Identifying the deficiencies of currently available CGM to improve uptake and benefit.

BACKGROUND AND AIMS: The use of diabetes technologies is increasing worldwide, with health systems facilitating improved access to devices. Continuous glucose monitoring is a complex intervention that provides information on glucose concentration, rate and direction of change, historical data and alerts and alarms for extremes of glucose. These data do not themselves change glycaemia and require translation to a meaningful action for impact. It is, therefore, crucial that such systems advance to better meet the needs of individuals using them. METHODS: Narrative review of the use of, engagement with, limitations and unmet needs of continuous glucose monitoring systems. RESULTS: CGM devices have made a significant contribution to the self-management of diabetes; however, challenges with access and user experience persist, with multiple limitations to uptake and benefit. These limitations include physical size and implementation, with associated stigma, alarm fatigue, sleep disturbance and the challenge of addressing large volumes of real-time data. Greater personalisation throughout the continuous glucose monitoring journey, with a focus on usability, may improve the benefits derived from the device and reduce the burden of self-management. Healthcare professionals may have unconscious biases that affect the provision of continuous glucose monitors due to deprivation, education, age, ethnicity and other characteristics. CONCLUSIONS: Continuous glucose monitoring exerts a dose-dependent response; the more it is used, the more effective it is. For optimal use, continuous glucose monitors must not just reduce the burden of management in one dimension but facilitate net improvement in all domains of self-management for all users.

Human RORγt(+)CD34(+) cells are lineage-specified progenitors of group 3 RORγt(+) innate lymphoid cells.

Group 3 innate lymphoid cells (ILC3s) are defined by the expression of the transcription factor RORγt, which is selectively required for their development. The lineage-specified progenitors of ILC3s and their site of development after birth remain undefined. Here we identified a population of human CD34(+) hematopoietic progenitor cells (HPCs) that express RORγt and share a distinct transcriptional signature with ILC3s. RORγt(+)CD34(+) HPCs were located in tonsils and intestinal lamina propria (LP) and selectively differentiated toward ILC3s. In contrast, RORγt(-)CD34(+) HPCs could differentiate to become either ILC3s or natural killer (NK) cells, with differentiation toward ILC3 lineage determined by stem cell factor (SCF) and aryl hydrocarbon receptor (AhR) signaling. Thus, we demonstrate that in humans RORγt(+)CD34(+) cells are lineage-specified progenitors of IL-22(+) ILC3s and propose that tonsils and intestinal LP, which are enriched both in committed precursors and mature ILC3s, might represent preferential sites of ILC3 lineage differentiation.

RORγt⁺ innate lymphoid cells acquire a proinflammatory program upon engagement of the activating receptor NKp44.

RORγt⁺ innate lymphoid cells (ILCs) are crucial players of innate immune responses and represent a major source of interleukin-22 (IL-22), which has an important role in mucosal homeostasis. The signals required by RORγt⁺ ILCs to express IL-22 and other cytokines have been elucidated only partially. Here we showed that RORγt⁺ ILCs can directly sense the environment by the engagement of the activating receptor NKp44. NKp44 triggering in RORγt⁺ ILCs selectively activated a coordinated proinflammatory program, including tumor necrosis factor (TNF), whereas cytokine stimulation preferentially induced IL-22 expression. However, combined engagement of NKp44 and cytokine receptors resulted in a strong synergistic effect. These data support the concept that NKp44⁺ RORγt⁺ ILCs can be activated without cytokines and are able to switch between IL-22 or TNF production, depending on the triggering stimulus.

Tissue distribution and dependence of responsiveness of human antigen-specific memory B cells.

Memory B cells (mBCs) are a key to immunologic memory, yet their distribution within lymphoid organs and the individual role of these for mBC functionality remain largely unknown. This study characterized the distribution and phenotype of human (Ag-specific) mBCs in peripheral blood (PB), spleen, tonsil, and bone marrow. We found that the spleen harbors most mBCs, followed by tonsils, BM, and PB, and we detected no major differences in expression of markers associated with higher maturity. Testing the distribution of tetanus toxoid-specific (TT(+)) mBCs revealed their presence in PB during steady state, yet absolute numbers suggested their largest reservoir in the spleen, followed by tonsils. To explore the role of both tissues in the maintenance of reactive B cell memory, we revaccinated controls and splenectomized and tonsillectomized individuals with TT. All donor groups exhibited comparable emergence of anti-TT IgG, TT(+) plasma cells, and TT(+) mBCs in the PB, together with similar molecular characteristics of TT(+) plasma cells. In summary, human mBCs recirculate through PB and reside in different lymphoid organs that do not reflect different mBC maturity stages. The spleen and tonsil, although harboring the largest number of overall and TT(+) mBCs, appear to be dispensable to preserve adequate responsiveness to secondary antigenic challenge.

Human NK cells at early stages of differentiation produce CXCL8 and express CD161 molecule that functions as an activating receptor.

Human natural killer (NK) cell development is a step-by-step process characterized by phenotypically identified stages. CD161 is a marker informative of the NK cell lineage commitment, whereas CD56, CD117, and CD94/NKG2A contribute to define discrete differentiation stages. In cells undergoing in vitro differentiation from CD34(+) umbilical cord blood (UCB) progenitors, LFA-1 expression allowed to discriminate between immature noncytolytic CD161(+)CD56(+)LFA-1(-) and more differentiated cytolytic CD161(+)CD56(+)LFA-1(+) NK cells. CD161(+)CD56(+)LFA-1(-) NK cells produce large amounts of CXCL8 after phorbol myristate acetate (PMA) or cytokine treatment. Remarkably, CXCL8 mRNA expression was also detected in fresh stage III immature NK cells isolated from tonsils and these cells expressed CXCL8 protein on PMA stimulation. Within in vitro UCB-derived CD161(+)CD56(+)LFA-1(-) NK cells, CXCL8 release was also induced on antibody-mediated cross-linking of NKp44 and CD161. Such unexpected activating function of CD161 was confined to the CD161(+)CD56(+)LFA-1(-) subset, because it did not induce cytokine release or CD107a expression in CD161(+)CD56(+)LFA-1(+) cells or in mature peripheral blood NK cells. Anti-CXCL8 neutralizing antibody induced a partial inhibition of NK cell differentiation, which suggests a regulatory role of CXCL8 during early NK cell differentiation. Altogether, these data provide novel information that may offer clues to optimize NK cell maturation in hematopoietic stem cell transplantation.

Concept and Implementation of a Novel Continuous Glucose Monitoring Solution With Glucose Predictions on Board.

The recently CE-marked continuous real-time glucose monitoring (rtCGM) solution Accu-Chek® (AC) SmartGuide Solution was developed to enable people with diabetes mellitus (DM) to proactively control their glucose levels using predictive technologies. The comprehensive solution consists of three components that harmonize well with each other. The CGM device is composed of a sensor applicator and a glucose sensor patch whose data are transferred to the connected smartphone by Bluetooth® Low Energy. The user interface of the CGM solution is powered by the AC SmartGuide app delivering current and past glucose metrics, and the AC SmartGuide Predict app providing a glucose prediction suite enabled by artificial intelligence (AI). This article describes the innovative CGM solution.

Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring.

Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.

Fear of Hypoglycemia and Diabetes Distress: Expected Reduction by Glucose Prediction.

BACKGROUND: Extended glucose predictions are novel in diabetes management. Currently, there is no solution widely available. People with diabetes mellitus (DM) are offered features like trend arrows and limited predictions linked to predefined situations. Thus, the impact of extended glucose predictions on the burden of diabetes and person-reported outcomes (PROs) is unclear. METHODS: In this online survey, 206 people with type 1 and type 2 diabetes (T1D and T2D), 70.9% and 29.1%, respectively, who participated in the dia·link online panel and were current continuous glucose monitoring (CGM) users, were presented with different scenarios of hypothetical extended glucose predictions. They were asked to imagine how low glucose predictions of 30 minutes and overnight as well as glucose predictions up to 2 hours would influence their diabetes management. Subsequently, they completed the Hypoglycemia Fear Survey II (HFS-II) and the T1 Diabetes Distress Scale (T1-DDS) by rating each item on a 5-point scale (-2: strong deterioration to +2: strong improvement) according to the potential change due to using glucose predictions. RESULTS: For all glucose prediction periods, 30 minutes, up to 2 hours, and at nighttime, the surveyed participants expected moderate improvements in both fear of hypoglycemia (HFS-II: 0.57 ± 0.49) and overall diabetes distress (T1-DDS = 0.44 ± 0.49). The T1-DDS did not differ for type of therapy or diabetes. CONCLUSIONS: People with T1D and T2D would see glucose predictions as a potential improvement regarding reduced fear of hypoglycemia and diabetes distress. Therefore, glucose predictions represent a value for them in lowering the burden of diabetes and its management.

Characteristics of Nocturnal Hypoglycaemic Events and Their Impact on Glycaemia.

BACKGROUND: Nocturnal hypoglycaemia is a burden for people with diabetes, particularly when treated with multiple daily injections (MDI) therapy. However, the characteristics of nocturnal hypoglycaemic events in this patient group are only poorly described in the literature. METHOD: Continuous glucose monitoring (CGM) data from 185 study participants with type 1 diabetes using MDI therapy were collected under everyday conditions for up to 13 weeks. Hypoglycaemic events were identified as episodes of consecutive CGM readings <70 mg/dl or <54 mg/dl for at least 15 minutes. Subsequently, the time <54 mg/dl (TB54), time below range (TBR), time in range (TIR), time above range (TAR), glucose coefficient of variation (CV), and incidence of hypoglycaemic events were calculated for diurnal and nocturnal periods. Furthermore, the effect of nocturnal hypoglycaemic events on glucose levels the following day was assessed. RESULTS: The incidence of hypoglycaemic events <70 mg/dl was significantly lower during the night compared to the day, with 0.8 and 3.8 events per week, respectively, while the TBR, TB54, and incidence of events with CGM readings <54 mg/dl was not significantly different. Nocturnal hypoglycaemic events <70 mg/dl were significantly longer (60 vs 35 minutes) and enveloped by less rapidly changing glucose levels. On days following nights containing hypoglycaemic events, there was a decrease in TAR, mean CGM glucose level and morning glucose levels and an increase in TB54, TBR, and CV. CONCLUSIONS: The results showed that nocturnal hypoglycaemic events are a common occurrence in persons with type 1 diabetes using MDI with significant differences between the characteristics of nocturnal and diurnal events.

Clinical Usage and Potential Benefits of a Continuous Glucose Monitoring Predict App.

Continuous glucose monitoring (CGM) has become an increasingly important tool for self-management in people with diabetes mellitus (DM). In this paper, we discuss recommendations on how to implement predictive features provided by the Accu-Chek SmartGuide Predict app in clinical practice. The Predict app's features are aimed at ultimately reducing diabetes stress and fear of hypoglycemia in people with DM. Furthermore, we explore the use cases and potential benefits of continuous glucose prediction, predictions of low glucose, and nocturnal hypoglycemia.

Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.

BACKGROUND: Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations. METHODS: The app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226). RESULTS: On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively. CONCLUSIONS: The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.

Recognition strategies of group 3 innate lymphoid cells.

During the early phase of an inflammatory response, innate cells can use different strategies to sense environmental danger. These include the direct interaction of specific activating receptors with pathogen-encoded/danger molecules or the engagement of cytokine receptors by pro-inflammatory mediators produced by antigen presenting cells in the course of the infection. These general recognition strategies, which have been extensively described for innate myeloid cells, are shared by innate lymphoid cells (ILC), such as Natural Killer (NK) cells. The family of ILC has recently expanded with the discovery of group 2 (ILC2) and group 3 ILC (ILC3), which play an important role in the defense against extracellular pathogens. Although ILC3 and NK cells share some phenotypic characteristics, the recognition strategies employed by the various ILC3 subsets have been only partially characterized. In this review, we will describe and comparatively discuss how ILC3 sense environmental cues and how the triggering of different receptors may regulate their functional behavior during an immune response.