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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.

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).

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

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.