A systematic review of the effect of prior hypoglycaemia on cognitive function in type 1 diabetes

Background: The effect of prior hypoglycaemia on cognitive function in type 1 diabetes is an important unresolved clinical question. In this systematic review, we aimed to summarize the studies exploring the impact of prior hypoglycaemia on any aspect of cognitive function in type 1 diabetes. Methods: We used a multidatabase search platform Healthcare Database Advanced Search to search Medline, PubMed, EMBASE, EMCARE, CINAHL, PsycINFO, BNI, HMIC, and AMED from inception until 1 May 2019. We included studies on type 1 diabetes of any age. The outcome measure was any aspect of cognitive function. Results: The 62 studies identified were grouped as severe hypoglycaemia (SH) in childhood (⩽18 years) and adult-onset (>18 years) diabetes, nonsevere hypoglycaemia (NSH) and nocturnal hypoglycaemia (NH). SH in early childhood-onset diabetes, especially seizures and coma, was associated with poorer memory (verbal and visuospatial), as well as verbal intelligence. Among adult-onset diabetes, SH was associated with poorer cognitive performance in the older age (>55 years) group only. Early versus late exposure to SH had a significant association with cognitive dysfunction (CD). NSH and NH did not have any significant association with CD, while impaired awareness of hypoglycaemia was associated with poorer memory and cognitive-processing speeds. Conclusion: The effect of SH on cognitive function is age dependent. Exposure to SH in early childhood (55 years) was associated with a moderate effect on the decrease in cognitive function in type 1 diabetes [PROSPERO ID: CRD42019141321]

Comparing Glucose Outcomes Following Face-to-Face and Remote Initiation of Flash Glucose Monitoring in People Living With Diabetes.

Background: When launched, FreeStyle Libre (FSL; a flash glucose monitor) onboarding was mainly conducted face-to-face. The COVID-19 pandemic accelerated a change to online starts with patients directed to online videos such as Diabetes Technology Network UK for education. We conducted an audit to evaluate glycemic outcomes in people who were onboarded face-to-face versus those who were onboarded remotely and to determine the impact of ethnicity and deprivation on those outcomes. Methods: People living with diabetes who started using FSL between January 2019 and April 2022, had their mode of onboarding recorded and had at least 90 days of data in LibreView with >70% data completion were included in the audit. Glucose metrics (percent time in ranges) and engagement statistics (previous 90-day averages) were obtained from LibreView. Differences between glucose variables and onboarding methods were compared using linear models, adjusting for ethnicity, deprivation, sex, age, percent active (where appropriate), and duration of FSL use. Results: In total, 935 participants (face-to-face 44% [n = 413]; online 56% [n = 522]) were included. There were no significant differences in glycemic or engagement indices between onboarding methods and ethnicities, but the most deprived quintile had significantly lower percent active time (b = −9.20, P = .002) than the least deprived quintile. Conclusions: Online videos as an onboarding method can be used without significant differences in glucose and engagement metrics. The most deprived group within the audit population had lower engagement metrics, but this did not translate into differences in glucose metrics.</p

Toward an Optimal Definition of Hypoglycemia with Continuous Glucose Monitoring

Background and Objective: As continuous glucose monitoring (CGM) becomes common in research and clinical practice, there is a need to understand how CGM-based hypoglycemia relates to hypoglycemia episodes defined conventionally as patient reported hypoglycemia (PRH). Data show that CGM identify many episodes of low interstitial glucose (LIG) that are not experienced by patients, and so the aim of this study is to use different PRH simulations to optimize CGM parameters of threshold (h) and duration (d) to provide the best PRH detection performance. Methods: The algorithm uses particle Markov chain Monte Carlo optimization to identify the optimal h and d which maximize an objective function for detecting PRH. We tested our algorithm by creating three different cases of PRH simulations. Results: We added three types of simulated PRH events to 10 weeks of anonymized CGM data from 96 type 1 diabetes people to see if the algorithm can detect the optimal parameters set out in the simulations. In simulation 1, we changed the locations of PRHs with respect to LIG episodes in the CGM signal to simulate random optimal LIG parameters for every individual. In simulation 2, the PRHs are CGM glucose 0.11 mmol/L/min. Simulation 3 is like simulation 2 but with glucose threshold of 3.0 mmol/L. The median [interquartile range] of deviation between the optimized (found by the algorithm) and the optimal (known) h and d are −0.07% [−0.4, 1.9] and −1.3% [−5.9, 6.8], respectively across the subjects for simulation 1. The mean [min max] of the optimized LIG parameters are h = 3.8 [3.7, 3.8] mmol/L and d = 12 [10, 14] min for simulation 2 and they are h = 3.0 [2.9, 3] mmol/L and d = 10 [8, 14] min for simulation 3 across a 10-fold cross validation. Conclusions: This work demonstrates the feasibility of the algorithm to find the best-fit definition of CGM-based hypoglycemia for PRH detection. In a prospective clinical study collecting CGM and PRH, the current algorithm will be used to optimize the definition of hypoglycemia with respect to PRH with the ambition of using the resulted definition as a surrogate for PRH in clinical practice

Current provision and HCP experiences of remote care delivery and diabetes technology training for people with type 1 diabetes in the UK during the Covid‐19 pandemic

BackgroundThe COVID-19 pandemic has led to the rapid implementation of remote care delivery in type 1 diabetes. We studied current modes of care delivery, healthcare professional experiences and impact on insulin pump training in type 1 diabetes care in the United Kingdom (UK).MethodsThe UK Diabetes Technology Network designed a 48-question survey aimed at healthcare professionals providing care in type 1 diabetes.ResultsOne hundred and forty-three healthcare professionals (48% diabetes physicians, 52% diabetes educators and 88% working in adult services) from approximately 75 UK centres (52% university hospitals, 46% general and community hospitals), responded to the survey. Telephone consultations were the main modality of care delivery. There was a higher reported time taken for video consultations versus telephone (p ConclusionThis survey highlights UK healthcare professional experiences of remote care delivery. While supportive of virtual care models, a number of factors highlighted, especially patient digital literacy, need to be addressed to improve virtual care delivery and device training.</div

A Roadmap to an Equitable Digital Diabetes Ecosystem.

ObjectivesDiabetes management presents a substantial burden to individuals living with the condition and their families, healthcare professionals, and healthcare systems. Although an increasing number of digital tools are available to assist with tasks such as blood glucose monitoring and insulin dose calculation, multiple persistent barriers continue to prevent their optimal use.MethodsAs a guide to creating an equitable connected digital diabetes ecosystem, we propose a roadmap with key milestones that need to be achieved along the way.ResultsDuring the Coronavirus 2019 pandemic, there was an increased use of digital tools to support diabetes care, but at the same time, the pandemic also highlighted problems of inequities in access to and use of these same technologies. Based on these observations, a connected diabetes ecosystem should incorporate and optimize the use of existing treatments and technologies, integrate tasks such as glucose monitoring, data analysis, and insulin dose calculations, and lead to improved and equitable health outcomes.ConclusionsDevelopment of this ecosystem will require overcoming multiple obstacles, including interoperability and data security concerns. However, an integrated system would optimize existing devices, technologies and treatments to improve help to improve outcomes

Understanding the clinical implications of differences between glucose management indicator and glycated haemoglobin

Laboratory measured glycated haemoglobin (HbA1c) is the gold standard for assessing glycaemic control in people with diabetes and correlates with their risk of long-term complications. The emergence of continuous glucose monitoring (CGM) has highlighted limitations of HbA1c testing. HbA1c can only be reviewed infrequently and can mask the risk of hypoglycaemia or extreme glucose fluctuations. While CGM provides insights in to the risk of hypoglycaemia as well as daily fluctuations of glucose, it can also be used to calculate an estimated HbA1c that has been used as a substitute for laboratory HbA1c. However, it is evident that estimated HbA1c and HbA1c values can differ widely. The glucose management indicator (GMI), calculated exclusively from CGM data, has been proposed. It uses the same scale (% or mmol/mol) as HbA1c, but is based on short-term average glucose values, rather than long-term glucose exposure. HbA1c and GMI values differ in up to 81% of individuals by more than ±0.1% and by more than ±0.3% in 51% of cases. Here, we review the factors that define these differences, such as the time period being assessed, the variation in glycation rates and factors such as anaemia and haemoglobinopathies. Recognizing and understanding the factors that cause differences between HbA1c and GMI is an important clinical skill. In circumstances when HbA1c is elevated above GMI, further attempts at intensification of therapy based solely on the HbA1c value may increase the risk of hypoglycaemia. The observed difference between GMI and HbA1c also informs the important question about the predictive ability of GMI regarding long-term complications

Glycaemic measures for 8914 adult FreeStyle Libre users during routine care, segmented by age group and observed changes during the COVID-19 pandemic

Aim: To evaluate the impact of the stay-at-home policy on different glucose metrics for time in range (%TIR 3.9-10 mmol/L), time below range (%TBR 10 mmol/L) for UK adult FreeStyle Libre (FSL) users within four defined age groups and on observed changes during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Data were extracted from 8914 LibreView de-identified user accounts for adult users aged 18 years or older with 5 or more days of sensor readings in each month from January to June 2020. Age-group categories were based on self-reported age on LibreView accounts (18-25, 26-49, 50-64 and ≥65 years). Results: In January, prior to the COVID-19 pandemic, the 65 years or older age group had the highest %TIR (57.9%), while the 18-25 years age group had the lowest (51.2%) (P <.001). Within each age group, TIR increased during the analysed months, by 1.7% (26-49 years) to 3.1% (≥65 years) (P <.001 in all cases). %TBR was significantly reduced only in the 26-49 years age group, whereas %TAR was reduced by 1.5% (26-49 years) to 3.0% (≥65 years) (P <.001 in both cases). The proportion of adults achieving both of the more than 70% TIR and less than 4% TBR targets increased from 11.7% to 15.9% for those aged 65 years or older (P <.001) and from 6.0% to 9.1% for those aged 18-25 years (P <.05). Mean daily glucose-sensor scan rates were at least 12 per day and remained stable across the analysis period. Conclusions: Our data show the baseline glucose metrics for FSL users in the UK across different age groups under usual care. During lockdown in the UK, the proportion of adults achieving TIR consensus targets increased among FSL users

Molecular mechanisms linking type 2 diabetes mellitus and late-onset Alzheimer's disease: A systematic review and qualitative meta-analysis

Research evidence indicating common metabolic mechanisms through which type 2 diabetes mellitus (T2DM) increases risk of late-onset Alzheimer's dementia (LOAD) has accumulated over recent decades. The aim of this systematic review is to provide a comprehensive review of common mechanisms, which have hitherto been discussed in separate perspectives, and to assemble and evaluate candidate loci and epigenetic modifications contributing to polygenic risk linkages between T2DM and LOAD. For the systematic review on pathophysiological mechanisms, both human and animal studies up to December 2023 are included. For the qualitative meta-analysis of genomic bases, human association studies were examined; for epigenetic mechanisms, data from human studies and animal models were accepted. Papers describing pathophysiological studies were identified in databases, and further literature gathered from cited work. For genomic and epigenomic studies, literature mining was conducted by formalised search codes using Boolean operators in search engines, and augmented by GeneRif citations in Entrez Gene, and other sources (WikiGenes, etc.). For the systematic review of pathophysiological mechanisms, 923 publications were evaluated, and 138 gene loci extracted for testing candidate risk linkages. 3 57 publications were evaluated for genomic association and descriptions of epigenomic modifications. Overall accumulated results highlight insulin signalling, inflammation and inflammasome pathways, proteolysis, gluconeogenesis and glycolysis, glycosylation, lipoprotein metabolism and oxidation, cell cycle regulation or survival, autophagic-lysosomal pathways, and energy. Documented findings suggest interplay between brain insulin resistance, neuroinflammation, insult compensatory mechanisms, and peripheral metabolic dysregulation in T2DM and LOAD linkage. The results allow for more streamlined longitudinal studies of T2DM-LOAD risk linkages.</p

Celebrating the Data from 100,000 Real-World Users of the MiniMed™ 780G System in Europe, Middle East, and Africa Collected Over 3 Years: From Data to Clinical Evidence

Introduction: The present report celebrates the benchmarking of 100,000 MiniMed™ 780G system users in Europe, Middle East, and Africa (EMEA) and summarizes the major insights into the usability and outcomes of this system. Methods: Carelink Personal data (August 2020-August 2023) of users living in EMEA were analyzed. Continuous glucose monitoring-based endpoints were aggregated for (1) the full cohort and (2) a 12-month longitudinal cohort. Subanalyses were done for users on optimal settings (those spending ≥95% of time with glucose target of 100 mg/dL, and ≥95% of time with active insulin time of 2 h), for self-reported age groups (≤15 and ≥56 years) and for various countries/regions. Results: Data from 101,629 users (34 countries) were analyzed. Mean time in range (TIR) was 72.3%, glucose management indicator (GMI) was 7%, time below 70 mg/dL (TBR70) was 2.0% and time below 54 mg/dL (TBR54) was 0.4%. In terms of international targets, 59.6% of users achieved a GMI 70%, 88.4% a TBR70 70% = 86.3%) while safety remained (TBR70 = 2.2% and TBR54 = 0.4%). Data showed consistency across self-reported age groups and geographies. In the longitudinal cohort, TIR reached 75.5% in the first month and remained 73.3% or higher over the 12-month period. Conclusion: Over 100,000 users of the MiniMed™ 780G system have demonstrated consistency in achieving target control of glycemia

variation in the current use of technology to support diabetes management in UK hospitals: Results of a survey of healthcare professionals

Background: There has been a significant increase in the use of wearable diabetes technologies in the outpatient setting over recent years, but this has not consistently translated into inpatient use. Methods: An online survey was undertaken to understand the current use of technology to support inpatient diabetes care in the United Kingdom. Results: Responses were received from 42 different organizations representing 104 hospitals across the United Kingdom. Significant variation was found between organizations in the use of technology to support safe, effective inpatient diabetes care. Benefits of the use of technology were reported, and areas of good practice identified. Conclusion: Technology supports good inpatient diabetes care, but there is currently variation in its use. Guidance has been developed which should drive improvements in the use of technology and hence improvements in the safety and effectiveness of inpatient diabetes care. Key recommendations include implementation of this guidance (especially for continuous glucose monitoring), ensuring specialist support is available for the use of wearable diabetes technology in hospital, optimizing information sharing across the health care system, and making full use of data from networked glucose and ketone meters.</p