoglobin (HbA1c) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA1c testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.

AIMS/HYPOTHESIS: This study aimed to explore the infancy growth trajectories of 'recent' and 'earlier' offspring of mothers with gestational diabetes mellitus (OGDM), each compared with the same control infants, and investigate whether 'recent' OGDM still exhibit a classical phenotype, with macrosomia and increased adiposity. METHODS: Within a prospective observational birth cohort, 98 'earlier' OGDM born between 2001 and 2009 were identified using 75 g oral glucose tolerance testing at 28 weeks gestation, 122 recent OGDM born between 2011 and 2013 were recruited postnatally through antenatal diabetes clinics, and 876 normal birthweight infants of mothers with no history of diabetes were recruited across the full study period as the control group. All infants followed the same study protocol (measurements at birth, 3, 12 and 24 months, including weight, length and skinfold thickness indicating adiposity, and detailed demographic data). In all cases, GDM was defined using the International Association of Diabetes and Pregnancy Study Group criteria. RESULTS: Earlier OGDM had higher birthweight SD scores (SDS) than control infants. Conversely, recent OGDM had similar birthweight- and length SDS to control infants (mean ± SD, 0.1 ± 1.0 and- 0.1 ± 0.9, respectively), but lower mean skinfold thickness SDS (-0.4 ± 0.6 vs 0.0 ± 0.9; p < 0.001). After birth, earlier OGDM showed reduced gains in weight and length between 3 and 12 months. In contrast, recent OGDM had increased weight and skinfold thickness gains until 3 months, followed by reduced gains in those variables from 3 to 12 months, compared with control infants. At 24 months, recent OGDM had lower adiposity than control infants (mean skinfold thickness SDS -0.3 ± 0.7 vs 0.0 ± 0.8; p < 0.001). At all time points recent OGDM had lower growth measurements than earlier OGDM. CONCLUSIONS/INTERPRETATION: Recent OGDM showed different growth trajectories to the earlier group, namely normalisation of birthweight and reduced adiposity at birth, followed by initial rapid weight gain but subsequent reduced adiposity postnatally. While avoidance of macrosomia at birth may be advantageous, the longer-term health implications of these changing growth trajectories are uncertain.

Aims To identify key gaps in the research evidence base that could help improve how technology supports people with diabetes, and provide recommendations to researchers and research funders on how best to address them. Methods A research workshop was conducted, bringing together research experts in diabetes, research experts in technology, people living with diabetes and healthcare professionals. Results The following key areas within this field were identified, and research recommendations for each were developed: Matching the pace of research with that of technology development Time in range as a measure Health inequalities and high risk groups How to train people to use technology most effectively Impact of technology usage on mental health CONCLUSIONS: This position statement outlines recommendations through which research could improve how technology is employed to care for and support people living with diabetes, and calls on the research community and funders to address them in future research programmes and strategies.

In December 2020, the National Institute for Health and Care Excellence (NICE) reviewed the evidence and updated their recommendations on intermittently scanned (commonly known as Flash) and Continuous Glucose Monitoring (CGM) during pregnancy for women with type 1 diabetes (1). The NICE guidelines now recommend offering CGM to all pregnant women with type 1 diabetes to help them meet their pregnancy glucose targets and improve neonatal outcomes. Their evidence review, based on the CONCEPTT randomised trial (2) and a Swedish observational study (3) found that, compared to capillary glucose monitoring, CGM resulted in more women achieving their blood glucose targets, fewer caesarean sections and fewer neonatal intensive care admissions.

Funder: Nuffield Department of Population Health, University of Oxford, AIMS: To undertake a Priority Setting Partnership (PSP) to establish priorities for future research in diabetes and pregnancy, according to women with experience of pregnancy, and planning pregnancy, with any type of diabetes, their support networks and healthcare professionals. METHODS: The PSP used established James Lind Alliance (JLA) methodology working with women and their support networks and healthcare professionals UK-wide. Unanswered questions about the time before, during or after pregnancy with any type of diabetes were identified using an online survey and broad-level literature search. A second survey identified a shortlist of questions for final prioritisation at an online consensus development workshop. RESULTS: There were 466 responses (32% healthcare professionals) to the initial survey, with 1161 questions, which were aggregated into 60 unanswered questions. There were 614 responses (20% healthcare professionals) to the second survey and 18 questions shortlisted for ranking at the workshop. The top 10 questions were: diabetes technology, the best test for diabetes during pregnancy, diet and lifestyle interventions for diabetes management during pregnancy, emotional and well-being needs of women with diabetes pre- to post-pregnancy, safe full-term birth, post-natal care and support needs of women, diagnosis and management late in pregnancy, prevention of other types of diabetes in women with gestational diabetes, women's labour and birth experiences and choices and improving planning pregnancy. CONCLUSIONS: These research priorities provide guidance for research funders and researchers to target research in diabetes and pregnancy that will achieve greatest value and impact.

Measurement of glycated hemoglobin (HbA1c) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA1c testing and self-monitoring of blood glucose. Although both provide themeans to move beyond the HbA1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to themost appropriate glucose monitoring methodologies, as well as standardized advice about howbest to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.