Background Closed-loop insulin delivery systems are expected to become a standard treatment for patients with type 1 diabetes. We aimed to assess whether the Diabeloop Generation 1 (DBLG1) hybrid closed-loop artificial pancreas system improved glucose control compared with sensor-assisted pump therapy.Methods In this multicentre, open-label, randomised, crossover trial, we recruited adults (aged ≥18 years) with at least a 2 year history of type 1 diabetes, who had been treated with external insulin pump therapy for at least 6 months, had glycated haemoglobin (HbA 1c ) of 10% or less (86 mmol/mol), and preserved hypoglycaemia awareness. After a 2-week run-in period, patients were randomly assigned (1:1) with a web-based system in randomly permuted blocks of two, to receive insulin via the hybrid closed-loop system (DBLG1; using a machine-learning-based algorithm) or sensor-assisted pump therapy over 12 weeks of free living, followed by an 8-week washout period and then the other intervention for 12 weeks. The primary outcome was the proportion of time that the sensor glucose concentration was within the target range (3•9-10•0 mmol/L) during the 12 week study period. Efficacy analyses were done in the modified intention-totreat population, which included all randomly assigned patients who completed both 12 week treatment periods. Safety analyses were done in all patients who were exposed to either of the two treatments at least once during the study. This trial is registered with ClinicalTrials.gov, number NCT02987556.

RESULTS -Clinical characteristics, pharmacological therapies, lifestyle, and prevalence of diabetes-related complications were similar in both patient groups. LDL size correlated negatively with plasma triglycerides (TGs) (R 2 = 0.52) and positively with HDL cholesterol (R 2 = 0.14). However, an inverse correlation between the TG-to-HDL cholesterol molar ratio and LDL size was even stronger (R 2 = 0.59). The ratio was Ͼ1.33 in 90% of the patients with small LDL particles (95% CI 79.3-100) and 16.5% of those with larger LDL particles. A cutoff point of 1.33 for the TG-to-HDL cholesterol ratio distinguishes between patients having small LDL values better than TG cutoff of 1.70 and 1.45 mmol/l.CONCLUSIONS -The TG-to-HDL cholesterol ratio may be related to the processes involved in LDL size pathophysiology and relevant with regard to the risk of clinical vascular disease. It may be suitable for the selection of patients needing an earlier and aggressive treatment of lipid abnormalities.

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Background: Sleep behavior is changing toward shorter sleep duration and a later chronotype. It results in a sleep debt that is acquitted on work-free days, inducing a small but recurrent sleep misalignment each week, referred to as "social jetlag". These sleep habits could affect health through misalignment with circadian rhythms. Objectives: The primary objective is to address the impact of sleep behavior on glycemic control, assessed by HbA1c, in patients with type 1 diabetes, independently of other lifestyle or sleep-related factors. The secondary objective is to address whether circadian phase affects glycemic control. Design: In total, 80 adult patients with type 1 diabetes (46% female) were included in a clinical cohort study. Methods: Sleep behavior was addressed objectively by a 7-day actimetry, lifestyle by questionnaires, sleep breathing disorders by nocturnal oximetry and circadian phase by dim light melatonin onset (DLMO). Results: Univariate analyses showed that chronotype (r = 0.23, P = 0.042) and social jetlag (r = 0.30, P = 0.008) were significantly associated with HbA1c. In multivariable analysis, social jetlag was the only sleep habit independently associated with HbA1c (β = 0.012 (0.006; 0.017), P < 0.001). HbA1c was lower in patients with a social jetlag below versus above the median (7.7% (7.1-8.7) and 8.7% (7.6-9.8), P = 0.011). DLMO was not associated with HbA1c. However, the later the DLMO, the worse the sleep efficiency (r = −0.41, P < 0.001) and fragmentation index (r = 0.35, P = 0.005). Conclusions: Social jetlag, a small but recurrent circadian misalignment, is associated with worse glycemic control in type 1 diabetes, whereas circadian phase is not. Further intervention studies should address the potential improvement of glycemic control by correcting social jetlag.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.